Arts, Faculty of
Vancouver School of Economics
DSpace
UBCV
Tavakoli, Amirhossein
Doctor of Philosophy - PhD
University of British Columbia
Chapter 2 examines the incidence of R&D tax credits on workersâ wages and the underlying mechanisms. Leveraging a regression kink design with matched employerâemployee tax records, I show that R&D tax credits substantially increase firmsâ R&D expenditures. Responses are concentrated among R&D-intensive firms, where higher spending leads to gains in profitability, productivity, and wages. These benefits accrue primarily to incumbent workers, with high-skill, long-tenured, and older employees experiencing the largest earnings increases: a 10 percent rise in expenditure limit raises their annual wages by 1.2â1.9 percent. In contrast, entrants and less-skilled, younger, or short-tenured workers experience no significant wage effects. The results are consistent with a rent-sharing framework and highlight the role of R&D policy in shaping within-firm wage inequality. Chapter 3 connects changes in employer characteristics through job transitions to employee earnings following mergers and acquisitions (M&As). Using firm balance sheet data linked to individual earnings data in Canada and a matched difference-in-differences design, we find that workers at target firms experience a post-M&A earnings decline, driven largely by those who leave. Although movers transition to larger and more profitable firms, they face wage losses, likely reflecting the erosion of firm-specific human capital or backloaded contracts. The evidence suggests that the loss of match-specific premiums is the primary mechanism behind post-M&A wage declines. Chapter 4 examines the impact of corporate M&As on firm profitability and markups. Using financial data (2010 - 2018) for 10 European countries and a matched difference-in-differences design, we find that acquirersâ and targetsâ markups remain unchanged, while their profitability declines substantially. Heterogeneity analyses across sectors and deal types confirm that these patterns are inconsistent with a market power channel. Instead, the evidence indicates that acquisitions are associated with weaker medium-run performance rather than increasing market power.
Essays on Firms and WorkersbyAmirhossein TavakoliB.S. Sharif University of Technology, 2016M.S. Sharif University of Technology, 2018A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDoctor of PhilosophyinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Economics)The University of British Columbia(Vancouver)October 2025c© Amirhossein Tavakoli, 2025The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled:Essays on Firms and Workerssubmitted by Amirhossein Tavakoli in partial fulfillment of the requirements forthe degree of Doctor of Philosophy in Economics.Examining Committee:Terry S. Moon, Assistant Professor, Economics, UBCCo-SupervisorKevin Milligan, Professor, Economics, UBCCo-SupervisorThomas Lemieux, Professor, Economics, UBCSupervisory Committee MemberPaul Schrimpf, Associate Professor, Economics, UBCUniversity ExaminerJan Bena, Professor, Sauder Business School, UBCUniversity ExamineriiAbstractChapter 2 examines the incidence of R&D tax credits on workers wages and theunderlying mechanisms. Leveraging a regression kink design with matched em-ployeremployee tax records, I show that R&D tax credits substantially increasefirms R&D expenditures. Responses are concentrated among R&D-intensive firms,where higher spending leads to gains in profitability, productivity, and wages.These benefits accrue primarily to incumbent workers, with high-skill, long-tenured,and older employees experiencing the largest earnings increases: a 10 percent risein expenditure limit raises their annual wages by 1.21.9 percent. In contrast, en-trants and less-skilled, younger, or short-tenured workers experience no significantwage effects. The results are consistent with a rent-sharing framework and high-light the role of R&D policy in shaping within-firm wage inequality.Chapter 3 connects changes in employer characteristics through job transitionsto employee earnings following mergers and acquisitions (M&As). Using firmbalance sheet data linked to individual earnings data in Canada and a matcheddifference-in-differences design, we find that workers at target firms experience apost-M&A earnings declines, driven largely by those who leave. Although moverstransition to larger and more profitable firms, they face wage losses, likely reflect-ing the erosion of firm-specific human capital or backloaded contracts. The evi-dence suggests that the loss of match-specific premiums is the primary mechanismbehind post-M&A wage declines.Chapter 4 examines the impact of corporate M&As on firm profitability andmarkups. Using financial data (2010 - 2018) for 10 European countries and amatched difference-in-differences design, we find that acquirersâ and targetsâ markupsiiiremain unchanged, while their profitability declines substantially. Heterogeneityanalyses across sectors and deal types confirm that these patterns are inconsistentwith a market power channel. Instead, the evidence indicates that acquisitions areassociated with weaker medium-run performance rather than increasing marketpower.ivLay SummaryThis dissertation consists of three chapters in labor and public economics. Thefirst chapter investigates the impacts of R&D tax credits on firmsâ performanceand workersâ earnings. Among eligible firms, only those with substantial pre-vious R&D expenditures respond to the tax incentives. These firms experiencean increase in performance measures such as revenue and profit margins. Thesefirm-level gains are passed onto incumbent workersâ earnings without impactingthe entrants. The second chapter connects changes in employer characteristicsthrough job transitions to employee earnings following mergers and acquisitions(M&A). Earnings of workers at target firms decrease after M&As, largely drivenby those who move to other firms. The third chapter studies how M&As affectfirmsâ medium-run profitability and markups in Europe. Both the acquiring andtarget firms experience a decline in their profitability, while their markups remainunchanged. The evidence suggests that M&As are unlikely to contribute to the risein firmsâ market power.vPrefaceThe research in Chapter 2 is an original, unpublished, and independent work by theauthor of this dissertation, Amirhossein Tavakoli. Chapter 3 is a joint work withTerry S. Moon (UBC), Kevin Milligan (UBC), and David Arnold (UCSD). Chapter 4is a joint work with Terry S. Moon (UBC) and David Arnold (UCSD). Amirhosseincompiled the data and implemented the empirical strategy. The development of theempirical strategy and interpretation of the results were joint work by authors.The use of generative artificial intelligence (ChatGPT) in the preparation of thisdissertation was minimal and limited to the revision of original text by the author.The purpose of the revision was purely to improve on the clarity and readability ofthe text.viTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xivList of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviii1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 R&D Expenditures, Productivity, and Rent-Sharing: Evidence fromR&D Tax Credits . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1 Institutional Background . . . . . . . . . . . . . . . . . . . . . . 162.2 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . 192.2.1 Estimating the Impact on Firms using a Regression KinkDesign . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.2.2 Estimating the Impact on Workers using a Regression KinkDesign . . . . . . . . . . . . . . . . . . . . . . . . . . . 22vii2.2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.2.4 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . 242.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.3.1 The Impact of Tax Credits on Firm-level Outcomes . . . . 262.3.2 Worker-Level Earnings and Job Transition . . . . . . . . . 312.3.3 Robustness and Internal Validity . . . . . . . . . . . . . . 322.4 Rent-Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322.4.1 Pass-Through Estimation . . . . . . . . . . . . . . . . . . 322.4.2 Within-Firm Pass-Through Heterogeneity . . . . . . . . . 342.5 Within-Firm Wage Inequality . . . . . . . . . . . . . . . . . . . . 352.5.1 Skill . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352.5.2 General Experience and Firm-Specific Experience . . . . 362.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Job Transitions and Employee Earnings After Acquisitions: LinkingCorporate and Worker Outcomes . . . . . . . . . . . . . . . . . . . 593.1 Institutional Background . . . . . . . . . . . . . . . . . . . . . . 663.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673.2.1 SDC Platinum . . . . . . . . . . . . . . . . . . . . . . . 673.2.2 Canadian Employer Employee Dynamics Database (CEEDD) 703.3 Potential Impacts of M&As on Firms and Workers . . . . . . . . 713.3.1 Firm-Level Impacts . . . . . . . . . . . . . . . . . . . . . 713.3.2 Impacts on Workers . . . . . . . . . . . . . . . . . . . . 743.4 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . 773.4.1 Matched Samples . . . . . . . . . . . . . . . . . . . . . . 793.4.2 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . 823.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 833.5.1 Post-M&A Firm Size and Average Payrolls . . . . . . . . 833.5.2 Post-M&A Worker-level Earnings and Job Transitions . . 853.6 Potential Mechanisms and Economic Interpretations . . . . . . . . 873.6.1 Market Power Channel . . . . . . . . . . . . . . . . . . . 873.6.2 Impacts on Job Movers . . . . . . . . . . . . . . . . . . . 903.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95viii4 Corporate Acquisitions, Profitability, and Markups: Evidence fromEurope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1134.1 Institutional Background . . . . . . . . . . . . . . . . . . . . . . 1184.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1194.2.1 SDC Platinum . . . . . . . . . . . . . . . . . . . . . . . 1194.2.2 Amadeus Database . . . . . . . . . . . . . . . . . . . . . 1214.2.3 Variable Definitions . . . . . . . . . . . . . . . . . . . . 1224.3 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . 1234.3.1 Estimating M&A Effects on Main Outcomes . . . . . . . 1234.3.2 Matched Samples . . . . . . . . . . . . . . . . . . . . . . 1254.3.3 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . 1264.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1264.4.1 Profitability and Markups After M&As . . . . . . . . . . 1264.4.2 Total Revenue, Expenses, and Leverage Ratio After M&As 1284.5 Market Power Channel and Economic Interpretations . . . . . . . 1294.5.1 Heterogeneity by Tradable vs. Non-Tradable Sectors . . . 1294.5.2 Heterogeneity by Domestic vs. Cross-border M&As . . . 1304.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1315 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144A Appendices to Chapter 2 . . . . . . . . . . . . . . . . . . . . . . . . 156A.1 Robustness and Validity . . . . . . . . . . . . . . . . . . . . . . . 156A.2 Estimating Total Factor Productivity . . . . . . . . . . . . . . . . 160A.3 Tax-Adjusted Price Elasticity . . . . . . . . . . . . . . . . . . . . 161B Appendices to Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . 167B.1 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . 167B.1.1 Different Clustering . . . . . . . . . . . . . . . . . . . . 167B.1.2 Using Matched Control Firms in Different Markets . . . . 168B.1.3 Outcomes in Levels (Replacing Missing Observations withZeros) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168ixB.1.4 By One-time vs. Repeat Acquirers . . . . . . . . . . . . . 168B.1.5 Using Private Firms Only . . . . . . . . . . . . . . . . . 169B.1.6 Use Sample of Unmatched Firms and Workers . . . . . . 169B.1.7 AKM and Match Effects . . . . . . . . . . . . . . . . . . 171B.2 Additional Heterogeneity Results . . . . . . . . . . . . . . . . . . 194B.2.1 By Local vs. National M&As . . . . . . . . . . . . . . . 194B.2.2 By Acquisition vs. Merger . . . . . . . . . . . . . . . . . 194B.2.3 By Within vs. Across Labor Markets . . . . . . . . . . . 195B.2.4 Distribution of HHI Across Markets . . . . . . . . . . . . 195B.2.5 By Initial Level of Concentration . . . . . . . . . . . . . 196B.2.6 By Initial Level of (flows-adjusted) Concentration . . . . 197B.2.7 Results on Revenue, Markups, and Realized Capital Gains 199B.2.8 By Tradable vs. Non-Tradable Sectors . . . . . . . . . . . 201B.2.9 By Within vs. Between Industry M&As . . . . . . . . . . 201B.2.10 By Worker Age . . . . . . . . . . . . . . . . . . . . . . . 202C Appendices to Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . 223C.1 Institutional Details . . . . . . . . . . . . . . . . . . . . . . . . . 223C.2 Fuzzy Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . 229C.2.1 SDC Platinum Data . . . . . . . . . . . . . . . . . . . . . 229C.2.2 Amadeus Data . . . . . . . . . . . . . . . . . . . . . . . 230C.2.3 Fuzzy Matching . . . . . . . . . . . . . . . . . . . . . . 230C.3 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234C.3.1 Different Clustering . . . . . . . . . . . . . . . . . . . . 234C.3.2 Perfectly Matched Sample . . . . . . . . . . . . . . . . . 234C.3.3 By One-time vs. Repeat Acquirers . . . . . . . . . . . . . 234C.3.4 Using Private Firms Only . . . . . . . . . . . . . . . . . 235C.3.5 Other Measures of Profitability . . . . . . . . . . . . . . . 235C.3.6 Firm Exit Rate . . . . . . . . . . . . . . . . . . . . . . . 235xList of TablesTable 2.1 Descriptive Statistics on Firms . . . . . . . . . . . . . . . . . 46Table 2.2 Descriptive Statistics on Workers . . . . . . . . . . . . . . . . 47Table 2.3 Tax Credits Impact on R&D Expenditure . . . . . . . . . . . . 48Table 2.4 R&D Expenditures and Wages . . . . . . . . . . . . . . . . . 49Table 2.5 Growth and Profitability . . . . . . . . . . . . . . . . . . . . . 50Table 2.6 Worker Composition . . . . . . . . . . . . . . . . . . . . . . . 51Table 2.7 Entry and Separation . . . . . . . . . . . . . . . . . . . . . . . 52Table 2.8 Tax Credits Impact on Earnings . . . . . . . . . . . . . . . . . 53Table 2.9 Rent-Sharing Estimates . . . . . . . . . . . . . . . . . . . . . 54Table 2.10 Heterogeneity based on AKM Worker Fixed Effects . . . . . . 55Table 2.11 Heterogeneity based on Within-Firm Earnings Distribution . . 56Table 2.12 Heterogeneity based on Tenure . . . . . . . . . . . . . . . . . 57Table 2.13 Heterogeneity based on Worker Age . . . . . . . . . . . . . . 58Table 3.1 Descriptive Statistics on the Matched Sample of Firms and Work-ers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105Table 3.2 Difference-in-differences Estimates on Firm Outcomes . . . . 106Table 3.3 Difference-in-differences Estimates on Worker Outcomes . . . 107Table 3.4 Workers Moving from Targets . . . . . . . . . . . . . . . . . . 108Table 3.5 Difference-in-differences Estimates on Firmsâ Profitability . . . 109Table 3.6 Changes in Average Firm Characteristics of Workers Movingfrom Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . 110Table 3.7 Workers Moving from Targets â By Worker Tenure . . . . . . 111xiTable 3.8 Workers Moving from Targets â By Within-Firm Earnings Dis-tribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112Table 4.1 Descriptive Statistics on the Matched Sample of Firms . . . . . 135Table 4.2 Profitability and Markups After M&As . . . . . . . . . . . . . 138Table 4.3 Total Revenue, Expenses, and Leverage Ratio After M&As . . 139Table 4.4 Profitability After M&As: By Tradable sectors vs. Nontradablesectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140Table 4.5 Profitability After M&As: By Cross-border M&As vs. Domes-tic M&As . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141Table A.1 Tax Credits Impact on R&D Expenditure . . . . . . . . . . . . 158Table A.2 Intensive Margins vs. Extensive Margins . . . . . . . . . . . . 159Table A.3 Evolution of Small Business Deduction Threshold and SREDTax Credit Threshold . . . . . . . . . . . . . . . . . . . . . . 166Table A.4 Smoothness Assumption Test . . . . . . . . . . . . . . . . . . 166Table B.1 Different Clustering . . . . . . . . . . . . . . . . . . . . . . . 184Table B.2 Matched Control Firms in Different Markets . . . . . . . . . . 185Table B.3 Outcomes in Levels (Replacing Missing with Zeros) . . . . . . 186Table B.4 By One-time vs. Repeat M&A (Acquirer) . . . . . . . . . . . 187Table B.5 Using Private Firms Only . . . . . . . . . . . . . . . . . . . . 188Table B.6 Including Unmatched Eligible Firms and Workers . . . . . . . 189Table B.7 Changes in Firm Effects and Worker-Employer Match Effects(Targets) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190Table B.8 Mean Log Wage Before and After Employer Change by Quar-tile of Mean Coworkers Wages at Origin and Destination Firms 191Table B.9 Descriptive Statistics on Unmatched Eligible Firms and Workers 192Table B.10 Additional Descriptive Statistics on Workers at Targets . . . . . 193Table B.11 Worker-level Earnings By National M&As vs. Local M&As(Targets) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213Table B.12 Worker-level Earnings By Acquisition vs. Merger (Targets) . . 214Table B.13 Worker-level Earnings By Within Market M&As vs. AcrossMarket M&As (Targets) . . . . . . . . . . . . . . . . . . . . . 215xiiTable B.14 Distribution of HHI across labor markets . . . . . . . . . . . . 216Table B.15 Worker-level Earnings By Initial Level of HHI (Targets) . . . . 217Table B.16 Worker Earnings - By Initial Level of Flows-Adjusted HHI (Tar-gets) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218Table B.17 Difference-in-differences Estimates on Firmsâ Sales, Markups,and Payouts . . . . . . . . . . . . . . . . . . . . . . . . . . . 219Table B.18 Worker-level Earnings By Tradable Sectors vs. Non-tradableSectors (Targets) . . . . . . . . . . . . . . . . . . . . . . . . . 220Table B.19 Worker-level Earnings By Within Industry vs. Across Industry(Targets) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221Table B.20 Workers Moving from Targets â By Worker Age (Targets) . . . 222Table C.1 Threshold changes . . . . . . . . . . . . . . . . . . . . . . . . 224Table C.2 Summary of Regulations . . . . . . . . . . . . . . . . . . . . 226Table C.3 M&A Deals Across Countries (2010 - 2018) . . . . . . . . . . 231Table C.4 Summary Statistics on M&A Activities Across Countries . . . 232Table C.5 Total Number of M&A Deals Across Other European Countries 233Table C.6 Different Clustering . . . . . . . . . . . . . . . . . . . . . . . 242Table C.7 Perfectly-Matched Sample . . . . . . . . . . . . . . . . . . . . 243Table C.8 By One-time M&As vs. Repeat M&As (Acquirers) . . . . . . 244Table C.9 Private Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . 245Table C.10 Other Measures of Profitability . . . . . . . . . . . . . . . . . 246xiiiList of FiguresFigure 2.1 Expenditure Limit Schedule . . . . . . . . . . . . . . . . . . 40Figure 2.2 McCrary Test For Manipulation at the Kink . . . . . . . . . . 41Figure 2.3 The Kink in Average SRED Expenditure . . . . . . . . . . . 42Figure 2.4 Rent-Sharing by Wage Decile . . . . . . . . . . . . . . . . . 43Figure 2.5 Tax Credits Impact by Bandwidth . . . . . . . . . . . . . . . 44Figure 2.6 Placebo Kink Point . . . . . . . . . . . . . . . . . . . . . . . 45Figure 3.1 Number of M&A Events . . . . . . . . . . . . . . . . . . . . 97Figure 3.2 Employment and Average Payrolls After M&As . . . . . . . 98Figure 3.3 Worker Earnings and Job Transitions After M&As . . . . . . 99Figure 3.4 Workers Moving from Targets . . . . . . . . . . . . . . . . . 100Figure 3.5 Firmsâ Profitability After M&As . . . . . . . . . . . . . . . . 101Figure 3.6 Firm Characteristics of Workers Moving from Targets . . . . 102Figure 3.7 Workers Moving from Targets â By Worker Tenure . . . . . . 103Figure 3.8 Workers Moving from Targets â By Within-Firm Earnings Dis-tribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104Figure 4.1 Number of M&A Events . . . . . . . . . . . . . . . . . . . . 132Figure 4.2 Profitability and Markups After M&As . . . . . . . . . . . . 133Figure 4.3 Total Revenue, Expenses, and Leverage Ratio After M&As . . 134Figure 4.4 Profitability After M&As: By Tradable sectors vs. Nontrad-able sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . 136Figure 4.5 Profitability After M&As: By Cross-border M&As vs. Do-mestic M&As . . . . . . . . . . . . . . . . . . . . . . . . . . 137xivFigure A.1 Expenditure Limit Schedule . . . . . . . . . . . . . . . . . . 165Figure B.1 Different Clustering . . . . . . . . . . . . . . . . . . . . . . 175Figure B.2 Matched Control Firms in Different Markets . . . . . . . . . 176Figure B.3 Outcomes in Levels (Replacing Missing with Zeros) . . . . . 177Figure B.4 By One-time vs. Repeat M&As (Acquirers) . . . . . . . . . . 178Figure B.5 Using Private Firms Only . . . . . . . . . . . . . . . . . . . . 179Figure B.6 Including Unmatched Eligible Firms and Workers . . . . . . . 180Figure B.7 Workers Moving from Targets â Employer FE and Match Ef-fects (Targets) . . . . . . . . . . . . . . . . . . . . . . . . . . 181Figure B.8 Exogenous Mobility Assumption . . . . . . . . . . . . . . . . 182Figure B.9 Probability of Exit . . . . . . . . . . . . . . . . . . . . . . . 183Figure B.10 Worker-level Earnings By National M&As vs. Local M&As(Targets) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203Figure B.11 Worker-level Earnings By Acquisition vs. Merger (Targets) . . 204Figure B.12 Worker-level Earnings By Within Market M&As vs. AcrossMarket M&As (Targets) . . . . . . . . . . . . . . . . . . . . 205Figure B.13 Distribution of HHI across markets . . . . . . . . . . . . . . 206Figure B.14 Worker-level Earnings By Initial Level of HHI (Targets) . . . 207Figure B.15 Worker-level Earnings By Initial Level of Flows-adjusted HHI(Targets) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208Figure B.16 Difference-in-differences Estimates on Firmsâ Sales, Markups,and Payouts . . . . . . . . . . . . . . . . . . . . . . . . . . . 209Figure B.17 Worker-level Earnings By Tradable Sectors vs. Non-tradableSectors (Targets) . . . . . . . . . . . . . . . . . . . . . . . . 210Figure B.18 Worker-level Earnings By Within Industry vs. Across Industry(Targets) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211Figure B.19 Workers Moving from Targets â By Worker Age (Targets) . . 212Figure C.1 Different Clustering . . . . . . . . . . . . . . . . . . . . . . 236Figure C.2 Perfectly-Matched Sample . . . . . . . . . . . . . . . . . . . 237Figure C.3 By One-time M&As vs. Repeat M&As (Acquirers) . . . . . . 238Figure C.4 Private Firms . . . . . . . . . . . . . . . . . . . . . . . . . . 239xvFigure C.5 Other Performance Measures after M&As . . . . . . . . . . . 240Figure C.6 Share of Target Firms Exiting the Sample . . . . . . . . . . . 241xviList of AbbreviationsCCPCs Canadian-Controlled Private CorporationsCEEDD Canadian Employer-Employee Dynamics DatabaseDiD Difference-in-DifferencesEBITDA earnings before interest, tax, depreciation, and amortizationEL Expenditure LimitIV Instrumental VariableRK Regression KinkSRED Scientific Research and Experimental DevelopmentTFP total factor productivityxviiAcknowledgmentsCompleting this Ph.D. has been one of the most challenging and rewarding experi-ences of my life. It taught me a great deal about the world and, more importantly,about myself.Words fall short when it comes to my family. To my wonderful parents, Mo-hammadreza and Soheyla, and my amazing sister, Sanam: your unwavering sup-port, love, and belief in me have carried me through every step of this journey.Without you, none of this or anything else would have been possible.I am deeply grateful to my supervisors, Terry Moon and Kevin Milligan, fortheir steady guidance and encouragement. I also thank Thomas Lemieux, SergeiSeverinov, Raffaele Saggio, and many other faculty members at the VancouverSchool of Economics whose insights helped shape this work. Special thanks toPaul Schrimpf and Jan Bena for serving as referees and offering thoughtful feed-back.To my friends and colleagues â thank you for making this journey not onlybearable but often joyful. The list is long, but Id like to mention Deivis, Alejan-dro, Federico, Mehdi, Sofia, Ceci, Parsa, and Milad. Your presence made all thedifference.xviiiChapter 1IntroductionThe dramatic increase in wage inequality and the role of firms in determiningworker earnings call for a deeper understanding of corporate decisions such asmergers and acquisitions, as well as public policies, such as R&D tax credits,aimed at workersâ employment and earnings. This dissertation consists of threechapters exploring the interplay of public policies, corporate decisions, and workerearnings. In particular, it aims to provide empirical evidence on the following ques-tions: How do R&D tax credits affect firmsâ decisions to invest in R&D activities,and what is the pass-through of these activities to worker earnings? (Chapter 2)How do M&As affect firmsâ performance, such as growth and profitability (Chap-ters 3 and 4), and how do these changes relate to worker earnings? (Chapter 3)Chapter 2 examines how R&D tax credits affect worker earnings. R&D taxcredits are an important policy tool to lower the cost of R&D inputs, particularlythe cost of R&D workers. For instance, Canada in 2021 provided $3.9 billion intax credits to over 22,000 businesses. However, inelastic supply of R&D workers[Goolsbee, 1998] or firm owners retaining tax benefits as profits [Saez et al., 2019]could limit efficacy of the tax credits to stimulate R&D activities.I use the unique design of a tax policy named Scientific Research and Exper-imental Development (SRED) and an administrative employer-employee matcheddataset from tax records in Canada to identify the causal impact of the R&D taxcredits on firm-level and worker-level outcomes. Under SRED, small and medium-sized Canadian-Controlled Private Corporations (CCPCs) are eligible for a 35 per-1cent credit rate on R&D expenditure up to a threshold called the ExpenditureLimit (EL). The EL is a decreasing function of firmsâ lagged taxable income downto zero benefits. This creates a kink in the tax credits schedule, which I exploit ina regression kink design to casually identify the impact of higher EL on firm-leveland worker-level outcomes.The results show that a $100k increase in EL leads to an average $5,976 in-crease in annual R&D expenditure. In elasticity terms, a 10 percent increase in ELtranslates into a 2.3 percent increase in annual R&D expenditures. The firms showsignificant heterogeneity in response to the eligibility for tax credits. Firms in thetop quartiles of R&D intensity â R&D-intensive firms â respond more strongly totax credit eligibility, while firms in the bottom three quartiles â non-R&D-intensivefirms â show limited response. Additionally, R&D-intensive firms experience sig-nificant increases in average wage, profitability, and total factor productivity (TFP)improvements, while non-R&D-intensive firms show minimal or no changes inthese metrics.The concurrent increase in the average wage, profitability, and TFP is consis-tent with a rent-sharing framework. Using an Instrumental Variable (IV) design, Iestimate that 82 percent of the surplus induced by the tax credits is passed throughto the workers. Moreover, I show that the top 30 percent of workers (based onwithin-firm earnings distribution) capture roughly 60 percent of the rents passedthrough to workers.To address a potential change in worker composition, I analyze the heterogene-ity among a fixed cohort of workers. Among incumbent workers at R&D-intensivefirms, high-skill, high-tenure, and experienced workers benefit the most from thetax credits, while low-skill, low-tenure, and young workers experience minimalchange. High-skill workers, whose skill is measured through worker fixed effectsor within-firm earnings distribution, experience a 1.6 percent increase in earningsfor a 10 percent increase in EL. Additionally, high-tenure workers and older work-ers, those in their 40s and 50s, experience a 1.2 to 1.9 percent increase in earningsfor a 10 percent increase in EL.Chapter 2 makes three contributions to the literature. First, it extends the litera-ture on firm-level drivers of worker compensation [Aghion et al., 2017, Barth et al.,2016, Carbonnier et al., 2022, Faggio et al., 2010, Howell and Brown, 2023, Kline2et al., 2019, Saez et al., 2019]. I estimate how firm-specific productivity shocksinduced by tax credits are passed onto worker earnings. This chapter also relatedto the literature studying the impact of tax policies on small innovative firms andtheir workers [Chen and Hshieh, 2024, Howell and Brown, 2023]. With a focuson R&D tax credits and access to earnings and job transition data, I estimate thetax incidence on incumbent and entrant workers. Moreover, I build on an exist-ing literature on innovation policies [Agrawal et al., 2020, DechezlepreËtre et al.,2023, Lokshin and Mohnen, 2013, Melnik and Smyth, 2024, Rao, 2016]. I findthat, in my setting, R&D tax credits crowd out private R&D expenditure becausemy baseline sample is larger than other studies, and only R&D-intensive firms areresponsive to tax credit eligibility. Finally, this chapter relates to technologicalchange and skill demand. In contrast to Bøler [2015], Lindner et al. [2022], I findthat R&D tax credits lead to a higher retention rate among incumbent workers. Myfindings suggest that technological change, e.g., increased productivity, is unlikelyto be skill-biased.Chapter 3 connects changes in employer characteristics through job transitionsto employee earnings following mergers and acquisitions (M&As). M&As oftenresults in important changes in corporate structure and worker composition for bothtarget and acquiring firms. While there exists a large literature on how M&As affectfirm performance and market value, there is growing interest in understanding labormarket impacts of M&As.In this chapter, we provide new empirical evidence on changes in workersâlabor market outcomes following corporate M&As. We use linked detailed firmbalance sheet data from corporate tax returns and worker-level earnings data fromindividual tax returns in Canada in a Difference-in-Differences (DiD) design toconnect and pin down potential firm-level and worker-level mechanisms behindour findings.In our setting, around 80 percent of M&A events involve acquisitions, wherethe target sells its assets, divisions, or only a part of its share and remains a separateentity following the event. Focusing on these events, we find that acquirers ex-pand, while targets shrink substantially. Acquirersâ employment increases by 18.8log points, without much change in average payrolls, on average relative to theirmatched control firms after M&As. By contrast, targetsâ employment and average3payrolls decrease by 8.9 log points and 2.8 log points on average, respectively.Since the changes in targetsâ average payrolls could be driven by changes inworker composition, the next part of this chapter estimates the impact of M&Ason worker-level earnings. For workers at acquiring firms, we find relatively stableearnings after M&As. However, for target workers, we find their earnings decreaseby 1.2 percent. This decline in worker earnings may stem from two channels. First,workers at target firms may face an increased risk of job displacement, implyingthe impacts on earnings are driven by job transitions. Second, workers at targetfirms may experience earnings losses due to changes in target firmsâ wage setting.To test this, we first estimate the impact of M&As on job transitions. For targetworkers, we find there is a large increase in the probability of job transitions inthe year following an M&A event. Given that we observe the universe of workersin the formal labor market in Canada, we check that most of these post-M&A jobtransitions are to different companies within the same industry, rather than to self-employment or unemployment. By contrast, for workers at acquiring firms, wefind no change in the probability of job transitions.For individuals who remain at target firms, we observe gradual and medium-run declines in their earnings, resulting in a statistically insignificant decrease inearnings (0.8 percent) on average. However, target workers who move jobs afteran M&A event suffer immediate earnings losses that continue to accumulate overtime, resulting in earnings that are about 4 percent less on average after the M&Aevent.Using the firm balance-sheet information, we find that target workers move tolarger, more profitable firms. Thus, the decline in job moversâ earnings cannot beexplained by these workers moving to observably worse firms. Importantly, mostof these job movers from target firms move to non-acquiring firms, meaning thatthis transition to larger firms is not mechanically driven by worker reallocationtoward acquirers.The results are consistent with a pay-complementary model between an indi-vidual worker and a given firm, which suggests that high-tenure (high-wage) work-ers will be especially harmed by job displacement. Consistently, we find that thedeclines are almost entirely driven by workers with longer tenure. Individuals withshorter tenure are not impacted by the M&A-induced job transition. Furthermore,4we find that the earnings losses are much larger among workers at the top quintileof the within-firm earnings distribution.While we interpret these results as a loss of productivity driven by worker-firm complementarities, an alternative interpretation is that the M&A event allowsfirms to displace overpaid workers with backloaded contracts. We cannot directlyobserve worker productivity in our dataset, but firms displacing overpaid workersmay increase their profits. We find a decrease in target firmsâ profitability, whichseems inconsistent with this mechanism. While it is possible for these firms to loseprofits after M&As despite displacing overpaid workers, there is another piece ofevidence against the backloaded contract channel. If M&As are targeting overpaidworkers to renege on their contracts, we should expect to see larger job transitionsamong high-tenure and high-wage workers. However, we find that changes in jobtransitions are similar between low-tenure (or low-wage) workers and high-tenure(or high-wage) workers after M&As. Therefore, this finding, combined with theresult on decreased profits, suggests that the earnings losses among target workersappear most consistent with a loss of firm-specific human capital.Chapter 3 makes three contributions to the literature. First, we contribute toa literature on the impacts of M&As on the labor market [Arnold, 2019, Brownand Medoff, 1988, Prager and Schmitt, 2021, Siegel and Simons, 2010]. Recently,both Prager and Schmitt [2021] and Arnold [2021] study how M&A events thatlead to large changes in labor market concentration affect worker earnings. In thischapter, despite finding small shifts in market concentration, we find that M&Aevents still have important impacts on job transitions that translate to changes inworker outcomes. Relative to a recent work documenting that M&A events createsignificant displacements of workers from target firms with higher wage premi-ums and reallocation to smaller firms with lower wage premiums in a developingcountry [Lagaras, forthcoming], our work shows that the majority of workers dis-placed from target firms move to larger and higher-paying firms. This distinctionis important because while the prior study implies that M&As are similar to masslayoffs to the extent that displaced workers are forced to move to lower-payingfirms, our findings suggest that M&As are different from mass layoffs; thus, amechanism through which displacements affect workers is distinct in our setting.Moreover, exploiting the balance-sheet data, we can rule out alternative mecha-5nisms such as back-loaded contracts. Finally, our work complements recent stud-ies [Dessaint et al., 2017, Ouimet and Zarutskie, 2020, Tate and Yang, 2024] docu-menting human-capital considerations and labor restructuring as important driversof M&As. While these studies show that acquirers engage in M&As to hire more(productive) workers, we find that most workers at targets move to non-acquiringfirms after the event, implying that acquirers engage in M&As to purchase partic-ular assets or intellectual properties of target firms, rather than to poach productiveworkers.Second, our paper relates to a literature on how job transitions impact wages.In particular, a large literature studies the role of firm-specific and match-specificcomponents to explain changes in displaced workersâ wages [Lachowska et al.,2020, Schmieder et al., 2023]. Much of the recent work leverages mass layoffs toidentify impacts of job transitions on workersâ earnings. In this chapter, we findthat M&A events can lead to significant job transitions for workers from targetfirms, leading to a decline in their earnings. In contrast to findings in the masslayoff literature, we find not only that the earning losses are smaller on average butalso that displaced workers move to larger and more profitable firms on average,likely because M&A events tend to be pro-cyclical in aggregate [Rhodes-Kropfand Viswanathan, 2004].Third, our work relates to the corporate finance and industrial organization lit-erature that studies how M&As affect firm performance. This chapter estimates theimpact of M&As on a variety of firm-level outcomes and finds results consistentwith the set of studies reporting negative impacts of M&As on firm profitability.We then use these firm-level outcomes to rule out alternative mechanisms, such aschanges in firmsâ market power or implicit contracts, behind the decline in workerearnings. Our ability to link these firm-level outcomes to the worker-level out-comes is unique to the literature.Chapter 4 studies how corporate M&As impact firms profitability and markupsin the medium run. We compare the outcomes of firms that go through M&As witha matched sample of non-M&A firms before and after the event. Using financialdata merged with M&A data at the firm level across 10 European countries from2010 to 2018, we exploit a large number of M&A activities to implement a matcheddifference-in-differences design.6Similar to Chapter 3, in the European setting, around 80 percent of M&Aevents involve acquisitions. Focusing on these events, we find that acquirersâ andtargetsâ profitability (measured by returns on assets or profit margins) declines af-ter M&As. Acquirersâ profitability decreases by 0.6 percentage points on averagerelative to their matched control firms after M&As. Similarly, targetsâ profitabilitydecreases by one percentage point, on average. At the aggregate (pooling targetsand acquirers) level, we also find similar declines in profitability in both mergersand acquisitions. By contrast, we find almost no changes in firmsâ markups afterM&As.There are two primary channels in which M&As can affect overall profits:changes in product-market power and changes in productivity. On the one hand,a productâs price increase via increased product market power could lead to anoverall increase in firm profits. On the other hand, productivity synergies from anM&A can also boost profitability. There are also prior studies that find negativeimpacts of M&As on firm performance. Possible motivations for productivity-reducing M&As include misaligned empire-building motives [Jensen, 1986a] orCEO overconfidence [Malmendier and Tate, 2005]. Decreases in productivity willlead to declines in profits. This is a key distinction; increases in product-marketpower should not be associated with profit decreases, while decreases in produc-tivity can lead to lower profits. Our results are consistent with the latter, indicatingthat decreased productivity drives the observed decline in firm performance.Next, we explore whether the estimated impacts on profitability are smallerin nontradable sectors, the logic being that tradable sectors are close to compet-itive and a single merger is unlikely to impact global competition. We find thatprofits decline in both tradable and nontradable M&As. We also consider the dis-tinction between domestic versus cross-border mergers. Contrary to the marketpower channel, we find no significant difference in profitability or markup out-comes between these two types of mergers. Taken together, our findings suggestthat a recent rise in M&A activities across Europe in the last decade may not havebeen associated with a rise in profitability, markups, or market power.This chapter makes three contributions to the literature. First, it contributes tothe literature on the effects of mergers and acquisitions. Following the recent âret-rospectiveâ merger analysis [Ashenfelter et al., 2013, 2015, Dafny et al., 2019], we7document a decline in firmsâ profitability by examining M&A activities across awide range of countries in Europe. The empirical findings in this literature aresomewhat mixed. Braguinsky et al. [2015b], Healy and Ruback [1992], Mak-simovic and Phillips [2001], and Devos et al. [2009] document efficiency gains,while Blonigen and Pierce [2016] and Cohn et al. [2014] find little evidence of in-creases in productivity or operating improvements. Moreover, Boucly et al. [2011]and Erel et al. [2015] find that after leveraged buyouts, target firms become moreprofitable, grow faster, and issue more debt, using a sample of French firms be-tween 1994 and 2004 and a sample of European firms between 2001 and 2008, re-spectively. While their findings contrast our overall results, this chapter focuses onmore recent years and on a broader set of M&A deals, which can lead to differencesin results. Second, this chapter relates to the literature on the relationship betweenmarket power and corporate outcomes. Gutie´rrez and Philippon [2017] argues thatdeclining competition in the U.S. has led to lower investment rates. Similarly, DeLoecker et al. [2020] document a rise in market power in the U.S., which is consis-tent with patterns of declining competition. In this chapter, we study one potentialsource of changing competition â changing ownership structure. While we find noevidence of increased markups after M&As, we find that the decline in profitabil-ity is likely driven by other factors, given that the declines documented in mergerslikely have negligible impacts on market power. Finally, our results contribute tothe literature in corporate finance that studies negative stock-market reactions tomerger announcements. Researchers have interpreted such findings as evidenceof empire building [Jensen, 1986b], misaligned incentives [Morck et al., 1988], orCEO overconfidence [Malmendier and Tate, 2005]. These hypotheses of acquisi-tion behavior imply that some M&A events may not be profit-maximizing. Ourresults are consistent with this interpretation, as we find declines in profitability forboth target firms and acquiring firms following M&As [Betton et al., 2008].8Chapter 2R&D Expenditures, Productivity,and Rent-Sharing: Evidencefrom R&D Tax CreditsResearch and Development (R&D) tax credits aim to promote R&D activity andare widely used worldwide. 1 Such tax credits effectively lower the costs of R&Dinputs, particularly the cost of R&D workers. While there is substantial evidencedemonstrating tax credits increase R&D expenditure at the firm level2. Little isknown about the incidence of such tax credits on workers. This question is crucialfor research and policy. The primary objective of such tax credits is to stimulatefirmsâ R&D efforts by incentivizing the hiring of additional R&D workers; how-ever, there are concerns that the inelastic supply of R&D workers [Goolsbee, 1998]or firm owners retaining tax benefits as profits [Saez et al., 2019] could limit theimpact on innovation.Extensive empirical research has shown how firm-specific productivity shocksaffect worker wages using proxies for productivity shocks such as payroll tax cuts[Carbonnier et al., 2022, Saez et al., 2019], one-time cash windfalls [Howell andBrown, 2023], patent grants [Kline et al., 2019], innovation [Van Reenen, 1996],1Appelt et al. [2019] in a 2018 survey report that 33 of 42 OECD and EU countries providedsignificant tax incentives to encourage firmsâ R&D investment.2For a literature review, see Becker [2015] and Howell [2024].9and changes in value added per worker [Card et al., 2014]. The impact of firm-specific productivity shocks is particularly important in the context of innovativefirms since they create higher-paying jobs, hire a disproportionately larger share ofhigh-skill workers, and drive economic growth [Aghion et al., 2017, Romer, 1990,Van Reenen, 1996]. Moreover, increased R&D expenditure induced by tax creditsmay contribute to skill-biased technological change, which, in turn, increases therelative skill demand and premium at the firm level [Bøler, 2015, Lindner et al.,2022]. So, R&D expenditures induced by tax credits have a theoretically ambigu-ous impact on worker wages, and it is essential to evaluate such tax creditsâ impactempirically.This paper studies how R&D tax credits affect wages and investigates the un-derlying worker- and firm-level mechanisms. I use the unique design of a tax policynamed SRED and an administrative employer-employee matched dataset from taxrecords in Canada to identify the causal impact of the R&D tax credits on firm-level and worker-level outcomes. Under SRED, small and medium-sized CCPCSare eligible for a 35 percent credit rate on R&D expenditure up to a threshold calledthe EL. The EL is a decreasing function of firmsâ lagged taxable income down tozero benefits. This creates a kink in the tax credits schedule, which I exploit in aregression kink design to casually identify the impact of higher EL on firm-leveland worker-level outcomes.The Regression Kink (RK) design exploits the presence of a kinked schedulein the relationship between the running variable (lagged taxable income) and theassignment variable (EL) to identify the impact of a higher EL. The identifica-tion comes from an induced kink in the mapping between the assignment variableand some outcome variable that coincides with the kink in the policy scheduleand compares the relative magnitudes of the two kinks. The identification in RKdesign relies on two assumptions: First, the density of the unobserved heterogene-ity should evolve smoothly with the running variable at the kink (no-manipulationassumption). Second, the direct marginal effect of the running variable on the out-come should be smooth (smoothness assumption). [Card et al., 2015]. I providegraphical and empirical evidence to show that the probability density function offirms around the kink point, indeed, evolves smoothly with the running variable. Ialso show that the direct marginal effect of the running variable on observed prede-10termined covariates evolves smoothly with the running variable at the kink point.I first examine the effects of the tax credits on firm-level R&D expenditures.A $100k increase in EL leads to an average $5,976 increase in annual R&D ex-penditure. In elasticity terms, a 10 percent increase in EL translates into a 2.3percent increase in annual R&D expenditures. Although SRED aims to stimulateinnovation in small and medium-sized firms, the response to eligibility for R&Dtax credits may show significant heterogeneity due to lack of inventive capacity orburdensome administrative requirements [Agrawal et al., 2020, Cui et al., 2022,DechezlepreËtre et al., 2023]. To build on this insight, I explore the heterogene-ity of firmsâ responses to tax credit eligibility based on their R&D intensity. Idefine two measures of R&D intensity: R&D expenditure scaled by revenue andR&D wages scaled by total payroll. I find that firms in the top quartiles of R&Dintensity â R&D-intensive firms â respond more strongly to tax credit eligibility,while firms in the bottom three quartiles â non-R&D-intensive firms â show lim-ited response. This distinction reflects the difference between the intensive marginâ where firms already engaged in R&D increase their expenditure and the exten-sive margin, where non-R&D-intensive firms might begin investing in R&D. Theresults are robust to the measure of R&D intensity. The results also demonstratethat R&D-intensive firms respond to tax credits with substantial increases in R&Dexpenditures, primarily driven by R&D wages and salaries rather than outsourcing,capital, or material inputs.Additionally, R&D-intensive firms experience significant increases in averagewage, profitability, and TFP improvements, while non-R&D-intensive firms showminimal or no changes in these metrics. The increase in the average wage, prof-itability, and TFP may be driven by a change in worker composition. For instance,if R&D activities lead to a skill-biased technological change, treated firms increasetheir relative demand for skill, which increases the skill premium and productivityat the firm level [Bøler, 2015, Lindner et al., 2022]. I show that the results are notattributable to changes in worker composition, as the gender and age composition,entrantsâ average wage before joining, moversâ average wage before leaving, andthe share of high-skill workers â proxied by worker fixed effects â remain stableacross firms. Finally, I find a large positive, although statistically insignificant, im-pact on employment for R&D-intensive firms. The increase comes from a decline11in the separation rate at these firms, while the entry rate does not change. This evi-dence suggests that the rise in productivity induced by R&D tax credits is unlikelyskill-biased.Tax credit eligibility raises the average wages at R&D-intensive firms and exac-erbates within-firm inequality. Focusing on incumbent workers at R&D-intensivefirms, I show that treated workers experience a 1.1 log point increase in annualearnings for a $100k increase in the EL relative to the control workers. In elasticityterms, a 10 percent increase in EL leads to a 0.7 percent increase in annual earn-ings. The increase in earnings is concentrated among high-skill, high-tenure, andexperienced workers. High-skill workers, measured through worker fixed effectsor within-firm earnings distribution, benefit the most from increased R&D expen-diture â with earning elasticity around 1.6 with respect to EL â while low-skillworkers see no significant change in earnings. Additionally, high-tenure workersand older workers, those in their 40s and 50s, experience more significant gains inearnings, with earning elasticities ranging from 1.2 to 1.9 percent with respect toEL. Finally, consistent with the decline in separation rate at R&D-intensive firms,I find that retention rate, for the most part, is concentrated among high-skill andolder workers. Incumbent workers at non-R&D-intensive firms experience a smallbut statistically significant decline in their earnings (-0.035 percent for a 10 percentincrease in EL). This decline is driven by movers from non-R&D-intensive firms.Robustness checks confirm the stability of the results. First, I estimate the im-pact of tax credits on average R&D expenditure using a wider range of bandwidthsaround the kink point and find a comparable impact in terms of magnitude and sig-nificance. Second, I address the issue of potential functional dependence betweenthe running variable and the outcome [Ganong and Ja¨ger, 2014, Landais, 2015].In practice, the relationship between these variables could either exhibit a kink orfollow a quadratic pattern. To address this issue, I run a series of regressions fora wider selection of placebo kink points and show that the relationship betweenR&D expenditure and the EL (hence, the running variable) quickly disappears asthe placebo kink points get further from the true kink point. This suggests that therelationship between the running variable and the outcome is unlikely driven bytheir functional dependence.The contributions of this paper are threefold. First, I contribute to the literature12on firm-level drivers of worker compensation. There is a growing body of evidencein labor economics that studies the role of firms in wage setting [Aghion et al.,2017, Barth et al., 2016, Carbonnier et al., 2022, Faggio et al., 2010, Howell andBrown, 2023, Kline et al., 2019, Saez et al., 2019].3 I estimate how firm-specificproductivity shocks induced by tax credits are passed onto workersâ wages. Mypaper is closest to Carbonnier et al. [2022], Saez et al. [2019]. Both papers studythe impact of a tax credit on firm-level outcomes and its incidence on differentgroups of workers. Exploiting a payroll tax cut for firms employing young work-ers in Sweden, Saez et al. [2019] find that firms with a larger windfall experienceincreased employment, sales, and profits. Also, they find evidence of rent sharingof the tax windfall as the wages of all workers, young and old, increase. Exploitinga corporate income tax credit for firms employing low-wage workers in France,Carbonnier et al. [2022] also find significant rent sharing where wage gains areconcentrated among workers in high-skill occupations and an increase in retentionrate among incumbent workers. The R&D tax credit is another tax policy target-ing a specific population of workers, R&D workers. Similar to Carbonnier et al.[2022], I find a significant pass-through of the productivity shock to wages.My estimated pass-through elasticity of approximately 0.8 suggests that a largeshare of the surplus generated by R&D tax credits is transmitted to workers throughhigher wages. This magnitude differs from the benchmark estimate of around 0.3reported by Kline et al. [2019], who examine patent-induced innovation shocks.While patents may generate a larger total surplus for firms, that surplus is sharedacross a broader group of stakeholders â such as executives, officers, and highlyskilled inventors â reflecting realized technological success. In contrast, the sur-plus from R&D tax credits operates through cost-based relief targeted to R&Dexpenditure, so its incidence is likely concentrated among a narrower group ofR&D workers and internal technical staff. Consequently, the higher pass-throughestimate in my setting does not necessarily imply larger overall worker gains butrather reflects the concentration of the impact among a specific subset of employeesdirectly linked to R&D activities.Studying the impact of tax policies on small innovative firms, my paper relates3For a literature review, see Card et al. [2018].13to Howell and Brown [2023] and Chen and Hshieh [2024]. Howell and Brown[2023] shows that a one-time cash windfall at small and innovative firms increasesincumbent workersâ wages, which rise with job tenure. Their results are consis-tent with a rent-sharing framework where workers receive backloaded contractsand earnings rise once the firms can pay. While my paper also studies innovativefirms, I focus on R&D tax credits instead of R&D grants. R&D tax credits andgovernment grants differ in several important dimensions. First, tax credits arean indirect, broad-based, and market-driven instrument, as firms self-select intoclaiming credits based on their eligibility, whereas grants are a direct, selective,and government-driven mechanism, allocated through competitive application andevaluation processes. Second, tax credits give firms greater flexibility in allocat-ing resources across projects of their choice, but they may subsidize activities thatwould have occurred in any case; by contrast, grants enable governments to targetspecific sectors, technologies, or socially desirable projects, though at the cost ofhigher administrative complexity and potential misallocation. Third, tax creditstypically reimburse firms ex post, which makes them less effective in encourag-ing high-risk or financially constrained R&D, while grants are provided ex ante,thereby sharing risk with firms and potentially stimulating additional, more uncer-tain innovation. So, there will be significant heterogeneity among firms in take-up.4 Consistently, I show that R&D-intensive firms increase their R&D expenditurewhile non-R&D-intensive firms do not. Chen and Hshieh [2024] study the im-pact of a policy â Protecting Americans from Tax Hikes (PATH) Act of 2015 âthat allowed firms to monetize R&D tax credits against payroll taxes. They ex-ploit a regression discontinuity design based on firm age (5-year-old firms vs. 6-year-old firms) to estimate the impact of eligibility on firm-level job posting andemployment. They show that treated firms increase job postings, especially for po-sitions in STEM and those that require more education and work experience. Theyalso found that eligible firms increase employment by 17.7 percent, concentratedamong high-skill positions. My paper differs from theirs in a few aspects: First,access to earnings and the universe of job transition allows estimation of tax inci-4Relatedly, this paper also contributes to the literature on heterogeneous response to governmentpolicies Criscuolo et al. [2019], Zwick and Mahon [2017], in that firmsâ characteristics determinethe extent to which they can benefit from government policies.14dence and rent-sharing on incumbent and new entrants. Second, the average firmin my sample is older (15 years old) than their sample (5 and 6 years old), whichmight explain the large employment effect since younger firms are more likely tobe financially constrained [Criscuolo et al., 2019].Second, this paper builds on the existing literature that studies the efficacy ofinnovation policies such as R&D tax credits. The availability of high-quality dataon private firmsâ R&D expenditure, investment, and sales has made estimation ofthe impact in a broader set of firm outcomes with more credible designs possible[Lokshin and Mohnen, 2013, Melnik and Smyth, 2024, Rao, 2016]. My paper isclosest to Agrawal et al. [2020] and DechezlepreËtre et al. [2023], who use firm-leveldata to estimate the impact of R&D tax credits. Agrawal et al. [2020], in a sim-ilar context as mine but using a difference-in-differences design, studies how thechanges in the user-cost of R&D encourage firms to increase their R&D expendi-ture. They find that firms with lower fixed costs respond more to R&D tax credits.Using a regression discontinuity design based on changes in the asset-based sizethreshold for tax credit eligibility in the UK, DechezlepreËtre et al. [2023] estimatethe impact of tax credits on small and medium-sized enterprises and find a large andsignificantly positive impact on patenting and R&D expenditure. My paper differsfrom these papers in an important aspect: While they focus on R&D expenditureand patenting activities, I look at a broader set of outcomes, in particular, averagewage and detailed worker-level outcomes, to understand the implications of taxcredits on the wage policy of firms. I find a tax-adjusted price elasticity of 0, whichsuggests a significant crowding out of private R&D expenditure. The potential rea-sons for my smaller elasticity are (i) the baseline sample is, on average, larger thanthe sample used by Agrawal et al. [2020], and (ii) the fact that only R&D-intensivefirms respond to eligibility for the tax credits suggests that these firms would havehad R&D expenditure even in the absence of the tax credits. This crowd-out effectsuggests that R&D tax credits likely increase firm-level rents, which may be passedonto worker wages.Finally, this paper relates to the literature on technological change and skill de-mand. The closest paper to mine is Lindner et al. [2022], who find an increase inthe college premium and the relative demand for skill. Exploiting the introductionof a new R&D tax credit in Norway, they estimate that treated firms experience a1510.4 percent increase in the college-to-non-college employment ratio. 5 However,I do not find evidence of a change in worker composition in favor of high-skillworkers. I find that treated firms increase employment primarily by retaining in-cumbent (skilled and unskilled) workers rather than hiring more skilled workers.My findings suggest that technological change, e.g., increased productivity, is un-likely to be skill-biased. Relatedly, I contribute to the endogenous growth literaturehighlighting the impact of R&D expenditure on productivity [Hall and Mairesse,1995, Romer, 1990]. For instance, constructing an endogenous growth model, Do-raszelski and Jaumandreu [2013] show how R&D affects the productivity growthacross firms and over time. Exploiting the firm-level data and estimating total fac-tor productivity a la´ Ackerberg et al. [2015], I provide empirical evidence that R&Dtax credits increase total factor productivity. Also consistent with Doraszelski andJaumandreu [2013], I find that interaction between current and past R&D expendi-tures contributes to productivity growth in that R&D expenditure is persistent, andR&D-intensive firms tend to respond more strongly to tax credit eligibility.The rest of the paper is organized as follows. Section 2.1 summarizes theinstitutional background of SRED tax credits. Section 2.2 presents the regressionkink design framework and introduces the data. Section 2.3 presents the mainresults. Section 2.5 discusses the heterogeneity results and potential mechanismexplaining the results. Section A.3 computes the implied tax-adjusted user cost ofR&D tax credits. Section 2.6 concludes.2.1 Institutional BackgroundThe Canadian government has a long history of supporting R&D activities throughtax policies. As early as 1994, firms could deduct 100 percent of their scientific re-search expenditure from their taxable income. In 1986, the government expandedtheir definition of R&D expenditure by including experimental development andintroduced the term Scientific Research and Experimental Development (SRED),which encouraged R&D activities. 6 The Scientific Research and ExperimentalDevelopment (SRED) is the main federal tax incentive program to encourage all5Bøler [2015] also explore the impact of this tax credit on change in skill demand and find similarresults.6See the link for details about the evolution of the SRED program in Canada.16companies to conduct research and development in Canada. The program providesan investment tax credit for qualifying R&D expenditures, which fall under fourmain categories: (i) Basic Research (aimed at advancing the scientific knowledgewithout a practical application in view), (ii) Applied Research (aimed at advancingscientific knowledge with a practical application in view), (iii) Experimental Devel-opment (aimed at generating or discovering technological knowledge or know-howin order to develop or improve materials, devices, products, or processes, includingincremental improvements), and (iv) Support work (tasks which directly supportbasic research, applied research, or experimental development work such as engi-neering, design, and data collection). Moreover, the following work is ineligibleR&D expenditure under SRED: market research, quality control tests, prospecting,exploring or drilling for, or producing, minerals, petroleum, or natural gas, as wellas purchasing proprietary knowledge such as patents. 7To claim SRED tax credits, firms must complete a detailed filing with theCanada Revenue Agency (CRA). The process requires preparing technical projectdescriptions that demonstrate scientific or technological advancement, document-ing all eligible expenditures (wages, materials, contracts), and completing special-ized tax forms (T661 and related schedules). In addition, firms may face follow-upreviews or audits, which require extensive record-keeping and technical justifica-tions. While the program is formally accessible to firms of all sizes, these compli-ance costs can be substantial. Many smaller firms rely on specialized consultants toprepare their claims, which reduces the net benefit of the credit, and some may bedeterred from applying altogether. Larger and more established firms are generallybetter positioned to manage these costs. 8SRED is a two-tier tax credit that provides an investment tax credit at the gen-eral rate of 20 percent for all firms. 9 Small and medium-sized Canadian ControlledPrivate Corporations (CCPCs) are eligible for a 35 percent credit rate on R&D ex-penditures up to a threshold called the Expenditure Limit (EL). The EL is a kinked7In this paper, I cannot distinguish between different categories of qualifying R&D expenditure;however, in future research, one can explore the importance of SRED tax credits for basic researchvs. other R&D activities as emphasized by Akcigit et al. [2021].8In an interview, Tobi Lu¨tke, CEO of Shopify, remarked that firms must submit âan ungodlyburden of documentation,â which discourages R&D claims.9As of January 1, 2014, the general credit rate is 15 percent.17function of prior-year taxable income and prior-year taxable capital employed inCanada. 10 The function, as shown in Figure 2.1, can be characterized by three pa-rameters: maximum expenditure limit, ELmaxt , start of phase-out threshold, ztopât ,and end of phase-out threshold, zbottomât . The EL function for firm j in year t canbe written as:EL jt =ï£±ï£´ï£´ï£´ï£²ï£´ï£´ï£´ï£³ELmaxt if TYjt â¤ ztopâtELmaxt â10(ztopât âTYj(tâ1)) if ztopât < TYj(tâ1) â¤ zbottomât0 if TYjt > zbottomât(2.1)where EL jt is firm jâs EL at year t, T I j(tâ1) is lagged taxable income. In my em-pirical design, I exploit the kinked relationship of EL and lagged taxable income,which provides a credible exogenous variation in tax credits generosity. WhileAgrawal et al. [2020], DechezlepreËtre et al. [2023] exploit a change in the eli-gibility threshold over time to identify the impact of R&D tax credits, here theidentification comes from the kinked relationship between lagged taxable incomeand EL (i.e. cross-sectional variation in the data). I will discuss the identificationin more details in Section 2.2.Reforms. The SRED tax credit schedule, part of the Canadian Federal Bud-get, is revised annually and publicly announced by the Ministry of Finance. Firmsare generally unaware of specific changes to the tax credit schedule within eachtax year. 11 Over this period, the SRED tax credits schedule underwent foursignificant reforms in 2004, 2006, 2008, and 2009, illustrated in Figure A1. In2004, the phase-out thresholds were adjusted, with the lower bound moving from$200,000 to $300,000 and the upper bound from $400,000 to $500,000. In 2006,the thresholds were further adjusted to range from $400,000 to $600,000. In 2008,two substantial changes were introduced: an increase in the maximum expenditure10Since taxable capital is only relevant for a small share of firms in the analysis sample, I abstractit from my formulation.11In 20012019 period, three federal administrations governed Canada, each with distinct fiscalpriorities, making the predictability of SRED tax credit generosity challenging. The Liberal govern-ment, led by Jean Chrtien (until 2003) and Paul Martin (20032006), was succeeded by the Conser-vative government under Stephen Harper (20062015), followed by the Liberal government led byJustin Trudeau (20152025).18limit to $3 million and an extension of the phase-out upper threshold to $700,000.Finally, in 2009, the phase-out region was extended once more, from $500,000 to$800,000. Despite the reforms, in all years, the kinked relationship between thelagged taxable income and EL, and more importantly, the slope of this relationshipin the phase-out region, remained unchanged.Top Kink vs. Bottom Kink. Figure 2.1 shows the presence of two kink pointsin the relationship between lagged taxable income and the EL: the top kink at ztopâtand the bottom kink at zbottomât . For the rest of the paper I focus only on the bottomkink for two reasons. First, the top kink coincides with another policy aimed atsmall CCPCs. The small business deduction is an important policy that provides areduced corporate tax rate for their business income for small CCPCs. In particular,Table A1 shows that the small business deduction threshold coincides with the topkink, ztopât . Since the policy creates a cross-sectional variation among firms belowand above the threshold, it can bias the estimate of the R&D tax credits. Second,the policy implications of firms around the top kink and the bottom kink are quitedifferent. The estimand in the bottom kink identifies the response of firms that justbecome eligible for R&D tax credits. The estimand in the top kink identifies theresponse of firms that have close to the maximum expenditure limit, ELmaxt . Sincea small share of firms reach or cross the maximum expenditure limit, the policyimplications of the estimands are different and potentially less interesting.2.2 Empirical StrategyThis section describes the empirical design to estimate the causal effect of SREDtax creditsâ generosity on firm-level and worker-level outcomes. As discussed inSection 2.1, I exploit the kink in the relationship between firmsâ lagged taxableincome and their expenditure limit to identify the impact of the tax credits. Next Idescribe the data and outcome variables of interest and close with some descriptivestatistics on firms and workers.192.2.1 Estimating the Impact on Firms using a Regression KinkDesignSuppose firm j is eligible for the more generous tax credits up to the expenditurelimit, EL j. Suppressing time-related considerations, I write the outcome y j (e.g.R&D expenditure, employment) asy j = κ+EL jθ +u j (2.2)where θ is the marginal impact of an increase in tax credit generosity andu j represents all other determinants of the outcome. But R&D expenditure maybe correlated with other firm characteristics. For instance, larger firms may haveaccess to more capital â physical or human â to invest in R&D activities. Thesefirms may also have a higher incentive to invest in R&D activities to keep theircompetitive advantage in the market, which will yield an upward-biased estimateof θ .To causally estimate θ , I exploit the presence of a kinked schedule in the re-lationship between lagged taxable income and EL. Following Bell et al. [2024],Card et al. [2017], Landais [2015], I model firm jâs outcome, y j, as a polynomialfunction of its lagged taxable income (the running variable) z j, allowing the slopeof the relationship to differ on either side of the cutoff z j = 0.12y j = Î±+Pâp=1[Î²p(z j)p+ γp(z j)p Â·1{z j â¥ 0}]+X j + ε j (2.3)where X j is a set control variables such as lagged dependent variable, firm age,industry fixed effects, and province fixed effects.13 Standard errors are clustered atthe firm level. Here γ1 is the change in the slope of the relationship between theoutcome variable and the running variable at the kink point. To interpret this pa-rameter as the causal effect of an increase in EL, I scale it based on the relationshipbetween the running variable and the EL. As mentioned in Section 2.1, I focus on12The kink cutoff is normalized to zero throughout the paper.13As discussed by Lee and Lemieux [2010], inclusion of controls is unnecessary for identificationin RD [and similarly RK] designs. But to exploit the panel structure of the data and to increaseprecision, I include the lagged dependent variable as a baseline covariate. Lee and Lemieux [2010]say âIn case where Yit is highly persistent over time, Yitâ1 may well be a very good predictor and hasa very good chance of reducing the sampling error.â20the bottom kink where the expenditure limit can be written asEL j(z j) =â10z jÃ1{z j < 0}+0Ã1{z j â¥ 0} (2.4)where z j is the normalized lagged taxable income (the running variable). If firm jâslagged taxable income is below the kink, EL is a linear function of z j. If firm jâslagged taxable income is above the kink, EL is equal to zero. Since EL scheduleis a deterministic function of lagged taxable income, the parameter of interest θ inEquation 2.2 can be written asÎ¸Ë =âyâ zâ£â£z=0+â âyâ zâ£â£z=0ââELâ zâ£â£z=0+â âELâ zâ£â£z=0â=Î³Ë110Note that since I observe all firms with positive or zero R&D expenditure, theestimates should be interpreted as Intention-To-Treat (ITT) effect. Moreover, thisallows me to study the extensive margin as well as the intensive margin.Identification Assumptions and Testing Their Validity. The identificationin RK design relies on two assumptions: First, the density of the unobservedheterogeneity should evolve smoothly with the running variable at the kink (no-manipulation assumption). Second, the direct marginal effect of the running vari-able on the outcome should be smooth (smoothness assumption).The SRED tax credits schedule is part of the Canadian Federal Budget and isannually revised and announced by the Ministry of Finance. Firms are unlikelyto be aware of the exact changes in the tax credit schedule during each tax year.Although the firms could manipulate their taxable income somewhat, I do not findany evidence of such manipulation around the kink. To present the graphical ev-idence supporting the no-manipulation assumption, I pool data from all the kinksin 2002-2019. I plot the running variableâs pooled probability density function todetect potential manipulation at the kink point.Figure 2.2 shows the number of firms observed in each bin of lagged taxable in-come around the kink. The graph shows no sign of discontinuity in the relationshipbetween the number of firms and the running variable at the kink point. The corre-sponding McCrary [2008] test yields a discontinuity estimate of â0.9802 (p-value= 0.3270), which is not statistically different from zero. I also extend the McCrary21test to validate the continuity assumption of the first derivative of the p.d.f. aroundthe kink. Following Card et al. [2015], Landais [2015], I fit a series of polynomialmodels that allow the first- and higher-order derivatives of the number of firms atthe kink. The change in the first-order derivative is not statistically different fromzero.Another testable implication of the RK design assumption is that the directmarginal effect of the running variable on observed covariates evolves smoothlywith the running variable at kink. To test the smoothness assumption, I run a re-gression analogous to equation 2.3 where the outcome variables are covariates suchas R&D expenditure, employment, and investment at the year before the treatment(Ï =â1). Controls include firm age and lagged dependent variable, i.e. covariatesat t =â2. Table A2 reports the estimates showing that, before the treatment, thereis no statistically significant difference between firms below and above the thresh-old across a range of covariates. In particular, estimates for R&D expenditure havea negative sign (column (1)), which suggests that RK design captures the lowerbound of eligibility on firm outcomes.Sample Restriction. For the analysis sample, I impose the following restric-tions. First, I focus on all R&D firms, i.e., firms with positive R&D expenditure inat least one year before the treatment, that operated as CCPC throughout the sam-ple period. Second, I exclude firms with fewer than five workers the year before thetreatment, thereby dropping small businesses with less economic impact. Third, Ifocus on firms operating in only one province the year before the treatment to avoidcomplications arising from firms re-allocating their R&D activities in response todifferences in provincial supports. Finally, to account for survival bias, firms thatexited after the treatment are kept in the sample and are given zero values for R&Dexpenditures, patents, and employment.2.2.2 Estimating the Impact on Workers using a Regression KinkDesignI estimate a similar RK model to Equation 2.3 to evaluate the impact of tax creditson worker-level outcomes:22yi = Î±+Pâp=1[Î²p(z j(i))p+ γp(z j(i))p Â·1{z j(i) â¥ 0}]+Xi+ εi (2.5)where y j is the outcome of worker i, z j(i) is the lagged taxable income of firm jwhere worker i was employed at treatment, Xi is a set control variables such aslagged dependent variable, worker age, industry fixed effects, and province fixedeffects. Here γ1 is the change in the slope of the relationship between the outcomeand the running variable at the kink point. To interpret this parameter as the causaleffect of an increase in EL, I scale it based on the relationship between the runningvariable and the EL. Since EL schedule is a deterministic function of lagged taxableincome, the parameter of interest θ , as in Equation 2.2, is γ110 . Standard errors areclustered at the worker level.Sample Restriction. For the analysis sample, I impose the following restric-tions. First, I drop workers with multiple jobs the year before the treatment. Sec-ond, I limit the sample to workers continuously employed in a treated or controlfirm at least one year before the treatment, following Arnold et al. [2023], Duanand Moon [2024]. This tenure restriction ensures that the analysis is on a sampleof workers with attachment to the treated or control firms.2.2.3 DataThis section describes the main datasets used for my analysis. The firm-level andworker-level information comes from the Canadian Employer-Employee Dynam-ics Database.Canadian Employer-Employee Dynamics Database (CEEDD). The CEEDDis a matched employer-employee dataset that covers the universe of workers andfirms in Canada over the 2001-2019 period. The CEEDD draws information fromindividual (T1) and corporate (T2) tax return records. It also includes job-level in-formation from employee tax records (T4) and Record of Employment (ROE) data,as well as firm-level information from the National Accounts Longitudinal Micro-data File (NALMF). A significant advantage of this data set that allows using anRK design is that taxable income, hence firmsâ eligibility, is directly reported inthe tax return records. This is useful to estimate the extensive margin since I ob-23serve firmsâ eligibility even when they have zero R&D expenditures. The primaryoutcome variable used in the firm-level analysis is R&D expenditure. R&D expen-diture consists of multiple components separately reported in the data. Firms reporttheir in-house R&D expenditure, armâs-length, and non-armâs-length contacts. Thein-house R&D expenditure consists of wages and salaries to R&D workers, R&Dcapital expenditure, and other R&D costs such as material. Other outcome vari-ables used in the firm-level analysis are employment, average payrolls, investment,and profit margins. Employment is defined as the number of employees reportedfrom the T4s.At the worker level, the key outcome is annual earnings, which are aggregatedacross all employers in a given year. While I include earnings across all employ-ers, I associate workers with the âdominantâ employer, i.e., the employer fromwhich the employee receives the highest pay in the year. I also use information onworkersâ age and gender derived from the T1 income tax form.Finally, following Abowd et al. [1999] (AKM), I estimate the firm-specificand worker-specific components of workersâ annual earnings following a two-wayfixed effect model. I borrow the notation used in Arnold et al. [2023], Duan andMoon [2024]. Let yit denote the log earnings of worker i in year t and j(i, t)index the workers employer. We regress yit against the worker fixed effects Ïi, theemployer fixed effects Ï j(i,t), and year fixed effects Ït :yit = Ïi+Ï j(i,t)+ Ït +uit . (2.6)I use the estimated worker fixed effects ÏËi to categorize workers into high skillsversus low skills. In particular, I label workers in the top 25 percentiles of workerfixed effects distribution across the entire workers sample as high skill, and workersin the bottom 75 percentiles of worker fixed effects distribution as low skill. Theresults are robust to various measures of skill (not reported).2.2.4 Descriptive StatisticsThe baseline sample contains 5,210 firms within a $67,600 bandwidth of laggedtaxable income around the normalized kink threshold, with 2,960 treated firms and2,250 control firms. The choice of baseline bandwidth is based on Calonico et al.24[2014] optimal bandwidth approach using R&D expenditure as the main outcomevariable. The results are robust to alternative bandwidths (Figure 2.5. All outcomesare winsorized at 1 percent to mitigate the impact of outliers. R&D expendituresand patents are winsorized at 1 percent of non-zero values.Table 2.1 shows the average value of key variables measured in the year beforetreatment (Ï =â1), separately for treated firms and control firms. Column (1) and(2) show the averages for all the firms in the baseline sample. Column (3) and (4)show the averages for R&D intensive firms - measured by R&D expenditure scaledby revenue. Reassuringly, treated firms and control firms, in both samples, aresimilar in terms of SRED Expenditure, revenue, investment, employment, averagepayroll, and average number of patents owned. R&D intensive treated firms andtheir control firms have higher R&D expenditure in levels, higher ratio of R&Dwages to total payroll, higher average wage, but have lower levels of employmentand investment in physical capital relative to firms in the baseline sample.Table 2.2 shows the average value of key variables in the worker sample in theyear before treatment (Ï =â1), separately for treated workers and control workers.Column (1) and (2) show the averages for workers at firms in the baseline sample.Column (3) and (4) show the averages for workers at R&D intensive firms. Reas-suringly, treated workers and control workers, in both sample, are similar in termsof earnings. Moreover, the worker composition at treated firms and control firms interms of age, gender and tenure are quite similar. Workers at R&D intensive firms,on average, earn more but are similar to the workers in the baseline sample.The majority of R&D firms in the baseline sample are in manufacturing andservices sector. In the subsample of R&D-intensive firms, the share of firms inservices sector substantially increases. The largest industries in this subsample are5415 (Computer systems design and related services), 5416 (Management, scien-tific and technical consulting services), 5417 (Scientific research and developmentservices).2.3 ResultsThis section presents the main results, demonstrating that treated firms, on average,increase their R&D expenditure relative to control firms. I then explore whether25the results are driven by the intensive or extensive margin and the implications forthe pass-through of tax credits to workers. Next, I examine worker-level results, fo-cusing on the impact on incumbent workersâ earnings and retention rates. Finally,I show that my results are robust to alternative specification tests.In all tables, I report the estimates of the average treatment effect, Î¸Ë = Î³Ë110 ,where Î³Ë1 is the estimated change in slope in the relationship between the outcomevariables and the running variable at the kink point from Equation 2.3 and 2.5. Thedenominator represents the deterministic change in slope of the EL schedule at thekink point from Equation 2.4. Each estimate should be interpreted as the effectof a $100k increase in EL on the mean outcome over a post-treatment window.I also report the elasticity with respect to the EL, ε = θ ELmaxy¯ j, where y¯ j is themean outcome variable before the treatment, and ELmax is the maximum EL in thesample, e.g., $676,000 in the baseline sample. Note that in the baseline sample, theaverage change in EL is $26k with a standard deviation, which suggests 20 percentas the mean percentage change in EL. To side on caution, I report the results withrespect to a 10 percent change in EL.2.3.1 The Impact of Tax Credits on Firm-level OutcomesSRED Expenditure. Table 2.3 presents the RK estimate results corresponding toEquation 2.3. Columns (1) to (3) report the estimates of the impact of tax creditson R&D expenditure across various post-treatment windows, controlling for laggeddependent variables and firm age. Column (4) includes additional controls for in-dustry fixed effects, while Column (5) adds province fixed effects. The preferredspecification is in Column (3), where I estimate a large and statistically significantimpact on R&D expenditure. A $100k increase in EL translates into an average$5,976 increase (3.5 percent) in annual R&D expenditure. In elasticity terms, a10 percent increase in EL leads to a 2.3 percent increase in annual R&D expendi-ture. Point estimates are stable across specifications. Table 2.3 also shows that theimpact of eligibility on R&D expenditure increases over time. In Section A.3, Idiscuss the price elasticity of R&D expenditure.R&D Intensity. To differentiate between the impacts on the extensive mar-gin and the intensive margin, I estimate the effects of R&D tax credits separately26for R&D-intensive firms and non-R&D-intensive firms. To do so, I divide all the(treated and control) firms in the baseline sample based on their R&D intensityprior to the treatment. I label firms in the top 25 percentiles of R&D intensitydistribution as R&D-intensive, and firms in the bottom 75 percentiles of R&D in-tensity distribution as non-R&D-intensive. I use two measures to define R&D in-tensity. The first measure, R&D expenditure scaled by revenue, is commonly usedin the literature [Bøler, 2015, Griliches, 2007]. For the second measure, I lever-age detailed firm-level data, specifically the total wages and salaries paid to R&Dworkers. Using this variable, I define a novel measure of R&D intensity as R&Dwages and salaries scaled by total payroll. Since I cannot distinguish R&D workersfrom non-R&D workers in my data, the share of wages and salaries that goes toR&D workers is a crude proxy of number of R&D workers at the firm.If the extensive margin is significant, one would expect to see a substantialpositive effect among non-R&D-intensive firms. The difference between the inten-sive margin and extensive margin also has important implications for between-firmwage inequality. R&D activity contributes to total factor productivity at the firmlevel. A small extensive margin means that new firms are unable or unwilling toincrease their R&D expenditure even with access to tax credits, leading to a widen-ing gap in between-firm productivity and wage inequality [Bøler, 2015, Song et al.,2019].R&D tax credits differs from other government supports such as loans andgrants in firmsâ leeway to choose and fund projects. So, there will be significantheterogeneity among firms in take-up. Not all eligible firms take advantage of thepolicy, and this is important for several reasons. First, some firms may lack theinventive capacitysuch as laboratory equipment, administrative expertise, or theknowledge baseto invest in R&D activities. For instance, Agrawal et al. [2020]find that firms with initial investments in R&D capital respond more strongly toR&D tax credits. Similarly, DechezlepreËtre et al. [2023] show that the impact ofR&D tax credits is concentrated in firms with prior R&D expenditures, previouspatents, or those in high-patenting industries. This suggests that firms with lowerR&D fixed costs or R&D-intensive production functions are more likely to respondto tax credit eligibility. Second, unlike direct subsidies such as grants, tax creditsdo not create an immediate cash windfall. Eligible firms must first invest in R&D27activities and then receive the refund or credit later. Additionally, and particularlyrelevant for SRED tax credits, firms must submit substantial evidence to supporttheir R&D claims. Indeed, I find significant heterogeneity in firmsâ responses toeligibility for tax credits. 14Table 2.4 presents the RK estimate results separately for R&D-intensive firmsand non-R&D-intensive firms, based on two measures of R&D intensity prior to thetreatment. Columns (1) and (5) show that R&D-intensive firmsthose in the top 25percentiles of R&D intensityrespond to R&D tax credits by increasing their R&Dexpenditures, while firms in the bottom 75th percentile do not. Columns (2) and(6) show that most of the impact stems from increases in R&D wages and salariesrather than changes in R&D capital, materials, or outsourcing contracts. Columns(3) and (7) show that the average wages at R&D-intensive firms increases signif-icantly, whereas the change in average wages at other treated firms is small andstatistically insignificant. In elasticity terms, a 10 percent increase in EL translateinto 17 percent increase in annual average wage at firm level. Columns (4) and (8)reports the similar results where the outcome variable is log of average wages.The concurrent increase in R&D expenditure and average wages aligns withthe literature on the role of firms in wage inequality. Similarly, Aghion et al.[2017] find that R&D-intensive firms pay higher wages on average. Addition-ally, Van Reenen [1996] shows that innovative firms pay higher wages, driven bysharing in the rents generated by innovation.Profitability and Growth. Table 2.5 presents the RK estimate results sepa-rately for R&D-intensive firms and non-R&D-intensive firms. Columns (1) and(4) show that R&D-intensive firms experience a large and statistically significantincrease in profitability. While non-R&D-intensive firms also see a statistically sig-nificant increase in profitability (Column(1) only), the magnitude is much smallerthan R&D-intensive firms. Columns (2) and (5) reveal that R&D-intensive firmsalso experience a large and statistically significant increase in their total factor pro-ductivity â which I describe its estimation in Appendix A.2. The results for non-14Appendix A.1 examines the effects of R&D tax credits on both R&D and non-R&D firms. Ishow that the increase in R&D expenditure primarily stems from the intensive margins, while theextensive margin remains unchanged. Accordingly, these findings indicate that R&D tax credits donot encourage non-R&D firms to turn into R&D firms.28R&D-intensive firms are much smaller than those of R&D-intensive firms but lessprecisely estimated. Finally, R&D-intensive firms experience a substantial increasein their size based on employment (Columns (3) and (7)) and revenue (Columns(4) and (8)) relative to non-R&D-intensive firms. Using the standard errors of esti-mates in Columns (3) and (4), the 95 percent confidence interval centered on zeroexcludes elasticities above 0.079 percent for employment, and 0.018 for revenuefor non-R&D-intensive firms.The evidence suggest that the increase in R&D expenditures leads to an in-crease in firm-level productivity and profitability which may increase the averagewage. The sizable increase in profitability following R&D tax credits can be under-stood through several reinforcing mechanisms. First, the credit directly lowers theafter-tax cost of R&D inputs, effectively increasing firms post-tax margins evenif revenues remain constant, which is in essence different than firm-level shockssuch as rents from patent grants. Second, because many of the firms in my sampleare already R&D-intensive and have substantial fixed costs, the marginal dollar oftax-induced R&D spending has a leveraged impact on profitabilitysmall percent-age increases in revenue can translate into disproportionately large increases in netincome. Finally, part of the observed rise reflects accounting and selection effects:the credit itself reduces tax liabilities and is most accessible to larger, more capablefirms that were already relatively profitable. Together, these mechanisms explainwhy profitability rises sharply among treated firms, without implying that the entireeffect represents new economic rents.A potential concern is that the observed increase in reported R&D expendituresfollowing the tax credit expansion could reflect accounting relabeling rather thangenuine new R&D activity. Several pieces of evidence suggest this is unlikely.First, given the SRED programs strict eligibility and audit requirements, large-scale relabeling would be costly and risky. Second, the rise in R&D spending doesnot coincide with reductions in non-R&D investmenteither tangible or intangibleassetsindicating that firms are not diverting other forms of investment to qualifyfor the credit. For comparison, Chen et al. [2021] find that roughly 30 percent ofthe measured response to a Chinese fiscal-year R&D incentive reflects relabeling;in contrast, the stability of other asset categories in my setting suggests that theobserved R&D increase under the SRED represents genuine additional activity.29Third, the increase in reported R&D expenditures is accompanied by proportionalgrowth in overall employment and payroll. If firms were merely reclassifying ex-isting expenses, such parallel increases in workforce and wage costs would not beexpected.The concurrent increase in profitability and average wages is also consistentwith the literature on productivity dispersion and wage inequality [Faggio et al.,2010], where the widening gap in between-firm productivity explains a substantialpart of the wage inequality. But this observed increase is potentially driven bythe change in worker composition, in particular, changes in the degree to whichdifferent groups of workers are assigned to different firms [Card et al., 2018]. Next,I explore whether the increase in average wages is driven by changes in workercomposition or changes in worker compensation.Worker Composition. Table 2.6 presents the RK estimates for changes inworker composition separately for R&D-intensive firms and non-R&D-intensivefirms. The results suggest that neither the share of female employees nor the shareof high-skill workers, as defined in Section 2.2.3, shows a meaningful change. Thefindings on average age are less conclusive. While the signs of the estimates inColumns (2) and (7) are opposite, in both cases the impact is economically andstatistically insignificant. Taken together, these results do not provide evidence ofa change in worker composition.Furthermore, I investigate whether the composition of entrant workers andmovers changes after the treatment. Column (3) and (9) show that the entrantworkersâ average wage, a year prior to entry, at R&D-intensive firms is not statis-tically different than that of entrant workers at non-R&D intensive firms. Columns(4) and (9) show that the moversâ average wage, a year prior to separation fromR&D-intensive firms, is also not statistically different than that of movers fromnon-R&D-intensive firms. Despite the insignificance of estimates, the results onchanges in worker composition, specially for entrants, is less than convincing sincethe coefficient estimates are quite large and comparable with estimated impact onaverage wages in Table 2.5.Entry Rate and Separation Rate. The size and imprecision of the estimatesmay be due to (i) mean reversion, or (ii) the small number of entrants at the firmsafter the treatment. Mean reversion may be likely since the average earnings of30entrants a year prior to the entry is significantly lower than average wage at the firm(16k vs. 46.9k). So, the large estimate might just capture the reversion of entrantswage to its mean. To address the lack of precision, following Carbonnier et al.[2022], I use the share of entrant and the share of movers as dependent variables inTable 2.7. Column (1) and (3) show that the share of entrants does not change forneither R&D-intensive nor non-R&D-intensive firms. On the other hand, the shareworkers separating from the firms decrease by 1.2 - 1.8 percent (5 - 8 percent).While the share of workers separating from non-R&D-intensive firms does notchanges relative to the control group.Motivated by the increase in R&D expenditure, total factor productivity, prof-itability, and average wages, and the decrease in turnover rate at the firm-level, therest of the paper focuses on R&D-intensive firms to investigate how changes inR&D expenditures affect within-firm wage inequality.2.3.2 Worker-Level Earnings and Job TransitionTo control for potential changes in worker composition, I focus on incumbentworkers with at least one year of tenure at their (R&D-intensive) firms.15 Table2.8 presents the RK estimate results corresponding to Equation 2.5). Columns(1) to (3) report the estimates for the impact of tax credits on the log of averageearnings of all incumbent workers across various post-treatment windows, withcontrols for the lagged dependent variable. Column (4) reports the estimates forthe log of average earnings of incumbent workers during their time at the firm. Onaverage, treated incumbent workers experience a 1.1 log point increase in earningsrelative to control workers. In elasticity terms, a 10 percent increase in EL leadsto a 0.7 percent increase in annual earnings. Point estimates are similar for stayersand across post-treatment windows. Column (5) reports the estimates for the logof average earnings of workers who move from the firms. These movers do notexperience any change in their annual earnings relative to control workers. Theresults are consistent with the rent-sharing framework, where incumbent workersbenefit from increased R&D expenditure at the firm level. In Section 2.5, I explorepotential drivers behind the changes in incumbent workersâ earnings.15I report the results based on âR&D expenditure scaled by revenueâ as the measure of R&Dintensity. The results are qualitatively and quantitatively similar with the second measure of intensity.312.3.3 Robustness and Internal ValidityI conduct several robustness checks to strengthen the internal validity of the results.First, I estimate the impact of tax credits on average R&D expenditure over a 5-year window after the treatment, using a wider range of bandwidths around the kinkpoint. Figure 2.5 displays the coefficients and their 95 percent confidence intervalsestimated from Equation 2.3. All coefficients are comparable in magnitude andsignificance to those in the baseline sample in Table 2.3.Second, I address the issue of potential functional dependence between the run-ning variable and the outcome. In practice, the relationship between these variablescould either exhibit a kink or follow a quadratic pattern. As a result, RKD estimatesmay capture this functional dependence between z j and y j rather than the actual ef-fect of EL j on y j. To address this issue, I run a regression similar to Equation 2.3for a wider selection placebo kink points. The placebo kink point selection rangesfrom -$100k to $100k with $10k steps. Figure 2.6 shows the estimates for theseregressions. The figures show that the relationship between the R&D expenditureand the EL quickly disappears as the placebo kink points get further from the truekink point. This suggests that the relationship between the running variable andthe outcome is unlikely driven by their functional dependence. Finally, AppendixA.1 discusses other specifications, their advantages, and caveats compared to thespecification in equation 2.3.2.4 Rent-SharingResults so far show, on average, incumbent workers benefited from increased R&Dexpenditures induced by the tax credits. This section explores how the improve-ment in firm performance is passed through to worker earnings. First, I explain thepass-through estimation using average worker earnings as the outcome variable.Second, I investigate how the pass-through on workers varies across the wage dis-tribution.2.4.1 Pass-Through EstimationFollowing Kline et al. [2019], I estimate a rent-sharing regression which relatesincumbent wages at the firm to various measures of rent.32log(wage j) = α+λ log(rent j)+X j + ε j (2.7)where on the left hand side, wage j is the average worker earnings at the firm leveland on the right hand side, rent j is a measure of rent such as R&D expenditure,surplus per worker, and X j is firm-level control such as firm age and lagged inde-pendent variable. λ is the rent-sharing estimate which shows how a change in firmperformance will affect worker earnings. As discussed in Card et al. [2018], Klineet al. [2019], this regression can suffer from omitted-variable bias. For instance, apositive market-level shocks to firm productivity can lead to a rise in market-levelwages which will lead to an upward bias in OLS rent-sharing estimate.To address the endogeneity problems, I use the special design on the SRED taxcredit policy as an instrument for the firmsâ surplus. The identification assumptionsand their validity are discussed in the detail in Section 2.2. The R&D tax creditscould influence firm-level rents through (1) increased in the received subsidies, or(2) increased in the firmâs productivity. Both mechanisms provide valid identifyingassumptions.Table 2.9 presents pass-through estimates, in elasticity term, for the averageworker earnings based on various measures of rent. The rent measures are in-strumented using the kinked relationship of the lagged taxable income and SREDExpenditure Limit. In the first stage, I use Equation 2.3 to estimate the impact of achange in tax credit eligibility on rent measures (not reported). I use various mea-sures of rent for comparison to the existing literature. Column (1) reports that a 1percent change in the total R&D expenditure leads to a 0.09 percent increase in theaverage worker earnings which is consistent with the concurrent increase in R&Dexpenditure and average wages discussed in Section 2.3. Moreover, this estimateis of the same order of magnitude as the share of R&D wages scaled by total wagebill. This suggests that an increase in the total R&D expenditure is most likelygoing toward R&D workersâ wage.Column (2) reports the a 82 percent pass-through estimate using surplus perworker as the rent measure.16 Column (3) reports the pass-through estimate using16I closely follow Arnold et al. [2023] to define the surplus which is earnings before interest, tax,depreciation, and amortization (EBITDA) + total wage bill.33value added per worker as the rent measure. In line with Card et al. [2018] andCarbonnier et al. [2022], I also find that using value added as the rent measureyields to a larger pass-through estimate because value added contains a number ofcost components that cannot be captured by workers [Kline et al., 2019]. Column(4) reports the pass-through estimate using profit per worker as the rent measure.For every 1 percent increase in the R&D-induced profit per worker, the averageworker earnings increases by 0.676 percent. This finding also suggests a largepass-through of the R&D-induced profitability to worker earnings.A potential reason for why the preferred pass-through estimate (0.824) is largerthan Kline et al. [2019] and Carbonnier et al. [2022] is that the tax-credits inducedrents are captured, at least at the first pass, by workers. The SRED tax creditspolicy mandates strict tax return reports and close examination to ensure firmsdo not subsidize non-R&D activities through this tax credit. Second, similar toVan Reenen [1996] and Kline et al. [2019], my focus is on innovative firms, par-ticularly R&D-intensive firms. The role of human capital in these firms may beanother potential reason for why the pass-through estimate is larger than the exist-ing literature. These estimates should be interpreted with caution, as the F-statisticin all columns falls below the standard threshold of 10. This indicates that theinstrument has a weak correlation with the endogenous rent measure, which mayintroduce biases into the pass-through estimates.2.4.2 Within-Firm Pass-Through HeterogeneityTo further explore the incidence of the tax credits, I investigate the rent-sharingframework by using other moments of earnings distribution besides the mean. Inthis section, I use various earnings deciles (D1, ..., D9) as the outcome variable, butotherwise I follow the same methodology. Panel A of Figure 2.4 presents the pass-through estimates. The estimates are increasing across deciles, with only the topthree decile estimates statistically significant at 5 percent, implying the larger im-pact of the R&D tax credits on the high-earners. Panel B of Figure 2.4 presents therent capture across the earnings distribution. This panel shows how a dollar passedonto workersâ earnings is distributed across different deciles of worker earningsdistribution. I find that the top 30 percent of workers capture roughly 60 percent34of the rents passed through to workers, with the top decile of workers capturinga disproportionately larger share of the rents. The results point to an increase inthe within-firm wage inequality, with the top 30 percent of workers capturing thelionsâ share of passed-through rents.A change in worker composition, i.e., a shift towards skilled workers, can po-tentially drive our rent-sharing results [Bøler, 2015, Lindner et al., 2022]. Section2.3.1 provides detailed firm-level tests to show that this mechanism is unlikely thedriver of my findings. For further corroboration, the section provides heterogeneityanalysis on earnings of a fixed cohort of workers.2.5 Within-Firm Wage InequalityThis section explores how the increase in R&D expenditures affects within-firmwage inequality. I investigate how the impact on workers varies across skills,tenure, within-firm wage distribution, and age.2.5.1 SkillA growing body of evidence shows significant heterogeneity in the pass-throughof rents to workers [Carbonnier et al., 2022, Kline et al., 2019, Saez et al., 2019].Heterogeneous demand for skill can help us understand the resulting within-firmwage dynamics. For instance, Carbonnier et al. [2022] examine the impact of apolicy aimed at firms with a higher proportion of low-wage workers and find asignificant pass-through to high-skill incumbents, with no effect on the earningsof low-skill workers. Similarly, Kline et al. [2019] show that the grant of high-quality patents results in increased earnings for inventors and workers in the top25 percentiles of a firmâs wage distribution, arguing that less easily substitutableincumbents receive a higher wage premium. Additionally, Lindner et al. [2022]demonstrate that technological changes at the firm level lead to shifts in relativedemand for skills and an associated skill premium.Conversely, Howell and Brown [2023], in their study of a one-time cash flowshocks at small innovative firms, find no variation in earnings based on skill proxiessuch as initial wage or education. A key limitation of this study, in contrast toworks like Carbonnier et al. [2022], Kline et al. [2019], is the need for more data35on workersâ specific occupations, making it impossible to differentiate betweenR&D and non-R&D workers. To address this, I rely on proxies for worker skillsand job complexity to analyze how increases in R&D expenditures affect earningsheterogeneity.Workers Fixed Effects. How does an increase in firmsâ R&D expenditureimpact workersâ earnings based on their skill levels? I use worker fixed effects,as estimated in Section 2.2.3, to proxy for skill. Table 2.10 presents estimatesfor incumbent workers and stayers, distinguishing between high-skill and low-skillworkers. The results show that low-skill workers experience no significant changesin either their annual earnings or retention rates. However, high-skill workers attreated firms see a 1.6 log points increase in annual earnings and a 0.7 percentagepoint rise in retention rate compared to control workers with similar skill levels. Interms of elasticity, a 10 percent increase in EL leads to a 1 percent rise in annualearnings and a 0.4 percent increase in the retention rate. These findings suggestthat high-skill workers benefit most from increased firm rents, supporting the rent-sharing framework.Within-Firm Earnings Distribution. Another way to approximate workersâskill is by using the within-firm earnings distribution, which serves as a roughindicator of job complexity and the workerâs value to the firm. Table 2.11 pro-vides estimates for incumbent workers and stayers, broken down by quartiles ofthe within-firm earnings distribution. The results show that workers in the bottomhalf of the distribution experience no significant changes in their annual earnings orretention rates. However, workers in the top half of the distribution at treated firmssee a 1.6 to 1.7 log point increase in their annual earnings. In terms of elasticity, a10 percent increase in EL results in a 1.1 percent increase in annual earnings.2.5.2 General Experience and Firm-Specific ExperienceTenure. Returns to tenure arise for several reasons, including firm-specific humancapital [Topel, 1991], hiring costs [Oi, 1962], implicit employee financing [Guisoet al., 2013, Howell and Brown, 2023], and rent-sharing [Card et al., 2014]. Table2.12 presents estimates for incumbent workers and stayers, separating high-tenureworkers (those with more than 4 years at the firm) from low-tenure workers (those36with 3 years or less). The results show no significant changes in the annual earningsof low-tenure workers, while high-tenure workers at treated firms experience a1.8 to 2 log point increase in their annual earnings compared to control workerswith similar tenure. In terms of elasticity, a 10 percent increase in EL leads to a1.2 to 1.3 percent rise in annual earnings. Although low-tenure workers have aslightly higher retention rate than high-tenure workers, the difference is small andstatistically insignificant.Age. Prior studies have found that firm-level shocks can have varying effectson workersâ earnings depending on their age [Saez et al., 2019]. Table 2.13 reportsthe estimates for incumbent workers and stayers, broken down by different agegroups. In contrast to Saez et al. [2019], I find that the largest impact is on workersin their 40s and 50s, with little to no effect on those under 40. In terms of elasticity,a 10 percent increase in EL leads to a 1.2 to 1.9 percent rise in annual earnings.These results align with the rent-sharing framework, suggesting that older workers(with more general knowledge) and high-tenure workers (with greater firm-specificknowledge) gain the most from an increase in firm rentsin this case, induced byR&D tax credits.Overall, the results indicate that the rents generated by R&D tax credits atR&D-intensive firms disproportionately benefit high-skill workersmeasured by workerfixed effects or within-firm earnings distributionalong with older workers and thosewith longer tenure at their firms. My results are complement to Bøler [2015], Lind-ner et al. [2022], in that, they show that innovation increases firmsâ relative demandfor skill. However, I do not find evidence of change in worker composition at firm-level. This is consistent with theoretical framework of Caselli [1999] that show theadoption rate of new technologies depends on workersâskill (in my context R&Dintensity). The difference in adoption rate predicts the increased wage dispersionbetween (and within) firms, as well as, increased productivity, but no change insorting of workers among firms.2.6 ConclusionThis paper contributes to the understanding of how R&D tax credits influence wageinequality among innovative firms. The findings demonstrate that while R&D tax37credits increase R&D expenditures, productivity, and wages, they also exacerbatewage inequality both between and within firms. Between-firm wage inequality isdriven by the stronger response of R&D-intensive firms to the tax credits, allow-ing these firms to widen the productivity and wage premium gap over less R&D-intensive firms. Within-firm wage inequality is amplified by rent-sharing mecha-nism, where high-skill, long-tenured, and older workers capture most of the wagegains, while low-skill workers experience little to no benefit.Future research can build on these results by using the kinked relationship be-tween lagged taxable income and the EL as an instrumental variable to directlyevaluate the impact of changes in R&D expenditure on within- and between-firmwage inequality [Bøler, 2015]. This approach could provide more causal insightsinto the link between innovation policies and wage dynamics.Moreover, the role of R&D in developing firmsâ absorptive capacity â wherefirms increase productivity without necessarily increasing innovative output â hasreceived limited attention in the literature. This paper provides suggestive evi-dence that R&D expenditure enhances absorptive capacity, enabling firms to boostproductivity by improving their ability to leverage external knowledge. Furtherresearch leveraging firm-level data could explore within-country knowledge flowsand productivity patterns, similar to cross-country analyses by Griffith et al. [2004].Similar to findings of Faggio et al. [2010], the tax credits in this study lead to anincrease in TFP dispersion, suggesting that technological differences across firmsmay be driving the observed wage and productivity disparities. Faggio et al. [2010]also highlights that the majority of the growth in wage and productivity dispersionoccurs within industries, implying that industry-level data alone may not capturethe underlying causes of these changes. This underscores the importance of firm-level analyses in understanding the impact of new technologies and R&D activitieson wage inequality.This study emphasizes the importance of situating R&D tax credits within thebroader innovation policy suite. The evidence shows that the benefits of tax creditsare concentrated among R&D-intensive firms with substantial internal resourcesand profitability, suggesting that financial constraints are not the primary barrier toinnovation in this context, which may not be true for smaller and younger firmsthat lack such resources. More broadly, effective innovation policy may also re-38quire tools beyond fiscal incentives, such as improving firms access to advisoryservices, international markets, or pools of high-skill workers. Taken together,these considerations highlight that while tax credits can stimulate innovation andbenefit incumbent workers, their effectiveness is shaped by firm characteristics,and they are most impactful when deployed alongside other targeted policies.Future work could also examine additional mechanisms, such as worker mobil-ity, complementary between firm-level and worker-level characteristics, and com-plementary between government policies to deepen our understanding of how in-novation policies shape labor market outcomes. Expanding the analysis to othercountries or tax regimes would provide valuable insights into the generalizabilityof these findings across different economic contexts.39Figure 2.1: Expenditure Limit ScheduleRefundable35% R&D tax creditNon-refundable20% R&D tax creditEL tmaxExpenditure Limitzttop* ztbottom*Lagged Taxable IncomeNotes: This graph shows the general SRED tax credit schedule. All Canadian-controlled privatecorporations are eligible for 35 percent refundable tax credits up to a threshold called ExpenditureLimit â the black solid line â which is a function of lagged taxable income. If a firmâs R&Dexpenditure exceeds the Expenditure Limit, its marginal tax credit rate drops to 20 percent, and thetax credits will be nonrefundable. ELmaxt is the maximum expenditure limit, ztopât is the start of thephase-out threshold, and zbottomât is the end of the phase-out threshold.40Figure 2.2: McCrary Test For Manipulation at the Kink050010001500Number of Observations-20 -15 -10 -5 0 5 10 15 20Lagged Taxable Income ($ in ten thous.)Notes: This figure shows the probability density function of the assignment variable (lagged taxableincome) for the tax credit schedule normalized at the kink point. I pooled the data from all kinkpoints across the 2002-2019 period. The estimation sample includes Canadian-controlled privatecorporations with lagged taxable income within $200k of the kink point. Section 2.2 describes thesample selection criteria.41Figure 2.3: The Kink in Average SRED Expenditure100,000140,000180,000220,000Average SRED Expenditure (in $)-20 -15 -10 -5 0 5 10 15 20Lagged Taxable Income (in $ ten thous.)Notes: This figure shows the average R&D expenditure for firms in $10k bins of the lagged taxableincome normalized at the kink point. I pooled the data from all kink points across the 2002-2019period. The estimation sample includes Canadian-controlled private corporations with laggedtaxable income within $200k of the kink point. Section 2.2 describes the sample selection criteria.The figure shows evidence of a kink in the relation of lagged taxable income and R&Dexpenditures. The lines display the predicted values of two linear regressions separately for eachside of the kink point.42Figure 2.4: Rent-Sharing by Wage Decile(a) Pass-Through Estimate0.511.522.5Pass-Through Estimate1 2 3 4 5 6 7 8 9Wage Decile(b) Rent Capture0.000.050.100.150.200.250.300.35Capture of Rent1 2 3 4 5 6 7 8 9Wage DecileNotes: This figure reports the within-firm pass-through heterogeneity. I use various earning deciles,D1, ..., D9, as the outcome variable in the rent-sharing framework discussed in Section 2.4. Panel Apresents the pass-through estimates across the earnings deciles from Equation 2.7. Panel B presentsrent capture across the earnings distribution. This panel shows the distribution of every dollarpassed onto worker earnings. This panel shows how a dollar passed onto workersâ earnings isdistributed across different deciles of worker earnings distribution. For example, the top 10 percentof workers capture roughly 30 percent of the rents passed onto worker earnings.43Figure 2.5: Tax Credits Impact by Bandwidth-5,00005,00010,00015,00020,000Estimated Coefficient on R&D Expenditure4.0 5.0 6.0 7.0 8.0 9.0 10.0Bandwidth (in $ ten thous.)Notes: This figure plots the regression kink estimates corresponding to Equation 2.3 using a widerselection of bandwidth around the kink point. The outcome variable is R&D expenditure. Thebandwidth selection is ranging from $40k to $100k with $5k steps. The red dash line shows thebaseline estimate based on $67,600 bandwidth, which is suggested by Calonico et al. [2014]âsmethod.44Figure 2.6: Placebo Kink Point-10,000-5,00005,00010,000Coefficient Estimate-10 -5 0 5 10Distance from the Kink Point (in $ ten thous.)Notes: This figure plots the regression kink estimates corresponding to Equation 2.3 for a wideselection of placebo kink points. The placebo kink point selection is ranging from -$100k to $100kwith $10k steps. The true kink point is at 0 after normalization. The red dashed line shows thebaseline estimate for this kink point. The figure shows that the relationship between R&Dexpenditure and the EL (and lagged taxable income) quickly disappears as the placebo kink pointgets further from 0.45Table 2.1: Descriptive Statistics on FirmsAll Firms R&D-Intensive FirmsTreated Control Treated ControlFirm CharacteristicsR&D Exp (â000) 167.4 164.8 453.5 493.4R&D Exp/Total Revenue 0.07 0.06 0.20 0.19R&D Wages/Total Payroll 0.03 0.03 0.12 0.10Employment 52.2 56.0 39.2 41.7Average Wage (â000) 47.6 47.3 54.0 51.8Incumbent Wage (â000) 48.8 48.0 56.7 54.4Entrant Wage (â000) 14.1 14.8 16.8 16.5Profit Margins 0.12 0.13 0.15 0.16Leverage 0.54 0.52 0.54 0.48Retained Earnings/Total Assets 0.42 0.44 0.41 0.47Firm Age 15.5 16.1 12.9 13.5Number of Firms 2,960 2,250 740 520SectorsUtility/Mining 0.01 0.01 0.01 0.01Construction 0.05 0.06 0.03 0.03Manufacturing 0.53 0.52 0.47 0.49Wholesale Trade 0.11 0.11 0.06 0.06Transportation 0.01 0.01 0.01 0.00Information 0.02 0.02 0.05 0.03Services 0.12 0.12 0.27 0.28Other 0.14 0.14 0.10 0.10Notes:This table reports descriptive statistics on the firm sample, measured one year prior to thetreatment. Panel A reports firm characteristics such as R&D expenditure, R&D expenditurescaled by total revenue, share of R&D wages out of total payroll, employment, average wages,incumbentsâ average wage, entrantsâ average wage, profit margins, leverage ratio, retainedearnings scaled by total assets, and firm age. Columns (1) and (3) report these statistics,respectively, for all treated firms and for treated R&D-intensive firms, based on R&Dexpenditure scaled by revenue. See section 2.3 for more details. Column (2) and (4) report thesestatistics for their respective control firms. Panel B reports the distribution of firms in theanalysis sample across 2-digit NAICS sectors. Other sectors include (1) agriculture, forestry,and fishing; (2) real estate and rental and leasing; (3) arts, entertainment, and recreation; (4)accommodation and food services; (5) retail trades; (6) other services; and (9) publicadministration.46Table 2.2: Descriptive Statistics on WorkersAll Firms R&D Intensive FirmsTreated Control Treated ControlWorker CharacteristicsEarnings (â000) 43.5 45.1 46.9 48.6Age 39.2 39.3 37.8 38.3Female 0.29 0.29 0.30 0.29Tenure 3.82 3.87 3.20 3.02Number of Workers 92,750 80,110 17,410 11,830SectorsUtility/Mining 0.01 0.01 0.01 0.01Construction 0.05 0.06 0.03 0.03Manufacturing 0.63 0.59 0.55 0.55Wholesale Trade 0.08 0.07 0.04 0.03Transportation 0.02 0.01 0.01 0.01Information 0.02 0.01 0.04 0.02Services 0.11 0.15 0.22 0.26Other 0.08 0.10 0.08 0.09Notes: This table reports descriptive statistics on the worker sample, measured one yearprior to the treatment. Panel A reports worker characteristics such as worker earnings,age, gender, and tenure at the firm. Columns (1) and (3) report these statistics,respectively, for workers at all treated firms and for workers at treated R&D-intensivefirms, based on R&D expenditure scaled by revenue. See section 2.3 for more details.Columns (2) and (4) report these statistics for their respective control workers. Panel Breports the distribution of firms in the analysis sample across 2-digit NAICS sectors.Other sectors include (1) agriculture, forestry, and fishing; (2) real estate and rental andleasing; (3) arts, entertainment, and recreation; (4) accommodation and food services;(5) retail trades; (6) other services; and (9) public administration.47Table 2.3: Tax Credits Impact on R&D Expenditure(1) (2) (3) (4) (5)Dependent Variable: R&D ExpenditureWindow: 3 Years 4 Years 5 Years 5 Years 5 YearsEligible Ã Z 5428.6** 5704.0** 5975.5** 6162.0** 6100.2**(2362.4) (2413.5) (2473.9) (2533.8) (2520.1)Mean at t = -1 167436.5 167436.5 167436.5 167664.5 167664.5Adj. R squared 0.670 0.642 0.620 0.632 0.632Observations 5,200 5,200 5,200 5,160 5,160Notes: This table reports the regression kink estimates of the average treatment effect Î¸Ë = Î³Ë110 whereÎ³Ë1 is the estimated change in slope in the relationship between the outcome variables and the runningvariable at the kink point from Equation 2.3. The baseline sample includes R&D firms with laggedtaxable income within $67,600 of the kink. The running variable is the lagged taxable income. InColumns (1) to (3), controls include (i) lagged dependent variable, and (ii) firm age. In Column (4),controls include (i) lagged dependent variable, (ii) firm age, and (iii) industry fixed effects. In Column(5), controls include (i) lagged dependent variable, (ii) firm age, (iii) industry fixed effects, and (iv)province fixed effects. Standard errors are clustered by firm.48Table 2.4: R&D Expenditures and Wages(1) (2) (3) (4) (5) (6) (7) (8)Measure of Intensity: R&D Exp. Scaled by Revenue R&D Wages Scaled by Total PayrollDependent Variable: R&D Exp. R&D Wages Wages log(Wages) R&D Exp. R&D Wages Wages log(Wages)Eligible Ã Z (R&D-Int. = 1) 24424.3*** 18489.9*** 1367.4*** 0.016*** 20184.5** 16488.1*** 1186.1*** 0.016***(8860.0) (5750.9) (422.9) (0.006) (8266.6) (5583.7) (437.4) (0.006)Eligible Ã Z (R&D-Int. = 0) 629.5 916.3 -233.0 -0.004 1488.4 871.7 -263.0 -0.004(1705.6) (1112.3) (213.8) (0.013) (1807.2) (1115.0) (210.8) (0.003)Difference: 23943.0*** 17663.6*** 1692.7*** 0.020*** 19001.5** 15860.5*** 1458.6*** 0.020***(8959.5) (5860.9) (461.9) (0.006) (8482.8) (5735.9) (474.8) (0.006)Mean at t = -1 (R&D-Int. = 1) 474536.3 292138.8 54734.8 10.79 442493.7 291083.9 52893.3 10.76Mean at t = -1 (R&D-Int. = 0) 71548.0 47611.5 46572.5 10.64 77410.6 44560.6 46302.1 10.63Adj. R squared (R&D-Int. = 1) 0.504 0.507 0.687 0.807 0.536 0.507 0.678 0.791Adj. R squared (R&D-Int. = 0) 0.438 0.397 0.690 0.826 0.457 0.445 0.704 0.838Observations (R&D-Int. = 1) 1,260 1,260 1,240 1,240 1,300 1,300 1,290 1,290Observations (R&D-Int. = 0) 3,770 3,770 3,740 3,740 3,900 3,900 3,870 3,870Notes: This table reports the regression kink estimates of the average treatment effect Î¸Ë = Î³Ë110 where Î³Ë1 is the estimated change in slope in the relationship between the outcomevariables and the running variable at the kink point from Equation 2.3, separately by R&D intensity measure. R&D-intensive firms include R&D firms in the top 25 percentiles ofdistribution, and non-R&D-intensive firms include R&D firms in the bottom 75th percentile. The baseline sample includes R&D firms with lagged taxable income within $67,600of the kink. The running variable is the lagged taxable income. Controls include (i) lagged dependent variable, and (ii) firm age. Standard errors are clustered by firm.49Table 2.5: Growth and Profitability(1) (2) (3) (4) (5) (6) (7) (8)Measure of Intensity: R&D Exp. Scaled by Revenue R&D Wages Scaled by Total PayrollProfit Margins TFP log(Revenue) log(Employment) Profit Margins TFP log(Revenue) log(Employment)Eligible Ã Z (R&D-Int. = 1) 0.024* 0.084* 0.039 0.013 0.031** 0.080* 0.050** 0.011(0.013) (0.049) (0.026) (0.012) (0.014) (0.044) (0.025) (0.011)Eligible Ã Z (R&D-Int. = 0) 0.009* 0.021 0.005 0.001 0.006 0.020 0.002 0.002(0.005) (0.024) (0.011) (0.006) (0.005) (0.025) (0.011) (0.006)Difference: 0.015 0.067 0.036 0.012 0.025* 0.065 0.046 0.009(0.014) (0.056) (0.028) (0.013) (0.015) (0.051) (0.028) (0.013)Mean at t = -1 (R&D-Int. = 1) 0.16 2.92 15.27 3.33 0.15 2.75 1539 3.22Mean at t = -1 (R&D-Int. = 0) 0.15 2.09 159.93 3.73 0.16 2.13 15.89 3.74Adj. R squared (R&D-Int. = 1) 0.046 0.769 0.438 0.779 0.018 0.782 0.449 0.771Adj. R squared (R&D-Int. = 0) 0.004 0.844 0.641 0.825 0.031 0.840 0.637 0.830Observations (R&D-Int. = 1) 1,250 1,080 1,250 1,240 1,280 1,150 1,280 1,290Observations (R&D-Int. = 0) 3,760 3,330 3,750 3,740 3,730 3,260 3,720 3,870Notes: This table reports the regression kink estimates of the average treatment effect Î¸Ë = Î³Ë110 where Î³Ë1 is the estimated change in slope in therelationship between the outcome variables and the running variable at the kink point from Equation 2.3, separately by R&D intensity measure.R&D-intensive firms include R&D firms in the top 25 percentiles of distribution, and non-R&D-intensive firms include R&D firms in the bottom 75thpercentile. The baseline sample includes R&D firms with lagged taxable income within $67,600 of the kink. The running variable is the laggedtaxable income. Controls include (i) lagged dependent variable, and (ii) firm age. Standard errors are clustered by firm.50Table 2.6: Worker Composition(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Measure of Intensity: R&D Exp. Scaled by Revenue R&D Wages Scaled by Total PayrollShare Average log(Wage) log(Wage) Share Share Average Share log(Wage) log(Wages)Female Age Entrants Movers High-Skill Female Age High-Skill Entrants Moversbefore Joining before leaving before Joining before leavingEligible Ã Z (R&D-Int. = 1) 0.004** -0.012 0.014 0.009 0.001 0.003 0.044 0.013 0.010 0.001(0.002) (0.070) (0.016) (0.014) (0.003) (0.002) (0.070) (0.015) (0.013) (0.003)Eligible Ã Z (R&D-Int. = 0) 0.001 -0.002 -0.006 -0.004 -0.000 0.001 -0.016 -0.007 -0.005 -0.000(0.001) (0.039) (0.007) (0.007) (0.001) (0.001) (0.040) (0.007) (0.007) (0.001)Difference: 0.004* -0.007 0.020 0.011 0.001 0.002 0.063 0.021 0.013 0.001(0.002) (0.079) (0.018) (0.016) (0.003) (0.002) (0.081) (0.017) (0.015) (0.003)Mean at t = -1 (R&D-Int. = 1) 0.28 37.94 10.03 10.19 0.38 0.29 38.06 10 10.15 0.37Mean at t = -1 (R&D-Int. = 0) 0.28 39.27 9.93 10.1 0.32 0.28 39.15 9.93 10.1 0.32Adj. R squared (R&D-Int. = 1) 0.87 0.755 0.148 0.299 0.729 0.858 0.756 0.147 0.297 0.723Adj. R squared (R&D-Int. = 0) 0.907 0.784 0.286 0.0380 0.751 0.905 0.784 0.283 0.39 0.749Observation (R&D-Int. = 1) 1,240 1,240 1,040 980 1,240 1,286 1,286 1,090 1,010 1,290Observation (R&D-Int. = 0) 3,736 3,736 3,360 3,140 3,740 3,867 3,867 3,450 3,240 3,870Notes: This table reports the regression kink estimates of the average treatment effect Î¸Ë = Î³Ë110 where Î³Ë1 is the estimated change in slope in therelationship between the outcome variables and the running variable at the kink point from Equation 2.3, separately by R&D intensity measure.R&D-intensive firms include R&D firms in the top 25 percentiles of distribution, and non-R&D-intensive firms include R&D firms in the bottom 75thpercentile. The baseline sample includes R&D firms with lagged taxable income within $67,600 of the kink. The running variable is the laggedtaxable income. Controls include (i) lagged dependent variable, and (ii) firm age. Standard errors are clustered by firm.51Table 2.7: Entry and Separation(1) (2) (3) (4)Measure of Intensity: R&D Exp. Scaled by Revenue R&D Wages Scaled by Total PayrollShare of Entrants Share of Movers Share of Entrants Share of MoversEligible Ã Z (R&D-Int. = 1) 0.000 -0.012* -0.002 -0.018***(0.003) (0.007) (0.003) (0.007)Eligible Ã Z (R&D-Int. = 0) 0.002 -0.005 0.002 -0.003(0.002) (0.004) (0.002) (0.004)Difference: -0.002 -0.008 -0.004 -0.016**(0.003) (0.008) (0.003) (0.008)Mean at t = -1 (R&D-Int. = 1) 0.25 0.22 0.26 0.22Mean at t = -1 (R&D-Int. = 0) 0.25 0.25 0.25 0.25Adj. R squared (R&D-Int. = 1) 0.148 0.052 0.219 0.052Adj. R squared (R&D-Int. = 0) 0.238 0.117 0.219 0.123Observations (R&D-Int. = 1) 1,240 1,240 1,290 1,290Observations (R&D-Int. = 0) 3,740 3,740 3,870 3,870Notes: This table reports the regression kink estimates of the average treatment effect Î¸Ë = Î³Ë110 where Î³Ë1 is the estimated change in slope inthe relationship between the outcome variables and the running variable at the kink point from Equation 2.3, separately by R&D intensitymeasure. R&D-intensive firms include R&D firms in the top 25 percentiles of distribution, and non-R&D-intensive firms include R&Dfirms in the bottom 75th percentile. The baseline sample includes R&D firms with lagged taxable income within $67,600 of the kink.The running variable is the lagged taxable income. Controls include (i) lagged dependent variable, and (ii) firm age. Standard errors areclustered by firm.52Table 2.8: Tax Credits Impact on Earnings(1) (2) (3) (4) (5)Dependent Variable: Log(Earnings)Sample: All Workers Stayers MoversW Window: 3 Years 4 Years 5 Years 5 YearsEligible Ã Z 0.011*** 0.012*** 0.011** 0.009** 0.006(0.004) (0.004) (0.004) (0.004) (0.007)Mean at t = -1 10.36 10.36 10.36 10.36 10.14Adj. R squared 0.459 0.426 0.395 0.558 0.268Observations 29,230 29,230 29,230 29,230 17,060Notes: This table reports the regression kink estimates of the average treatment effect Î¸Ë = Î³Ë110where Î³Ë1 is the estimated change in slope in the relationship between the outcome variables andthe running variable at the kink point from Equation 2.5, for workers at R&D-intensive firms.The baseline sample includes workers with at least one year of tenure at the firm. I drop workerswith multiple jobs. The running variable is the lagged taxable income. Controls include (i)lagged dependent variables. Standard errors are clustered by worker.53Table 2.9: Rent-Sharing Estimates(1) (2) (3) (4)Dependent Variable: log(Average Wages)Method: IV: Eligible Ã Zlog ofR&D Exp. 0.094*(0.055)Surplus per Worker 0.824**(0.341)Value Added per Worker 2.192(2.105)Profit per Worker 0.676**(0.310)Mean at t=-1 12.76 11.19 11.35 11.18Centered R squared 0.998 0.999 0.993 0.999Observations 1,240 1,240 1,240 1,240F-Stats 2.55 3.23 2.61 2.78Notes: This table reports the IV estimates of pass-through for the average workerearnings. The first stage is the estimated change in slope in the relationship betweenthe rent measure and the running variable at the kink point from Equation 2.5, forR&D-intensive firms. The second stage is the estimated change in the averageworker earnings on the predicted rent. Column (1) to (4) report the pass-throughestimates using R&D expenditure, surplus per worker, value-added per worker, andprofit per worker, respectively, as their measure of rent. R&D-intensive firmsinclude R&D firms in the top 25 percentiles of R&D distribution. Baseline sampleincludes R&D firms with lagged taxable income within $67,600 of the kink. Therunning variable is the lagged taxable income. Controls include (i) lagged dependentvariable, and (ii) firm age. Standard errors are clustered by firm.54Table 2.10: Heterogeneity based on AKM Worker Fixed Effects(1) (2) (3)Dependent Variable: log(Earnings) RetentionSample: All Stayers AllEligible Ã Z (High Skill) 0.016*** 0.011** 0.007***(0.005) (0.004) (0.002)Eligible Ã Z (Low Skill) 0.000 0.002 -0.003(0.006) (0.005) (0.002)Difference 0.017** 0.009 0.009***(0.008) (0.007) (0.002)Mean at t = -1 (High Skill) 10.9 10.9 1Mean at t = -1 (Low Skill) 10.03 10.03 1Adj. R squared (High Skill) 0.388 0.543 0.003Adj. R squared (Low Skill) 0.261 0.427 0.002Observations (High Skill) 10,750 10,750 10,750Observations (Low Skill) 16,820 16,820 16,820Notes: This table reports the regression kink estimates of the average treatmenteffect Î¸Ë = Î³Ë110 where Î³Ë1 is the estimated change in slope in the relationshipbetween the outcome variables and the running variable at the kink point fromEquation 2.5, for workers at R&D-intensive firms. The baseline sample includesworkers with at least one year of tenure at the firm. I drop workers with multiplejobs. The running variable is the lagged taxable income. Controls include (i)lagged dependent variables. Standard errors are clustered by worker.55Table 2.11: Heterogeneity based on Within-Firm Earnings Distribution(1) (2) (3)Dependent Variable: log(Earnings) RetentionSample: All Stayers AllEligible Ã Z (4th Quartile) 0.016** 0.012*** 0.006***(0.006) (0.004) (0.002)Eligible Ã Z (3rd Quartile) 0.017** 0.014*** 0.000(0.007) (0.005) (0.002)Eligible Ã Z (2nd Quartile) 0.009 0.007 0.002(0.009) (0.007) (0.003)Eligible Ã Z (1st Quartile) -0.007 -0.007 -0.001(0.013) (0.012) (0.003)Mean at t = -1 (4th Quartile) 11.04 11.04 1Mean at t = -1 (3rd Quartile) 10.45 10.45 1Mean at t = -1 (2nd Quartile) 10.05 10.05 1Mean at t = -1 (1st Quartile) 9.43 9.43 1Observations (4th Quartile) 8,595 8,595 8,595Observations (3rd Quartile) 8,535 8,535 8,535Observations (2nd Quartile) 7,255 7,255 7,255Observations (1st Quartile) 4,850 4,845 4,850Notes: This table reports the regression kink estimates of the average treatmenteffect Î¸Ë = Î³Ë110 where Î³Ë1 is the estimated change in slope in the relationshipbetween the outcome variables and the running variable at the kink point fromEquation 2.5, for workers at R&D-intensive firms. The baseline sample includesworkers with at least one year of tenure at the firm. I drop workers with multiplejobs. The running variable is the lagged taxable income. Controls include (i)lagged dependent variables. Standard errors are clustered by worker.56Table 2.12: Heterogeneity based on Tenure(1) (2) (3)Dependent Variable: log(Earnings) RetentionSample: All Stayers AllEligible Ã Z (High Tenure) 0.018** 0.020*** 0.001(0.008) (0.006) (0.002)Eligible Ã Z (Low Tenure) 0.007 0.005 0.003*(0.005) (0.004) (0.001)Difference: 0.018* 0.019*** -0.002(0.010) (0.007) (0.002)Mean at t = -1 (High Tenure) 10.84 10.84 1Mean at t = -1 (Low Tenure) 10.22 10.22 1Adj. R squared (High Tenure) 0.495 0.624 0.001Adj. R squared (Low Tenure) 0.355 0.522 0.002Observations (High Tenure) 6,240 6,240 6,240Observations (Low Tenure) 22,990 22,990 22,990Notes: This table reports the regression kink estimates of the average treatmenteffect Î¸Ë = Î³Ë110 where Î³Ë1 is the estimated change in slope in the relationship betweenthe outcome variables and the running variable at the kink point from Equation 2.5,for workers at R&D-intensive firms. The baseline sample includes workers with atleast one year of tenure at the firm. I drop workers with multiple jobs. The runningvariable is the lagged taxable income. Controls include (i) lagged dependentvariables. Standard errors are clustered by worker.57Table 2.13: Heterogeneity based on Worker Age(1) (2) (3)Dependent Variable: log(Earnings) RetentionSample: All Stayers AllEligible Ã Z (50s) 0.024** 0.029*** 0.005*(0.012) (0.008) (0.003)Eligible Ã Z (40s) 0.019** 0.012* 0.002(0.008) (0.006) (0.002)Eligible Ã Z (30s) -0.002 0.001 0.006**(0.008) (0.006) (0.002)Eligible Ã Z (20s) 0.007 0.012* -0.000(0.008) (0.007) (0.003)Mean at t = -1 (50s) 10.62 10.62 1Mean at t = -1 (40s) 10.62 10.62 1Mean at t = -1 (30s) 10.54 10.54 1Mean at t = -1 (20s) 10.08 10.08 1Observations (50s) 4,300 4,300 4,300Observations (40s) 7,230 7,230 7,230Observations (30s) 7,930 7,930 7,930Observations (20s) 7,130 7,130 7,130Notes: This table reports the regression kink estimates of the averagetreatment effect Î¸Ë = Î³Ë110 where Î³Ë1 is the estimated change in slope in therelationship between the outcome variables and the running variable atthe kink point from Equation 2.5, for workers at R&D-intensive firms.The baseline sample includes workers with at least one year of tenureat the firm. I drop workers with multiple jobs. The running variable isthe lagged taxable income. Controls include (i) lagged dependentvariables. Standard errors are clustered by worker.58Chapter 3Job Transitions and EmployeeEarnings After Acquisitions:Linking Corporate and WorkerOutcomesMergers and acquisitions (M&As hereinafter) often result in important changes incorporate structure and worker composition for both target and acquiring firms.While there exists a large literature on how M&As affect firm performance andmarket value, there is growing interest in understanding labor market impacts ofM&As.1 In theory, M&As may have positive or negative impacts on worker earn-ings. If M&As lead to efficiency gains for the involved parties, incumbent workersat acquiring firms or target firms may experience a wage increase through rentsharing. By contrast, M&As may lead to layoffs through labor restructuring whichcould lead to earnings losses for displaced workers. Furthermore, M&As can resultin increased labor-market or product-market power, again leading to lower wages.1There exists a large body of literature that examines the M&A impacts on firm performance,including Schoar [2002], Hoberg and Phillips [2010], Boucly et al. [2011], Davis et al. [2014],Braguinsky et al. [2015b], Blonigen and Pierce [2016], Malmendier et al. [2018], and Demirer andKaraduman [2024]. A growing body of studies assesses the impacts on workers, including Brownand Medoff [1988], Siegel and Simons [2010], Dessaint et al. [2017], Ouimet and Zarutskie [2020],Prager and Schmitt [2021], Arnold [2021], Lagaras [forthcoming], and He and le Maire [2022].59Since M&As may bring significant changes in both corporate and labor structure,a key challenge in answering this question is to link worker and firm outcomesfollowing M&As to delve into potential mechanisms behind changes in workerearnings.In this paper, we provide new empirical evidence on changes in workersâ la-bor market outcomes following corporate M&As. While prior studies focus onlyon either firm-level outcomes or worker-level outcomes, in our work we are ableto link detailed firm balance sheet data from corporate tax returns to worker-levelearnings data from individual tax returns in Canada.2 This allows us to not only as-sess employee-level outcomes, but also to study firm outcomes to connect and pindown potential mechanisms behind our findings. Specifically, the detailed joint in-formation on firms and workers allows us to study the connection between changesin employer characteristics via job transitions and employee earnings after M&As.We first examine the impacts of M&As on employment and average payrollsat the firm level. To do so, we implement a matched DID design, where we matchfirms that go through M&A events to otherwise similar firms that never undergoM&As during our sample period. Importantly, in our setting, around 80 percentof M&A events involve acquisitions, where the target sells its assets, divisions, oronly a part of its share, and remains operating as a separate entity following theM&A event. This allows us to keep track of the targetâs and the acquirerâs out-comes after the event, so that we can assess the impacts on targets and on acquirersseparately.3 By contrast, in mergers, the entirety of the target is sold to (or mergedwith) the acquirer, meaning there is a definite change of control. Given that therecan be qualitative or quantitative differences in firm-level impacts depending onwhether there is a change of control, we separate our firm-level analyses into thoseinvolved in acquisitions and those involved in mergers, although we do not findmeaningfully different results on firm-level outcomes in the aggregate between thetwo types.4 Given that the vast majority of M&A activities in Canada (and in the2While Arnold [2021] also uses employer-employee matched data in the U.S., the LEHD datasetdoes not have detailed firm balance sheet information, such as profit margins, and therefore cannotbe used to simultaneously evaluate firm-level and worker-level outcomes.3As we discuss in detail later, our worker-level results depend substantially on whether the workerwas originally at the target or the acquiring firm.4As discussed in Section 3.2, most acquisitions in our data likely result in a change of control60United States) involve acquisitions, analyzing these activities in addition to merg-ers allows us to exploit more events and to examine a larger and broader sample ofrelevant firms and workers.Focusing on these acquisitions, we find that acquirers expand, while targetsshrink substantially after M&As. Acquirersâ employment increases by 18.8 logpoints, without much change in average payrolls, on average relative to their matchedcontrol firms after M&As. By contrast, targetsâ employment and average payrollsdecrease by 8.9 log points and 2.8 log points on average, respectively. At the ag-gregate (pooling targets and acquirers) level, however, we find almost no changesin employment or average payrolls in both mergers and acquisitions. These nullresults on firm size in the aggregate could be driven by reallocation of workers be-tween targets and acquirers, so we use worker-level data to assess changes in theirearnings and job transitions after M&As.Since the changes in targetsâ average payrolls could be driven by changes inworker composition, the next part of our paper estimates the impact of M&As onworker-level earnings. Here, we do not make a distinction between acquisitionsand mergers, not only because we can track individual worker transitions, but alsobecause we find similar results in both cases (Appendix B.2). For workers at ac-quiring firms, we find relatively stable earnings after M&As. However, for targetworkers, we find their earnings decrease by 1.2 percent. This decline in workerearnings may stem from two channels. First, workers at target firms may face anincreased risk of job displacement, implying the impacts on earnings are driven byjob transitions. Second, workers at target firms may experience earnings losses dueto changes in target firmsâ wage setting.To test this, we first estimate the impact of M&As on job transitions. For targetworkers, we find there is a large increase in the probability of job transitions inthe year following an M&A event. Given that we observe the universe of workersin the formal labor market in Canada, we check that most of these post-M&A jobtransitions are to different companies within the same industry, rather than to self-employment or unemployment. By contrast, for workers at acquiring firms, wethrough the acquisition of majority interest or assets. In both cases, however, the acquirer may notbuy the entire target (known as partial acquisitions), so there could be still quantitative differences infirm-level impacts relative to mergers.61find no change in the probability of job transitions.For individuals who remain at target firms, we observe gradual and medium-run declines in their earnings, resulting in a statistically insignificant decrease inearnings (0.8 percent) on average. However, target workers who move jobs afteran M&A event suffer immediate earnings losses that continue to accumulate overtime, resulting in earnings that are about 4 percent less on average after the M&Aevent. Given the relatively substantial losses for workers in this group, the nextpart of the paper explores the source of this decline in earnings.First, we explore whether changes in the characteristics of the firm employingthe worker can explain the drop in earnings, following a recent literature on jobdisplacement [Schmieder et al., 2023] and [Lachowska et al., 2020]. The firmbalance-sheet dataset allows us to test whether there are observable differencesbetween the target firm and the new firm that target workers move to. We find thatthe new firms are in fact larger (in terms of employment and revenue) and moreprofitable on average compared to the target firms.5 This result suggests that thedecline in job moversâ earnings cannot be explained by these workers moving toobservably worse firms. Importantly, most of these job movers from target firmsmove to non-acquiring firms, meaning that this transition to larger firms is notmechanically driven by worker reallocation toward acquirers.Interestingly, these workers still suffer earnings losses despite moving to theseobservably better firms. This finding suggests there is a pay complementarity be-tween an individual worker and a given firm, which can arise for two reasons.First, models of firm-specific human capital accumulation [Lazear, 2009] implythat a high-tenure worker is especially productive at a given firm. Second, firm paypolicies, such as backloaded contracts [Lazear, 1979] and [Saez et al., 2023], implythat high-tenure workers within a firm will be making in excess of their marginalproduct of labor, meaning that layoffs will lower these workersâ pay. Both of these5Additionally, we estimate firm-specific wage premiums (AKM) to study whether job moverstransition to systematically lower-paying firms. We find that target workers move to higher-payingfirms on average after M&As (see Appendix B.1). One concern with the AKM estimation is thatthe effects are residuals which attempt to explain why a given firm pays more relative to other firms;in other words, there is a potential for omitted variables driving the estimated AKM effects. Thishighlights the importance of using firm balance-sheet data to directly assess their observable charac-teristics, such as their profitability.62models suggest that high-tenure (or high-wage) workers will be especially harmedby job displacement. To assess this channel empirically, we conduct heterogeneityanalyses based on worker tenure measured one year before the event. We find thatthe declines are almost entirely driven by workers with longer tenure. Individualswith shorter tenure are not impacted by the M&A-induced job transition. Further-more, we find that the earnings losses are much larger among workers at the topquintile of the within-firm earnings distribution. This result is also consistent withthe result based on worker tenure, since workers at the top of the within-firm earn-ings distribution are more likely to be the ones who have accumulated firm-specifichuman capital.While we interpret these results as a loss of productivity driven by worker-firmcomplementaries, an alternative interpretation is that the M&A event allows firmsto displace overpaid workers with backloaded contracts. For example, Shleiferand Summers [1988] conjecture that takeovers allow new managers to renege onimplicit contracts and lay off workers paid more than their productivity. We can-not directly observe worker productivity in our data, but firms displacing overpaidworkers may increase their profits. We find a decrease in target firmsâ profitability,which seems inconsistent with this mechanism. While it is possible for these firmsto lose profits after M&As despite displacing overpaid workers, there is anotherpiece of evidence against the backloaded contract channel. If M&As are target-ing overpaid workers to renege on their contracts, we should expect to see largerjob transitions among high-tenure and high-wage workers. However, we find thatchanges in job transitions are similar between low-tenure (or low-wage) workersand high-tenure (or high-wage) workers after M&As. Therefore, this finding, com-bined with the result on decreased profits, suggests that the earnings losses amongtarget workers appear most consistent with a loss of firm-specific human capital.This paper contributes to several distinct literatures. Most directly, we con-tribute to a literature on the impacts of M&As on the labor market [Brown andMedoff, 1988, Siegel and Simons, 2010]. Recently, both Prager and Schmitt [2021]and Arnold [2021] study how M&A events that lead to large changes in labor mar-ket concentration affect worker earnings. In these papers, the goal of the analysis isto argue that other changes, such as shifts in productivity or product market power,are stable across M&A events that differentially impact labor market concentration.63In this paper, we seek to directly estimate these impacts using firm balance-sheetdata. Additionally, the sample of M&A events in this paper is quite different. Inboth our analysis sample and the whole sample of M&A events, we find that shiftsin labor market concentration are relatively rare mainly because most M&A eventshappen across different labor markets. However, we find that these M&A eventsstill have important impacts on job transitions that translate to changes in workeroutcomes.Relative to a recent work documenting that M&A events create significant dis-placements of workers from target firms with higher wage premiums and realloca-tion to smaller firms with lower wage premiums in a developing country [Lagaras,forthcoming], our work shows that the majority of workers displaced from tar-get firms moves to larger and higher-paying firms. This distinction is importantbecause while the prior study implies that M&As are similar to mass layoffs tothe extent that displaced workers are forced to move to lower-paying firms, ourfindings suggest that M&As are different from mass layoffs; thus, a mechanismthrough which displacements affect workers is distinct in our setting. Besides find-ing different mechanisms, our study differs from the prior work by additionally us-ing firm balance sheet data in a developed country with a larger set of M&A eventsand by assessing important firm outcomes in a coherent setting, such as changesin profitability. This helps us rule out alternative mechanisms, such as backloadedcontracts, which could also explain the earnings decline in the prior study. Ouranalysis complements the study by He and le Maire [2022], which focuses on therole of replacing managers in target firms. Theoretically, high-wage managers maybe replaced after M&A events [Shleifer and Summers, 1988] leading to wage de-clines in target firms. While we cannot distinguish managers from other workersin our data, job displacements at targets may have been led by changes in man-agement. Moreover, our work complements recent studies [Dessaint et al., 2017,Ouimet and Zarutskie, 2020, Tate and Yang, 2024] documenting human-capitalconsiderations and labor restructuring as important drivers of M&As. While thesestudies show that acquirers engage in M&As to hire more (productive) workers, wefind that most workers at targets move to non-acquiring firms after the event, im-plying that acquirers engage in M&As to purchase particular assets or intellectualproperties of target firms, rather than to poach productive workers.64Second, our paper relates to a literature on how job transitions impact wages.In particular, a large literature studies the role of firm-specific and match-specificcomponents to explain changes in displaced workersâ wages [Lachowska et al.,2020]; [Schmieder et al., 2023]. Much of the recent work leverages mass layoffsto identify impacts of job transitions on workers earnings. In this paper, we findthat M&A events can lead to significant job transitions for workers from targetfirms, leading to a decline in their earnings. In contrast to findings in the masslayoff literature, we find not only that the earning losses are smaller on average, butalso that displaced workers move to larger and more profitable firms on average,likely because M&A events tend to be pro-cyclical in aggregate [Rhodes-Kropfand Viswanathan, 2004].Lastly, there is a large literature in corporate finance and industrial organizationthat studies how M&As affect firm performance, finding mixed empirical results[Schoar, 2002], [Hoberg and Phillips, 2010], [Boucly et al., 2011], [Davis et al.,2014], [Braguinsky et al., 2015b], [Blonigen and Pierce, 2016], and [Malmendieret al., 2018]. Declines in firm performance have been attributed to misalignedincentives, such as empire-building motives [Jensen, 1986b] and CEO overconfi-dence [Malmendier and Tate, 2005]. Furthermore, acquiring firmsâ profitabilitymay also decrease if the acquisition was covertly intended to reduce competitionand retain market power in the long run at the cost of losing profits in the mediumrun [Cunningham et al., 2021]. Our paper contributes to this literature by estimat-ing the impact of M&As on a variety of firm-level outcomes and by finding resultsconsistent with the set of studies reporting negative impacts of M&As on firm prof-itability. We then use these firm-level outcomes to rule out alternative mechanisms,such as changes in firmsâ market power or implicit contracts, behind the decline inworker earnings. Our ability to link these firm-level outcomes to the worker-leveloutcomes is unique to the literature.This paper is organized as follows. Section 3.1 provides institutional details,Section 3.2 describes our data, Section 3.3 discusses potential impacts of M&As,Section 3.4 describes our design, Section 3.5 shows our main results, and Section3.6 discusses potential mechanisms behind our findings.653.1 Institutional BackgroundThis section describes relevant institutional details about competition policy andlabor regulation. Competition policy in Canada is administered by the Competi-tion Bureau, which enforces a two-step process for merger reviews, similar to theAmerican process with notification thresholds and a supplementary informationrequest. The thresholds for a pre-merger notification requirement are set by theCompetition Act. The two most relevant thresholds are the size of parties and thesize of transaction. Both of these must be met to trigger a pre-merger notification.6In our analysis sample, most M&A deals and involved parties are not large enoughto trigger any of these thresholds.Until recently, the Competition Act featured an âefficiencies defenceâ of merg-ers, allowing anti-competitive mergers to proceed if potential cost savings out-weighed the losses to consumers through higher prices. In the U.S. and manyother jurisdictions, efficiency is also considered as a factor in antitrust decisionsfor mergers but is given less weight.7 Recent legislation repealed the efficienciesdefence for merger reviews after December 15, 2023. Our work, assessing changesin acquirers and targets performance after M&As through a retrospective study,provides evidence that can inform policy discussions about how to weigh potentialefficiency gains from M&As.The Canadian and the U.S. labor markets share many similarities. Labor reg-ulation is the jurisdiction of the provinces for most industries, but unemploymentinsurance is national through the Employment Insurance program. Measures ofemployment protection from the OECD show Canada close to the U.S. with muchlower levels of protection from dismissals than European countries [OECD, 2020].Dismissing a worker requires several weeks of advance notice to the worker and6The parties together must have aggregate assets in Canada, or aggregate annual gross revenuefrom sales in, from or into Canada, in excess of 400 million Canadian dollars. The aggregate valueof the assets in Canada to be acquired, or the aggregate annual gross revenue from sales in or fromCanada generated from those assets, must be greater than 93 million Canadian dollars. There is alsoa size of equity threshold that less frequently comes into play: the acquirer holds 20 percent of theshares of a public corporation, 35 percent of the share of a private corporation, or 35 percent of theprofits or assets on dissolution of a non-corporate entity.7Ware and Winter [2016] assert (p. 366) for Canada that âin no other jurisdiction in the worldwould a court accept evidence of substantial price effects from a merger and yet allow the merger.âA comparison of efficiency defences across many OECD countries is found in OECD [2013].66mandatory severance depending on years of service. Non-compete clauses haverecently been banned in certain provinces, but were generally unenforceable evenbefore the explicit banning [Hanson and Cohen, 2012]. Therefore, non-competeclauses are unlikely to be a substantial issue for worker transitions after M&As inour setting. One difference for labor markets in Canada is the degree of unioniza-tion. During our sample period, the overall coverage in the private sector by unioncontracts is 18 percent on average, compared to 8 percent in the U.S.8 While thehigher union coverage in Canada may render it more difficult to lay off workersin general, high unionization may also make M&As an attractive opportunity torestructure the company, i.e. via layoffs.3.2 DataThis section describes two datasets used for our analysis. First, we use the SDCPlatinum database which contains information on M&A activities around the globe.Second, we use the Canadian Employer-Employee Dynamics Database, a matchedemployee-employer dataset that covers the universe of firms and workers in Canadafrom 2001 to 2017. To prevent disclosure of confidential information, StatisticsCanada requires researchers to round estimates and observation counts.3.2.1 SDC PlatinumThe SDC Platinum database allows detailed search on M&A activities around theworld, covering the near universe of transactions involving either publicly tradedor private companies in Canada.9 This database contains variables that indicate thetype of a transaction, such as merger or different kind of acquisition, and identifythe target firm and the acquiring firm in each transaction.10 Our main analyses8The source for the Canadian statistics is Statistics Canada, Table 14-10-0070-01, âUnion Cov-erage by Industry.â The source of the U.S. statistics is Bureau of Labor Statistics, Series IDLUU0204906700, âPercent of Employed, Private Wage and Salary Workers Represented by Unions.â9While Barrios and Wollmann [2024] notes that the SDC data may not cover all M&A deals inthe U.S., identifying potentially missing M&As in Canada (which might be a small share) is beyondthe scope of our study.10In a relatively rare transaction with multiple ownership changes at the same time due to mul-tiple acquiring firms involved for a given target firm, it identifies all the acquirers. Relatedly, if atransaction involves multiple ownership changes across different years by the same or a differentacquiring firm, the SDC data keeps tracks of these transactions over time and separately lists each of67include both mergers and acquisitions as well as show the results separately. Sincethe literature focuses on the change of control as a key determinant of post-M&Aoutcomes, we include acquisitions given that many of them may result in a changeof control. To understand this choice, we provide details on how the SDC databasecategorizes each M&A transaction.In our database, a merger is defined as an event in which either two partiesbecome one entity or an acquirer buys 100 percent of a targetâs stock. By contrast,an acquisition is defined as taking over either a targetâs assets or a part of its stock.In the sample of all M&As that are matched with our firm-level data in Canadabetween 2001 and 2017, mergers account for 25 percent (20 percent in our analy-sis sample) of the cases, while acquisitions account for the rest. Acquisitions arefurther categorized into the following: (1) acquisition of majority interest, meaningdeals in which the acquirer holds less than 50 percent and is seeking to purchase50 percent or more, but less than 100 percent of the target companys stock, (2)acquisition of remaining interest, meaning deals in which the acquirer holds over50 percent and is seeking to purchase 100 percent of the target companys stock, (3)acquisition of partial interest, meaning deals in which the acquirer holds less than50 percent and is seeking to acquire less than 50 percent, or the acquirer holds over50 percent and is seeking less than 100 percent of the target companys stock, (4)acquisition of assets, meaning deals in which the assets of a company, subsidiary,division, or branch are acquired, and (5) acquisition of certain assets, meaningdeals in which certain assets of a company, subsidiary, division, or branch are ac-quired. Except for (but possibly including some of) the acquisition of assets, theseevents are also known as partial acquisitions. While the acquirer may not purchasethe entirety of the target in an acquisition, the acquirer gains the controlling stakeof the target in the acquisition of majority interest (10 percent of all M&As). Inthis sense, an acquisition of majority interest is conceptually similar to a merger tothe extent that there is a change of control.By contrast, when the acquirer buys the remaining interest (3 percent of allM&As), there is no change of control even though the acquirer already had thecontrolling stake and now has the full control. Similarly, when the acquirer buysthese transactions for each year. We describe how we treat this type of transactions in our analysis inSection 3.4.68a partial interest (14 percent of all M&As), there is no change of control and theacquirer does not have the controlling stake unless it already had more than 50percent of the targetâs stock prior to the event.On the other hand, when an acquirer buys the assets of the target (47 percentof all M&As), the acquirer can buy the entire companyâs assets or its divisionâsassets, so the acquirer may or may not have the controlling stake in the case ofasset purchases. For example, if an acquirer buys two out of three factories ownedby a target, then it appears that the acquirer now has the controlling stake. However,even if the acquirer buys only one factory, it still controls that specific division ofthe target. Therefore, it is difficult to say whether there is an absolute change ofcontrol in the acquisition of assets without more information from the data. If theacquisition of assets leads to a change of control, then again, it is conceptuallysimilar to a merger. In the acquisition of certain assets (1 percent of all M&As),however, it is unlikely that there is a change of control.Since M&As may lead to different outcomes depending on whether there isa change of control (see Section 3.3), we divide our firm-level analyses into thoseinvolving mergers and those involving acquisitions. Furthermore, M&As may havequantitatively different impacts depending on whether the acquirer buys a part orthe entirety of the target, regardless of a change of control. Again, in the acquisitionof majority interest, there is a definite change of control and most asset acquisitionslikely result in a change of control as well, despite not buying the entire target.11Finally, the SDC dataset includes names of the parties, NAICS industry codes,and other identifying information such as addresses and phone numbers. Thedataset on M&A activities was merged with the firm-level data from the CanadianEmployer-Employee Dynamics Database using all available identifying variables,such as firm names, addresses, and industry codes. The match rate is around 75percent on average from 2001 to 2017.11Our main results remain qualitatively similar even if we exclude the acquisition of remaininginterest, partial interest, and certain assets (together 18 percent of all M&A deals in our data) wherethere is no change of control. These results can be provided upon request.693.2.2 Canadian Employer Employee Dynamics Database (CEEDD)The Canadian Employer-Employee Dynamics Database draws information fromboth individual (T1) and corporate (T2) tax return records, merged with job-levelinformation using T4 employee tax records (like a W-2 in the United States, withinformation on annual earnings) and Record of Employment (ROE) data with in-formation on work history, and with firm-level information from the National Ac-counts Longitudinal Micro-data File (NALMF). This database has rich informationon the universe of firms and workers in Canada from 2001 to 2017.The main firm-level outcome variables are employment, average payrolls, andprofitability. Employment is defined as the average number of employees reportedon the NALMF. Average payrolls are defined as the total wage bill divided by thenumber of employees. We use three different measures of profitability: (1) profitmargins, defined as total revenue minus total expenses scaled by total revenue, (2)returns on assets, defined as net income divided by total assets, and (3) EBITDA perworker. In Appendix B.2, we look at other outcomes such as markups and realizedcapital gains. We define markup as the elasticity of output with respect to variablecosts as well as the variable costs share [De Loecker and Warzynski, 2012]. Forthe elasticity of output with respect to variable costs, we use estimates from DeLoecker et al. [2020] based on the U.S. data that allow for different elasticitiesacross two-digit NAICS industry codes and years. Given the elasticity estimates,this allows us to estimate firm-level markups as the output elasticity multipliedby the inverse of the variable costs (total wage bills and material costs) share:Î¸Ëst â SalesCosts o f Goods . Furthermore, we link ownership data with the firm-level datato compute realized capital gains by owners for a given firm in each year. Aftermerging individual tax returns data with the ownership data at the investor level,we aggregate ownersâ realized capital gains for each firm in each year to computetotal realized capital gains by these investors at the firm level.At the worker level, the key outcome variable is annual earnings, aggregatedacross all employers for that worker in a given year. While we include earningsacross all employers, we associate workers with the âdominantâ employer fromwhich the employee receives the highest pay in the year. We also use informationon workersâ gender and age derived from the T1 income tax filing for creating a70matched control group and for heterogeneity analyses (Appendix B.2).3.3 Potential Impacts of M&As on Firms and WorkersIn this section, we discuss potential impacts of M&As on firms and workers. Thefirst subsection focuses on firm-level impacts, while the second one discusses howthese firm-level impacts may spill over to workers, with a particular focus on therole of job displacement.3.3.1 Firm-Level ImpactsThere can be several motivations for a M&A. Acquiring firms often argue thatM&As can lead to increased productivity through synergies. However, antitrustauthorities often focus on the potential for M&As to increase prices. Further-more, models in corporate finance discuss alternative, profit-reducing motivationsfor M&As, such as empire-building [Jensen, 1986b] or CEO overconfidence [Mal-mendier and Tate, 2005]. Overall, these various motivations may lead to hetero-geneous impacts of M&As on firm performance. In the next section, we discusshow these different motivations may impact firm outcomes. In particular, we focuson two core outcomes: profitability and firm size. As we discuss further in thenext subsection, changes in these outcomes are crucial for understanding differentmechanisms behind worker outcomes.Another issue to keep in mind when interpreting firm-level impacts of M&As isthat there are two main types of events that we consider: mergers and acquisitions.The discussion below applies to both types of events, although there may exist twokey differences between these types, as described in Section 3.2. First, mergersresult in a definite change of control, while acquisitions may not necessarily leadto a change of control. There can be qualitative or quantitative differences in firmoutcomes depending on whether there is a change of control, which we describebelow. Second, regardless of a change of control, there can be quantitative differ-ences depending on whether the acquirer merges with the target or buys only a partof it. Furthermore, in most acquisitions, target firms continue to operate afterwards,whereas many target firms disappear from the data after mergers.12 Therefore, we12Even if some target firms remain in the data after mergers, they are now subsidiaries of their71can evaluate firm outcomes separately by the target and the acquirer in the case ofacquisitions, while we assess their joint outcomes in the case of mergers.ProfitsThere are three primary channels in which M&As can increase overall profits:changes in product-market power, changes in labor-market power, and changes inproductivity. The primary focus of the industrial organization literature on M&Ashas been the potential for price increases via increased product market power,which would lead to an overall increase in firm profits.13 Similarly, increases inlabor-market power will also lead to increased profits through decreased wages.A common justification for M&As by firms is the possibility of increased prof-itability through productivity synergies. For example, Braguinsky et al. [2015b]and Demirer and Karaduman [2024] find evidence of increased productivity afteracquisitions. However, there are also prior studies that find negative impacts ofM&As on firm performance. For example, Malmendier et al. [2018] finds that ac-quiring firms that win bidding contests for target firms suffer losses in stock returnsrelative to competitors. Possible motivations for productivity-reducing M&As in-clude misaligned empire-building motives [Jensen, 1986b] or CEO overconfidence[Malmendier and Tate, 2005]. Decreases in productivity will lead to declines inprofits. This is a key distinction: increases in product-market power and labor-market power should not be associated with profit decreases, while decreases inproductivity can lead to lower profits.It is possible that there may not be any change in product-market power, labor-market power, or productivity unless there is a change of control, i.e., via merg-ers. However, even without a change of control, an acquirer may still increase itsmarket share by buying a part of the targetâs businesses in the same industry. Fur-thermore, by purchasing particular divisions of the target with potential synergies,the acquirer may enhance its productivity. Therefore, the impacts on firmsâ prof-itability may be qualitatively similar in both mergers and acquisitions, althoughacquirers, so it is difficult to assess their outcomes separately because their parentsâ financial de-cisions directly impact the subsidiariesâ financial statements (which is not the case in the case ofacquisitions).13See Whinston [2007] and Asker and Nocke [2021] for review articles on antitrust evaluation inthe industrial organization literature.72there could be still quantitative differences. Furthermore, if the acquirer buys onlyproductive units of the target in the case of acquisitions, then the acquirerâs profitsmay increase, while the targetâs profits would go down, so profitability may remainsimilar in the aggregate. By contrast, the acquirer can sell off unproductive unitsof the target after a merger, and the combined entityâs profits may still increase.Firm SizeThe impacts of M&As on firm size, measured by the average number of employeesand average payrolls, are ambiguous overall. Increases in product-market powerwill lead to declines in employment. This is because firms will increase prices,which will reduce demand, leading to a decrease in overall employment. Bycontrast, when the labor-market power increases, the impact on employment isambiguous. In static monopsony models, increases in labor-market power willcause firms to decrease employment in order to decrease wages. However, inwage-bargaining models [Abowd and Lemieux, 1993], wages can decrease withno changes in employment.Moreover, changes in productivity can lead to either higher or lower levels ofemployment. If the M&A leads to a more efficient production process, then wecould see increases in employment as the firm expands. However, one potentialsource of synergies is through a reduction in redundant workers, which would leadto declines in employment. Lastly, if the M&A is motivated by empire-buildingmotives [Jensen, 1986b] or CEO overconfidence [Malmendier and Tate, 2005],then we might expect to see productivity declines accompanied by employmentlosses if the acquiring firm is now less productive and loses market shares. Giventhe ambiguous impacts of M&As on productivity, the impacts on employment andpayrolls are also ambiguous.Similar to the predictions on firmsâ profitability, there could be both qualitativeand quantitative differences on firm-size impacts between mergers and acquisi-tions. However, if either a merger or an acquisition leads to a pure reallocation ofworkers from the target to the acquirer, then the overall employment would staythe same in the aggregate in both cases.In summary, there are overall ambiguous impacts of M&As on both firm size73and profitability depending on the motivations and the type of transaction. Next, wediscuss potential impacts of M&As on workers and how changes in firm outcomesmay spill over to workers.3.3.2 Impacts on WorkersIn this section we discuss how the firm-level changes in profitability and sizestranslate to impacts on workers. To help guide the discussion, we first set up aparsimonious conceptual framework.Assume that the value of worker iâs output at firm j is given by pi j = Ï j +ξi j,which is composed of a firm component (Ï j) and a match component (ξi j). Thefirm component of pay is common to all workers within a firm, while the matchcomponent is specific to the worker-firm match. The workerâs outside option isgiven by Oi. A simple Nash bargaining model implies the wage paid to the workeris given by14:wi j = γ j · pi j +(1â γ j) Â·Oi (3.1)Following Lazear [1979] and Saez et al. [2023], we assume a firm pays a firingcost f in the event of a layoff. We will return to this firing cost when discussing thepotential impact of M&As on job displacement, but first, we discuss the potentialimpacts on wages.WagesGiven this framework, we can now discuss how M&As will impact wages, drawingon the previous discussion in the firm-level impacts section. On the one hand, thevalue of output, pi j, may increase either through increased productivity or throughhigher prices via increased product-market power. The increase in value of outputthrough this higher Ï j would lead to higher wages.On the other hand, M&As may also lead to decreased productivity, resultingin a decline in workersâ wages through lower pi j. Lastly, if the firm gains labor-14Many wage-determination models have a similar form. For example, in union-bargaining mod-els [Abowd and Lemieux, 1993], standard static monopsony models [Manning, 2003], and job-differentiation models [Card et al., 2018], the wage is an average of productivity and the outsideoption of workers. The relevant measures of productivity, the determination of the weights, and thedetermination of the outside option vary across these models.74market power, then wages may decrease through two channels. First, the bargain-ing parameter γ j could decrease, implying that workers get less of the overall rentsin the firm, leading to lower wages. Second, the outside option Oi could alsodecrease if M&As reduce the number of competing employers in a given labormarket, leading to lower wages.To the extent that there can be both qualitative and quantitative differences inimpacts on firmsâ productivity between mergers and acquisitions, the subsequentimpacts on workersâ wages may be also different between the two types. By con-trast, if the impacts on firmsâ profits are similar in both cases in the aggregate, theworker-level impacts would be also similar in both types. Furthermore, changes inpi j would only have direct impacts on wages of workers who continue to stay atthe firm going through a M&A. However, changes in firmsâ productivity or prof-itability may lead to layoffs, which can still affect workers leaving the M&A firmthrough job displacement.Job DisplacementChanges in productivity may lead to job displacement for workers at either targetor acquiring firms. For example, M&As may lead to layoffs due to shifts in theproduction process or organization structure, leading to changes in the worker-firmmatch component (ξi j). If this falls far enough (or to zero, if the workerâs job isnow redundant), then this will lead to layoffs.There are two potential channels through which ξi j may fall after an M&Aevent. First, there could be a set of overpaid workers at the firm whose marginalproduct is below their current wage due to backloaded contracts. Lazear [1979]illustrates a model in which contracts are structured in a way such that workersare eventually paid above their marginal product. These backloaded contracts areoptimal in equilibrium as they solve a principal-agent problem. Importantly, firmshave an incentive to lay off older workers whose marginal product is lower thantheir wage. However, laying off these workers involves a firing cost, which may beeither explicit or implicit. Explicit firing costs stem from employment protectionregistration that may impact the cost of firing a worker, which is studied in Saezet al. [2023]. Implicit costs involve reputation concerns: if the firm lays off older75workers, then younger workers would not accept the lower pay initially. How-ever, Shleifer and Summers [1988] posit that M&As may allow new managers todisplace overpaid workers and reduce salaries by breaching the trust developedbetween the previous manager and the workers.Another source of the fall in ξi j is through the destruction of firm-specific hu-man capital. Models of firm-specific human capital, such as Lazear [2009], predictthat workers with high tenure will accumulate skills that make pi j particularly largeat their firm. The key difference relative to the previous channel is that in the caseof backloaded contracts, firing overpaid workers with wages above their produc-tivity will increase profits. By contrast, in the case of firm-specific human capital,the M&A may destroy this worker-firm match, leading to a drop in productivityboth for the worker and the firm. Because the acquiring firm may not have full in-formation on ξi jâs across all workers and the target firm may not have full controlover which workers to retain, certain workers with high ξi j (concentrated amonghigh-tenure and high-wage groups within a firm) may experience a drop in produc-tivity when being forced to move to a different firm after a M&A. This may alsohave negative consequences on the target firmâs performance as well, leading tolower profits. Therefore, the firm-level outcomes will be important for disentan-gling these two channels.Interestingly, both channels predict that the fall in ξi j and the subsequent earn-ings losses would be larger among high-tenure and high-wage workers within afirm. However, the key distinction is that in the backloaded contract channel, post-M&A job transitions will also be higher among high-tenure and high-wage work-ers if the new manager is targeting overpaid workers, while that is not necessarilythe case in the firm-specific human capital channel. In Section 3.6, we conductheterogeneity analyses based on workersâ tenure and the within-firm earnings dis-tribution, and assess changes in firmsâ profits to test which of these models seemsmost consistent with our findings.Finally, a fall in the worker-firm match will cause short-run losses in workerearnings, which may or may not diminish over time as displaced workers seek tofind a comparable match. A different source of long-run losses in earnings maystem from the firm-specific component of pay Ï j, which can be proxied by theirsize or profitability. Therefore, we also examine whether workers who leave target76firms after the event move to smaller or less profitable firms in Section 3.6.3.4 Empirical StrategyThis section describes our empirical design and provides descriptive statistics onour matched sample. To estimate the effects of M&As on firm-level outcomes, weimplement a matched difference-in-differences design by estimating a regressionof the following form:Yjt =5âk=â4Î²MAk 1(t j = tâ+ k)ÃMA j + Ït +Ï j +u jt (3.2)where Yjt is an outcome variable for firm j at year t, MA j is an indicator for anM&A firm, 1(t j = tâ+ k) indicates an M&A event occurred k years in the past (orfuture) relative to the period of the M&A event tâ, Ït are period fixed effects thatvary by the year of the M&A event, Ï j are firm fixed effects, and u jt is an errorterm. To absorb any industry-specific shock affecting M&A activities in a givenyear [Maksimovic and Phillips, 2001], we include 4-digit industry dummies inter-acted with year dummies as control variables. Furthermore, we include a quarticfunction in firm age to ensure that our results are not driven by differences in finan-cial constraints of firms, although dropping these control variables does not quali-tatively affect our results. The standard errors are clustered at the firm level. Notethat for firms that go through M&As more than once, we focus on their first M&Aevent although we still allow the subsequent M&A event(s) to affect their outcomesin the post-event period.15 Furthermore, most of the M&A firms and their matchedcontrol firms are private companies (96 percent) in our analysis sample, so our re-sults remain nearly unchanged even if we focus our analyses exclusively on privatefirms (Appendix B.1).In our matched sample described in the next subsection, roughly 80 percent of15When focusing on the first acquisition or merger, we still cover the subsequent event(s) in thepost-event period for firms that go through M&As more than once. While the majority (roughly 70percent) of firms goes through a merger or an acquisition only once during our sample period, thereexists a handful of acquirers that engage in M&As more than once. Our main analysis sample stillincludes these acquirers involved in multiple M&As over time. In addition, we separately examineacquirers that go through multiple M&As in Appendix B.1.77M&A events involve acquisitions. Relative to a merger, an acquisition results inthe target firm continuing its operation as an independent entity, which allows us tokeep track of the targetâs outcomes separately from the acquirerâs outcomes afterthe event, as described in Sections 3.2 and 3.3. To have a clearer picture on howfirm size changes after M&As, we examine our firm-level outcomes separately forthose involved in acquisitions and for those involved in mergers. Specifically, whenassessing acquisitions, we look at targets and acquirers separately. In contrast,when assessing mergers, we look at the aggregate (targets and acquirers pooled)outcomes, although we also look at the aggregate outcomes for acquisitions as acomparison. Additionally, we use the worker-level data to examine the extent towhich workers reallocate from targets to acquirers.To assess worker-level impacts, we estimate a similar matched difference-in-difference design of the following form:yit =5âk=â4Î²MAk 1(ti = tâ+ k)ÃMAi+ Ït +Ïi+µit (3.3)where yit is an outcome variable for incumbent worker i at year t, Ïi are workerfixed effects, and all other variables are defined as in Equation (3.2). The standarderrors are two-way clustered at the worker-by-firm level.16The key identifying assumption is that outcomes for M&A firms and workersand for control firms and workers would have trended similarly in the absenceof the M&A event. This assumption may be strong since M&As are the resultof firmsâ decisions. For example, acquiring firms may target firms that will beprofitable in the future, whose earnings may grow even absent the acquisition. Bycontrast, acquirers may target mismanaged and underperforming businesses whoseemployment and workersâ earnings would fall regardless of M&As. One way todetermine the direction of the potential bias is by comparing outcomes for M&Afirms and workers to the control firms and workers in the years prior to the M&Aevent. Parallel pre-trends in firm-level and worker-level outcomes help alleviatethe aforementioned concerns. In fact, we find that M&A firmsâ outcomes shareparallel pre-trends with those of control firms, not only in terms of their size and16Our results are robust to two-way clustering standard errors at the worker and the market (4-digitindustry by commuting zone) level â see Appendix B.1.78profitability (Sections 3.5 â 3.6), but also with regards to their sales and markups(Appendix B.2).While verifying common pre-trends is reassuring for a causal interpretation,contemporaneous shocks that occur with M&A events could still bias our results.For example, there could be a negative demand shock that hits a particular mar-ket and causes both a decline in wages and an increase in M&A activities as firmsget purchased before they shut down. In this case, M&A activities are correlatedwith shocks that decrease labor demand. We can also have the opposite scenario;in fact, merger activities tend to be pro-cyclical in aggregate [Rhodes-Kropf andViswanathan, 2004]. We address this concern by also looking at M&As that areless likely to have been triggered by local economic conditions. Specifically, weconsider the impact of national M&As that occur among domestic firms with mul-tiple establishments across different commuting zones, and therefore are less likelyto be driven by local economic conditions faced by the firms or workers (AppendixB.2).3.4.1 Matched SamplesBefore performing a matching procedure between M&A firms and potential con-trol firms, we make the following restrictions. First, we require a firm to have atleast 10 workers one year prior to the event. This choice focuses our sample oneconomically active firms with enough pre-period observations, and drops mostsmall businesses that are not comparable to either acquiring or target firms. Addi-tionally, we drop a small share of firms that have missing observations for the keyvariables used for matching, such as employment, average payroll, and firm age,measured one year prior to the event. During our sample period, the total numberof M&A events is 765 per year on average among this sample of firms eligible formatching (see Figure 3.1). We then match each firm one year prior to an M&Aevent to a control firm in the same province and 2-digit NAICS industry. A firmis a potential control firm for firm j if: (1) the firm is never involved in an M&Aevent during our sample period, and (2) the firm is in the same decile bin of av-erage payrolls and is in the same 15-quantile bins in total revenue and firm age79one year prior to the M&A event of firm j.17 Of all the possible counterfactualfirms for a given M&A firm, we choose the firm with the closest propensity score(one-to-one matching within a caliper), which is estimated by predicting treatmentusing a linear probability model with a quadratic function in average payrolls, to-tal revenue, and firm age in year [t â 1]. This matching strategy is similar to anumber of recent papers implementing a dynamic difference-in-differences design[Arnold, 2021, Goldschmidt and Schmieder, 2017, Smith et al., 2019]. The match-ing strategy finds a control firm in about 80 percent of all cases among this eligiblesample.Choosing one counterfactual control firm per M&A firm in a given year ensuresthat the treated and control groups are comparable on the matched variables. Weconstruct an unbalanced panel of firms which extends 4 years prior to and 5 yearsafter the M&A event. M&A firms in our analysis sample are larger than thosethat fail to find a control group on average in part because we drop firms withfewer than 10 employees. Therefore, the M&A deals in our analysis sample aremeaningful and larger than an average M&A deal in Canada during the sampleperiod. Furthermore, there exist firms eligible for matching, i.e. with at least 10employees, but not matched to control firms, i.e. similar in sizes to M&A firmsbut located in a different province). We provide descriptive statistics on the sampleof unmatched M&A firms (the remaining 20 percent among the eligible firms) andshow robustness test results including these excluded firms in Appendix B.1.Matching on size, sector, and province finds treatment-control pairs that wouldplausibly exhibit common trends in the absence of M&A activity. While we do notexplicitly match firms based on a market (defined at the 4-digit NAICS by commut-ing zone level), it is possible that firms are matched within the same market. Thisraises a potential concern if M&As have impacts on local labor markets throughincreased concentration. If M&As have negative effects on control firms in thesame industry and commuting zone, then the impact of M&As on firms will be17While this specification yields the best parallel pre-trends on the key outcomes, which are im-portant for our identification strategy, our main results remain qualitatively similar when we usedifferent bin sizes or other related firm characteristics for matching. For example, we can make thematching criteria more restrictive so that the treated and control groups look even more comparable;we still find similar results with a smaller sample size. Results based on other variation in matchingcan be provided upon request.80biased towards zero. However, these potential spillover effects are minimal in oursetting given that the number of M&A deals within the same market is small andthe number of competitors in a given market is so large that most M&A deals donot contribute to a meaningful increase in concentration (see Appendix B.2). Nev-ertheless, to minimize this concern, we do a robustness check by matching firmswithin the same province, but in different markets (Appendix B.1).To construct the worker-level sample, we extract all workers who were contin-uously employed in the matched firms during the entire pre-event period (4 years).This tenure restriction is chosen to obtain a sample of workers with attachmentto the M&A firms and is similar to tenure restrictions used in the mass layoff lit-erature [Jacobson et al., 1993, Lachowska et al., 2020, von Wachter et al., 2009].Additionally, we restrict workers to have at least 4,000 CAD in annual earnings anddrop multiple jobholders to ensure that we study full-time workers with stable in-come and attached to their firms [Card et al., 2013, Sorkin, 2018], given that we donot observe work hours in our data. For each worker in a treated firm, we choose aworker in any of the matched control firms in the same sector, province, worker age(five-year) bin and gender. If more than one match is found, we choose the workerwith the closest propensity score to the treated worker (one-to-one matching withina caliper), where the propensity score is estimated by predicting treatment using alinear probability model with a quadratic function in worker age. In total, about60 percent of workers at treated firms are matched to control workers among thesample of eligible workers at matched control firms. We provide descriptive statis-tics for the unmatched workers and show robustness test results including theseexcluded workers in Appendix B.1. To compute earnings in the worker-level data,we aggregate annual earnings across all employers if a worker is employed at morethan one firm in a given year (due to job transitions).Since we use a matched control group that is never treated, the specificationsabove do not suffer from the identification issues that arise in conventional event-study designs [Borusyak et al., 2024] with staggered timing [Goodman-Bacon,2021]. Identification here comes from differences in always-treated and never-treated units over time, not from units coming in and out of treatment.1818We let the first M&A event in our analysis sample start from 2005 to ensure that our matchedcontrol firms did not go through any M&A event at least 4 years prior to (or after) the first M&A81Finally, we use only completed transactions in our analyses following the M&Aliterature. The number of withdrawn M&A events that meet the sample restrictionsdescribed above (or even without the restrictions) is very small in Canada. Whileusing firms (and workers) involved in withdrawn M&A events as a placebo groupyields null effects across the key outcomes, many of these results are also quitenoisy given the small sample size.193.4.2 Descriptive StatisticsWe close this section with descriptive statistics of our analysis sample. Panel A ofTable 3.1 shows the averages for key variables across firms, comparing M&A firmsto the set of matched control firms one year before the event. On average, M&Afirms are a bit larger than their control firms, in terms of total revenue, expenses,and employment. However, for average payrolls, leverage ratio, and markups,M&A firms are comparable to their control firms, suggesting that firms that gothrough M&As and firms that never get involved in M&As are comparable withregards to their average employee compensation and financing structure prior tothe event. Importantly, as we show in Sections 3.5 â 3.6 and Appendix B.2, M&Afirms and their control firms share parallel pre-trends on these variables, implyingthat they exhibited similar patterns not only in terms of employment and averagepayrolls, but also with regards to sales and profitability, prior to the M&A event.The dominant sectors are manufacturing, wholesale, and services (about 70 percentof our firm sample).Panel C of Table 3.1 shows average worker characteristics in our analysis sam-ple one year before the event. We distinguish between workers at acquiring firmsand workers at target firms. Annual earnings are 70,046 CAD and 71,386 CADamong workers at acquiring firms and workers at the matched control firms, re-spectively. Annual earnings are 70,625 CAD and 72,317 CAD among workersat target firms and workers at the matched control firms, respectively. Therefore,annual earnings of treated workers are comparable to those of control workers onaverage. The difference between average payrolls in Panel A and annual earningsevent, given that our data starts from 2001. In this way, we ensure that none of the matched controlfirms went through any M&A event from 2001 to 2017.19These placebo test results can be provided upon request.82in Panel C arises because not all workers from treated firms are matched to work-ers at control firms, as we impose restrictions on tenure, worker age, and genderfor matching. Hence, the average payrolls at a given firm may not equal the av-erage annual earnings of workers at a given firm in our matched sample. The ageand gender compositions are similar between treated workers and their matchedcontrol workers.3.5 ResultsThis section reports the results from the estimation of the difference-in-differencesmodel described in Section 3.5, and presents additional tests supporting the inter-pretations of our results.3.5.1 Post-M&A Firm Size and Average PayrollsFigure 3.2 plots estimates of βMAk from equation (3.2) across the main firm-leveloutcomes using our matched sample. As mentioned in Section 3.4, we show theseresults separately for targets and for acquirers involved in acquisitions (Panels (a)and (b)) and for the aggregate (targets and acquirers pooled, Panels (c) and (d)).Panel (a) shows that acquiring firmsâ and target firmsâ employment followed asimilar pattern as those of their matched control firms before the M&A event. Thispre-event stability is important evidence in support of our empirical strategy. Whileacquirersâ employment significantly increased after the event, targetsâ employmentdecreased after the event, compared to those of matched control firms. Panel (b)shows that the pre-event trends for average payrolls are also similar between M&Afirms and their control firms. While acquirersâ average payrolls stayed flat afterthe M&A event on average, targetsâ average payrolls significantly decreased afterthe event, compared to control firmsâ average payrolls.20 In Panel (c) and Panel(d), we pool targets and acquirers and compare their outcomes with those of theirmatched control firms before and after the M&A event. Here, we simply drop thedistinction between acquirers and targets when estimating the equation (3.2) so that20To account for a small share of firms (especially among targets) that exit the sample after theM&A event, we repeat the same analysis using outcomes measured in levels instead of in logs, wherewe replace the missing observations with zeros. We find qualitatively similar results when we usethe outcomes in levels (see Appendix B.1).83acquirers, targets, and their matched control pairs are all in the same estimation.21The black line indicates the aggregate outcomes for those involved in mergers andthe red line indicates the aggregate outcomes for those involved in acquisitions. Inthe case of acquisitions, employment and average payrolls did not change muchafter the event on average, except for the initial drop in average payrolls. Similarly,in the case of mergers, employment and average payrolls stayed roughly flat afterthe event on average, except for the initial increase in employment.To interpret the magnitude of these results, Table 3.2 presents the difference-in-differences estimates on these outcomes, separately for targets and acquirersinvolved in acquisitions (Column 1 and 2) and for the aggregate (Column 3 and4). Column (1) shows that target firmsâ employment and average payrolls fell by8.9 log points and 2.8 log points, respectively, after the event. Column (2) showsthat acquiring firmsâ employment increased by 18.8 log points, without signifi-cant changes in average payrolls, after the event. Columns (3) and (4) show theseoutcomes in the aggregate, separately for those involved in acquisitions and forthose involved in mergers. Columns (3) and (4) show that employment and aver-age payrolls did not change much on average in the aggregate in the case of eitheracquisitions or mergers.22 Overall, our results show that M&As lead to mechanicalfirm-size adjustments when we look at targets and acquirers separately, but in theaggregate, firm size remains relatively flat both in acquisitions and in mergers. Toget a better sense of the impacts on worker reallocation and average payrolls, wenext turn to worker-level data to assess the impacts of M&As on worker earningsand job transitions.21An alternative approach would be to combine acquirers and targets into single entities, i.e. sum-ming up their observations, re-do the firm-level matching to find their control firms, and estimate theequation (3.2) using the combined entities. However, this new matched sample would be likely dif-ferent from the original analysis sample, given that finding matched control firms for the combinedentities may result in a lower match rate and therefore a smaller matched sample.22In Column (3), the average estimates are weighted by the relative sample sizes between targets(Column 1) and acquirers (Column 2), where the target sample size is much larger. Therefore,the negative coefficient estimates from the targets carry a larger weight than the positive coefficientestimates from the acquirers, leading to zero (or slightly negative and insignificant) effects on averageat the aggregate level.843.5.2 Post-M&A Worker-level Earnings and Job TransitionsGiven the considerable turnover at target firms after M&As, changes in averagepayrolls may reflect changes in worker composition. For example, the decrease infirm-level wages could be driven by laying off high-wage workers or reducing wagegrowth for existing workers, or a combination of both. Therefore, we next turn tothe worker-level data that allows us to flexibly control for composition by trackingthe same workers over time. We focus on incumbent workers who were alreadyemployed at their firms before the event and follow their outcomes throughout oursample period. Here, we do not make a distinction between workers involved inacquisitions and workers involved in mergers, although the results are very similarwhen we assess them separately (Appendix B.2).Panel (a) of Figure 3.3 shows that annual earnings for incumbent workers attarget firms trend similarly to those of their matched control workers in the yearsprior to the event, but fall significantly afterwards. By contrast, annual earningsof incumbent workers at acquiring firms trend similarly to those of their matchedcontrol workers and stayed flat after the event. Column (1) of Table 3.3 shows thatworkers at target firms experience a decline of 1.2 log points in annual earningsafter the M&A event on average. This decline could be due to M&A workersmoving to lower-paying firms or M&A firms reducing earnings for their incumbentworkers. Column (2) shows that the annual earnings of workers at acquiring firmsdid not change much on average.The drop in employment at target firms, as shown in Table 3.2, suggests thatjob transitions could explain a part of the decline in worker-level earnings. Thereduction in employment could come primarily through decreased hiring, implyingincumbent workers may be relatively unaffected. We first consider the impact ofM&As on the probability of worker transitions from a job. This transition could beto another firm or to non- or self-employment. In our data, most of the workers wholeave their original employers do so involuntarily, but find a job afterwards.23 Panel(b) of Figure 3.3 plots the estimates of equation (3.3) with an indicator for a job23Our data has an indicator for reasons for job separations, which can be broadly categorized intoinvoluntary, i.e. shortage of work, takeovers, or retirement or voluntary, i.e. personal or medicalreasons. Roughly half of the observations in the relevant sample has these reasons as âunknown.âAmong the other half, about three quarters of workers moving from target firms left involuntarilyafter the M&A event.85transition as the outcome, which equals one in year t if a worker moved to anotherfirm in tâ1.24 As shown in the figure, job transitions spike in t = 1, meaning thatmany workers transitioned to another firm in the year of the M&A event. Workersin target firms are about 20 percentage points more likely to transition jobs in theyear of the M&A event. Transition rates then drop, but remain elevated relativeto workers in the control group. Given this notable increase in job separationsespecially in the year of the M&A event, part of the effects on earnings may becoming from departures from their employers rather than within-firm decreasesin earnings. This also helps explain the immediate drop in target firmsâ averagepayrolls in t = 0 (Panel (b) of Figure 3.2), which reflects a large compositionalchange due to the significant worker departures. By contrast, we find that workersat acquiring firms do not experience any increased probability of a job transitionrelative to their control counterparts.To study the impacts solely due to within-firm changes in compensation, Panel(c) of Figure 3.3 restricts the analysis to workers who stay in the same firm in theyears following the event. We make this restriction for both treated workers andcontrol workers so that the treatment group does not mechanically contain workerswho have more stable job histories [Walker, 2013]. Column (3) of Table 3.3 showsthat annual earnings for stayers in M&A firms do not change much at either targetor acquiring firms on average in the years following the event.25By contrast, Panel (a) of Figure 3.4 shows large and immediate drops in earn-ings of workers who move from target firms to other firms within the first two yearsafter the event. Column (1) of Table 3.4 shows that these job movers experiencea decline of 4.0 log points in their earnings on average. Panel (b) also shows thatroughly 70 percent of these job transitions happen in the year of the event (t = 0),with the rest in the second year (t = 1).26 Note that the reason why the earning24This is because we impose that workers are continuously employed at their firms until the M&Aevent. Workers are allowed to move to other firms in the year of the event, but have to be employedat their original firm first at t = 0 before the transition. Therefore, there is an one-year lag in theindicator for a job transition.25Note that the coefficient estimates on stayersâ earnings at target firms are negative and statisti-cally significant in years 4 and 5. This result seems to suggest that post-event changes in target firmsâcharacteristics, such as decreases in profitability (Section 3.6), may help partly explain the decline instayersâ earnings as discussed in Section 3.3.26We allow these workers to move to other firms in subsequent years after their first transition,86drops in t = 1 even though most of these workers moved to other firms in t = 0is because the annual earning in t = 0 reflects the earnings from both the originalemployer and the new employer, and most job transitions at t = 0 happened in thelate calendar year, meaning the annual earning mostly reflects the earning from theoriginal firm on average. Importantly, about 80 percent of these workers move tonon-acquiring firms, meaning that most of these job transitions are not driven bya simple reallocation of workers from targets to acquirers. Interestingly, as Panel(c) and Columns (3) â (4) show, target workers who move to non-acquiring firmsexperience larger and more immediate drops in their earnings on average, relativeto workers who move to acquiring firms. Panel (d) and Column (5) show that thevast majority of these workers are moving to other firms within the same industry,implying that switching an occupation via moving to a different industry is unlikelythe reason why these job movers experience a decline in their earnings. In the nextsection, we explore potential mechanisms behind the post-M&A decline in workerearnings.3.6 Potential Mechanisms and Economic InterpretationsIn this section, we discuss and empirically test potential mechanisms, as discussedin detail in Section 3.3, behind the responses in workersâ earnings after M&As.3.6.1 Market Power ChannelIn principle, both increased labor-market power and increased product-market powermay impact worker earnings. For example, Prager and Schmitt [2021] and Arnold[2021] study how M&As that generate large shifts in concentration generate market-level declines in wages. Similarly, shifts in product-market power could impactearnings through two channels. First, if product-market power increases, firmsmay cut quantity in order to increase price. A decrease in labor demand in an in-dustry may therefore lower wages. However, in models of rent-sharing, increasesin product-market power will increase wages for workers in merging firms.27which explains why there exists a small share of workers moving after the second year.27Kroft et al. [2020] study a setting featuring both labor market power and product market powerto quantify markdowns of wages and markups of prices simultaneously.87In our setting, we find limited evidence for these channels. In particular, it isimportant to note that most M&A events (around 90 percent) happen across differ-ent labor markets in our setting, implying that there is a limited scope for a givenM&A to increase concentration in a given market. Even for the M&A events thathappen within the same labor market, the number of competitors in that market isso large initially that any given M&A does not lead to a meaningful rise in con-centration in our setting. To get direct evidence on the market power channels, weturn to the firm balance-sheet data. For both increases in product-market powerand labor-market power, firmsâ profits should increase after M&As. We use threedifferent measures of profitability: profit margins, returns on assets, and EBITDAper worker. Panel (a) of Figure 3.5 shows the results on profit margins, separatelyfor targets and for acquirers involved in acquisitions. Both targetsâ and acquirersâprofitability were in a parallel trend with those of their matched control firms priorto the event. We find that while acquirersâ profit margins decrease significantlyafter the event, targetsâ profit margins spiked in the year of M&A, and decreasedafterwards. The initial increase in profit margins for targets is likely mechanical,due to a larger saving in fixed costs from downsizing relative to the initial fall insales at the event year. Panel (d) shows the aggregate outcomes (targets and ac-quirers pooled) separately for those involved in acquisitions and for those involvedin mergers. In both cases, we find that profit margins decreased after the event inthe aggregate. We find similar results on return on assets (Panel (b) and (e)) andEBITDA per worker (Panel (c) and (f)).28To interpret the magnitude of these results, Table 3.5 presents the difference-in-differences estimates on these outcomes. Columns (1) and (2) show that targetsâprofit margins decrease by 0.7 percentage points (albeit not statistically signifi-cant), while acquirersâ profit margins decrease by 1.9 percentage points on averageafter the event. Columns (3) and (4) show that in the aggregate, profit marginsdecrease by 0.9 percentage points in the case of acquisitions, while they decreaseby 1.9 percentage points in the case of mergers. Columns (1) and (2) show thattargetsâ returns on assets decrease by 3.3 percentage points, while acquirersâ re-turns on assets decrease by 1.3 percentage points on average. Columns (3) and (4)28These results on firmsâ profitability remain qualitatively similar even if they are weighted byfirm size (which can be provided upon request).88show that in the aggregate, returns on assets decrease by 2.7 percentage points inthe case of acquisitions, while they decrease by 2.9 percentage points in the caseof mergers. Columns (1) and (2) show that targetsâ EBITDA decreases by 2,887CAD per worker, while acquirersâ EBITDA decreases by 4,570 CAD per workeron average. Columns (3) and (4) show that in the aggregate, EBITDA decreasesby 3,428 CAD per worker in the case of acquisitions, while it decreases by 7,106CAD per worker in the case of mergers.While efficiency gains through synergies are often argued as potential benefitsof M&As, we do not find any evidence of increased profitability for either acquir-ing or target firms after M&As. This has potentially important implications forpro-merger arguments that rely on predicted synergy gains. This result might besurprising if M&A events are undertaken by profit-maximizing firms, but this isconsistent with a corporate finance literature that finds negative effects of M&Aevents on firm performance or market valuation. These results also have importantimplications for the impact on workers. If M&As did increase productivity, wemight expect some workers to benefit through rent sharing. In this setting, how-ever, the lack of productivity gains suggests that workers may not benefit from theM&A events if there are no increased rents to share. Note that our results captureshort to medium run outcomes, so firmsâ profitability may improve in the long run.Whether or not profits actually increase in the long run, these negative results onfirm performance suggest that increased market power via M&As seems unlikelyto drive our worker-level results.In Appendix B.2, we perform a variety of heterogeneity analyses to providemore evidence against the market power channel. For example, we do not findlarger declines in target workersâ earnings when an M&A event happens within thesame labor market or in the markets with a high initial level of concentration or innon-tradable sectors or in within-industry (horizontal) M&As. The overall take-away from these results is that we find declines in worker earnings even in M&Aevents that are unlikely to have any impact on labor or product market power.29Given these results, we focus on workers who experience immediate and signif-29Note that we continue to find a lack of support for increased market power explaining our resultseven when we focus on a subset of large firms whose mergers or acquisitions may have larger impactson market concentration. The results from this subsample analysis may be provided upon request.89icant declines in their earnings after M&As: job movers from target firms. Wefocus on those who move within the first two years after the event because mostjob transitions from target firms happen within the first two years and are likelyinduced by the M&A event. Note that control workers who were matched to thesejob movers are also allowed to move to other firms after the event, but empirically,we find that most of these control workers stay at their original firm.3.6.2 Impacts on Job MoversIn this section, we empirically assess mechanisms through which job transitionsimpact workers. A large body of evidence studies job transitions through masslayoffs. M&A events provide a different and interesting source of variation to studyjob transitions. Both the motivations and impacts of job transitions may be verydifferent in this setting. For example, while we find similar patterns of earningslosses, the magnitude is much lower in our setting, with a long-run earnings loss ofaround 4 percent. By contrast, Jacobson et al. [1993] find a long-run earnings lossof 25 percent. Furthermore, M&A events tend to be more procyclical on average[Rhodes-Kropf and Viswanathan, 2004] in contrast to mass layoffs. As we willshow later, this may help explain why job movers from target firms were able tomove to larger and more profitable firms. Therefore, while M&A events are similarto mass layoffs in the sense that they both create job displacements, the mechanismthrough which M&As affect job moversâ earnings could be different relative to thechannel through which mass layoffs affect displaced workersâ earnings.We consider two broad classes of mechanisms. First, job transitions may causeimpacts purely from the type of firms individuals are employed at. If M&A eventscause workers to move from highly productive and large firms to low productivityand small firms, then these compositional impacts could rationalize the results. Forexample, Lachowska et al. [2020] and Schmieder et al. [2023] seek to understandwhether the characteristics of the firm the individual is employed at can explainlong-term earnings losses due to mass layoffs. Similarly, in the next section, wetest whether the firm plays a role in earnings losses of job movers after M&Aevents. In contrast to their studies, our work looks at firm characteristics from thebalance-sheet data, such as profitability, to directly assess whether target workers90are moving to less productive firms. We show that target workers move to largerand more profitable firms on average after M&As, therefore finding limited evi-dence for this channel.30 Therefore, we next consider heterogeneity in the typesof workers. We find significant heterogeneity that can be rationalized by modelsof the labor market in which there is a complementarity between the worker andthe firm that is specific to a given employment relationship, as discussed in Section3.3. Then we empirically assess which of these models seems most consistent withour overall findings.Compositional EffectsTo understand the role of the firm, we estimate an analogue to equation (3.3), sub-stituting in as the outcome variable the observable firm characteristics, such asemployment, total revenue, and profit margins. The goal is to assess whether theearnings losses following job transitions can be attributed to a moverâs reemploy-ment by an employer with a different size or profitability than the employer fromwhich the mover left. Similar to Figure 3.4 and Table 3.4, we compare job moversfrom targets to their matched workers at control firms who may or may not moveafter the event.Figure 3.5 shows changes in average firm characteristics of target workers whomove to other firms after the M&A event. Again, we define a job transition asoccurring in time t if the worker is in a different firm at t relative to tâ1. Therefore,if a M&A occurs in time t = 0, and the worker appears in a new firm at t = 1, thenwe define the job transition as occurring at t = 1. This is why we observe a largespike in job transitions at t = 1 in the data. Because of the tenure restriction,any change in pre-event firm characteristics is driven by yearly changes in targetfirmsâ characteristics. The change in firm characteristics in event year (t = 0) stillreflects the change in target firmsâ characteristics, given that the first job transitionis reflected at t = 1. Starting from t = 1, changes in firm characteristics reflectboth changes in new employers where target workers moved, and changes in targetfirms of workers who had not left yet, relative to changes in firm characteristics oftheir matched control workers. Figure 3.5 shows that workers transition to firms30We also find that target workers move to firms with higher firm-specific wage premiums (Ap-pendix B.1).91with more employees, higher sales, and higher profit margins. Table 3.5 presentsthe difference-in-differences results summarizing these impacts, finding workersfrom target firms move to firms with higher employment (51.8 log points), higherrevenue (49.9 log points) and higher profit margins (1.8 percentage points).To summarize, we find that workers who transition jobs after M&A eventsmove to better firms along a number of dimensions. Despite this, their actual earn-ings are 4.0 log points lower on average. In the next section we explore potentialmechanisms for this earnings decline.Heterogeneity by WorkersIn this section, we explore heterogeneity by workers in order to understand who ismost impacted by these job transitions. This will allow us to understand varioustheoretical mechanisms through which workers are affected by M&As. In partic-ular, it will allow us to understand why workers still suffer earnings losses despitemoving to on-average larger and more profitable firms.First, we consider heterogeneity by the workerâs tenure. As discussed in Sec-tion 3.3, there are two conceptual models that would predict impacts on earningsincreasing in tenure. First, a model of firm-specific human capital, i.e. [Lazear,2009] predicts that high-tenure workers would lose more from moving after anM&A event because the high-tenure workerâs productivity is much lower at otherfirms. Second, a model of backloaded contracts, i.e. [Lazear, 1979] also predictshigh-tenure workers would lose more by moving to other firms, as these workerscurrently enjoy wages above their marginal product at the current employer.To study impacts by tenure, we divide workers into two groups: those with 4years of tenure (shorter tenure), and those with 7 or more years of tenure (longertenure).31 In Figure 3.6, for individuals with 4 years of tenure we find small, short-run negative impacts that quickly disappear 2 years after the event. Individualswith 7 or more years of tenure experience a large decline in their earnings afterM&As. As shown in Table 3.6, on average, they suffer earnings losses of about 4.531For this analysis, we implement another matching strategy to ensure control workers are in thesame tenure bin as the target workers. We find qualitatively similar results when we use a differentcut to define short tenure and longer tenure workers. Results based on a different cut can be providedupon request.92percent relative to workers with 4 years of tenure whose earnings stay similar onaverage.32These results are consistent with models of the labor market that include somecomplementarity between the worker and the firm. To understand this channelfurther, we next exploit heterogeneity by within-firm earnings quintiles. Intuitively,both the productivity and bargaining channels may be more relevant for highly-skilled labor. For example, in hospital mergers, Prager and Schmitt [2021] findsimpacts for nurses, but no impacts on unskilled workers such as cafeteria workers,whose skills were not tied to the hospital industry. In our context, we do not observethe workerâs occupation. Instead, we use within-firm earnings quintiles to capturea crude measure of the complexity of the job. Additionally, this analysis is alsoinformative about distributional consequences of M&A events.In Figure 3.7 we plot the impacts of M&As for individuals at the bottom quin-tile of the earnings distribution versus individuals at the top quintile of the earningsdistribution.33 For workers at the top quintile, we see a 4.6 log points larger declinein earnings when they move to other firms after the event, relative to job movers atthe bottom quintile of the earnings distribution, as shown in Column (1) of Table3.7. These differences are not due to the types of firms these workers move to.34As discussed above, these results are consistent with either the worker suf-fering losses due to a loss of firm-specific human capital or the worker sufferinglosses due to a backloaded contract that pays higher wages later in an employmentspell. Conceptually, one way to distinguish these two would be to estimate workerproductivity. This would allow one to compare the productivity of the worker toher wage, and therefore we would be able to understand whether a worker is over-paid.35 However, in practice, even at a conceptual level, it is difficult to measure32Furthermore, both short-tenure and long-tenure workers are moving to better-paying firms onaverage relative to their original firm. These results can be provided upon request.33For this analysis, we implement the matching strategy again to ensure control workers are in thesame quintile of the earnings distribution within their firm as the target workers.34Similar to the results by workersâ tenure, both bottom-quintile workers and top-quintile workersare moving to better-paying firms on average. These results can be provided upon request.35An alternative explanation could be that target workers move to larger and more profitable firmsand accept lower wages due to non-wage amenities, such as geographical proximity or job stability.However, it is also likely that low-tenure or low-wage workers have preferences for non-wage ameni-ties, i.e. good locations or job security, so this channel would be inconsistent with our heterogeneityresults based on worker tenure or prior earnings.93worker-level productivity for most jobs.While we cannot observe worker-level productivity, one observable that is in-formative for this discussion is firmsâ profitability. Note that if firms are firingworkers for whom pi jâwi jâO j < 0 then these firms should see increases in profitmargins. Recall the simple model in Section 3.3. In this case, the outside option O jis greater than the profits from worker i (pi jâwi j). However, as shown earlier, weno evidence of increased profitability for either acquiring firms or target firms fol-lowing the M&A event. Therefore, in order to rationalize the worker-level impactsthrough firing of overpaid workers, one would need to believe that there are multi-ple off-setting decisions being made by new management. In other words, in thisstory, management is laying off unproductive workers, so that overall productivityshould increase. But they are not realizing any gains from this, so they must bemaking different, productivity-reducing decisions as well to rationalize the over-all decline in profitability. M&As are of course heterogeneous events, so multiplechannels may be at play.While the decline in firmsâ profits alone is not definitive evidence against thebackloaded contract channel, there is another result that potentially rules it out. Asdiscussed in Section 3.3, we should expect to see larger job transitions among high-tenure and high-wage workers if overpaid workers are displaced through M&As,but we find similar changes in job transitions among short-tenure (or low-wage)workers. This result, combined with the finding on decreased profits, suggests thatthe earnings losses among workers who leave target firms after M&As are mostlikely driven by a loss of firm-specific human capital.To summarize, we find that the decrease in earnings in our setting is primar-ily driven by target workers who transition jobs after an M&A event and move todifferent firms. While some M&A events may increase labor market or productmarket power, we find the number of such events is limited in our setting. Work-ers who transition to new firms after M&A events move to firms that have betterobservable characteristics along a number of dimensions. These firms have higherrevenue, employment, and profit margins. Despite this, workers who transition tothese firms still experience decreases in earnings overall. Our results are consistentwith a story in which a complementarity between the worker and firm that resultsin higher wages is destroyed through M&As.943.7 ConclusionIn this paper, we use linked employer-employee data to connect the impact of M&Aevents on firms to the impact on their employees. Previous research has looked atthe financial impact on firms and the impact on workersâ outcomes separately, butour paper is the first to link these impacts directly using our administrative datafrom tax records on both firms and workers. This allows us to look deeper intofirm-based mechanisms than has been possible in previous research.Using a matched difference-in-differences design, we compare firms that wentthrough an M&A to matched control firms of the same size bins, province, andsector. Our results show that acquiring firms expand, but target firms shrink sig-nificantly, without much change in the aggregate (targets and acquirers pooled).For workers at target firms, we find that their earnings decline and job separationsincrease significantly after an M&A event.Leveraging detailed firm balance sheet data, we investigate different potentialmechanisms for the decline in workersâ earnings at target firms. Since we see nomeaningful change in labor market concentration after M&As, we conclude that arise in market concentration is unlikely the key mechanism behind post-M&A wagedeclines. Furthermore, since profit margins decrease at both targets and acquirers,a rise in product market power is unlikely to have driven the decline in workerearnings either. Instead, we find that the decrease in earnings of workers at targetsis largely driven by those who move to other firms after the M&A event. Whilethese workers move to larger firms with higher profit margins on average, theyexperience a decline in their earnings likely due to a loss of firm-specific humancapital.Our findings provide important context for research investigating the labor mar-ket consequences of corporate M&As. Whichever mechanism is under investi-gation, care should be taken to account for how firm-level outcomes, especiallytheir profitability and growth, as well as worker-level outcomes, may change afterM&As. Our results also matter for public policy. In Canada, recent changes tothe Competition Act targeted both the impact of M&As on labor and how potentialefficiency gains are weighed in merger decisions. Similar debates are happeningin other countries, including the United States [Naidu et al., 2018]. Our results95provide new evidence of the negative impact of M&As on wages that add perspec-tive to these policy debates, and our findings on post-M&A firm performance raisedoubts about the efficiency arguments made in support of M&As.96Figure 3.1: Number of M&A Events300400500600Number of Deals in Matched Sample 6007008009001000Number of Deals2005 2007 2009 2011 2013 2015 2017YearNumber of DealsNumber of Deals in Matched Sample Notes: During our sample period, the number of M&A events is 765 per year (including multipleevents for a given firm) on average among the sample of firms eligible for matching as explained inSection 3.4 (black line). Among these eligible firms, the number of M&A events in the matchedsample is 422 per year (including multiple events for a given firm) on average (red line). Section 3.4describes how we construct our matched sample of firms. During our sample period, the uniquenumber of firms eligible for matching is 5,200. Among these firms, the unique number of matchedfirms (both targets and acquirers) is 4,100. Therefore, the matching strategy finds a counterfactualfirm in about 79 percent of all cases among this sample of eligible firms.97Figure 3.2: Employment and Average Payrolls After M&As(a) log(Employment)-0.4-0.3-0.2-0.10.00.10.20.30.4Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(b) log(Average Payrolls)-0.10-0.050.000.050.10Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(c) log(Employment)-0.4-0.3-0.2-0.10.00.10.20.30.4Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquisition Merger(d) log(Average Payrolls)-0.10-0.050.000.050.10Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquisition MergerNotes: These figures display event-study estimates for the impact of M&As on employment andaverage payrolls. Panel (a) and (b) show the estimates for the impact of acquisitions, separately foracquiring firms (navy line) and for target firms (orange line). Panel (c) and (d) show the estimateson the aggregate firm-level (targets and acquirers pooled) outcomes, separately for those involved inacquisitions (red line) and for those involved in mergers (black line). The dashed lines indicate 95percent confidence intervals where the standard errors are clustered at the firm level. The M&Aevent is in year 0 and the coefficient is normalized to be zero in year -1.98Figure 3.3: Worker Earnings and Job Transitions After M&As(a) log(Earnings)-0.06-0.04-0.020.000.020.040.06Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventWorker at Acquirer Worker at Target(b) Job Transition-0.10.00.10.20.3Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventWorker at Acquirer Worker at Target(c) log(Earnings) for Stayers-0.06-0.04-0.020.000.020.040.06Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventWorker at Acquirer Worker at TargetNotes: These figures display event-study estimates for the impact of M&As on worker-leveloutcomes, separately for workers at acquiring firms (navy line) and for workers at target firms(orange line). Panel (a) shows the estimates for log of total earnings. Panel (b) shows the estimatesfor job transition probabilities. Panel (c) shows the estimates for log of total earnings for firmstayers. The dashed lines indicate 95 percent confidence intervals where the standard errors aretwo-way clustered at the worker and firm level. The M&A event is in year 0 and the coefficient isnormalized to be zero in year -1.99Figure 3.4: Workers Moving from Targets(a) log(Earnings)-0.12-0.08-0.040.000.040.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventMovers from Targets(b) Job Transition-0.100.100.300.500.70Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventMovers from Targets(c) log(Earnings) â By Destination-0.12-0.08-0.040.000.040.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventTo Acquirers To Other Firms(d) Transition to Different Industry-0.100.100.300.500.70Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventMovers from TargetsNotes: These figures display event-study estimates for the impact of M&As on workers movingfrom target firms within the first two years after the event, as described in Section 3.5. Panel (a)shows the estimates for log of total earnings. Panel (b) shows the estimates for job transitionprobabilities. Panel (c) shows the estimates for log of total earnings based on their destination. Asmall share of workers also moves within their original parent company; however, we do notobserve any impact for these workers, so we do not report their estimates here. Panel (d) shows theestimates for the probability of transition to a different industry. The dashed lines indicate 95percent confidence intervals where the standard errors are two-way clustered at the worker and firmlevel. The M&A event is in year 0 and the coefficient is normalized to be zero in year -1.100Figure 3.5: Firmsâ Profitability After M&As(a) Profit Margins-0.08-0.06-0.04-0.020.000.020.040.06Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(b) Return on Assets-0.08-0.06-0.04-0.020.000.020.04Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(c) EBITDA per Worker-20,000-10,000010,00020,000Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(d) Profit Margins-0.08-0.06-0.04-0.020.000.020.040.06Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquisition Merger(e) Return on Assets-0.08-0.06-0.04-0.020.000.020.04Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquisition Merger(f) EBITDA per Worker-20,000-10,000010,00020,000Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquisition MergerNotes: Panels (a) â (c) display event-study estimates for the impact of M&As on firm-level profitability for firms involved in acquisitions, separatelyfor acquiring firms (navy line) and for target firms (orange line). Panel (a) shows the estimates for profit margins. Panel (b) shows the estimates forreturn on assets. Panel (c) shows the estimates for EBDITA per worker. Panels (d) â (f) display event-study estimates for the impact of M&As on theaggregate firm-level (targets and acquirers pooled) profitability, separately for those involved in acquisitions (red line) and for those involved inmergers (black line). Panel (d) shows the estimates for profit margins. Panel (e) shows the estimates for return on assets. Panel (f) shows the estimatesfor EBDITA per worker. The dashed lines indicate 95 percent confidence intervals where the standard errors are clustered at the firm level. The M&Aevent is in year 0 and the coefficient is normalized to be zero in year -1.101Figure 3.6: Firm Characteristics of Workers Moving from Targets(a) log(Employment)-0.200.000.200.400.600.80Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventMovers from Targets(b) log(Revenue)-0.200.000.200.400.600.80Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventMovers from Targets(c) Profit Margins-0.020.000.020.040.060.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventMovers from TargetsNotes: These figures display event-study estimates for changes in average firm characteristics ofworkers moving from target firms within the first two years after the event, as described in Section3.5. Panel (a) shows the estimates for log of employment. Panel (b) shows the estimates for log oftotal revenue. Panel (c) shows the estimates for profit margins. The dashed lines indicate 95 percentconfidence intervals where the standard errors are two-way clustered at the worker and firm level.The M&A event is in year 0 and the coefficient is normalized to be zero in year -1.102Figure 3.7: Workers Moving from Targets â By Worker Tenure(a) log(Earnings)-0.10-0.050.000.05Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Event4 Years of Tenure 7+ Years of Tenure(b) Job Transition0.000.200.400.600.80Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Event4 Years of Tenure 7+ Years of TenureNotes: These figures display event-study estimates for the impact of M&As for workers movingfrom target firms within the first two years after the event, separately for those with 4 years of tenureand for those with 7 or more years of tenure by one year before the event. Panel (a) shows theestimates for log of total earnings. Panel (b) shows the estimates for job transition probabilities. Thedashed lines indicate 95 percent confidence intervals where the standard errors are two-wayclustered at the worker and firm level. The M&A event is in year 0 and the coefficient is normalizedto be zero in year -1.103Figure 3.8: Workers Moving from Targets â By Within-Firm Earnings Dis-tribution(a) log(Earnings)-0.15-0.10-0.050.000.05Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventQ1 Q5(b) Job Transition0.000.200.400.600.80Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventQ1 Q5Notes: These figures display event-study estimates for the impact of M&As for workers movingfrom target firms within the first two years after the event, separately for those at the bottom quintile(Q1) and for those at the top quintile (Q5) of the within-firm earnings distribution. Panel (a) showsthe estimates for log of total earnings. Panel (b) shows the estimates for job transition probabilities.The dashed lines indicate 95 percent confidence intervals where the standard errors are two-wayclustered at the worker and firm level. The M&A event is in year 0 and the coefficient estimate isnormalized to be zero in year -1.104Table 3.1: Descriptive Statistics on the Matched Sample of Firms and Work-ers(1) (2) (3) (4)Acquirer Control Target ControlPanel A: Firm CharacteristicsTotal Revenue (in millions) 62 47 34 29Total Expenses (in millions) 58 44 32 28Profit Margins 0.02 0.05 0.02 0.05Number of Employees 187 125 106 87Average Payrolls 52,472 51,956 50,380 49,175Leverage Ratio 0.65 0.67 0.70 0.65Markups 1.88 1.68 1.88 1.68Number of Firms 1,040 1,040 3,060 3,060Panel B: Sectors (Firms)Construction 0.06 0.06 0.05 0.05Manufacturing 0.23 0.23 0.26 0.26Wholesale 0.15 0.15 0.14 0.14Retail 0.03 0.03 0.05 0.05Transportation 0.03 0.03 0.04 0.04Information 0.04 0.04 0.04 0.04Services 0.33 0.33 0.32 0.32Other Sectors 0.13 0.13 0.11 0.11Panel C: Worker CharacteristicsTotal Earnings 70,046 71,386 70,625 72,317Age 46.7 46.6 47.2 47.0Female 0.33 0.33 0.31 0.31Number of Workers 42,780 42,780 64,520 64,520Panel D: Sectors (Workers)Construction 0.03 0.03 0.02 0.02Manufacturing 0.49 0.49 0.5 0.5Wholesale 0.16 0.16 0.10 0.10Retail 0.01 0.01 0.03 0.03Transportation 0.03 0.03 0.03 0.03Information 0.02 0.02 0.02 0.02Services 0.19 0.19 0.21 0.21Other Sectors 0.05 0.05 0.08 0.08Notes: This table reports descriptive statistics on the matched sample of firms and workers,measured one year prior to the event. Panel A reports firm characteristics such as total revenue, totalexpenses, profit margins, number of employees, average payrolls, leverage ratio, and markups.Columns (1) and (3) report these statistics for acquiring firms and for target firms, respectively, andcolumn (2) and (4) report these statistics for their respective matched control firms. Panel B reportsthe distribution of firms in the matched sample across 2-digit NAICS sectors. Panel C reportsworker characteristics such as total annual earnings, age, and gender. Columns (1) and (3) reportthese statistics for workers at acquirers and for workers at targets, respectively, and column (2) and(4) report these statistics for their respective matched control workers. Panel D reports thedistribution of workers in the matched sample across 2-digit NAICS sectors. Other sectors include(1) Agriculture, forestry, and fishing, (2) Mining, quarrying, and oil and gas extraction, (3) Utilities,(4) Real estate and rental and leasing, (5) Arts, entertainment and recreation, (6) Accommodationand food services, (7) Other services, and (8) Public administration.105Table 3.2: Difference-in-differences Estimates on Firm Outcomes(1) (2) (3) (4)Acquisition AggregateTarget Acquirer Acquisition Mergerlog(Employment) -0.089*** 0.188*** -0.006 0.024(0.023) (0.033) (0.018) (0.038)Mean at t = -1 4.00 4.55 4.14 4.02Adj. R squared 0.875 0.891 0.879 0.880Firm-Year Obs. 66,680 21,930 89,230 19,100log(Average Payrolls) -0.026** 0.011 -0.009 -0.009(0.011) (0.014) (0.008) (0.017)Mean at t = -1 10.69 10.74 10.70 10.73Adj. R squared 0.796 0.817 0.799 0.800Firm-Year Obs. 66,590 21,870 89,080 19,040Profit Margins -0.007 -0.019** -0.009* -0.019(0.007) (0.009) (0.005) (0.012)Mean at t = -1 0.02 0.02 0.02 0.00Adj. R squared 0.356 0.355 0.352 0.380Firm-Year Obs. 68,910 22,200 91,730 19,520Return on Assets -0.033*** -0.013 -0.027*** -0.029***(0.006) (0.008) (0.005) (0.010)Mean at t = -1 0.08 0.05 0.07 0.04Adj. R squared 0.384 0.420 0.387 0.433Firm-Year Obs. 69,900 22,390 92,900 19,690Notes: This table reports the difference-in-differences estimates for the impact of M&As onemployment and average payrolls. Columns (1) and (2) report the estimates for the impact ofacquisitions on target firms and on acquiring firms, respectively. Columns (3) and (4) report theestimates on the aggregate firm-level (targets and acquirers pooled) outcomes for those involved inacquisitions and for those involved in mergers, respectively. The standard errors are clustered at thefirm level.106Table 3.3: Difference-in-differences Estimates on Worker Outcomes(1) (2) (3)log(Earnings) Transition log(Earnings) - StayersWorkers at Target -0.012** 0.067*** -0.008(0.005) (0.007) (0.006)Mean at t = -1 11.01 0.00 11.01Adj. R squared 0.739 0.187 0.797Worker-Year Obs. 2,023,130 2,0264,40 1,581,880Workers at Acquirer 0.004 0.004 0.002(0.007) (0.006) (0.008)Mean at t = -1 11.02 0.00 11.02Adj. R squared 0.732 0.171 0.786Worker-Year Obs. 1,343,370 1,345,330 1,084,030Notes: This table reports the difference-in-differences estimates for the impacts of M&As onworkersâ outcomes, separately for those at acquiring firms and for those at target firms. Thedependent variables in columns (1) and (2) are log of total earnings and the probability of jobtransitions. Column (3) reports the estimates on log of total earnings for firm stayers. The standarderrors are two-way clustered at the worker and firm level.107Table 3.4: Workers Moving from Targets(1) (2) (3) (4) (5)log(Earnings) Transition log(Earnings) - By Destination TransitionTo Acquirer To Other Firms To Diff IndustryWorkers Moving -0.040*** 0.224*** -0.016 -0.050*** 0.064***from Targets (0.007) (0.006) (0.013) (0.007) (0.004)Mean at t = -1 10.98 0.00 10.96 10.93 0.00Adj. R squared 0.739 0.317 0.765 0.736 0.167Worker-Year Obs. 689,900 691,120 144,550 430,750 691,120Notes: This table reports the difference-in-differences estimates for the impacts of M&As forworkers moving from target firms within the first two years after the event. Column (1) displays theestimates for log of total earnings. Column (2) displays the estimates for the job transitionprobabilities. Columns (3) and (4) display the estimates for log of total earnings based on workersâdestination (to acquiring firms and to other firms, respectively). A small share of workers alsomoves within their original parent company; however, we do not observe any impact for theseworkers, so we do not report the estimates here. Column (5) displays the estimates for theprobability of transition to a different industry. The standard errors are two-way clustered at theworker and firm level.108Table 3.5: Difference-in-differences Estimates on Firmsâ Profitability(1) (2) (3) (4)Acquisition AggregateTarget Acquirer Acquisition MergerProfit Margins -0.007 -0.019** -0.009* -0.019(0.007) (0.009) (0.005) (0.012)Mean at t = -1 0.02 0.02 0.02 0.00Adj. R squared 0.356 0.355 0.352 0.380Firm-Year Obs. 68,910 22,200 91,730 19,520Return on Assets -0.033*** -0.013 -0.027*** -0.029***(0.006) (0.008) (0.005) (0.010)Mean at t = -1 0.08 0.05 0.07 0.04Adj. R squared 0.384 0.420 0.387 0.433Firm-Year Obs. 69,900 22,390 92,900 19,690EBITDA per worker -2886.774* -4570.210** -3427.987*** -7105.831***(1484.173) (2329.311) (1204.799) (2730.995)Mean at t = -1 15,880.58 17,906.32 16,375.09 14,431.66Adj. R squared 0.441 0.458 0.436 0.449Firm-Year Obs. 65,810 21,600 88,050 18,690Notes: This table reports the difference-in-differences estimates for the impact of M&As ondifferent measures of firmsâ profitability. Columns (1) and (2) report the estimates for target firmsand for acquiring firms involved in acquisitions, respectively. Columns (3) and (4) report theestimates on the aggregate firm-level (targets and acquirers pooled) outcomes, for those involved inacquisitions and for those involved in mergers, respectively. The standard errors are clustered at thefirm level.109Table 3.6: Changes in Average Firm Characteristics of Workers Moving fromTargets(1) (2) (3) (4)Employer Fixed Effect Match Effect log(Revenue) Profit MarginsWorkers Moving From Targets 0.032*** -0.090*** 0.499*** 0.018***(0.006) (0.004) (0.055) (0.007)Mean at t = -1 0.20 0.06 18.22 0.05Adj. R squared 0.881 0.205 0.803 0.466Worker-Year Obs. 684,800 677,750 649,220 650,510Notes: This table reports the difference-in-differences estimates for changes in average firmcharacteristics of workers moving from target firms within the first two years after the event.Column (1) displays the estimates for log of employment. Column (2) displays the estimates for logof revenue. Column (3) displays the estimates for profit margins. The standard errors are two-wayclustered at the worker and firm level.110Table 3.7: Workers Moving from Targets â By Worker Tenure(1) (2)log(Earnings) TransitionPost Ã Treated 0.006 0.180***(0.011) (0.008)Post Ã Treated Ã 7+ Years of Tenure -0.045*** 0.012(0.014) (0.010)Mean at t = -1 (4 Years of Tenure) 10.96 0.00Mean at t = -1 (7+ Years of Tenure) 11.03 0.00Adj. R squared 0.745 0.404Worker-Year Obs. (4 Years of Tenure) 186,170 186,660Worker-Year Obs. (7+ Years of Tenure) 251,020 251,340Notes: This table reports the difference-in-differences estimates for the impacts of M&As forworkers moving from targets within the first two years after the event, separately for those with 4years of tenure measured one year prior to the event. The triple interaction term captures thetriple-difference estimates for those with 7 or more years of tenure measured one year prior to theevent. Column (1) displays the estimates for log of total earnings. Column (2) displays theestimates for the job transition probabilities. The standard errors are two-way clustered at theworker and firm level.111Table 3.8: Workers Moving from Targets â By Within-Firm Earnings Distri-bution(1) (2)log(Earnings) TransitionPost Ã Treated -0.028** 0.181***(0.011) (0.006)Post Ã Treated Ã Q5 -0.046** 0.007(0.021) (0.005)Mean at t = -1 (Q1 = 1) 10.45 0.00Mean at t = -1 (Q5 = 1) 11.56 0.00Adj. R squared 0.745 0.324Worker-Year Obs. (Q1 = 1) 126,710 126,980Worker-Year Obs. (Q5 = 1) 119,950 120,160Notes: This table reports the difference-in-differences estimates for the impacts of M&As forworkers moving from targets within the first two years after the event, separately for those at thebottom quintile (Q1) of the within-firm earnings distribution measured one year prior to the event.The triple interaction term captures the triple-difference estimates for workers at the top quintile(Q5) of the within-firm earnings distribution. Column (1) displays the estimates log of totalearnings. Column (2) displays the estimates for the probability of transition. The standard errors aretwo-way clustered at the worker-firm level.112Chapter 4Corporate Acquisitions,Profitability, and Markups:Evidence from EuropeCompetition among firms is crucial for spurring innovation and growth in the econ-omy. Firms with market power may have incentives to reduce the supply of theirgoods to maintain higher prices [Harberger, 1995], which will dampen investmentin capital and innovation [Aghion et al., 2005]. An important policy tool the gov-ernment can use to promote competition is the regulation on mergers and acqui-sitions (M&As henceforth), as M&As may allow firms to exploit higher marketpower, leading them to increase markups and profits. However, theoretical pre-dictions on the effects of M&As on firmsâ profits or markups are ambiguous. Onthe one hand, M&As may allow firms to be more productive and to reduce costs,which could increase their profit margins. On the other hand, M&As may be ini-tiated by empire-building motives [Jensen, 1986b] or misaligned CEO incentives[Morck et al., 1988], which can lead to a fall in profits. Therefore, how M&As willaffect corporate profits and markups is an empirical question.In this paper, we study how corporate M&As impact firmsâ profitability andmarkups in the medium-run. We compare the outcomes of firms that go throughM&As with a matched sample of non-M&A firms before and after the event. Usingfinancial data merged with M&A data at the firm-level across 10 European coun-113tries from 2010 to 2018, we exploit a large number of M&A activities to implementa matched difference-in-differences design. Given that these countries all share acomparable set of antitrust regulations and are governed by the European Union,our main estimates pool all countries together.We first examine the impacts of M&As on profitability and markups at the firmlevel. Importantly, in our setting, around 80 percent of M&A events involve acqui-sitions, where the target firm sells its assets, divisions, or only a part of its share,and remains operating as a separate company following the event. This allows us totrack the targetâs and the acquirerâs outcomes after an acquisition, so that we can as-sess the impacts on targets and on acquirers separately. By contrast, in mergers, thewhole target is sold to (or merged with) the acquirer, meaning there is an absolutechange of control. Given that there can be qualitative or quantitative differences infirm-level impacts depending on whether there is a change of control, we separateour analyses into those involved in acquisitions and those involved in mergers, al-though we do not find meaningfully different results on firm-level outcomes in theaggregate between the two types. Given that the vast majority of M&A activitiesin Europe involve acquisitions, analyzing these activities in addition to mergersallows us to exploit more events and to examine a larger and broader sample ofrelevant firms.It is possible that there may not be any change in product-market power orproductivity unless there is a change of control (i.e., via mergers). However, evenwithout a change of control, an acquirer may still increase its market share bybuying a part of the targetâs businesses in the same industry. Furthermore, bypurchasing particular divisions of the target with potential synergies, the acquirermay enhance its productivity. Therefore, the impacts on firmsâ profitability may bequalitatively similar in both mergers and acquisitions, although there could be stillquantitative differences. Furthermore, if the acquirer buys only productive unitsof the target in the case of acquisitions, then the acquirerâs profits may increase,while the targetâs profits would go down, so profitability may remain similar in theaggregate. By contrast, the acquirer can sell off unproductive units of the targetafter a merger, and the combined entityâs profits may still increase.Focusing on these acquisitions, we find that acquirersâ and targetsâ profitability(measured by returns on assets or profit margins) declines after M&As. Acquir-114ersâ profitability decreases by 0.6 percentage points on average relative to theirmatched control firms after M&As. Similarly, targetsâ profitability decreases byone percentage points, on average. At the aggregate (pooling targets and acquirers)level, we also find similar declines in profitability in both mergers and acquisitions.By contrast, we find almost no changes in firmsâ markups after M&As.There are two primary channels in which M&As can increase overall profits:changes in product-market power and changes in productivity. The primary fo-cus of the industrial organization literature on M&As has been the potential forprice increases via increased product market power, which would lead to an over-all increase in firm profits.1 On the other, productivity synergies from an M&Acan also boost profitability. For example, Braguinsky et al. [2015a] and Demirerand Karaduman [2024] find evidence of increased productivity after acquisitions.However, there are also prior studies that find negative impacts of M&As on firmperformance. For example, Malmendier et al. [2018] finds that acquiring firms thatwin bidding contests for target firms suffer losses in stock returns relative to com-petitors. Possible motivations for productivity-reducing M&As include misalignedempire-building motives [Jensen, 1986a] or CEO overconfidence [Malmendier andTate, 2005]. Decreases in productivity will lead to declines in profits. This is a keydistinction: increases in product-market power should not be associated with profitdecreases, while decreases in productivity can lead to lower profits. Our results areconsistent with the latter indicating that decreased productivity drives the observeddecline firm performance.The fall in profitability and unchanged markups are consistent with the ideathat M&As are not associated with a rise in firmsâ market power [De Loeckeret al., 2020]. Next, we explore whether the estimated impacts on profitability aresmaller in nontradable sectors, the logic being that tradable sectors are close tocompetitive and a single merger is unlikely to impact global competition. We findthat profits decline in both tradable and nontradable M&As. We also consider thedistinction between domestic versus cross-border mergers, where a cross-bordermerger involves at least one of the parties being a foreign company. Contrary to themarket power channel, we find no significant difference in profitability or markup1See Whinston [2007] and Asker and Nocke [2021] for review articles on antitrust evaluation inthe industrial organization literature.115outcomes between these two types of mergers. Taken together, our findings suggestthat a recent rise in M&A activities across Europe in the last decade may not havebeen associated with a rise in profitability, markups or market power.In all of our results, the key identification assumption is that M&A firms andnon-M&A firms would have followed similar trajectories in the absence of a mergeror an acquisition. This may be a strong assumption in our setting. For example,M&A events may be undertaken by productive acquiring firms that will continue toexpand. In this case, high profitability could be correlated with M&A events, bias-ing our estimates. Furthermore, spillover effects could contaminate our estimatesif increased market power from an M&A event impacts all firms in an industry, andnot just the merging parties.We provide a number of tests to corroborate our findings. First, we find paralleltrends in our main outcomes between M&A firms and non-M&A firms prior to theM&A event. Second, our results are robust across various specifications. Whilewe show there are no pre-trends in key outcomes for M&A firms, it is still possiblethat coinciding shocks could bias our estimates. For example, acquiring firms mayface positive productivity shocks in the year of the merger, which would not beapparent in pre-trends. In this case, our estimates would be attenuated toward zero.Therefore, one may interpret our estimated effects on investment as a lower boundof the true effects. The opposite story, of coinciding negative shocks, is potentiallyless relevant as a firm that receives a negative shock has the option to delay themerger.This paper contributes to several distinct literatures. First, it contributes to theliterature on the effects of mergers and acquisitions. There is a long theoreticaland empirical literature in industrial organization studying the impacts of M&Ason consumer welfare [Dansby and Willig, 1979, Farrell and Shapiro, 1990, Hartet al., 1990, Kaplow and Shapiro, 2007, Nevo, 2000]. While much prior workin this literature has focused on simulation of merger effects, there is a growingbody of evidence that identifies effects in completed mergers, an approach oftenreferred to âretrospectiveâ merger analysis in industrial organization [Ashenfelteret al., 2013, 2015, Dafny et al., 2019]. Unlike much prior work on retrospectivemerger analysis, we take a broader approach by considering the impacts across alarge number of M&A events and industries.116In terms of âretrospectiveâ merger analysis, this paper is most closely relatedto papers that study the impacts of mergers on firm-level outcomes across manyacquisitions, rather than a case study of a particular merger or acquisition. Forexample, Braguinsky et al. [2015b] utilizes detailed firm-level data to study theeffects of acquisitions in the Japanese cotton spinning industry on productivity.Most closely related to this work are papers in the United States that use firm-level data (in manufacturing industries) to estimate impacts on productivity, forexample Maksimovic and Phillips [2001] and Blonigen and Pierce [2016]. In par-ticular, Blonigen and Pierce [2016] consider both productivity and market powerchanges separately by applying the method by De Loecker and Warzynski [2012]to estimate markups. Overall, empirical findings in this literature are somewhatmixed. Braguinsky et al. [2015b], Maksimovic and Phillips [2001], Healy andRuback [1992], and Devos et al. [2009] document efficiency gains while Blonigenand Pierce [2016] and Cohn et al. [2014] find little evidence of increases in pro-ductivity or operating improvements. Relative to these papers, we focus on howM&As affect corporate profits and markups, examining M&A activities across awide range of countries in Europe. Lastly, Boucly et al. [2011] and Erel et al.[2015] find that after leveraged buyouts, target firms become more profitable, growfaster, and issue more debt, using a sample of French firms between 1994 and 2004and a sample of European firms between 2001 and 2008, respectively. While theirfindings contrast our overall results, our paper focuses on more recent years andwith a broader set of M&A deals, which can lead to differences in results.This paper also contributes to a recent literature on the relationship betweenmarket power and corporate outcomes. Gutie´rrez and Philippon [2017] argues thatdeclining competition in the U.S. has led to lower investment rates. Similarly, DeLoecker et al. [2020] document a rise in market power in the U.S., which is con-sistent with patterns of declining competition. In this paper, we study one potentialsource of changing competition â changing ownership structure. While we find noevidence of increased markups after M&As, we find that the decline in profitabil-ity is likely driven by other factors, given that the declines documented in mergerslikely have negligible impacts on market power.Finally, our results contribute to the literature in corporate finance that studiesnegative stock-market reactions to merger announcements. Researchers have in-117terpreted such findings as evidence of empire building [Jensen, 1986b], misalignedincentives [Morck et al., 1988], or CEO overconfidence [Malmendier and Tate,2005]. These hypotheses of acquisition behavior imply that some M&A eventsmay not be profit-maximizing. Our results are consistent with this interpretation,as we find declines in profitability for both target firms and acquiring firms follow-ing M&As [Betton et al., 2008].The remainder of the paper is organized as follows. Section 4.1 describes theinstitutional setting. Section 4.2 describes our data and Section 4.3 explains ourempirical design. Section 4.4 presents our main results. Section 4.5 discusses po-tential mechanisms and economic interpretations of our results. Section 4.6 con-cludes.4.1 Institutional BackgroundThe regulations regarding M&As are comparable across 10 European countrieswe study: Belgium, Finland, France, Germany, Italy, Netherlands, Poland, Spain,Sweden, and United Kingdom.2 Each country has its own antitrust agency whichoversees and regulates M&A activities and bidding processes that occur both withinthe country or across the border. At a broader level, all competition authorities en-force a comparable set of rules when reviewing the proposed M&As, and followthe guidelines set by the European Union. These countries may differ primarily interms of which types of firms they grant exemptions on regulations. For example,in Finland, foreign target companies are not subject to the regulations. We includethe details on regulations regarding M&As across these countries in Appendix .All of the countries in our sample have pre-merger notification rules to blockpotentially anticompetitive M&As. Typically, those rules are based on firm size:(1) domestic sales and (2) global sales. Each country has its own thresholds, butthey are typically set higher than the thresholds in North America [Wollmann,2019]. Appendix summarizes the regulations regarding pre-merger notificationrules in each country. There have been several legislation changes on the antitrust2We choose these 10 countries based on the total number of M&A events in each country withinthe European Union, as these countries give us enough variation to estimate the effects of M&Ason firm outcomes. See Table 4.1 in Appendix C.1 for the number of M&A transactions across thesecountries over time as well as other countries within the European Union.118regulations, especially regarding pre-merger notification rules, across these coun-tries during our sample period. These legislative changes, however, depend onfirmsâ global sales, which we do not observe in our data. Furthermore, these thresh-olds are generally set at a high level, likely affecting only a tiny share of very largefirms. In other words, in our analysis sample, most M&A deals and involved partiesare not large enough to trigger any of these thresholds.4.2 DataThis section describes two main datasets for our analysis. First, the SDC Platinumdatabase contains information on M&A activities around the world, including thecountries in our analysis sample. Second, we use the Amadeus database, whichcontains financial information about firms in those countries.4.2.1 SDC PlatinumThe SDC Platinum database covers the near universe of M&A activities aroundthe globe involving either publicly traded or private companies in the Europeancountries that are part of our analysis. This database includes names of the parties,NAICS industry codes, and other identifying information such as addresses andphone numbers. The dataset on M&A activities was merged with the firm-leveldata from Amadeus Database using all available identifying variables, such as firmnames, addresses, and industry codes. The match rate is around 60 percent onaverage from 2010 to 2018.Importantly, this database contains variables that identify the target firm andthe acquiring firm in each transaction, and indicate the type of a transaction, suchas merger or acquisition. Our analyses include both mergers and acquisitions andpresent the results separately. Because prior studies on M&As use the change ofcontrol as a key determinant of post-M&A outcomes, we also look at acquisitionsgiven that many of them can result in a change of control. First, we provide detailson how the SDC database categorizes each M&A transaction.In the SDC database, a merger is an event in which a target and an acquirerbecome one entity. Additionally, a merger can be an event where an acquirer buys100 percent of a targetâs stock. By contrast, an acquisition is an event where an119acquirer takes over either a targetâs assets or a part of its stock. In the sample of allM&As that are matched with our firm-level data in our analysis sample between2010 and 2018, mergers account for 17 percent (19 percent in our analysis sample)of the cases. Acquisitions are further classified into the following categories: (1)acquisition of majority interest, in which the acquirer holds less than 50 percent andis seeking to purchase 50 percent or more, but less than 100 percent of the targetcompanyâs stock, (2) acquisition of remaining interest, in which the acquirer holdsover 50 percent and is seeking to purchase 100 percent of the target companyâsstock, (3) acquisition of partial interest, meaning deals in which the acquirer holdsless than 50 percent and is seeking to acquire less than 50 percent, or the acquirerholds over 50 percent and is seeking less than 100 percent of the target companyâsstock, (4) acquisition of assets, in which the assets of a company, subsidiary, divi-sion, or branch are acquired, and (5) acquisition of certain assets, in which certainassets of a company, subsidiary, division, or branch are acquired. While the ac-quirer may not purchase the entirety of the target in the case of an acquisition, theacquirer gains the controlling stake of the target in the acquisition of majority in-terest (12 percent of all M&As). In this sense, an acquisition of majority interest issimilar to a merger to the extent that there is a change of control.In contrast, when the acquirer buys the remaining interest (3 percent of allM&As), there is no change of control. Similarly, when the acquirer buys a partialinterest (16 percent of all M&As), there is no change of control either. Further-more, the acquirer does not have the controlling stake unless it already had themajority of the targetâs stock prior to the event.When an acquirer buys the targetâs assets (51 percent of all M&As), the ac-quirer may purchase the entire companyâs assets or its divisionâs assets. Therefore,in the case of asset purchases, the acquirer may or may not have the controllingstakes, and it is difficult to know whether there is an absolute change of controlwithout more information from the data. If the acquisition of assets leads to achange of control, then it is also similar to a merger. In the acquisition of certainassets (1 percent of all M&As), however, there is unlikely to be a change of control.Because M&As may lead to different outcomes depending on whether thereis a change of control, we separate our firm-level analyses into those involvingmergers and those involving acquisitions. Furthermore, M&As may have different120quantitative impacts depending on whether the acquirer buys a part or the entiretyof the target, regardless of a change of control. In the acquisition of majorityinterest, there is a definite change of control and most asset purchases likely resultin a change of control as well, despite not buying the entire target.4.2.2 Amadeus DatabaseThe Amadeus Database contains financial information for both private and publiclytraded firms in Europe from 2010 to 2019 across 10 European countries: Belgium,Finland, France, Germany, Italy, Netherlands, Poland, Spain, Sweden, and UnitedKingdom. We choose these countries as they give use enough variation based onthe number of M&A deals in each year to conduct an event-study analysis.3 Theyinclude a wide range of firm characteristics and outcome variables, which we de-tail in the next subsection. Importantly, they include unique identifying variables,such as firm names, addresses, phone and fax numbers, which we use along withother identifying variables (i.e., postal codes) to perform fuzzy matching with theM&A dataset. As a robustness check, we also show the results where we only usefirms that are perfectly matched between the financial dataset and M&A dataset(Appendix ).We perform fuzzy matching between the financial data and M&A data at thefirm-level. We standardize the name, city, address, website, email, fax and phonenumber of each firm. Then, we perform fuzzy matching using matchit command inSTATA which matches two variables (firm names here) based on similar text pat-terns. Other variables are used to increase both quality and the number of matches(see Appendix for more details). Since different establishments of a firm may beactive in different industries, matching based on industry codes will not be perfectand a firm might be matched with an establishment that is not active in its industry.4Therefore, we do not use the NAICS industry code as the unique identifying vari-able. The final dataset contains firm financial data matched with M&A informationfor 10 European countries from 2010 to 2018.3Table C.5 in Appendix shows the number of M&A deals in other European countries not in-cluded in our analysis. Except for Denmark, all other countries have a substantially lower number ofM&A events relative to 10 European countries in our analysis during the sample period.4For example, Airbus SE is a company primarily active in âManufacturingâ sector; however, italso has establishments active in âRetailâ and âManagement of Companies and Enterprisesâ sectors.121Our analysis sample consists of both publicly traded (mostly large) and private(small to large) firms across 10 European countries from 2010 to 2018. The shareof listed firms in our analysis sample is about 5 percent on average, and droppingthem does not quantitatively affect our main estimates. In our main specification,we consider firms that completed at least one transaction (whether as an acquireror target) as treated firms and firms without any completed transaction as controlfirms. We checked that our controls have never been involved in any M&A trans-action since 2005 (i.e., at least six years before the start of the first M&A event inour analysis sample).4.2.3 Variable DefinitionsThe main firm-level outcome variables are different measures of profitability, markups,total revenue and expenses, and leverage ratio. We use two different measures ofprofitability: (1) returns on assets, defined as net income divided by total assetsand (2) net profit margins directly observed in our data. In Appendix , we look attwo additional measures of profitability as a robustness check: (1) returns on assetsdirectly observed in our data and (2) EBITDA (Earnings Before Interest, Taxes,Depreciation, and Amortization) divided by total revenue which is also directlyobserved in our data. We define markup as the elasticity of output with respectto variable costs as well as the variable costs share [De Loecker and Warzynski,2012]. For the elasticity of output with respect to variable costs, we use esti-mates from De Loecker et al. [2020] based on the U.S. data that allow for differentelasticities across two-digit NAICS industry codes and years. Given the elastic-ity estimates, this allows us to estimate firm-level markups as the output elasticitymultiplied by the inverse of the variable costs (total wage bills and material costs)share: Î¸Ëst â SalesCosts o f Goods . Total revenue is the sum of operating revenue, financialrevenue, and other revenue from our data. Total expenses is the sum of operatingexpense, financial expense, and other expense from our data. Finally, leverage ratiois defined as total liabilities divided by total assets.1224.3 Empirical StrategyThis section describes our empirical strategy for estimating the impact of mergersor acquisitions on firm-level outcomes.4.3.1 Estimating M&A Effects on Main OutcomesTo assess the effects of mergers or acquisitions on firm-level outcomes, we imple-ment a matched difference-in-differences design by estimating a regression of thefollowing form:Yjt =4âk=â4Î²MAk 1(t j = tâ+ k)ÃMA j + Ït +Ï j +u jt (4.1)where Yjt is an outcome variable for firm j at year t, MA j is an indicator for anM&A firm, 1(t j = tâ+ k) indicates an M&A event occurred k years in the past(or future) relative to the period of the M&A event tâ, Ït are period fixed effectsthat vary by the year of the M&A event, Ï j are firm fixed effects, and u jt is anerror term. To absorb any country-specific shock or industry-specific shock affect-ing M&A activities in a given year [Maksimovic and Phillips, 2001], we includeindustry by country by year fixed effects as control variables. In this way, our varia-tion is coming from cross-firm comparisons (i.e., M&A firms versus their matchednon-M&A firms) within an industry and a country. The standard errors are clus-tered at the firm level. Note that we focus on their first M&A event for firms thatgo through M&As more than once, although we still allow the subsequent M&Aevent(s) to affect their outcomes in the post-event period.5 Furthermore, most ofthe M&A firms and their matched control firms are private (95 percent) in our anal-ysis sample, so our results remain similar even if we focus our analyses exclusivelyon private firms (Appendix ).In our matched sample (described in the next subsection), roughly 81 percentof M&A events involve acquisitions. Compared to a merger, an acquisition results5When focusing on the first acquisition or merger, we still include the subsequent event(s) in thepost-event period for firms that go through M&As more than once. The vast majority (roughly 74percent) of firms goes through a merger or an acquisition only once during our sample period. Ourmain analysis sample still includes these acquirers involved in multiple M&As over time. In addition,we separately examine acquirers that go through multiple M&As in Appendix .123in the target firm continuing its operation as an independent company, allowingus to track the targetâs outcomes separately from the acquirerâs outcomes after theevent. To have a clearer picture on where the effects are coming from, we assessour firm-level outcomes separately for those involved in acquisitions and for thoseinvolved in mergers. In particular, when looking at acquisitions, we look at targetsand acquirers separately. By contrast, when looking at mergers, we assess theaggregate (targets and acquirers pooled) outcomes, although we also assess theaggregate outcomes for acquisitions as a comparison.The main identifying assumption is that outcomes for M&A firms and for con-trol firms would have trended similarly in the absence of the M&A event. Thisassumption seems strong since M&As are firmsâ endogenous decisions. For in-stance, acquiring firms may plan to purchase firms that will be profitable in thefuture, whose profits may grow even absent the acquisition. By contrast, acquir-ers may buy underperforming businesses whose profits would fall regardless ofM&As. One way to determine the direction of the potential bias is by comparingoutcomes for M&A firms to the control firms in the years prior to the M&A event.Parallel pre-trends in firm-level outcomes would help alleviate the aforementionedconcerns. In fact, we find that M&A firmsâ outcomes share parallel pre-trends withthose of control firms, not only in terms of their profitability and size, but also withregards to their capital structure.While verifying parallel pre-trends is helpful for a causal interpretation, ourestimates can be still biased due to contemporaneous shocks that occur with M&Aevents. For example, there could be a negative shock that hits a particular marketand causes both a decline in profits and an increase in M&A activities as firmsget purchased before they shut down. In this case, M&A activities are correlatedwith shocks that decrease firmsâ profits. We can also have the opposite scenario;in fact, merger activities tend to be pro-cyclical in aggregate [Rhodes-Kropf andViswanathan, 2004]. Therefore, rather than claiming our results as causal effectsof M&As, we focus on whether we find strong evidence of increased profitabilityand markups consistent with the increased market power after M&As.1244.3.2 Matched SamplesTo perform a matching procedure between M&A firms and control firms, we im-pose the following restrictions. First, we drop firms that have missing observationsfor the key variables used for matching, such as total revenue, returns on assets,and firm age, measured one year prior to the event. During our sample period, weobserve, on average, 387 M&A events per year-country among this sample of firmseligible for matching (see Figure 4.1). We then match each firm one year prior toan M&A event to a control firm in the same 2-digit NAICS sector. A firm is apotential control firm for firm j if: (1) the firm is never involved in an M&A eventbetween 2005 and 2018, and (2) the firm is in the same ventile bin of total revenueand is in the same quintile bins in returns on assets and firm age one year prior tothe M&A event of firm j.6 Of all possible counterfactual firms for a given M&Afirm, we choose one with the closest propensity score (one-to-one matching withina caliper), which is estimated by predicting treatment using a linear probabilitymodel with a quadratic function in total revenue, returns on assets, and firm age inyear [tâ1]. This matching strategy is similar to a number of recent papers imple-menting a matched difference-in-differences design [Arnold, 2021, Goldschmidtand Schmieder, 2017]. The matching strategy finds a control firm in about 98 per-cent of all cases among the eligible sample. Choosing one counterfactual controlfirm for each M&A firm ensures that both groups are comparable on the matchedvariables. We construct an unbalanced panel of firms extending 4 years prior toand 4 years after the M&A event.Since we use a matched control group that is never involved in M&A during oursample period, the specifications above do not suffer from the identification issuesthat arise in conventional event-study designs with staggered timing [Borusyaket al., 2024, Goodman-Bacon, 2021]. Our identification comes from differencesin always-treated and never-treated units over time, not from units coming in andout of treatment.7 Moreover, we use only completed M&A deals in our analyses6While this specification yields the best parallel pre-trends on the key outcomes, our main resultsremain qualitatively similar when we use different bin sizes or other related firm characteristics (e.g.,leverage ratio) for matching. Results based on other variation in matching can be provided uponrequest.7We let the first M&A event in our analysis sample start from 2011 to ensure that our matchedcontrol firms did not go through any M&A event at least 5 years prior to (or after) the first M&A125following the literature. The number of withdrawn M&A events that meet thesample restrictions described above is very small in our data. While using firmsinvolved in withdrawn M&A events as a placebo group yields null effects acrossthe key outcomes, many of these results are also underpowered due to the smallsample size.4.3.3 Descriptive StatisticsWe close this section with descriptive statistics of our analysis sample. Panel Aof Table 4.1 shows the averages for main variables across firms, comparing M&Afirms to the set of matched control firms one year before the event. On average,M&A firms are a bit larger than their control firms, in terms of total revenue. How-ever, for returns on assets, profit margins, leverage ratio, and markups, M&A firmsare comparable to their control firms, suggesting that firms that go through M&Asand firms that never get involved in M&As are comparable with regards to theirprofitability and financial structure prior to the event. Importantly, as we showin Sections 4.4, M&A firms and their control firms share parallel pre-trends onthese variables, implying that they exhibited similar patterns not only in terms ofsales, but also with regards to profitability, markups, and leverage ratio prior to theM&A event. The dominant sectors are manufacturing, wholesale, and services (66percent of our analysis sample).4.4 ResultsThis section shows results from the estimation of the difference-in-differencesmodel in Section 4.3.4.4.1 Profitability and Markups After M&AsFigure 4.2 plots estimates of βMAk from equation (3.2) across the key firm-leveloutcomes based on our matched sample. As mentioned in Section 4.3, we showthese results separately for targets and for acquirers involved in acquisitions (Panels(a) â (c)) and for the aggregate (targets and acquirers pooled, Panels (d) â (f)). Panelevent, given that our SDC Platinum data starts from 2005. In this way, we ensure that none of thematched control firms went through any M&A event from 2005 to 2018.126(a) shows that acquiring firmsâ and target firmsâ returns on assets followed a similarpattern as those of their matched control firms before the M&A event. Both targetsâand acquirersâ returns on assets decreased after the event, compared to those ofmatched control firms. Panel (b) shows that the pre-event trends for profit marginsare also similar between M&A firms and their control firms. We see declines inboth targetsâ and acquirersâ profit margins after M&As, compared to control firmsâprofit margins. Panel (c) shows that targetsâ and acquirerâs marks remain flat bothbefore and after M&As, compared to those of their control firms. In Panels (d) â(f), we pool targets and acquirers and compare their outcomes with those of theirmatched control firms before and after the M&A event. Here, we simply drop thedistinction between acquirers and targets when estimating the equation (3.2) so thatacquirers, targets, and their matched control pairs are all in the same estimation.8The black line indicates the aggregate outcomes for those involved in mergers andthe red line indicates the aggregate outcomes for those involved in acquisitions.In both mergers and acquisitions, returns on assets and profit margins decreasedafter M&As, relative to their control firms. By contrast, markups stayed roughlyflat after acquisitions, whereas we see a small increase in markups in the case ofmergers.To interpret the magnitude of these results, Table 4.2 presents the difference-in-differences estimates on these outcomes, separately for targets and acquirersinvolved in acquisitions (Column 1 and 2) and for the aggregate (Column 3 and4). Column (1) shows that target firmsâ returns on assets and profit margins fell by1.1 percentage points and 1 percentage points, respectively, after the event. Col-umn (2) shows that acquiring firmsâ returns on assets and profit margins decreasedby 0.6 percentages points and 0.7 percentage points, respectively, after the event.Columns (3) and (4) show these outcomes in the aggregate, separately for thoseinvolved in acquisitions and for those involved in mergers. Columns (3) and (4)show that returns on assets decreased by 0.9 percentage points in the case of acqui-sitions and 0.4 percentage points in the case of mergers. Similarly, profit margins8Alternatively, we could combine acquirers and targets into single entities (i.e., summing up theirobservations), re-do the firm-level matching to find their control firms, and estimate the equation(3.2) using the combined entities. However, this new matched sample would likely differ from theoriginal analysis sample, given that finding matched control firms for the combined entities mayresult in a lower match rate and therefore a smaller matched sample.127decreased by 0.9 percentage points in both acquisitions and mergers. By contrast,we do not see much changes in markups either for targets or acquirers, or either inthe case of acquisitions or mergers. Overall, our results show that M&As lead de-clines in profitability for both targets and acquirers either in the case of acquisitionsor mergers. By contrast, markups stay relatively flat after M&As.4.4.2 Total Revenue, Expenses, and Leverage Ratio After M&AsTo help understand the results on decreased profitability, Figure 4.3 plots estimatesof βMAk from equation (3.2) for total revenue, expenses, and leverage ratio. Panels(a) â (c) show that acquiring firmsâ and target firmsâ total revenue, expenses andleverage ratio followed a similar pattern as those of their matched control firms be-fore the M&A event. Both targetsâ and acquirersâ revenue, expenses, and leverageratio increased after the event, compared to those of their control firms. Panels (d)â (f) show that both mergers and acquisitions lead to higher revenue, expenses, andleverage ratio in the aggregate.To interpret the magnitude of these results, Table 4.3 presents the difference-in-differences estimates on these outcomes, separately for targets and acquirersinvolved in acquisitions (Column 1 and 2) and for the aggregate (Column 3 and4). Column (1) shows that target firmsâ revenue and expenses increased by 18.3log points and 15.7 log points, respectively, after the event. Column (2) shows thatacquiring firmsâ revenue and expenses increased by 34.1 log points and 34.9 logpoints, respectively, after the event. Columns (3) and (4) show these outcomes inthe aggregate, separately for those involved in acquisitions and for those involvedin mergers. Therefore, for target firms, revenue increased more than expenses,while for acquirers, revenue increased slightly less compared to expenses after theevent. Columns (3) and (4) show that revenue increased by 24.6 log points in thecase of acquisitions and 21.7 log points in the case of mergers. Similarly, expensesincreased by 23.3 log points in the case of acquisitions and 26 log points in thecase of mergers. Finally, leverage ratio increased by 1.1 percentage points fortargets, by 2.67 percentage points for acquirers, 1.7 percentage points in the caseof acquisitions in the aggregate, and 1.5 percentage points in the case of mergersin the aggregate. Overall, our results show that M&As lead to rises in revenue and128expenses for both targets and acquirers either in the case of acquisitions or mergers.4.5 Market Power Channel and EconomicInterpretationsIn this section, we discuss and empirically test whether profitability and markupresponses following an M&A event are consistent with a rise in market power. Un-derstanding potential mechanisms behind these responses is important for policy-makers designing an effective antitrust system. The main channel in which M&Ascan induce higher profits or markups is through the product market power: firmsthat gain market power after M&As may have incentives to decrease investment inorder to suppress output and to increase prices, which will result in higher profitsand markups.We find that the both acquiring and target firms experienced a decline in prof-itability, which is inconsistent with these firms gaining market power after M&As[De Loecker et al., 2020]. While efficiency gains through synergies are often ar-gued as potential benefits of M&As, we do not find any evidence of increasedprofitability for either acquiring or target firms after M&As. This result can haveimportant implications for pro-merger arguments that rely on predicted synergygains; if M&A events are undertaken by profit-maximizing firms, why do we ob-serve a decline in profitability? This result is consistent with a corporate financeliterature that finds negative effects of M&A events on firm performance or marketvaluation. Note that our findings capture short to medium run outcomes, so firmsâprofitability may improve in the long run. Whether or not profits increase in thelong run, the decline in firm performance suggests that increased market power viaM&As is unlikely. To further corroborate this conclusion, we also conduct hetero-geneity analyses by sectors and types of M&As to test the market power channel.4.5.1 Heterogeneity by Tradable vs. Non-Tradable SectorsIf firms gain more market power after M&As, we should observe that a decrease inprofitability is concentrated among tradable sectors relative to nontradable sectors.The intuition is that an M&A would have a larger impact on firmsâ market powerif they do not face competition outside their product or geographical (i.e., inter-129national) markets. We define firms as active in tradable good sectors if they fallunder Agriculture, Forestry, Fishing and Hunting, Mining, Quarrying, and Oil andGas Extraction, and Manufacturing. Firms active in other sectors (i.e., Construc-tion, Retail, Real Estate, Services, etc) are labeled as active in nontradable sectors[Delgado et al., 2014, ?].Figure 4.4 shows the results separately for tradable sectors (27 percent of M&Adeals in our analysis sample) and for nontradable sectors. Panels (a) and (c) showthat the declines in returns on assets in the case of acquisitions (in the aggregate)are similar for both tradable sectors and nontradable sectors. Panel (b) shows thatthe decline in returns on assets is smaller for nontradable sectors compared to thedecline in tradable sectors in the case of mergers. While this result is partly con-sistent with the market power channel, the difference is not statistically differentfrom zero, as shown in Table 4.4. Furthermore, we see similar declines in profitmargins for both tradable and nontradable sectors in the case of mergers, implyingthat M&As unlikely lead target firms to gain more market power.4.5.2 Heterogeneity by Domestic vs. Cross-border M&AsIn our pooled sample across 10 European countries, cross-border M&As accountfor 34 percent of total M&A deals. We test whether the effects of M&As on prof-itability are different depending on whether a M&A deal was international ratherthan domestic. The intuition is that changes in competition would be higher ifM&A deals involve firms within the same country i.e., the same domestic marketrather than at least one of the parties outside of the country. We define a deal asdomestic if both target and acquiring firms are within the same country, and de-fine the deal as cross-border if the parties are from different countries. Figure 4.5shows that the effects of M&A on returns on assets and profit margins in the caseof acquisitions are not statistically different between domestic and cross-borderM&As. In the case of mergers, the declines in returns on assets and profit marginsare larger (albeit not statistically different from zero) for domestic deals comparedto cross-border deals (see Table 4.5), which is inconsistent with the market powerchannel.1304.6 ConclusionIn this paper, we study how corporate M&As affect firmsâ profitability and markupsin the medium run. We compare the outcomes of firms that go through M&As witha matched sample of non-M&A firms before and after the event. Using financialdata merged with M&A data at the firm level across 10 European countries from2010 to 2018, we exploit a large number of M&A activities based on a pooledsample of firms across these countries, given that they all share a comparable set ofantitrust regulations and are governed by the European Union. Our results show aneconomically and statistically significant decline in profitability for both acquiringfirms and target firms, while their markups remain unchanged. Additional hetero-geneity analyses based on sectors and types of M&A deals suggest that the changesin profitability and markups are unlikely to be driven by the market power chan-nel. Our findings cast doubts on the efficiency effects of M&A through synergiesand raise concerns regarding the efficiency-defence argument commonly used bymerging parties.131Figure 4.1: Number of M&A Events30004000500060007000Number of Events2010 2012 2014 2016 2018YearWhole Sample Eligible SampleMatched SampleNotes: During our sample period, the number of M&A events is 575 per year (including multipleevents for a given firm) on average among the among all firms (eligible and ineligible for matching)as explained in Section ?? (blue line). The number of eligible M&A events is 387 per year(including multiple events for a given firm) on average (red line). Finally, the number of matchedM&A events is 375 per year. Section ?? describes how we construct our matched sample of firms.During our sample period, the unique number of firms eligible for matching is 36,516. Among thesefirms, the unique number of matched firms (both targets and acquirers) is 35,698. Therefore, thematching strategy finds a counterfactual firm in about 98 percent of all cases among this sample ofeligible firms.132Figure 4.2: Profitability and Markups After M&As(a) Return on Assets-.02-.010.01Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventTarget Acquirer(b) Profit Margins-.02-.015-.01-.0050.005Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventTarget Acquirer(c) Markups-.04-.020.02.04Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventTarget Acquirer(d) Return on Assets-.015-.01-.0050.005.01Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventAcquisition Merger(e) Profit Margins-.02-.010.01Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventAcquisition Merger(f) Markups-.050.05Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventAcquisition MergerNotes: Panels (a) â (c) display event-study estimates for the impact of M&As on firm-level profitability for firms involved in partial acquisitions,separately for acquiring firms (navy line) and for target firms (orange line). Panel (a) shows the estimates for return on assets. Panel (b) shows theestimates for profit margins. Panel (c) shows the estimates for markups. Panels (d) â (f) display event-study estimates for the impact of M&As on theaggregate firm-level (targets and acquirers pooled) profitability, separately for those involved in partial acquisitions (red line) and for those involved inmergers (black line). Panel (d) shows the estimates for return on assets. Panel (e) shows the estimates for profit margins. Panel (f) shows the estimatesfor markups. The dashed lines indicate 95 percent confidence intervals where the standard errors are clustered at the firm level. The M&A event is inyear 0 and the coefficient is normalized to be zero in year -1. Return on assets is defined as firmsâ net income divided by its total assets. We definemarkup as the elasticity of output with respect to variable costs as well as the variable costs share [De Loecker and Warzynski, 2012].133Figure 4.3: Total Revenue, Expenses, and Leverage Ratio After M&As(a) Total Revenue0.1.2.3.4.5Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventTarget Acquirer(b) Total Expenses0.2.4.6Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventTarget Acquirer(c) Leverage Ratio-.020.02.04.06Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventTarget Acquirer(d) Total Revenue-.10.1.2.3.4Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventAcquisition Merger(e) Total Expenses-.20.2.4.6Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventAcquisition Merger(f) Leverage Ratio-.020.02.04Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventAcquisition MergerNotes: Panels (a) â (c) display event-study estimates for the impact of M&As on firm-level performance for firms involved in partial acquisitions,separately for acquiring firms (navy line) and for target firms (orange line). Panel (a) shows the estimates for logarithm of total revenue. Panel (b)shows the estimates for logarithm of total expense. Panel (c) shows the estimates for leverage ratio. Panels (d) â (f) display event-study estimates forthe impact of M&As on the aggregate firm-level (targets and acquirers pooled) profitability, separately for those involved in partial acquisitions (redline) and for those involved in mergers (black line). Panel (d) shows the estimates for logarithm of total revenue. Panel (e) shows the estimates forlogarithm of total expense. Panel (f) shows the estimates for leverage ratio. The dashed lines indicate 95 percent confidence intervals where thestandard errors are clustered at the firm level. The M&A event is in year 0 and the coefficient is normalized to be zero in year -1.134Table 4.1: Descriptive Statistics on the Matched Sample of Firms(1) (2) (3) (4)Acquirer Control Target ControlPanel A: Firm CharacteristicsTotal Revenue (in millions) 65.1 47.5 29.7 24.0Return on Assets 0.044 0.049 0.034 0.044Profit Margins 0.059 0.054 0.046 0.048Leverage Ratio 0.600 0.617 0.631 0.613Markups 2.884 3.294 2.482 2.619Number of Firms 8,096 8,096 14,636 14,636Panel B: Sector CompositionConstruction 0.06 0.06 0.06 0.06Manufacturing 0.24 0.24 0.26 0.26Wholesale 0.14 0.14 0.13 0.13Retail 0.03 0.03 0.04 0.04Transportation 0.04 0.04 0.04 0.04Information 0.06 0.06 0.06 0.06Services 0.28 0.28 0.25 0.25Other Sectors 0.17 0.17 0.18 0.18Notes: This table reports descriptive statistics on the matched sample of firms measured one yearprior to the event. Panel A reports firm characteristics such as total revenue, return on assets, profitmargins, leverage ratio, and markups. Columns (1) and (3) report these statistics for acquiring firmsand for target firms, respectively, and column (2) and (4) report these statistics for their respectivematched control firms. Panel B reports the distribution of firms in the matched sample across 2-digitNAICS sectors. Other sectors include (1) Agriculture, forestry, and fishing, (2) Mining, quarrying,and oil and gas extraction, (3) Utilities, (4) Real estate and rental and leasing, (5) Arts,entertainment and recreation, (6) Accommodation and food services, (7) Other services, and (8)Public administration.135Figure 4.4: Profitability After M&As: By Tradable sectors vs. Nontradablesectors(a) Return on Assets - Acquisitions-.02-.010.01Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventTradable Nontradable(b) Return on Assets - Merger-.04-.020.02Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventTradable Nontradable(c) Profit Margins - Acquisitions-.02-.010.01Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventTradable Nontradable(d) Profit Margins - Merger-.04-.020.02Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventTradable NontradableNotes: Panels (a) â (d) display event-study estimates for the impact of M&As on firm-levelprofitability for firms active in tradable sectors (orange line) and for firms in nontradable sectors(navy line). Panel (a) shows the estimates for return on assets for firms involved in partialacquisitions. Panel (b) shows the estimates for profit margins for firms involved in partialacquisitions. Panel (c) shows the estimates for return on assets for firms involved in mergers. Panel(d) shows the estimates for profit margins for firms involved in mergers. The dashed lines indicate95 percent confidence intervals where the standard errors are clustered at the firm level. The M&Aevent is in year 0 and the coefficient is normalized to be zero in year -1. We define firms as active intradable good sectors if they fall under Agriculture, Forestry, Fishing and Hunting, Mining,Quarrying, and Oil and Gas Extraction, and Manufacturing.136Figure 4.5: Profitability After M&As: By Cross-border M&As vs. DomesticM&As(a) Return on Assets - Acquisitions-.03-.02-.010.01Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventInternational Domestic(b) Return on Assets - Merger-.04-.020.02.04Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventInternational Domestic(c) Profit Margins - Acquisitions-.03-.02-.010.01Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventInternational Domestic(d) Profit Margins - Merger-.020.02.04.06Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventInternational DomesticNotes: Panels (a) â (d) display event-study estimates for the impact of M&As on firm-levelprofitability for firms involved in international M&As (orange line) and for firms involved ininternational M&As (navy line). Panel (a) shows the estimates for return on assets for firmsinvolved in partial acquisitions. Panel (b) shows the estimates for profit margins for firms involvedin partial acquisitions. Panel (c) shows the estimates for return on assets for firms involved inmergers. Panel (d) shows the estimates for profit margins for firms involved in mergers. The dashedlines indicate 95 percent confidence intervals where the standard errors are clustered at the firmlevel. The M&A event is in year 0 and the coefficient is normalized to be zero in year -1.137Table 4.2: Profitability and Markups After M&As(1) (2) (3) (4)Acquisition AggregateTarget Acquirer Acquisition MergerReturn on Assets -0.011*** -0.006*** -0.009*** -0.004(0.002) (0.002) (0.001) (0.003)Mean at t=-1 0.03 0.04 0.04 0.05Adj. R squared 0.467 0.431 0.460 0.449Firm-Year 198,406 108,377 311,436 64,347Profit Margins -0.010*** -0.007*** -0.009*** -0.009***(0.002) (0.002) (0.001) (0.003)Mean at t=-1 0.04 0.06 0.05 0.06Adj. R squared 0.522 0.521 0.524 0.500Firm-Year 177,146 97,401 279,210 57,546Markups -0.000 -0.009 -0.004 0.012(0.006) (0.008) (0.005) (0.011)Mean at t=-1 0.37 0.48 0.41 0.26Adj. R squared 0.935 0.941 0.938 0.949Firm-Year 158,616 85,475 248,441 49,430Notes: This table reports the difference-in-differences estimates for the impact of M&As on firmprofitability. Columns (1) and (2) report the estimates for the impact of partial acquisitions on targetfirms and on acquiring firms, respectively. Columns (3) and (4) report the estimates on the aggregatefirm-level (targets and acquirers pooled) outcomes for those involved in partial acquisitions and forthose involved in mergers, respectively. The standard errors are clustered at the firm level.138Table 4.3: Total Revenue, Expenses, and Leverage Ratio After M&As(1) (2) (3) (4)Acquisition AggregateTarget Acquirer Acquisition MergerTotal Revenue 0.183*** 0.341*** 0.246*** 0.217***(0.016) (0.021) (0.013) (0.030)Mean at t=-1 15.41 16.28 15.71 15.72Adj. R squared 0.881 0.889 0.884 0.891Firm-Year 196,676 107,573 308,888 63,806Total Expense 0.157*** 0.349*** 0.233*** 0.260***(0.021) (0.025) (0.016) (0.039)Mean at t=-1 12.03 13.06 12.39 12.34Adj. R squared 0.880 0.896 0.888 0.889Firm-Year 187,155 103,171 295,015 60,150Leverage Ratio 0.011*** 0.027*** 0.017*** 0.015**(0.003) (0.004) (0.002) (0.006)Mean at t=-1 0.63 0.60 0.62 0.61Adj. R squared 0.727 0.761 0.737 0.729Firm-Year 208,286 112,438 325,357 67,224Notes: This table reports the difference-in-differences estimates for the impact of M&As on firmperformance measures such as total revenue, total expense, and leverage ratio. Columns (1) and (2)report the estimates for the impact of partial acquisitions on target firms and on acquiring firms,respectively. Columns (3) and (4) report the estimates on the aggregate firm-level (targets andacquirers pooled) outcomes for those involved in partial acquisitions and for those involved inmergers, respectively. The standard errors are clustered at the firm level.139Table 4.4: Profitability After M&As: By Tradable sectors vs. Nontradablesectors(1) (2) (3) (4)Return on Assets Profit MarginsAcquisition Merger Acquisition MergerPost x Treated -0.006*** -0.008 -0.008*** -0.014***(0.002) (0.005) (0.002) (0.005)Post x Treated x Nontradable -0.004 0.006 -0.002 0.006(0.003) (0.006) (0.003) (0.006)Mean at t=-1 (Tradable = 1) 0.04 0.05 0.05 0.05Mean at t=-1 (Nontradable = 1) 0.03 0.04 0.05 0.06Adj. R squared 0.460 0.449 0.524 0.500Firm-Year (Tradable = 1) 84,461 19,396 78,311 18,031Firm-Year (Nontradable = 1) 230,179 49,154 204,810 43,872Notes: This table reports the difference-in-differences estimates for the impact of M&As on firmprofitability. Columns (1) and (2) report the estimates for the impact on return on assets for partialacquisitions and merger, respectively. Columns (3) and (4) report the estimates for the impact onprofit margins for partial acquisitions and merger, respectively. The triple interaction term capturesthe triple-difference estimates for firms active in nontradable sectors. The standard errors areclustered at the firm level. We define firms as active in tradable good sectors if they fall underAgriculture, Forestry, Fishing and Hunting, Mining, Quarrying, and Oil and Gas Extraction, andManufacturing140Table 4.5: Profitability After M&As: By Cross-border M&As vs. DomesticM&As(1) (2) (3) (4)Return on Assets Profit MarginsAcquisition Merger Acquisition MergerPost x Treated -0.011*** 0.003 -0.012*** 0.004(0.002) (0.003) (0.002) (0.003)Post x Treated x Domestic 0.001 -0.006 -0.003 -0.007(0.006) (0.007) (0.006) (0.007)Mean at t=-1 (Cross-border = 1) 0.03 0.04 0.05 0.06Mean at t=-1 (Domestic = 1) 0.04 0.05 0.05 0.06Adj. R squared 0.460 0.449 0.524 0.500Firm-Year (Cross-border = 1) 90,672 19,087 79,429 16,721Firm-Year (Domestic = 1) 223,968 49,463 203,692 45,182Notes: This table reports the difference-in-differences estimates for the impact of M&As on firmprofitability. Columns (1) and (2) report the estimates for the impact on return on assets forcrossborder M&As and domestic M&As, respectively. Columns (3) and (4) report the estimates forthe impact on profit margins for crossborder M&As and domestic M&As, respectively. The tripleinteraction term captures the triple-difference estimates for firms involved in a domestic. Thestandard errors are clustered at the firm level.141Chapter 5ConclusionsThis dissertation is composed of three chapters studying the interplay of publicpolicies, corporate decisions, firm growth, and worker earnings. Chapter 2 stud-ies how R&D tax credits influence worker earnings. I show that R&D-intensivefirms respond to tax credits with substantial increase in R&D expenditures, lead-ing to significant gains in wages. These firm-level gains are passed onto high-skillincumbent workers without impacting low-skill incumbent workers or entrants.The chapter contributes to the literature on the role of firms in determiningworker earnings, with a focus on small and medium-sized innovative firms. Theresults are consistent with a rent-sharing framework and highlight the role of R&Dtax credits in contributing to within-firm wage inequality.Chapter 3 explores the connection between M&A events and changes in work-ersâ labor market outcomes. Using a matched difference-in-differences design, weshow that acquiring firms expand, but target firms shrink compared to their controlgroups. For workers at target firms, we find that their earnings decline and jobseparations increase significantly after an M&A event.The chapter contributes to the literature on labor market consequences of cor-porate M&As. We show, despite minimal changes in labor market concentrations,these events have consequential impact on worker earnings through changes infirm-level profitability and growth.Chapter 4 relatedly explores how corporate M&As affect firmsâ profitabilityand markups in the medium run across 10 European countries. We find an econom-142ically and statistically significant decline in profitability for both acquiring firmsand target firms, while their markups remain unchanged. The heterogeneity anal-ysis based on sectors and types of M&As suggests that the changes in profitabilityand markups are unlikely driven by the market power channel.Chapters 3 and 4 contribute to the literature on the impact of M&As on firm per-formance, which finds mixed empirical results. 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The log transforma-tion may artificially over-smooth the distribution of the outcome around the kink,hence, attenuate the results towards zero, and (ii) R&D expenditure has a right-skewed distribution â where for many observations R&D expenditure is zero. Alog-linear specification may produce biased estimates [Agrawal et al., 2020]. Ta-ble A.1 reports the estimates for two alternative specifications. Columns (1) to(3) use a log transformation, log(R&D Exp +1), and Columns (4) to (6) use in-verse hyperbolic sine transformation, asinh(R&D Exp). In Columns (1) and (4),I control for lagged dependent variable and firm age. In Columns (2) and (5), Icontrol for lagged dependent variable, firm age, and industry fixed effects. And inColumns (3) and (6), I control for lagged dependent variable, firm age, industryfixed effects, and province fixed effects. Reassuringly, the results in all specifica-tions are still economically and statistically significant, although, as expected, theyare smaller than my baseline estimates in Table 2.3.156Intensive Margin vs. Extensive Margin. I examine the effects of R&D taxcredits on both R&D and non-R&D firms. Table A.2 reports the RK estimateresults on intensive and extensive margins. Columns (1) to (3) report the estimateof the impact of the tax credits on the intensive margin using, respectively, all firms,R&D firms (baseline sample), and non-R&D firms around the kink point. Theimpact on all firms is much smaller, still statistically significant, than the impact onR&D firms because non-R&D firms do not respond to eligibility for the R&D taxcredits. Columns (4) and (5) report the estimate of the impact of the tax credits onthe extensive margin using, respectively, R&D firms and non-R&D firms aroundthe kink point. Consistently, I find that the extensive margin for non-R&D firmsis zero. Moreover, the extensive margin for R&D firms is also relatively smallbecause even among these firms, R&D-intensive firms tend to respond to tax crediteligibility. Therefore, the increase in R&D expenditure primarily stems from theintensive margins, while the extensive margin remains unchanged. Accordingly,these findings indicate that R&D tax credits do not encourage non-R&D firms toturn into R&D firms.157Table A.1: Tax Credits Impact on R&D Expenditure(1) (2) (3) (4) (5) (6)Dependent Variable: log(R&D Exp. + 1) asinh(R&D Exp.)Eligible Ã Z 0.092* 0.109* 0.111** 0.097* 0.115 0.118**(0.055) (0.056) (0.056) (0.058) (0.059) (0.059)Mean at t = -1 8.00 8.01 8.01 8.47 8.49 8.49Adj. R squared 0.506 0.535 0.537 0.503 0.533 0.534Observations 5,200 5,160 5,160 5,200 5,160 5,160Notes: This table reports the regression kink estimates of the average treatment effect Î¸Ë = Î³Ë110 where Î³Ë1is the estimated change in slope in the relationship between the outcome variables and the runningvariable at the kink point from Equation 2.3. The baseline sample includes R&D firms with laggedtaxable income within $67,600 of the kink. The running variable is the lagged taxable income. InColumns (1) to (3), controls include (i) lagged dependent variable, and (ii) firm age. In Column (4),controls include (i) lagged dependent variable, (ii) firm age, and (iii) industry fixed effects. In Column(5), controls include (i) lagged dependent variable, (ii) firm age, (iii) industry fixed effects, and (iv)province fixed effects. Standard errors are clustered by firm.158Table A.2: Intensive Margins vs. Extensive Margins(1) (2) (3) (4) (5)R&D Exp 1[R&D Exp > 0]All Firms R&D Firms non-R&D Firms R&D Firms non-R&D FirmsEligible Ã Z 1077.8*** 5975.5** 139.9 0.008* 0.000(404.227) (2473.874) (126.891) (0.005) (0.001)Mean at t = -1 26366.93 167436.5 0 0.69 0Adj. R squared 0.669 0.62 0.003 0.45 0.005Observation 34,000 5,200 28,810 5,200 28,810Notes: This table reports the regression kink estimates of the average treatment effect Î¸Ë = Î³Ë110 where Î³Ë1 is the estimatedchange in slope in the relationship between the outcome variables and the running variable at the kink point fromEquation 2.3. The running variable is the lagged taxable income. Columns (1) to (3) include all firms, R&D firms, andnon-R&D firms, respectively, with lagged taxable income within $67,600 of the kink. The outcome variable in thesecolumns is R&D expenditure. Columns (4) and (5) include R&D firms and non-R&D firms, respectively, with laggedtaxable income within $67,600 of the kink. The outcome variable in these columns is whether the firm had positiveR&D expenditure in the 5-year window after eligibility.159A.2 Estimating Total Factor ProductivityIn this appendix, we provide details on the productivity estimation. To begin, weassume that total revenue of a firm is given by the following production function:y jt = β0+βkk jt +βll jt +Ï jt +η jt (A.1)where y jt is log total revenue, k jt is log total capital, which we capture throughthe variable fixed assets, and l jt is log total employment. The goal of the productionfunction estimation for our purposes is to retrieve an estimate of ÏË jt = y jtâ Î²Ëkk jtâÎ²Ëll jt which captures the productivity of firm j at time t. To allow for differentproduction functions across countries and industries, the estimation procedure isimplemented separately for each four-digit NAICS code and country.Estimating Equation (1) by linear regression would face well-known endogene-ity issues, as η jt is generally unobserved. To circumvent this issue, we followAckerberg et al. [2015] and use a control function that allows us to control for un-observed productivity. To derive the control function, we assume the demand formaterials is a function of both capital and labor. Modeling materials as a functionof capital and labor and including labor as a state variable of the firm are the keydistinctions between Ackerberg et al. [2015] and earlier approaches developed byOlley and Pakes [1992] and Levinsohn and Petrin [2003], which model labor as acompletely variable input that does not appear as a state variable:m jt = mt(k jt , l jt ,Ï jt)Under an invertibility condition, this allows us to invert the demand function toget productivity as a function of labor, capital, and materials:Ï jt = mâ1t (k jt , l jt ,m jt) = ht(k jt , l jt ,m jt)The estimation procedure proceeds in two stages. In stage one, we model rev-enue of a firm as:y jt = β0+βkk jt +βll jt +ht(k jt , l jt ,m jt) (A.2)160Note that k jt and l jt appear both directly, as well as indirectly through ht . There-fore, in the first stage, neither βk nor βl are identified. However, the function Φtcan be estimated by approximating the nonparametric function with a polynomialin labor, capital, and materials.Φt(k jt , l jt ,m jt) = β0+βkk jt +βll jt +Ï jtAdditionally, we assume productivity follows an exogenous first-order Markovprocess:Ï jt = E(Ï jt |Ï j,tâ1)+Î¾ jt = g(Ï j,tâ1)+Î¾ jt = g(Î¦tâ1âÎ²0âÎ²ll j,tâ1âÎ²kk j,tâ1)Using the first-stage estimates, we can now rewrite revenue in time t as:y jt = β0+βll jt +βkk jt +gt(Î¦Ëtâ1âÎ²0âÎ²ll j,tâ1âÎ²kk j,tâ1)+Î¾ jt +η jt (A.3)To estimate this equation requires an additional moment, as ξ jt and l jt are notorthogonal. A standard option is to assume lagged employment is orthogonal tothe error term ξ jt +η jt . This implies the parameters βl and βk can be computed bya generalized methods of moments estimator:E[(ξ jt +η jt)(k jtl j,tâ1)]= 0which yields estimates for Î²Ëk and Î²Ël . The estimates of productivity Ï jt canthen be retrieved as:ÏË jt = y jt â Î²Ëkk jt â Î²Ëll jtA.3 Tax-Adjusted Price ElasticityTo evaluate the tax credit, I calculate the implied R&D elasticity with respect to thetax-adjusted user cost of R&D [Agrawal et al., 2020, DechezlepreËtre et al., 2023,161Hall and Jorgenson, 1967]. The elasticity is calculated by the following formula:ηR&D,uc =âRDâuc.u¯cRÂ¯DBy rearranging the right hand side, I haveηRD,uc =âRDâELâucâEL.u¯cRÂ¯Dwhere âRDâEL is the change in R&D expenditure with respect to the EL, as estimatedin Equation 2.3, âucâEL is the change in tax-adjusted user cost of R&D with respectto the EL, RÂ¯D is the average R&D expenditure, and u¯c is the average tax-adjusteduser cost of R&D.Change in tax-adjusted user cost with respect to the EL, âucâEL . I use Halland Jorgenson [1967] formula for the steady-state tax-adjusted user cost of R&D:uc =(1âAdâAc)1â Ï (r+δ )where r is the real interest rate, δ is the depreciation rate, Ï is corporate tax rate,and Ad is the present value of deductions and depreciation allowances, and Acis the value of tax credits. In Canada, Ad = Ï , the corporate tax rate, becauseR&D expenditures (including the capital) are fully deductible. Moreover, Ac =Ï(1â Ï) because R&D tax credits are taxable income [Agrawal et al., 2020]. Ï isthe marginal credit rate. Therefore,uc = (r+δ )(1âÏ)The marginal credit rate varies across firms base on their tax liability, and R&Dexpenditure relative to EL. There are four possible scenarios:1. RDt <= ELt : The marginal credit rate is 0.35 and the credit is refundable.2. RDt > ELt and Taxt > 0 and t â¤ 2013: The marginal credit rate is 0.20 andthe credit offsets the taxes.3. RDt > ELt and Taxt > 0 and t â¥ 2014: The marginal credit rate is 0.15 andthe credit offsets the taxes.1624. RDt > ELt and Taxt <= 0: The marginal credit rate is 0.08 and the credit isrefundable.1For each scenario I calculate the tax-adjusted user cost of R&D and its changerelative to EL, âucâEL , then calculate the average value for tax-adjusted user cost ofR&D, u¯c, for the analysis sample based on their tax liability, R&D expenditurerelative to the EL. 0.15*0.25+0.2*0.35RDt <= ELt :uc = (r+δ )(1â0.35) = 0.65(r+δ )In this case a change in EL (i.e. an increase in the EL) does not change the marginalcredit rate. âucâEL = 0.RDt > ELt and Taxt > 0 and t â¤ 2013:uc = (r+δ )(1â0.20) = 0.80(r+δ )In this case a change in EL (i.e. an increase in the EL), âucâEL , is equal to â0.15(r+Î´ ).RDt > ELt and Taxt > 0 and t â¥ 2014:uc = (r+δ )(1â0.20) = 0.85(r+δ )In this case a change in EL, i.e. an increase in the EL, âucâEL , is equal toâ0.20(r+Î´ ).The average tax-adjusted user cost of R&D ,u¯c is then as follows:uc= {PR(RDt <=ELt)Ã0.65+PR(RDt >ELt , t â¥ 2014)Ã0.8+PR(RDt >ELt , t < 2013)Ã0.85}(r+Î´ )In the baseline sample, I have PR(RDt <= ELt) = 0.40, PR(RDt > ELt , t â¥2014) = 0.35, and PR(RDt > ELt , t < 2013) = 0.25. So, the average tax-adjusted1This case is unlikely in my sample analysis since all firms have positive taxable income, so Iexclude this scenario for the rest of the calculations. As discussed by Agrawal et al. [2020], in thiscase the credit can be carried forward to future years and are âsomewhat more valuableâ. They alsodiscuss the 8 percent marginal credit rate in more detail.163user cost of R&D is =u¯c = (0.65Ã0.4+0.8Ã0.35+0.25Ã0.85)(r+Î´ ) = 0.7525(r+δ ).Now, the elasticity of R&D expenditure with respect to tax-adjusted user ofR&D equals:ηR&D,uc =âRDâELâucâEL.RÂ¯Du¯c=Î¸Ë Ã0.7525(r+Î´ )â0.1075(r+Î´ )Ã RÂ¯DUsing the baseline estimates and average R&D expenditure from Table 2.3,I compute ηR&D,uc = 0. This elasticity suggests a significant crowd-out effect ofR&D tax credits. Since the extensive margin is small in the baseline sample, ifI focus on R&D-intensive firms, assuming similar distribution around the EL, theimplied elasticity is still 0, which suggests the tax credits are subsidizing firms thatwould have invested in R&D even in the absence of the tax credits.164Figure A.1: Expenditure Limit Schedulepre 2004â 2004â 2006â 2008â 2009â0123Expenditure Limit ($ mil)0 1 2 3 4 5 6 7 8Lagged Taxable Income ($ hundred thous)Notes: This graph shows the general SRED tax credit schedule over 2001-2019 period. All CCPCfirms are eligible for 35 percent refundable tax credits up to a threshold called Expenditure Limit âthe black solid line â which is a function of lagged taxable income. If a firmâs R&D expenditureexceed the Expenditure Limit, its marginal tax credit rate drops to 20 percent and the tax credits willbe nonrefundable (the general was reduced to 15 percent in 2014). ELmaxt is maximum expenditurelimit, ztopât is start of phase-out threshold, and zbottomât . is end of phase-out threshold.165Table A.3: Evolution of Small Business Deduction Threshold and SRED TaxCredit ThresholdYear SBD Limit Top Kink Bottom Kink2001 - 2002 200 200 4002003 225 200 4002004 250 300 5002005 300 300 5002006 - 2007 300 400 6002008 400 400 7002009 - 2019 500 400 700Notes: This table report the changes in Small Business Deduction threshold and it coincidence withthe top kink and bottom kink of the tax credits schedule across the 2001-2019 period.Table A.4: Smoothness Assumption Test(1) (2) (3) (4)Dependent Variable R&D Exp. R&D Wages Employment WageEligible Ã Z -1108.618 179.385 0.115 -228.105(2030.544) (1264.877) (0.191) (212.674)Adj. R squared 0.812 0.818 0.976 0.655Observations 4,880 4,880 4,870 4,870Notes: This table reports the regression kink estimates corresponding to Equation 2.3) .The runningvariable is lagged taxable income a year before the treatment. Baseline sample controls include firmage and lagged dependent variable (i.e. covariates at t =â2). Columns (1) to (4) report thepre-treatment covariates test for R&D expenditure, and R&D wages, employment, and averagewage, respectively. Standard errors are clustered at the firm-level.166Appendix BAppendices to Chapter 3B.1 Robustness ChecksIn Appendix B.1, we provide results from robustness tests discussed in Sections3.4 â 3.6.B.1.1 Different ClusteringOur main firm-level results are based on clustering at the firm level and our mainworker-level results are based on two-way clustering at both firm level and workerlevel. We also do robustness tests on the key firm-level and worker-level outcomes,where standard errors are clustered at the market level (defined at the four digitNAICS by commuting zone) for firm-level results, and are two-way clustered atthe worker and market level for worker-level results. Figure A1 and Table A1 showthat the results on employment and average payrolls, and worker-level earnings aresimilar to the main estimates. Note that the coefficient estimates on these outcomesare slightly different from our main estimates, even though we only change the waywe cluster our standard errors. This is because some firms and workers have miss-ing commuting zone information, so they are dropped from our analysis samplewhen we cluster the standard errors at the market level.167B.1.2 Using Matched Control Firms in Different MarketsMatching on size, sector, and province finds firms that would plausibly exhibitcommon trends in the absence of an M&A activity. However, it is possible thatfirms can be matched within the same market (defined at the four-digit NAICS in-dustry by commuting zone), which is potentially concerning if M&As have impactson local labor markets through increased concentration. If M&As have negative ef-fects on firms in the same market, then the impact of M&As on firms and workerswill be biased towards zero. To minimize this concern, we do a robustness checkby matching firms within the same province, but across different markets, and findsimilar results to our main results where we allow M&A firms to be matched withcontrol firms within the same market. Figure A2 and Table A2 show that the effectson the key firm-level and worker-level outcomes from this approach are qualita-tively similar to our main estimates.B.1.3 Outcomes in Levels (Replacing Missing Observations withZeros)We show results on the key outcomes, such as employment, average payrolls, andworker-level earnings, in logs. A potential concern with this approach is that wecannot account for firms and workers exiting the sample after M&As. This con-cern is especially relevant for target firms and for displaced workers from targetsthat might exit our sample. Although we find the the vast majority of target firmscontinue to operate independently and most displaced workers find a job at a differ-ent firm, we run our main analyses in levels, replacing missing observations withzeros, to account for a small share of firms and workers who exit the sample afterM&As. Figure A3 and Table A3 show that the main results are qualitatively robustto the specification where the outcomes are measured in levels.B.1.4 By One-time vs. Repeat AcquirersPrior research points out that a part of the motive behind M&As involves empire-building, which could result in losses in efficiency and profitability after the event[Jensen, 1986b]. Even though it is practically difficult to discern whether a par-ticular acquirer has an empire-building motive in our data, we test whether the168effects on firm size and average payrolls are different depending on whether an ac-quirer engages in multiple M&A transactions (roughly 30 percent of our analysissample). Specifically, we compare the outcomes of acquirers involved in a singleacquisition relative to the outcomes of acquirers involved in multiple acquisitionsduring our sample period. Figure A4 and Table A4 show that acquirers involvedin multiple M&A transactions tend to grow a bit larger, in terms of the numberof employees and average payrolls after the event, compared to acquirers involvedin a single M&A deal. While these results provide suggestive evidence consistentwith the empire-building story, these differences could be also simply driven by thefact that acquirers involved in multiple deals might mechanically increase in sizemore.B.1.5 Using Private Firms OnlyPrior research points out that publicly listed firms engage in M&As more than pri-vate firms during merger waves, and that acquisitions can be efficiency improving,especially when buyers and sellers are publicly listed firms during on-the-wavemergers [Maksimovic et al., 2013]. While the vast majority (96 percent) of firmsthat go through M&As in our matched sample are private, we repeat our analysisfocusing only on private firms, since the effects of M&As on firm-level outcomesmight be different between listed firms and private firms. Figure A5 and Table A5show that the results on firm and worker outcomes are similar to our main resultswhere we include publicly listed companies, implying that our results are robust tojust focusing on private firms in our sample.B.1.6 Use Sample of Unmatched Firms and WorkersIn our main analysis sample, roughly 20 percent of M&A firms eligible for match-ing (as described in Section 3.4) are not matched to their control group either be-cause of their sector, location, or firm characteristics. Correspondingly, about 68percent of workers in M&A firms (eligible for matching) are not matched to theircontrol group either because their firms were not matched or there are not enoughcontrol workers to be matched (as we impose that only one worker is matched witha treated worker). In Table A8, we show characteristics of these unmatched firms169and workers. Panel A shows that unmatched firms are quite similar to our matchedcontrol firms on average in terms of total revenue, total expenses, and leverage ra-tio, but are much larger in terms of the number of employees, average payrolls, andmarkups. Panel C shows that unmatched workers have much lower earnings rela-tive to our matched control workers on average. Note that the number of workerseligible for matching includes those at unmatched M&A firms eligible for match-ing. Therefore, the match rate for workers decreases from about 60 percent (asindicated in Section 3.4) to 32 percent once we include all eligible workers acrossall eligible firms. The match rate for our main worker sample (around 60 percent)is computed using the sample of all eligible workers among the matched sample offirms only.An external validity concern is whether our matched sample of M&A firms andworkers are representative within Canada. We argue that our matched sample offirms and workers are representative of overall M&A activities in Canada, giventhat we have a good match rate among firms and workers eligible for matching.Still, we test whether our results significantly change once we incorporate thesepreviously unmatched set of firms and workers that were eligible for matching.Panel (a) of Figure A6 shows the results on employment, separately for targetsand for acquirers involved in acquisitions, including previously unmatched M&Afirms. Here, we just add these unmatched acquirers and targets as part of the treated(M&A) group, without matching them with possible control firms, so we still usethe same set of matched control firms in this analysis. As shown in the figure,the parallel pre-trend appears to get a bit weaker once we include previously un-matched firms, but we still find qualitatively similar results on employment afterM&As. We find similar results on average payrolls in Panel (b). Panel (c) showsthe results on worker-level earnings, separately for workers at targets and for work-ers at acquirers, including previously unmatched workers from M&A firms. Whilethe parallel pre-trends are not as good as those in our main results (Panel (a) of Fig-ure 3), we still find qualitatively similar results on worker-level earnings after theevent. Therefore, these results suggest that our results are not driven by a particularsample of matched M&A firms and workers in our data.170B.1.7 AKM and Match EffectsAKM effectsTo understand the role of the firm in explaining the earnings decline for targetworkers, we compare the observable characteristics of the targets firms and of thenew firms that target workers move to after M&As in Section 3.6. In this section,we estimate firm-specific pay premiums using standard AKM models. We thencharacterize a firm-specific wage premium of the old employer and of the newemployer for each worker who undergoes a separation following an M&A eventto understand the decline in earnings. Our implementation of the AKM modelregresses log earnings observed for individual i working at firm j in year t (yi jt)on employer-specific fixed effects which reflect firm characteristics that result inabove- or below-average earnings for all workers at firm j (Ï j(i,t)), individual fixedeffects (Ïi), and year effects (Ït):yi jt = Ï j(i,t)+Ïi+ Ït +ui jt (B.1)We can then assess the role played by employers by estimating an analogueto equation (3.3), substituting in as the outcome variable the estimated firm fixedeffects ÏË j. The goal is to estimate the share of earnings losses following job tran-sitions that can be attributed to a moverâs reemployment by an employer with adifferent ÏË j than the employer from which the mover left. Note that we omit M&Afirms in the year of the event to avoid changes in composition affecting firm effectsestimation, although including them does not affect our estimates by much.Panel (a) of Figure A7 shows firm-specific wage premiums of workers at targetfirms who move to other firms after the M&A event. Relative to their matchedcontrol workers, job movers from target firms show a significant increase in theirfirm fixed effects after the event, implying that on average, they move to employerswith higher wage premiums. Column (1) of Table A7 shows that workers whomove from target firms experience 3.2 log points increase in firm-specific wagepremiums after the event.This suggests that workers are actually moving to firms with higher wage pre-miums after M&As. Importantly, most of these workers who leave target firms171move to non-acquiring firms, meaning that this transition to larger firms with higherpays is not mechanically driven by worker reallocation toward acquirers. In prac-tice, many factors may contribute to a firm having a higher wage premium. Onecommon issue in this literature is that AKM effects may capture whether somefirms pay more relative to others, but cannot control for other aspects of the firm.Therefore, to get into the black box of the AKM effects, we take advantage ofour firm balance sheet data to characterize the types of firms that target workerstransition to after M&As (see Section 3.6).We test the main assumptions underlying the estimation of the AKM specifica-tion following Lachowska et al. [2020].AKM Assumption 1: Sufficient MobilityThe firm wage premium in the AKM model is identified by workers who movebetween firms. For this reason, the sample formation strategy (and the underlyingmobility pattern) need to exhibit sufficient mobility to allow the firm wage pre-miums to be estimated. In the average year during our sample period, roughly18 workers per employer move to other firms. Among full-time workers, the av-erage number of movers per employer is about 8. Across the entire sample, 77percent of workers make at least one move to a different firm during our sampleperiod. Therefore, the mobility rates in the sample we use to estimate the AKMmodel appear to be high and comparable to the mobility rate in the sample used byLachowska et al. [2020].AKM Assumption 2: Exogenous MobilitySince the firm wage premium in the AKM model is identified by workers whomove between firms, the model requires an assumption of exogenous mobility ofworkers between firms. If this assumption fails, then the firm wage premium wouldbe biased because the workers who move would be different than those who do notmove. We test this using a similar test as in Lachowska et al. [2020] who builds offan exercise in Card et al. [2013]. We group firms into quartiles by their estimatedfirm fixed effects and study the wages of workers who move between firms. Ifwages of movers are determined by the quartile of the firm effects symmetrically172both when moving from high to low and low to high firms, this symmetry supportsthe assumption that mobility is exogenous. In contrast, if movers show systematicwage gains regardless of the fixed effects of the origin firms and destination firms,then the assumption of exogenous mobility could be violated.In Figure A8, we plot the log wages of job movers for eight different quartile-to-quartile transitions. The top of the figure shows workers moving from the top(fourth) to the top quartile of firms. The wages of these workers are high and stayhigh. The same stable pattern can be seen for those going from the bottom (first)to the bottom quartile; their wages are low and stay relatively low. In contrast, forworkers going from the fourth to the first quartile of firm fixed effects, their wagesdrop significantly. Symmetrically, those going from the first to the fourth quartileof firm fixed effects see a strong increase in wages. Because the wage quality ofthe firm drives the wage change of the moving worker, this provides support forthe exogenous mobility assumption.Match EffectsOur results shown in Section 3.6 point to the idea that the decline in target workersâearnings is likely driven by these workers losing either firm-specific human capitalor backloaded contracts. Following prior research that draws a connection betweenthis mechanism and a loss in âmatch effectsâ, we estimate match effects based onLachowska et al. [2020] which implements a strategy based on Woodcock [2015].For each employee-employer spell, we first calculate the average of residualizedlog earnings (yi j) by removing calendar-year effects and regressing this adjustedlog earnings on years of job tenure and worker-employer match indicators. We thencompute within-match averages of the outcome after subtracting the contributionof job tenure. Then we estimate a model similar to the AKM model in equation(B.1), but using within-match averages as the dependent variable:yi j = αi+pi j(i,t)+ ei j (B.2)where αi, pi j(i,t), and ei j denote the worker fixed effects, employer fixed effects, andan error term independent of individual and firm fixed effects, respectively.We then calculate the residuals from equation (B.2) and interpret them as173worker-employer match effects averaged over the years we observe a given worker-employee match:eËi j = yi jâ Î±Ëiâ pËi j(i,t) (B.3)We proceed to take the estimated eËi j terms relevant for the employee in eachtime period and use them as the dependent variable in equation (3.3) to see thecontribution of match effects in explaining the earnings loss of target workers whomove to other firms after the event.Panel (b) of Figure A7 shows match effects of workers at target firms whomove to other firms after the M&A event. Relative to their control workers, moversfrom target firms experience a significant and large decrease in their match effectsafter the event, implying that on average, they move to employers with a lowermatch quality. The decrease in match effects in each post-M&A year comes bothfrom workers who had already left target firms and from workers who leave ineach subsequent year. Column (2) of Table A7 shows that workers who movefrom target firms within the first two years after the event experience 9 log pointsdecrease in match effects.The decline in match effects may imply that these workers lose the benefit of aspecific employee skill set that fits better with the previous employer. Also, the de-crease in match effects could simply indicate that these workers lose an employer-specific contract that yields a better work environment or amenity. Taken together,it appears that after target firms shrink through an M&A event, workers move tolarger firms with higher wage premiums and take a modest wage cut in the mediumrun due to worse matches with new employers. Relative to previous work (Lagarasforthcoming; He and le Maire 2022) documenting that M&A events create signif-icant displacements of workers from target firms with higher wage premiums andreallocation to firms with lower wage premiums, we find that a significant shareof workers earnings losses after M&As stems primarily from a decline in matchquality.174Figure B.1: Different Clustering(a) log(Employment)-0.4-0.3-0.2-0.10.00.10.20.30.4Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(b) log(Average Payrolls)-0.10-0.050.000.050.10Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(c) log(Earnings)-0.06-0.04-0.020.000.020.040.06Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventWorker at Acquirer Worker at TargetNotes: These figures display event-study estimates for the impact of M&As on the main firm-leveland worker-level outcomes. Panel (a) shows the estimates for log of employment for those involvedin acquisitions. Panel (b) shows the estimates for log of average payrolls for those involved inacquisitions. Panel (c) shows the estimates for log of worker-level earnings. The dashed linesindicate 95 percent confidence intervals where the standard errors are clustered at the market-level(by 4-digit NAICS Ã commuting zone) for Panels (a) and (b), and at the worker and market levelfor Panel (c). The M&A event is in year 0 and the coefficient estimate is normalized to be zero inyear -1.175Figure B.2: Matched Control Firms in Different Markets(a) log(Employment)-0.4-0.3-0.2-0.10.00.10.20.30.4Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(b) log(Average Payrolls)-0.10-0.050.000.050.10Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(c) log(Earnings)-0.06-0.04-0.020.000.020.040.06Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventWorker at Acquirer Worker at TargetNotes: These figures display event-study estimates for the impact of M&As on the main firm-leveland worker-level outcomes. For this analysis, we implement the same matching procedure (Section3.4), except that we restrict M&A firms to be at different markets (defined by 4-digit NAICS Ãcommuting zone) from matched control firms. Panel (a) shows the estimates for log of employmentfor those involved in acquisitions. Panel (b) shows the estimates for log of average payrolls forthose involved in acquisitions. Panel (c) shows the estimates for log of worker-level earnings. Thedashed lines indicate 95 percent confidence intervals where the standard errors are clustered at thefirm level for Panels (a) and (b), and at the worker and firm level for Panel (c). The M&A event is inyear 0 and the coefficient estimate is normalized to be zero in year -1.176Figure B.3: Outcomes in Levels (Replacing Missing with Zeros)(a) Employment-60-40-200204060Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(b) Average Payrolls-20,000-15,000-10,000-5,00005,000Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(c) Worker Earnings-3,000-1,50001,5003,000Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventWorker at Acquirer Worker at TargetNotes: These figures display event-study estimates for the impact of M&As on the main firm-leveland worker-level outcomes in levels, where we replace missing observations with zeros if the firmsor workers exit the sample. Panel (a) shows the estimates for employment for those involved inacquisitions. Panel (b) shows the estimates for average payrolls for those involved in acquisitions.Panel (c) shows the estimates for worker-level earnings. The dashed lines indicate 95 percentconfidence intervals where the standard errors are clustered at the firm level for Panels (a) and (b),and at the worker and firm level for Panel (c). The M&A event is in year 0 and the coefficientestimate is normalized to be zero in year -1.177Figure B.4: By One-time vs. Repeat M&As (Acquirers)(a) log(Employment)-0.4-0.3-0.2-0.10.00.10.20.30.4Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(b) log(Average Payrolls)-0.10-0.050.000.050.10Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer TargetNotes: These figures display event-study estimates for the impact of M&As on the main firm-leveloutcomes, separately for acquiring firms that go through an M&A only once throughout our sampleperiod and for acquiring firms with multiple M&A events. Panel (a) shows the estimates for log ofemployment. Panel (b) shows the estimates for log of average payrolls. The dashed lines indicate 95percent confidence intervals where the standard errors are clustered at the firm level. The M&Aevent is in year 0 and the coefficient estimate is normalized to be zero in year -1.178Figure B.5: Using Private Firms Only(a) log(Employment)-0.4-0.3-0.2-0.10.00.10.20.30.4Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(b) log(Average Payrolls)-0.10-0.050.000.050.10Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(c) log(Earnings)-0.06-0.04-0.020.000.020.040.06Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventWorker at Acquirer Worker at TargetNotes: These figures display event-study estimates for the impact of M&As on the main firm-leveland worker-level outcomes using only private firms/workers involved in acquisitions in the matchedsample. In our matched sample, 96 percent of firms are private during our sample period. Panel (a)shows the estimates for log of employment. Panel (b) shows the estimates for log of averagepayrolls. Panel (c) shows the estimates for log of worker-level earnings. The dashed lines indicate95 percent confidence intervals where the standard errors are clustered at the firm level for Panels(a) and (b), and at the worker and firm level for Panel (c). The M&A event is in year 0 and thecoefficient estimate is normalized to be zero in year -1.179Figure B.6: Including Unmatched Eligible Firms and Workers(a) log(Employment)-0.4-0.3-0.2-0.10.00.10.20.30.4Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(b) log(Average Payrolls)-0.10-0.050.000.050.10Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(c) log(Earnings)-0.06-0.04-0.020.000.020.040.06Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventWorker at Acquirer Worker at TargetNotes: Panel (a) and (b) display event-study estimates for the impact of M&As on the mainfirm-level outcomes separately for targets and for acquirers involved in acquisitions, including theunmatched sample of M&A firms eligible for matching. Panel (c) displays event-study estimates forthe impact of M&As on worker-level earnings for workers at targets and workers at acquirer,including the unmatched sample of eligible workers at M&A firms. The dashed lines indicate 95percent confidence intervals where the standard errors are clustered at the firm level for Panels (a)and (b), and at the worker and firm level for Panel (c). The M&A event is in year 0 and thecoefficient estimate is normalized to be zero in year -1.180Figure B.7: Workers Moving from Targets â Employer FE and Match Effects(Targets)(a) Employer Fixed Effect-0.020.000.020.040.060.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventMovers from Targets(b) Match Effect-0.10-0.08-0.06-0.04-0.020.000.02Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventMovers from TargetsNotes: This figure displays event-study estimates of the impact of M&As for workers moving fromtarget firms within the first two years after the event. Panel (a) shows the estimates for employerfixed effects. Panel (b) shows the estimates for worker-employer match effects. The dashed linesindicate 95 percent confidence intervals where the standard errors are two-way clustered at theworker and firm level. The M&A event is in year 0 and the coefficient is normalized to be zero inyear -1.181Figure B.8: Exogenous Mobility Assumption9.09.510.010.511.011.5Mean Log Earnings of Movers-3 -2 -1 0 1Time (-1=Last Year at Origin; 0=First Year at Destination)4 to 4 4 to 3 4 to 2 4 to 11 to 4 1 to 3 1 to 2 1 to 1Notes: This figure displays the estimates of the average log earnings of job movers during thefour-year period around their transition. The estimates are classified based on the quartile of firmeffects at origin firms (t =â1) and destination firms (t = 0).182Figure B.9: Probability of Exit(a) Firm Exit Rate-0.100.000.100.200.30Exit Rate-4 -3 -2 -1 0 1 2 3 4 5tTarget Firms Control Firms(b) Worker Exit Rate-.1-.08-.06-.04-.020.02.04.06.08.1Exit Rate-4 -3 -2 -1 0 1 2 3 4 5tWorkers at Target Firms Workers at Control FirmsNotes: Panel (a) displays the exit rate of firms in the matched sample, separately for target firms andtheir control firms. After the event, the probability of exiting the sample increases by 2.4 percentagepoints (SE = 0.006) for target firms (mostly those involved in mergers) relative to control firms onaverage. Panel (b) displays the exit rate of workers in the matched sample, separately for workers attarget firms and for their control workers. After the event, the probability of exiting the sampleincreases by 0.6 percentage points (SE = 0.002) for target workers relative to their control workerson average.183Table B.1: Different Clustering(1) (2) (3)log(Employment) log(Average Payrolls) log(Earnings)Target -0.087*** -0.026*** -0.021***(0.023) (0.009) (0.008)Mean at t = -1 4.00 10.69 11.02Adj. R squared 0.876 0.798 0.650Firm/Worker-Year Obs. 66,380 66,260 1,954,480Acquirer 0.183*** 0.011 0.002(0.038) (0.013) (0.012)Mean at t = -1 4.55 10.74 11.03Adj. R squared 0.892 0.819 0.642Firm/Worker-Year Obs. 21,830 21,760 1,296,770Notes: This table reports the difference-in-differences estimates for the impacts of M&As on the mainfirm-level and worker-level outcomes. The outcome variables in Columns (1) to (3) are log of employment,log of average payrolls, and log of worker-level earnings. Columns (1) and (2) show the results for firmsinvolved in acquisitions. The standard errors of the firm-level estimates are clustered at the market level(defined by 4-digit NAICS Ã commuting zone). The standard errors of the worker-level estimates aretwo-way clustered at the worker and market level. Note that the coefficient estimates also changed slightlyfrom clustering at the market-level because the information on commuting zone is missing for a smallshare of firms and workers, resulting in a slightly different sample compared to our main analysis sample.184Table B.2: Matched Control Firms in Different Markets(1) (2) (3)log(Employment) log(Average Payrolls) log(Earnings)Target -0.103*** -0.026** -0.016***(0.023) (0.011) (0.005)Mean at t = -1 4.00 10.69 11.01Adj. R squared 0.877 0.798 0.739Firm/Worker-Year Obs. 65,740 65,620 1,960,640Acquirer 0.167*** 0.018 -0.001(0.033) (0.014) (0.007)Mean at t = -1 4.56 10.73 11.02Adj. R squared 0.891 0.815 0.731Firm/Worker-Year Obs. 21,620 21,560 1,282,810Notes: This table reports the difference-in-differences estimates for the impacts of M&As on the mainfirm-level and worker-level outcomes. For this analysis, we implement the same matching procedure(Section 3.4), except that we restrict M&A firms to be at different markets (defined by 4-digit NAICS Ãcommuting zone) from matched control firms. The outcome variables in Columns (1) to (3) are log ofemployment, log of average payrolls, and log of worker-level earnings. Columns (1) and (2) show theresults for firms involved in acquisitions. The standard errors of firm-level estimates are clustered at thefirm level. The standard errors of worker-level estimates are two-way clustered at the worker and firm level.185Table B.3: Outcomes in Levels (Replacing Missing with Zeros)(1) (2) (3)Employment Average Payrolls EarningsTarget -16.784*** -11,447*** -681*(2.535) (732) (353)Mean at t = -1 105.69 49,191 70,625Adj. R squared 0.845 0.696 0.790Firm/Worker-Year Obs. 73,430 73,340 2,026,430Acquirer 23.330*** 1,304 154(5.172) (1,003) (465)Mean at t = -1 177.75 51,060 70,046Adj. R squared 0.860 0.732 0.793Firm/Worker-Year Obs. 23,300 23,250 1,345,330Notes: This table reports the difference-in-differences estimates for the impacts of M&Ason the firm-level and worker-level outcomes in levels, where we replace missingobservations with zeros if the firms or the workers exit the sample. The outcome variablesin Columns (1) to (3) are log of employment, log of average payrolls, and log ofworker-level earnings. Columns (1) and (2) show the results for firms involved inacquisitions. The standard errors of firm-level estimates are clustered at the firm level. Thestandard errors of worker-level estimates are two-way clustered at the worker and firmlevel.186Table B.4: By One-time vs. Repeat M&A (Acquirer)(1) (2)log(Employment) log(Average Payrolls)Post Ã Treated 0.130*** 0.008(0.038) (0.016)Post Ã Treated Ã Repeat 0.130** 0.005(0.066) (0.027)Mean at t = -1 (One-Time Acquirer = 1) 4.40 10.73Mean at t = -1 (Repeat Acquirer = 1) 4.85 10.75Adj. R squared 0.892 0.817Firm-Year Obs. (One-Time Acquirer = 1) 15,400 15,360Firm-Year Obs. (Repeat Acquirer = 1) 7,110 7,090Notes: This table reports the difference-in-differences estimates of the impact of M&As on the mainfirm-level outcomes for acquiring firms with only one M&A event during our sample period. The tripleinteraction term captures the triple-difference estimates for acquiring firms with repeated M&A events.Column (1) displays the estimates for log of employment. Column (2) displays the estimates for log ofaverage payrolls. The standard errors are clustered at the firm level.187Table B.5: Using Private Firms Only(1) (2) (3)log(Employment) log(Average Payrolls) log(Earnings)Target -0.088*** -0.026** -0.012**(0.023) (0.011) (0.005)Mean at t = -1 4.01 10.69 11.01Adj. R squared 0.876 0.797 0.739Firm/Worker-Year Obs. 65,560 65,450 1,990,940Acquirer 0.178*** 0.008 0.003(0.033) (0.014) (0.007)Mean at t = -1 4.56 10.73 11.02Adj. R squared 0.894 0.818 0.732Firm/Worker-Year Obs. 21,040 20,990 1,308,270Notes: This table reports the difference-in-differences estimates for the impacts of M&As on the mainfirm-level and worker-level outcomes using only private firms/workers in the matched sample. In ourmatched sample, 96 percent of firms are private during our sample period. The outcome variables inColumns (1) to (3) are log of employment, log of average payrolls, and log of worker-level earnings.Columns (1) and (2) show the results for firms involved in acquisitions. The standard errors of firm-levelestimates are clustered at the firm level. The standard errors of worker-level estimates are two-wayclustered at the worker and firm level.188Table B.6: Including Unmatched Eligible Firms and Workers(1) (2) (3)log(Employment) log(Average Payrolls) log(Earnings)Target -0.105*** -0.021** -0.010*(0.021) (0.010) (0.005)Mean at t = -1 4.00 10.65 10.83Adj. R squared 0.872 0.794 0.732Firm/Worker-Year Obs. 73,670 73,580 3,815,300Acquirer 0.178*** 0.022 0.010(0.032) (0.014) (0.007)Mean at t = -1 4.53 10.69 10.93Adj. R squared 0.890 0.815 0.697Firm/Worker-Year Obs. 24,610 24,580 3,143,520Notes: Columns (1) and (2) report the difference-in-differences estimates for the impacts of M&As on themain firm-level outcomes separately for targets and for acquirers involved in acquisitions, including theunmatched sample M&A firms eligible for matching. Column (3) reports the difference-in-differencesestimates for the impact of M&As on worker-level earnings, including the unmatched sample of eligibleworkers at M&A firms. The standard errors of firm-level estimates are clustered at the firm level. Thestandard errors of worker-level estimates are two-way clustered at the worker and firm level.189Table B.7: Changes in Firm Effects and Worker-Employer Match Effects(Targets)(1) (2)Employer FE Match EffectWorkers Moving From Target 0.032*** -0.090***(0.006) (0.004)Mean at t = -1 0.20 0.06Adj. R squared 0.881 0.205Worker-Year Obs. 684,800 677,750Notes: This table reports the difference-in-differences estimates for theimpacts of M&As for workers moving from target firms within the first twoyears after the event. Column (1) displays the estimates for the employer fixedeffects. Column (2) displays the estimates for worker-employer match effects.The standard errors are two-way clustered at the worker and firm level.190Table B.8: Mean Log Wage Before and After Employer Change by Quartileof Mean Coworkers Wages at Origin and Destination Firms(1) (2) (3) (4) (5) (6) (7)Mean Log Wage of Movers Change from t = -2 to t=+1Origin to Number of Two Years One Year One Year Two Year Unadjusted AdjustedDestination Observations Before Before After AfterQuartile1 to 1 1,355,190 9.70 9.64 9.70 9.87 0.16 0.001 to 2 834,130 9.77 9.73 9.92 10.15 0.38 0.221 to 3 429,280 9.68 9.66 10.00 10.33 0.65 0.491 to 4 306,100 9.64 9.65 10.12 10.51 0.86 0.702 to 1 384,560 10.05 9.89 9.80 9.99 -0.06 -0.182 to 2 1,110,570 10.30 10.22 10.26 10.42 0.12 0.002 to 3 777,090 10.38 10.33 10.44 10.63 0.26 0.142 to 4 383,820 10.27 10.23 10.48 10.77 0.51 0.393 to 1 160,360 10.30 10.05 9.82 10.03 -0.27 -0.383 to 2 429,880 10.51 10.35 10.31 10.50 -0.01 -0.123 to 3 1,009,290 10.66 10.61 10.65 10.77 0.11 0.003 to 4 956,990 10.72 10.70 10.79 10.92 0.20 0.094 to 1 97,140 10.57 10.26 9.87 10.08 -0.49 -0.624 to 2 187,550 10.68 10.45 10.30 10.51 -0.17 -0.304 to 3 413,630 10.85 10.72 10.71 10.88 0.03 -0.104 to 4 1,250,900 11.02 10.99 11.04 11.15 0.13 0.00Notes: This table reports the average of log of earnings for workers who move and are observed forat least two years prior to a job transition, and for two years after. We define quartiles based on theaverage earnings of coworkers at the origin firm in the year prior to transition, and at the destinationfirm in the year after. The adjusted earnings change is estimated as the average change for eachorigin-destination group, minus the average change for switchers from the same origin quartile whoremain in the same quartile.191Table B.9: Descriptive Statistics on Unmatched Eligible Firms and Workers(1) (2)Acquirer TargetPanel A: Firm CharacteristicsTotal Revenue (in millions) 42 24Total Expense (in millions) 40 24Number of Employees 238 155Average Wage Bill 64,637 64,653Leverage Ratio 0.67 0.73Markups 2.97 2.87Number of Firms 290 810Panel B: Sectors (Firms)Construction 0.01 0.02Manufacturing 0.14 0.13Wholesale 0.05 0.08Retail 0.04 0.03Transportation 0.03 0.06Information 0.15 0.19Services 0.12 0.19Other Sectors 0.46 0.30Panel C: Worker CharacteristicsTotal Earnings 61,611 55,731Age 47.0 45.0Female 0.39 0.43Number of Workers 115,866 112,539Panel D: Sectors (Workers)Construction 0.03 0.01Manufacturing 0.15 0.23Wholesale 0.07 0.08Retail 0.10 0.43Transportation 0.37 0.02Information 0.06 0.02Services 0.08 0.08Other Sectors 0.13 0.13Notes: This table reports descriptive statistics on the the unmatched sample of M&A firms andworkers eligible for matching, measured one year prior to the event. Panel A reports firmcharacteristics such as total revenue, total expenses, number of employees, average payrolls,leverage ratio, and markups. Panel B reports the distribution of firms in the matched sample across2-digit NAICS sectors. Panel C reports worker characteristics such as total annual earnings, age,and gender. Panel D reports the distribution of workers in the matched sample across 2-digit NAICSsectors. Other sectors include (1) Agriculture, forestry, and fishing, (2) Mining, quarrying, and oiland gas extraction, (3) Utilities, (4) Real estate and rental and leasing, (5) Arts, entertainment andrecreation, (6) Accommodation and food services, (7) Other services, and (8) Public administration.192Table B.10: Additional Descriptive Statistics on Workers at Targets(1) (2) (3) (4) (5) (6)Movers from Targets By DestinationMovers Stayers Voluntary Separation Involuntary Separation To Acquirer To Other FirmsTotal Earnings 68,023 72,551 65,800 64,531 67,820 65,082Age 46.0 48.6 40.9 46.6 46.7 45.7Female 0.31 0.31 0.35 0.32 0.36 0.32Number of Workers 21,890 33,130 2,510 7,880 4,560 13,660Notes: This table reports descriptive statistics on the matched sample of workers at target firms, measured one year prior to the event. Columns (1) and(2) report these statistics, respectively, for workers moving from target firms within the first two years after the event and for workers who stay at targetfirms throughout the entire post-event period. Column (3) reports these statistics for workers moving voluntarily from target firms and column (4)reports these statistics for workers displaced from targets. Column (5) reports these statistics for workers who move to acquiring firms after the M&Aevent and column (6) reports the statistics for workers who move to other firms after the M&A event.193B.2 Additional Heterogeneity ResultsIn Appendix B.2, we provide results from heterogeneity analyses in addition tothose discussed in Sections 3.4 â 3.6. Here, for worker-level analyses, we focuson target workers to see whether alternative mechanisms can explain the declinein their earnings after M&As. For most of the heterogeneity results, we separatelylook at stayers at targets and job movers from targets to get a better sense wherethe effects are concentrated.B.2.1 By Local vs. National M&AsWhile checking common pre-trends is reassuring for a causal interpretation whenimplementing a difference-in-differences design, contemporaneous shocks that oc-cur with M&A events could still bias our results. For example, there could be anegative demand shock that affects a commuting zone and causes both a decline inemployment and wages and an increase in M&A activities as firms get purchasedbefore they shut down. In this case, M&A activities are correlated with shocks thatdecrease labor demand. We can also have the opposite scenario.We address this concern by also looking at M&As that are less likely to havebeen triggered by local economic conditions of the firm. Specifically, we considerthe impact of national M&As that occur among domestic firms with multiple es-tablishments across different commuting zones. The intuition is that these changesin ownership are less likely to be driven by the local economic conditions of thefirms or workers. Figure B1 and Table B1 show that the effect of local M&Ason earnings of target workers is economically indistinguishable from the effect ofnational M&As on the same outcome. Therefore, the decline in earnings of work-ers at target firms is unlikely driven by local economic conditions of firms whereM&A activities occur.B.2.2 By Acquisition vs. MergerIn our analysis sample, roughly 80 percent of M&A events are acquisitions (75percent among the entire M&A events). In other words, the vast majority of M&Aevents in Canada (and in North America) involves an acquirer purchasing a partof a targetâs businesses. As discussed in Sections 3.2 and 3.3, it is possible that a194wage decline is larger (or smaller) in the case of a merger, where there is a completetransfer of ownership. We explore whether impacts on worker earnings are largerin the case of a merger, compared to a acquisition. We examine these impactsseparately for stayers and job movers, so that we can see in which group the effectsare concentrated. In Figure B2 and Table B2, we find that decreases in workersâearnings in target firms in the case of mergers were not larger than decreases inworkersâ earnings in the case of acquisitions, suggesting that our results are notdriven by the fact the majority of our M&A events involves acquisitions. In otherwords, both mergers and acquisitions create job separations of workers from targetfirms, resulting in a wage loss that we observe in the data.B.2.3 By Within vs. Across Labor MarketsWe explore whether impacts on worker earnings are larger in markets where merg-ing firms are located in the same labor market (defined at the four-digit industry bycommuting zone level), following Prager and Schmitt [2021]. Once again, we lookat these impacts separately for stayers and job movers, so that we can see wherethe effects are concentrated. In Figure B3 and Table B3, we find that decreases inworkersâ earnings in target firms where M&As occur within the same market werenot larger than decreases in workersâ earnings where M&As occur across differentmarkets, suggesting a limited role for the change in concentration in explaining thechange in worker earnings after M&As.B.2.4 Distribution of HHI Across MarketsBefore we assess the heterogeneity based on the initial level of concentration (mea-sured in Herfindahl-Hirschman Index) in labor markets, we examine the distribu-tion of labor market concentration across different markets (defined at the four-digitindustry by commuting zone level) in our data. Panel (a) in Figure B4 shows thatthe distribution of HHI across the entire labor market is a bit skewed towards theleft, with some spikes around the middle and at the very right tail. Column (1)in Table B4 shows that the average and median HHI are 0.38 and 0.28 across theentire labor market in Canada during our sample, with roughly 12 percent of labormarkets have a single employer in each market. Focusing on labor markets in the195entire sample with any M&A event during our sample period, however, the distri-bution becomes a lot more left-skewed, with only 3 percent of labor markets havinga single firm, as shown in panel (b) of Figure B3. Column (2) of Table B4 showsthat the average and median HHI are 0.264 and 0.175. These statistics becomeeven smaller (to 0.138 and 0.069, respectively, as shown in Column (3) of TableB4) when we zoom in on labor markets with any M&A event in our matched anal-ysis sample, with less than one percent of labor markets having a single employer.Therefore, while the labor markets in the entire sample are reasonably concen-trated, the markets in our analysis sample are not very concentrated on average tobegin with, which can explain why M&A events in our analysis sample have littleimpacts on concentration in our setting.B.2.5 By Initial Level of ConcentrationOur findings on the decreases in employment and worker-level earnings in targetfirms can be potentially rationalized by an increase in monopsony power of firmsthrough increased concentration. Prager and Schmitt [2021] and Arnold [2021]study this channel in the U.S. context and find that M&A events that generatelarge shifts in concentration result in market-level declines in earnings. While thischannel may still be a factor for a subset of the M&A events we study in Canada,we rule it out as being the only factor determining wage losses.In particular, we find that 99 percent of M&A events have a zero predictedchange in local labor-market concentration, and even among the remaining 1 per-cent, the predicted change is not large enough to generate any meaningful vari-ation. This is consistent with the description on the distribution of HHI in ouranalysis sample in the previous subsection. In contrast, Prager and Schmitt [2021]and Arnold [2021] isolate M&A events with statistically significant increases inconcentration.1To examine this in more detail, in Figure B5 and Table B5, we turn to a met-ric that antitrust authorities consider: the initial concentration level. To study this1In particular, when conducting market-level analyses, Arnold [2021] focuses on the top 5 percentof the U.S. labor markets that experience a sufficiently large predicted change in concentration afterM&As, implying that the remaining 95 percent do not experience any meaningful (predicted) changein concentration.196channel, we split the analysis sample by quintiles in the HHI measured one year be-fore the event. In both the high-concentration markets (fifth quintile) and the low-concentration markets (first quintile), we find similar levels of declines in earningsof either stayers at or job movers from target firms.2 This is not surprising givenmost of these events do not actually increase concentration in the local labor mar-ket.Taken together, these results, along with the results from Section B3, suggestthat increased monopsony power due to changes in local labor-market concentra-tion or outside options is unlikely to be the primary driver of declines in workerearnings in our setting.3B.2.6 By Initial Level of (flows-adjusted) ConcentrationIn the previous subsection, we show our estimates on worker earnings, separatelyfor markets with low level (below the first quintile) of HHI and for markets withhigh level (above the last quintile) of HHI measured one year before the event. Astandard Herfindahl-Hirschmann Index (HHI) takes as given the definition of themarket and then computesHHI =âjs2j , (B.4)as the measure of concentration. We describe a flows-adjusted concentration mea-sure that takes into account transitions across markets, following Arnold [2021].To begin, let market m be defined by the interaction between 4-digit NAICS andcommuting zone. The flows-adjusted concentrated measure (denoted C) requirescomputing transition rates across markets. While, in theory, transition rates across2Given that the change in concentration after M&As was close to zero for about 99 percent oflabor markets in our sample, cutting our analysis sample based on the predicted change in HHI afterM&As, as in Arnold [2021], is not meaningful in our setting due to the lack of variation in thepredicted change in HHI.3Even if the results are not driven by changes in concentration, changes in bargaining power ofworkers may be independent of concentration changes. For example, He and le Maire [2022] findsthat M&A events in Denmark result in high-wage managers being replaced in target firms. Such achange in management may result in shifts in bargaining power of workers at target firms. However,a change in bargaining power through a change in management is only relevant for incumbent ornew workers at target firms, and thus is unlikely to explain the decline in earnings of workers leavingtarget firms.197markets may change, we instead choose to pool the entire sample in order to re-trieve a consistent and more precise measure of the rate of transitions across mar-kets. The share of firm j in market m is given by:sË jm =l jmâkÎ±mâkLk(B.5)whereÎ±mâk =P(k|m)P(m|m)LmLk(B.6)where P(k|m) is the probability an individual from market m transitions to mar-ket k conditional on experiencing a transition. The intuition behind this formula-tion is that jobs in other markets likely provide viable options for workers. Arnold[2021] shows that one can use a discrete choice model and empirical flows acrossmarkets (i.e., Î±kâm) to measure the value a worker from a given market places onanother market.Intuitively, if we observe a large number of flows from market m to k, then klikely serves as a viable outside option. Additionally, we need to take into accountthe relative sizes of the markets. For example, if k is a relatively small market, butwe still observe high rates of flows to this market, it must provide a particularlygood option for the workers. This is why the relative size between m and k is takeninto account when determining the value individuals from m place in receiving ajob in market k. The concentration in market m is given by:CË =âjsË2jm (B.7)One key difference in this formulation relative to Arnold [2021] is that transi-tions across both industries and locations are taken into account. In Arnold [2021],the market shares depend only on employment in other industries within the samecommuting zone. In this more general version, the market share depends on em-ployment in other commuting zones as well.Second, the relative size term in Î±mâk (i.e. E[ LkLm ]) is now the expected relativesize of industries across commuting zones. To understand this factor, imagine thereare two equally sized industries that use similar workers but are generally locatedin different areas. For example, imagine plastic manufacturing and rubber man-198ufacturing plants hire similar workers, but plastic manufacturing primarily takesplace in Texas while rubber manufacturing primarily takes place in Ohio. In thiscase, the aggregate relative size of the industries will be quite different than theexpected relative size within a commuting zone given the two industries primarilyoperate in different commuting zones. Therefore, a low volume of flows betweenthe two industries does not necessarily reflect low substitutability, but rather theyare generally located in different areas.The flows-adjusted local labor market concentration measure, Cmc, is definedas:Cmc =âjâc(sË jmc)2 (B.8)Figure B6 and Table B6 show the effects of M&As on earnings of workers attarget firms, separately for markets with high (flow-adjusted) HHI and for marketswith low (flow-adjusted) HHI. Similar to our main results based on the regular HHI,these results show that the decline in worker earnings is not larger for high-HHImarkets.B.2.7 Results on Revenue, Markups, and Realized Capital GainsTo get a better sense of why acquirersâ and targetsâ profitability decreases afterM&As (discussed in Section 3.6), we next examine what happens to their revenue,markups, and cash-out responses. Panel (a) of Figure B7 shows the results on totalrevenue, separately for targets and for acquirers involved in acquisitions. Bothtargetsâ and acquirersâ sales were in a parallel trend with those of their matchedcontrol firms prior to the event. We find that while acquirersâ revenue increasessignificantly after the event, targetsâ revenue declines after the event. Even thoughacquirersâ sales increase after M&As, their profit margins can decrease if the costsof acquisitions outweigh the benefits in the short to medium run. Furthermore,while the decline in targetsâ revenue is mechanical in the case of acquisitions, thedecrease in profitability happens if they sold a profitable part of their businesses toacquirers. Panel (d) shows the aggregate outcomes (targets and acquirers pooled)separately for those involved in acquisitions and for those involved in mergers. Inboth cases, revenue significantly decreases after the event in the aggregate.Panel (b) of Figure B7 shows the results on markups, separately for targets and199for acquirers involved in acquisitions. Both targetsâ and acquirersâ markups werein a parallel trend with those of their matched control firms prior to the event, andremained relatively unchanged after the event. Panel (e) shows the aggregate out-comes (targets and acquirers pooled) separately for those involved in acquisitionsand for those involved in mergers. In the case of acquisitions, markups do notchange much after the event. In the case of mergers, markups decrease after theevent in the aggregate. These results, in conjunction with the results on profitabil-ity, provide more direct evidence that firmsâ product market power did not increaseafter the event at least in the short to medium run.Panel (c) of Figure B7 shows the results on realized capital gains, separatelyfor targets and for acquirers involved in acquisitions. We find that while acquirersârealized capital gains decreased slightly after the event, targetsâ realized capitalgains increase significantly after the event. The increase in realized capital gainsat targets is driven by their initial investors selling a part of their shares (âcash-ing outâ) to those in acquiring firms, while the slight decrease in realized capitalgains at acquiring firms indicate that their investors were net buyers of shares afterthe event. Panel (f) shows the aggregate outcomes (targets and acquirers pooled)separately for those involved in acquisitions and for those involved in mergers. Inboth cases, we see increases in realized capital gains. These results imply that eventhough targetsâ profitability did not improve after the event, initial investors stillbenefitted from the transactions by selling a part of their shares to acquirers.To interpret the magnitude of these results, Table B7 presents the difference-in-differences estimates on these outcomes. Columns (1) and (2) show that targetsârevenue decreases by 54.5 log points, while acquirersâ revenue increases by 26.9log points on average. Columns (3) and (4) show that in the aggregate, revenuedecreases by 30.7 log points in the case of acquisitions, while it decreases by 10.3log points in the case of mergers. Columns (1) and (2) show that acquirersâ andtargetsâ markups do not change much after the event on average. Columns (3) and(4) show that in the aggregate, markups do not change in the case of acquisitions,while they decrease by 4.2 log points in the case of mergers. Columns (1) and (2)show that targetsâ realized capital gains increase by 27,558 CAD, while acquirersârealized capital gains decrease by 13,071 CAD on average. Columns (3) and (4)show that in the aggregate, realized capital gains increase by 21,646 CAD in the200case of acquisitions, while they increase by 28,002 CAD in the case of mergers.Overall, the decrease in profitability, without much change in markups, suggeststhat neither targets nor acquirers experienced an increase in market power afterM&As at least in the short to medium run (De Loecker et al. 2020).B.2.8 By Tradable vs. Non-Tradable SectorsThere are several results that suggest product market power is not the main driverof the observed labor-market impacts. First, we find that profitability decreased forboth target and acquiring firms. If product market power increased on average afterM&A events, we would expect to see a rise in profits, not a decline (De Loeckeret al. 2020). Additionally, we estimate markups and do not find any statisticallysignificant impacts of M&A events on markups for either target or acquiring firms.To explore this channel further, we also conduct our analysis separately by trad-able and non-tradable goods sectors. The intuition is that an M&A would have alarger impact on a firmâs market power if the firm does not face competition outsideits geographical (i.e., international) markets. We define firms as active in tradablegood sectors if they fall under Agriculture, Forestry, Fishing, Mining, Quarrying,and Oil and Gas Extraction, and Manufacturing. Firms active in other sectors (i.e.,Construction, Retail, Real Estate, Services, etc) are defined as falling under non-tradable sectors [Berger et al., 2022, Delgado et al., 2014]. Figure B8 and TableB8 show that the declines in worker earnings at target firms are not larger for non-tradable sectors. Therefore, this finding, along with the results on decreased profitmargins without much change in markups, suggests that an increase in productmarket concentration seems to be unlikely the key driver behind the decline intarget workersâ earnings.B.2.9 By Within vs. Between Industry M&AsIn the previous subsection, we show that the decline in earnings of workers attarget firms is similar between M&As that happen in tradable sectors and thosethat happen in non-tradable sectors, suggesting a limited role for the change inproduct market power in explaining the change in worker earnings. To furthersupport this conclusion, we also conduct our analysis separately by within-industry201M&As and between-industry M&As. The intuition is that an M&A would have alarger impact on firmsâ market power if the acquirer buys another firm within thesame industry (i.e., horizontal mergers). We divide our sample of all M&A firmsbased on the industries of the parties involved in a transaction. An M&A is within-industry if the industries (4-digit NAICS) of both parties are identical and it isbetween-industry (i.e., vertical mergers) if the industries are different. For firmswith one transaction, we define a firm as âwithinâ if it participated in a within-industry M&A and as âbetweenâ if it participated in a between-industry M&A.For firms with multiple M&A deals, we consider the majority of transactions todetermine the within- and between-indicator. Figure B9 and Table B9 show that thedecline in worker earnings is larger for those involved in within-industry M&As.These results imply that a rise in product market power is unlikely the main driverbehind the results on worker earnings.B.2.10 By Worker AgeWe additionally explore whether the decline in earnings of workers at target firmsis different based on workersâ age. Prior studies have found differential impactsof firm-level shocks on worker earnings depending on their age (Kline et al. 2019;Saez et al. 2019). As shown in Section 3.5, the decline in workersâ earnings attarget firms is entirely driven by those who move to other firms after the M&Aevent. Therefore, we focus on job movers from target firms, and estimate whathappens to their earnings, separately across different age groups. Figure B10 andTable B10 show that while we observe declines in earnings across all age groupsfor workers moving from target firms, the decline in earnings is largest amongmovers who are at least 50 years old before the event. Taken together, these resultsimply that there exists a substantial degree of heterogeneity across age groups forchanges in worker earnings after the M&A event. These results are consistent withour main results based on worker tenure in Section 3.6.202Figure B.10: Worker-level Earnings By National M&As vs. Local M&As(Targets)(a) Stayers-0.30-0.20-0.100.000.100.20Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventNational Local(b) Movers-0.12-0.08-0.040.000.040.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventNational LocalNotes: These figures display event-study estimates for the impact of M&As on log of total earningsfor workers at target firms, separately for those involved in national M&A deals and for thoseinvolved in local M&A deals. Panel (a) displays the estimates for stayers and panel (b) displays theestimates for workers moving from target firms within the first two years after the event. The dashedlines indicate 95 percent confidence intervals where the standard errors are two-way clustered at theworker-firm level. The M&A event is in year 0 and the coefficient estimate is normalized to be zeroin year -1.203Figure B.11: Worker-level Earnings By Acquisition vs. Merger (Targets)(a) Stayers-0.12-0.08-0.040.000.040.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquisition Merger(b) Movers-0.12-0.08-0.040.000.040.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquisition MergerNotes: These figures display event-study estimates for the impact of M&As on log of total earningsfor workers at target firms, separately for those involved in acquisitions and for those involved inmergers. Panel (a) displays the estimates for stayers and panel (b) displays the estimates for workersmoving from target firms within the first two years after the event. The dashed lines indicate 95percent confidence intervals where the standard errors are two-way clustered at the worker-firmlevel. The M&A event is in year 0 and the coefficient estimate is normalized to be zero in year -1.204Figure B.12: Worker-level Earnings By Within Market M&As vs. AcrossMarket M&As (Targets)(a) Stayers-0.30-0.20-0.100.000.100.20Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcross Market Within Market(b) Movers-0.12-0.08-0.040.000.040.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcross Market Within MarketNotes: These figures display event-study estimates for the impact of M&As on log of total earningsfor workers at target firms, separately for M&As that happen within the same labor market and forM&As that happen across different labor markets. Panel (a) displays the estimates for stayers andpanel (b) displays the estimates for workers moving from target firms within the first two years afterthe event. The dashed lines indicate 95 percent confidence intervals where the standard errors aretwo-way clustered at the worker-firm level. The M&A event is in year 0 and the coefficient estimateis normalized to be zero in year -1.205Figure B.13: Distribution of HHI across markets(a) All Markets0.05.1.15Share0.00 0.20 0.40 0.60 0.80 1.00HHI(b) Treated Markets0.000.010.020.030.04Share0.00 0.20 0.40 0.60 0.80 1.00HHI(c) Matched Markets0.000.050.100.15Share0.00 0.20 0.40 0.60 0.80 1.00HHINotes: These figures display the distribution of concentration (measured by HHI) across labormarkets. Panel (a) displays the distribution across all labor markets in the entire sample. Panel (b)displays the distribution across labor markets with at least one M&A deal in the entire sampleduring our sample period. Panel (c) displays the distribution across labor markets with at least oneM&A deal in our matched sample (used for the main analyses).206Figure B.14: Worker-level Earnings By Initial Level of HHI (Targets)(a) Stayers-0.12-0.08-0.040.000.040.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventQ1 Q5(b) Movers-0.12-0.08-0.040.000.040.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventQ1 Q5Notes: These figures display event-study estimates for the impact of M&As on log of total earningsfor workers at target firms, separately for those in markets with low initial level of concentration(first quintile) and for those in markets with high initial level of concentration (fifth quintile). Panel(a) displays the estimates for stayers and panel (b) displays the estimates for workers moving fromtarget firms within the first two years after the event. The dashed lines indicate 95 percentconfidence intervals where the standard errors are two-way clustered at the worker-firm level. TheM&A event is in year 0 and the coefficient estimate is normalized to be zero in year -1.207Figure B.15: Worker-level Earnings By Initial Level of Flows-adjusted HHI(Targets)(a) Stayers-0.12-0.08-0.040.000.040.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventQ1 Q5(b) Movers-0.12-0.08-0.040.000.040.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventQ1 Q5Notes: These figures display event-study estimates for the impact of M&As on log of total earningsfor workers at target firms, separately for those in markets with low initial level of concentration(first quintile) and for those in markets with high initial level of concentration (fifth quintile). Panel(a) displays the estimates for stayers and panel (b) displays the estimates for workers moving fromtarget firms within the first two years after the event. The dashed lines indicate 95 percentconfidence intervals where the standard errors are two-way clustered at the worker-firm level. TheM&A event is in year 0 and the coefficient estimate is normalized to be zero in year -1.208Figure B.16: Difference-in-differences Estimates on Firmsâ Sales, Markups, and Payouts(a) log(Revenue)-0.8-0.6-0.4-0.20.00.20.40.6Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(b) log(Markups)-0.20-0.100.000.100.20Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(c) Realized Capital Gains-100,000-50,000050,000100,000150,000Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquirer Target(d) log(Revenue)-0.80-0.60-0.40-0.200.000.200.400.60Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquisition Merger(e) Markups-0.20-0.100.000.100.20Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquisition Merger(f) Realized Capital Gains-100,000-50,000050,000100,000150,000Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventAcquisition MergerNotes: Panels (a) â (c) display event-study estimates for the impact of M&As on other firm-level outcomes for firms involved in acquisitions,separately for acquiring firms (navy line) and for target firms (orange line). Panel (a) shows the estimates for log of total revenue. Panel (b) shows theestimates for log of markups. Panel (c) shows the estimates for ownersâ realized capital gains aggregated at the firm level. Panels (d) â (f) displayevent-study estimates for the impact of M&As on the aggregate firm-level (targets and acquirers pooled) outcomes, separately for those involved inacquisitions (red line) and for those involved in mergers (black line). Panel (d) shows the estimates for log of total revenue. Panel (e) shows theestimates for log of markups. Panel (f) shows the estimates for ownersâ realized capital gains aggregated at the firm level. The dashed lines indicate 95percent confidence intervals where the standard errors are clustered at the firm level. The M&A event is in year 0 and the coefficient is normalized tobe zero in year -1.209Figure B.17: Worker-level Earnings By Tradable Sectors vs. Non-tradableSectors (Targets)(a) Stayers-0.12-0.08-0.040.000.040.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventTradable Nontradable(b) Movers-0.12-0.08-0.040.000.040.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventTradable NontradableNotes: These figures display event-study estimates for the impact of M&As on log of total earningsfor workers at target firms, separately for those in tradable sectors and for those in non-tradablesectors. Panel (a) displays the estimates for stayers and panel (b) displays the estimates for workersmoving from target firms within the first two years after the event. The dashed lines indicate 95percent confidence intervals where the standard errors are two-way clustered at the worker-firmlevel. The M&A event is in year 0 and the coefficient estimate is normalized to be zero in year -1.210Figure B.18: Worker-level Earnings By Within Industry vs. Across Industry(Targets)(a) Stayers-0.12-0.08-0.040.000.040.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventVertical M&A Horizontal M&A(b) Movers-0.12-0.08-0.040.000.040.08Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventVertical M&A Horizontal M&ANotes: These figures display event-study estimates for the impact of M&As on log of total earningsfor workers at target firms, separately for those involved in (horizontal) M&As that happen withinthe same industry and for those involved in (vertical) M&As that happen across different industries.Panel (a) displays the estimates for stayers and panel (b) displays the estimates for workers movingfrom target firms within the first two years after the event. The dashed lines indicate 95 percentconfidence intervals where the standard errors are two-way clustered at the worker-firm level. TheM&A event is in year 0 and the coefficient estimate is normalized to be zero in year -1.211Figure B.19: Workers Moving from Targets â By Worker Age (Targets)(a) log(Earnings)-0.12-0.08-0.040.000.040.08Coefficient Estimates - [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to Event20 - 30 30 - 4040 - 50 50+(b) Transition0.000.200.400.600.80Coefficient Estimates - [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to Event20 - 30 30 - 4040 - 50 50+Notes: These figures display event-study estimates of the impact of M&As on worker-leveloutcomes for workers moving from target firms within the first two years after the event, separatelyfor various age groups. Panel (a) shows the estimates for log of total earnings. Panel (b) shows theestimates for job transition probabilities. Panel (c) shows the estimates for employer fixed effects.The dashed lines indicate 95 percent confidence intervals where the standard errors are two-wayclustered at the worker and firm level. The M&A event is in year 0 and the coefficient estimate isnormalized to be zero in year -1.212Table B.11: Worker-level Earnings By National M&As vs. Local M&As(Targets)(1) (2)Stayers MoversNational -0.008 -0.033***(0.006) (0.007)Mean at t = -1 11.00 10.95Adj. R squared 0.799 0.741Worker-Year Obs. 1,373,290 549,560Local -0.006 -0.035**(0.030) (0.014)Mean at t = -1 11.09 11.08Adj. R squared 0.790 0.744Worker-Year Obs. 193,180 134,420Notes: This table reports the difference-in-differencesestimates of the impact of M&As on log of totalearnings for workers at target firms, separately for thoseinvolved in national M&As and for those involved inlocal M&As. Columns (1) and column (2) display theestimates for stayers and for workers moving fromtarget firms within the first two years after the event,respectively. The standard errors are two-way clusteredat the worker and firm level.213Table B.12: Worker-level Earnings By Acquisition vs. Merger (Targets)(1) (2)Stayers MoversAcquisition -0.008 -0.035***(0.006) (0.007)Mean at t = -1 11.01 10.97Adj. R squared 0.800 0.743Worker-Year Obs. 1,365,580 601,180Merger 0.001 -0.030*(0.013) (0.016)Mean at t = -1 11.01 11.02Adj. R squared 0.785 0.726Worker-Year Obs. 216,050 88,190Notes: This table reports the difference-in-differencesestimates of the impact of M&As on log of totalearnings for workers at target firms, separately for thoseinvolved in acquisitions and for those involved inmergers. Columns (1) and column (2) display theestimates for stayers and for workers moving fromtarget firms within the first two years after the event,respectively. The standard errors are two-way clusteredat the worker and firm level.214Table B.13: Worker-level Earnings By Within Market M&As vs. AcrossMarket M&As (Targets)(1) (2)Stayers MoversAcross Market -0.007 -0.034***(0.006) (0.007)Mean at t = -1 11.00 10.95Adj. R squared 0.799 0.741Worker-Year Obs. 1,387,150 547,370Within Market -0.005 -0.040***(0.033) (0.015)Mean at t = -1 11.1 11.09Adj. R squared 0.786 0.743Worker-Year Obs. 170,870 120,900Notes: This table reports the difference-in-differencesestimates of the impact of M&As on log of totalearnings for workers at target firms, separately for thoseinvolved in M&As that happen across different labormarkets (defined at the four-digit NAICS by commutingzone) and for those involved in M&As that happenwithin the same labor market. Columns (1) and column(2) display the estimates for stayers and for workersmoving from target firms within the first two years afterthe event, respectively. The standard errors are two-wayclustered at the worker and firm level.215Table B.14: Distribution of HHI across labor markets(1) (2) (3)All Markets Markets with M&A Markets with M&A(Entire Sample) (Matched Sample)Average 0.3827 0.264 0.1383Median 0.28 0.1753 0.0694Min 0.0002 0.0003 0.0004Max 1 1 1Market-Year Obs. 745,849 177,629 10,120Notes: This table reports summary statistics (average, median, min and max) on concentration,measured by HHI, (1) across all labor markets in the entire sample, (2) across labor markets withat least one M&A event in the entire sample, and (3) across labor markets with at least one M&Aevent in the matched analysis sample.216Table B.15: Worker-level Earnings By Initial Level of HHI (Targets)(1) (2)Stayers MoversQ1 -0.003 -0.023**(0.009) (0.011)Mean at t = -1 11.04 11.03Adj. R squared 0.814 0.748Worker-Year Obs. 384,470 186,790Q5 -0.010 -0.026*(0.008) (0.015)Mean at t = -1 11.03 11.00Adj. R squared 0.777 0.744Worker-Year Obs. 379,700 122,390Notes: This table reports thedifference-in-differences estimates of the impact ofM&As on log of total earnings for workers at targetfirms, separately for those in markets with initiallylow level of concentration (first quintile) and forthose in markets with initially high level ofconcentration (fifth quintile). Columns (1) andcolumn (2) display the estimates for stayers and forworkers moving from target firms within the firsttwo years after the event, respectively. The standarderrors are two-way clustered at the worker and firmlevel.217Table B.16: Worker Earnings - By Initial Level of Flows-Adjusted HHI (Tar-gets)(1) (2)Stayers MoversQ1 0.007 -0.052***(0.012) (0.016)Mean at t = -1 10.97 10.95Adj. R squared 0.812 0.735Worker-Year Obs. 166,590 66,840Q5 -0.015 -0.044***(0.010) (0.012)Mean at t = -1 11.06 10.97Adj. R squared 0.792 0.747Worker-Year Obs. 395,720 182,240Notes: This table reports the difference-in-differencesestimates of the impact of M&As on log of totalearnings for workers at target firms, separately forthose in markets with initially low level ofconcentration (first quintile) and for those in marketswith initially high level of concentration (fifthquintile). Columns (1) and column (2) display theestimates for stayers and for workers moving fromtarget firms within the first two years after the event,respectively. The standard errors are two-wayclustered at the worker and firm level.218Table B.17: Difference-in-differences Estimates on Firmsâ Sales, Markups,and Payouts(1) (2) (3) (4)Acquisition AggregateTarget Acquirer Acquisition Mergerlog(Revenue) -0.545*** 0.269*** -0.307*** -0.103*(0.038) (0.048) (0.030) (0.058)Mean at t = -1 16.37 17.02 16.53 16.45Adj. R squared 0.820 0.861 0.829 0.840Firm-Year Obs. 68,460 22,060 91,140 19,400NIBTEI per Worker -2,887* -4,570** -3,428*** -7,106***(1,484) (2,329) (1,205) (2,731)Mean at t = -1 15,881 17,906 16,375 14,432Adj. R squared 0.441 0.458 0.436 0.449Firm-Year Obs. 65,810 21,600 88,050 18,690log(Markups) -0.008 0.015 -0.008 -0.042(0.019) (0.024) (0.014) (0.034)Mean at t = -1 0.42 0.40 0.42 0.39Adj. R squared 0.758 0.811 0.769 0.746Firm-Year Obs. 40,800 12,760 54,150 11,170Realized Capital Gains 27,558*** -13,071 21,646*** 28,002***(4,310) (9,386) (3,736) (9,603)Mean at t = -1 37,938 51,147 40,770 35,449Adj. R squared 0.282 0.209 0.245 0.304Firm-Year Obs. 42,150 10,820 53,670 9,410Notes: This table reports the difference-in-differences estimates for the impact of M&As onfirmsâ sales, markups, and realized capital gains. Columns (1) and (2) report the estimates fortarget firms and for acquiring firms involved in acquisitions, respectively. Columns (3) and (4)report the estimates on the aggregate firm-level (targets and acquirers pooled) outcomes, forthose involved in acquisitions and for those involved in mergers, respectively. The standarderrors are clustered at the firm level.219Table B.18: Worker-level Earnings By Tradable Sectors vs. Non-tradableSectors (Targets)(1) (2)Stayers MoversTradable -0.006 -0.031***(0.009) (0.010)Mean at t = -1 10.97 10.95Adj. R squared 0.788 0.739Worker-Year Obs. 826,140 320,350Nontradable -0.009 -0.025***(0.007) (0.008)Mean at t = -1 11.06 11.00Adj. R squared 0.804 0.748Worker-Year Obs. 755,700 369,300Notes: This table reports the difference-in-differencesestimates of the impact of M&As on log of totalearnings for workers at target firms, separately forthose in tradable sectors and for those in non-tradablesectors. Columns (1) and column (2) display theestimates for stayers and for workers moving fromtarget firms within the first two years after the event,respectively. The standard errors are two-wayclustered at the worker and firm level.220Table B.19: Worker-level Earnings By Within Industry vs. Across Industry(Targets)(1) (2)Stayers MoversVertical M&A -0.002 -0.037***(0.007) (0.010)Mean at t = -1 10.97 10.88Adj. R squared 0.798 0.755Worker-Year Obs. 679,760 233,960Hoirzontal M&A -0.011 -0.032***(0.007) (0.007)Mean at t = -1 11.04 11.03Adj. R squared 0.798 0.732Worker-Year Obs. 901,990 455,670Notes: This table reports the difference-in-differencesestimates of the impact of M&As on log of totalearnings for workers at target firms, separately forthose involved in M&As that happen across differentindustries and for those involved in M&As thathappen within the same industry. Columns (1) andcolumn (2) display the estimates for stayers and forworkers moving from target firms within the first twoyears after the event, respectively. The standard errorsare two-way clustered at the worker and firm level.221Table B.20: Workers Moving from Targets â By Worker Age (Targets)(1) (2)log(Earnings) Transition20s -0.036* 0.172***(0.021) (0.011)Mean at t = -1 10.69 0.00Adj. R squared 0.764 0.282Worker-Year Obs. 32,390 32,41030s -0.001 0.169***(0.009) (0.006)Mean at t = -1 10.98 0.00Adj. R squared 0.73 0.267Worker-Year Obs. 154,250 154,37040s -0.037*** 0.188***(0.007) (0.006)Mean at t = -1 11.05 0.00Adj. R squared 0.778 0.333Worker-Year Obs. 246,160 246,51050+ -0.056*** 0.200***(0.009) (0.007)Mean at t = -1 10.95 0.00Adj. R squared 0.761 0.369Worker-Year Obs. 256,090 256,800Notes: This table reports the difference-in-differencesestimates for the impacts of M&As on workers moving fromtarget firms within the first two years after the event acrossvarious age groups. Column (1) displays the estimates log oftotal earnings. Column (2) displays the estimates for thetransition probabilities. Column (3) displays the estimatesfor the employer fixed effects. The standard errors aretwo-way clustered at the worker and firm level.222Appendix CAppendices to Chapter 4C.1 Institutional DetailsIn Appendix C.1, we provide additional institutional details on M&A regulationsfor 10 European countries in our main analysis sample.Table C.1 summarizes the regulations regarding pre-merger notification. Incolumn âJointâ of Table C.1, Xsuggests it is a joint threshold. If the column isempty, then it means if one threshold is satisfied, then the firm has to file a pre-merger notification.Table C.2 summarizes overall regulations regarding M&As. In column âReg-ulationâ of Table C.2, the first agency is in charge of takeover bid process, and thesecond agency is responsible for merger control. For âExceptionsâ in Germany,the Takeover Act is applicable to foreign companies whose voting shares are ex-clusively listed in Germany at the organized market. For âExceptionsâ in Italy,small and medium firms are subject to some special rules. Takeover Directives donot apply to some public offers in EU. The concentration that has a communitydimension in EU Merger Regulation is defined with turnovers.223Table C.1: Threshold changesCountry Time Thresholds Value JointBelgium 2009-2018domestic combined turnover EUR 100 million Xdomestic individual turnover of at least two ofthe undertakings concernedEUR 40 million XFinland 2009-2018global combined turnover EUR 350 million Xdomestic individual turnover of at least two ofthe undertakings concernedEUR 20 million XFrance 2009-2018global combined turnover EUR 150 million Xdomestic individual turnover of at least two ofthe undertakings concernedEUR 50 million XGermany2009-2017global combined turnover EUR 500 million Xdomestic turnover of at least one participating EUR 25 million Xdomestic individual turnover of at least onefurther participating undertakingEUR 5 million X2017-2018global combined turnover EUR 500 million Xdomestic turnover of at least one participating EUR 25 million Xdomestic individual turnover of at least onefurther participating undertakingEUR 5 million Xtransaction value EUR 400 millionItaly2009domestic combined turnover EUR 461 milliondomestic individual turnover of targets EUR 46 million2009-2012domestic combined turnover EUR 474 milliondomestic individual turnover of targets EUR 47 million2013-2017domestic combined turnover EUR 499 million Xdomestic individual turnover of targets EUR 50 million X2018domestic combined turnover EUR 498 million Xdomestic individual turnover of at least two ofthe undertakings concernedEUR 30 million X224(continued): Threshold changesCountry Time Thresholds Value JointNetherlands2009-2018global combined turnoverhealth care: EUR55 millionXpension funds:EUR 500 millionXdomestic individual turnover of at least twoconcerned undertakingshealth care: EUR10 millionXpension funds:EUR 100 millionX2009-2014global combined turnover EUR 113.45 mil-lionXdomestic individual turnover of at least twoconcerned companiesEUR 30 million X2015-2018global combined turnover EUR 150 million Xdomestic individual turnover of at least twoconcerned companiesEUR 30 million XPoland2009-2014global combined turnover EUR 1 billion Xdomestic combined turnover EUR 50 million X2015-2018global combined turnover EUR 1 billion Xdomestic combined turnover EUR 50 million Xdomestic individual turnover EUR 10 million XSpain2009-2018domestic combined turnover EUR 240 million Xdomestic individual turnover of at least two ofthe undertakings concernedEUR 60 million X2009-2010 domestic market share acquired or increased 30%2011-2018domestic market share acquired or increased 50% Xdomestic individual turnover of targets EUR 10 million XSweden2008domestic combined turnover SEK 4 billion Xdomestic individual turnover of at least two ofthe parties concernedSEK 100 million X2009-2018domestic combined turnover SEK 1 billion Xdomestic individual turnover of at least two ofthe parties concernedSEK 200 million XUnited Kingdom 2009-2018domestic individual turnover of targets GBP 70 milliondomestic market share acquired or increased 25%225Table C.2: Summary of RegulationsCountries Regulation Who is applied to Exceptions TimelineBelgium agencies: FSMA,Belgian CompetitionAuthorityvoluntary or mandatory publictakeovers bids if securities are inBelgium, primary market is inBelgium, or registered office isin Belgium and stocks are tradedon Belgian stock exchange.any public squeeze-out bid.registered officeand primarymarket of tar-get outsideBelgiumwithout pre-mergernotification: 4 to 16weeks.with permerger no-tification: 6 to 36weeksFinland agencies: FinancialSupervisory Author-ity,FCCApublic takeovers.firms listed on Nasdaq Helsinki.foreign targetfirmswithout pre-mergernotification: 20 to 24weeks.with pre-merger no-tification: 24 to 48weeksFrance agencies: Autorit desMarchs Financiers,Autorit de la Concur-renceirrespective of targets corporateform.Foreign buyers of certain sectors(energy, water, defense etc.) aresubject to approval by the Min-ister of Economy.Banking, insurance, etc. aresubject to approval regardless ofbuyers nationality.Listed com-panies haveslightly differ-ent rules regard-ing corporategovernance.without pre-mergernotification: 12 to 16weeks.with pre-merger no-tification: 16 to 32weeks226(continued): Summary of RegulationsCountries Regulation Who is applied to Exceptions TimelineGermany regulation: TakeoverAct.agencies: FederalFinancial SupervisoryAuthority,Federal Cartel Officeonly applies entirely to German-registered German-traded firms.Only part ofTakeover Actis applicable ifa company isregistered out-side Germanyor is tradedonly outsideGermany.without pre-mergernotification: 1 to 16weeks.with pre-merger no-tification: 5 to 36weeksItaly regulation: ItalianCivil Code, ItalianFinancial Act (TUF).agencies: NationalCommission for Com-panies and the StockExchange,Italian CompetitionAuthorityjoint-stock companies traded onItalian markets. Both publicand private transactions subjectto Italian Civil Code. The TUFapplies to listed companies.Small/mediumenterprises havespecial rules.without pre-mergernotification: 4 to 10weeks.with pre-merger no-tification: 6 to 24weeksNetherlands agencies: Authorityfor the Financial Mar-kets,Authority for Con-sumers and Marketstarget admitted to trading onNetherlands regulated market.N/A without pre-mergernotification: 10 to 24weeks.with pre-merger no-tification: 14 to 41weeksPoland agencies: PolishFinancial SupervisionAuthority,Office of Competi-tion and ConsumerProtectionTarget is public company regis-tered in Poland with shares in aPolish regulated market.non-Polishcompanies nottraded in Polandwithout pre-mergernotification: 24 to 48weeks.with pre-merger no-tification: 28 to 68weeks227(continued): Summary of RegulationsCountries Regulation Who is applied to Exceptions TimelineSpain agencies: SecuritiesExchange Commis-sion,Competition AuthorityN/A N/A without pre-mergernotification: 6 to 12weeks.with pre-merger no-tification: 10 to 32weeksSweden agencies: Swedish Fi-nancial Authority,Swedish CompetitionAuthoritytargets whose shares are admit-ted to a regulated or alternativemarket in Sweden.No special rulesfor foreign buy-ers except somerestrictions inenergy, nuclear,and defensesectors.without pre-mergernotification: 4 to 14weeks.with pre-merger no-tification: 6 to 36weeksUnitedKingdomagencies: TakeoverPanel,Competitions andMarkets Authoritypublic companies registered inthe UK whose shares are tradedon UK markets.Foreign buyersrestricted in avi-ationwithout pre-mergernotification: 4 to 16weeks.with pre-merger no-tification: 4 to 184weeksEU regulation: TakeoverDirective 2004/25/EC,Council Regulation(EC) No 139/2004(the EU Merger Regu-lation)agencies: EuropeanCommissionTakeover Directive: (1)publicoffers not made by the targetcompany itself; (2)objective ofcontrol; (3)not issued by EUmember statesâ central banks.EU Merger Regulation: all con-centrations with a CommunitydimensionTakeover Direc-tive: (1)madeby the targetcompany it-self; (2)do nothave as theirobjective theacquisition ofcontrol; (3)byEU memberstatesâ centralbanks.without pre-mergernotification: 2 to 10weeks.with pre-merger no-tification: 7 to 35weeks.228C.2 Fuzzy MatchingIn this appendix, we explain the steps to merge the firm-level datasets from theSDC Platinum database and from the Amadeus database.C.2.1 SDC Platinum DataFirst, we drop transactions with missing dates of submission from the SDC dataset.Second, the SDC dataset lists a target firm and an acquiring firm for each transac-tion. However, the names of the parties are not always disclosed. For example,an investor group acquired âAlbingia SAâ in 2018 but the names of acquiring par-ties are not disclosed or âAnimagi Oyâ acquired a set of companies whose namesare not disclosed. We label these undisclosed parties as unmatchable and do notinclude them in our fuzzy matching. Roughly 7% of M&A parties are labeledunmatchable and dropped.Moreover, a firm may have participated in multiple transactions during the pe-riod of our study. However, information such as phone number, website, and postalcode might not be reported for all of the transactions. The SDC Platinum does nothave a unique identifier for firms, hence, we use firmsâ original (non-standardized)names to recover these unique identifying variables (UIVs). We explain below howUIVs are used in our fuzzy matching.To perform fuzzy matching, we standardize names of the firms. This task isparticularly difficult since names are in various languages such as English, French,German, Italian and Finnish. First, we remove punctuation such comma, colon,dot, and ampersand from firmsâ names. For example, âA. & J. VOEGELâ willturn into âA J VOEGELâ and common words such âANDâ, âTHEâ, âOFâ (in var-ious languages). Second, we identify common phrases. For example, the Ger-man phrase âGesellschaft mit beschrnkter Haftungâ or âGmbHâ and the Swedishphrase âAktiebolagâ or âABâ are equivalent to âLtd.â used in the U.K. or âInc.â inthe U.S. Third, we harmonize these common phrases. For exmaple, âGesellschaftMBHâ, âGes MBHâ and âGesell MBHâ are all various formats of âGesellschaftmit beschrnkter Haftungâ, all of which we replace with the phrase âGMBHâ.As another example, âPHARMACOLOGIQUESâ, âPHARMACIESâ, âFARMA-CEUTICOâ are all various formats âPHARMACEUTICALSâ, all of which we229replace with the phrase âPHARMâ. Fourth, we remove phrases that represent thelegal status of a firm but are not specific to the firm such as âPLCâ, âLTDâ, âINCâ(in various languages). Fifth, we identify and harmonize phrases related to thecountry of the firm e.g. âITALIENNEâ, âITALIEâ, and âITALIANOâ are all re-lated to Italy, and we replace them with the phrase âITâ.C.2.2 Amadeus DataThe Amadeus database contains financial and accounting information for both pri-vate and publicly traded firms in Europe from 2010 to 2018. We drop firms withmissing names and standardize the names of firms, as described above.C.2.3 Fuzzy MatchingWe perform fuzzy matching between SDC Platinum and Amadeus data using theSTATA command matchit.1 We tokenize standardized names by splitting on spaces.For instance, in âSEVEN NETWORKSâ there are two tokens: SEVEN and NET-WORKS. These match perfectly with âSEVEN NETWORKSâ and âNETWORKSSEVENâ (score = 1), imperfectly with âNETWORK SEVENâ or âSEV NET-WORKSâ (score = 0.5) but does not match with âSEVE NETâ (score = 0). Thescore is calculated by dividing number of matched tokens by the total number oftokens. We keep all the matches with a score equal to or above 0.5.We use unique identifying variables (UIV) such as phone number, email, web-site, postal code, ticker symbol if available to identify perfect matches. This helpsus to improve both quality and quantity of identified matches. We define fourclasses of quality in our matched data with class 1 having the highest quality: (1)Identical standardized names and at least one matching UIV (2) Imperfect fuzzy-matched names with at least one matching UIV (3) Identical standardized nameswith no conflicting UIVs but same industry codes, and (4) Identical standardizednames with conflicting UIVs. At this point, a firm might have multiple matches.We keep matches with the highest quality. If a firm has two or more matches of thesame quality, we choose one randomly.1See the STATA documentation for details.230Table C.3: M&A Deals Across Countries (2010 - 2018)Panel A: Fuzzy Match Rate (Perfect Match Rate)(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)Belgium Finland France Germany Italy Netherlands Poland Spain Sweden UK Average2010 53 (84) 58 (88) 48 (82) 52 (93) 40 (37) 41 (77) 66 (91) 36 (81) 50 (82) 59 (93) 51 (86)2011 51 (83) 59 (93) 46 (81) 54 (93) 42 (37) 41 (78) 65 (93) 38 (81) 52 (87) 62 (93) 52 (86)2012 52 (81) 59 (86) 48 (81) 57 (92) 44 (40) 44 (73) 64 (90) 43 (77) 51 (83) 62 (92) 54 (85)2013 54 (81) 58 (85) 48 (81) 58 (91) 49 (39) 46 (70) 60 (90) 49 (74) 53 (82) 67 (93) 56 (84)2014 60 (83) 63 (84) 48 (79) 66 (88) 49 (39) 51 (74) 63 (87) 51 (72) 59 (83) 69 (92) 59 (83)2015 56 (79) 62 (86) 45 (77) 68 (89) 49 (39) 51 (69) 62 (86) 54 (71) 61 (79) 72 (91) 59 (81)2016 59 (68) 68 (85) 48 (73) 70 (89) 52 (42) 57 (68) 62 (85) 56 (71) 67 (75) 74 (91) 62 (79)2017 68 (77) 72 (83) 54 (77) 74 (91) 56 (41) 60 (72) 67 (84) 59 (70) 69 (73) 72 (86) 65 (78)2018 69 (78) 76 (83) 59 (76) 73 (88) 63 (42) 63 (75) 69 (85) 61 (66) 73 (75) 65 (72) 66 (74)Average 59 (79) 64 (86) 49 (78) 64 (90) 51 (40) 51 (73) 64 (87) 49 (73) 59 (79) 67 (88) 59 (81)Panel B: Total Number of M&A DealsBelgium Finland France Germany Italy Netherlands Poland Spain Sweden UK Average2010 398 484 2,795 2,474 1,164 1,113 623 1,724 1,522 4,575 1,6872011 431 497 3,299 2,856 1,054 1,207 459 1,753 1,548 4,758 1,7862012 365 379 3,208 2,608 872 1,089 347 1,454 1,373 4,686 1,6382013 355 365 3,072 2,515 846 982 441 1,252 1,239 4,172 1,5242014 414 381 4,238 2,804 992 998 721 1,543 1,030 4,801 1,7922015 538 404 5,336 2,763 1,409 1,101 1,162 1,626 1,115 5,016 2,0472016 538 494 5,186 2,855 1,674 1,338 888 1,521 1,236 4,849 2,0582017 577 432 4,167 2,709 1,862 1,222 658 1,699 1,330 6,075 2,0732018 554 430 3,671 2,563 1,930 1,258 659 1,894 1,398 6,689 2,105Average 463 430 3,886 2,683 1,311 1,145 662 1,607 1,310 5,069 1,857Notes: Panel A reports the match rate between SDC Platinum (M&A data) and Amadeus (firm-level financial data). In the parentheses, we report theshare of perfectly matched observations. A match is perfect if two observations match on at least one unique identifying variables (i.e., phone number,website, email, and ticker symbol). Panel B reports the total number of deals (matched and unmatched) across years and countries in our sample.231Table C.4: Summary Statistics on M&A Activities Across Countries(1) (2) (3) (4) (5) (6)Per Year Tradable (%) Within Indusry (%) International (%) Merger (%) ValueBelgium 122 0.22 0.48 0.53 0.15 214.41Finland 92 0.23 0.56 0.35 0.26 58.10France 597 0.24 0.44 0.18 0.09 131.32Germany 2045 0.39 0.52 0.34 0.18 160.46Italy 319 0.39 0.54 0.35 0.22 106.96Netherlands 42 0.23 0.48 0.45 0.19 181.61Poland 131 0.28 0.43 0.28 0.24 39.17Spain 331 0.26 0.52 0.31 0.16 94.63Sweden 226 0.19 0.52 0.26 0.24 75.50UnitedKingdom 572 0.22 0.53 0.34 0.18 150.94Total 263 0.27 0.50 0.34 0.19 121.31Notes: This table displays summary statistics of M&A deals across countries in our matched sample. Column (1) reports the average number of M&Adeals. Column (2) reports the share of M&A deals in tradable goods sectors. Column (3) reports the share of deals that were within the same 4-digitindustries. Column (4) reports the share of crossborder deals. Column (5) reports the share of deals that were mergers between two parties, rather thanacquisitions of assets. Column (6) reports the average value of transactions in million euros.232Table C.5: Total Number of M&A Deals Across Other European Countries(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)Switzerland Norway Denmark Ireland Austria Ukraine Cyprus CzechRepublic Luxembourg Portugal Average2010 796 932 605 232 360 534 476 287 145 253 4622011 906 967 548 236 366 325 383 282 157 126 4302012 787 961 534 292 309 373 367 259 147 123 4152013 706 861 553 257 304 340 218 225 188 139 3792014 843 559 457 337 270 137 241 188 203 111 3352015 772 571 462 405 314 218 211 288 261 190 3692016 805 524 536 351 304 239 222 373 291 203 3852017 812 487 522 396 262 230 206 252 314 242 3722018 766 449 466 462 256 175 139 301 293 248 356Average 799 701 520 330 305 286 274 273 222 182 389Notes: This table reports the number of deals in the 2010-2018 period for other European countries based on their total number of deals in our studyperiod. We exclude these countries because of low match rate233C.3 RobustnessIn this section, we provide results from robustness tests discussed in Sections 4.2 â4.4.C.3.1 Different ClusteringOur main firm-level results are based on clustering at the firm level. We also dorobustness tests on the key firm-level outcomes, where standard errors are clusteredat the market level (defined at the four digit NAICS by country) for firm-levelresults. Figure C.1 and Table C.6 show that the results on returns on assets andprofit margins, and the estimates are almost identical to our main estimates.C.3.2 Perfectly Matched SampleIn Table C.7 of Appendix C.3, we show that among the fuzzy-matched sample(between SDC data and Amadeus data), roughly 80 percent of firms are perfectlymatched using the unique identifying variables. We repeat the main analysis us-ing the perfectly matched sample. Figure C.2 and Table C.7 show the estimateson returns on assets and profit margins that are qualitatively similar to our mainestimates (where we include the fuzzy matched sample).C.3.3 By One-time vs. Repeat AcquirersPrior research points out that a part of the motive behind M&As involves empire-building, which could result in losses in profitability or productivity after the event[Jensen, 1986a]. While it is difficult to discern whether a particular acquirer has anempire-building motive in our data, we test whether the effects on returns on assetsand profitability are different depending on whether an acquirer engages in multi-ple M&A transactions (roughly 26 percent of our analysis sample). Specifically,we compare the outcomes of acquirers involved in a single acquisition relative tothe outcomes of acquirers involved in multiple acquisitions during our sample pe-riod. Figure C.3 and Table C.8 show that acquirers involved in multiple M&Atransactions experience similar declines in profitability after the event, comparedto acquirers involved in a single M&A deal.234C.3.4 Using Private Firms OnlyPrior research shows that publicly listed firms engage in M&As more than privatefirms during merger waves, and that acquisitions can be efficiency improving, espe-cially when buyers and sellers are publicly listed firms during on-the-wave mergers[Maksimovic et al., 2013]. While the vast majority (95 percent) of firms that gothrough M&As in our matched sample are private, we repeat our analysis focusingonly on private firms, since the effects of M&As on firm-level outcomes might bedifferent between listed firms and private firms. Figure C.4 and Table C.9 show thatthe results on returns on assets and profit margins are similar to our main resultswhere we include publicly listed companies, implying that our results are robust tojust focusing on private firms in our sample.C.3.5 Other Measures of ProfitabilityFor our main analysis, we focus on two measures of profitability: returns on as-sets and profit margins. We use two additional measures of profitability directlyobserved in our data: returns on assets (labeled as RTAS in our data) and Earn-ings Before Interest and Taxes divided by total revenue (labeled as EBMA in ourdata). Figure C.5 and Table C.10 show the results on these additional measures ofprofitability that are qualitatively similar to the estimates on our main measures ofprofitability.C.3.6 Firm Exit RateOne potential concern regarding the decline in profitability after acquisitions isthat the result could be driven by treated firms exiting the sample more comparedto control firms after the event. This is a potential issue especially for target firmsinvolved in acquisitions because it is possible that some of them may shut downand exit the sample after acquisitions. To check whether this is indeed the case,Figure C.8 shows the share of target firms exiting the sample after the event. Itshows that the share of firms existing the sample is actually quite similar betweentarget firms and for their matched control firms, which would help alleviate theaforementioned concern.235Figure C.1: Different Clustering(a) Return on Assets-.02-.010.01.02Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventAcquisition Merger(b) Profit Margins-.02-.010.01.02Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventAcquisition MergerNotes: Panels (a) and (b) display event-study estimates for the impact of M&As on the aggregatefirm-level (targets and acquirers pooled) profitability, separately for those involved in partialacquisitions (red line) and for those involved in mergers (black line). Panel (a) shows the estimatesfor return on assets. Panel (b) shows the estimates for profit margins. The dashed lines indicate 95percent confidence intervals where the standard errors are clustered at the industry-by-country level.The M&A event is in year 0 and the coefficient is normalized to be zero in year -1. Return on assetsis defined as firmsâ net income divided by its total assets. We define markup as the elasticity ofoutput with respect to variable costs as well as the variable costs share [De Loecker and Warzynski,2012].236Figure C.2: Perfectly-Matched Sample(a) Return on Assets-.02-.010.01.02Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventAcquisition Merger(b) Profit Margins-.02-.010.01.02Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventAcquisition MergerNotes: Panels (a) and (b) display event-study estimates for the impact of M&As on the aggregatefirm-level (targets and acquirers pooled) profitability, separately for those involved in partialacquisitions (red line) and for those involved in mergers (black line). We restrict our sample toperfectly-matched observations. A match is perfect if two observations match on at least one uniqueidentifying variables (i.e., phone number, website, email, and ticker symbol). Panel (a) shows theestimates for return on assets. Panel (b) shows the estimates for profit margins. The dashed linesindicate 95 percent confidence intervals where the standard errors are clustered at the firm level. TheM&A event is in year 0 and the coefficient is normalized to be zero in year -1. Return on assets isdefined as firmsâ net income divided by its total assets. We define markup as the elasticity of outputwith respect to variable costs as well as the variable costs share [De Loecker and Warzynski, 2012].237Figure C.3: By One-time M&As vs. Repeat M&As (Acquirers)(a) Return on Assets-.02-.010.01.02Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventOne-Time RepeatOne-Time (4861), Repeat (1713)(b) Profit Margins-.03-.02-.010.01Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventOne-Time RepeatOne-Time (4625), Repeat (1660)Notes: Panels (a) and (b) display event-study estimates for the impact of M&As on firm-levelprofitability, separately for acquiring firms that go through an M&A only once throughout oursample period (blue line) and for acquiring firms with multiple M&A events (navy line). Panel (a)shows the estimates for return on assets. Panel (b) shows the estimates for profit margins. Thedashed lines indicate 95 percent confidence intervals where the standard errors are clustered at thefirm level. The M&A event is in year 0 and the coefficient is normalized to be zero in year -1.238Figure C.4: Private Firms(a) Return on Assets-.02-.010.01.02Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventAcquisition Merger(b) Profit Margins-.02-.010.01.02Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventAcquisition MergerNotes: Panels (a) and (b) display event-study estimates for the impact of M&As on the aggregatefirm-level (targets and acquirers pooled) profitability, separately for those involved in partialacquisitions (red line) and for those involved in mergers (black line). We restrict our sample toprivate firms. Panel (a) shows the estimates for return on assets. Panel (b) shows the estimates forprofit margins. The dashed lines indicate 95 percent confidence intervals where the standard errorsare clustered at the firm level. The M&A event is in year 0 and the coefficient is normalized to bezero in year -1. Return on assets is defined as firmsâ net income divided by its total assets. Wedefine markup as the elasticity of output with respect to variable costs as well as the variable costsshare [De Loecker and Warzynski, 2012].239Figure C.5: Other Performance Measures after M&As(a) RTAS-.02-.010.01Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventAcquisition Merger(b) EBMA-.02-.010.01Coefficient Estimates [Year Before Event = 0]-4 -3 -2 -1 0 1 2 3 4Year Relative to EventAcquisition MergerNotes: Panels (a) and (b) display event-study estimates for the impact of M&As on the aggregatefirm-level (targets and acquirers pooled) profitability, separately for those involved in partialacquisitions (red line) and for those involved in mergers (black line). We restrict our sample toprivate firms. Panel (a) shows the estimates for return on assets as reported in the balance sheet data.Panel (b) shows the estimates for EBIDTA scaled by total revenue. The dashed lines indicate 95percent confidence intervals where the standard errors are clustered at the firm level. The M&Aevent is in year 0 and the coefficient is normalized to be zero in year -1.240Figure C.6: Share of Target Firms Exiting the Sample-.15-.1-.050.05.1.15Exit Rate-4 -3 -2 -1 0 1 2 3 4 5PeriodTarget ControlNotes: This figure displays the exit rate of target firms in the matched sample, separately for targetfirms and their control firms.241Table C.6: Different Clustering(1) (2)Acquisition MergerReturn on Assets -0.009*** -0.004(0.002) (0.004)Mean at t=-1 0.04 0.05Adj. R squared 0.460 0.449Firm-Year 311,436 64,347Profit Margins -0.009*** -0.009**(0.002) (0.004)Mean at t=-1 0.05 0.06Adj. R squared 0.524 0.500Firm-Year 279,210 57,546Notes: This table reports the difference-in-differences estimates for the impact of M&As on firmprofitability. Columns (1) and (2) report the estimates on the aggregate firm-level (targets andacquirers pooled) outcomes for those involved in partial acquisitions and for those involved inmergers, respectively. The standard errors are clustered at the industry-by-country level.242Table C.7: Perfectly-Matched Sample(1) (2)Acquisition MergerReturn on Assets -0.008*** -0.003(0.001) (0.004)Mean at t=-1 0.04 0.05Adj. R squared 0.458 0.436Firm-Year 228,085 47,470Profit Margins -0.008*** -0.007*(0.001) (0.004)Mean at t=-1 0.05 0.06Adj. R squared 0.530 0.492Firm-Year 205,656 42,620Notes: This table reports the difference-in-differences estimates for the impact of M&As on firmprofitability. Columns (1) and (2) report the estimates on the aggregate firm-level (targets andacquirers pooled) outcomes for those involved in partial acquisitions and for those involved inmergers, respectively. We restrict our sample to perfectly-matched observations. A match is perfectif two observations match on at least one unique identifying variables (i.e., phone number, website,email, and ticker symbol). The standard errors are clustered at the firm level.243Table C.8: By One-time M&As vs. Repeat M&As (Acquirers)(1) (2)Return on Assets Profit MarginsPost x Treated -0.006** -0.006***(0.002) (0.002)Post x Treated x Repeat -0.000 -0.003(0.004) (0.004)Mean at t=-1 (One-Time = 1) 0.04 0.06Mean at t=-1 (Repeat = 1) 0.04 0.07Adj. R squared 0.431 0.521Firm-Year (One-Time = 1) 82,460 74,290Firm-Year (Repeat = 1) 30,130 27,581Notes: This table reports the difference-in-differences estimates for the impact of M&As on firmprofitability, separately for acquiring firms that go through an M&A only once throughout oursample period and for acquiring firms with multiple M&A events. Columns (1) reports theestimates for the impact on return on assets. Columns (2) reports the estimates for the impact onprofit margins. The triple interaction term captures the triple-difference estimates for firms withmultiple M&A events. The standard errors are clustered at the firm level.244Table C.9: Private Firms(1) (2)Acquisition MergerReturn on Assets -0.008*** -0.003(0.001) (0.003)Mean at t=-1 0.04 0.05Adj. R squared 0.455 0.447Firm-Year 297,032 60,460Profit Margins -0.008*** -0.007**(0.001) (0.003)Mean at t=-1 0.05 0.06Adj. R squared 0.526 0.503Firm-Year 266,516 54,150Notes: This table reports the difference-in-differences estimates for the impact of M&As on firmprofitability. Columns (1) and (2) report the estimates on the aggregate firm-level (targets andacquirers pooled) outcomes for those involved in partial acquisitions and for those involved inmergers, respectively. We restrict our sample to private firms. The standard errors are clustered atthe firm level.245Table C.10: Other Measures of Profitability(1) (2)Acquisition MergerRTAS -0.010*** -0.004(0.001) (0.003)Mean at t=-1 0.06 0.07Adj. R squared 0.513 0.505Firm-Year 307,172 63,597EBMA -0.009*** -0.007**(0.001) (0.003)Mean at t=-1 0.06 0.07Adj. R squared 0.572 0.560Firm-Year 283,111 58,425Notes: This table reports the difference-in-differences estimates for the impact of M&As on firmprofitability. Columns (1) and (2) report the estimates on the aggregate firm-level (targets andacquirers pooled) outcomes for those involved in partial acquisitions and for those involved inmergers, respectively. The top panel shows the estimates for return on assets as reported in thebalance sheet data. The bottom panel shows the estimates for EBIDTA scaled by total revenue. Thestandard errors are clustered at the firm level.246
Thesis/Dissertation
eng
Economics
Vancouver : University of British Columbia Library
Attribution-NoDerivatives 4.0 International
Graduate
Moon, Terry S.
Milligan, Kevin
Essays on firms and workers
Text