Arts, Faculty of
Vancouver School of Economics
DSpace
UBCV
Plotkin, Pascuel
Doctor of Philosophy - PhD
University of British Columbia
Chapter 2 examines the impact of online-delivery platforms on the labor market. These platforms, emblematic of recent technological shifts, alter both workforce composition and product demand. While they can create new opportunities for workers with limited outside options, they may also displace âgood jobs.â Using novel Brazilian data that link employer-employee records to both restaurants and a major delivery platform, and applying a matched event-study design, I estimate the effects of platform adoption on labor market outcomes. On average, adopting restaurants substitute in-house labor hours for outsourced platform worker hours nearly one-for-one. Workers at adopting restaurants see modest earnings losses, as most transition into new formal sector jobs. However, workers at non-adopting restaurants face higher risks of job loss and larger earnings declines, as these establishments are more likely to downsize or close. Despite these losses, the aggregate earnings gains for gig workers outweigh the losses among traditional restaurant employees, highlighting important distributional trade-offs. Chapter 3 studies rent-sharing between firms and outsourced workers. Using unique Argentinian administrative data that match temp agencies to user firms, we estimate how wage premia differ between regular and temp workers. Results show that high-wage user firms paying regular employees a 10% premium relative to low-wage firms, pay temp workers only a 4.9% premium. This is midway between full rent-sharing with insiders and a competitive spot-labor market, indicating partial but limited sharing of firm-specific wage premia with outsourced workers. Chapter 4 explores how institutional settings affect domestic outsourcing in Italy, exploiting a novel administrative identifier for outsourcing events. We estimate the impact of outsourcing across various occupations without restricting to continuously employed workers. Results show substantial earnings losses for outsourced workers, mostly due to increased non-employment shortly after outsourcing. The losses are largest for non-routine manual jobs, while workers in abstract, cognitive roles experience some earnings gains following an outsourcing event. Overall, the thesis provides new evidence on how technology and organizational change through platforms and outsourcing reshape labor market outcomes, highlighting trade-offs between flexibility, earnings, and job security.
Three Essays on Labor EconomicsbyPascuel PlotkinM.Sc., Universidad de San Andres, 2017B.Sc., Universidad de Buenos Aires, 2015A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THEREQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Economics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)June 2025© Pascuel Plotkin, 2025The following individuals certify that they have read, and recommend to the Facultyof Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Three Essays on Labor Economicssubmitted by Pascuel Plotkin in partial fulfillment of the requirements for the degreeof Doctor of Philosophy in Economics.Examining Committee:Thomas Lemieux, Professor, Vancouver School of Economics, UBCCo-SupervisorRaffaele Saggio, Associate Professor, Vancouver School of Economics, UBCCo-SupervisorClaudio Ferraz, Professor, Vancouver School of Economics, UBCSupervisory Committee MemberSam Norris, Assistant Professor, Vancouver School of Economics, UBCSupervisory Committee MemberFlorian Hoffmann, Associate Professor, Vancouver School of Economics, UBCUniversity ExaminerGuillermo Marshall, Associate Professor, Sauder School of Business, UBCUniversity ExaminerJohannes Schmieder, Professor, Department of Economics, Boston UniversityExternal ExamineriiAbstractChapter 2 examines the impact of online-delivery platforms on the labor market. Theseplatforms, emblematic of recent technological shifts, alter both workforce compositionand product demand. While they can create new opportunities for workers withlimited outside options, they may also displace âgood jobs.â Using novel Brazilian datathat link employer-employee records to both restaurants and a major delivery platform,and applying a matched event-study design, I estimate the effects of platform adoptionon labor market outcomes. On average, adopting restaurants substitute in-house laborhours for outsourced platform worker hours nearly one-for-one. Workers at adoptingrestaurants see modest earnings losses, as most transition into new formal sector jobs.However, workers at non-adopting restaurants face higher risks of job loss and largerearnings declines, as these establishments are more likely to downsize or close. Despitethese losses, the aggregate earnings gains for gig workers outweigh the losses amongtraditional restaurant employees, highlighting important distributional trade-offs.Chapter 3 studies rent-sharing between firms and outsourced workers. Using uniqueArgentinian administrative data that match temp agencies to user firms, we estimatehow wage premia differ between regular and temp workers. Results show that high-wage user firms paying regular employees a 10% premium relative to low-wage firms,pay temp workers only a 4.9% premium. This is midway between full rent-sharing withinsiders and a competitive spot-labor market, indicating partial but limited sharing offirm-specific wage premia with outsourced workers.Chapter 4 explores how institutional settings affect domestic outsourcing in Italy,exploiting a novel administrative identifier for outsourcing events. We estimate theimpact of outsourcing across various occupations without restricting to continuouslyemployed workers. Results show substantial earnings losses for outsourced workers,iiimostly due to increased non-employment shortly after outsourcing. The losses arelargest for non-routine manual jobs, while workers in abstract, cognitive roles experiencesome earnings gains following an outsourcing event. Overall, the thesis providesnew evidence on how technology and organizational change through platforms andoutsourcing reshape labor market outcomes, highlighting trade-offs between flexibility,earnings, and job security.ivLay SummaryThis thesis examines how the intersection between alternative work arrangements andinstitutions impact, workers, firms and inequality. Chapter 2 studies the winners andlosers of online delivery platforms and finds that restaurant workers suffer earningslosses, but these losses are outweighed by the potential gains of gig workers. Chapter 3estimates how much firms differentiate pay premia between regular and outsourcedworkers in temp agency work arrangements and finds that firms share pay-premiumwith outsourced workers at a midpoint between the benchmarks for insiders (one) andthe competitive spot-labor market (zero). Chapter 4, exploits a novel identifier ofoutsourcing events present in Italian administrative data and finds that outsourcingleads to substantial earnings losses, primarily driven by negative employment effectson workers.vPrefaceThe research in Chapter 2 is an original, unpublished, independent work by the authorof this thesis, Pascuel Plotkin. This project was approved by the University of BritishColumbiaâs Research Ethics Board [certificate #H22-02662].Chapter 3 is an original, joint work with Andres Drenik (UT Austin), Simon Jager(Princeton University) and Benjamin Schoefer (UC Berkeley). The authors collabo-rated on the definition of the research questions. Pascuel conducted the data collection,processing, and empirical analysis. The authors contributed equally to the design ofthe estimation strategy, the interpretation of the results, and the writing. A version ofChapter 3 is published in the Review of Economics and Statistics, 105, no. 1 (2023):206-216.Chapter 4 is an original, unpublished, joint work with Diego Daruich (University ofSouthern California), Martino Kuntze (Fondazione Eni Enrico Mattei) and RaffaeleSaggio (University of British Columbia). The authors collaborated on the definitionof the research questions. Pascuel conducted the processing of the data, and empiricalanalysis. The authors contributed equally to the design of the estimation strategy,the interpretation of the results, and the writing.No generative AI tools or technologies were used in any part of the creation, analysis,or writing of this work.viTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Dinner at Your Door: How Delivery Platforms Affect Workers andFirms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.2 Theoretical Framework . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2.1 Model Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2.2 Impact of online-delivery platforms . . . . . . . . . . . . . . . 132.2.3 Comparative Statics . . . . . . . . . . . . . . . . . . . . . . . 142.3 Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.4.1 Delivery Platform Data . . . . . . . . . . . . . . . . . . . . . . 172.4.2 Employer-Employee Data: RAIS . . . . . . . . . . . . . . . . 18vii2.5 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.5.1 Research Design: Adopting Establishments . . . . . . . . . . . 202.5.2 Research Design: Spillovers . . . . . . . . . . . . . . . . . . . 232.5.3 Research Design: Workers . . . . . . . . . . . . . . . . . . . . 242.6 Impact of Online-Delivery Platforms on Restaurants Labor Demand . 262.6.1 Main Results: Effect of Adoption of the Platform . . . . . . . 262.6.2 Effects by Occupation . . . . . . . . . . . . . . . . . . . . . . 292.6.3 Effects by Establishment Pay Premium . . . . . . . . . . . . . 312.6.4 Effects by Restaurant Density . . . . . . . . . . . . . . . . . . 322.6.5 Spillovers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342.6.6 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352.7 Restaurant Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372.7.1 Main Results: Effect of Employer Adoption on the Platform . 382.7.2 Spillovers on Workers . . . . . . . . . . . . . . . . . . . . . . . 402.8 Winners & Losers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422.8.1 Limitations & Discussion . . . . . . . . . . . . . . . . . . . . . 452.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 482.10 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502.11 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 Paying Outsourced Labor: Direct Evidence from Linked TempAgency-Worker-Client Data . . . . . . . . . . . . . . . . . . . . . . . 653.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653.2 Institutions and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 683.3 Wages for Temp Agency Work in Argentina . . . . . . . . . . . . . . 703.4 Do High-Wage Firms Share Pay Premia With Temp Agency Workers? 743.5 Interpretation and Implications . . . . . . . . . . . . . . . . . . . . . 783.6 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 813.7 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 854 The Consequences of Domestic Outsourcing on Workers: NewEvidence from Italian Administrative Data . . . . . . . . . . . . . . 864.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 864.2 Outsourcing in Italy . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.2.1 Institutional Background . . . . . . . . . . . . . . . . . . . . . 91viii4.2.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 934.2.3 Measuring Outsourcing . . . . . . . . . . . . . . . . . . . . . . 934.3 Econometric Framework . . . . . . . . . . . . . . . . . . . . . . . . . 964.4 The Effects of Outsourcing on Workers . . . . . . . . . . . . . . . . . 974.4.1 Main Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 984.4.2 Factors Contributing to Non-Employment Following Outsourcing 994.4.3 Estimates Based on the Flow-Based Approach of GS . . . . . 1034.5 Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1044.6 The Role of Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1074.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1094.8 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1124.9 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1145 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128A Appendix to Chapter 2 . . . . . . . . . . . . . . . . . . . . . . . . . . 145A.1 Appendix: Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145A.2 Appendix: Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174A.3 Appendix: Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177A.3.1 Main Model: Derivations . . . . . . . . . . . . . . . . . . . . . 177A.3.2 Model Extension: Selection into Delivery Platform . . . . . . 180A.3.3 Extension: Product Demand . . . . . . . . . . . . . . . . . . . 183A.4 Appendix: Empirics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187A.4.1 Imputation of informality: restaurants . . . . . . . . . . . . . 187A.4.2 Estimation of Informal/Unemployed to Employed Wage Ratio 187A.4.3 Imputation of Wage on Delivery Platform per Restaurant . . . 188A.4.4 Procedure to Rescale Spillover Effects . . . . . . . . . . . . . . 188A.4.5 AKM Estimation Details . . . . . . . . . . . . . . . . . . . . . 190A.4.6 Estimation of Firm Values V ej using PageRank . . . . . . . . . 191A.4.7 Estimation of the Effect of Delivery Platforms on Market LevelOutcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193A.4.8 Geolocation details . . . . . . . . . . . . . . . . . . . . . . . . 194ixB Appendix to Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . . . 196B.1 Appendix: Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196B.2 Appendix: Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201B.3 Appendix: Sample Construction . . . . . . . . . . . . . . . . . . . . . 203B.4 Appendix: Measurement Error Correction . . . . . . . . . . . . . . . 205C Appendix to Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . . . 206C.1 Appendix: Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206C.2 Appendix: Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213xList of Tables2.1 Summary Statistics for Adopting and Control Restaurants . . . . . . 602.2 Effect of Platform Adoption on Labor Demand per Occupation . . . . 612.3 Heterogeneous Effects of Platform Adoption on Labor Demand perOccupation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 622.4 Summary Statistics for Workers whose Employer Adopts the DeliveryPlatform and Control Workers . . . . . . . . . . . . . . . . . . . . . . 632.5 Total Wage Bill Effect of Platform Adoption . . . . . . . . . . . . . . 643.1 Temp Agency Work Arrangement Pay Penalty . . . . . . . . . . . . . 854.1 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144.2 Summary of Main Results . . . . . . . . . . . . . . . . . . . . . . . . 125A.1 Summary Stats Restaurant Workers (RAIS & Census) . . . . . . . . 174A.2 Summary Stats for Platform Workers . . . . . . . . . . . . . . . . . . 175A.3 Crosswalk of Occupations from CBO to Waiters, Cooks and DeliveryWorkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176A.4 Share of Restaurants that Geocode in Each Year . . . . . . . . . . . . 195B.1 Summary Statistics: All Formal Employees . . . . . . . . . . . . . . . 201B.2 Summary Statistics: All Temporary Work Agents in User Firms (SIPA-Registro Version) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202C.1 Outsourced Workersâ Occupations and Sector . . . . . . . . . . . . . 214C.2 Sample Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . 215C.3 Effects by Task Content of Outsourced Jobs . . . . . . . . . . . . . . 216xiList of Figures2.1 Restaurant Structure before and after online-delivery platforms . . . 502.2 AKM Firm FE and usage of the Platform . . . . . . . . . . . . . . . 512.3 Trends in hours Hired for In-House and Platform Workers . . . . . . 522.4 Effects of Online-Delivery Platform Adoption on In-House Size and Wages 532.5 Effects of Online-Delivery Platform Adoption on Total Hours Hired . 542.6 Effects of Online-Delivery Platform Adoption on Total Hours Hiredand In-House Size by AKM Firm Effect . . . . . . . . . . . . . . . . . 552.7 Effects of Online-Delivery Platform Adoption on Total Hours Hiredand In-House Size by Restaurant Density . . . . . . . . . . . . . . . . 562.8 Spillover Effects of Platform Adoption on Non-Adopting Restaurants 572.9 Effects of Employer Adoption of Online-Delivery Platform on WorkerEarnings and Employment . . . . . . . . . . . . . . . . . . . . . . . . 582.10 Spillover Effects of Platform Adoption on Restaurant Workers . . . . 593.1 Measurement Challenges: Regular and Temp Agency Work Arrangements 813.2 Firm Pay Premia (AKM Effects) For User and Non-User Firms and byWork Arrangement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823.3 Firm-Level Pay Premia Sharing Between Workers in Temp Agency andRegular Work Arrangements . . . . . . . . . . . . . . . . . . . . . . . 833.4 Industry Heterogeneity in Firm-Level Pay Premia Sharing BetweenWorkers in Temp Agency and Regular Work Arrangements . . . . . . 844.1 Earnings and Employment of Outsourced and Non-Outsourced Workers 1124.2 Conditioning on Outsourced Workers Being Employed by the DaughterFirm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1134.3 The Effect of Outsourcing on Employment Contract and CBA . . . . 1154.4 Reason for Job Separation after Outsourcing . . . . . . . . . . . . . . 116xii4.5 Comparison with the Flow-Based Approach of Goldschmidt and Schmieder(2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1174.6 Effects by Worker Characteristics . . . . . . . . . . . . . . . . . . . . 1184.7 Effects by Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1194.8 Effects by Occupation . . . . . . . . . . . . . . . . . . . . . . . . . . 1204.9 Effects by Task Content . . . . . . . . . . . . . . . . . . . . . . . . . 1214.10 Effects by Value Added per Worker of Mother Firm or Labor Share . 1224.11 Trends of Outsourcing Firms . . . . . . . . . . . . . . . . . . . . . . . 1234.12 Effects after Matching on Trends of Value Added per Worker . . . . . 124A.1 Rollout of the Platform . . . . . . . . . . . . . . . . . . . . . . . . . . 146A.2 Restaurants within 1 Kilometer Adopting the Platform . . . . . . . . 147A.3 Distribution of â¦ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148A.4 Distribution of Wages of Restaurant Workers (2018) . . . . . . . . . . 149A.5 Trends in Closure for Adopting and Control Restaurants . . . . . . . 150A.6 Effect of Platform Adoption on Avg. Worker Fixed Effects at Restaurants151A.7 Effect of Platform Adoption on Total Hours for Service Workers . . . 152A.8 Effect of Platform Adoption on Total Hours for Service Workers byAKM Firm Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153A.9 Relation Between Establishment Level Treatment Effect on Number ofWaiters and Usage of Delivery Platform by Restaurants . . . . . . . . 154A.10 Ratio of Delivery Workers and Service Workers at Treated Establish-ments by Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155A.11 Effect of Adopting the Platform using Borusyak et al. (2024) estimator 156A.12 Effect of Platform Entry on Restaurant Sector Size . . . . . . . . . . 157A.13 Establishment level treatment effect and COVID-19 Cases . . . . . . 158A.14 Effect of Platform Adoption on Log Firm In-House Size by COVID-19period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159A.15 Effect of Platform Adoption on Log Firm In-House Size with Statetimes Year Fixed Effects . . . . . . . . . . . . . . . . . . . . . . . . . 160A.16 Effect of Platform Adoption on Log Firm Size By Informality of theMunicipality of the Establishment . . . . . . . . . . . . . . . . . . . . 161A.17 Spillover Effects of Platform Adoption on Non-Adopting RestaurantsClosure and Platform Adoption . . . . . . . . . . . . . . . . . . . . . 162xiiiA.18 Spillover Effects of Platform Adoption on Non-Adopting Restaurants(2KM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163A.19 Histogram of the Difference Between Restaurant Opening Date andFirst Appearance in RAIS . . . . . . . . . . . . . . . . . . . . . . . . 164A.20 Effect of Delivery Platform Availability on Restaurant Entry . . . . . 165A.21 Effect of Employer Platform Adoption on Restaurant Workers LogEarnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166A.22 Effect of Employer Platform Adoption on Worker Probability of Chang-ing Firm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167A.23 Effect of Employer Platform Adoption on Worker Probability of Workingat Delivery Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . 168A.24 Spillover Effects of Platform Adoption on Restaurant Workers by Em-ployer Closure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169A.25 Trends of Formal Employment and Earnings for Platform Workers . . 170A.26 Total Wage Bill Effect from Platform Adoption as a Function of Non-Formal platform-workers Outside Option . . . . . . . . . . . . . . . . 171A.27 Histogram of Values (V ej ) for all Restaurants . . . . . . . . . . . . . . 172A.28 Relationship Between Values (V ej ) and Earnings . . . . . . . . . . . . 173B.1 Industry Distribution and Hours of Work of Temp Agency and RegularWorkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197B.2 Sorting of Regular and Temp Agency Workers: Estimated WorkerEffects Against Firm Effects (by Work Arrangement) . . . . . . . . . 198B.3 Sorting in the Temporary Agency Market: Temporary Firm FixedEffects Against Regular Firm Fixed Effects . . . . . . . . . . . . . . . 199B.4 Average Tenure vs Regular Firm Fixed Effects . . . . . . . . . . . . . 200C.1 Selection of Outsourced Workers within the Firm . . . . . . . . . . . 207C.2 Effect on Log Daily Wages . . . . . . . . . . . . . . . . . . . . . . . . 208C.3 Conditioning on Mother Firm Survival . . . . . . . . . . . . . . . . . 209C.4 Distribution of Exposure across Outsourcing Events . . . . . . . . . . 210C.5 Effects by Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211C.6 Effect on Log Daily Wages Using the Flow-Based Approach of Gold-schmidt and Schmieder (2017) . . . . . . . . . . . . . . . . . . . . . . 212xivAcknowledgementsThis journey would not have been possible without the unwavering support of myfamily and friends. I am deeply grateful to Valentina, who has accompanied me inthis ride, and has been the best partner I could have ever dreamed of. My heartfeltthanks also go to my parents, Andrea and Mariano, and the rest of my family Nico,Tomi, Ariel and Lili, whose belief in me surpassed even my own, and whose love andencouragement have sustained me in every circumstance.I am also thankful to my friends in Argentina, who have flown across continents toshare memories with me and welcomed me every time I returned, as if I had neverleft. I am also thankful to my friends in Vancouver for making this city feel like home:Pablo, Cata, Daniel, and many others, who have shared with me countless momentsof joy and laughter.Finally, I offer special thanks to my advisors Thomas Lemieux, Raffaele Saggio, ClaudioFerraz, and Sam Norris, whose invaluable mentorship, patience, and encouragementhave been instrumental in guiding me through this endeavor.xvâTo my family: Valentina, Andrea, Mariano, Nico, Tomi, Ariel and Lili.xviChapter 1IntroductionThis thesis comprises three chapters exploring the interplay of alternative work ar-rangements, institutions and inequality. Specifically, it studies the implications ofdomestic outsourcing on workers and firms pay policies. Chapter 2 investigates howonline-delivery platforms affect both in-house workers and gig workers. Chapter 3examines how firms share rents with outsourced workers hired through temporary workagencies. Finally, Chapter 4 focuses on the interaction between domestic outsourcingand institutions, by studying the impact that these events have on the extensivemargin for workers.Chapter 2 estimates the impact of delivery platforms on in-house restaurant employeesand gig workers in Brazil. Using a novel data set linking adopting restaurants andplatform-workers to administrative employer-employee data, I exploit the staggeredrollout of online-delivery platforms across regions to measure the causal effects ofplatform adoption. To account for informal workers, I supplement the data withhousehold surveys. The empirical strategy matches adopting restaurantsâand theirworkersâin regions with platform access to those in regions without it.The results show that adopting restaurants substitute in-house non-cooking-staff hourswith outsourced platform-worker hours, but the earnings and employment effects onrestaurant workers are only modestly negative,as many displaced workers reallocateto new formal sector jobs.Additionally, delivery platforms have spillover effects: Theyincrease the likelihood that non-adopting restaurants will shrink or close. However, thenet earnings gains for gig workers more than offset the losses for in-house restaurantworkers. These effects not only affect total earnings but potentially shift the income1distribution, as most gig workers were previously employed informally or unemployed.Chapter 3 studies the wage setting for outsourced workers. We study employmentmediated by temporary employment agencies (âtemp agenciesâ), where the workplace isat a user firm even though the temp agency serves as the formal employer. Temp agencywork is a facet of outsourcing and, more broadly, nonstandard work arrangements,which have been associated with lower wages and increased inequality (Weil, 2014).Specifically, we focus on firmsâ wage policies in the form of pay premia. The between-firm wage dispersion arising from pay premia constitutes a deviation from the lawof one price that would arise in spot labor markets. A long-standing hypothesisis that nonstandard work arrangementsâand specifically, outsourced, temp agencyworkâerode such pay premia by plausibly operating closer to a spot labor market orby lowering workersâ bargaining power.Our research design identifies pay premia through wage changes that accompanyworker moves across employers (Abowd et al., 1999b). We measure the degree towhich high-wage firms for regular work arrangements are also high-wage firms foroutsourced labor. Here, a view of temp workers treated as insiders in wage set-ting would predict a slope of one. By contrast, either the competitive spot labormarket benchmark or the treatment of temp workers as a separate class of workerswould predict a flat line. We find a reduced-form slope of 0.490 for temp agency workarrangements and quantitatively similar results when correcting for measurement error.Chapter 4 provides a novel quantification of the effects of outsourcing by usingan explicit identifier of outsourcing events encoded in Italian administrative data.This identifier provides an improvement relative to the existing literature along twodimensions. First, while existing estimates of the effect of outsourcing are confinedto specific occupations or sectors (e.g., Dube and Kaplan, 2010; Goldschmidt andSchmieder, 2017), we can precisely compute the effects of outsourcing across a widerange of jobs. Second, this identifier allows us to study the impact of outsourcingalong the extensive margin, an aspect typically not addressed in the current literature.The latter can be particularly relevant in contexts of high employment protectionâlikeItalyâwhere anecdotal and court evidence suggests that some firms use outsourcingto effectively terminate workers (Autor, 2003; Speziale et al., 2006; Fisac-CGIL, 2014).We find that outsourcing has large and persistent negative effects on earnings and2these losses are primarily driven by job loss. Five years after being outsourced, work-ersâ cumulative earnings losses map to around 13,500 Euros in present discountedvalue (PDV) terms, corresponding to about two-thirds of their pre-outsourcing annualearnings. These earnings losses are mainly driven by the extensive margin; ignoringthis margin, as much of the existing literature does, would lead us to underestimatethe earnings losses by a factor of four. These losses are also very heterogeneous. Out-sourced workers in non-routine manual jobsâsuch as cooks or beauticiansâhave thelargest earnings losses, while workers in jobs involving abstract, cognitive tasksâsuchas software developers or consultantsâexperience some earnings gains following theoutsourcing event.3Chapter 2Dinner at Your Door: HowDelivery Platforms Affect Workersand Firms2.1 IntroductionTechnological progress profoundly affects both businesses and their employees. Mostdirectly, innovation reshapes labor demand, displacing some jobs while creating newtasks and occupations (Autor et al., 2003; Acemoglu and Autor, 2011; Acemoglu et al.,2024). However, technology also reshapes demand for various goods and services, whichin turn affects the fortunes of firms and workers (Bakos, 1997; Dolfen et al., 2023).1Although the labor and product market impacts of technological change are commonlydiscussed separately, they often overlap. In the restaurant sector, online-deliveryplatforms such as Grubhub and DoorDash allow restaurants to expand the marketfor their products while potentially outsourcing part of their labor. Advocates of thegig economy argue that these platforms enable restaurants to grow their market andsimultaneously provide workers with flexibility while creating opportunities for low-income workers with limited outside options. Critics argue that these platform replace1Several industries illustrate how technology simultaneously affects labor and product markets.E-commerce has shifted the retail sector toward online operations, altering job composition andforcing some retailers to close (Chava et al., 2024). Streaming services have transformed mediaconsumption patterns (Aguiar and Waldfogel, 2018), while digital banking has reduced bank-tellerjobs but increased demand for software-development professionals (Jiang et al., 2021).4higher-wage, less insecure jobs with lower-wage, more insecure jobs.2 Understandinghow delivery platforms affect both in-house employees and gig workers is crucial forassessing the broader effects of these emerging technologies on welfare and inequality,as well as for informing regulators.Measuring the effects of delivery platforms is empirically challenging because it requiresinformation on both gig workers and the restaurants that adopt the technology, whichis often not available in standard datasets. Moreover, adopting a new technology is astrategic decision that may correlate with productivity or demand shocks at the firmlevel. This self-selection makes it difficult to identify the causal effect of online-deliveryplatforms in settings in which the technology is widespread.This paper estimates the impact of delivery platforms on in-house restaurant employeesand gig workers in Brazil. Using a novel data set linking adopting restaurants andplatform-workers to administrative employer-employee data, I exploit the staggeredrollout of online-delivery platforms across regions to measure the causal effects ofplatform adoption. To account for informal workers, I supplement the data withhousehold surveys. The empirical strategy matches adopting restaurantsâand theirworkersâin regions with platform access to those in regions without it. The resultsshow that adopting restaurants substitute in-house non-cooking-staff hours with out-sourced platform-worker hours, but the earnings and employment effects on restaurantworkers are only modestly negative, as many displaced workers reallocate to newformal sector jobs. Additionally, delivery platforms have spillover effects: They in-crease the likelihood that non-adopting restaurants will shrink or close. However, thenet earnings gains for gig workers more than offset the losses for in-house restaurantworkers. These effects not only affect total earnings but potentially shift the incomedistribution, as most gig workers were previously employed informally or unemployed.Brazil is a particularly suitable setting to study who wins and loses from online-deliveryplatforms. The restaurant industry accounts for about 4 percent of formal employment,offering a chance to assess the impact of these platforms on a large portion of thelabor market. Additionally, the country has a sizable gig economy, with 1.5 millionpeople working through digital platforms and more than 600,000 of them workingon online-delivery platforms (IBGE, 2022). Brazilâs high levels of informality and2For arguments in favor of the gig economy, see, for example, this article in The Economist, October2018. For arguments against the gig economy, see, for example, this article in The New York Times,January 2024.5unemployment create conditions in which delivery platforms may offer opportunitiesto workers with limited alternatives (Ulyssea, 2020).I start by building a stylized model that illustrates the potential effects of online-delivery platforms on the labor demand of restaurants. The model highlights twochannels through which adoption of the technology influences demand for in-houseworkers. First, there is an outsourcing effect, in which the platform increases theproductivity of delivery workers relative to substitutable in-house workers, such asservers, similarly to a skill-biased technological change. Beyond the impact on themarginal rate of transformation between servers and delivery drivers, the platformcreates a product-demand effect by reducing search costs, which can increase overalldemand for restaurant meals and, in turn, boost restaurant employment.3 I proposea microfoundation of the product-demand effect through a consumer search modeland show that restaurants in less dense areas (or suburbs) are more likely to benefitfrom the platformâs expanded customer base. The overall impact of the platformon in-house employment and earnings is thus ambiguous, depending on the relativestrength of these two forces across different locations. Importantly, the shift in theproduct-demand curve not only affects adopters but can have spillover effects onnearby non-adopting restaurants by crowding out demand for their goods.Next, I study empirically the effect of adopting online-delivery platforms on thenumber of in-house workers, wages, and workforce composition. I first document thatrestaurants that adopt a delivery platform are larger and pay higher wages to theirworkers, as measured by their AKM firm fixed effects (Abowd et al., 1999b). I usea matched difference-in-differences design that compares restaurants that adopt theplatform to observationally similar restaurants located in regions where no platformis available at the time, thus mitigating concerns about self-selection into adoption.The identifying assumption is that matched treated and control restaurants wouldhave exhibited similar trends in labor demand in the absence of platform adoption.Adopting restaurants reduce the number of in-house workers by 6 percent a year afteradoption. This decline is fully explained by non-cooking-staff hours being substitutedone to one for outsourced platform-driver hours. In contrast, the number of cooksremains stable after adoption, suggesting that the outsourcing effect dominates the3In an extension of the model, I show how this framework maps onto a model of selection into theplatform similar to the model of Bilal and Lhuillier (2022), in which high-paying firms are morelikely to outsource workers to reduce costs.6product-demand effect for the average adopting restaurant.The effects of platform adoption on labor demand for in-house workers are heteroge-neous across the wage setting of firms and regions. I find that higher-paying restaurantsreduce their in-house workforce by a greater margin than lower-paying restaurants(8 percent vs. 4 percent) a year after adopting the platform. The difference is againdriven by non-cooking-staff that are replaced by outsourced workers, while cooks inboth groups of adopting restaurants are unaffected. In line with models of selectioninto outsourcing, the estimates are consistent with the outsourcing effect being strongerfor high-paying restaurants (Bilal and Lhuillier, 2022), while there is no evidence of adifferential product-demand effect between high- and low-paying restaurants.Does this mean the product-demand effect is irrelevant for all restaurants? I answerthis question by studying heterogeneous effects of adoption on restaurants locatedin areas with high and low restaurant density. Establishments in low-density areastypically experience less walk-in traffic and therefore face limited reductions in in-housedemand but stand to gain significantly from the platformâs expanded customer base.Supporting this hypothesis, I find that restaurants in low-density regions expand theirtotal workforce by 6 percent more than those in high-density areas. This workforcegrowth in low-density locations is primarily due to an increase in cooks, while thenumber of non-cooking- staff remains unchanged. Together, these findings highlightthe importance of the product-demand effect for restaurants in less central locations(Overby and Forman, 2015; Kitchens et al., 2018; Couture et al., 2021).Online-delivery platforms might affect not only restaurants that adopt these tech-nologies but can also influence the demand of non-adopting restaurants. To examinethe indirect effects of the platform on the labor demand of non-adopters, I focus onsudden, large increases in the share of restaurants using delivery platforms withina one-kilometer radius of non-adopters. In line with the idea that adopting estab-lishments crowd out non-adopting restaurantsâ demand (Chava et al., 2024), I findevidence that these platforms also affect restaurants that do not use these technologies.Restaurants that do not adopt but are located near a significant share of adopters are5 percentage points more likely to close a year after the shock. Importantly, theserestaurants are not on a stronger downward trend than adopters, indicating thatpreexisting conditions are unlikely to explain the effects.I then leverage the granularity of my data to study the effect these platforms haveon employment and earnings of workers in the restaurant sector. Following the job-7displacement and outsourcing literature (Goldschmidt and Schmieder, 2017; Jacobsonet al., 1993; Bertheau et al., 2023), I define a worker as treated if their employeradopts an online-delivery platform and match them to a control restaurant worker ina location where no platform is available. I find that treated workers suffer a modestearnings and employment loss of only 1.5 percent a year after their employer adoptsthe platform. However, treated workers find a new job at a quick pace: 75 percent oftreated workers find a new job by the fifth quarter after their former employer adoptsa delivery platform. Remarkably, these new jobs are also in the formal restaurantsector and not in the gig economy.In contrast, workers at non-adopting restaurants experience a greater (unconditional)earnings loss compared to those at adopting restaurants. One year after a largeshare of nearby restaurants adopt the platform, earnings for workers at non-adoptingrestaurants decrease by 6.6 percent. The impact is larger because the risk of displace-ment is higher, as many non-adopting restaurants close. As a result, these workersâlikelihood of remaining employed drops more sharply: One year after the shock, theyare 3.8 percentage points less likely to be employed than control-group workers.The reduced-form estimates indicate that online-delivery platforms have a persistentnegative impact on the earnings of in-house workers at adopting restaurants andnegative spillovers on workers at non-adopting restaurants. However, the earningslosses might not tell the full story. It is possible that the earnings gained by platformworkers outweigh the earnings lost by in-house workers. To assess the overall impactof online-delivery platforms on workersâ earnings, I compute the present-value netgains (or losses) per adopting restaurant for in-house workers at adopting restaurants,workers affected by spillovers, and platform workers. For restaurant workers, I alsoconsider the impact on those hired informally (Ulyssea, 2018), imputing the numberof informal workers per formal restaurant using the Brazilian Census and householdsurvey (PNAD-C). I find that in-house workers at adopting restaurants and workersat restaurants affected by spillovers lose earnings equivalent to 7.3 and 17.1 percent ofthe pre-platform wage bill of adopting restaurants, respectively.Turning to the potential gains for gig workers, I compare their platform earnings (netof maintenance and transportation costs) to their outside option. I separate workersinto two groups: those employed in the formal sector when joining the platform (22percent) and those without formal employment when starting (78 percent). For thefirst group, using their formal sector wage as the outside option, I find a gain equivalent8to 2.1 percent of the pre-platform wage bill of adopting restaurants. The second group,without prior formal employment, presents a greater challenge, as their outside optionis not directly observable. I propose different scenarios for their outside options and,using my best estimateâwhich is a regression-adjusted earnings estimate from thePNAD-Câfind that their net earnings gain per adopting restaurant is 26.9 percentof the pre-platform wage bill. Summing the gains and losses across all groups, thetotal wage effect per adopting restaurant is positive and represents 6.8 percent of thepre-platform wage bill.While earnings are a key part of worker utility, nonwage characteristics of thesejobsâsuch as job security, flexibility, and health risksâcan also significantly influenceworker welfare. If gig work provides valued flexibility, the benefits to workers mayextend beyond wages alone. Conversely, if these jobs entail higher health risks or lacksecurity, they may be less desirable than other employment options. To account forthese nonwage factors, I use job-to-job transitions (Sorkin, 2018) to rank how workersvalue gig work compared to other restaurant jobs. This method captures the broaderquality of employers by considering both monetary and nonmonetary job attributes.My findings reveal that app-based jobs rank in the 90th percentile among restaurantjobs in Brazil, suggesting that the welfare effects of delivery platforms may extendwell beyond earnings, potentially because of these platformsâ flexibility.Although my analysis focuses on the impact of incumbent firmsâ adoption of online-delivery platforms, these platforms may also influence the entry of new firms. Deliveryplatforms can enable businesses to offer goods with less capital requirements , therebyincreasing the likelihood of new firm entry. However, they may also intensify compe-tition, potentially discouraging smaller, less efficient firms from opening. I use datacollected from the National Registry of Legal Entities (CNPJ) combined with anevent study at the microregion level to examine the effect of online-delivery platformavailability on firm entry. I find that restaurants are on average 10.5 percent morelikely to open following the platformâs entry in a given microregion. This suggeststhat focusing solely on incumbent restaurants represents a lower bound on the overallimpact of these platforms on workers.This paper contributes to several strands of the literature. First, it integrates researchon online-delivery platforms into the broader literature on technology adoption. Partof this strand focuses on the impact of technology in labor markets, particularlythe substitutability between workers and technology and the shifts in worker tasks9(Autor et al., 2003; Acemoglu and Autor, 2011).4 Firms play a crucial role in thediffusion of technologies, making the analysis of firm-level adoption particularlyrelevant (Corn, 1957; Mokyr, 2003; Bloom et al., 2016).5 Another strand examineshow technology can influence product demand by enabling firms to reach new marketsand alter consumption patterns (Bakos, 1997; Dolfen et al., 2023).6 Typically, thesetwo dimensions of technological impactâlabor and product demandâare studiedindependently. This paper bridges the gap by proposing a simple framework thatdecomposes the effects of technology adoption on labor demand into a product-marketchannel and a labor-replacement channel. It then offers empirical evidence of how atechnology that influences both margins, online-delivery platforms, affects workers.This paper also relates to the growing literature on the rapid growth of alternativework arrangements (Katz and Krueger, 2019b) and, more specifically, the gig economy(Harris and Krueger, 2015; Garin et al., 2023, 2024).7 Previous research has examinedthe impact of ride-sharing apps on self-employed workers (Berger et al., 2018; Abrahamet al., 2024). This paper extends the literature by exploring the effects of online-delivery platforms on workers in both standard (employee) jobs and gig roles. Unlikeride-sharing platforms, which compete directly with self-employed workers, online-delivery platforms are adopted by establishments, allowing them to outsource laborwhile also influencing product demand.8 By matching data from a major gig companywith administrative employer-employee records, I am able to track workers and firmsover time, overcoming limitations often associated with survey or tax data (Garin4Acemoglu et al. (2024) synthesize the literature and presents a framework showing how technologicalchange can affect labor through changes in productivity, automation, or the creation of new tasks.5A growing literature has focused on firm-level technological changes to study skill demand (Boler,2015; Aghion et al., 2019). Using a framework closer to mine, Lindner et al. (2022) show thatskill-biased technological adoption changes both the skill ratio and premium.6Studying eBay, Dinerstein et al. (2018) show that search-ranking algorithms play a significant role inreducing consumer search frictions. Fang et al. (2024) find that search tools in e-commerce increasecustomer orders. More closely related to the setting in this paper, Zheng et al. (2023) show that aquery recommender system on online-delivery platforms increases the probability that customersplace a food order.7In the US, the gig economy has been found to smooth consumption (Koustas, 2018) and alleviatethe negative effects of job displacement (Jackson, 2022).8Delivery platforms represent a form of domestic outsourcing that has not been studied extensively.Most micro-level evidence has focused on outsourcing events in which workers transfer from anoutsourcing firm to a firm offering outsourced services (Goldschmidt and Schmieder, 2017; Daruichet al., 2024) or to temporary work agencies and contract firms (Deibler, 2023; Drenik et al., 2023;Felix and Wong, 2024). Because of data limitations, there is limited evidence on the substitutabilitybetween outsourced and in-house workers in core occupations. This paper overcomes this challengeby observing both in-house and outsourced workers following the adoption of the technology.10et al., 2024). This paper also extends the literature by studying the gig economy in amiddle-income country, where gig jobs may serve as a safety net because high levelsof informality and unemployment are high (Ulyssea, 2018). Notably, I find that themain benefits of online-delivery platforms accrue to workers who did not hold formaljobs when joining the platform. This may contrast with developed countries, wherenonwage benefits, such as flexibility, are the main driver of value for workers (Chenet al., 2019, 2020; Angrist et al., 2021).2.2 Theoretical FrameworkThis section presents a theoretical framework that illustrates the different mechanismsthrough which online-delivery platforms can affect the labor demand of restaurants.The model is a wage posting model following Card et al. (2018a) with three laborfactors: cooks (C), waiters (W ) and delivery (D). Workers are defined by theiroccupation rather than their skill, reflecting the relatively homogeneous workforce inthe restaurant sector; this approach contrasts with recent task-based models, wheredifferent skill groups can perform multiple tasks (Acemoglu et al., 2024). The focus ofthis model is to illustrate in a simplified framework how the adoption of online-deliveryplatforms can change the demand for in-house labor by enabling firms to outsourcepart of their workforce while potentially shifting the demand for their products. 92.2.1 Model SetupThe model features heterogeneous workers defined by their occupations. Cooks andwaiters vary in their preferences for firmsâ non-wage attribute. Worker heterogeneitygives rise to imperfect competition in the labor market. Restaurants faces an upwardlabor supply curve for cooks and waiters which yields a monopsonistic labor marketfor these occupations. Unlike a competitive market where firms take wages as given,restaurants post wages for cooks and waiters and hire any worker willing to work at9This approach more closely resembles models of skill-biased technological change (Acemoglu, 2002;Lindner et al., 2022), where new technology augments one skill. In this case, the technology isfactor-augmenting for an occupation rather than a skill.11that wage. The labor supply of cooks and waiters is given by:ln(Cj) = λjC + β ln(wC) (2.1)ln(Wj) = λjW + β ln(wW ) (2.2)Where λj{C,W} represents the firm specific amenities, common to all workers in theoccupation. For simplicity, I assume that the labor supply elasticity (β) is commonto both types of workers.10 In contrast, delivery workers are hired in a competitivemarket.Waiters and delivery drivers compose what I define as service workers (S). Cooks andservice workers are complements in a production function that has constant returnsto scale. Waiters an delivery drivers are related through a constant elasticity ofsubstitution function (CES) with elasticity of substitution Ï. The relative productivityof waiters and delivery drivers is governed by the parameter θ. Motivated by thefact that the presence of delivery workers prior to delivery platforms was very limited(as discussed in Section 2.4.2), these workers are assumed to have a productivityclose to 0 in the pre-platform period. Therefore, restaurants essentially do not hirethem previous to adopting a platform service. As described later in this section, theintroduction of delivery platforms increases the productivity of the delivery workforcerelative to waiters, implying a reduction in θ.Specifically, the production function is given by:Yj = TjCαj S1âÎ±j (2.3)S = [θW Ï + (1â Î¸)DÏ] 1Ï (2.4)Where the elasticity of substitution is equal to Ï = (1 â Ï)â1 and Tj reflects theHicks netural firm specific productivity term. Restaurants compete in a competitivemonopolistic market. Each firm faces a negatively sloped demand curve for the goodthey produce with product demand elasticity of ϵ > 1. The demand curve containsa firm (and location) specific product demand shifter P0j, that allows for firms toset different prices for the same quantities. The product demand function takes the10This assumption implies that the difference between the quantities demanded (and effectivelyhired) of both types of labor will be a function of the output elasticities, amenities, and relativeproductivities of each type of labor. None of the derivations depend on this assumption.12following form:Pjl = P0jlY â1Ïµ (2.5)Firms Profit Maximization Problem Restaurants must choose between betweenthe three types of labor. Within a market l restaurants post wages for waiters and cooksand choose the number of delivery workers solving the following profit maximizationproblem:maxwW ,wC ,DPjlYj â wWW â wCC â wDDsubject to (2.1)-(2.5). The first order conditions yield the following wage equationsfor in-house cooks and waiters (disregarding the j notation for simplicity):wW =β1 + β︸ ︷︷ ï¸¸MarkdownÏµâ 1ϵVS θ (1â Î±)(WS)Ïâ1ï¸¸ ︷︷ ︸MRPL(2.6)wC =β1 + Î²Ïµâ 1ϵVC α (2.7)Where VS and VC are the revenue R per service worker S and per cook C, respectively(RS, RC), and wC , wW are the wages of cooks and waiters. The wage equation illustratesthe typical monopsonistic setting (Manning, 2011), where the markdown is a functionof the labor supply elasticity and the marginal revenue product of labor is a functionof the output elasticity and the relative productivities of each type of labor.2.2.2 Impact of online-delivery platformsOutsourcing Effect Under this setting, the introduction of online-delivery platformswill have two direct effects. First delivery platforms increase the relative productivityof delivery workers respect to waitersâa decrease in θ. This effect mimics closelythe impact that skill-biased technology has on the production function (Lindneret al., 2022), by changing the marginal rate of transformation (MRT) of one typeof occupation respect to the other. The increase in the productivity of deliveryworkers represents the increasing returns to scale that online-delivery platforms have13by specializing in these services.11 I call this the outsourcing effect.Product Demand Effect The introduction of online-delivery platforms also im-pacts the demand for goods produced by the restaurant (P0jl). On the one hand,the technology may enable restaurants to access new markets that were previouslyinaccessible. However, delivery platforms may also substitute or crowd out in-housedining (in favor of delivery) in markets already accessible to the restaurant, having anet zeroâor potentially negativeâimpact on the total demand for the restaurantâsgoods. Ultimately, the effect on product demand will depend on several factors, such asthe location of the firm and the baseline competition it faces. For example, restaurantsin low-density areas typically have less walk-in traffic (Leonardi and Moretti, 2023)and therefore face limited losses in in-house demand but stand to gain significantlyfrom the platformâs expanded customer base. I call this potential shift in demand theproduct demand effect.12Spillovers The product demand effect can also have spillovers on restaurants that donot adopt the technology. Empirical evidence suggests that the adoption of e-commercecrowds out demand for brick-and-mortar stores in the retail sector (Chava et al., 2024).Similarly, the adoption of online-delivery platforms may crowd out demand for nearbynon-adopting restaurants. Intuitively, if delivery platforms increase the visibility ofenrolled restaurants, making it more likely for customers to order food online insteadof dining in, then firms previously benefiting from agglomeration externalities mayexperience a reduction in demand. Appendix A.3.3 provides a microfoundation of theproduct demand effect through a consumer search model to illustrate how firm locationand platform adoption can influence demand for goods produced by restaurants.2.2.3 Comparative StaticsThe changes in labor demand for in-house workers after a restaurant adopts an online-delivery platform depend on the relative magnitude of the product demand effect and11A microfoundation of the comparative advantages of firms that provide outsourcing services can befound in Bilal and Lhuillier (2022).12One could also consider a model with two types of goods produced by restaurants: in-house anddelivery goods. In such a model, online-delivery platforms could increase demand for delivery goodsat the expense of in-house goods, depending on the level of substitutability between the two typesof products. In Section 2.6.3, I provide evidence that this model is unlikely to hold.14the outsourcing effect. Specifically, by log-linearizing equation (2.6) with and withoutthe adoption of the technology, and comparing the two, one arrives at the followingexpression for the change in labor demand for waiters:â ln(W ) = Î²ÏÎ² + Ï[â ln(VS)ï¸¸ ︷︷ ï¸¸â Revenue p/Service Worker+ 1Ïâ ln(S)ï¸¸ ︷︷ ï¸¸â Service SectorSizeï¸¸ ︷︷ ︸Product demand effect+ â ln(θ)︸ ︷︷ ï¸¸â relative productivitywaitersï¸¸ ︷︷ ︸Outsourcing Effect](2.8)The effect on product demand can be decomposed into the change in revenue perservice worker and an additional term that reflects the relative change in the servicesector size after adopting the platform. This second term is scaled by the level ofsubstitutability between waiters and delivery drivers.13 Specifically, if waiters anddelivery drivers are complements (Ï â 0), an increase in the number of delivery driversshould lead to an increase in the number of waiters hired by the firm. In contrast, asÏ ââ, waiters do not benefit from the overall increase in delivery drivers, makingthis second term irrelevant. Taken together, equation (2.8) provides guidance on theforces behind the impact of delivery platform adoption on the demand for in-housewaiters. On the one hand, the outsourcing effect will most certainly reduce demandfor waiters. On the other, the product demand effect is uncertain, and its impact onin-house labor will depend on certain characteristics of the adopters, such as theirlocation.The model presented so far assumes a constant outsourcing effect across all adoptingrestaurants. A limitation of this assumption is that it does not account for thepossibility that high-paying restaurants may selectively opt for outsourcingâanoutcome highlighted in both the empirical and theoretical outsourcing literature(Goldschmidt and Schmieder, 2017; Drenik et al., 2023; Bilal and Lhuillier, 2022). InAppendix Section A.3.2, I extend the model to allow for different tasks within theservice sector and show that this framework enables a similar decomposition of theproduct effect and outsourcing effect while allowing high-paying establishments toselect into outsourcing.The impact of the adoption of delivery platforms on the number of in-house cooks13The change in the service sector is analogous to the scale effect in Hicksâ law of labor demand ifthe technology did not affect product demand. However, because P0jl is also impacted, the changein S also reflects the change in product demand.15hired is expressed as follows:â ln(C) = β â ln(VC)ï¸¸ ︷︷ ï¸¸â Revenue p/Cook(2.9)The change in the number of in-house cooks will depend on the change in revenue percook at the firm. Because the output elasticity of cooks is unaffected by the adoptionof the platform (i.e., cooks are equally complementary to waiters and delivery drivers),the change in the number of cooks will be solely determined by the change in theirmarginal productivity.142.3 Settingonline-delivery platforms operate as intermediaries in a contractual relationship be-tween restaurants, consumers, and workers. Figure 2.1 illustrates this relationship:consumers order a product from a restaurant through the app, which then coordinatesthe delivery by assigning the task to a worker. Under this arrangement, the restau-rant is responsible for producing the good, while the worker typically operates as anindependent contractor. When a consumer places an order, the app offers the task toworkers who are logged on at the time and are near the restaurant. The first workerto accept the task receives the assignment and earns a fee based on factors such asthe distance covered, time spent, and any additional bonuses available.To work for an online-delivery service in Brazil, individuals must pass a backgroundcheck and own a smartphone. Deliveries can be made by bike, motorbike, or car.15According to Brazilian law, these workers are classified as independent contractors,meaning they are not entitled to social security contributions from the company.Consequently, they do not receive benefits such as maternity leave or paid vacations,and they are not subject to the federal minimum wage, which only applies to formalemployees. online-delivery workers are required to pay income taxes on their earnings14Importantly, if online-delivery platforms retain all the additional rents generated by adoption, evenif demand increases, the revenue per cook may not change. In other words, the revenue per cookreflects the gains in the marginal revenue productivity of cooks net of the platform costs.15The majority of deliveries are made by motorbikes. In my sample, 81 percent of workers and 87percent of deliveries use motorbikes. Bicycles account for 11 percent of deliveries (16 percent ofworkers), while cars represent 2 percent of deliveries and 3 percent of workers.16if they exceed a threshold that applies to all self-employed individuals (in 2023, thisthreshold was roughly equivalent to two minimum wages). Workers earning above thisthreshold have 10 percent of their income subject to taxation.16For the context of this paper, I focus on a large online-delivery company operating inBrazil. Since it began operations, the platform has enrolled over 100,000 establishments.It operates in all major cities and holds a significant market share. The companyprovides two main services to businesses: (1) delivery services, where consumers orderthrough the app and the company coordinates the delivery; and (2) marketplaceservices, where consumers order through the app, but the establishment handles thedelivery. Fees are charged for both services, though they vary depending on the specificservice provided.17 Restaurants must provide a tax identifier and, therefore, must beformally registered with the government to offer services through the platform.2.4 Data2.4.1 Delivery Platform DataThe data comes from a major online-delivery company in Brazil. It includes detailedinformation on establishments enrolled in the app, delivery drivers working through theservice, and orders placed between 2018 and 2021. Crucially, both the establishmentand worker data contain unique identifiers, allowing me to link them to Brazilâsadministrative employer-employee database (RAIS). The establishment panel datacovers businesses offering delivery through the app. Specifically, it provides monthlyinformation on the number of hours workers spent on deliveries for each business andwhether more than 50 percent of the businessâs revenue on the platform came fromdeliveries.The worker panel data includes monthly details on each driver, such as the numberof deliveries completed, hours worked and logged in, distance traveled, and earnings16This is in contrast with ridesharing workers, 60 percent of whose earnings are taxable. For moredetails, see: this article.17The company also employs two types of delivery drivers: (1) independent contractors, who are paidper delivery and make up the majority of the workforce; and (2) drivers affiliated with logisticscompanies, who are formally employed by an intermediary firm and receive a wage independentof their deliveries. These workers typically work for firms that collaborate with several deliveryservices and represent a minority of the workforce. For the purposes of this paper, I focus only onindependent contractors.17(broken down by tips, base earnings, and bonuses). It also tracks the primarymunicipality where the driver operated, the main mode of transportation used (i.e.,bike, motorbike, or car), and identifies workers by their contract with the company.Descriptive Statistics: Platform Workers Table A.2 summarizes the maincharacteristics and performance of platform-workers in my sample. Column (1)provides a snapshot of the characteristics of platform-workers in the first quarterthey worked on the delivery platform.18 platform-workers are on average 29 yearsold, predominantly non-Black and male. Those who are formally employed whileworking on the platform have close to two years of tenure in their formal job, workfull-time, and earn average monthly wages of approximately 1,500 BRL (1.57 timesthe minimum wage in 2018). Columns (2) and (3) show the platform performance fordrivers who, in the quarter prior to starting on the platform, held a formal job (formalplatform workers) and for workers who did not hold a formal job prior to workingon the platform (non-formal app workers), respectively. On average, non-formal appworkers earn 13 percent more than formal app workers per month on the platform (646BRL-2018 vs. 571 BRL-2018). This is explained by non-formal workers working morehours (35 hours vs. 30 hours), spending more time logged on the platform (76 hoursvs. 62 hours), completing more deliveries (92 vs. 78), and covering more distance (480kilometers vs. 426 kilometers).2.4.2 Employer-Employee Data: RAISThe other main data source is the administrative employer-employee dataset fromBrazil, called Relação Anual de Informações Sociais (RAIS).19 Employers are requiredto submit yearly reports to the federal government detailing all formal job contractsestablished in the previous year.20 This information is used to calculate variousbenefits for both workers and firms. Compliance with this reporting requirement ishigh, as incomplete submissions result in substantial penalties. This dataset allowsresearchers to follow workers over time and includes the universe of formal workers and18Demographic characteristics are only available for workers who at some point in their career held aformal job.19This data has been used in several contexts, such as studying the impact of trade on labor marketconcentration (Felix, 2022), the impact of the minimum wage on inequality (Edgel et al., 2023),and racial pay differences (Gerard et al., 2021), among others.20Formal employment represented 62 percent of all employment in Brazil in 2023.18their employersâestablishmentsâfrom 2003 to 2021. The data contains informationon the worker, such as average monthly wages, hours worked, occupation, age, gender,education, and race. Importantly for the restaurant sector, wages in RAIS also includetips. Each worker is identified by a unique tax identifier: Cadastro de Pessoas Físicas(CPF). On the employer side, the data includes information on the industry of theemployer and the location of the establishment. Each establishment is identifiedthrough a unique tax identifier: Cadastro Nacional de Pessoas Jurídicas (CNPJ). Formy analysis, I deflate wages to 2018 Reais. I restrict the sample to employment spellswhere the worker was employed for at least two months and earned at least 20 Reaisper month (approximately 5 USD at the time).21Descriptive Statistics: RAIS Column (1) of Table A.1 reports summary statisticsfor restaurant workers in RAIS during 2018.22 The average formal restaurant workerin RAIS earned 1,574 BRL in 2018, significantly above the minimum wage of 954BRL-2018.23 Restaurant workers are older than platform-workers (35 years vs. 30)and are more likely to be female (56 percent vs. 8 percent). In terms of occupations,waiters and cooks represent 77 percent of all restaurant workers, while delivery driversrepresented only 2 percent of the restaurant sector workforce during the same period.Columns (2) and (3) of Table A.1 show the same statistics for the 2010 BrazilianCensus. A key advantage of the census is that it allows observation of informal workersas well. Informal workers in the restaurant sector earn 37 percent less than formalworkers, which could be partially driven by differences in occupations. Specifically,informal workers are more likely to be waiters and less likely to be cooks than formalworkers in the restaurant sector. Importantly, the delivery sector was essentiallynon-existent in the restaurant sector in 2010 (1 percent of the workforce for bothformal and informal workers).21The vast majority of workers earn above the minimum wage (see Figure A.4). However, it ispossible that some may earn less due to low hours, suspensions, or leaves.22Restaurants are defined as establishments with the two-digit Classificação Nacional de AtividadesEconómicas (CNAE) code of 56, which includes âRestaurants and other food and beverage servicesâand âCatering services, buffet, and other prepared food services.â23This, along with the fact that restaurants in Brazil predominantly employ full-time workers,contrasts with other countries, such as the US, where part-time work and tips are key componentsof restaurant sector employment.192.5 Research DesignThis section discusses the research design to estimate the causal effect of online-deliveryplatforms on restaurantsâ labor demand and workersâ labor market outcomes. Section2.5.1 presents the research design to study the impact of online-delivery platforms onestablishments that enroll on the platform. Section 2.5.2 outlines the research design toexamine the spillover effects of competitorsâ adoption of the platform. Finally, Section2.5.3 presents the empirical strategy to study the impact of employer enrollment inthe platform on the labor market outcomes of restaurant workers.2.5.1 Research Design: Adopting EstablishmentsThe ideal experiment to estimate the causal effect of platform adoption on restaurantswould involve random assignment of delivery services across establishments. However,this is not feasible in practice. Instead, I leverage the staggered rollout of the platformacross Brazil.24 Comparing restaurants across regions presents a trade-off. On theone hand, the staggered rollout provides quasi-experimental variation in platformavailability, preventing establishments from self-selecting into the control group. Ad-ditionally, if delivery apps create spillovers for non-adopting restaurants, comparingestablishments within the same region could violate the Stable Unit Treatment ValueAssumption (SUTVA). On the other hand, comparing across regions may introduceconfounding factors due to differences in the characteristics of establishments and theirmarkets. To address this, I apply a matching algorithm to create a balanced sampleof adopting restaurants and control establishments that did not have the option toadopt the platform.Matching I start by considering the pool of treated restaurants that, at the timeof adopting the platform, generated more than 50 percent of their revenue through24Figure A.1 shows the staggered rollout of the platform in Brazil. The platform began its operationsin the largest cities (primarily in the Southeast region of Brazil). As the platform grew in popularityover the following years, it expanded its operations to other regions of the country without a cleargeographic pattern. To operate in a microregion, the platform must fulfill two requirements: (i) itmust have a set of client restaurants willing to use the service, and (ii) it must have a sufficientlylarge driver force to provide service during all business hours. Both requirements take time to meetand can explain why the platform did not extend operations across the entire country at once.20the platform using delivery services.25 At the time of enrollment, treated restaurantsmust be located in microregions where the platform holds more than 50 percent of themarket share in the online-delivery industry. To ensure a balanced sample, I furtherlimit the sample to restaurants that had been open for at least two years before thequarter prior to enrolling on the platform and were treated at least five quarters beforethe end of my sample period.To build an appropriate comparison group, I use a matching procedure.26 The potentialcontrol group of establishments includes restaurants located in labor markets whereno online-delivery platform technology was available up to the time of treatment, andwhere the platform did not enter for at least five quarters after treatment.27 Eachtreated restaurant is matched to a control restaurant within the same cell the quarterprior to treatment. A control restaurant belongs to the same cell as firm j treated inquarter t if it has been open for at least two years and belongs to the same quartile offirm size and average wages among restaurants in the year. Additionally, to ensurethat treated and control restaurants face similar competition, both must fall eitherabove or below the median share of restaurants within a 1-kilometer radius relative tothe total number of restaurants in their microregion. Within each cell, treated andcontrol restaurants are paired based on their propensity score, which is estimatedusing a logit model that predicts the probability of being treated based on log firmsize in quarters [tâ 8, tâ 1], log average earnings in quarters [tâ 4, tâ 1], firm age,share of waiters, average tenure, age of workers, share of female workers, and averagehours of workers.Given the high volatility of the restaurant sector (Parsa et al., 2011, 2021), matchingon lagged size and earnings captures control restaurants that are following similartrends as the treated establishments. However, matching on lagged outcomes may raiseconcerns regarding mean reversion post-treatment. Therefore, I do not match earningsin three quarters of the pre-period and show that treated and control restaurants havesimilar trends even in these non-targeted periods.25The restaurants that meet this criterion represent 36 percent of all establishments that enrolled onthe app between 2018 and 2021.26Similar empirical strategies have been used to study worker substitutability (Jäger and Heining,2022), the effects of mergers and acquisitions (Arnold, 2019), and the effects of outsourcing(Goldschmidt and Schmieder, 2017; Daruich et al., 2024), among others.27By only considering firms that are never treated during the entire period as control groups, I ensurethat I donât include "forbidden controls" (Borusyak et al., 2024).21Econometric Framework I estimate the causal effect of restaurant enrollment onthe labor demand of establishment j by estimating the following event-study modelon the matched sample of treated and control establishments:Y{j,i}t = Î²0+Î±{j,i}+Î´t+k=5âk=â7Î¸k1{t = tâ(j)+k}+k=5âk=â7Î²k1{t = tâ(j)+k}ÃTreated{j,i}+Ïµ{j,i}t(2.10)Here, Yjt represents the outcome of interest (e.g., average wages, establishment size) forrestaurant j at time t; αj denotes establishment fixed effects, and δt denotes quarter-year fixed effects. Treatedj is a dummy variable indicating whether establishment jenrolls on the platform, while tâ(j) indicates the date on which the restaurant firstenrolled in the platform.28 1{t = tâ(j) + k} represents the event-study indicators thatreference the time relative to the treatment date. The main coefficient of interest is βk,which, under the parallel trends assumption, captures the causal effect of establishmentenrollment on the platform on the outcome of interest. The event-study indicatorsare normalized relative to Î²â2. Therefore, the coefficients βk for k â¥ 0 capture theeffect of the restaurant enrolling on the platform on outcome Yjt, k quarters after theenrollment relative to two quarters prior to enrollment. Standard errors are clusteredat the establishment level.Identification The key identifying assumption of this model is that, in the absenceof treatment, the differences in the outcomes of interest between treated and controlestablishments would have remained constant (the parallel trend assumption). Thisassumption may be strong, as the decision to enroll on the platform is strategicand could correlate with both potential outcomes and past trends.29 To address this,treated restaurants are matched with control restaurants located in microregions wherethe platform is unavailable. By comparing restaurants in areas with and withoutplatform access, I ensure that control restaurants are not self-selecting into treatmentstatus. Under this framework, the parallel trend assumption can be divided into twoparts: (i) treated and control restaurants share the same trends, conditional on the28In the matched sample, control restaurants are assigned the treatment date of their matched treatedpair.29For example, restaurants may decide to enroll if they face a negative demand shock. In this case,wages or employment could decline at enrolling restaurants even without the platform, leading to adownward bias in the estimates. Conversely, if firms enroll after a positive shock that enables themto expand, the estimates would be biased upwards.22observables used for matching, and (ii) common trends affecting all restaurants aresimilar across treatment and control regions.To enhance the likelihood of (i), I leverage the detailed nature of my data. First,restaurants are matched based on their average wages, size, composition, and thenumber of restaurants in surrounding areas. Second, I compare the outcomes fortreated and control restaurants in the quarters prior to the treated establishmentsâadoption of the platform, as shown in Figure 2.4. Both earnings and size follow similartrends before the treated establishments adopt the platform, even for periods notdirectly targeted in the matching process (e.g., average wages between quarters -7 and-4).Regarding (ii), a potential concern is that enrollment by treated restaurants couldincentivize competitors in the same market to adopt the platform. If competitoradoption generates spillover effects, the estimates could reflect both direct and indirecteffects. However, Figure A.2 shows no discrete increase in the likelihood of nearbyrestaurants (within 1 km) adopting the platform after a treated firm adopts theplatform. Another concern is that different microregions could be exposed to varyingshocks over time, especially during the COVID-19 pandemic in 2020 and 2021. Toaddress this, I present results for a set of restaurants that were treated at the beginningof 2019 and therefore were not affected by the pandemic. Additionally, I presentresults controlling for state-date fixed effects (which absorb state-specific COVIDpolicies) and demonstrate that the individual-level treatment effects are insensitive tothe number of COVID cases reported in the municipality of the treated establishment(Chauvin, 2021).2.5.2 Research Design: SpilloversTo capture the spillover effects, I define a set of firms affected by a sudden, largeincrease in the number of nearby restaurants that enroll on the platform (spilloverevent). First, I restrict potential restaurants exposed to these events to: (i) restaurantsthat, at the time of the event, had never adopted the platform before; (ii) restaurantsthat had at least five other restaurants within a 1 kilometer radius; and (iii) restaurantsthat did not belong to a multi-establishment firm where at least one establishmenthad enrolled on the platform in another location. Restriction (i) is intended to capture23non-adopting restaurants at the moment of treatment.30 Restriction (ii) ensures thattreated restaurants are exposed to a minimum level of competition, while restriction(iii) excludes firms that may be affected by within-firm spillovers generated by otheradopting establishments.For each potentially treated establishment j, I calculate the share of restaurants within1 kilometer that are enrolled in the platform in each quarter (Ïjt) and calculate thedifference in this share between quarter t and quarter tâ 1. That is, â¦jt = ÏjtâÏjtâ1.Treated restaurants are defined as establishments in the top 5th percentile of thedistribution of â¦. When more than one observation of the same establishment fallswithin the top 5th percentile, I only keep the first event. Appendix Figure A.3 plotsthe distribution of â¦. The mean of â¦ is 0.01, while the top 5th percentile has a meanof 0.11 (with a standard deviation of 0.04). Treated establishments are matched tocontrol establishments using the same procedure as in the baseline analysis. Finally, Iestimate the same event-study model as in Equation (2.10).2.5.3 Research Design: WorkersThis section provides an empirical strategy to estimate the causal effect of online-delivery platforms on restaurant workersâ labor market outcomes. The first step in thisestimation is to construct a control group. Workers employed in restaurants that doand do not enroll on the platform may differ in various dimensions. Furthermore, firmsthat enroll on the platform may differ from firms that do not, and these differencesmay spill over to workers by affecting their potential outcomes. Similar to theestablishment-level analysis, I build my control group using a matching algorithm,where the pool of control workers is located in labor markets where no online-deliveryplatform technology is available at the time of treatment (and up to five quarters aftertreatment).Matching I start by considering the pool of treated workers as those whose employerenrolls on the platform while they are employed at the restaurant. I restrict the sampleto workers who are treated up to December 2020, allowing me to observe at least fourquarters after treatment. Additionally, I impose a requirement that these workers must30No restriction is imposed on the posterior probability of adopting the platform to avoid conditioningon an outcome.24have at least two quarters of tenure.31 For each cohort (quarter Ã year) of treatedworkers, the potential control group includes all restaurant workers employed duringthe same period in a microregion where no online-delivery platforms were availableprior to the treatment date and for the subsequent five quarters after treatment.Each treated worker is then matched to a control worker based on their characteristicsthe quarter prior to treatment. Workers are matched exactly on gender, occupation,firm size quartile, average firm wages (within restaurants in the year), and the medianshare of restaurants in the microregion within a 1 kilometer radius.32 I use one-to-onematching with a caliper algorithm (Stepner and Garland, 2017).33 Matching variablesinclude earnings in tâ â 3 to tâ â 1 (with a bandwidth of 200 BRL), age (with abandwidth of 2 years), and tenure (with a bandwidth of ±1 quarter).Econometric Framework I estimate the effect of employer enrollment on restaurantworkersâ labor market outcomes by estimating Equation (2.10) on workers. Under thisspecification, Yit is the outcome of interest (e.g., earnings, employment) for individual iat time t. αi represents worker fixed effects, and δt represents quarter-year fixed effects.I additionally control for a quadratic in age. Treated is a dummy variable indicatingwhether the employer of individual i enrolls on the platform, while t(i) indicates thedate on which the employer enrolled in the platform. 1{t = tâ(i) + k} represents theevent-study coefficients that reference the time relative to the treatment date. Themain coefficient of interest is βk, which captures the effect of employer enrollment onthe platform on the outcome of interest. The event-study indicators are normalizedrelative to Î²â1 and standard errors are clustered at the worker level. Similar to theestablishment-level analysis, the research design for workers relies on the paralleltrend assumption. Although an employerâs decision to enroll on the platform is likelyexogenous to the worker, it is not feasible to test this identifying assumption directly.In Section 2.7.1, Figure 2.9 provides evidence that both targeted and non-targetedmoments in the matching display parallel trends prior to treatment.31Tenure restrictions are commonly used in the job displacement literature (e.g., Jacobson et al., 1993;Schmieder et al., 2023; Bertheau et al., 2023). This restriction is imposed to measure outcomes forworkers with a certain degree of attachment to the firm.32Occupations are based on the six-digit CBO code (ClassificaçãÃÂ£o Brasileira de Ocupações). Idefine four groups of occupations: waiters, cooks, administrative, and other. More details on theconstruction of these four categories are available in Appendix A.3.33Workers are matched without replacement within each cohort of treatment.252.6 Impact of Online-Delivery Platforms on Restau-rants Labor DemandThis section discusses the impact that online-delivery platforms have on restaurantsâlabor demand. Section 2.6.1 covers the main results of the event study analysis,and Sections 2.6.2-2.6.4 discuss effects by occupation, restaurant pay policy, andrestaurant density. Finally, Section 2.6.5 examines potential spillover effects fromplatform adoption by competitors and Section 2.6.6 presents some robustness checks.2.6.1 Main Results: Effect of Adoption of the PlatformDescriptive Evidence Columns (1) and (2) of Table 2.1 present a snapshot of thecharacteristics of the matched control and treated restaurants in 2017, respectively.Workers at treated and control restaurants average 11 years of formal educationâslightly less than complete high schoolâand earn approximately 1,500 BRL per month(in real terms of 2018). Workers at control establishments have slightly higher tenure(2.6 years vs. 1.4 years at treated establishments) and are also slightly older (33.4years vs. 32.5 years at treated establishments). Both treated and control restaurantspredominantly hire Brazilian, full-time workers (97 percent) and have a similar share offemale workers (59 percent). These restaurants have an average size of 11 workers, andwaiters represent approximately 50 percent of their workforce. Columns (3) and (4)provide a snapshot of the characteristics for all potential treated restaurants (includingthose that didnât match) and all restaurants in Brazil, respectively. Overall, treatedrestaurants are larger than the average restaurant in Brazil and pay slightly higherwages.Figure 2.2 panel (a) plots the distribution of the AKM firm fixed effects for restaurantsthat at some point enroll on the platform and those that never enroll, using themethod pioneered by Abowd et al. (1999b).34 Restaurants that adopt the platformhave, on average, 8 log points higher AKM firm fixed effects. This suggests that firmsthat enroll on the app have a wage-setting policy that results in higher wages for alltheir workers. Interestingly, AKM firm fixed effects also have predictive power for theintensive margin. Figure 2.2 panel (b) shows a positive correlation between the AKM34Gerard et al. (2021) have tested the validity of the AKM model in Brazil. Appendix A.4.5 presentsdetails on the AKM estimation.26firm fixed effects and the number of hours platform drivers work for each restaurant.Figure 2.3 presents trends in total hours hired for the matched establishments thatenroll on the platform. Panel (a) shows that total hours of in-house workers decreasedby 27.6 percent between tââ2 and tâ+5. Only part of this decrease is compensated byoutsourced platform workers. When accounting for app workers, total hours worked atthe establishment decrease by 21.3 percent. Panel (b) shows that as an establishmentenrolls on the platform, it increasingly relies on platform workers. App workersrepresent 2.4 percent of the total hours worked at the establishment in the quarter ofenrollment. This share increases over time, reaching 7.5 percent after five quarters ofenrollment. Both of these patterns suggest a replacement of in-house workers withplatform-workers following platform adoption. However, understanding the effect ofplatform enrollment on labor demand requires a comparison with a counterfactualscenario. In the next sections, I present the results of the event study analysis.Trajectories of treated and control restaurants Figure 2.4 panel (a) shows thetrajectories for log average wages paid to employees at treated and control restaurants.Both groups exhibit similar trends in earnings in the quarters prior to treatment.Average wages for both groups are mostly stable between tâ â 7 and tâ â 2. Wagesshow a sharp decrease between tâ â 1 and tâ + 5 of approximately 17 log points fortreated establishments and 13 log points for control establishments. This patternsuggests that treated establishments may start making adjustments the quarter priorto enrollment in the platform, which motivates using tââ 2 as the baseline in the eventstudy analysis. Following enrollment in the platform, differences in wages betweentreated and control establishments remain fairly constant over time.Panel (c) reports the trends for log firm sizeâas measured by workers formally hired(in-house) by the establishmentâat treated and control restaurants. Both groups showsimilar trends (and levels) in size in the quarters prior to treatment. As with wages,firm size for both types of establishments remains mostly stable between quarterstâ â 7 and tâ â 2 and experiences a large decrease between tâ â 1 and tâ + 5. Firmsize decreases by approximately 10 log points for treated establishments and 9 logpoints for control establishments during that period. In contrast to wages, the size oftreated and control establishments persistently diverges after enrollment, up to fivequarters after the event. After five quarters of enrollment, treated establishments areon average 7 log points smaller than control establishments.27Figure A.5 illustrates the trends for the extensive margin of restaurants.35 Five quartersafter the event, approximately 12 percent of the restaurants in the sample have closed,highlighting the high turnover in the sector. The average difference between the twogroups at the extensive margin appears marginal, with treated restaurants being 1.5percent more likely to close after five quarters. To investigate these patterns moreformally, I present the estimated results for the regression model in the next section.Effect of Platform Adoption on Size and Hours Figure 2.4 panel (b) presentsthe estimated βk from Equation 2.10 for log firm size and shows that treated restaurantsdecrease the number of in-house workers after adopting the platform, and that thiseffect is persistent. Five quarters after enrollment, the number of in-house workers attreated firms decreases by 6 percent.Figure 2.5 shows the estimated effect of platform adoption on the log number ofhours worked by in-house workers (red line) and the log number of hours includingplatform-workers (blue line).36 Consistent with a decrease in the intensity of in-houselabor, the results show that treated restaurants reduce the number of hours worked byin-house workers by 7.6 percent after five quarters of platform adoption. This decreasein in-house hours is entirely compensated by the hours worked by outsourced workers.The total number of hours worked at the establishment, when including platformworkers, is essentially unchanged five quarters after enrollment.In summary, these results indicate that restaurants adopting the platform are decreas-ing their labor demand for in-house workers and replacing in-house labor with platformdelivery drivers. However, to study the relative importance of each component shownin Equations (2.8) and (2.9), we must focus on the effects by occupation, which Idiscuss in the next section.Effect of Platform Adoption on Wages Figure 2.4 panel (d) plots the effects ofenrollment in the platform on average wages paid at the restaurants. Average wagesat treated firms decrease by 2.8 percent in the quarter prior to enrollment, suggestingthat firms may adjust wages before enrolling. This decrease in average wages persistsduring the post-enrollment period. Average wages are approximately 2 percent lower35By construction, restaurants cannot close prior to the event. The outcome measured in this figureis closure (after opening) and thus can only differ from 0 in the post-period.36App worker hours are measured as the time a platform driver dedicates to delivering food from aspecific establishment.28between tâ+1 and tâ+4 for treated establishments and only seem to recover in tâ+5.The wage level effects are smaller in absolute terms compared to the effects found forestablishment size. The disparity between wage and size effects could be associatedwith two main factors. First, treated restaurants may be changing their compositionof occupations as they reduce their size. If lower-paid jobs are being replaced at ahigher rate than higher-paid jobs by outsourced platform workers, then the effect ofadoption on the average wage of in-house workers would be smaller than the effect onfirm size.37 Second, employment may be elastic such that a relatively smaller changein wages is needed to decrease a larger amount of labor hired. To untangle these twocomponents, I zoom in on the impact of platform adoption on the labor demand ofcooks and waiters separately.382.6.2 Effects by OccupationThis section presents the treatment effects of adoption on labor demand for in-housecooks and waiters separately. I start by examining the change in the overall servicesector (that is, all workers who are not cooks, including platform workers). FigureA.7 provides evidence that the total hours hired for service workers (including appworkers) remain essentially unaffected five quarters after platform adoption. Throughthe lens of Equation (2.8), the null effect on the size of the service sector indicatesthat changes in the number of waiters will only reflect changes in the firmâs revenueand the relative productivity of waiters.Table 2.2 presents difference-in-difference estimates for cooks and waiters using themethodology developed by Borusyak et al. (2024). Column (1) reports the causaleffect of adopting the online-delivery platform on the number of in-house waitershired. Treated restaurants decrease the number of in-house waiters by 5.4 percentafter adoption.As discussed in Section 2.4.2, the vast majority of restaurants did not hire delivery37For instance, if cooks are on average paid more than waiters, a decrease in θ could yield a smallereffect on average wages at the firm level.38A model that allows for worker heterogeneity could also explain the disparity between wagesand size through a change in the type of workers (more or less productive)âconditional on theoccupational structure of the establishment. Figure A.6 panel (a) plots the effect of platformenrollment on the average worker fixed effects at the establishment and finds a null effect. FigureA.6 panel (b) leverages the matched design to construct establishment-level treatment effects onaverage worker effects following Schmieder et al. (2023). The figure plots these treatment effects onaverage worker effects on firm effects and finds a flat 0, discarding the platform enhancing sorting.29drivers prior to adopting delivery services through the app. Although the power of theanalysis is limited due to the sparsity of delivery workers in the pre-treatment period,the estimates in Column (3) show that restaurants that did have delivery workersprior to adopting the app decrease the number of in-house delivery drivers by 10.5percent.Column (2) reports the changes in the number of cooks after platform adoption andshows that enrollment in the app has a sharp zero effect on the demand for cooks.This null effect on cooks has two important implications. First, the adoption ofonline-delivery platforms has no impact on the revenue of the average restaurant.Furthermore, the null effect on revenue, combined with the zero effect on total serviceworkers, implies that changes in the number of waiters reflect the relative productivitybetween waiters and delivery workersâthe outsourcing effect. Taken together, columns(1) and (2) suggest that the adoption of online-delivery platforms allows the averagerestaurant to shift its business from in-house dining to delivery services without aclear effect on the product demand for the goods produced by adopting restaurants.39Column (4) reports the effect of platform adoption on the wages of waiters. Onaverage, the wages of waiters decrease by 2.25 percent after platform adoption. Adirect implication of the model presented in Section 2.2 is that the labor supplyelasticity can be recovered from the following ratio:β = â ln(W )â ln(wW )(2.11)Using this ratio and my estimates for the number of waiters and wages, the impliedlabor supply elasticity for waiters is 2.43. This is on the higher end of what has beenfound in other empirical studies (e.g., Card et al., 2018a; Manning, 2021), indicatingthat the labor market for waiters is fairly competitive.40Column (5) reports the effect of platform adoption on the wages of cooks. Consistent39A model where restaurants produce two substitutable, separate goods (in-house dining and deliveryservices) that use different types of workers could also explain the patterns. In such a model,all effects would be driven by changes in consumer demand, as there would be no room for theoutsourcing effect. Importantly, this type of model would predict either a strong negative correlationor a null correlation between the effect on the number of waiters and the intensity of platform salesby the restaurant. In Figure A.9, I plot the establishment-level treatment effects on the number ofwaiters and the hours of platform-workers used by the restaurant and find evidence of a positivecorrelation for a set of establishments.40This is not surprising, given that the restaurant sector is usually considered a sector with close-to-nullrent-sharing (Card et al., 2016).30with the null effect on labor demand for cooks, the effect on wages is not statisticallydifferent from zero. In summary, the results suggest that the outsourcing effectdominates for the average firm, leading to a decrease in the number of waiters,while the number of cooks remains unaffected. I now explore heterogeneities acrossrestaurants that allow to potentially isolate the outsourcing effect from the productdemand effect.412.6.3 Effects by Establishment Pay PremiumUp to this point, the analysis has centered around the average treatment effect ofplatform adoption on the labor demand of restaurants. However, it is possible thatthe returns to adoption are heterogeneous, depending on the wage setting policy ofthe firm. Several models of outsourcing suggest that higher-paying firms are morelikely to substitute in-house workers with outsourced workers to decrease labor costs(Bilal and Lhuillier, 2022; Spitze, 2022).42To test this, Figure 2.6 plots the effects of platform adoption on firm size by medianAKM firm effects. Panel (a) shows that high AKM firms decrease their in-house labormore than low AKM firms. Five quarters after adoption, high-paying firms decreasetheir in-house size by approximately 4 log points more than low-paying firms. Panel(b) of Figure 2.6 shows that the change in total hours (including platform workers) isnot significantly different from zero after five quarters of adoption for either high orlow AKM restaurants, although the difference in total hours between high and lowAKM restaurants is significant.Table 2.3 panel (a) presents the results for waiters and cooks separately. Column (1)shows that low AKM firms decrease the number of waiters by 3.5 log points afterenrollment, while Column (3) reports that high AKM firms decrease the number ofwaiters by 6.2 log points, suggesting that high-paying firms outsource a larger portionof their workforce.43However, it is still possible that the product demand effect differs between high and41Column (6) reports the effect of platform adoption on the wages of delivery workers. The estimatesshow that the wages of delivery workers essentially do not decrease, but these estimates must betaken with caution given the small sample size.42Appendix A.3.2 extends the baseline model presented in Section 2.2 to allow for heterogeneousoutsourcing effects by restaurant pay premium.43These findings are consistent with what has been found empirically in other settings, such asGermany and Argentina, where higher-paying firms have greater incentives to outsource to avoidpaying higher rents to all their workers (Goldschmidt and Schmieder, 2017; Drenik et al., 2023).31low AKM firms. This would be the case if firms facing a large ex-ante market fortheir product experience a relatively smaller increase in their market upon adoptingthe platform. To test this, I start by plotting the effects of adoption on total hourshired in the service sector by AKM firm effects. Figure A.8 shows that the total hourshired for service workers are unaffected in restaurants with both high and low paypremiums. With that result in hand, it is possible to once again interpret the effectson the number of waiters as a reflection of the outsourcing effect and the change inrevenue at adopting firms. Column (2) and Column (4) of Table 2.3 panel (a) showthat the number of cooks is not significantly different from zero for both high andlow AKM firms, suggesting that there is no significant impact on revenue for eitherhigh- or low-paying firms. Taken together, these results suggest that the differencebetween high- and low-paying restaurants is due to the outsourcing effect being higherfor high-paying restaurants compared to those with a lower pay policy.2.6.4 Effects by Restaurant DensityAs of now, the estimates show limited impact of the product demand effect afteradoption. However, it is possible that the impact on demand for goods is morepronounced in firms located in certain areas. Under the assumption of a constantoutsourcing effect across local areas, the difference in the effect of platform adoptionon in-house labor between high- and low-density areas will reflect differences in theproduct demand effect. Consumer search models often predict that the agglomerationof establishments reflects demand externalities and proximity to the average consumer(Vitali, 2022; Leonardi and Moretti, 2023).44 In such models, as online-deliveryplatforms decrease transportation costs for consumers, the demand for restaurantsfurther from the average consumerâand thus located in areas with lower restaurantdensityâshould increase more than for restaurants located in more central or denserareas.4544Appendix A.3.3 provides an extension to the model presented in Section 2.2 in which I microfoundthe product demand of restaurants through a consumer search model. In that model, the densityof restaurants reflects both the quality of restaurants in the area (demand externalities) and thedistance to the average consumer.45Another way to think about this is that restaurants with lower demand for their goods priorto delivery platforms have more to gain from these platforms as they expand to new markets.By contrast, restaurants that had a higher baseline demand prior to adopting an online-deliveryplatform are less likely to benefit as much from expanding to new markets. Instead, these restaurantsmay experience a shift from in-house dining to delivery, resulting in a net zero effect on overall32In this section, I explore heterogeneous effects of delivery platform adoption on labordemand for restaurants located in areas with high and low restaurant density. Todefine restaurant density, I start by geocoding the universe of formal restaurants inBrazil between 2017 and 2021. For each restaurant, I count the number of nearbyrestaurants within a 1-kilometer radius of the establishment and define this numberas Ïjt. I then calculate quartiles for Ï by micro-region and year, defining restaurantsas being in high-density areas if their Ïjt is above the median in the quarter beforethey adopt the delivery platform.46The next step is to study whether there are heterogeneities in the outsourcing effectacross locations. Although there are no strong ex-ante reasons to presume that thenumber of surrounding restaurants would impact the outsourcing effect, I take twosteps to ensure that the outsourcing effect remains constant across high- and low-density areas. First, I compare the share of the wage bill and hours that deliveryworkers represent relative to total service workers in high- and low-density areas.47Figure A.10 shows that by quarter 5, both the share of the wage bill and hours thatdelivery workers represent relative to total service workers is essentially the sameacross restaurants located in high- and low-density areas, suggesting that locationis independent of the outsourcing effect. Second, to ensure that the distribution offirm pay premiums is similar across high- and low-density areas, I re-weight treatedrestaurants located in areas with lower density following the methodology pioneeredby DiNardo et al. (1996)âthe DFL estimator.I proceed to plot the effects of adoption on firm size and total hours (includingplatform workers) by restaurant density. Figure 2.7 panel (a) shows that restaurantsin high-density areas reduce their in-house labor substantially more than those indemand for the restaurant.46Appendix A.4.8 describes the steps taken to geolocate the restaurants in Brazil and the share ofrestaurants I was able to geolocate per year.47The CES production function presented in Section 2.2 implies that differences across locations inthe ratio of the wage bill and hours of delivery workers to in-house service workers should identifythe differential change in the ratio of productivities between the two types of workers:[âH ln(WB WWB S)ââL ln(WB WWB S)]â Ï[âH ln(WS)ââL ln(WS)]= âH ln(Î¸1â Î¸)ââL ln(Î¸1â θ).Here, H and L define areas with high and low restaurant density, respectively. Ideally, one wouldwant to see if the percentage difference (between pre- and post-adoption) in wage bills and hoursbetween delivery workers and in-house workers is consistent across high- and low-density areas.However, the sparsity of data on delivery workers in the pre-adoption period makes this analysisunfeasible.33less dense areas. Restaurants in high-density areas decrease their in-house laborby approximately 8 percent five quarters after adopting the platform. In contrast,restaurants in less dense areas do not change their in-house labor after adoptingthe platform. Panel (b) shows large differences in the effect on total hours whenaccounting for platform workers. Total hours at restaurants in high-density areasremain essentially unchanged, while hours at restaurants in less dense areas increaseby 6 percent five quarters after platform adoption.Table 2.3 panel (b) presents the results for waiters and cooks separately. Column(1) shows that the number of waiters at restaurants in high-density areas decreasesby 7.1 percent after platform adoption. In contrast, column (3) reports that theeffect on the number of waiters at restaurants in less dense areas is not significantlydifferent from zero. Consistent with the overall effect on hours, the number of cooksis not significantly different from zero for restaurants in high-density areas (column 2),while the number of cooks increases by 3.6 percent for restaurants in less dense areas(column 4), suggesting an overall increase in the product demand for the latter groupafter platform adoption.In sum, the results presented in this section suggest that online-delivery platformshave varying impacts on the product demand of restaurants depending on theirlocation. These heterogeneous effects are reflected in the overall impact on labordemand. Importantly, the results indicate that online-delivery platforms may providean alternative path for restaurants to expand their business without needing to locatein central areas or rely on the demand externalities that accompany agglomeration(Vitali, 2022; Leonardi and Moretti, 2023).2.6.5 SpilloversOnline-delivery platforms can impact the labor demand of restaurants beyond thosethat adopt these platforms. Specifically, if establishments that start selling goodsthrough online-delivery platforms crowd out demand for restaurants that do not adoptsuch platforms, the labor demand of non-adopting restaurants could be affected (Chavaet al., 2024).48 Importantly, understanding the impact of online-delivery platforms on48The model presented in Appendix A.3.3 highlights that the adoption of online-delivery platformscan affect the market beyond the adopters. Specifically, the "spillover" term in Equation A.23implies that as the share of neighboring restaurants enrolled in the platform increases, the consumersearch costs associated with the product of restaurant j will increase, impacting its demand.34non-adopting restaurants is crucial for assessing the overall effect of these platformson workers in the sector.Following the discussion in Section 2.5.2, I define an event for non-adopters as a large,sudden increase in the share of restaurants within a 1-kilometer radius that adopt theonline-delivery platform. Figure 2.8 panels (a) and (c) show the trends in firm size andaverage wages for firms exposed to a spillover event and their matched pairs. Priorto the event, non-adopting firms do not appear to experience a larger negative shockthan adopting restaurants (as reported in Figure 2.4), suggesting that restaurants arenot selecting into earlier adoption based on their ex-ante trends.49Figure 2.8 panel (b) plots the estimated treatment effects of the spillover event onthe log firm size of non-adopters. The estimates show that firm size decreases by3 percent on impact. However, this negative effect on firm size is not persistentover time; five quarters after the event, the effect on firm size is essentially null.50However, these patterns in firm size could be influenced by changes in compositiondue to firm closures. Figure A.17 panel (a) shows the trends in closure for treated andcontrol establishments. Restaurants exposed to a large share of neighbors adoptingthe technology are approximately 5 percent more likely to close within five quartersof the event compared to control establishments. Figure 2.8 panel (d) shows thatthe average wages of non-adopters decrease by 2 percent after five quarters. Takentogether, these results suggest that restaurants adopting online-delivery platforms maycrowd out demand for non-adopters, leading to a decrease in their size and averagewages.2.6.6 RobustnessIn this section, I explore the sensitivity of the results to several robustness checks.Alternative Estimators First, following the recent difference-in-difference litera-ture with staggered treatment, Figure A.11 shows that the main results are robust tousing the estimator proposed by Borusyak et al. (2024). This is not surprising, as the49Recall that I do not condition on post-event adoption of the platform. Figure A.17 panel (b) showsthat approximately 7 percent of the ex-ante non-adopting restaurants adopt the delivery platformwithin 5 quarters following the spillover event.50Figure A.18 shows similar results when considering an alternative group of nearby restaurants,such as those within a 2-kilometer radius using a donut design.35control establishments are drawn from regions where treatment is not available and,therefore, are never treated in my setting (Roth et al., 2023).Matching An additional concern is that by matching on pre-treatment outcomes,the estimates may be capturing reversions to the mean.51 Figure A.12 shows theestimated effect of the entry of the platform on the size of the restaurant sector(without matching) using the regression described in Appendix A.4.7. These aggregatelevel estimates show that 5 quarters after the entry of the platform to a micro-region,the size of the restaurant sector decreases by 5 percent. Importantly, these estimatesresemble closely the micro-level estimates found when using the matching algorithm,suggesting that the results are not driven by the research design.Covid The period of consideration overlaps with the COVID-19 pandemic, raisingthe concern that the results could be driven primarily by firms that adopt the platformto cope with the pandemic. In this case, the negative impact of platform enrollmentcould be more related to a response to the pandemic than to the platform itself.Indeed, if firms most negatively affected by the pandemic are also the most responsiveto the platform, the external validity of the results could be limited. To address thisconcern, I take three approaches. First, leveraging the matching strategy, I plot inFigure A.13 the establishment-level treatment effects on size and the change in thenumber of COVID-19 cases per capita in the post-period. The figure shows thattreatment effects on size are not correlated with increases in COVID-19 cases. Second,I estimate the effect of platform enrollment on firm size for establishments treated upto five quarters before the pandemic and compare the estimates to restaurants treatedduring the pandemic. Figure A.14 shows that, if anything, the effects on in-housesize are more negative for establishments treated in the pre-COVID period. Lastly, Ileverage within-state variation in platform availability and consider a specificationthat includes state-year fixed effects to control for state-specific policies that may haveaffected restaurants during COVID-19 (Chauvin, 2021). Figure A.15 shows that theresults are not sensitive to the inclusion of these fixed effects.Informality An additional concern regarding the external validity of my estimatesis related to the relatively high levels of informality in Brazil compared to developed51This concern is also alleviated by the fact that I leave untargeted moments in the pre-treatmentperiod during the matching procedure.36countries such as the US and Canada. If restaurants that adopt the platform hirea large portion of their workers informally, it is possible that part of the changes inlabor demand are not observed in my data (which only covers formal workers). Tounderstand the potential implications of this, I estimate the share of formal workersin the restaurant sector in each municipality using the 2010 Brazilian census (thelatest census available). I then divide my matched sample into groups above andbelow the median of the share of formal workers, based on the informality levels oftheir municipality. Figure A.16 shows that restaurants located in municipalities withbelow-median informality decrease their in-house size by approximately 9 percentafter five quarters of adoption. In contrast, restaurants in municipalities with highinformality in the sector decrease their formal labor by just 2 percent, suggestingthat these restaurants may be adjusting their informal (unobserved) labor instead. Ifanything, these results indicate that the main results are conservative estimates of theeffect of platform adoption on labor demand. When considering the overall effect ofthe platform on workers in Section 2.8, I will account for the possibility that adoptionof the delivery platform also affects informal workers.2.7 Restaurant WorkersThe results presented in Section 2.6 provide evidence that delivery platforms reshapelabor demand, with restaurants replacing in-house waiters with outsourced deliveryworkers. To fully understand the labor market consequences, it is essential to examinethe impact on the workers themselves. This section analyzes the impact of online-delivery platform adoption on restaurant workersâ labor market outcomes, revealinghow much of the decline in demand for in-house workers is borne by the workers. Thisanalysis sets the stage for Section 2.8, where I assess the overall effects of deliveryplatforms by examining both restaurant workers affected by adoption and spillovers,as well as the impact on gig workers, providing a comprehensive view of the winnersand losers. Section 2.7.1 presents summary statistics and the main results of theevent study analysis, while Section 2.7.2 explores the spillover effects on workers atnon-adopting establishments.372.7.1 Main Results: Effect of Employer Adoption on thePlatformSummary Statistics Table 2.4 presents summary statistics for matched treatedworkers (Column 1), matched control workers (Column 2), and all potential treatedworkersâincluding those who did not matchâ(Column 3) in the quarter prior totreatment. Matched treated and control workers earn, on average, 1,500 BRL-2018per month, work full-time, have 11 years of formal education, and are 33 years old.The sample of matched workers is composed of 60 percent female workers. In terms ofoccupations, 65 percent of the matched workers are waiters, 18 percent are cooks, andthe remaining 17 percent are other types of workers. The average tenure of matchedworkers is 3.7 years. When compared to all potentially treated workers (Column 3),the matched workers have lower earnings (1,501 vs. 1,788 BRL-2018) and a slightlyhigher share of female workers and waiters.Trajectories of Treated and Control Workers Figure 2.9 panel (a) presentsthe earnings trajectories for treated and control workers. Both groups show similarearnings levels prior to treatment. Following their employerâs adoption of the platform,treated workers experience a slightly stronger negative impact on their formal sectorearnings compared to the control group, which persists five quarters after the event.52Specifically, treated workers experience a 11.3 percent decrease in their earnings thequarter after the event, while control workers experience a 9.7 percent decrease. Thedecline in earnings is persistent, with treated workersâ earnings decreasing by 37.1percent five quarters after the event, while control workersâ earnings decrease by 35.4percent.Panel (c) reports the trends in formal employment for treated and control workers.Similar to earnings, both groups show a negative and persistent trend in employment.Five quarters after the event, treated workers experience a slightly stronger negativeimpact on their formal employment compared to the control group (a 32.7 percentvs. 31 percent decrease). To further investigate these patterns, I next present theestimated results for the regression model.52Since control workers are also required to have two quarters of tenure at the time of the eventâbutdo not have any tenure requirements after the eventâsome may experience a decline in earningspost-event due to non-employment.38Earnings & Employment Figure 2.9 Panel (b) presents the estimated βk fromEquation (2.10) for total monthly earnings in the formal sector. In the quarter followingthe employerâs adoption of the platform, treated workersâ earnings are reduced by20 BRL-2018âapproximately 1.5 percent of their pre-event average earnings. Thisslightly negative impact on earnings persists five quarters after the event, althoughthe estimates become noisier in the fifth quarter.53 Panel (c) presents the estimatedtreatment effects for employment, showing that treated workers experience a smallnegative impact on their formal employment following the event. Workers whoseemployer adopts the delivery platform experience a 1.5 percentage point decreasein their probability of being employed in the formal sector, a negative impact thatpersists five quarters after the event.54Figure A.22 panel (a) presents the cumulative likelihood of workers leaving theirpre-event establishment (either for another firm or to non-employment). Workerswhose employer adopts the delivery platform have a higher likelihood of leaving thefirm after the event; specifically, these workers are 2 percent more likely to leave theiremployer in the quarter following the event. This cumulative likelihood increasesover time, reaching 6 percent five quarters after the employer adopts the platform.These estimates reflect the worker-side dynamics shown in Figure 2.4 panel (b), whichreported similar patterns when estimating the effect of platform adoption on in-housefirm size. Combined with the employment effects of employer adoption of the platformshown in Figure 2.9 panel (d), these results suggest that a significant share of restaurantworkers leave their job as their employer adopts the platform but manage to finda new formal job relatively quickly afterward. Figure A.22 panel (b) supports this:approximately 75 percent of the workers who leave their employer are employed by a53For context, the negative earnings effect of mass layoffs in Brazil is estimated to be 40 percent (Brittoet al., 2022a). In Germany, workers exposed to a domestic outsourcing event (who remain employed)lose approximately 5 percent of their earnings in the year following the event (Goldschmidt andSchmieder, 2017). In Italy, outsourced workers lose 23 percent of their pre-outsourced earningsafter the event, an effect mostly driven by the loss of formal employment (Daruich et al., 2024).54Figure A.21 panel (a) shows treatment effects of employer platform adoption on workersâ logearnings (conditional on employment). These estimates show that, conditional on employment,treated workersâ earnings decrease by approximately 3 percent five quarters after the event. FigureA.21 panel (b) reports estimates on log monthly earnings after excluding pairs of workers where thetreated worker is not employed but the control is. The underlying assumption is that the controlworker is a complierâthat is, a worker who, if their employer had adopted the platform, wouldalso have been non-employed (Lee, 2009). By dropping these compliers, I can evaluate the effectof platform adoption among the always takersâthose who remain employed even after platformadoption. The results essentially do not change after conditioning on always takers.39new employer five quarters after the event. This dynamic likely explains why workersâearnings are not as negatively affected as one might expect from the firm-level analysis.Transition to Delivery Platform Work The richness of the data allows me toexamine whether workers whose employer adopts the delivery platform are more likelyto transition to work as gig delivery drivers themselves. This analysis helps determinewhether some of the negative impact on earnings is offset by income generated fromplatform work. Figure A.23 presents the estimated treatment effects for the likelihoodof working on the delivery platform. In the quarter following their employerâs adoptionof the platform, workers are 0.1 percent more likely to begin working on the platform.This likelihood slightly increases over time, so that five quarters after the event, treatedworkers are 0.4 percent more likely to work on the platform. The small increase intransition to platform work suggests that waiters in the restaurant sector are beingreplaced by a different set of workers, rather than being outsourced to a new employer.The characteristics of these gig workers are explored further in Section 2.8.2.7.2 Spillovers on WorkersTo fully capture the effects of online-delivery platforms on workers, it is essential toconsider the impact on those who are not directly employed by restaurants that adoptthese platforms. Section 2.6.5 discussed the spillover effects of platform adoptionby nearby restaurants on non-adopting establishments, showing that non-adoptingrestaurants are more likely to close. Figure 2.10 panel (b) presents the estimatedresults of Equation (2.10) on total earnings for workers employed at non-adoptingrestaurants exposed to a sharp increase in the share of nearby restaurants on theplatform, as defined in Section 2.5.2.55 The results show that workers at non-adoptingrestaurants experience a negative impact on their earnings after the event, which islarger than that experienced by workers at restaurants that do adopt the platform.Workers at non-adopting restaurants suffer a 3 percent decrease in earnings the quarterafter the event, with the effect persisting and increasing to 6.6 percent five quartersafter. Panel (c) presents the estimated treatment effects for employment, showingthat workers at non-adopting restaurants also experience a negative impact on theirformal employment following the event. Specifically, these workers are 2.3 percentage55Panels (a) and (c) display the raw means for matched treated and control restaurants.40points less likely to be employed in the formal sector in the quarter following theevent, with the negative impact persisting and increasing to 3.8 percentage pointsafter five quarters.The stronger impact on earnings and employment is driven by the large fraction ofnon-adopting restaurants that close. Figure A.24 illustrates the differences in effectsbetween workers whose employer (the quarter prior to the event) eventually closesand those whose employer remains open. Panel (a) shows that workers employed atrestaurants that do not eventually close see their earnings decrease by approximately 2.6percent five quarters after the event. This group represents 92 percent of the matchedsample, and the effects on these workers closely mirror those on in-house workersdirectly affected by their employerâs platform adoption. In contrast, workers whoseemployer at tââ1 eventually closesâ8 percent of the matched sampleâexperience a 37percent decrease in earnings five quarters after the event. This effect is largely drivenby the extensive margin, as these workers are 40 percent less likely to be employedfive quarters following the event.Taken together, these results suggest that online-delivery platforms impact formalemployment and earnings beyond the direct effects on workers at restaurants thatadopt these platforms. The spillover effects on non-adopting restaurants are largerthan the direct effects of employer adoption due to restaurant closures. Although adetailed exploration of the exact mechanisms driving these spillovers in the productmarket is beyond the scope of this paper, a search cost model, like the one proposedin Section A.3.3, offers a potential explanation. In this model, the spillover effectsof platform adoption by nearby restaurants on workers at non-adopting restaurantscould be driven by a crowding-out effect in the product market. From the modelâsperspective, as nearby restaurants adopt the platform, consumers face higher searchcosts when looking for non-adopting restaurants. This increase in search costs wouldlead to a decline in demand for the products of non-adopting restaurants, subsequentlyreducing their demand for labor. Importantly, a complete assessment of the winnersand losers from online-delivery platforms must consider not only the direct effecton workers at adopting restaurants but also the spillover effects on workers at non-adopting establishments. The next section examines the trade-offs of online-deliveryplatforms, factoring in platform workers.412.8 Winners & LosersIn this section, I decompose the total effect of online-delivery services on workersâearnings. Following the discussion in Section 2.7, I differentiate the platformâs effectinto a direct effect on in-house workers at adopting restaurants, an indirect effecton restaurant workers at non-adopting establishments, and the impact on app-baseddelivery workers. Unlike a welfare analysis, which would require a comprehensiveassessment of the platformâs costs and benefits beyond financial outcomes, I focusspecifically on earnings effectsâan outcome that is comparable across different groupsof workers. Although this approach narrows the scope of conclusions, it offers a clearview of the trade-offs online-delivery platforms pose across a key outcome of interestfor workers, while minimizing assumptions about the welfare function.56To study the total effect of online-delivery platforms on workersâ earnings, I aggregatethe earnings effects for each restaurant that adopts the delivery service.57 Specifically,for each adopting restaurant j, I conduct the following accounting exercise:Ïjappï¸¸ ︷︷ ︸Total Wage-Bill EffectApp= Ïjin-houseï¸¸ ︷︷ ︸Wage-Bill EffectIn-House Workers+ Ïk Ì¸=jspilloversï¸¸ ︷︷ ︸Wage-Bill EffectSpillovers+ Ïjapp-workersï¸¸ ︷︷ ︸Wage-Bill EffectApp-Workers(2.12)The wage bill effect is calculated as the sum of total earnings effects for each group ofworkers, multiplied by the average number of workers affected per adopting restaurant.So far, I have discussed that the adoption of online-delivery platforms has a slightlynegative impact on the earnings of in-house restaurant workers and a stronger negativeimpact on the earnings of workers at non-adopting restaurants. Table 2.5 summarizesthe wage bill effect for each restaurant that adopted delivery services, covering bothcomponents.Column (1) shows the total earnings lost by in-house restaurant workers each quarterafter the restaurant adopts the delivery service. The cumulative discounted present56If restaurant and gig jobs offered similar amenities, then, under a utility function that dependslinearly on earningsâsuch as the one proposed in Section A.3.1âdifferences in earnings acrossjobs would lead to proportional differences in welfare. A similar argument could be made if thevalue placed on non-pecuniary job characteristics were low compared to the marginal utility ofincome. While the first assumption is unlikely to hold given the differing job characteristics, thelatter may be plausible given the relatively low income of the individuals studied in this setting.57Summarizing the effects by restaurant provides a clear unit of analysis and enables straightforwardextrapolation of results to contexts with varying intensities of platform adoption (assuminghomogeneity in treatment effects).42value of this loss, five quarters after adoption, is 3,192 BRL (in 2018 Reais). Thisamount represents 5.5 percent of the average wage bill for restaurants that adopt theplatform before offering deliveries through the app. Column (2) reports the effectsfor the same group, net of social security contributions. As discussed in Section2.3, considering net wages is particularly relevant when comparing gains and losses,as app workers are not required to make these contributions. When social securitycontributions are excluded, the effect on in-house workers represents 5.2 percent ofthe pre-platform wage bill of restaurants that adopt the technology.Column (4) reports the earnings effects of spillovers per restaurant that adopts deliveryservices. To estimate this, I re-scale the per-worker estimates presented in Section2.7.2 by the impact of spillover events on the adoption of delivery services by nearbyrestaurants. This re-scaling provides a per-worker effect in units of neighboringrestaurant adoption (Ï). I then multiply this adjusted coefficient by the average Ïand the average restaurant size in my sample.58 The cumulative discounted presentvalue of spillovers per adopting restaurant, calculated over five quarters after startingdelivery services, is -8,055 BRL (2018 Reais), which is equivalent to 13.9 percent ofthe pre-platform wage bill of these restaurants. When netting out contributions, theeffect reduces to 12.7 percent (Column 5).Assessing the total earnings impact of online-delivery services requires examininghow they affect the income of delivery workers, which poses several challenges. Asignificant share of these workers may have been employed informally or unemployedbefore joining the platform and thus are not visible in RAIS. Generally, app workershave low attachment to the formal sector. Seven quarters before their first delivery,29 percent of drivers held formal jobs. This drops to 22 percent at the time of theirfirst delivery, then slightly recovers to 24 percent after five quarters.59 Additionally,one must consider not only the earnings from deliveries but also outside options andwork-related costs (e.g., gas, vehicle maintenance). I use estimates from prior studiesto calculate the average maintenance cost for platform drivers in Brazil.60To estimate outside options, I divide workers into two groups: (i) those who held aformal job the quarter before starting on the app and were not laid off, and (ii) those58For full details on this re-scaling procedure, see Appendix A.4.4.59Figure A.25 panel (a) presents trends in formal employment for workers before and after they starton the platform.60The estimated maintenance cost for motorcycle delivery drivers is 3.7 BRL-2018 per hour worked(Callil and Pincaço, 2023).43without a formal job or who were laid off the quarter prior to or during their firstquarter on the platform. For the first group, I take a conservative approach, assumingtheir outside option is their pre-platform hourly wage, implying they could havecontinued earning the same wage had they not decided to work for the delivery service.For the second group, I estimate their outside option using the (regression-adjusted)average wage of formal platform workers, adjusted by the non-formal-to-formal wageratio from PNAD-C data for their state and quarter.61Table 2.5 Column (7) reports the average wage billâper adopting restaurantâofplatform-workers who held a formal job prior to working on the platform.62 Theseworkers made, on average, a cumulative present discounted value of 5,080 BRL (2018),equivalent to 8.7 percent of the pre-adoption wage bill of restaurants. Column (8)presents the total wage bill, net of opportunity costs and transportation/maintenancecosts, for the same group. When considering the pre-platform wage per hour as theopportunity cost for these workers, the cumulative present discounted value decreasesto 1,195 BRL (2018), equivalent to 2.1 percent of the pre-adoption wage bill.63Column (9) reports the wage bill generated by the platform, per adopting restaurant,for workers who did not hold formal employment (or were laid off) the quarter beforejoining the app. The cumulative present discounted wage bill for these workers is 25,169BRL (2018 Reais), equivalent to 43.3 percent of the pre-adoption wage bill. Column (10)presents the wage-bill effects for this group, accounting for transportation/maintenancecosts and outside options as estimated using PNAD-C. The cumulative presentdiscounted value for these workers is 15,647 BRL (2018), equivalent to 26.9 percent ofthe pre-adoption wage bill of restaurants.Taken together, when considering the total effect of the delivery service on workersâwages, I find that the platform generates a wage-bill surplus equivalent to 11.1 percentof the average pre-adoption wage bill of restaurants. Put differently, the gains for61The mean ratio of informal and unemployed to formal wages is 0.32. Details are provided inAppendix A.4.2.62A limitation of my data is that I do not observe which worker delivered for each restaurant at anyspecific time. Instead, I observe the earnings of each worker per hour and the total number ofhours that restaurants used the appâs delivery services per month/municipality. Appendix A.4.3discusses how I estimate hourly earnings for each adopting restaurant.63Figure A.25 panel (b) shows trends for formal and delivery earnings for app workers who held aformal job the quarter prior to working on the platform. Consistent with the platform allowingindividuals to smooth consumption over time, formal workers experience a negative income shockin the months before their first delivery. Similar results have been found in the US (e.g., Koustas,2018; Jackson, 2022).44delivery workers (net of their costs) are 62 percent larger than the combined directand indirect impact on formal restaurant workers.2.8.1 Limitations & DiscussionInformal Restaurant Workers The effects presented so far do not account forthe impact that the platform may have on workers hired informally in the restaurantsector. Workers can be hired informally either by formal restaurantsâfirms that havea tax identifier but may employ some workers informally (intensive margin)âor byfirms that are entirely informal, meaning they do not have a tax identifier (extensivemargin) (Ulyssea, 2018). To account for the intensive margin of informality, I use datafrom the 2010 Brazilian census and the household survey (PNAD-C) to impute thenumber of informal workers per restaurant.64 I estimate that approximately 25 percentof the workforce in each restaurant is informal. Column (3) shows that the effects oninformal workers at adopting restaurants represent 1.2 percent of the pre-adoptionwage bill.65 In Column (6), I estimate the impact on informal workers affected byspillovers, using the same methodology as for estimating the direct effects on in-houseworkers. The effect on informal workers represents 3.1 percent of the wage bill ofadopting restaurants prior to adoption. When accounting for informality, the totalearnings effect of the platform on workers remains positive but decreases to 6.8 percentof the pre-adoption wage bill.However, a limitation of my data is that I cannot account for the extensive marginof informality. Specifically, I do not observe the number or size of informal firms.66Informal firms are likely smaller than formal firms (Ulyssea, 2018). To the extentthat informal firms are also impacted by spillover effects, the negative impact of theplatform may be underestimated. Specifically, if the spillover effects on non-formalfirms account for 53 percent of the impact on formal firms, the total effect of theplatform on workers would break even.6764Appendix A.4.1 provides the details of the imputation procedure.65For all columns in the table, the pre-adoption wage bill is calculated including informal workers.66Using data from Cadastro Nacional de Pessoas Jurídicas (CNPJ), a dataset that includes informationon the start of operations for all formal firms in Brazil, I find that, among all formal restaurantscreated between 2015 and 2017, 43 percent never hire a formal employee and therefore do notappear in RAIS. This data is the same as used in Feinmann et al. (2022) and was kindly shared bythe authors.67This implies that if the earnings effect is the same for workers at both formal and informal firms,and if the size of informal firms is comparable to that of formal firms, there would need to be45Alternative Outside Options The outside options estimated through PNAD-Cprovide a reasonable benchmark; however, gig workers may have outside options notfully captured in survey data. Figure A.26 shows the total effect of the delivery serviceas a function of the outside option for non-formal app workersâexpressed as the ratioof their wages to formal workersâ wages (Φ). The figure highlights several interestingscenarios: if delivery workers without formal jobs had been unemployed (outsideoption of 0), the total surplus would be approximately 15 percent of the pre-adoptionwage bill. Conversely, if all non-formal app workers had the same outside option asformal workers, the delivery service would generate a wage-bill deficit of 10 percent.The break-even point occurs when non-formal workers have an outside option equalto 60 percent of formal workersâ, resulting in a wage-bill surplus of zero.Amenities The exercise presented in this section considers only the monetary valuecreated (and destroyed) by delivery platforms, without accounting for other non-pecuniary aspects, such as amenities, that may affect the overall welfare impact ofthese platforms. For instance, if app workers highly value flexibility (Mas and Pallais,2020), then the gains from the platform for these workers may exceed those presentedhere. Conversely, if working on the platform poses risks related to health, safety,or job security more broadly (Jarosch, 2023), the benefits of the platform for appworkers may be overestimated by focusing solely on earnings. To benchmark thequality of the jobs created by the platform relative to the rest of the restaurant sector,I estimate firm (and platform) valuation using the PageRank algorithm developedby Sorkin (2018). This revealed preference measure leverages employer-to-employertransitions to quantify the value of working at an establishment (V ej ).68 Importantly,this valuation includes both pecuniary and non-pecuniary characteristics of the firm.Figure A.27 shows the distribution of V ej across all Brazilian restaurants where it waspossible to estimate value (approximately 30 percent of all formal establishments).The red dashed line represents the platformâs value. The gig job on the platform ranks0.53 informal firms for each formal firm affected by a spillover effect to break even. Given thatthe average share of informal restaurant workers in my sample is 25 percent, this scenario seemsunlikely.68Appendix A.4.6 provides details on this estimation method and validation tests. Figure A.28 Panel(a) shows that firm value correlates with pay policies, as measured by AKM firm effects. Higher-paying firms also have a higher V ej . Panel (b) plots this correlation for restaurants specifically.Interestingly, the correlation between firm value and AKM firm effects is strong for low-valuationrestaurants but seems to weaken for highly valued restaurants, suggesting that amenities may playa larger role in the utility derived from these jobs.46within the 90th percentile of V ej among all formal restaurants in Brazil, implying thatamenities offered by the platform (such as flexibility) may enhance workersâ totalwelfare beyond the earnings generated by the platform.Firm Creation Estimating the overall effects of delivery platforms requires consid-ering not only the impacts on incumbent firms but also the effects on new firm entry.Anecdotal evidence suggests that delivery platforms have facilitated the entry of âdarkkitchensââestablishments that operate exclusively through delivery services withoutin-house dining.69 More broadly, delivery platforms may enable firms to offer goodswith lower capital requirements, thereby increasing firm entry. However, they mayalso intensify competition, potentially discouraging smaller, less efficient firms fromopening. I use data collected from the Cadastro Nacional de Pessoas Jurídicas (CNPJ)to measure firm entry. Figure A.20 panel (a) shows the estimated effect of deliveryplatform availability on restaurant entry.70 The data indicate that restaurants aremore likely to open in each quarter after the platform becomes available in a givenmicroregion, with an average increase in entry likelihood of 7.5 percent. Panel (b)presents the same estimates, this time weighted by the average number of workersin the restaurant sector in each microregion as of 2017. These weighted results areconsistent with the unweighted estimates but show a larger effect, indicating thatrestaurants are 10.5 percent more likely to open after platform entry when accountingfor microregion size. These findings suggest that focusing solely on incumbent firmsmay underestimate the total impact of delivery platforms on the labor market.Limitations The approach used to assess the costs and benefits of the platformfocuses on its partial-equilibrium impact and does not consider the potential realloca-tion effects it may induce. For instance, the platform may serve as a stepping stonefor non-formal workers (Booth et al., 2002; Jahn and Rosholm, 2014), potentiallyenabling them to enter the formal sector after working on the platform. Alternatively,delivery platforms might redirect workers toward less advantageous career paths bysending negative signals to the market (Neumark, 2018) or by reducing their job searchefforts (Le Barbanchon et al., 2024). In-house workers displaced by outsourced appdrivers may also reallocate to other types of firms (Dustmann et al., 2022), poten-69For a discussion of dark kitchens in Brazil, see this article from Globo in 2022.70Appendix A.4.7 discusses the empirical design used to estimate these effects and provides additionalresults indicating no substantial change in the composition of new entries.47tially affecting their long-term career trajectoriesâan effect that the time frame ofthis data does not allow to measure. These potential general-equilibrium effects arebeyond the scope of this paper but present important avenues for future research tobetter understand the overall impact of online-delivery platforms on the labor market.Additionally, although the platform studied in this paper accounts for a large shareof the delivery market, gig drivers may work on multiple platforms simultaneously(Caldwell and Oehlsen, 2023) or combine platform work with other forms of non-formalemployment, which my analysis cannot account for. If workers are able to generateadditional income sources due to the platformâs flexibility, the earnings effect of theplatform on formal workers could be underestimated. Conversely, if platform workrestricts workersâ opportunities to secure higher-paying jobs, the platformâs effect onapp workers could be overestimated.2.9 ConclusionOver the past 15 years, online-delivery platforms have expanded rapidly worldwide,transforming interactions between workers and employers in the restaurant sector.This paper provides evidence on the impact of online-delivery platforms on restaurantsand workers in Brazil. Using a unique data set that combines detailed administrativeemployer-employee records with platform-worker data, I show that platform adoptionprofoundly affects labor demand. Specifically, adopting a platform leads to a substantialreduction in the number of servers employed, who are replaced by gig delivery drivers.However, these workers do not experience persistent earnings losses, as they areable to get new jobs quickly. Online-delivery platforms also have spillover effects onnon-adopting restaurants, indicating that restaurants adopting these platforms maycrowd out demand for those that do not.I assess the overall impact of delivery platforms by comparing the losses experiencedby in-house workers with the gains of platform workers. Accounting for gig workersand informal workers in the restaurant sector, I find that the platform generatesa wage-bill surplus of 6.8 percent relative to the average pre-platform wage bill ofrestaurants. This surplus is primarily driven by the gains of workers who did not holdformal employment when joining the platform.The results presented in this paper carry significant implications for policy makersconsidering regulating these platforms. The findings demonstrate that online-delivery48platforms substantially affect both traditionally employed workers and gig workers.Regulatory efforts should carefully balance the benefit of protecting traditional workersagainst displacement with the potential benefits for gig workers. Policy makers mightexplore mechanisms to ensure gig workers gain access to basic protections, such associal safety nets, without diminishing the flexibility and low entry costs that makethese jobs appealing to many. This consideration is particularly relevant in settingslike Brazil, where gig work can serve as a critical buffer in an economy with high levelsof informality and unemployment.A limitation of this paper is its inability to capture the sources of the nonwage gainsand losses that workers may experience while working on the platform. Future researchshould investigate the nonpecuniary aspects of gig work and how these platformsinfluence the overall welfare of workers. A particularly interesting direction would beto examine how delivery platforms affect workersâ health and how these individualsvalue specific job characteristics, such as flexibility. Additionally, this paper does notaddress the long-term effects of the platform on workersâ careers. Given the largeshare of informal and unemployed workers on delivery platforms, future researchcould explore how these platforms affect the job ladder for individuals. Specifically, itwould be worthwhile to determine whether these platforms serve as stepping stonesfor workers to enter the formal sector or whether they, in fact, limit these workersâopportunities to find higher-quality jobs in the future.492.10 FiguresFigure 2.1: Restaurant Structure before and after online-delivery platformsFigure 1.1: Restaurant StructurePrevious to Delivery PlatformsManaged by the RestaurantIn-house dining Pick-up DeliveryRestaurantFigure 1.2: Restaurant StructureAfter Delivery PlatformsRestaurantIn-house dinningPick-up DeliveryOnline Delivery appFigure 1.3: Main Relationship Studied in thePaperRestaurantIn-house diningPick-up DeliveryOnline Delivery appNotes: The figure illustrates the structure of restaurants before and after the adoption of online-delivery platforms. Panel (a) shows the structure of restaurants before the adoption of online-deliveryplatforms. When not enrolled in delivery platforms, restaurants usually operate in-house dinning,pick-ups and deliveries (if offered). Panel (b) and panel (c) show the structure of restaurants after theadoption of online-delivery platforms. When restaurants start offering services through online-deliveryplatforms, these platforms often take over the pick-up and deliveries of the restaurants. In this paperI focus on the impact of online-delivery platforms that offer delivery services, as highlighted in red inpanel (c).50Figure 2.2: AKM Firm FE and usage of the PlatformFigure 2.1: Distribution of AKM firm FE forAdopters and Non-Adoptersâ = 0.08ââ0.03.06.09.12-2 -1 0 1Firm FERestaurants With AppRestaurants W/O AppMean With AppMean W/O AppFigure 2.2: Intensive Margin of Platform and AKMFirm FE44.555.56Log Hours using Delivery-1.6 -1.2 -.8 -.4 0 .4 .8 1.2Firm FERegression Slope: 0.7694Notes: Panel (a) reports the histogram of AKM workplace effects for restaurants in Brazil. It plotsthe selection into offering deliveries through an online platforms. The figure plots the histogram ofAKM firm effects for restaurants that at some point in time offered deliveries through the deliveryplatform and restaurants that never offered services through the app. The distributions fo AKM firmeffects are normalized such that the average workplace effect in the group of firms that never offeredservices through online-delivery platforms is zero. The distribution for restaurants that offer servicesthrough the platform is shifted to the right by 8 log points, indicating that firms with higher wagepolicies for formal workers are more likely to offer services through the platform. Panel (b) plots abinned scatter plot of the logarithm of the total hours reported in my sample that platform-workersworked as delivery drivers through the platform for restaurants, plotted against the AKM firm effectsof restaurants that offer services through the app (slope is 0.77; SE XXX). The hours worked at eachfirm through the delivery platform cover the years 2018 to 2021. Estimated firm effects are restrictedto establishment in the largest connected set that, at any point in my sampling window, offeredservices through delivery platforms. The AKM specification was estimated using data from RAISbetween the years 2012 and 2018. Restaurants are defined as establishments that have a CNAE twodigit code equal to 56.51Figure 2.3: Trends in hours Hired for In-House and Platform WorkersFigure 3.1: Total Hours by Type of Labor05001000150020002500300035004000450050005500Total Hours-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformApp Workers In-House WorkersAllFigure 3.2: Share of Outsourced Labor0.02.04.06.08Share App/In-House Hours-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformNotes: Panel (a) reports the total hours per quarter hired by restaurants that offer delivery servicesthrough the platform and match to a control restaurant. The red line reports the average quarterlyhours for workers hired formally by these restaurants (in-house workers). The blue line reports theaverage quarterly hours that delivery platform-workers worked for these restaurants (app workers).The solid black line is the sum of in-house workers and app workers hours. Panel (b) shows the shareof average quarterly hours that delivery platform-workers contributed to these restaurants, relative tothe total hours worked by both in-house employees and app workers. The x-axis reflects the quarterrelative to the first quarter in which each restaurant offered delivery services through the platformfor the first time.52Figure 2.4: Effects of Online-Delivery Platform Adoption on In-House Size and WagesFigure 4.1: Log In-House Size ofRestaurants1.651.71.751.81.85Log Firm Size-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformControlTreatedFigure 4.2: Event-Study Estimates on Log In-House Size of Restaurants-.08-.06-.04-.020.02Effect on Log Firm Size-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformFigure 4.3: Avg. Wages Paid byRestaurants77.057.17.157.2Log Earnings in Month-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformControlTreatedFigure 4.4: Event-Study Estimates on Log Avg.Monthly Wages Paid by Restaurants-.06-.04-.020.02Effect on Log Earnings in Month-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformNotes: Panel (a) and Panel (c) reports the trajectories of the logarithm of the size of the restaurantsâas measured by the number of workers hired in a quarterâ (Panel a) and wages as measured bythe average wages paid by a restaurant in a quarter (Panel c) of restaurants that offer deliveryservices through the online platform and their matched-controls. Panel (b) and Panel (d) reportthe corresponding event-study estimates obtained after fitting equation (2.10) on log restaurant size(Panel b) or average wages paid at the establishment (panel d). The x-axis reflects the quarterrelative to the first quarter in which each restaurant offered delivery services through the platform forthe first time. Quarter 0 is the quarter when the treated restaurant starts offering delivery servicesthrough the platform. A formal worker is defined to be employed in a quarter if they have at least oneday of work recorded in RAIS. Quarterly size is constructed by taking the average of the number ofworkers hired formally each month in the quarter by the establishment after applying the restrictionsdescribed in Section 2.3. Average monthly wages in a quarter are constructed by taking the quarterlyaverage of all the monthly wages reported in RAIS by the establishment in the corresponding quarter.Wages are expressed in real terms (2018 CPI). Both panels report 95% confidence intervals based onstandard errors clustered at the establishment level.53Figure 2.5: Effects of Online-Delivery Platform Adoption on Total Hours Hired-.1-.08-.06-.04-.020.02.04.06Log Total Hours in Month-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformApp + In-House WorkersIn-House WorkersNotes: This figure reports the corresponding event-study estimates obtained after fitting equation(2.10) on log total hours hired quarterly for workers hired formally (the red dashed line) and log totalhours hired quarterly when accounting for formal workers and platform-workers (the solid blue line).The x-axis reflects the quarter relative to the first quarter in which each restaurant offered deliveryservices through the platform for the first time. Quarter 0 is the quarter when the treated restaurantstarts offering delivery services through the platform. The panel reports 95% confidence intervalsbased on standard errors clustered at the establishment level.54Figure 2.6: Effects of Online-Delivery Platform Adoption on Total Hours Hired andIn-House Size by AKM Firm EffectFigure 6.1: Log In-House Size of Restaurants-.1-.08-.06-.04-.020.02.04.06.08.1Log Firm Size-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformBelow Median AKM Firm FEAbove Median AKM Firm FEFigure 6.2: Log Total Hours Hired-.1-.08-.06-.04-.020.02.04.06.08.1Log Hours (including App)-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformBelow Median AKM Firm FEAbove Median AKM Firm FENotes: Panel (a) reports the corresponding event-study estimates obtained after fitting equation (2.10)on log restaurant size as defined by the average number of formal workers hired by the establishment.A formal worker is defined to be employed in a quarter if they have at least one day of work recordedin RAIS. Quarterly size is constructed by taking the average of the number of workers hired formallyeach month in the quarter by the establishment after applying the restrictions described in Section2.3. The red dashed line reports the estimates for restaurants that are above the median of AKMfirm effects. The solid blue line reports the estimates for restaurants that are below the medianof AKM firm effects. The median of AKM firm effects is calculated using the AKM firm effects ofall treated and control restaurants that match that belong to the largest connected set. When arestaurant does not have an AKM firm effect, I input the firm effect of their matched pair. TheAKM specification was estimated using data from RAIS between the years 2012 and 2018. Panel (b)reports the corresponding event-study estimates obtained after fitting equation (2.10) on log totalhours hired in the quarter when accounting for workers hired formally and workers working throughthe platform. The x-axis reflects the quarter relative to the first quarter in which each restaurantoffered delivery services through the platform for the first time. Quarter 0 is the quarter when thetreated restaurant starts offering delivery services through the platform. Both panels report 95%confidence intervals based on standard errors clustered at the establishment level.55Figure 2.7: Effects of Online-Delivery Platform Adoption on Total Hours Hired andIn-House Size by Restaurant DensityFigure 7.1: Log In-House Size of Restaurants-.12-.1-.08-.06-.04-.020.02.04.06Log Firm Size-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformLow Density of RestaurantsHigh Density of RestaurantsFigure 7.2: Log Total Hours Hired-.06-.04-.020.02.04.06.08.1Log Hours (including App)-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformLow Density of RestaurantsHigh Density of RestaurantsNotes: Panel (a) reports the corresponding event-study estimates obtained after fitting equation (2.10)on log restaurant size as defined by the average number of formal workers hired by the establishment.A formal worker is defined to be employed in a quarter if they have at least one day of work recordedin RAIS. Quarterly size is constructed by taking the average of the number of workers hired formallyeach month in the quarter by the establishment after applying the restrictions described in Section2.3. The red dashed line reports the estimates for restaurants that are above the median of restaurantdensity within their microregion. The solid blue line reports the estimates for restaurants that arebelow the median of restaurant density within their microregion. Restaurant density is calculated asthe number of restaurants that are located in a 1 kilometer radius of each restaurant (Ï). The medianof restaurant density is calculate using the distribution of Ï corresponding to the microregion of eachrestaurant in each quarter. The density assigned to each restaurant corresponds to the Ï calculatedusing the quarter prior to the first quarter in which the treated restaurant of the pair started offeringdelivery services through the platform. Panel (b) reports the corresponding event-study estimatesobtained after fitting equation (2.10) on log total hours hired in the quarter when accounting forworkers hired formally and workers working through the platform. The x-axis reflects the quarterrelative to the first quarter in which each restaurant offered delivery services through the platform forthe first time. Quarter 0 is the quarter when the treated restaurant starts offering delivery servicesthrough the platform. Both panels report 95% confidence intervals based on standard errors clusteredat the establishment level.56Figure 2.8: Spillover Effects of Platform Adoption on Non-Adopting RestaurantsFigure 8.1: Log In-House Size ofRestaurants1.51.61.71.8Log Firm Size-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the EventControlTreatedFigure 8.2: Event-Study Estimates on Log In-House Size of Restaurants-.06-.04-.020.02.04Effect on Log Firm Size-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the EventFigure 8.3: Avg. Wages Paid byRestaurants6.977.17.2Log Earnings in Month-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the EventControlTreatedFigure 8.4: Event-Study Estimates on Log Avg.Monthly Wages Paid by Restaurants-.04-.020.02.04Effect on Log Earnings in Month-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the EventNotes: This figure reports event study estimates for restaurants that are exposed to a spilloverevent. A spillover event is defined as a restaurant that is exposed to a large increase in the share ofrestaurants within a 1 kilometer radius that start offering services through the platform. As describedin Section 2.5.2, a large increase is defined as the top 5 percentile of the distribution of quarterlychanges in the share of nearby restaurants that adopt the platform. Panel (a) and Panel (c) reportthe trajectories of the logarithm of the size of the restaurantsâas measured by the number of workershired in a quarterâ (Panel a) and wages as measured by the average wages paid by a restaurant ina quarter (Panel c) of restaurants exposed to a spillover event and their matched-controls. Panel(b) reports the corresponding event-study estimates obtained after fitting equation (2.10) on logrestaurant size as defined by the average number of formal workers hired by the establishment. Aformal worker is defined to be employed in a quarter if they have at least one day of work recordedin RAIS. Quarterly size is constructed by taking the average of the number of workers hired formallyeach month in the quarter by the establishment after applying the restrictions described in Section2.3. Panel (d) presents the estimates for the log average monthly wages paid at restaurants. Thex-axis reflects the quarter relative to the quarter in which the spillover event occurs. Average monthlywages in a quarter are constructed by taking the quarterly average of all the monthly wages reportedin RAIS by the establishment in the corresponding quarter. Wages are expressed in real terms(2018 CPI). Both panels report 95% confidence intervals based on standard errors clustered at theestablishment level.57Figure 2.9: Effects of Employer Adoption of Online-Delivery Platform on WorkerEarnings and EmploymentFigure 9.1: Avg. Monthly Earnings8001000120014001600Total Earnings in Month-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Employer Adoption of the PlatformControlTreatedFigure 9.2: Event-Study Estimates on TotalEarnings-60-40-200204060Effect on Tot. Earnings in Month-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Employer Adoption of the PlatformFigure 9.3: Employment.6.7.8.91Employed-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Employer Adoption of the PlatformControlTreatedFigure 9.4: Event-Study Estimates on Employ-ment-.03-.02-.010.01Effect on Employed-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Employer Adoption of the PlatformNotes: Panel (a) and Panel (c) reports the trajectories of total earnings and employment of restaurantworkers whose employer starts offering delivery through the platform and their matched controlworkers. Panel (b) and Panel (d) report the corresponding event-study estimates obtained afterfitting equation (2.10) on the quarterly average of monthly earnings (Panel b) or employment (paneld). The mean earnings for treated workers the quarter prior to treatment is 1,488 BRL-2018. Averagemonthly earnings reflects the quarterly average of the monthly formal wages that a worker earns inthe corresponding quarter. If the worker did not hold a formal job during the period, the earningsare equal to 0. The x-axis reflects the quarter relative to the first quarter in which the employer ofthe treated worker offered delivery services through the platform for the first time. Quarter 0 is thequarter when the restaurant that employs the treated worker starts offering delivery services throughthe platform. A formal worker is defined to be employed in a quarter if they have at least one dayof work recorded in RAIS. Wages are expressed in real terms (2018 CPI). Both panels report 95%confidence intervals based on standard errors clustered at the worker level.58Figure 2.10: Spillover Effects of Platform Adoption on Restaurant WorkersFigure 10.1: Avg. Monthly Earnings8001000120014001600Total Earnings in Month-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the EventControlTreatedFigure 10.2: Event-Study Estimates on TotalEarnings-100-50050Effect on Tot. Earnings in Month-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the EventFigure 10.3: Employment.6.7.8.91Employed-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the EventControlTreatedFigure 10.4: Event-Study Estimates on Em-ployment-.06-.04-.020.02Effect on Employed-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the EventNotes: This figure reports event study estimates for workers who are employed at restaurants thatare exposed to a spillover event. A spillover event is defined as a restaurant that is exposed to a largeincrease in the share of restaurants within a 1 kilometer radius that start offering services throughthe platform. As described in Section 2.5.2, a large increase is defined as the top 5 percentile of thedistribution of quarterly changes in the share of nearby restaurants that adopt the platform. Panel(a) and Panel (b) reports the trajectories of total earnings and employment of restaurant workerswhose employer is exposed to a spillover event and their matched control workers. Panel (b) reportsthe corresponding event-study estimates obtained after fitting equation (2.10) on on the quarterlyaverage of monthly earnings. The mean earnings for treated workers the quarter prior to treatmentis 1,496 BRL-2018. Average monthly earnings reflects the quarterly average of the monthly formalwages that a worker earns in the corresponding quarter. If the worker did not hold a formal jobduring the period, the earnings are equal to 0. Panel (d) presents the estimates for employment. Aformal worker is defined to be employed in a quarter if they have at least one day of work recordedin RAIS. The x-axis reflects the quarter relative to the quarter in which the spillover event occurs.Wages are expressed in real terms (2018 CPI). Both panels report 95% confidence intervals based onstandard errors clustered at the worker level.592.11 TablesTable 2.1: Summary Statistics for Adopting and Control Restaurants(1) (2) (3) (4)Matched Control Matched Treated Potential Treated All RestaurantsYears of Education 11.02 11.29 11.29 11.01(1.38) (1.27) (1.43) (1.62)Tenure (in years) 2.58 1.43 1.25 2.04(1.78) (1.92) (1.90) (2.43)Monthly Wage (2018 - R$) 1,483.81 1,530.43 1,477.52 1,438.13(412.83) (571.16) (553.56) (507.57)Age 33.38 32.48 32.71 34.95(7.02) (6.94) (7.68) (8.42)Share of Brazilians 1.00 0.99 0.99 0.99Female 0.59 0.59 0.59 0.60Hours 43.10 43.05 43.18 43.09(2.75) (3.05) (2.81) (3.02)Full-Time 0.97 0.97 0.97 0.97Establishment Size 11.34 11.50 9.12 7.36(24.75) (22.83) (42.77) (34.84)Share of Waiters 0.48 0.50 0.49 0.49(0.32) (0.31) (0.35) (0.36)Number of Establishments 7,836 14,862 57,962 296,263Notes: Column 2 presents the summary statistics of the restaurants that offer delivery servicesthrough the platform and for whom I can find a matched control restaurant. These summary statisticsare calculated in 2017. Potential matched control restaurants are those who in tâj â 1 do not haveplatform delivery services available in their microregion (and the platform does not start offeringservices the following five quarters). Additionally, these control restaurants must have been openfor at least two years, belong to the same quartile of firm size, quartile of average earnings andmedian of number of restaurants within a radius of 1 kilometers (with respect to the distribution ofrestaurants in their corresponding microregion). A propensity score matching based on log size intââ 8 to tââ 1, log average monthly wages paid in quarters tââ 4 to tââ 1, firm age, share of waiters,average tenure, age of workers, share of female workers and average hours of workers is conductedto assign exactly one matched control restaurant to each treated restaurant. Column 1 reports thecharacteristics of matched control restaurants, and Column 3 reports the summary statistics forthe entire set of restaurants that offer delivery services through the platform identified in RAIS.Column 4 reports the summary statistics for all restaurants found in RAIS in 2017. Restaurants aredefined as establishments that have a CNAE two digit code equal to 56. Wages are expressed in realterms (2018 CPI). All statistics are person-month-weighted, and standard deviations are reported inparentheses60Table 2.2: Effect of Platform Adoption on Labor Demand per Occupation(1) (2) (3) (4) (5) (6)Number of Waiters Number of Cooks Number of Delivery Workers Log Wage Waiters Log Wage Cooks Log Wage Deliveryβ(Diff-in-Diff) -0.3088*** 0.0001 -0.0162* -0.0247*** -0.0112 -0.0027(0.0581) (0.0255) (0.0094) (0.0067) (0.0081) (0.0243)Mean prior to Adoption 5.5339 2.0368 0.1539 7.1397 7.2294 7.2784Observations 363,129 363,129 363,129 292,984 207,408 29,466Notes: The table reports the corresponding difference-in-difference estimates obtained after fittingequation (2.10) on the number of waiters (column 1), the number of cooks (column 2), the number ofin-house delivery drivers (3) and their respective log wages (column 4, 5 and 6) at each establishmentj using the estimator from Borusyak et al. (2024). A formal worker is defined to be employed in aquarter if they have at least one day of work recorded in RAIS. Quarterly number of workers for eachoccupation is constructed by taking the average of the number of workers hired in that occupationformally each month in the quarter by the establishment after applying the restrictions described inSection 2.3. Average monthly wages in a quarter are constructed by taking the quarterly average ofall the monthly wages reported in RAIS by the establishment in the corresponding quarter. Wagesare expressed in real terms (2018 CPI). Standard errors clustered at the establishment level. *** issignificant at the 0.01 level, ** is significant at the 0.05 level, and * is significant at the 0.1 level.61Table 2.3: Heterogeneous Effects of Platform Adoption on Labor Demand per Occu-pation(1) (2) (3) (4)Above Median Below MedianNumber of Waiters Number of Cooks Number of Waiters Number of CooksPanel A: Heterogeneity by AKM Firm Effectsβ(Diff-in-Diff) -0.4100*** 0.0140 -0.1452** -0.0273(0.0747) (0.0324) (0.0673) (0.0302)Mean Dependent Variable Prior to App Adoption 6.4699 2.2369 3.7786 1.6845Observations 236,494 236,494 120,848 120,848Panel B: Heterogeneity by Restaurant Densityβ(Diff-in-Diff) -0.4151*** -0.0247 -0.1161 0.0695*(0.0714) (0.0324) (0.1038) (0.0386)Mean Dependent Variable Prior to App Adoption 5.7365 2.0888 4.9867 1.9286Observations 232,545 232,545 105,887 105,887Notes: Panel (a) and Panel (b) of the table report the corresponding difference-in-difference estimatesobtained after fitting equation (2.10) on the number of waiters and the number of cooks at eachestablishment j using the estimator from Borusyak et al. (2024). Panel (a) reports the heterogeneityby AKM firm effects. Panel (b) reports the heterogeneity by restaurant density. Columns (1) and(2) present the results for above the median of each measure, while columns (3) and (4) present theresults for below the median of each measure. Restaurant density is calculated as the number ofrestaurants that are located in a 1 kilometer radius of each restaurant (Ï). The median of restaurantdensity is calculate using the distribution of Ï corresponding to the microregion of each restaurantin each quarter. The density assigned to each restaurant corresponds to the Ï calculated usingthe quarter prior to the first quarter in which the treated restaurant of the pair started offeringdelivery services through the platform. The median of AKM firm effects is calculated using the AKMfirm effects of all treated and control restaurants that match that belong to the largest connectedset. When a restaurant does not have an AKM firm effect, I input the firm effect of their matchedpair. The AKM specification was estimated using data from RAIS between the years 2012 and2018. A formal worker is defined to be employed in a quarter if they have at least one day of workrecorded in RAIS. Quarterly number of workers for each occupation is constructed by taking theaverage of the number of workers hired in that occupation formally each month in the quarter bythe establishment after applying the restrictions described in Section 2.3. Average monthly wagesin a quarter are constructed by taking the quarterly average of all the monthly wages reported inRAIS by the establishment in the corresponding quarter. Wages are expressed in real terms (2018CPI). Standard errors clustered at the establishment level. *** is significant at the 0.01 level, ** issignificant at the 0.05 level, and * is significant at the 0.1 level.62Table 2.4: Summary Statistics for Workers whose Employer Adopts the DeliveryPlatform and Control Workers(1) (2) (3)Matched Control Workers Matched Treated Workers Potential Treated WorkersYears of Education 10.92 11.07 11.42(2.18) (1.87) (2.05)Avg Tenure (in years) 3.68 3.68 2.91(1.35) (1.35) (1.58)Avg Monthly Earnings (in 2018-R$) 1,501 1,503 1,789(377) (389) (1,475)Age 32.51 32.45 33.14(9.96) (9.99) (11.10)Share of Brazilians 1.00 0.99 0.99Female 0.59 0.59 0.51Hours 43.13 41.29 41.69(3.22) (6.95) (6.66)Share of Black Workers 0.05 0.07 0.06Delivery 0.01 0.01 0.01Kitchen 0.18 0.18 0.18Waiters 0.67 0.67 0.52Other 0.14 0.14 0.29Number of Workers 15,283 19,610 132,861Notes: Column 2 presents the summary statistics of workers working at restaurants when theystart offering delivery services through the platform and for whom I can find a matched controlworker. These summary statistics are calculated the quarter prior to the employers adoption of theplatform, that is, tâ â 1. Potential matched control workers are those who in tâj â 1 are working at arestaurant that is located in a microregion where there are no platform delivery services available(and the platform does not start offering services the following five quarters). Additionally, thesecontrol workers must have the same gender, occupation as their corresponding treated worker andtheir employer must belong to the same quartile of wage, size and median of number of restaurantswithin a radius of 1 kilometers (with respect to the distribution of restaurants in their correspondingmicroregion). A caliper matching method based on earnings in tâ â 1 to tâ â 4, age and tenureis then conducted to assign exactly one matched control worker for each treated worker withoutreplacement ((Stepner and Garland, 2017)). Column 1 reports the characteristics of matched controlworkers, and Column 3 reports the summary statistics for the entire set of workers that work ata restaurant that offer delivery services through the platform identified in RAIS. Restaurants aredefined as establishments that have a CNAE two digit code equal to 56. Occupations are based onthe 6 digit Classificação brasileira de ocupações (CBO). Wages are expressed in real terms (2018CPI). Standard deviations are reported in parentheses.63Table 2.5: Total Wage Bill Effect of Platform Adoption(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)In-House RestaurantWorkers (Formal)In-House RestaurantWorkers (Informal) Spillovers (Formal) Spillovers (Informal) App Workers (From Formal Sector) App Workers (From Non-Formal Sector)Gross Wage Net Wage Gross Wage Net Wage Wage Bill AppWage Bill Netof Opportunity &Transportation CostsWage Bill AppWage Bill Netof Opportunity &Transportation CostsQuarter 0 -61.6 -69.9 -16.6 -40.0 -37.6 -9.2 330.2 88.2 1,494.2 966.7Quarter 1 -693.4 -651.2 -154.5 -307.6 -284.0 -69.6 770.8 189.6 3,660.8 2,319.0Quarter 2 -714.4 -666.8 -158.1 -443.0 -409.2 -100.3 868.9 198.9 4,317.2 2,672.1Quarter 3 -507.8 -479.8 -113.8 -1,225.1 -1,124.7 -275.6 1,075.7 269.4 5,290.5 3,324.9Quarter 4 -933.0 -860.3 -204.1 -2,483.2 -2,278.2 -558.3 1,110.6 259.9 5,547.8 3,426.9Quarter 5 -395.6 -381.9 -90.6 -3,974.7 -3,638.6 -891.6 1,115.1 233.3 5,820.2 3,530.6Cumulative PDV -3,192.3 -3,003.5 -712.4 -8,055.5 -7,389.2 -1,810.7 5,080.6 1,195.7 25,169.1 15,647.2Percentage of Pre-PlatformWage Bill -5.5% -5.2% -1.2% -13.9% -12.7% -3.1% 8.7% 2.1% 43.3% 26.9%Notes: This table reports the estimated wage bill surplus generated by the platform for each component of equation (2.12). Column 1 presentsthe effects on in-house restaurant workers whose employer starts offering delivery services through the platform. Each quarter report thecorresponding event-study estimates obtained after fitting equation (2.10) on monthly earnings, converting the coefficient to quarterly andmultiplying it by the average firm size of restaurants that adopt the platform in tâ â 2. Column presents the same estimates but using thewages net of social security contributions. Column 3 presents the estimated effects for informal workers at adopting restaurants. The numberof informal workers is imputed to each establishment using the average share of informal workers in the restaurant sector in each municipalityestimated using the 2010 Census. The earnings of informal workers are imputed to each establishment using the ratio of informal to formalwages in the restaurant sector in each state-quarter estimated using the PNAD-C. Column 4 presents the estimated coefficients for workersthat are employed at restaurants that are exposed to a spillover event. A spillover event is defined as a restaurant that is exposed to a largeincrease in the share of restaurants within a 1 kilometer radius that start offering services through the platform. As described in Section 2.5.2,a large increase is defined as the top 5 percentile of the distribution of quarterly changes in the share of nearby restaurants that adopt theplatform. To estimate a per adopting restaurant effect of spillovers, the earnings effects of workers exposed to spillover events are re-scaled bythe effect of spillover on restaurant adoption, multiplied by the average adoption of the platform and multiplied by the average firm size ofrestaurants in my sample. Column 5 and Column 6 present the net wage effect and the spillover effects on informal workers estimated usingthe same methodology as for in-house workers. Column 7 reports the total earnings gained by workers who held a formal job the quarterprior to working on the platform (and were not laid-off). Column 8 reports the total earnings gained for these workers net of their outsideoption and their transportation costs. The outside option is equivalent to the average per hour wage that workers held prior to working on theplatform. Transportation costs are based on estimates from Callil and Pincaço (2023). Column 9 and 10 report the same estimates for workersthat did not hold a formal job the quarter prior to working on the app. The outside option for these workers is imputed using the averagewage of informal workers in the restaurant sector in each state-quarter estimated using the PNAD-C. PDV means present discounted valuewhich was calculated using the average quarterly inflation of Brazil between 2019 and 2021 (0.012). Pre-platform wage bill is the averagequarterly wage bill of restaurants that adopt the platform between tâ â 2 and tâ â 7 Wages are expressed in real terms (2018 CPI).64Chapter 3Paying Outsourced Labor: DirectEvidence from Linked TempAgency-Worker-Client Data3.1 IntroductionWe shed direct light on wage setting for outsourced workers. We study employmentmediated by temporary employment agencies (âtemp agenciesâ), where the workplace isat a user firm even though the temp agency serves as the formal employer. Temp agencywork is a facet of outsourcing and, more broadly, nonstandard work arrangements,which have been associated with lower wages and increased inequality (Weil, 2014).Specifically, we focus on firmsâ wage policies in the form of pay premia (defined asfirm fixed effects in Abowd et al., 1999b, henceforth AKM, specifications). Thebetween-firm wage dispersion arising from pay premia constitutes a deviation from thelaw of one price that would arise in spot labor markets (see, e.g., Slichter, 1950; Lester,1967). These premia can arise in imperfectly competitive labor markets throughbargaining, search frictions, or monopsony (see, e.g., Mortensen, 2003; Hornsteinet al., 2011; Card et al., 2018a). A long-standing hypothesis is that nonstandardwork arrangementsâand specifically, outsourced, temp agency workâerode such paypremia by operating closer to a spot labor market or by lowering workersâ bargainingpower.1 However, forces such as equity concerns (Card et al., 2012; Breza et al., 2017;1For instance, Katz (2017) describes this view as follows: âWhen janitors work at Goldman Sachs asGoldman Sachs employees, they tend to share in the firmâs huge productivity benefits and huge65Dube et al., 2019; Saez et al., 2019) or the imperfect observability of effort (Akerlofand Yellen, 1986; Katz, 1986) may lead firms to extend firm-specific pay premia evento outsourced labor.User firmsâ wage setting for outsourced labor compared to regular workers has so farlargely escaped measurement because typical datasets exclusively associate outsourcedworkers with their formal employer, in our case the temp agency, rather than theworkplace, the user firm. This is true for surveys (Abraham and Amaya, 2018;Abraham et al., 2018; Katz and Krueger, 2019b,a). And the challenge extends totypical administrative matched employer-employee datasets, which generally do notshow links between temp agency workers and user firms (Goldschmidt and Schmieder,2017, constitute an exemption as they circumvent the missing link problem by studyingoutsourcing events of clusters of workers in low-skilled service occupations). Weillustrate this issue in Figure 3.1.Our paper overcomes this fundamental measurement challenge by drawing on uniqueadministrative data on the universe of workers in temporary work arrangements thatcontain information on both their temp agency and user firms. This linkage permitsus to directly study the differentiation of pay premia between regular and temp agencyworkers within a workplace.Our research design identifies pay premia through wage changes that accompanyworker moves across employers (Abowd et al., 1999b). Such workplace pay premia forregular workers are associated with higher productivity (as documented by, e.g., Cardet al., 2018a) and can hence be interpreted as facets of rent sharing that are directlyobservable in matched employer-employee data. We also document that worker tenureis longer in firms with higher AKM firm effects, consistent with higher-rent jobs. Weask whether these pay premia, whatever their source, are shared with outsourcedlabor.2rents. But if they work for Joeâs Janitorial Services, they no longer share in those rents.â Similarly,Autor (2008) argues that labor market intermediaries more broadly and specifically includingtemp agencies, âshare a common function, which is to redressâand in some cases exploitâa set ofendemic departures of labor market operation from the efficient neoclassical benchmark.â Empirically,Abraham (1990); Dube and Kaplan (2010) and Goldschmidt and Schmieder (2017) present evidenceon the wage penalty associated with nonstandard work arrangements and outsourcing.2Our work thus complements growing evidence documenting that firms may not set pay premiapolicies equally for all worker types. Using an AKM approach, Card et al. (2016) link the genderpay gap with differential rent sharing in Portugal. Gerard et al. (2021) link the racial wage gap withAKM premia differentials and sorting across employers in Brazil. Daruich et al. (2023) documentdifferential rent sharing with workers on fixed-term contracts and open-ended contracts in Italy.66In a first step, we compare cross-sectional dispersion measures of workplace-level paypremia separately for regular and temp agency workers. The competitive benchmark fortemp workers and the associated law of one price would imply little dispersion amongtemp workers. Though somewhat smaller compared with regular work arrangements,the dispersion of pay premia of temp agency workers is substantial. User firm paypremia for temp workers have a standard deviation of 17.2 log points, which rises to 20.7log points in regular work arrangements for the same sample of user firms. Hence, thelarge degree of wage dispersion that characterizes regular work arrangements extendsto the market for temporary agency work, even though this market is plausibly lesssubject to standard labor search frictions (consistent with Andreas Hornstein, PerKrusell and Giovanni Violante, 2011).In a second step, we compare workplace pay premia estimates (AKM firm effects) fortemp agency and regular work arrangements within firms. We therefore measure thedegree to which high-wage firms for regular work arrangements are also high-wagefirms for outsourced labor. Here, a view of temp workers as insiders in wage settingwould predict a slope of one. By contrast, either the competitive spot labor marketbenchmark or the treatment of temp workers as a separate class of workers wouldpredict a flat line. We find a reduced-form slope of 0.490 for temp agency workarrangements and quantitatively similar results when correcting for measurement error.Normalizing the firm effects for both work arrangements to zero for the lowest wageuser firms, our estimates thus imply that temp agency workers receive 49% of theworkplace-specific pay premia earned by regular workers in user firmsâa substantialmarkdown and the half point between the benchmark for insiders (one) and thecompetitive spot-labor market (zero).3 This pass-through, of around one half, ispresent even in low-tenure industries, where regular workers are more comparableto temp workers, and in firms less or more subject to wage floors from the nationalminimum wage or collectively bargaining.We discuss interpretations and implications of our findings in the conclusion section.3Temp workersâ share of regular workersâ pay premia would be even lower if even low-wage firmsâpremium is positive.673.2 Institutions and DataTemporary Work Agencies and Regulation The Argentinian labor market fortemporary work shares characteristics with those of other countries along variousdimensions. First, temp agencies in Argentina pay below-average wages (Beccaria andMaurizio, 2017). Second, their business model and regulatory environment are similarto those of temp agencies in OECD countries (OECD/IDB EPL Database, 2015). Forexample, Argentinian law (Decreto 1694/2006) mandates that temp agency workersshould be treated no worse than regular workers in the same capacity, similar toprovisions in the European Unionâs Temporary Agency Work Directive 2008. Finally,about 1.7% of employees were employed through a temp agency in 2005 (source: owncalculations, using the employer industry code in SIPA, whereas all our subsequentidentification uses the âSRâ data set available from 2008 onward, described below),compared with 0.9% in temp agencies and 1.4% through contract firms in the US(calculations based on February-2005 CPS, see Table 2 in Katz and Krueger, 2019b).Temp workersâ labor earnings and payroll taxes are paid by the temp agency (typicallymonthly, the frequency at which we see administrative earnings). We draw on arepresentative labor force survey (Encuesta Permanente de Hogares) to compareweekly hours of work of temp agency and regular workers and find that they aresimilar; if anything, temporary workers appear to work slightly more hours (36.18hrs/week, SD 12.15, vs. 34.61 hrs/week, SD 13.16, respectively; see Appendix FigureB.1 Panel (b)). As in many countries, there are a number of formal regulations fortemp agency pay. De jure, the temp agency ought to pay the worker the wage specifiedby the collective bargaining agreement corresponding to the actual job, or the wageeffectively paid in the user company. An open question is the degree to which suchcommon regulations are binding and complied with, or whether firms find ways tocircumvent the policies. For example, temp wage penalties and associated cost savingsmay point to imperfect compliance. In our study, partial compliance may be a formalinstitutional factor that contributes to similar pay policies across types within a firm,although we cannot definitely distinguish this channel from others, as we discuss inSection 3.5.Wage Setting in Argentina The Argentinian labor market features substantialscope for firm-level wage setting, consistent with the dispersion in between-firm wages68we will document. First, the minimum wage is not very binding in Argentina, withmore than 99% and 94% of formal workers having wages above the minimum wagein 2003 and 2012, respectively (Bértola and Williamson, 2017). Second, sector-widecollective bargaining agreements (CBAs) specify wage floors by occupation for allemployers. Third, some firm-specific CBAs are negotiated by the trade union withlarge firms that must weakly deviate upwards from the sectoral agreements. Fourth,specific employers can always deviate upwards on a discretionary basis. Consistentwith this scope for firm-level wage setting, although more than 80% of formal employeesare covered by CBAs, in the mid 2000s, the average monthly wage in the formal sectorwas 23% higher than the average monthly wage stipulated by sector-wide CBAs (seePalomino and Trajtemberg, 2006).Administrative Social Security Records (SIPA) We use monthly administrativeemployer-employee matched data from 1996 to 2018 from the national social securitysystem (Sistema Integrado Previsional Argentino, or SIPA). Details on the sampleconstruction are in Online Appendix B.3. The dataset (described in further detail in,e.g., Tortarolo, 2019) covers the universe of formal workers employed in all regions,industries, and types of contracts. This corresponds to more than 15 million workersand 40 million job spells. The dataset includes information on workers (gender andage) and their jobs (type of contract, part-time/full-time indicator, compensationcomponents), as well as some characteristics of the firm (sector and province). SIPAalso provides firm and worker tax identifiers, and reports total wages earned in eachmonth, which include all forms of payment that are taxable or subject to social securitycontributions. These measures are not top-coded. We CPI-deflate all payments tocorrespond to January 2008 Argentine Pesos.Administrative Worker-Client-Agency Linkage (SR) In addition, we exploitadministrative data linking the temp agency employing the worker and the user firmsvia tax identifiers of the temp workers, temp agencies, and clients (SimplificacionRegistral, or SR), which is available since 2008. This unique data source stems from a2006 reform of temp agency work, which required that temp agencies register tempworkers with the Ministry of Labor, at a bimonthly frequency, and submit informationon the worker, user company, position type, remuneration, and contract start and enddates. These filings are sworn statements and audited, and hence are of administrative69quality.Defining Earnings Concepts We use SIPA, reporting the monthly nominal pretaxcompensation paid by formal employers, to construct our earnings measures. Fortemp workers, compensation is paid by the temp agency. To remove ambiguity aboutearnings sources (workplaces) and hours and days worked, we restrict our sample oftemp workers to those providing services to a single user firm in a given month, anddrop temp spells with simultaneous user firms or partial-month spells (by omittingthe first and last month of employment in each job spell, as we do not observe precisestart dates of the temp agency-client firm spells). We winsorize earnings at the 1%level on both sides. We also drop earnings with real income less than half the real2008 minimum wage (in 2008, the real minimum earnings were USD340 per month)adjusted by the average annual growth rate (1.5%) of real income for the entire sample.3.3 Wages for Temp Agency Work in ArgentinaSummary Statistics In Appendix Tables B.1 and B.2, we provide descriptiveevidence on the types of workers in regular and temp agency arrangements, alongwith the characteristics of user firms. Overall, we find that temp agency workers tendto be younger (mean age of 28 vs. 38), and are more likely to be men (79% vs. 70%).For each industry, Appendix Figure B.1 Panel (a) plots temp agency employment as ashare of total national temp agency employment against its share in national regularemployment. Deviations from the 45-degree line indicate that a firm accounts formore or less temp employment than predicted by its regular employment share. Wefind, e.g., that manufacturing relies particularly strongly on temp agency employment,while education and health services and professional business services draw relativelyless on such outsourced labor. Our data set does not contain information on hours, butin Section 3.2, supplementary data suggested that hours are if anything higher amongtemp workers, making it unlikely that hours differences explain the lower earnings wedocument below.Estimating the Average Temp Agency Work Pay Penalty We next estimatethe pay effect associated with temp agency work. We regress log wages earned by70worker i in period t on an indicator for temp work, TempAgencyArrangementit:lnwit = αi + ÏJit + ÏÃ TempAgencyArrangementit +Xâ²itÎ² + ϵit. (3.1)As basic controls, Xit, we include gender and a cubic polynomial in workerâs age aswell as industry and year, or industry-by-year effects. Due to the panel nature ofthe data, we can also include worker effects, αi, which address selection based onpermanent differences between workers. As a novel feature of our dataset, we alsoinclude workplace J fixed effects, ÏJit , which allows us to estimate the temp agencywork penalty by comparing temp workers with regular workers in the same workplace.The coefficient of interest will capture pay premia differences between regular andtemp agency work arrangements, but may also pick up potential differences in hoursor productivity between arrangements (the former of which we can rule out on averageas we noted above). We estimate (3.1) based on the procedure in Correia (2017) andcluster standard errors at the worker level.We report results for specification (3.1) in Table 3.1. Column (1) reports the raw tempeffect of -0.133 (SE 0.0005) with only year effects. This effect is reduced substantiallyto -0.075 (SE 0.001) once we include gender and age controls, particularly since tempagency workers tend to be younger than regular workers (see Appendix Tables B.1 andB.2). We next report specifications with industry or industry-by-year effects, whichincreases the temp penalty to -0.191 (SE 0.001). When we include worker effects in thenext column, we find a point estimate for the penalty of -0.0795 (SE 0.0005), consistentwith the previous specificationâs overestimation of the temp penalty due to negativeworker selection. Next, we add firm effects and find a larger temp penalty of -0.140(SE 0.0005). Overall, the estimated wage penalty of -0.140, controlling for workplaceand worker effects, is similar to the estimates from the event studies of outsourcingof low-skilled service workers in Germany (-15 to -10 percent, see Goldschmidt andSchmieder, 2017) and for janitors and security guards in the US (-24 to -4 percent,see Dube and Kaplan, 2010).Estimating Workplace Premia for Regular and Temp Agency Workers Wenext estimate modified AKM specifications, in which we allow for separate workplaceeffects for regular and temp agency workers, which we will then juxtapose in Section713.4. Formally, we estimate the following specification:lnwit = αi + ÏWitJit + ξTempAgencyTAit+X â²itÎ² + ϵit, (3.2)where αi are worker fixed effects and ÏWitJit are work-arrangement-specific workplaceeffects.4 The superscript Wit â R, T indicates whether worker i is employed througha temp agency (T ) or a regular employment relationship (R) in period t, and Jitdenotes the workplace. In addition, we include temp agency effects, ξTempAgencyTAit , for thetemp agency TAit at which a temp agency worker i is formally employed in period t.The temp agency fixed effects also absorb potential average differences between workarrangements, such as potential differences in productivity or hours. We include ascontrol variables, Xit, a cubic term in worker age and year fixed effects. Intuitively, thewage changes of movers between different workplaces and work arrangements identifythe fixed effects in a connected set.5 We estimate (3.2) in the largest connected set,which captures 60.8% of firms and 95.9% of worker-year-spell observations.Which Firms Hire Temp Workers? In Panel (a) of Figure 3.2, we plot thedistribution of regular firm effects separately for those firms that ever or never hiredtemp workers (weighting observations by the number of workers). The histogramsshow that user firmsâ pay policies are shifted to the right, with a mean difference inthe firm effect of 0.27. That is, high-paying firms are more likely to have outsourcedlabor. This pattern is consistent with cost-saving theories of outsourcing, by whichhigh-wage firms seek to lower their wage bill by hiring temp workers. Alternatively, itcould reflect selection by which more productive firms pay higher wages and engagein more complex modes of production. Lastly, it could reflect industry composition orfirm size effects.Assortative Matching We further investigate the assortative matching relatingAKM worker effects for the two types of workers to firmsâ (regular) AKM pay premiain Appendix Figure B.2. We find positive slopes of 0.27 for regular workers and only4We estimate the model simultaneously for both work arrangements; our temp-agency fixed effectshence absorb, e.g., average differences between the arrangements.5David Card, Jörg Heining and Patrick Kline (2013) suggest a test for the exogeneity of these movesbased on the symmetry of wage changes of job switchers between firms ranked by coworker wages,which we leave for future research due to data access restrictions. The key test is for symmetry oftemp wage changes between user firms (ranked by temp coworker wages).72somewhat lower at 0.22 for temp agency workers. This assortative matching, whichamplifies between-firm wage dispersion, may reflect temp agencies assigning theirmost productive workers to their most productive clients, or high-wage temp workersobtaining the best-paying assignments. By contrast, we do not find that high-wagefirms hire from high-wage temp agencies. Here, we find a flat slope of -0.007 (AppendixFigure B.3).Between-firm Dispersion in Pay Policies for Regular and Temp WorkersMost importantly in Panel (b) of Figure 3.2, we plot the distribution of workplaceeffects for regular and temp work arrangements in the sample of user firms. Thespecification does not include temp agency fixed effects, so that we can directly comparethe average workplace fixed effects in regular and temp agency work arrangements.6The histogram, as in Panel (a), weights firm observations by total worker-monthobservations in order to give equal weight to similarly sized firms irrespective of theshare of regular vs. temp workers.7 The firms relying on temp labor are larger, asthey make up 32.2% (1.6%) of our connected set sample of worker-month (total firms)observations. We find a downward shift in workplace effects for temp compared toregular work arrangements. The average pay premium is 17 log points lower for tempwork arrangements compared with regular ones.8Importantly, the dispersion of the workplace effects is nearly as high for temp agencyworkersâ user firms as for the workplaces of regular workersâa stark rejection of thelaw of one price for temp agency workers. Specifically, the raw standard deviationin the pay premia is 17.2 log points for temp workers and 20.7 log points for regularworkers.We also implement a measurement error correction based on a split-sample IV pro-cedure, leading us to scale down the standard deviation for the pay premia of tempagency workers to 15.2 and that of regular workers to 20.5 log points.9 The large re-6Here, the difference may also capture average productivity or hours differences between the twoarrangements besides true temp pay penalties.7Moreover, unlike later in Figure 3.3, we do not normalize workplace fixed effects to zero for a baselinelow-wage set of firms, but shift both distributions such that the mean of the regular workplaceeffects is zero.8Instead weighting firm observations by the number of temporary (rather than all) workers yields aweighted-mean difference of 0.13, akin to the relative wage-setting effect in the terminology of Cardet al. (2016) in the context of the gender wage gap, suggesting that temporary workers are morelikely to work for firms that pay them higher wages.9Specifically, we split our worker sample into two random groups and estimate the AKM specification73maining degree of dispersion following this simple split-sample approach also validatesour AKM fixed effect as a measure of heterogeneous firmsâ pay policies.Overall, the standard deviation for temp workers is therefore around a quarter belowthat of regular workers, indicating that temp labor markets appear somewhat closertoâbut still considerably far fromâcomplying with the law of one price that wouldbe predicted to prevail in a competitive spot labor market.3.4 Do High-Wage Firms Share Pay Premia WithTemp Agency Workers?Our core specification compares the workplace pay premia earned by temp agencyand regular workers in the same workplace. Their relationship could, for example,reflect the relative degree of rent sharing and/or the degree to which employers candifferentiate the pay of outsourced labor.Strategy: Comparing Temp and Regular Pay Premia Within Client FirmsBuilding on (3.2), we use the estimated workplace pay premia received by temp agencyworkers, ÏTJ , and compare them with those of their peers in regular employmentrelationships at the same workplace, ÏRJ :ÏTJ = α + Î³ÏRJ + νJ . (3.3)Our coefficient of interest is γ, capturing the elasticity of temp to regular pay premia.We estimate (3.3) with OLS.We normalize ÏTJ and ÏRJ to zero in the lowest respective vigintiles for each workarrangement. This normalization is inconsequential for our estimation of the slope,γ, and is absorbed by the intercept. However, the normalization matters wheninterpreting γ as the parameter governing the fraction of the percent premia earned byregular workers that temp agency workers receive on average in higher-paying firms.If, for example, workers in regular work arrangements in low-paying firms do earnrents, but temp workers do not, then the estimate of γ constitutes an upper boundfor the share of the premia earned by regular workers that temp workers receive on(3.2) separately. We calculate the covariance of these two sets of fixed effects by work arrangement.74average (cf. David Card, Ana Rute Cardoso and Patrick Kline, 2016, for a similarargument related to gender wage gaps).Polar Benchmarks: Law of One Price vs. Insiders We highlight two polarbenchmarks for the slope γ. First, if firmsâ pay policies for outsourced workers mirrorthose for insiders in regular work arrangements, we expect γ = 1. This benchmarkarises under similar degrees of rent sharing and rents to be shared, or institutionalnorms, formal or informal, curbing pay differentiation within the firm across workarrangements. Second, if firms pay a market price for temp agency workers, or if temppay premia are unrelated to regular premia, we expect γ = 0.Results We report binned scatter plots of ÏTJ plotted against ÏRJ in Figure 3.3. Panel(a) does so for levels, and Panel (b) for changes in pay premia (based on splittingour sample period in half). Here, we weight firm observations by total monthlyobservations. Panel (a) indicates that the empirical pay premia trace out a slope ofÎ³ËOLS = 0.490 (SE 0.0075). That is, comparing two firms, A and B, with B offering a10% pay premium for its regular workers compared with firm A, the correspondingpay premium for temp agency workers at B vs. A is predicted to be 4.9%. Hence,firms do appear to extend their pay premia to outsourced labor, but only pass on halfthe amount.10 Panel (b) broadly replicates these results by plotting the changes inthe fixed effects within user firms over time, where we split the data in two periodwindows, from 2009 to 2013 and from 2014 to 2017. This specification holds, forinstance, industry and region constant. We find a slope of 0.37 (SE 0.0308), perhapssmaller due to higher measurement error, but broadly consistent with our main resultsin Panel (a) for levels.Measurement Error Correction: Split Sample IV We now probe the robustnessof our findings. First, we account for the fact that measurement error may lead toa downward bias in Î³ËOLS. The effects ÏRJ are generated regressors such that thevariance of ÏRJ captures both true variation in regular workersâ pay premia acrossworkplaces and noise due to sampling variability (Andrews et al., 2008; Kline et al.,10Instead weighting firm observations by the number of temporary (rather than all) workers yieldsa slightly higher slope of 0.61 (SE 0.0055), suggesting that temporary workers are more likely towork for firms that share more rents with them, also consistent with our finding of a lower averagepay gap in that weighting scheme summarized in Footnote 8.752020). To gauge the quantitative importance of measurement error, we implement asimple split-sample procedure (see, e.g., Goldschmidt and Schmieder, 2017; Gerardet al., 2021, for similar resolutions and Online Appendix B.4 for more information).We find a corrected coefficient of Î³ËIV = 0.493 (SE 0.0077). Hence, the measurementerror correction has essentially no effect on our findings.Do High-Wage Firms Offer Better Jobs? We additionally assess whether high-wage firms offer better jobs by studying the cross-sectional relationship between tenureand pay premia. This line of analysis follows the revealed-preference approach, wherebygood jobs last longer (see, e.g., Krueger and Summers, 1988). If, for example, higherpay premia reflected only compensating differentials, workers would be indifferentbetween jobs with higher or lower pay premia. However, we find a strong positiverelationship between tenure and pay premia, as shown in Appendix Figure B.4 Panel(b). Quantitatively, a 10% higher AKM pay premium for regular workers is associatedwith 5 months longer tenure.11 Our evidence is thus consistent with high-wage firmsoffering better, higher-surplus jobs and sharing rents with their regular workers, ratherthan merely reflecting, e.g., compensating differentials or hours differences.Comparability of Temp and Regular Jobs If pay premia only accrue to new hiresonce they become stably employed incumbents (as in Kline et al., 2019, who documentdifferential rent sharing with new hires and incumbents) due to firm-specific humancapital, or if pay compression operates within comparable jobs rather than across allworker types, then our pooled pay premium may downward bias the estimated slope.To assess this concern, we separate our client firms into 4-digit industries with lower(below-median) and higher (above-median) average tenure for regular workers. Weconstruct industry leave-out means rather than potentially endogenous firm-leveltenure information.12 We report those results in Figure 3.4 Panel (a), which replicatesFigure 3.3 Panel (a) separately firms in high and for firms in low tenure industries. Wefind a lower slope of 0.45 in the sample of firms with below-median tenure, comparedto a slope of 0.54 in firm with above-median tenure. That is, if anything, pay premiumsharing decreases when temp and regular workers become more comparable in terms11A 5-month increase corresponds to about a 10% increase in tenure, so that the elasticity of tenureto pay premia is about one, consistent with Bassier et al. (2019) based on US data.12Our data do not contain occupation. We construct the sample and industry means again worker-weighted.76of tenure.Institutional Constraints: Collective Bargaining and the Minimum WageTo assess the role of CBA wage floors or the national minimum wage, we again split upour analysis sample along the median by three 4-digit-industry-level proxies reflectingthe severity of these concerns. First, in Figure 3.4 Panel (b), we split up firms bythe average dispersion (standard deviation) in regular-worker pay premia within theindustry, reflecting that potential CBA wage floors or the minimum wage bind forfewer firms. Here, we find that firms with more scope for firm-level wage setting have aslightly larger slope (0.53 (SE 0.0085) compared to 0.48 (SE 0.0155) for below-medianfirms), suggesting that a mechanical pay premia pass-through in industries with moreregulated pay is unlikely to explain our pattern of results.Second, in Figure 3.4 Panel (c), we split firms by the average level of the AKM fixedeffects. This measure proxies for the average distance from the minimum wage and forindustry rents (e.g., Krueger and Summers, 1988). We find a slope of 0.46 (SE 0.0105)for the firms below the median, only slightly lower than the slope for above-median(high-wage) firms (0.54 (SE 0.0145)).Third, as a direct measure of collective bargaining coverage, in Figure 3.4 Panel (d) wesplit the firms by the industry coverage of CBAs.13 Here, we find that firms above andbelow the median exhibit very similar slopes (0.50 (SE 0.0154) and 0.51 (SE 0.0088),respectively). Overall, our findings likely reflect patterns that would arise in settingswith large scope for firm-level wage setting and indeed reflect sharing of firm-specificrents.Heterogeneous Temp Penalties and Sorting Heterogeneous temp penaltiesacross workers combined with assortative matching of temp workers to firms canlead our specification to underestimate the relative degree of rent sharing with tempworkers. Specifically, if workers with high worker fixed effects sort into firms withhigh regular workplace effects (sidestepping sorting of workers into temp agencies, forwhich we include a set of fixed effects), and if the temp penalty increases in the workerfixed effect, then our estimated slope would also capture this effect. An alternativespecification with separate worker fixed effects by work arrangement would remove13We construct CBA coverage as the fraction of workers whose occupation has a CBA wage floor,using SIPA worker-level flags.77these confounders. Our heterogeneity cuts in Figure 3.3 show, if anything, a smallerslope for industries with lower regular worker pay premia or with lower tenure forregular workers. Lastly, in Figure 3.4 Panel (b), we documented that changes in firmsâwage policies result if anything in a lower slope.3.5 Interpretation and ImplicationsWe close with interpretations of our findingsâthat firms appear to pay at most half ofthe workplace-specific pay premia received by regular workers to temp workersâanda discussion of potential implications and also limitations of our design.14Why Do Firms Compress Pay Premia for Temp Workers? One reading ofthe estimate is that the glass is half empty: workers in temporary work arrangementsdo not appear to share in a firmâs rents as much as workers who are formally anddirectly employed at their place of work. One explanation draws on bargaining,with temp workers having lower bargaining power (analogous to the gender wagegap and rent sharing in Card et al., 2016). Relatedly, three-party bargaining ordouble marginalization may lead the temp agency to appropriate some of the rents.Alternatively, temp agency labor supply to specific firms may simply be more elastic(as in the model in Card et al., 2018a, which gives rise to an AKM specification).The attenuated slope is also consistent with findings by Daruich et al. (2023) thatlower firing costs (in fixed-duration jobs) are associated with lower rent sharing.The attenuation of pay policy premia may also contribute to the ongoing debateregarding the forces that motivate firms to outsource labor (see, e.g., Abrahamand Taylor, 1996; Houseman et al., 2003; Autor, 2003; Mas and Pallais, 2020, forexisting evidence). Here, our findings suggest that high-wage firms can moderately cutlabor costs by relying on temp workersâbut to a lesser degree than the competitivebenchmark would have suggested, as they still appear to pay a premium even tooutsourced labor.Why Do Firms Pass on Such a Large Share of Pay Premia to Temp Workers?Alternatively, the glass is half full: our estimates reveal considerable evidence thatpay premia are shared with temp workers, compared with the competitive spot labor14Here, we also draw on interviews with temp agency representatives.78market benchmark for temp agency labor with wages equalized across employers.The considerable degree of pay premia sharing is consistent with theories of fairnessnorms in the workplace reflected in workersâ dislike for pay differences that lead to paycompression (see, e.g., Bewley, 2009; Card et al., 2012; Breza et al., 2017; Saez et al.,2019; Dube et al., 2019). Alternatively, efficiency wage theories based on moral hazardwould imply that incentive compensation would pass through into pay for both regularand temp workers performing the same job. Finally, temp agencies themselves mayhave incentives to increase rent sharing with temp workers. Temp agenciesâ revenuesstem from fees charged to user firms, which are typically computed as a multiple of thetemp workerâs wage (e.g., about 1.5 to 2% based on conversations with leading tempagencies, and thus are small relative to the average wage gap and, as proportionatefees, do not affect the log-log slope we estimate).Viewed through the lens of labor market monopsony, the alignment of pay premiawould imply that the firm-specific supply of temp labor is far from perfectly elasticand far from a competitively supplied intermediate service. Sources of imperfectlyelastic supply include heterogeneity in workersâ preferences for certain employers ormobility costs, factors that also plausibly guide temp labor supply. It may also reflectmonopolistic behavior by the temp agency itself.Another interpretation is partial but considerable compliance with the standardregulatory framework, which would de jure mandate firms to pay equal wages acrosswork arrangements for the same job. It is beyond the scope of our paper to isolatethe role of this channel, even though we suspect that similar forces may operate injurisdictions with related provisions, such as in the European Union. Yet, Argentinaâsrelatively large informal sector suggests that our setting plausibly leaves some room fornoncompliance compared with other countries. We also point to analogous evidenceon differential rent sharing between men and women (Black and Strahan, 2001; Cardet al., 2016) despite laws that purport to ban discrimination based on gender.Limitations Our study relies on AKM firm fixed effects to study firmsâ pay policiesestimated separately for regular and temp work arrangements. Our preferred inter-pretation of such estimates concerns differential rent sharing patterns between thework arrangements. For data availability reasons, our analysis does not feature directproxies for rents (such as labor productivity). Nor does our data set permit us to assessthe comparability of the jobs performed by regular and temp workers. Future research79may estimate regular workersâ workplace fixed effects for those occupations that tempworkers are performing in user firms, by merging additional data. Moreover, due todata access constraints, we are unable to conduct two specification and robustnesschecks: an exogeneity test of movers as in Card et al. (2013), and an alternativespecification with worker fixed effects separated by work arrangement.1515We thank a reviewer for suggesting these checks.803.6 FiguresFigure 3.1: Measurement Challenges: Regular and Temp Agency Work ArrangementsFigure 1.1: Regular Work ArrangementsFigure 1.2: Temp Agency Work Arrange-mentsFigure 1.3: Measurement of Temp AgencyWorkArrangements in Typical MatchedEmployer-Employee DataFigure 1.4: Measurement of Temp AgencyWorkArrangements in Argentinian MatchedEmployer-Employee Data (Dual Regis-tration)Note: The figure illustrates regular and temp agency work arrangements and their measurement inadministrative data. Panel (a) plots regular work arrangements in which employer and workplacetypically coincide. Panel (b) illustrates the case of temp agency work arrangements in which a tempagency serves as the employer while the user firm is the actual workplace. The links between userfirms are generally not observed in matched employer-employee datasets (Panel (c)), as no directcontractual links exist between the user firm and the temp agency worker. Panel (d) illustrates thecase of Argentinian matched employer-employee data, which allow us to observe links between userfirms and temp agency workers due to dual registration.81Figure 3.2: Firm Pay Premia (AKM Effects) For User and Non-User Firms and byWork ArrangementFigure 2.1: Regular Work Arrangement Firm Effects of User and Non-UserFirmsâ = 0.27ââ0.03.06.09.12-2 -1 0 1Firm FEEver User FirmsNever User FirmsMean Ever User FirmsMean Never User FirmsFigure 2.2: Firm Effects, by Regular and Temporary Agency Work Ar-rangement (for Ever-User Firms)â = 0.17ââ0.04.08.12.16-1 -.5 0 .5 1Firm FEFirm FE for Temp WorkersFirm FE for Regular WorkersMean Firm FE for Temp WorkersMean Firm FE for Regular WorkersNote: The figures report histograms of AKM workplace effects. Panel (a) studies selection of firms into outsourcinglabor (i.e., becoming a user firm of temp agency workers). It plots the histogram of AKM firm effects for regular workarrangements, separately for firms that ever or never hired through temp agency arrangements in our observation period,normalizing the average workplace effect in the group of firms that never hired through temp agency arrangements tozero. The distribution for user firms is shifted to the right by 27 log points, indicating that firms with higher wagepolicies for regular workers are more likely to have outsourced labor. Panel (b) juxtaposes the workplace pay premiain temp agency and regular work arrangements within the same workplace as it draws on the sample of user firms.The specification underlying Panel (b) does not include temp agency fixed effects to permit a comparison across workarrangements, and shifts both distributions by normalizing the mean of regular work arrangement workplace effects tozero. The histograms indicate 17 log points higher workplace pay premia in regular work arrangements. Both panelsweight firm observations by total worker-month observations in order to give equal weight to similarly sized firmsirrespective of the share of regular vs. temp workers.82Figure 3.3: Firm-Level Pay Premia Sharing Between Workers in Temp Agency andRegular Work ArrangementsFigure 3.1: LevelsBenchmark for Insiders:γ = 1Competitive Benchmark:γ = 0γ = 0.49 (SE 0.0075)0.2.4.6.81Firm FE for Temp Workers0 .2 .4 .6 .8 1Firm FE for Regular WorkersFigure 3.2: ChangesCompetitive Benchmark:γ = 0Benchmark for Insiders:γ = 1γ = 0.37 (SE 0.0308)0.1.2.3.4.5Firm FE for Temp Workers0 .1 .2 .3 .4 .5Firm FE for Regular WorkersNote: The figure shows a binned scatter plot of estimated firm effects for firms acting as user firmsfor temp agency workers, ÏTJ , plotted against firm effects in regular work arrangements, ÏRJ . Panel(a) does so for a cross-sectional comparison using all years (slope 0.49; SE 0.0075); Panel (b) plotsthe changes in the fixed effects, splitting the data in two period windows, from 2009 to 2013 and from2014 to 2017 (slope 0.37; SE 0.0308). For ease of visualization, we normalize the respective levelsof the fixed effects in the lowest respective vingtiles to zero in Panel (a)) (and similarly the changeto zero in the bottom vingtile of regular fixed effects changes in Panel (b)). This normalization isinconsequential for our estimation of the slope, γ, and would be absorbed by the intercept, in bothpanels. Estimated firm effects are restricted to those firms in the largest connected set that, at anypoint in our sampling window, served as the workplace of temp agency workers. The red regressionline corresponds to the OLS regression line following specification (3.3).83Figure 3.4: Industry Heterogeneity in Firm-Level Pay Premia Sharing BetweenWorkers in Temp Agency and Regular Work ArrangementsFigure 4.1: Regular Worker TenureAbove MedianTenureγ = 0.54 (SE 0.0129)Below MedianTenureγ = 0.45 (SE 0.0110)0.2.4.6.81Firm FE for Temp Workers0 .2 .4 .6 .8 1Firm FE for Regular WorkersFigure 4.2: SD of Regular Firm FEAbove MedianSD of AKM Pay Premiaγ = 0.53 (SE 0.0085)Below MedianSD of AKM Pay Premiaγ = 0.48 (SE 0.0155)0.2.4.6.81Firm FE for Temp Workers0 .2 .4 .6 .8 1Firm FE for Regular WorkersFigure 4.3: Regular Firm Fixed EffectsAbove MedianAKM Pay Premiaγ = 0.54 (SE 0.0145)Below MedianAKM Pay Premiaγ = 0.46 (SE 0.0105)0.2.4.6.81Firm FE for Temp Workers0 .2 .4 .6 .8 1Firm FE for Regular WorkersFigure 4.4: CBA CoverageAbove MedianCBA Coverageγ = 0.50 (SE 0.0154)Below MedianCBA Coverageγ = 0.51 (SE 0.0088)0.2.4.6.81Firm FE for Temp Workers0 .2 .4 .6 .8 1Firm FE for Regular WorkersNote: This figure replicates our main result in Figure 3.3 Panel (a) separately for two halves ofour analysis sample of temp agency user firms. For each variable, we construct a 4-digit leave-outindustry mean (or standard deviation), weighting firms (in the analysis sample) by the worker count(consistent with our weighting in of the pooled analysis in Figure 3.3). We then sort our analysissample based on the leave-out mean for each variable into one subsample above and one below median(again weighted by worker count), and estimate the specification (3.3) and generate the binned scatterplot in Figure 3.3 Panel (a) separately in each subsample. Panel (a) does so for regular-worker tenure(above median average tenure is 43 months and below median average tenure is 26 months), Panel(b)does so for the within-industry standard deviation of AKM firm fixed effects (above median averageSD is 0.18 and below median average SD is 0.10), Panel (c) for the industry average of the AKMfirm fixed effect (above median average fixed effects is 0.28 and below median average fixed effects is0.12), and Panel (d) for the industry-level average of collective bargaining agreement coverage on thebasis of worker-level (population) SIPA data (above median average share of covered workers is 0.72and below median average share of covered workers is 0.58).843.7 TablesTable 3.1: Temp Agency Work Arrangement Pay PenaltyOutcome: Log Wage(1) (2) (3) (4) (5) (6)Temp Agency Arrangement -0.133*** -0.0745*** -0.191*** -0.193*** -0.0795*** -0.140***(0.000523) (0.00132) (0.00123) (0.00123) (0.000487) (0.000485)Year Yes Yes Yes Yes Yes YesGender No Yes Yes Yes No NoAge Cubic No Yes Yes Yes Yes YesIndustry FE No No Yes Yes Yes NoIndustry - Year FE No No No Yes No NoWorker FE No No No No Yes YesFirm FE No No No No No YesR-Squared 0.011 0.070 0.352 0.355 0.897 0.922Observations 52,167,733 49,580,782 49,561,798 49,561,794 48,463,435 48,419,633Note: The table reports coefficients for the temp agency arrangement pay penalty Ï in Mincerequations following regression specification (3.1). Standard errors clustered at the individual levelreported in parentheses. âââ p<0.01, ââ p<0.05, â p<0.1.85Chapter 4The Consequences of DomesticOutsourcing on Workers: NewEvidence from ItalianAdministrative Data4.1 IntroductionOutsourcing is a landmark of contemporaneous labor markets (Dey et al., 2010). Thisbusiness practice allows firms to outsource tasks (e.g., cleaning, HR, IT services) toexternal providers that may have a comparative advantage in performing them. Criticsof outsourcing, however, argue that this comparative advantage is often overstatedand firms use outsourcing to simply reduce costs, sometimes by circumventing existinglabor laws (Weil, 2014; The New York Times, 2023). Both hypotheses are reasonableex ante, highlighting the importance of an empirical analysis that could assess thepotential costs and benefits of outsourcing for workers.1In this paper, we provide a novel quantification of the effects of outsourcing by usingan explicit identifier of outsourcing events encoded in Italian administrative data.This identifier provides an improvement relative to the existing literature along twodimensions. First, while existing estimates of the effect of outsourcing are confinedto specific occupations or sectors (e.g., Dube and Kaplan, 2010; Goldschmidt and1As highlighted by our paperâs title, our analysis focuses on domestic outsourcing, as opposed tooffshoring.86Schmieder, 2017), we can precisely compute the effects of outsourcing across a widerange of jobs. Second, this identifier allows us to study the impact of outsourcingalong the extensive margin, an aspect typically not addressed in the current literature.The latter can be particularly relevant in contexts of high employment protectionâlikeItalyâwhere anecdotal and court evidence suggests that some firms use outsourcingto effectively terminate workers (Autor, 2003; Speziale et al., 2006; Fisac-CGIL, 2014).We find that outsourcing has large and persistent negative effects on earnings andthese losses are primarily driven by job loss. Five years after being outsourced,workersâ cumulative earnings losses map to around 13,500 euros in present discountedvalue (PDV) terms, corresponding to about two-thirds of their pre-outsourcing annualearnings. These earnings losses are mainly driven by the extensive margin; ignoringthis margin, as much of the existing literature does, would lead us to underestimatethe earnings losses by a factor of four. These losses are also very heterogeneous.Outsourced workers in non-routine manual jobsâsuch as cooks or beauticiansâhavethe largest earnings losses, while workers in jobs involving abstract, cognitive tasksâsuch as software developers or consultantsâexperience some earnings gains followingthe outsourcing event.Our analysis uses data from Italian social security records, which require employers toprovide explicit justifications for terminating employment contracts. This informationis key for determining severance payments and other benefits that might accrue tothe worker. Among possible reasons for separation, employers can list outsourcing.We leverage this information recorded in the social security administrative records toidentify outsourcing events. Over two-thirds of workers in our data are outsourced toa daughter firm whose sector code does not match that of a business service firm.2As a result, a wide range of jobs are part of our outsourcing events, from janitorsand security guards (as in Goldschmidt and Schmieder (2017) and Dube and Kaplan(2010)) to blue-collar workers in the manufacturing sectors (as in Dey et al. (2012)) toconsultants, accountants, or software developers.The data also reveal that several outsourced workers are not working for the daughterfirm one year after being outsourced. This finding aligns with existing lawsuits inwhich workers claim that being outsourced led to an unlawful termination (Corte diCassazione, 2006, 2017a). Relatedly, some legal scholars have raised concerns about2Following the literature, we refer to the firm that outsources the worker as the âmother firm,â whileâdaughter firmâ refers to the firm receiving the worker.87the inconsistency in Italian labor laws, noting that while it is very difficult for firms todirectly fire workers, it is relatively easy to outsource them to companies that mightsoon go out of business or may have stronger economic and legal grounds to eventuallyterminate the outsourced workersâ employment contract (Speziale et al., 2006).We use a matched difference-in-differences approach to study the causal impact ofoutsourcing on workers. Outsourced workers are matched to individuals who neverexperienced an outsourcing event and who share similar observable characteristics. Wefind that outsourcing leads to a loss in earnings of around 5,000 euros (approximately23% of their pre-outsourcing earnings) one year after the event. These earningslosses continue to persist five years after the event and amount to roughly 10% ofthe pre-outsourcing earnings. Job loss is a key driver of these losses. Employmentdecreases by 22 percentage points the year after the event, and the negative effectremains significant at almost 8 percentage points five years later.We then compare the estimated earnings losses using the outsourcing identifierwith those computed when applying the outsourcing definition of Goldschmidt andSchmieder (2017) (GS henceforth), which focuses on transitions of workers from agiven employer to a business service firm in the food, cleaning, logistics, and security(FCSL) sector. Outsourced FCSL workers identified by the flow approach of GS inour data have cumulative earnings losses (over five years) of around 11% of their pre-outsourcing yearly earnings, while outsourced FCLS workers flagged by our identifierof outsourcing events have cumulative losses of around 45%. These differences arisebecause the flow approach estimates null effects on employment. In this method, atreated/outsourced worker must be employed by the mother firm in year t and by thedaughter firm in year t + 1. Consequently, it excludes workers who lose their jobsbetween these years, thus underestimating employment losses due to outsourcing.But why are many outsourced workers eventually without a job after being outsourcedin our setup? A relevant fraction of workers are outsourced to daughter firms that arecreated precisely in the outsourcing event year, with many shutting down right after.Daughter firms also tend to offer jobs with fewer hours (e.g., a part-time job to aworker who used to have a full-time job with the mother firm) and lower employmentprotection (e.g., a temporary job to a worker who used to have a permanent job withthe mother firm), and apply a different collective bargaining agreement (CBA) relativeto the one applied by the mother firm. As a result, we also see an increase in thenumber of outsourced workers who voluntarily resign from these jobs. Taken together,88this suggests that the primary reason outsourcing leads to non-employment stems fromthe considerable instability of outsourced positions, combined with workersâ reluctanceto accept these lower-quality jobs.The effects of outsourcing tend to vary considerably across occupations. For exam-ple, outsourced cooks tend to experience very large negative earnings losses, whileoutsourced software developers experience slightly positive increases in earnings. Com-puting the effects of outsourcing according to the outsourced jobâs task contentâusingthe classification of Autor et al. (2003)âshows that the immediate losses of outsourc-ing are three times higher for workers in routine or non-routine manual jobs relativeto those in non-routine analytical or interactive jobs. The analysis at the sectorallevel of the mother firm yields comparable results, with large negative effects in thefood/restaurant sectors (and in some manufacturing sectors), null or slightly positiveeffects in IT-intensive sectors, and positive effects in the consulting sector.We view these heterogeneous effects as the manifestation of different motives driving afirmâs decision to outsource (a point previously highlighted in a structural frameworkby Bilal and Lhuillier, 2022). Some jobs may be outsourced simply because firms needto reduce firing and other related costs (Goldschmidt and Schmieder, 2017; Dreniket al., 2023; Daruich et al., 2023). Outsourcing could, however, also be pursued bysome firms to directly increase productivity, for instance, by exploiting economies ofscale or comparative advantages, factors that may eventually lead to higher pay foroutsourced workers (Bergeaud et al., 2021; Bostanci, 2021). This latter motive seemsto be more prevalent when analyzing workers in jobs involving more cognitive andabstract tasks, such as consultants and software developers.Consistent with the heterogeneous effects by sectors and occupations, we also findthat the negative effects of outsourcing are concentrated in low-value-added firms witha relatively high labor share. This result is somewhat surprising given GSâs findingsthat wage losses due to outsourcing are concentrated in workers outsourced by firmswith high wage premiums (and thus firms that tend to be more productive; see Cardet al., 2018b). We believe this discrepancy arises because GS could not account forthe possibility that firms use outsourcing to reduce firing costs (as highlighted byAutor, 2003), due to both data limitations and differences in labor market institutionsbetween Germany and Italy (Boeri, 2011). The negative effects of outsourcing onemployment that we find are consistent with outsourcing being used as a cost-cuttingstrategy that might allow low-value-added and high labor share firms to remain viable89and avoid closure (Acabbi and Alati, 2023; Dustmann et al., 2022).The fact that Italian firms tend to use outsourcing practices to circumvent firing costsis also apparent when analyzing the trends of firm-level outcomes of outsourcing firms.Five years before the outsourcing decision takes place, these firms tend to have highervalue added per worker than non-outsourcing firms in the same location and industry.However, three to four years before the outsourcing decision, outsourcing firms appearto be hit by a relatively persistent negative revenue shock. Interestingly, while thisnegative shock drives down sales and value-added, these firms essentially seem unableto adjust their size and labor costs, most likely due to high employment protection inthe country. It is only after the outsourcing decision that size and labor costs effectivelydecrease. Therefore, it appears that the average firm does not use outsourcing inâgoodâ times to directly increase productivity. Instead, it is predominately used tocope with a negative downturn.The dynamic evolution of revenues and other financial variables of outsourcing firmsraises the question of what is the relevant counterfactual for an outsourced worker. Ourbaseline estimates leverage non-outsourced workers who had a similar career trajectoryas outsourced workers to construct this counterfactual. This follows the original designof GS and is similar in spirit to the research design used in the job displacementliterature (Jacobson et al., 1993; Schmieder et al., 2023; Bertheau et al., 2023) thatcomputes what would have happened to the worker if the displacement/outsourcingevent had not occurred. However, another interesting counterfactual to consideris one in which the worker is not outsourced but their employer has experienced anegative trajectory in revenues similar to the one faced by outsourcing employers.Interestingly, when we match workers based also on their employerâs revenue trendsbefore outsourcing, our earnings estimates remain virtually identical. Therefore,workers employed by firms that are also experiencing a downturn trajectory in revenuesbut did not resort to outsourcing have significantly higher earnings than outsourcedworkers after the outsourcing event. This suggests that firms can pursue otherstrategies to cope with the negative shock that do not lead to persistent earningslosses for workers.More broadly, the partial-equilibrium effect of outsourcing on workers that we estimateis important when considering the general equilibrium effects of this business practice(Bostanci, 2021; Bertrand et al., 2021; Bilal and Lhuillier, 2022; Deibler, 2023; Felixand Wong, 2024). Our estimated negative effects are higher than the ones typically90used in this literature, which are often based on the analysis of a few selected sectors ordo not consider the extensive margin. Our results therefore suggest that any efficiencygains due to outsourcing must be substantially higher than originally thought to offsetthe negative effects faced by workers.4.2 Outsourcing in ItalySection 4.2.1 describes the Italian labor market and the evolution of labor laws ondomestic outsourcing. Section 4.2.2 introduces the Italian administrative data, andSection 4.2.3 explains how these data are used to measure outsourcing.4.2.1 Institutional BackgroundThe Italian labor market has historically been considered a very rigid one. Wagesare determined by employersâ organizations and unions via nationwide sectoral CBAsthat provide limited scope for firm-level adjustments (Devicienti et al., 2019; Boeriet al., 2021). In turn, this rigid wage structure is coupled with one of the highestemployment protection legislation in europe (Boeri, 2011). In the mid-1990s, Italybegan implementing a series of reforms aimed at enhancing the flexibility of its labormarket (Cappellari et al., 2012).3A landmark from this wave of reforms is Decree 18/2001, which reformed outsourcingpractices. Before this reform, Italian firms were prohibited from outsourcing some oftheir existing employees to other firms. The only way to formally transfer a workerfrom firm A to firm B was via a full merger between both firms (i.e., a situation inwhich all employees of one firm joined the other firm). This changed with Decree18/2001, which made it possible for Italian firms to outsource just âpieces" of itsbusiness to other firms (Emanuele et al., 2012).4 Decree 276/2003 introduced an3Three reforms were of particular significance. The âTreu Package" in 1997 introduced temporary workagencies, and Decree 368/2001 relaxed several restrictions on the usage of temporary employmentcontracts. Additionally, Decree 30 of 2003 (âBiagi Law"), followed by Decree 276/2003, reformedthe apprenticeship contract and introduced new temporary contracts limited to specific âprojectsâ(often referred to as âindependent contractors" in labor law, or âCo.Co.Pro" in Italian). Thesereforms contributed to creating a dual labor market where highly protected employment contractsâthe so-called permanent contracts that remain the most common contract in Italyâco-exist withtemporary onesâwhich are typically used to hire new, young workers (Daruich et al., 2023).4In Italian, this practice is called trasferimento di ramo di azienda and is disciplined by article 2112in the Italian civil law.91additional element of discretionality by stating that the branch to be outsourcedcan be identified at the time of transfer, whereas before this reform, the branch hadto be âpre-existing" within the organization. Importantly, firms can engage in thisoutsourcing practice without workersâ approval. Workers who refuse to transfer to thedaughter firm can be fired for just cause by the daughter firm (Corte di Cassazione,2017b).Two legal guardrails were adopted to avoid the indiscriminate use of this practiceby employers (Pedrazzoli et al., 2004). First, firms can only outsource well-definedâbranches,â i.e., parts that have a clear task within the organization (e.g., providingcustomer support). Second, the outsourced worker should not experience any significantworsening of their employment conditions. However, it is unclear whether theseguardrails were truly effective in protecting workers. In fact, several labor courts sincethe mid-2000s have presided over lawsuits filed by outsourced workers against theiremployers for illegal outsourcing practices (e.g., Corte di Cassazione, 2017a; TribunaleOrdinario di Milano, 2016; Undiemi, 2008).The typical case is one in which firms are accused of using outsourcing as a loopholeto essentially fire workers, thus violating Italian employment protection laws (Meucci,2015). Firms in Italy can appeal to either âsubjective" or âobjective" motives to legallyfire workers. Subjective motives refer to situations where the workerâs conduct violatedthe employment relationship, while objective reasons constitute situations where thefirm is experiencing financial difficulties and must fire part of its workforce. Firing forboth reasons is heavily regulated, and the burden of proof relies entirely on firms. Forinstance, companies seeking to implement a mass layoff must first communicate theirintentions to a labor court, which verifies that all necessary conditions for the masslayoff have been met. These requirements include verifying that the firm is facingsome pre-existing poor economic conditions, proving that none of the fired workerscan be relocated within the firm, and proving that the layoff is not âdiscriminatory"(i.e., targeting a few, selected, workers).Conversely, outsourcing does not require any pre-approval from a labor court and canonly be appealed after the outsourcing event. In practice, there are three commonreasons why outsourced workers may lose their jobs shortly after being outsourced.First, the worker may be outsourced to a firm with financial difficulties, which couldthus shut down shortly after the outsourcing event (see, e.g., Corte di Cassazione, 2006and Gragnoli, 2006). Second, outsourced workers may join new and/or smaller firms92with less than 15 employees or worker cooperatives with less stringent employmentprotection laws (Kugler and Pica, 2008) and would thus be easier to be fired. Last,outsourcing may involve an agreement where the daughter firm supplies a particularservice (e.g., call center) to the mother firm using the outsourced workers. Whenthe contract expires, these workers can be fired, as the economic rationale for theiremployment no longer exists (see, e.g., the Barcalys-Engo case study analyzed inFisac-CGIL, 2014).While the definition of illegal outsourcing practices remains a topic of debate amonglabor law scholars (Scarpelli, 2012; Iacobucci, 2023), there has never been a systematicaccounting of, say, how many outsourced workers are non-employed after beingoutsourced. This paper aims to fill that gap.4.2.2 DataWe use administrative data from the Italian Social Security Institute (Istituto Nazionaledi Previdenza Sociale, INPS) on the universe of employer-employee matches and socialsecurity payments in the private sector from 1983 to 2019, made available through theVisitINPS scholar program. For each employment spell recorded in a given year, wehave data on earnings, number of days worked, the type of employment contract (e.g.,temporary versus permanent, part versus full time), the employerâs identity, and somedemographic information about the employee.5 From 2009, it is possible to measurethe occupation of a job that was either created or destroyed in a given year. Firmsare defined based on their national tax identifier (codice fiscale). The latter is used tomerge the financial records of firms collected by CERVED, which are available for theuniverse of non-financial incorporated firms.64.2.3 Measuring OutsourcingSince 2005, employers have been required to report the reason for a job separation tothe INPS.7 Among the potential reasons listed by the INPS is the outsourcing practice5Earnings include overtime payment, bonuses, and shift work. We convert them to real euros (2015CPI) and top code them at 400.000 euros.6The CERVED dataset is derived from standardized reports that firms are required to file annuallyto their local chamber of commerce.7This requirement, affecting pension contributions and severance payments, officially began in 2005but has only been reliably enforced since 2008. Thus, we start measuring outsourcing events from2008.93(âtrasferimento di ramo di azienda") described in Section 4.2.1.8,9 We thus measureoutsourcing events by focusing on separations where the underlying reason behind thejob separation is âoutsourcing" according to official INPS records.10Comparison with Goldschmidt and Schmieder (2017). Our approach inmeasuring outsourcing events in administrative data differs from that of GS. GSidentify outsourcing events in Germany by focusing on large flows of workers movingfrom a particular establishment to a business service firm. They focus on five types ofbusiness service firms (those in the FCLS sector and temporary agencies) for whichit is relatively easy to find proper business service sector codes in the German data,reducing the likelihood of flagging non-outsourcing flows of workers.There are two key advantages of our approach of using the INPS identifier to measureoutsourcing relative to the GS approach based on worker flows. First, the INPS flagallows us to study the effects of outsourcing beyond the FCLS categories consideredby GS. Table C.1 shows the most common occupations associated with outsourcingevents in the INPS data. Salespersons operating in the retail and mass-retail sector(e.g., a product sampler in a supermarket) represent the most common outsourcedoccupation. The table also shows that outsourcing can extend to occupations that aretypically considered better paid, such as IT jobs.Second, our approach does not require outsourced workers to be employed by thedaughter firm (a requirement that instead must be imposed by the flow approach); itonly requires them to be outsourced by the mother firm. This is extremely importantbecause, as described in Section 4.2.1, abundant anecdotal evidence and lawsuitssuggest that several workers no longer have a job shortly after being outsourced.Overall, we find that the vast majority (79%) of workers are either outsourced to anon-business-service-providing firm or end up not working for the daughter firms (i.e.,8The other most common reasons for a job termination are that (i) the worker resigned, (ii) theworker was fired due to the firmsâ financial difficulties (objective reason), (iii) the worker was fireddue to misconduct (subjective reason), (iv) the temporary contract ended, and (v) (full) mergers.Data on reasons for job separations are available for approximately 75% of all separations observedin a given year among jobs that lasted at least three months. Two key factors associated withmissing data on reasons for separation are (i) the separation occurring in December and (ii) theemployer disappearing from the INPS data in the year of separation.9Di Addario et al. (2023) test the validity of the reason for separation variable in the INPS data,concluding that it accurately distinguishes between voluntary and involuntary job separations.10The INPS also supplies data on reasons for hiring, which include outsourcing. This informationallows us to isolate cases where the outsourced worker effectively joins the âdesignated" daughterfirm associated with its outsourcing event.94two cases that would not be captured by GSâs flow approach).Defining Outsourcing Events. The outsourcing events that we seek to analyzecorrespond to situations where the mother firm outsources to an external firm someof its workers. Ideally, these outsourced workers continue to perform the same jobas before but are now hired by the daughter firm (which GS labeled as âon-siteoutsourcing").One important caveat of the outsourcing identifier from the INPS data is that it mightflag situations different from the outsourcing event just highlighted. First, the flag issometimes used when workers are transferred within the firm. For example, it mayinaccurately flag cases in which workers are transferred from âFirm A - Productionâto âFirm A - ITâ and these two parts of firm A have different national tax identifiersdespite being controlled by the same owner. Additionally, some mother firms shut downright after the outsourcing event. This is puzzling under an outsourcing framework,as it leaves no tasks to be performed for the mother firm anymore.We thus exclude from our definition of outsourcing events situations where either ofthe following happens: (a) the mother and daughter firm share the same six-digitindustry code, (b) worker flows are also observed from the daughter to the motherfirm, and (c) 90% or more workers are flagged as outsourced by the mother firmin year t and the mother firm is dissolved between year t and t + 1. Restrictions(a) and (b) are imposed to ensure that the daughter firm is an external firm and,in particular, that the outsourcing event does not represent a transition of workersoccurring within an internal labor market (Cestone et al., 2023). Restriction (c) isimposed to limit cases in which the mother firm intended to shut down immediatelyafter the outsourcing event.11 These restrictions identify 267,030 workers involvedin an outsourcing event in our data.12 Imposing these conditions does not ensurethat outsourced workers will continue performing the same tasks once employed bythe daughter firm, a caveat also shared with the flow approach of GS. However, thequalitative evidence in (Emanuele et al., 2012)âof interviews with HR representativesof companies involved in outsourcing eventsâsuggests that this is the case in most11A similar condition is imposed by GS, who focus on mother firms that continue to be alive in theyear after the outsourcing event and whose firm size does not shrink by more than 50%. FigureC.5 shows the effects of outsourcing for various degrees of exposure, defined as the fraction of thewage bill of the mother firm related to outsourced workers.12Table C.2 presents details on the share and total number of observations dropped by the threerestrictions.95situations.4.3 Econometric FrameworkWe study the effects of outsourcing using a matched difference-in-differences researchdesign akin to the one originally proposed by GS. This approach compares the changesin labor market outcomes of an outsourced worker relative to a matched control workerwith a similar labor market history.Matching Strategy. To construct our matched sample, we begin by consideringthe pool of workers who experienced an outsourcing eventâas defined in Section4.2.3âand had at least two years of tenure with their mother firm when they wereoutsourced.13 Each worker treated/outsourced in year tâ is then matched with acontrol worker who never experienced an outsourcing event in their career and in yeartâ; had the same gender, tenure, CBA, and employment contract; lived in the sameregion; and worked for a firm in the same quartile of firm size (measured in tâ â 1).To perform a one-to-one match between treated and control workers, we conduct acaliper matching method (Stepner and Garland, 2017) without replacement, usingearnings in tâ â 3 and tâ â 2 (with a bandwidth of ± 500 euros) as well as age (with abandwidth of ± 2 years).Summary Statistics. As shown in Table 4.1, the matching algorithm successfullymatches 61,849 outsourced workers. The characteristics of matched outsourced workersare relatively similar relative to the overall sample of outsourced workers (see differencesbetween Columns 2 and 3). Most outsourced workers are male, have a permanentand full-time contract, and have been at the mother firm for around six to sevenyears. They are relatively low paid, with yearly gross earnings and daily wages ofapproximately 20,600 euros and 75 euros, respectively.14 These workers tend to workfor large firms and represent a significant part of the mother firmâs wage bill the yearbefore the outsourcing event, as measured by the exposure coefficient.13This restriction is also used by GS and mimics the one typically imposed in the job displacementliterature (e.g., Jacobson et al., 1993; Schmieder et al., 2023; Bertheau et al., 2023).14Average earnings and daily wages in Italy for this period are approximately 28,077 euros and97 euros. Moreover, Figure C.1 shows that outsourced workers tend to have lower earnings anddaily wages relative to their coworkers who did not experience an outsourcing event. The latter isparticularly true among higher-paying mother firms.96Event Study. To isolate the impact of outsourcing on workersâ careers, we fit thefollowing event-study specification on the matched sample of treated and controlworkers represented in Table 4.1:yit = αi + λt +bâk=aÎ²kRkit +bâk=aÎ¸k(Rkit ÃOutsourcedi) +X â²itÎ³ + rit, (4.1)where yit is the outcome of interest (e.g., total earnings) for individual i in year t, andαi and λt capture worker and time fixed effects, respectively. Xit is a quadratic in age,Outsourcedi is an indicator for whether worker i is outsourced, and Rkit = 1{tâi = t+k}represent the event-study indicators with tâi being the outsourcing year for worker i.15The event-study indicators are binned at a = â4 and b = 5 and are normalized relativeto Î¸â1. Therefore, the coefficients θk for k â¥ 0 capture the effect of outsourcing onoutcome yit, k years after the outsourcing event relative to the year before outsourcing.Standard errors are clustered at the worker level.Identification. The coefficients θk identify the causal effect of outsourcing under aparallel trend assumption; i.e., differences in outcomes between outsourced and controlworkers would have remained constant absent the outsourcing event. Although thisidentification assumption cannot be tested directly, our analysis leverages data fromyears before the outsourcing event and rich microdata to maximize its plausibility.First, counterfactual earnings differences in our research design are based on narrowlydefined pairs of treated and control workers who share the same tenure-gender-region-contract type-CBA-quartile of firm size up to the moment of the outsourcing event.Second, we check the evolution of outcomes in the years leading up to the outsourcingevent by computing the coefficients θk for k < 0.4.4 The Effects of Outsourcing on WorkersThis section shows how outsourcing impacts the employment and earnings of out-sourced workers. Section 4.4.1 introduces the baseline estimates, and Section 4.4.2contextualizes why outsourcing has large and negative effects on employment. Section15In the case of a control worker, tâi represents the outsourcing event year of the treated workermatched to the control worker. In the rare cases where an outsourced worker is outsourced morethan once, we let tâi be the earliest outsourcing year.974.4.3 compares our estimates to those obtained when applying the flow approach ofGS to identify outsourcing events.4.4.1 Main ResultsEarnings Trajectories of Outsourced and Control Workers. Figure 4.1, panel(a) presents the evolution of average annual earnings for both outsourced workersand their matched controls. Before the outsourcing event, the earnings of treatedand control workers follow a similar trend.16 After the event, the earnings of treatedworkers start diverging as they experience a substantial and persistent drop in pay.17Figure 4.1, panel (c) shows that a substantial part of the earnings drop may be drivenby outsourced workers being without a job. Treated workers see a roughly constant30% decline in their employment probability after being outsourced.Event-Study Estimates. We now analyze the event-study estimates from equation(4.1). Panel (b) of Figure 4.1 shows that the annual earnings of outsourced workersdecrease by 4,500 euros on impact (i.e., the year after outsourcing), or about 22%of pre-outsourcing average earnings. These losses are reduced over time but persistfive years after outsourcing, at about 2,000 euros, or 10% of pre-outsourcing averageearnings. Our main results on earnings and employment are also presented in Table 4.2,Column 1. Summing the earnings losses across the five years following the outsourcingevent leads to PDV losses of around 13,500 euros, corresponding to roughly two-thirdsof the average pay observed in the year before the outsourcing event.Panel (d) of Figure 4.1 shows that employment is heavily affected after outsourcing,with the probability of being employed decreasing by 22 percentage points one yearafter. This is equal to between a quarter and one-third of the employment sharesobserved in the years before outsourcing for which we did not impose a tenure16Recall that while earnings at tâi â 2 and tâi â 3 are directly targeted in our matching algorithm, theother pre-periods are not.17Because we imposed a tenure restriction in tâi â 1 and tâ, both control and treated workers are, byconstruction, employed in these two periods. However, this restriction is no longer imposed afterthe outsourcing event (or in periods before tâi â 1). This implies that some control workers are nowgoing to be non-employed after the outsourcing event, and it explains their drop in average earningsat tâi + 1 and in subsequent periods. The resulting hump-shaped earnings profile shown in Figure4.1, panel (a) is common when imposing tenure restrictions in matched difference-in-differencesstrategies (see, e.g., the discussion in Schmieder et al., 2023) and will be captured by the coefficientsβk in the event-study specification of equation (4.1).98restriction. Over time, the loss in employment decreases but remains significant, evenfive years after outsourcing, at almost 8 percentage points. To put these estimatesin context, Bertheau et al. (2023) recently found that Italian workers subject to amass layoff are 30 percentage points less likely to be employed immediately after themass-layoff event (18 percentage points after five years). Thus, our estimates suggestthat outsourcing leads to an immediate negative employment effect that, while smaller,is comparable to that of mass layoffs (i.e., 22 versus 30 percentage points).Appendix Figure C.2, panel (a) reports the results in log daily wages. Outsourcingleads to a decrease in wages of roughly 2 to 3 log points, a small share of the totalearnings loss. However, one concern with this analysis is outsourcingâs large impacton the extensive margin, which makes this decomposition nontrivial. To account forthis, panel (b) reports estimates from equation (4.1) on log daily wages after droppingpairs where the outsourced worker is not employed but the control worker is. Weassume that this control worker represents a âcomplier,â i.e., a worker who, if exposedto outsourcing, would be non-employed (Lee, 2009). Dropping these compliers fromthe estimating equation (4.1) thus allows us to evaluate the impact of outsourcing onlog daily wages just among âalways-takers,â i.e., workers who remain employed evenwhen outsourced. Overall, the estimates on this balanced sample reported in panel (b)are quite similar to the ones reported in panel (a), suggesting that the large negativeeffect of outsourcing on earnings is mostly due to employment losses. The next sectioninvestigates why several workers are without a job after being outsourced.4.4.2 Factors Contributing to Non-Employment FollowingOutsourcingIn this section, we discuss potential reasons why outsourcing might lead to large joblosses.Mislabeling. Employers may incorrectly report situations as âoutsourcing" thatare actually mass layoffs or plant closures, which are shown to have large negativeemployment effects, especially in Italy (e.g., Bertheau et al., 2023). To address this,Appendix Figure C.3 reports the results after fitting equation ((4.1)) on the subsampleof events where the mother firm does not shut down in tâ + 1. The point estimatesare very similar to our baseline estimates shown in Figure 4.1, suggesting that the99mother firmâs closure is not the primary driver of our effects.Appendix Figure C.5 expands on this finding by reporting the effects of outsourcingone or five years after the event across different exposure deciles (i.e., the fraction ofthe mother firmâs pre-event wage bill allocated to outsourced workers; see AppendixFigure C.4 for the histogram of this measure). The graph shows that the negative effectof outsourcing on earnings is qualitatively similar in events where exposure is relativelylow (i.e., where presumably only a handful of workers are being outsourced) and inevents where outsourced workers instead constitute a large fraction of employment(mass-layoff events).Overall, the evidence presented in both figures does not suggest that our results aredriven by employers that misclassify outsourcing events to the INPS. This also alignswith the fact that the INPS issues a distinct identifier for job separations resultingfrom mass layoffs.Never Attached to Daughter Firm. To further confirm that we are capturingoutsourcing events and that these events lead a sizable share of workers to be withouta job, we re-estimate equation (4.1) for outsourced workers who report working forthe daughter firm for at least a day in the outsourcing event year (which occurs forabout 76% of our outsourced workers).18 Panel (b) of Figure 4.2 shows that even afterimposing this employment restriction, several outsourced workers employed by thedaughter firm in the outsourcing event year are non-employed in the following years.The negative employment effect one year after the event is around 10 percentage pointsand remains significant at around 3 percentage points five years later. Panel (a) showsa qualitatively similar pattern for earnings, while Table 4.2, Column 2 summarizesthe estimates on earnings and employment just described.We draw two conclusions from the evidence presented in Figure 4.2. First, about halfof the baseline employment effect (as shown in Figure 4.1 and reproduced in Figure4.2) is driven by workers not joining the daughter firm (as shown by the differencebetween the baseline employment effects and those restricted to individuals who dowork for the daughter firm), potentially because they rejected the ânew" outsourcedjob. Second, the remaining half of the employment effect is driven by individualswho do work (shortly) for the daughter firm but are then found to be non-employed18Recall that the INPS provides data on the reasons for both job separations and job hirings (aspecific flag in the data indicates when hiring by the daughter firm is the result of an outsourcingevent).100according to INPS records in the years following the outsourcing event. We nextdiscuss reasons why outsourced workers might refuse or accept the job at the daughterfirm only to find themselves non-employed shortly after.Characteristics of Post-Outsourcing Jobs. According to Italian laws, outsourcedworkers cannot decline the outsourcing âofferâ and remain employed with their motherfirm. The rationale for this condition is that, in principle, the job at the daughter firmmust have the same attributes as the job at the mother firm. In practice, however, weoften find that this is not the case. Figure 4.3, panel (a) shows that outsourced workerswho had a permanent employment contract before being outsourced are significantlymore likely to be employed with a temporary contract after the outsourcing event.Similarly, outsourced full-time workers have a significant increase in the probability ofbeing employed part time after being outsourced.The worsening of employment conditions following an outsourcing event could be drivenby the fact that CBAs are allowed to change after an outsourcing event according toItalian laws. Panel (b) of Figure 4.3 shows that outsourcing significantly increases theprobability of workers being employed under a different CBA than the one observedbefore outsourcing. A typical situation involves an outsourced worker initially hiredunder a âstandardâ CBA (i.e., signed by one of the main unions in Italy; see Luciforaand Vigani, 2021) but is then outsourced to a job governed by a CBA designed fortemporary agencies or cooperative-type employers (Pencavel, 2013). Both situationscorrespond to cases where the new CBA is likely to offer the outsourced worker muchless protection and fewer other amenities.Figure 4.3 thus shows that outsourcing leads to jobs with fewer hours, lower employmentprotection, and with a different CBA that might provide lower wage floors and feweramenities relative to the workersâ original CBA with the mother firm.19 These factorscontribute to why workers might reject employment with the daughter firm, as theseoutsourced jobs generally offer inferior conditions compared to their previous job withthe mother firm. This is also confirmed by panel (a) of Figure 4.4, which shows thatthe probability of a worker resigning from their dominant job in year t significantly19Part of this effect could be attributed to workers rejecting the outsourced job and reallocating toanother job. However, using a standard revealed preferences argument, this would then suggestthat our estimates in Figure 4.3 are a lower bound of the changes in job quality associated withthe outsourced job created by the daughter firm.101increases after the outsourcing event.20However, outsourcing might lead to non-employment for reasons other than voluntaryresignation. Panels (b) and (c) of Figure 4.4 show that the probability of involuntaryseparations significantly increases after outsourcing. First, outsourced workers aremore likely to experience a mass layoff, consistent with anecdotal evidence and laborcourt cases where workers report being outsourced to financially unstable companies,leading to job displacement shortly thereafter. Additionally, these workers are alsomore likely to experience a job separation due to their new employersâ reluctance torenew temporary contracts (which are also more common after outsourcing, as shownin panel (c)).Last, panel (d) shows that outsourced workers are systematically more likely to workfor an employer that was created in the outsourcing event year. This is importantfor two reasons. First, it provides a rationale for the results in Figure 4.3, panel(b). It is often easier for newly created firms to adopt non-traditional CBAs (Cardet al., 2013; Dustmann et al., 2024) or impose conditions that might be illegal, suchas converting permanent contracts to temporary ones, which might be difficult forworkers to challenge (or even recognize) given the lack of union representation in thesefirms (Naidu, 2022). Second, newly created firms are typically more unstable or lesslikely to survive (e.g., Mata et al., 1995; Caves, 1998; Agarwal and Gort, 2002), whichcan help explain the reduction in employment experienced by outsourced workers. Inour sample, about 13% of daughter firms shut down the year after the outsourcingevent, a much higher number than the unconditional 3% shutdown probability forfirms that we find in our Italian data.Summary. Being outsourced leads to large and persistent negative earnings losses,primarily explained by the extensive margin. Many outsourced workers are employedonly briefly (or not at all) by daughter firms, which are often created the year inwhich the outsourcing event takes place and often shut down shortly thereafter.These daughter firms tend to offer jobs with fewer hours and lower employmentprotection (e.g., due to changed CBAs and economic conditions at the daughter firm).Taken together, these findings suggest that the primary reason for non-employmentfollowing outsourcing is the considerable instability of outsourced positions and workersâ20Note that the INPS reports resignation as the reason behind a job separation only in situationswhere the outsourced worker is employed for at least a day by the daughter firm.102reluctance to accept these lower-quality jobs.4.4.3 Estimates Based on the Flow-Based Approach of GSOne relevant question is why we find a much more negative impact of outsourcingon workersâ earnings compared to the findings of GS. To explore this, we computethe effects of outsourcing using GSâs definition of outsourcing events with Italiandata. We then compare these estimates with our baseline results, which are basedon outsourcing events explicitly flagged by INPS. The goal is to determine whetherthe two sets of outsourcing events ultimately lead to similar effects on Italian workers(i.e., suggesting that the difference between our findings and those of GS might stemfrom contextual differences between Germany and Italy) and, if disparities insteademerge, investigate potential reasons behind these differences.We implement the same steps as GS to identify outsourcing events in the FCSL sector.First, we identify flows of at least 10 workers moving between the mother firm in yeart and the daughter firm in year t + 1.21 We then drop events if the mother firm isa temporary agency, if it had less than 50 employees the year before, if it dies theyear after, if it shrinks by more than 50%, or if the flow represents more than 30%of the mother firmâs size the year before. Next, we drop events if the daughter firmis new and the flow represents less than 65% of its size, and drop events if daughterand mother firms share the same four-digit industry code. Last, we drop events if thedaughter firm does not belong to the business service associated with FCSL industries.As with our previous analysis, we focus on workers with at least two years of tenure.To compare our results with those from the flows approach, we restrict estimationof equation (4.1) to workers whose resulting daughter firm operates within an FCSLbusiness service firm.Figure 4.5 presents the results. Panel (a) shows that our outsourcing identificationstrategy yields an estimate of the drop in total earnings the year after outsourcingthat is more than three times larger than that produced by GSâs flow approach. Thisdifference is persistent over time, though the magnitudes become relatively smaller.Panel (b) shows that the difference in employment outcomes is driving the differencein earnings. We estimate employment losses between 10% and 5% over time, while theflow-based approach leads to much smaller losses. Table 4.2, Columns 3â4 compute21Following GS, we take a snapshot of employment at the firm in a particular moment in time, whichin our case corresponds to January of each year.103the associated PDV losses from each of the two approaches, and our methodologyestimates these losses to be around four times largerâwhen scaled by pre-outsourcingearningsâthan those found using the flow approach.The assumptions required to identify each approach are precisely what leads to thisdifference.22 While our approach does not require outsourced workers to work forthe daughter firm the year after the outsourcing event, the flow approach does. As aresult, the latter approach significantly underestimates the impact of outsourcing onthe extensive margin.4.5 HeterogeneityA key advantage of our method for measuring outsourcing is that it provides asufficiently large sample, allowing us to study the heterogeneous impact of outsourcingacross various characteristics, including occupations (and their task content) andsectors.23 This analysis will shed light on the different motives behind firmsâ decisions tooutsource and how these motives might ultimately impact a workerâs career trajectory.Worker Demographics. Panels (a) and (b) of Figure 4.6 display the effects ofoutsourcing by age and gender.24 One year after outsourcing, the effects across agegroups exhibit a reverse U shape, with the most pronounced negative effects onyounger and older workers. In contrast, five years after outsourcing, the effects aremore homogeneous across all groups except for those aged 50 and over, where weobserve small and insignificant effects. This trend appears to be driven by olderworkers in the control group retiring from the workforce. Panel (b) shows slightlymore negative effects for women, especially on impact.22Note that our estimates on earnings loss based on the GS approach for Italy (of approximately 4%)are qualitatively comparable to the ones of GS for Germany (though noisier toward the end) thatshow a 4%â7% effect on earnings (see their Appendix Figure A.6). Appendix Figure C.6 computesthe effects on log daily wages using the GS design. The coefficients present a pattern similar to theearnings results found in Figure 4.5, though these estimates are smaller than the ones reported byGS for Germany.23Recall that the GS approach focuses on outsourcing events for only five types of occupations (FCSLagencies). Moreover, our sample size is about three times the one of GS (despite being a shorterpanel).24All the heterogeneous effects presented in this section are computed by only re-estimating equation(4.1) for the set of treated and control workers belonging to the particular category under investiga-tion (e.g., women). The reported event-study coefficients are then rescaled by the average level ofearnings observed in the year before outsourcing for that particular group.104Labor Contract and Relative Pay. We find significantly more heterogeneity whenexamining workers with different contracts or relative pre-outsourcing wages. Panel(c) of Figure 4.6 compares part-time outsourced workers with those who work fulltime (in the year before the outsourcing event). Part-time workers experience largerearnings losses, especially on impact (30% versus 20%). Large gaps also emerge whencomparing workers in different quartiles of log daily wages observed at tâ â 1. Panel(d) shows that low-wage workers (i.e., those in the first quartile of the pre-outsourcinglog wage distribution) experience a 35% earning loss on impact compared to high-wage workers (i.e., those in the fourth quartile), who experience a 20% loss. Thesedifferences, however, tend to converge over time. Overall, these results suggest thatthe cumulative negative effects of outsourcing are more pronounced among workerswho have a weaker attachment to the labor market, such as women, young/older,part-time, and low-wage workers.Sector and Occupation. Figure 4.7 presents the effect of outsourcing one year(panel (a)) and five years later (panel (b)) by sector of the mother firm, showingconsiderable heterogeneity in impact on earnings. Initially, the points estimates acrosssectors range from â60% to +5%, with a standard deviation of 12 percentage pointsafter accounting for sampling error. While there is less dispersion in the effects fiveyears post-outsourcing, the heterogeneity still remains considerable. The negativeeffects are very pronounced in the food/restaurant sector but are also present in severalmanufacturing-intensive sectors (e.g., manufacturing of leather or vehicles). On theother hand, there are close to null effects on impact and slightly positive (but relativelyimprecise) effects in the medium-run for workers in more IT-intensive sectors (e.g.,IT services, software production, repair of computers). Another sector that does notappear to suffer from outsourcing is consulting: the effects on impact are virtuallyzero and five-years out become positive with a magnitude of approximately 17%. Theeffects by occupationsâreported in Figure 4.8âcomplement the analysis for sectors.For example, when a cook is outsourced, it leads to large negative effects on theirearnings both on impact (â40%) and in the medium run (â20%). In contrast, when asoftware developer is outsourced, their earnings are virtually unchanged right after theoutsourcing event and increase (by around 15%) five years after, though this estimateis somewhat noisy.105Task Content. To better understand the potential structural differences drivingthe heterogeneous effects of outsourcing across occupations, we use the task-basedoccupation classification of Autor et al. (2003). Figure 4.9 shows the effects ofoutsourcing for non-routine analytical, non-routine interactive, non-routine manual,routine cognitive, routine manual jobs (as defined by their most prevalent task content).The effect of outsourcing varies considerably depending on the outsourced jobâs taskcontent. Earnings losses one year after outsourcing are two-thirds smaller for workersin non-routine analytical/ interactive jobs compared to those in mainly (routine ornon-routine) manual jobs (e.g., cooks, beauticians, truck drivers). Five years post-outsourcing, the differences are still striking: workers in non-routine cognitive jobsreturn to their earnings levels, whereas those in manual jobs still earn significantlyless.25Summary. The estimates by sector and occupation suggest that the effects of out-sourcing vary significantly depending on the type of job being outsourcedâsomethingthat previous research has not been able to fully quantify. This heterogeneity maybe attributed to the diverse motives for outsourcing, which vary across sectors andoccupations, as previously highlighted in a quantitative structural framework by Bilaland Lhuillier (2022). Some jobs may be outsourced simply because firms need tocut down firing and other costs (Goldschmidt and Schmieder, 2017; Drenik et al.,2023). However, other firms could use outsourcing to directly increase productivity,for instance, by exploiting economies of scale or comparative advantages, factors thatmay eventually result in higher pay for workers (Bergeaud et al., 2021; Bostanci,2021). This latter motive seems to be more prevalent when analyzing workers whosejobs involved more cognitive and interactive tasks, such as consultants and softwaredevelopers (Autor et al., 2003).25Table C.3 shows that the results from Figure 4.9 are not driven by the decision to assign a job to atask based on the highest task content associated with that given occupation. Running a regressionof average losses from outsourcing against the various task contents associated with a particularjob shows that jobs involving non-routine cognitive tasks experience the smallest earnings lossesdue to outsourcing.1064.6 The Role of FirmsThe heterogeneous effects by occupation and sector point to different motives thatmight drive a firmâs decision to outsource. This section builds on these results byfurther examining the role played by (mother) firms in driving the effects of outsourcing.Firm Productivity and Labor Share. Figures 4.7 and 4.8 suggest that low-value-added occupations/sectors might be driving the negative effects of outsourcing.Figure 4.10, panel (a) confirms this by showing the effects of outsourcing one andfive years out by different deciles of mother firmsâ value added per worker, measuredin the year before the outsourcing event. A worker being outsourced by a firm atthe bottom of the productivity distribution leads to earnings losses of more than30%, while being outsourced by a firm at the top results in earnings losses thatare about two-thirds smaller (â 11%). A similar pattern is also observed whenexamining the effects five years post-outsourcing. While earnings losses remain largeand statistically significant for workers outsourced by firms at the bottom of theproductivity distribution, the effects are smaller and sometimes insignificant for thoseoutsourced by firms at the higher end of the productivity ladder. Consistent withthe evidence shown on occupations and sectors, outsourcing appears to lead to morepronounced earnings losses when workers are outsourced by low-productivity firms.Figure 4.10, panel (b) complements this finding by reporting estimates based on thelabor share of the mother firm, calculated in the year before the outsourcing event.The negative effects of outsourcing are concentrated in highly labor-intensive firms,which, following our previous discussion of outsourcing as a firing mechanism, may bemore motivated to cut labor costs. Conversely, workers outsourced by firms with arelatively small labor share experience smaller losses in earnings on impact and nearlyzero earnings losses in the medium run.The concentration of negative effects in low-productivity firms may seem surprising, asprevious research has shown that firms paying higher wages (and therefore more likelyto have higher value added per worker, as noted by Card et al., 2018b) are the onesdriving the negative effects of outsourcing on pay (Goldschmidt and Schmieder, 2017).We believe this potential discrepancy relates to our measure of outsourcing and itsability to capture events where workers lose their jobs shortly after being outsourced.As mentioned in Section 4.2.1, anecdotal and court-level evidence suggest that firms use107outsourcing to separate from workers and bypass Italyâs strict employment legislation;see also the related evidence for the US provided by Autor (2003). The large negativeemployment effects shown in Figure 4.1 align with the fact that some Italian employersoften use outsourcing to basically fire workers. Figure 4.10 strengthens this conclusionby highlighting that the negative impact of outsourcing on pay is most significant forworkers outsourced by low-productivity and labor-intensive firms. These firms may beparticularly motivated to use outsourcing as a cost-cutting strategy that allows themto remain viable and thus potentially avoid closure. In the next paragraph, we furtherelaborate on this point by showing trends in various firm-level characteristics in theyears leading to the outsourcing decision.Trends of the Average Outsourcing Firm. Figure 4.11 shows how variousfirm-level outcomes were trending in the years leading up to an outsourcing event.For comparison, we also report these averages for firms that do not outsource andare in the same two-digit sector and province of outsourcing firms. Five years beforethe outsourcing decision, outsourcing firms have a higher value added per workerthan an average non-outsourcing firm in the same region by sector cell. However,this changes in later years, as they experience a downward trajectory in value addedper worker (or revenues) of approximately 5% in each year. Despite this downwardtrajectory, we observe little adjustments in the firm in terms of labor costs per workeror firm size, likely due to Italyâs strict employment protection rules. These rules hinderfirmsâ ability to quickly adjust their employment in response to negative demandshocks. Faced with these institutional constraints, firms might turn to outsourcingto counteract the effects of such shocks. Overall, the evidence suggests that firmstypically do not use outsourcing in âgood times" to directly increase productivity.Instead, they often resort to it as a way to manage the combination of negativefirm-level shocks and high firing costs.An Alternative Counterfactual for the Outsourced Worker. One questionthat emerges from the previous analysis on firm-level trends is the following: Whatis the relevant counterfactual of an Italian worker being outsourced? In our baselineestimates, we construct this counterfactual by examining the outcomes of a non-outsourced worker who had a similar career trajectory as an outsourced worker upto the moment of the outsourcing decision. This research design mimics the one108used by GS for German outsourced workers and is based on the idea of using non-outsourced workers to infer what would have happened if the outsourced workerremained employed (at time tâ = 0) with the mother firm.26However, this counterfactual does not necessarily account for the dynamic firm-leveltrajectory displayed in Figure 4.11. An alternative counterfactual might be constructedby looking at a worker whose employer is experiencing a negative trajectory similarto the ones shown in Figure 4.11 but ultimately has decided not to outsource theworker. This approach addresses whether outsourcing is generally a âlesser evil" froma workerâs perspective, compared to other events that might unfold at the motherfirm during challenging times (e.g., mass layoffs, plant closure).We now augment our research design to include in the matching variables the trendsin value added per worker of a given employer, and report the results in Figure 4.12.27The point estimates on earnings are remarkably close to the ones obtained with ourbaseline event-study specification. The key conclusion from this analysis is that thenegative effects of outsourcing shown in Figure 4.1 are driven by the outsourcingdecision itself, rather than by the negative firm-level shock impacting the motherfirm. Workers employed by firms experiencing a similar negative trajectory in sales asoutsourcing firmsâbut who are ultimately not outsourced by their employerâhavesignificantly better labor market outcomes than outsourced workers. This suggeststhat employers in the control group are more effective at shielding their workersfrom the negative impacts of such shocks. This may occur if, instead of relying on acost-cutting strategy like outsourcing, these employers decide to invest in other formsof firm-level restructuring that could eventually raise revenues.4.7 ConclusionThis paper studies the impact of outsourcing on a workerâs career trajectory, usinga novel identifier of outsourcing events directly encoded in Italian social securityrecords. This identifier allows us to study outsourcing events previously unexplored.For instance, we can study events where the worker is outsourced to a non-businessservice firm and/or becomes non-employed shortly thereafter. The latter is particularly26This type of approach is also used by the job displacement literature (Jacobson et al., 1993;Lachowska et al., 2020; Schmieder et al., 2023; Bertheau et al., 2023).27Specifically, we now also match on values of log value added per worker in tâ â 3 and tâ â 2 using acaliper bandwidth of 10 log points.109important in the Italian context, given the extensive anecdotal and court evidencesuggesting that firms use outsourcing as a strategy to essentially fire workers.Our results suggest that the negative effects of outsourcing on workersâ pay are largerthan those documented in previous research. Thanks to our outsourcing identifierâsability to capture effects along the extensive margin of employment, we show thatoutsourced workers experience sizable earnings losses largely because they often losetheir jobs soon after being outsourced. These employment outcomes stem fromdaughter employers providing lower-quality, less stable jobs. The richness of ouroutsourcing identifier also reveals interesting patterns of heterogeneity: earningslosses from outsourcing are particularly pronounced in low-value-added sectors oroccupations (e.g., cooks) but are null or even positive in other occupations with ahigh non-routine cognitive task component (e.g., software developers). By analyzingthe dynamics of firms that opt to outsource, we note that these employers seem toresort to it as a response to negative shocks, likely due to the employment protectionsthat prevent them from quickly adjusting in-house employment. Importantly, however,workers employed by firms experiencing similar negative firm-level shocks but choosingnot to outsource have much higher earnings than outsourced workers following theoutsourcing event.There are three main takeaways from our analysis. First, our work shows that severaloutsourcing practices by Italian firms do eventually lead to workers becoming non-employed shortly thereafter. Absent the employment effects, the negative effect onworkersâ earnings would be 75% smaller. It would be useful to combine the informationon outsourcing from Italian social security data with additional investigations onoutsourcing firms to understand whether these outsourcing practices are actually legalaccording to the most recent court orders (Corte di Cassazione, 2017a). In fact, arecent Italian labor reform introduced stricter rules and harsher sanctions againstillegal outsourcing (Tagliabue, 2024), thus implicitly recognizing that firms may havebeen using outsourcing to circumvent employment protection. It will be interesting toevaluate this reformâs impact in the coming years and compare it to similar recentreforms conducted in other countries (Estefan et al., 2024; Elias and Silva, 2024).Second, the heterogeneity that we document across occupations, sectors, and firm pro-ductivity suggests that the reasons behind firmsâ decision to outsource can vary. Whilelow-productivity firms seem to rely on outsourcing as a cost-cutting strategy to bypasssome of the strict Italian employment protection laws, more productive firms engaging110in outsourcing tend to achieve better outcomes for their workers. Understanding thedeterminants of the outsourcing decision conducted by heterogeneous firmsâand howit ultimately impacts key firm-level outcomes, such as firm size, productivity, andlabor compensationâis an interesting avenue for future research.Last, we view our results as a manifestation of the second-best theorem. Policiesthat allow firms to outsource part of their workforce to an external firm are oftenviewed as efficiency-enhancing by their advocates. However, this view often overlooksrestrictive labor market conditions in many countries like Italy (or France, Spain,Portugal, among others), which include stringent employment protection legislationand rigid wage structures. As a result, allowing firms to outsource in such a heavilyregulated labor market might actually have negative net effects. Our results speakdirectly to this outcome by showing that workers unaffected by outsourcing, yetemployed by firms with similar declining revenues, experience significantly better labormarket outcomes than outsourced workers. Fully exploring the interaction betweenfirm strategies, labor market frictions, and existing labor regulations is an importantavenue for future research.1114.8 FiguresFigure 4.1: Earnings and Employment of Outsourced and Non-Outsourced WorkersFigure 1.1: Total Earnings14,00016,00018,00020,00022,000Total Earnings (â¬)-5 -4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventControlTreatedFigure 1.2: Event-Study Estimates on TotalEarnings-5000-4000-3000-2000-10000Effect on Total Earnings (â¬)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventFigure 1.3: Employment60708090100Employed (%)-5 -4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventControlTreatedFigure 1.4: Event-Study Estimates on Employ-ment-25-20-15-10-50Effect on Employmnet (p.p.)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventNotes: Panels (a) and (c) report the trajectories of earnings (panel (a)) and employment (panel(c)) of outsourced workers and their matched controls. Panels (b) and (d) report the correspondingevent-study estimates obtained after fitting equation (4.1) on annual earnings (panel (b)) or a dummyequal to 1 if the worker is employed in a given year (panel (d)). Year 0 is when the outsourcing eventtakes place. A worker is defined as employed if they have at least one day of work recorded in theItalian social security data. Total earnings is the sum of labor earnings obtained by the worker inthat year and are equal to zero if the worker is not employed according to official social securityrecords. Earnings are expressed in real terms (2015 CPI). The event-study coefficients in panel (d)are multiplied by 100. Panels (b) and (d) report 95% confidence intervals based on standard errorsclustered at the worker level.112Figure 4.2: Conditioning on Outsourced Workers Being Employed by the DaughterFirmFigure 2.1: Total Earnings-5,000-4,000-3,000-2,000-1,00001,000Effect on Total Earnings (â¬)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventBaseline1 + Day at Daughter FirmFigure 2.2: Employment-25-20-15-10-50Effect on Employment (p.p.)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventBaseline1 + Day at Daughter FirmNotes: This figure reports event-study results on total earnings (panel (a)) and employment (panel(b)) obtained from equation (4.1). The figure reproduces the baseline event-study estimates fromFigure 4.1 in blue and reports in red event-study estimates computed after estimating equation (4.1)only for outsourcing events where the outsourced worker was employed by the daughter firm for atleast one day in the outsourcing event year. The mean of earnings in the year before the outsourcingevent for the baseline specification is 20,566 euros and 21,898 euros when conditional on working atthe daughter firm. The event-study coefficients for employment are multiplied by 100. Both panelsreport 95% confidence intervals based on standard errors clustered at the worker level.1134.9 TablesTable 4.1: Summary Statistics(1) (2) (3)Matched Control Matched Outsourced All OutsourcedAge 38.59 38.62 40.31(10.74) (10.77) (10.77)Female 0.43 0.43 0.44Temporary contract 0.22 0.22 0.17Working full time 0.73 0.73 0.68Current tenure at firm 7.07 7.07 5.81(5.34) (5.34) (5.11)Total earnings 20,602 20,530 22,624(13,007) (12,812) (23,099)Daily wage 75.53 75.08 81.20(40.23) (42.56) (80.07)Firm size 1,352 1,146 973(5,197) (3,760) (3,120)Exposure - 0.58 0.57(0.31) (0.32)Number of workers 61,849 61,849 267,030Number of firms 40,694 20,248 55,024Notes: Column 2 presents the summary statistics of the outsourced workers who have at least twoyears of tenure when outsourced and for whom we can find a matched control worker. These summarystatistics are calculated in the year before the outsourcing event, i.e., in tâi â 1. Potential matchedcontrol workers are those who never experienced an outsourcing event in their career and, in tâi â 1,had the same tenure, region, CBA, gender, employment contract, and quartile of firm size as theircorresponding outsourced worker (defined as the unique combination of the full-time and permanentcontract indicators). A caliper matching method based on earnings in tâ â 3, tâ â 2, and age is thenconducted to assign exactly one matched control worker for each treated worker without replacement((Stepner and Garland, 2017)). Column 1 reports the characteristics of matched control workers,and Column 3 reports the summary statistics for the entire set of outsourced workers identified inthe INPS data (without imposing a two-year tenure restriction). Total earnings is the sum of laborearnings obtained by the worker in that year and are expressed in real 2015 euros. Exposure isdefined as the fraction of the mother firmâs total wage bill paid to outsourced workers in tâi â 1. Allstatistics are person-year-weighted, and standard deviations are reported in parentheses.114Figure 4.3: The Effect of Outsourcing on Employment Contract and CBAFigure 3.1: Employment Contract-2024681012Change of Empl. Contract (p.p.)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing Event1(No Longer with a Permanent Contract)1(No Longer with a Full-Time Contract)Figure 3.2: Collective Bargaining Agreement-404812162024Change in CBA (p.p.)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventNotes: The blue event studies in panel (a) are constructed by estimating equation (4.1) on alloutsourced workers with a permanent contract the year before the outsourcing event, and wherethe outcome is a dummy equal to 1 if the worker is on a temporary contract and 0 if they remainemployed with a permanent contract. The red event studies in panel (a) are constructed by estimatingequation (4.1) on all outsourced workers with a full-time contract the year before the outsourcingevent, and where the outcome is a dummy equal to 1 if the worker is on a part-time contract and0 if they remain employed with a full-time contract. Panel (b) reports estimates from equation(4.1) where the outcome variable is an indicator equal to 1 if the CBA observed the year before theoutsourcing event differs from the current one. The event-study coefficients reported in all panels aremultiplied by 100. Both panels are estimated on the sample of employed individuals and report 95%confidence intervals based on standard errors clustered at the worker level.115Figure 4.4: Reason for Job Separation after OutsourcingFigure 4.1: Worker Resignation-2024Worker Resignation (p.p.)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventFigure 4.2: Mass Layoff-1012Mass Layoff (p.p.)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventFigure 4.3: Temporary Contract Not Renewed-20246Temp. Cont. Not Renewed (p.p.)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventFigure 4.4: Employed by a Newly CreatedFirm01020304050Work for New Firm (p.p.)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventNotes: Panel (a) reports estimates from equation (4.1) where the outcome variable is an indicatorequal to 1 if the worker has resigned in year t from their dominant job observed in that year. Panel(b) reports estimates where the outcome variable is an indicator equal to 1 if the worker experiencesa job separation due to a mass layoff. Panel (c) reports estimates where the outcome variable isan indicator equal to 1 if the worker experiences a job separation because their temporary workcontract was not renewed. Finally, panel (d) reports event-study estimates where the outcome isan indicator equal to 1 if the dominant employer at time t was created in year t or year tâ 1. Thefraction of newly created firms in our matched sample in the year before the outsourcing event isequal to 0.09. The event-study coefficients reported in all panels are multiplied by 100 with 95%confidence intervals based on standard errors clustered at the worker level.116Figure 4.5: Comparison with the Flow-Based Approach of Goldschmidt and Schmieder(2017)Figure 5.1: Total Earnings-20-15-10-505Effect on Total Earnings (%)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventFCSL Workers---INPS identifier of Outsourcing EventsFCSL Workers---Flow approach of Goldschmidt-SchmiederFigure 5.2: Employment-20-15-10-505Effect on Employment (p.p.)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventFCSL Workers---INPS identifier of Outsourcing EventsFCSL Workers---Flow approach of Goldschmidt-SchmiederNotes: The event-study coefficients in blue are computed by fitting equation (4.1) on the sample ofoutsourced workers whose daughter firm in the year of the outsourcing event is a business service firmin the FCSL sector. The event-study coefficients in red are computed after identifying outsourcingevents using the flow-based approach of Goldschmidt and Schmieder (2017), where an outsourcingevent corresponds to a situation where a group of workers are employed in year t by a non-businessfirm and in year t+ 1 are instead employed by a FCSL business service firm (see Section 4.4.3 fordetails). Panel (a) reports event-study coefficients on total earnings, rescaled by the average totalearnings the year before the event observed in each of the two samples. Panel (b) reports event-studycoefficients on employment, defined as a dummy equal to 1 if the worker has at least one day ofemployment according to official social security records. The coefficients in panel (b) are multipliedby 100. Both panels report 95% confidence intervals based on standard errors clustered at the workerlevel.117Figure 4.6: Effects by Worker CharacteristicsFigure 6.1: Age-60-50-40-30-20-10010Effect on Total Earnings (%)<25 [25;30) [30;35) [35;40) [40;45) [45;50) [50;55) > 55Age At Moment of OutsourcingEffect 1-yr Post Outsourcing EventEffect 5-yrs Post Outsourcing EventFigure 6.2: Gender-50-40-30-20-100Effect on Total Earnings (%)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventMenWomenFigure 6.3: Full/Part Time-50-40-30-20-100Effect on Total Earnings (%)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventPart-TimeFull-TimeFigure 6.4: Pre-Outsourcing Wages-50-40-30-20-100Effect on Total Earnings (%)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventLow-Wage WorkersHigh-Wage WorkersNotes: This figure reports event-study estimates after fitting equation (4.1) separately for a givengroup of workers (e.g., women) and using total earnings as an outcome. Panel (a) presents results byage bin, panel (b) plots the estimated coefficients by gender, and panel (c) presents results for workerswho held either a part- or full-time job before the outsourcing event. Panel (d) presents results forworkers belonging to the first quartile of the log daily wages (âLow-Wage Workers") distributionobserved in the year before the outsourcing event. The red line reports estimates for workers whoinstead belong to the fourth quartile (âHigh-Wage Workers"). All event-study coefficients are rescaledby the average total earnings observed in the year before the outsourcing event in the reference group.Also reported are 95% confidence intervals based on standard errors clustered at the worker level.118Figure 4.7: Effects by SectorFigure 7.1: One Year after OutsourcingManuf. of vehiclesManuf. of leatherBuilding constructionMetallurgyNon resid. social assist.Printing & mediaRestaurant serv.Oth. serv. activ.Packaging of clothingFood industryTextile industryManuf. of non-metallicSpecial. constructionOth. retail tradeFinancial serv.Manuf. of plasticManuf. of furnitureHealthcareManuf. electrical equip.Land & pipelines transp.Manuf. of pharmaceuticalServ. for buildingsAccomodationOth. manuf. indus.Trade vehiclesOth. wholesale tradeManuf. of metalEducationWood industrySupport for businessManuf. of machineryResidential social serv.Repair of machineryAdvertisingAux. financial serv.Archit. & engineeringOth. prof. activ.TelecommunicationsHuman resourcesLegal/accounting serv.Manuf. of electronicsWaste collectionReal estate activ.Manuf. of chemicalsSoftware productionRepair of computersSupply electric. & gasRental & leasing activ.Transp. support activ.IT serv.ConsultingManuf. of oil-80 -60 -40 -20 0 20 40Effect on Total Earnings - Rescaled (%)Weighted SD of Effects: 12.19Figure 7.2: Five Years after OutsourcingMetallurgyNon resid. social assist.Manuf. of leatherFood industryManuf. of non-metallicOth. manuf. indus.Wood industryRestaurant serv.Oth. serv. activ.Manuf. of metalOth. wholesale tradeManuf. electrical equip.EducationTrade vehiclesOth. retail tradeAdvertisingRental & leasing activ.TelecommunicationsHealthcareResidential social serv.Special. constructionBuilding constructionPrinting & mediaManuf. of oilManuf. of furnitureRepair of machineryManuf. of chemicalsLand & pipelines transp.AccomodationPackaging of clothingManuf. of pharmaceuticalAux. financial serv.Real estate activ.Support for businessManuf. of plasticOth. prof. activ.Manuf. of machineryRepair of computersTransp. support activ.Textile industryServ. for buildingsLegal/accounting serv.Waste collectionArchit. & engineeringFinancial serv.Manuf. of vehiclesManuf. of electronicsHuman resourcesIT serv.Software productionSupply electric. & gasConsulting-80 -60 -40 -20 0 20 40Effect on Total Earnings - Rescaled (%)Weighted SD of Effects: 7.72Notes: This figure presents event-study estimates derived from equation (4.1), fitted separatelydepending on the mother firmâs two-digit ATECO sector code of the outsourced worker. We includeestimates for sectors that have at least 100 outsourced workers. Panel (a) presents the estimatedevent-study coefficients for the year following the outsourcing event, and panel (b) displays theestimated coefficients for five years after the outsourcing event. Both sets of coefficients are normalizedby the sector-specific mean of total earnings observed in the year before outsourcing. Additionally,both panels report 95% confidence intervals based on standard errors clustered at the worker level.We also report the weighted standard deviation of these rescaled effects (net of sampling error) wherethe weights are given by the number of person-year observations in a given sector.119Figure 4.8: Effects by OccupationFigure 8.1: One Year after OutsourcingHairdresser/beauticianMining/construc. laborerCar/motorcycle driverWood treaterGarment workerCookHeavy truck/bus driverBuilding frame workerCashiers clerkWaiter/bartenderBuilding finisher workerHandicraft workerOther sales workerFood prep. assistantAssemblerTextile machine operatorOther craft workerDomest./hotel/offi. cleanerOther elementary workerFood processingShop salespersonOther clerical supp. workerMetal workers/welderMachinery mechanicTelecommunications technic.Electric. equipment installerSecretaries generalGeneral office clerkAdministrative secretaryKeyboard operatorsOther health professionalFinancial/math professionalMaterial-recording clerkManuf. laborerEngineering technicianTransport/storage laborerTeller/money collectorPersonal care in health serv.Telecommunications repairClient information workerMetal plant operatorBlacksmiths/toolmakerSales agents and brokerNumerical clerkCommunications techniciansAdministration professionalBusiness services agentSoftware developerSales/marketing prof.-80 -60 -40 -20 0 20 40 60Effect on Total Earnings - Rescaled (%)Weighted SD of Effects: 10.27Figure 8.2: Five Years after OutsourcingBuilding finisher workerCookFood prep. assistantCashiers clerkGarment workerWood treaterOther craft workerMining/construc. laborerOther elementary workerHairdresser/beauticianHeavy truck/bus driverDomest./hotel/offi. cleanerMachinery mechanicOther sales workerOther health professionalFood processingShop salespersonMetal plant operatorMetal workers/welderGeneral office clerkBuilding frame workerOther clerical supp. workerManuf. laborerClient information workerMaterial-recording clerkAssemblerWaiter/bartenderSales agents and brokerElectric. equipment installerPersonal care in health serv.Administrative secretaryKeyboard operatorsTelecommunications technic.Financial/math professionalTransport/storage laborerTeller/money collectorHandicraft workerBlacksmiths/toolmakerCommunications techniciansNumerical clerkCar/motorcycle driverTelecommunications repairSecretaries generalTextile machine operatorEngineering technicianBusiness services agentSoftware developerAdministration professionalSales/marketing prof.-80 -60 -40 -20 0 20 40 60Effect on Total Earnings - Rescaled (%)Weighted SD of Effects: 5.53Notes: This figure presents event-study estimates derived from equation (4.1), fitted separatelybased on the occupation code of the outsourced worker. We report estimates for occupations thathave at least 100 outsourced workers. A job in our data is classified according to five-digit CP(âClassificazione delle professioni") codes from the INPS data, which are then cross-walked andaggregated to three-digit ISCO codes. Panel (a) presents the estimated event-study coefficients for theyear following the event, and panel (b) displays the estimated coefficients for five years after the event.Both sets of coefficients are normalized by the occupation-specific mean of total earnings observedin the year before outsourcing. Additionally, both panels report 95% confidence intervals basedon standard errors clustered at the worker level. We also report the weighted standard deviation(net of sampling error) of these effects where the weights are given by the number of person-yearobservations in a given occupation.120Figure 4.9: Effects by Task ContentFigure 9.1: One Year after Outsourcing0-10-20-30-40Effect on Total Earnings (%)Non-Routine AnalyticNon-Routine InteractiveNon-Routine ManualRoutine CognitiveRoutine ManualFigure 9.2: Five Years after Outsourcing100-10-20Effect on Total Earnings (%)Non-Routine AnalyticNon-Routine InteractiveNon-Routine ManualRoutine CognitiveRoutine ManualNotes: This figure reports the losses from outsourcing depending on the task content of an outsourcedjob. A job in our data is classified according to five-digit CP (âClassificazione delle professioni") codesfrom the INPS data, which are then cross-walked and aggregated to three-digit ISCO codes. We nextmeasure the tasks associated with each job using the classification of Mihaylov and Tijdens (2019),which extends the methodology of Autor et al. (2003) for ISCO codes. This provides a task contentmeasured for each job, i.e., how much of the job involves routine versus non-routine tasks and whetherit requires analytical, interactive, or manual skills. We assign a job to either non-routine analytical,non-routine interactive, non-routine manual, routine cognitive, or routine manual, depending on themost prevalent task of the job. We then average the occupation-specific effects of outsourcing for agiven task content, weighting by the number of observations associated with a given occupation. Theeffects on earnings are rescaled by the average level of earnings observed in a given occupation, oneyear before the outsourcing event. Ninety-five percent confidence intervals are based on standarderrors calculated via the delta method and are clustered at the worker level.121Figure 4.10: Effects by Value Added per Worker of Mother Firm or Labor ShareFigure 10.1: Log Value Added per Worker-50-40-30-20-10010Effect on Total Earnings (%)1 2 3 4 5 6 7 8 9 10Deciles of Log Value Added per Worker of Mother Firm 1 yr before OutsourcingEffect 1-yr Post Outsourcing EventEffect 5-yrs Post Outsourcing EventFigure 10.2: Labor Share-60-50-40-30-20-10010Effect on Total Earnings (%)1 2 3 4 5 6 7 8 9 10Deciles of Labor Share of Mother Firm 1yr before OutsourcingEffect 1-yr Post Outsourcing EventEffect 5-yrs Post Outsourcing EventNotes: This figure reports event-study estimates after fitting equation (4.1) separately for each decileof the mother firmâs log value added per worker (panel (a)) or labor share (panel (b)), where bothquantities are measured the year before the outsourcing event. The blue coefficients capture theeffects one year after the outsourcing event, and the red coefficients capture the effects five yearsafter. All reported coefficients are scaled by the mean of total earnings observed in the year beforethe outsourcing event in a given decile. The plot reports 95% confidence intervals based on standarderrors clustered at the worker level.122Figure 4.11: Trends of Outsourcing FirmsFigure 11.1: Log Revenue per Worker11.3511.411.4511.511.5511.6Log Revenue P/W-4 -3 -2 -1Year Relative to Outsourcing EventOutsourcing Mother FirmsFirms in the Same Province and SectorFigure 11.2: Log Value Added per Worker1010.0510.110.1510.210.25Log VA P/W-4 -3 -2 -1Year Relative to Outsourcing EventFigure 11.3: Log Labor Cost per Worker9.79.89.910Log Labor Costs P/W-4 -3 -2 -1Year Relative to Outsourcing EventFigure 11.4: Firm Size050100150200Firm Size-4 -3 -2 -1Year Relative to Outsourcing EventNotes: This figure reports (in red) the evolution of revenues, value added per worker, labor costs, andfirm size in the years before an outsourcing event. For comparison, we report in blue the trajectoriesof these outcomes for firms that do not conduct an outsourcing event but are in the same regionand two-digit sector code of outsourcing firms. The graph is computed on a balanced panel; i.e., wecondition on firms that are alive and report financial statements to CERVED for each of the fouryears before the outsourcing event.123Figure 4.12: Effects after Matching on Trends of Value Added per WorkerFigure 12.1: Total Earnings-30-25-20-15-10-505Effect on Total Earnings (%)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventBaselineMatching on VA p/WorkerFigure 12.2: Employment-30-25-20-15-10-50Effect on Employment (p.p.)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventBaselineMatching on VA p/WorkerNotes: This figure reports event-study estimates from equation (4.1). The blue line representsbaseline effects reported in Figure 4.1. The red line reports the event-study coefficients, computedafter augmenting our matching strategy to also include log value added per worker in tâ â 3 andtâ â 2 in the caliper matching (with a bandwidth of 10 log points). The effects on earnings arereported after rescaling the event-study coefficients by the average earnings observed in the yearbefore the outsourcing event. Both panels report 95% confidence intervals based on standard errorsclustered at the worker level.124Table 4.2: Summary of Main Results(1) (2) (3) (4) (5)Baseline Estimates At Least 1 Dayw/ Daughter Firm FCSL Workers OnlyFCSL Workers:Flow-Based approachMatching onValue Added p/ WorkerPanel (a): Effects on Total Earnings (Real 2015 Euros)1 year after the outsourcing event -4,643.2 -2,507.03 -2,257.3 -780.0 -3,144.2(74.1) (87.2) (189.9) (110.7) (260.1)2 years after -3,305.8 -1,633.03 -1,385.0 -611.3 -2,185.9(83.8) (101.4) (226.8) (166.2) (288.7)3 years after -2,730.8 -1,418.4 -1,106.1 -611.7 -2,000.0(93.2) (114.6) (260.2) (200.0) (320.7)4 years after -2,227.5 -945.8 -1,031.5 -490.3 -2,193.5(100.7) (124.8) (302.1) (237.8) (351.0)5 years after -2,023.5 -970.2 -1,031.8 388.7 -2,134.4(107.2) (134.1) (329.6) (263.8) (394.1)Panel (b): Effects on Employment1 year after the outsourcing event -.219 -.096 -.088 -.007 -.226(.003) (.003) (.009) (.001) (.011)2 years after -.139 -.050 -.059 -.003 -.131(.003) (.003) (.011) (.003) (.012)3 years after -.113 -.042 -.056 -.005 -.080(.003) (.004) (.012) (.003) (.013)4 years after -.088 -.017 -.043 -.004 -.077(.004) (.004) (.014) (.003) (.014)5 years after -.077 -.016 -.048 -.001 -.073(.004) (.005) (.015) (.004) (.015)Number of person-year observations 1,062,058 791,480 83,627 86,224 71,660Average Earnings one year before outsourcing event 20566.2 21898.3 13481.1 19298.7 16783.9PDV value losses in earnings -13,515.9 -6,787.3 -6,164.1 -1,958.6 -10,451.6Notes: This table presents the main results of the paper. Column 1 displays the estimated coefficients for total earnings (panel (a)) andemployment (panel (b)), also reported in in Figure 4.1. Column 2 displays the estimated coefficients for outsourced workers who were employedfor at least one day with the daughter firm, as presented in Figure 4.2. Columns 3 and 4 display the estimated coefficients for FCSL workers,using our identifier of outsourcing events from the INPS data (Column 3) and when using the flow-based definition of outsourcing events usedby Goldschmidt and Schmieder (2017) (Column 4); see also Figure 4.5. Column 5 displays the estimated coefficients when matching on valueadded trends of the mother firm, as presented in Figure 4.12. The PDV of earnings losses is calculated using a discount factor of 4%.125Chapter 5ConclusionsThis dissertation comprises three chapters that investigate evolving labor marketarrangements in response to technological innovations and regulatory frameworks.Each chapter delves into a distinct facet of labor market dynamics, offering novelempirical insights that both inform policy debates and set the stage for future research.In Chapter 2, I examine the rapid expansion of online-delivery platforms in Braziland their transformative effects on the restaurant sectorâs employment structure. Byleveraging a unique data set that links administrative employer-employee recordswith platform-worker data, the chapter documents how platform adoption leads to asignificant contraction in traditional server employment, a trend that is partially offsetby the growth of gig delivery drivers. Importantly, while displaced workers transitionquickly to new jobs and do not suffer persistent large earnings losses, the overallimpact on gig workers tends to outweigh these loses due to the new opportunities thatthese technologies offer to workers with limited outside options. These findings carrysubstantial policy implications, highlighting the need for regulatory frameworks thatbalance the protection of traditional workers with the flexible benefits that draw manyto gig workâespecially in contexts marked by high informality and unemployment.Chapter 3 shifts the focus to the dynamics of wage formation in temporary workarrangements. The chapter provides evidence that temp workers receive, on average,approximately half of the workplace-specific pay premia enjoyed by their regularlyemployed counterparts. This compression of pay premia is interpreted through multiplelenses. On one hand, the lower bargaining power of temp workers and the possibility ofrent appropriation by temp agencies suggest that market imperfections and multi-partybargaining play key roles. On the other hand, evidence of significant rent sharingâ126albeit more moderate than competitive benchmarks would predictâindicates thatfactors such as fairness norms and regulatory compliance also influence wage outcomes.Together, these findings contribute to the broader discussion on labor outsourcingand wage-setting mechanisms, enriching our understanding of how firms navigate thetrade-offs between cost-cutting and maintaining workforce morale and stability.Finally, in Chapter 4, we explore the long-term career consequences of outsourcing inthe Italian labor market. Utilizing a novel identifier for outsourcing events embeddedin Italian social security records, the chapter reveals that outsourcing can precipitatesubstantial earnings losses, largely due to the high likelihood of subsequent job loss.The analysis further uncovers marked heterogeneity across occupations and sectors.For example, while low-value-added roles such as cooks experience pronounced neg-ative effects, occupations requiring higher non-routine cognitive skills, like softwaredevelopment, may see null or even positive outcomes. These results not only under-score the complex interplay between outsourcing, employment protection legislation,and firm-specific shocks but also raise important questions about the efficiency ofoutsourcing practices in regulated labor markets.Together, these three chapters offer a comprehensive perspective on how evolving labormarket institutions and practicesâwhether driven by digital platforms, temporarywork arrangements, or outsourcing strategiesâare reshaping employment dynamicsand worker outcomes. While each chapter addresses distinct mechanisms and settings,a common thread emerges: changes in labor market structures, often initiated bypolicy shifts or technological advancements, create both opportunities and challengesfor workers.127BibliographyAbowd, J., F. Kramarz, and D. Margolis. 1999a. âHigh Wage Workers andHigh Wage Firms.â Econometrica, 67(2): 251â333.Abowd, J. M., F. Kramarz, and D. N. 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Li. 2023. âRecommendingWhat to Search: Sales Volume and Consumption Diversity Effects of a QueryRecommender System.â SSRN Electronic Journal.144Appendix AAppendix to Chapter 2A.1 Appendix: Figures145Figure A.1: Rollout of the PlatformFigure 1.1: 2018 Figure 1.2: 2019Figure 1.3: 2020 Figure 1.4: 2021Notes: This figure reports the microregions in which restaurants offered services through the deliveryplatform in each year. Treated means that at least one establishment offered a delivery servicethrough the platform. Panel (a) shows the presence of the platform in 2018, panel (b) in 2019, panel(c) in 2020 and panel (d) in 2021.146Figure A.2: Restaurants within 1 Kilometer Adopting the Platform-.1-.050.05.1Effect on Share of Nearby Restaurants on the Platform-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformNotes: This figure reports the corresponding event-study estimates obtained after fitting equation(2.10) on the share of restaurants within a 1 kilometer radius that offer delivery services through theplatform. The mean share of restaurant within 1 kilometer using the platform the quarter prior totreatment is 0.06. The x-axis reflects the quarter relative to the first quarter in which each restaurantoffered delivery services through the platform for the first time. Quarter 0 is the quarter when thetreated restaurant starts offering delivery services through the platform. The panel reports 95%confidence intervals based on standard errors clustered at the establishment level.147Figure A.3: Distribution of â¦0.1.2.3.4.5Fraction-.6 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 .6 .7 .8Change in Share of Restaurants with App Within 1KM RadiusNotes: This figure reports the histogram of the quarterly difference in the share of restaurants thatoffer delivery services through the platform within a 1 kilometer radius of each restaurant (â¦). Thedistribution is calculated using restaurants that have at least 5 restaurants within a 1 kilometerradius. A restaurant is considered to offer delivery services through the platform if they sold at leastone good through the platform using the delivery services of the platform in the quarter. The reddashed line shows the 95th percentile of the distribution. Restaurants are defined as establishmentsthat have a CNAE two digit code equal to 56.148Figure A.4: Distribution of Wages of Restaurant Workers (2018)0.02.04.06.080 1000 2000 3000 4000 5000Monthly Wage (R$ 2018)Monthly Wage (R$ 2018)Minimum Wage (2018)Notes: This figure reports the histogram of wages for restaurant workers in 2018. Restaurants aredefined as establishments that have a CNAE two digit code equal to 56. The vertical black dashedline shows the minimum wage in 2018 (954 BRL). Wages are winsorized at the 99 percentile and areexpressed in real terms (2018 CPI).149Figure A.5: Trends in Closure for Adopting and Control Restaurants0.05.1.151(Closed)-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformControlTreatedNotes: This figure reports the trajectories of establishment closure for matched restaurants thatoffered delivery services through the platform and matched control restaurants. The date of closure isdefined as the last quarter in which the establishment reported having workers to RAIS (previous tothe last quarter available in my sample). The x-axis reflects the quarter relative to the first quarterin which each restaurant offered delivery services through the platform for the first time. Quarter0 is the quarter when the treated restaurant starts offering delivery services through the platform.Potential matched control restaurants are those who in tâj â 1 do not have platform delivery servicesavailable in their microregion (and the platform does not start offering services the following fivequarters). Additionally, these control restaurants must have been open for at least two years, belongto the same quartile of firm size, quartile of average earnings and median of number of restaurantswithin a radius of 1 kilometers (with respect to the distribution of restaurants in their correspondingmicroregion). A propensity score matching based on log size in tâ â 8 to tâ â 1, log average monthlywages paid in quarters tâ â 4 to tâ â 1, firm age, share of waiters, average tenure, age of workers,share of female workers and average hours of workers was conducted to assign exactly one matchedcontrol restaurant to each treated restaurant. Restaurants are defined as establishments that have aCNAE two digit code equal to 56.150Figure A.6: Effect of Platform Adoption on Avg. Worker Fixed Effects at RestaurantsFigure 6.1: Avg. AKM Worker Fixed Effects-.010.01.02Effect on Avg. Worker FE-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformFigure 6.2: Relation Between Log In-House Size Treat-ment Effects and Avg. AKM Worker Fixed Effects-.02-.010.01.02â dd Worker FE-2 -1 0 1 2âdd Log sizeNotes: This figure reports the effect of the adoption of the platform on sorting at the establishmentlevel. Panel (a) reports the corresponding event-study estimates obtained after fitting equation(2.10) on the average AKM worker effect at the establishment level. The x-axis reflects the quarterrelative to the first quarter in which each restaurant offered delivery services through the platformfor the first time. Quarter 0 is the quarter when the treated restaurant starts offering deliveryservices through the platform. The panel reports 95% confidence intervals based on standard errorsclustered at the establishment level. Panel (b) displays a binscatter plot of the establishment leveldifference-in-difference estimates of the adoption of the platform on the average AKM worker effectsplotted against the effects on log in-house establishment size (slope -0.005, SE 0.003). âdd representsthe establishment level treatment effect estimate and is equivalent to âdT ââdC where âdT andâdC are the change before (-7 to -1 quarters) and after (0 to 5 quarters) the adoption of the platformfor the treated and control establishment of the matched pair. The AKM specification was estimatedusing data from RAIS between the years 2012 and 2018. For workers out of the AKM sample, Iimputed their worker effects using the corresponding firm effects, year effects, and age. A formalworker is defined to be employed in a quarter if they have at least one day of work recorded in RAIS.Quarterly size is constructed by taking the average of the number of workers hired formally eachmonth in the quarter by the establishment after applying the restrictions described in Section 2.3.151Figure A.7: Effect of Platform Adoption on Total Hours for Service Workers-1000100200Effect on Total Hours in Service Sector-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformNotes: This figure reports the corresponding event-study estimates obtained after fitting equation(2.10) on the total quarterly hours hired of service workers. The average hours hired of service sectorworkers prior to the adoption of the platform for treated restaurants is 4,160. Service workers aredefined by all workers in a restaurant that are not cooks as defined by their 6 digit Classificaçãobrasileira de ocupações (CBO). Appendix Table A.3 presents how occupations were classified usingthe CBO reported in RAIS. Service workers also include platform workers, once the restaurant startsoffering delivery services through the platform. The x-axis reflects the quarter relative to the firstquarter in which each restaurant offered delivery services through the platform for the first time.Quarter 0 is the quarter when the treated restaurant starts offering delivery services through theplatform. Total hours are defined as total hours hired in each quarter. The panel reports 95%confidence intervals based on standard errors clustered at the establishment level.152Figure A.8: Effect of Platform Adoption on Total Hours for Service Workers by AKMFirm Effects-300-200-1000100200300Effect in Total Hours in Service Sector-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformBelow Median AKM Firm FEAbove Median AKM Firm FENotes: This figure reports the corresponding event-study estimates obtained after fitting equation(2.10) on the total quarterly hours hired of service workers. The red dashed line reports the estimatesfor restaurants that are above the median of AKM firm effects. The solid blue line reports theestimates for restaurants that are below the median of AKM firm effects. The median of AKM firmeffects is calculated using the AKM firm effects of all treated and control restaurants that match thatbelong to the largest connected set. When a restaurant does not have an AKM firm effect, I inputthe firm effect of their matched pair. The AKM specification was estimated using data from RAISbetween the years 2012 and 2018. Service workers are defined by all workers in a restaurant that arenot cooks as defined by their 6 digit Classificação brasileira de ocupações (CBO). Appendix TableA.3 presents how occupations were classified using the CBO reported in RAIS. Service workers alsoinclude platform workers, once the restaurant starts offering delivery services through the platform.The x-axis reflects the quarter relative to the first quarter in which each restaurant offered deliveryservices through the platform for the first time. Quarter 0 is the quarter when the treated restaurantstarts offering delivery services through the platform. Total hours are defined as total hours hired ineach quarter. The panel reports 95% confidence intervals based on standard errors clustered at theestablishment level.153Figure A.9: Relation Between Establishment Level Treatment Effect on Number ofWaiters and Usage of Delivery Platform by Restaurants-.8-.6-.4-.20.2â dd Waiters2 4 6 8Log Avg. Quarterly Hours Using App (Restaurant)Notes: This figure displays a binscatter plot of the establishment level difference-in-differenceestimates of the adoption of the platform on the number of waiters plotted against the log quarterlyaverage hours of platform usage by restaurants (slope -0.0001441, SE 0.0000647). âdd represents theestablishment level treatment effect estimate and is equivalent to âdT ââdC where âdT and âdCare the change before (-7 to -1 quarters) and after (0 to 5 quarters) the adoption of the platformfor the treated and control establishment of the matched pair. Number of waiters is defined as thenumber of formal workers that have the waiter occupations as defined in Table A.3. A formal workeris defined to be employed in a quarter if they have at least one day of work recorded in RAIS. Thex-axis reflects the quarterly average hours that the restaurant used the platform for delivery servicesin the quarters tâ + 1 to tâ + 5. The figure conditions on restaurants that averaged at least 10 hoursin a quarter of usage of the platform. Both average of quarterly usage of the platform and âdd arewinsorized at the 98 percentile and latter is also winsorized at the 2 percentile.154Figure A.10: Ratio of Delivery Workers and Service Workers at Treated Establishmentsby DensityFigure 10.1: Wage Bill.15.2.25.3Ratio Delivery + App/Service (Wage Bill)0 1 2 3 4 5Quarter Relative to Adopting the PlatformLow ConcentrationHigh ConcentrationFigure 10.2: Hours.08.1.12.14Ratio Delivery + App/Service (Hours)0 1 2 3 4 5Quarter Relative to Adopting the PlatformLow ConcentrationHigh ConcentrationNotes: This figure reports the ratio of platform to service wage bill (panel a) and hours (panel b) attreated establishments after the restaurants starts offering services through the platform. The ratioin panel (a) is calculated by dividing the quarterly wage bill of platform-workers by the quarterlywage bill of service workers. The red dashed line reports the estimates for restaurants that areabove the median of restaurant density within their microregion. The solid blue line reports theestimates for restaurants that are below the median of restaurant density within their microregion.Restaurant density is calculated as the number of restaurants that are located in a 1 kilometer radiusof each restaurant (Ï). The median of restaurant density is calculate using the distribution of Ïcorresponding to the microregion of each restaurant in each quarter. The density assigned to eachrestaurant corresponds to the Ï calculated using the quarter prior to the first quarter in which thetreated restaurant of the pair started offering delivery services through the platform. The ratio inpanel (b) is calculated by dividing the total hours hired of platform-workers by the total number ofhours hired of service workers. The x-axis reflects the quarter relative to the first quarter in whicheach restaurant offered delivery services through the platform for the first time. Quarter 0 is thequarter when the treated restaurant starts offering delivery services through the platform. Serviceworkers are defined by all workers in a restaurant that are not cooks as defined by their 6 digitClassificação brasileira de ocupações (CBO). Appendix Table A.3 presents how occupations wereclassified using the CBO reported in RAIS. Service workers also include platform workers, once therestaurant starts offering delivery services through the platform.155Figure A.11: Effect of Adopting the Platform using Borusyak et al. (2024) estimatorFigure 11.1: Log In-House Firm Size-.08-.06-.04-.020.02Effect on Log Firm Size-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformPre-trend coefficientsTreatment effectsFigure 11.2: Log Monthly Wages-.04-.020.02.04Effect on Log Earnings in Month-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformPre-trend coefficientsTreatment effectsNotes: This figure reports the corresponding event-study estimates obtained after fitting equation(2.10) on the logarithm of firm size (panel a) and the logarithm of the average monthly wages paidby restaurants (panel b) using the estimator proposed by Borusyak et al. (2024). The x-axis reflectsthe quarter relative to the first quarter in which each restaurant offered delivery services throughthe platform for the first time. Quarter 0 is the quarter when the treated restaurant starts offeringdelivery services through the platform. A formal worker is defined to be employed in a quarter if theyhave at least one day of work recorded in RAIS. Quarterly size is constructed by taking the average ofthe number of workers hired formally each month in the quarter by the establishment after applyingthe restrictions described in Section 2.3. Average monthly wages in a quarter are constructed bytaking the quarterly average of all the monthly wages reported in RAIS by the establishment inthe corresponding quarter. Wages are expressed in real terms (2018 CPI). Both panels report 95%confidence intervals based on standard errors clustered at the establishment level.156Figure A.12: Effect of Platform Entry on Restaurant Sector Size-.1-.050.05Effect on Log Total Workers in Restaurant Sector-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the Entry of the PlatformNotes: This figure reports reports the corresponding event-study estimates obtained after fittingequation (A.38) on the log number of workers in the restaurant sector in microregion m in quarter t.The regression is weighted by the average number of workers in the sector in 2017. The x-axis reflectsthe quarter relative to the quarter in which the delivery platform entered the microregion. Bothpanels report 95% confidence intervals based on standard errors clustered at the microregion level.157Figure A.13: Establishment level treatment effect and COVID-19 CasesFigure 13.1: Log In-House Firm Size-.1-.050.05â dd Log size-.04 -.02 0 .02 .04âpost Covid Cases Per CapitaFigure 13.2: Log Monthly Wages-.1-.050.05â dd Log earnings-.04 -.02 0 .02 .04âpost Covid Cases Per CapitaNotes: This figure displays a binscatter plot of the establishment level difference-in-difference estimatesof the adoption of the platform on log in-house establishment size (panel a) and log average monthlywages paid by restaurants (panel b) plotted against the difference of average covid cases per capitain the municipality of the matched treated and control establishments between tâ + 5 and â0. âddrepresents the establishment level treatment effect estimate and is equivalent to âdT ââdC whereâdT and âdC are the change before (-7 to -1 quarters) and after (0 to 5 quarters) the adoption of theplatform for the treated and control establishment of the matched pair. Covid cases per capita arecalculated as the total covid cases reported in a quarter divided by the population of the municipality.Panel (a) has a linear slope of 0.145 (SE 0.357). Panel (b) has a linear slope of 0.289 (SE 0.246). Aformal worker is defined to be employed in a quarter if they have at least one day of work recordedin RAIS. Quarterly size is constructed by taking the average of the number of workers hired formallyeach month in the quarter by the establishment after applying the restrictions described in Section2.3. Average monthly wages in a quarter are constructed by taking the quarterly average of all themonthly wages reported in RAIS by the establishment in the corresponding quarter. Wages areexpressed in real terms (2018 CPI).158Figure A.14: Effect of Platform Adoption on Log Firm In-House Size by COVID-19period-.2-.15-.1-.050.05.1.15.2Log Firm Size-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformNo Covid CohortCovid CohortNotes: This figure reports the corresponding event-study estimates obtained after fitting equation(2.10) on the logarithm of in-house restaurant size. The blue line represents cohort of restaurantsthat started offering delivery services through the platform before the first quarter of 2019. That is,this quarter had five full quarters after offering services through the platform before the Covid-19pandemic started. The red dashed line represents the cohort of restaurants that started offeringdelivery services through the platform between the second quarter of 2920 and the last quarter of2020. This cohort started offering delivery services through the platform during the pandemic and Ican observe at least four quarters after they adopted the platform. The x-axis reflects the quarterrelative to the first quarter in which each restaurant offered delivery services through the platformfor the first time. Quarter 0 is the quarter when the treated restaurant starts offering deliveryservices through the platform. A formal worker is defined to be employed in a quarter if they have atleast one day of work recorded in RAIS. Quarterly size is constructed by taking the average of thenumber of workers hired formally each month in the quarter by the establishment after applying therestrictions described in Section 2.3. The panel reports 95% confidence intervals based on standarderrors clustered at the establishment level.159Figure A.15: Effect of Platform Adoption on Log Firm In-House Size with State timesYear Fixed Effects-.08-.06-.04-.020.02Effect on Log Firm Size-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformNotes: This figure reports the corresponding event-study estimates obtained after fitting equation(2.10) on the logarithm of in-house restaurant size when including state by year fixed effects. Thex-axis reflects the quarter relative to the first quarter in which each restaurant offered deliveryservices through the platform for the first time. Quarter 0 is the quarter when the treated restaurantstarts offering delivery services through the platform. A formal worker is defined to be employed in aquarter if they have at least one day of work recorded in RAIS. Quarterly size is constructed by takingthe average of the number of workers hired formally each month in the quarter by the establishmentafter applying the restrictions described in Section 2.3. The panel reports 95% confidence intervalsbased on standard errors clustered at the establishment level.160Figure A.16: Effect of Platform Adoption on Log Firm Size By Informality of theMunicipality of the Establishment-.14-.12-.1-.08-.06-.04-.020.02.04Log Firm Size-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Adopting the PlatformBelow Median InformalityAbove Median InformalityNotes: This figure reports the corresponding event-study estimates obtained after fitting equation(2.10) on the logarithm of in-house restaurant size. The plot presents the estimated coefficients bythe levels of informality in the restaurant sector at the municipality of each restaurant as estimatedusing the 2010 Brazilian Census. Informality is calculated as the share of informal workers in therestaurant sector in each municipality. The median of informality is calculated using the distribution ofinformality of restaurants that adopt the platform and match to a control group. Control restaurantsare assigned the level of informality of their matched treated restaurant. The blue line presents theestimated coefficients for treated restaurants located in municipalities that have above the medianlevels of informality. The red dashed line presents the estimated coefficients for treated restaurantslocated in municipalities that have below the median levels of informality. The x-axis reflects thequarter relative to the first quarter in which each restaurant offered delivery services through theplatform for the first time. Quarter 0 is the quarter when the treated restaurant starts offeringdelivery services through the platform. A formal worker is defined to be employed in a quarter ifthey have at least one day of work recorded in RAIS. Quarterly size is constructed by taking theaverage of the number of workers hired formally each month in the quarter by the establishmentafter applying the restrictions described in Section 2.3. The panel reports 95% confidence intervalsbased on standard errors clustered at the establishment level.161Figure A.17: Spillover Effects of Platform Adoption on Non-Adopting RestaurantsClosure and Platform AdoptionFigure 17.1: Closure0.05.1.15.21(Closed)-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the EventControlTreatedFigure 17.2: Likelihood of Platform Adoption0.02.04.06.08Effect on Firm using App for Delivery-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the EventNotes: Panel (a) reports the trajectories of establishment closure for matched restaurants that areexposed to a spillover event and matched control restaurants. A spillover event is defined as arestaurant that is exposed to a large increase in the share of restaurants within a 1 kilometer radiusthat start offering services through the platform. As described in Section 2.5.2, a large increaseis defined as the top 5 percentile of the distribution of quarterly changes in the share of nearbyrestaurants that adopt the platform. The date of closure is defined as the last quarter in which theestablishment reported having workers to RAIS (previous to the last quarter available in my sample).Panel (b) reports the corresponding event-study estimates obtained after fitting equation (2.10) forrestaurants that are exposed to a spillover event on the likelihood of offering delivery services throughthe platform. Both panels report 95% confidence intervals based on standard errors clustered at theestablishment level.162Figure A.18: Spillover Effects of Platform Adoption on Non-Adopting Restaurants(2KM)Figure 18.1: Log Firm Size-.050.05Effect on Log Firm Size-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the EventFigure 18.2: Log Avg. Monthly Wages-.06-.04-.020.02Effect on Log Earnings in Month-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the EventNotes: This figure reports event study estimates for restaurants that are exposed to a spillover event.In this figure, a spillover event is defined as a restaurant that is exposed to a large increase in theshare of restaurants within a 2 kilometer donut that start offering services through the platform. Asdescribed in Section 2.5.2, a large increase is defined as the top 5 percentile of the distribution ofquarterly changes in the share of nearby restaurants that adopt the platform. Panel (a) reports thecorresponding event-study estimates obtained after fitting equation (2.10) on log restaurant size asdefined by the average number of formal workers hired by the establishment. A formal worker isdefined to be employed in a quarter if they have at least one day of work recorded in RAIS. Quarterlysize is constructed by taking the average of the number of workers hired formally each month inthe quarter by the establishment after applying the restrictions described in Section 2.3. Panel (b)presents the estimates for the log average monthly wages paid at restaurants. The x-axis reflectsthe quarter relative to the quarter in which the spillover event occurs. Average monthly wages in aquarter are constructed by taking the quarterly average of all the monthly wages reported in RAIS bythe establishment in the corresponding quarter. Wages are expressed in real terms (2018 CPI). Bothpanels report 95% confidence intervals based on standard errors clustered at the establishment level.163Figure A.19: Histogram of the Difference Between Restaurant Opening Date and FirstAppearance in RAIS0.1.2.3Fraction-10 0 10 20 30Difference (in Quarters) Between Firm Opening and Appearning in RAISNotes: This figure reports the histogram of the difference between the opening date of restaurants asreported in Cadastro Federal and the date of the first appearance in RAIS. Restaurants appear inRAIS when they report that they are hiring formally an employee. The figure reports the histogramfor the years in te sample previous to the entry of the platform to any microregion, that is, for theyears 2015-2017. The average of the difference between both sources of reporting for new restaurantsis 3.6 quarters.164Figure A.20: Effect of Delivery Platform Availability on Restaurant EntryFigure 20.1: Effect on RestaurantEntry-5051015Effect on Restaurant Entry-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the Entry of the PlatformFigure 20.2: Effect on Restaurant En-try (Weighted)-50050100150Effect on Restaurant Entry-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the Entry of the PlatformFigure 20.3: Effect on NewRestaurants in RAIS-.04-.020.02.04.06Effect on Share of New Restaurants Ever in RAIS-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the Entry of the PlatformFigure 20.4: Effect on Share of NewRestaurants in RAIS (Weighted)-.04-.020.02.04Effect on Share of New Restaurants Ever in RAIS-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the Entry of the PlatformFigure 20.5: Effect on New Restau-rantsâ Log Size-.2-.10.1.2Effect on (mean) mean of log_firm_size-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the Entry of the PlatformFigure 20.6: Effect on New Restau-rantsâ Log Size (Weighted)-.2-.10.1Effect on (mean) mean of log_firm_size-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to the Entry of the PlatformNotes: This figure reports reports the corresponding event-study estimates obtained after fittingequation (A.38) on the number of new restaurants in microregion m in quarter t (panel a and b),on the share of new restaurants that appear in RAIS at some point (panel c and d) and the size ofnew restaurantsâconditional on reporting in RAIS (panel e and f). Panel (a),(c) and (e) report theunweighted estimates. The mean restaurant entry in tââ 1 is 52, the share of new restaurants that atsome point in time appear in RAIS is 0.48. Panel (b), (d) and (f) report estimates when weightingthe regression by the average number of workers in 2017. The mean restaurant entry in tâ â 1 whenweighting the sample is 410 while the share of new restaurants that at some point in time appear inRAIS is 0.53. The log size of new restaurants is calculated as an average of the reported employeesacross the first five quarters after the opening. Restaurant opening is defined using the reports ofCadastro Federal based on CNPJ. The x-axis reflects the quarter relative to the quarter in which thedelivery platform entered the microregion. Both panels report 95% confidence intervals based onstandard errors clustered at the microregion level.165Figure A.21: Effect of Employer Platform Adoption on Restaurant Workers LogEarningsFigure 21.1: Log Avg. Monthly Earnings-.03-.02-.010.01Effect on Log Earnings in Month-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Employer Adoption of the PlatformFigure 21.2: Log Avg. Monthly Earnings: Dropping Compliers-.03-.02-.010.01Effect on Log Earnings in Month-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Employer Adoption of the PlatformNotes: This figure reports event-study results on log daily wages (panel a) obtained after fittingequation (2.10) on the log daily wage of individual i in period t. Panel (b) estimates this equationafter dropping from the sample pairs where the treated worker is not employed but the controlworker is. I label this control worker as a âcomplier", one who would not be employed if exposed tooutsourcing. Average monthly earnings reflects the quarterly average of the monthly formal wagesthat a worker earns in the corresponding quarter. The x-axis reflects the quarter relative to the firstquarter in which the employer of the treated worker offered delivery services through the platform forthe first time. Quarter 0 is the quarter when the restaurant that employs the treated worker startsoffering delivery services through the platform. Both panels report 95% confidence intervals basedon standard errors clustered at the worker level. Wages are expressed in real terms (2018 CPI).166Figure A.22: Effect of Employer Platform Adoption on Worker Probability of ChangingFirmFigure 22.1: Likelihood of Leaving Employer from t*=0 (Non-Employment or New-Employer)0.02.04.06.08Effect on 1(Change of Firm)-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Employer Adoption of the PlatformFigure 22.2: Likelihood of Working for a different Employer thant*=00.01.02.03.04.05Effect on 1(Working at a Different firm than t*=0)-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Employer Adoption of the PlatformNotes: Panel (a) reports the corresponding event-study estimates obtained after fitting equation(2.10) on a dummy equal to one if individual i in time t no longer works for the employer they had intâ â 1 (either because of non-employment or they are employed at another establishment). Panel (b)reports the corresponding event-study estimates obtained after fitting equation (2.10) on a dummyequal to one if individual i is employed by a different employer than the one they had in tâ â 1.The employer in a quarter is defined as the employer that pays the highest total wages in a quarter(the dominant employer of the quarter). The x-axis reflects the quarter relative to the first quarterin which the employer of the treated worker offered delivery services through the platform for thefirst time. Quarter 0 is the quarter when the restaurant that employs the treated worker startsoffering delivery services through the platform. Both panels report 95% confidence intervals basedon standard errors clustered at the worker level.167Figure A.23: Effect of Employer Platform Adoption on Worker Probability of Workingat Delivery Platform-.0010.001.002.003.004Effect on 1(Working for App)-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Employer Adoption of the PlatformNotes: This figure reports the corresponding event-study estimates obtained after fitting equation(2.10) on a dummy equal to one if individual i in time t works in the platform. Employment at timet in the platform is defined as doing at least one delivery in quarter t. The x-axis reflects the quarterrelative to the first quarter in which the employer of the treated worker offered delivery servicesthrough the platform for the first time. Quarter 0 is the quarter when the restaurant that employsthe treated worker starts offering delivery services through the platform. The panel reports 95%confidence intervals based on standard errors clustered at the worker level.168Figure A.24: Spillover Effects of Platform Adoption on Restaurant Workers byEmployer ClosureFigure 24.1: Total Earnings-.4-.35-.3-.25-.2-.15-.1-.050Total Monthly Earnings-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to EventEmployer at T*-1 Not ClosedEmployer at T*-1 ClosedFigure 24.2: Employment-.4-.35-.3-.25-.2-.15-.1-.050Employed-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to EventEmployer at T*-1 Not ClosedEmployer at T*-1 ClosedNotes: This figure reports event study estimates for workers who are employed at restaurants thatare exposed to a spillover event. A spillover event is defined as a restaurant that is exposed to a largeincrease in the share of restaurants within a 1 kilometer radius that start offering services throughthe platform. As described in Section 2.5.2, a large increase is defined as the top 5 percentile of thedistribution of quarterly changes in the share of nearby restaurants that adopt the platform. Panel(a) reports the corresponding event-study estimates obtained after fitting equation (2.10) on on thequarterly average of monthly earnings. Average monthly earnings reflects the quarterly average of themonthly formal wages that a worker earns in the corresponding quarter. If the worker did not hold aformal job during the period, the earnings are equal to 0. The red dashed line shows the estimates forworkers whose employer at tâ â 1 closes within the period tâ0 and tâ + 5. The black line representsthe estimates for workers whose employer at tâ â 1 does not close the following five quarters after theevent. Panel (b) presents the estimates for employment. A formal worker is defined to be employedin a quarter if they have at least one day of work recorded in RAIS. The x-axis reflects the quarterrelative to the quarter in which the spillover event occurs. Wages are expressed in real terms (2018CPI). Both panels report 95% confidence intervals based on standard errors clustered at the workerlevel.169Figure A.25: Trends of Formal Employment and Earnings for Platform WorkersFigure 25.1: Formal Employment.2.22.24.26.281(Formal Worker)-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Working on the PlatformFigure 25.2: Total Earnings Conditional on Worker Employed intâ â 10500100015002000Monthly Earnings-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5Quarter Relative to Working on the PlatformEarnings on AppEarnings in Formal SectorNotes: Panel (a) presents the trajectories of formal employment for workers who at some point intime work as delivery workers in the platform. A worker is defined as a platform-worker if they did atleast one delivery through the platform. The worker is considered to be formally employed in quartert if they are employed in the quarter for at least one day and theyâre employment is recorded inRAIS. The x-axis reflects the quarter relative to the first quarter in which the individual i worked forthe first time on the platform. Panel (b) reports the monthly earnings made in formal employment(red line) and on the platform (blue line) for workers that held a formal employment the quarter priorto working on the platform (and were not laid-off either in tâ â 1 or in tâ0). Wages are expressed inreal terms (2018 CPI).170Figure A.26: Total Wage Bill Effect from Platform Adoption as a Function of Non-Formal platform-workers Outside Option0.0 0.2 0.4 0.6 0.8 1.0= Non Formal EarningsFormal Earnings-15% Wage-Bill-10% Wage-Bill-5% Wage-Bill0% Wage-Bill5% Wage-Bill10% Wage-Bill15% Wage-BillSurplus Generated by App (p/ Firm)Wage-surplus = 0( = 0.6)PNADC( =0.32)All unemployed( =0)Non-Formal Earn. = Formal Earn.( = 1)Notes: This figure reports the estimated total wage bill surplus generated by the platform as estimatedusing equation (2.12). The figure plots the wage bill surplus as a function of different outside optionsfor app workers that did not hold a formal employment prior to working on the platform. Workers areconsidered to not have a formal employment prior to working on the platform if they did not have aformal job the quarter prior to working on the app, or held a formal job but were laid-off the quarterprior (or the first quarter) to their first spell on the platform. The x-axis expresses the outside optionfor these workers as a share of the average hourly wage that platform-workers that did have a formalemployment prior to working on platform earned prior to working on the platform (Φ). The y-axisrepresents the surplus generated by each restaurant that adopts the platform as a percentage of theaverage quarterly wage bill of adopting restaurants the 4 quarters prior to offering delivery servicesthrough the platform (t*-7 to t*-2). The figure highlights four different points. All unemployedmeans that the outside option of platforms workers that did not hold a formal employment prior toworking on the platform is 0. PNADC is the estimated outside option using the Brazilian householdsurvey (PNAD-C). Wage-surplus equal 0 is the outside option for non-formal workers that makes thetotal wage bill surplus of the platform equal to 0. Non-Formal earnings equal to formal earningsmeans that the outside option of non-formal workers is the same as the outside option of formalworkers.171Figure A.27: Histogram of Values (V ej ) for all Restaurants0.01.02.03.04.05Fraction-6 -4 -2 0 2 4Value (Ve)Notes: This histogram reports the distribution of V ej for all restaurants in the sample for whomit was possible to estimate the value (30%). V ej was estimated using the algorithm developed bySorkin (2018) and represents the value of a firm. The average V ej in the sample is -0.158. The reddashed line represents the V ej of the delivery platform (2.014). This places the delivery platform inthe percentile 93 of the distribution. Values V ej are winsorized at the 0.05 and 99.5 level.172Figure A.28: Relationship Between Values (V ej ) and EarningsFigure 28.1: Restaurants.05.1.15.2.25.3AKM Firm Effects-4 -2 0 2 4Value (Ve)Figure 28.2: All Establishments0.2.4.6AKM Firm Effects-4 -2 0 2 4 6Value (Ve)Notes: This figure presents a binscatter plot of the relationship between the value of the AKMfirm effects and firm V ej . Panel (a) presents the relationship for restaurants. Panel (b) presentsthe relationship for all establishments in the sample. The value of the firm V ej was estimated usingthe algorithm developed by Sorkin (2018) and represents the value of a firm. The red dashed linerepresents the V ej of the delivery platform (2.014). Both AKM firm effects and Values V ej arewinsorized at the 0.05 and 99.5 level. The Binscatter plots 40 bins.173A.2 Appendix: TablesTable A.1: Summary Stats Restaurant Workers (RAIS & Census)(1) (2) (3)RAIS Census (2010)Formal Workers Informal WorkersMonthly Wage (2018 - R$) 1,574 1,353 842(686) (2,831) (860)Age 34.84 33.20 31.37(11.50) (11.29) (13.35)Share of Brazilians 0.99 0.74 0.75Female 0.56 0.56 0.60Hours 42.84(4.35)Share of Black Workers 0.06 0.09 0.09Delivery 0.02 0.01 0.02Kitchen 0.24 0.43 0.34Waiters 0.53 0.29 0.43Other 0.21 0.27 0.21Number of Workers 1,795,250 94,559 59,115Notes: Column 1 presents the summary statistics of restaurant workers in 2017 using data from RAIS.These statistics are weighted at the individual level. Column (2) and Column (3) present statisticsfor restaurant workers using the 2010 Brazilian Census (the latest census available). A worker isconsidered to be formal if they respond that they have a Carteira de trabalho assinada in their job.An individual is considered to work in a restaurant if they are working for an establishment that hasa two digit CNAE industry code equal to 56. Definitions of occupations are presented in Table A.3.174Table A.2: Summary Stats for Platform WorkersSummary StatsApp WorkersPerformance on the AppWorker Formal Before App Worker Non-Formal Before App(1) (2) (3)Demographics:Age 29.5(8.0)Female 0.1Monthly Earnings in Formal Job (Unconditional) 358.5(867.4)Monthly Earnings in Formal Job (Conditional on Employment) 1583.9(1176.1)Weekly Hours in Formal Job 41.7(6.5)Tenure in Formal Job (in Years) 2.2(2.9)Education (Years) 11.6(1.7)Full Time 0.9Black 0.1Brazilian 1.0Workers in RAIS at some point 0.6Performance on App:Monthly Earnings (Tips) 10.2 11.2(19.0) (20.8)Monthly Earnings (Trip) 571.2 646.6(727.3) (810.5)Monthly Earnings (Promotions) 51.7 47.8(92.2) (88.7)Monthly Hours Online 62.0 75.9(62.9) (73.0)Monthly Hours Worked 30.1 34.9(36.8) (41.6)Monthly Total Deliveries 79.5 91.8(104.4) (117.3)Monthly Total Distance (KMs) 425.9 480.0(593.5) (660.0)Number of Workers 624,141 143,996 480.145Notes: Column 1 presents the summary statistics of individuals that at some point in time work asdelivery workers in the platform. Statistics reflect the demographics at the first quarter in which theindividual worked for the platform. Demographic information is only available for individuals whoat some point in their career hold a formal employment. Unconditional formal earnings representsearnings that include zeros for individuals that are not employed in the formal sector. Workers inRAIS at some point, are workers that at some point in my sample hold a formal employment asreported in RAIS. Column 2 reports the performance on the platform for workers that the quarterprior to joining the platform held a formal employment (and were not laid-off either the quarterprior to joining the platform or the quarter in which they joined the platform). Column 3 reportsthe performance for workers that the quarter prior to joining the platform did not hold a formalemployment. Hours online are hours in which the individual had the platform open (either in thebackground or actively using it). Hours worked are hours in which the individual was actively doinga delivery. Standard deviations are reported in parentheses.175Table A.3: Crosswalk of Occupations from CBO to Waiters, Cooks and DeliveryWorkersCBO Code CBO Definition Occupation Crosswalk513405 Waiter Waiter513435 Snack Bar Attendant Waiter513505 Food Service Assistant Waiter513425 Pantry Worker Waiter521110 Retail Salesperson Waiter421125 Cashier Waiter513420 Bartender Waiter422105 Receptionist Waiter513415 Busser (Assistant Waiter) Waiter141510 Restaurant Manager Waiter271105 Head Chef Cook513205 General Cook Cook513215 Industrial Cook Cook513605 Barbecue Cook Cook513610 Pizza Maker Cook513615 Sushi Chef Cook841408 Food Preserver (Conservation of Food) Cook841416 Meat Cooker Cook841420 Fruit and Vegetable Cooker Cook848315 Pasta Maker Cook848310 Confectioner Cook848305 Baker Cook519110 Motorcyclist for Document and Small Parcel Delivery Delivery782310 Van or Similar Vehicle Driver Delivery782305 Passenger Car Driver Delivery782510 Truck Driver (Regional and International Routes) Delivery783225 Driverâs Assistant Delivery782820 Pedal Vehicle Driver Delivery176A.3 Appendix: ModelA.3.1 Main Model: DerivationsThis section presents the derivations of the model discussed in Section 2.2. The modelis a job posting model that builds on Card et al. (2018a). I start by providing amicrofoundation to the labor supply of each type of worker. I then derive the profitmaximization problem for restaurants, and finally derive the comparative statics forwaiters and cooks.There are J firms in the economy and two types of workers: cooks (C) and waiters(W ). Each firm j posts wages for both groups of workers. These wages are observedwith no cost by the workers, and restaurants will hire any worker willing to work at theposted wage. Each firm offers a unique workplace environment for each type of worker,and workers have heterogeneous preferences of these environments. Specifically, theindirect utility of worker i from working at firm j is given by:uiâµj = β ln (wâµj) + aâµj + Îµiâµj (A.1)Where âµ = {C,W}, aâµj is a restaurant specific amenity comon to each type of workerâµj, and Îµiâµj is an idisosyncratic worker specific taste shock. As in Card et al. (2018a),I assume that Îµiâµj is distributed as a standard type-I extreme value distribution.Following McFadden (1972) the probability of a worker âµ working at firm j is givenby:pâµj =exp(β ln (wâµj) + aâµj)âJk=1 exp(β ln (wâµk) + aâµk)(A.2)When assuming that the number of firms J is sufficiently largeâthat is, abstractingfrom strategic interactionsâ, the probability of a worker âµ working at firm j can beapproximated by:pâµj â Î»Ëâµ exp(β ln (wâµj) + aâµj) (A.3)Where Î»Ëâµ is constant across all firms. Taking logs gives the labor supply presented in177Section 2.2:ln(âµ) = Î»jâµ + β ln(wjâµ) (A.4)Where Î»jâµ = ln(Î»Ëâµ) + aâµj. In contrast to Card et al. (2018a), I subtract fromheterogeneity in labor supply and assume that the labor supply elasticity of cooksand waiters is constant and equal to β. The production function of each restaurant isgiven by:Yj = Tj Cαj S1âÎ±j (A.5)Sj = [θW Ï + (1â Î¸)DÏ]1Ï (A.6)Where cooks and service workers are complements in production, where the outputelasitcity is defined by α. Within the service sector, waiters and delivery driversoperate through a CES function with elasticity of substitution equal to Ï = (1â Ï)â1and relative productivity equal to θ. The profit maximization problem for restaurantj is given by:maxwW ,wC ,DPjlYj â wWW â wCC â wDD (A.7)subject to the following constraints:ln(W ) = λjW + β ln(wjW ) (A.8)ln(C) = λjC + β ln(wjC) (A.9)Pjl = P0jlYâ 1ϵj (A.10)Where wW and wC are the wages of waiters and cooks respectively and P0jl is thefirm specific demand shifter for the good produced by firm j. Motivated by marignalnumber of delivery drivers in the data previous to online-delivery platforms, I startby assuming that pre-delivery platforms the θ â 1. That is, that the productivitydelivery drivers was essentially 0 previous to delivery platforms, and so restaurantswould not hire them (neither through platforms or formally). Dropping the j suscripts178for simplicity, the first order conditions of the profit maximization problem for waiterscan be expressed as:(ξp,y + 1) P (Y )1ξwW + 1âYâW(A.11)WhereâYâW= Cα (1â α) SâÎ± [θW Ï + (1â Î¸)Ï] 1âÏÏ θ W Ïâ1 (A.12)Define R as P Y = P0l YÏµâ1Ïµ . Then pluggining equation A.12 into equation A.11 givesequation 2.6 of Section 2.2:wW =β1 + β︸ ︷︷ ï¸¸MarkdownÏµâ 1ϵRSθ (1â Î±)(WS)Ïâ1ï¸¸ ︷︷ ︸MRPL(A.13)Similarly, using the fact that that:âYâC= Î±CÎ±â1S1âÎ± = Y αC(A.14)We get equation 2.7 of Section 2.2:wC =β1 + Î²Ïµâ 1ϵRCα (A.15)To derive the comparative statics for waiters and cooks, define the difference betweenpre and post platform of X as âX. Taking logs, plugging in the labor supply ofwaiters and writing the post platform minus the pre platform FOC for waiters, we getthe following:â ln P0j +Ïµâ 1Ïµâ ln Y + 1â ÏÏâ ln S +â ln θ =(1β+ 1Ï)â ln W (A.16)Where I am using the fact that the amenities at the firm level λj do not vary with theplatform, and the labor supply elasticity is also fixed. This equation can be re-writte179as equation 2.8 of Section 2.2:â ln(W ) = Î²ÏÎ² + Ïï£®ï£¯ï£¯ï£¯ï£¯ï£¯ï£¯ï£¯ï£¯ï£¯ï£¯ï£°â ln(VS)ï¸¸ ︷︷ ï¸¸â Revenue p/Service Worker+ 1Ïâ ln(S)ï¸¸ ︷︷ ï¸¸â Service SectorSizeï¸¸ ︷︷ ︸Product demand effect+ â ln(θ)︸ ︷︷ ï¸¸â relative productivitywaitersï¸¸ ︷︷ ︸Outsourcing Effectï£¹ï£ºï£ºï£ºï£ºï£ºï£ºï£ºï£ºï£ºï£ºï£»(A.17)Where Vs is the revenue per service worker(RS). The comparative statics for cookscan be derived in a similar way.A.3.2 Model Extension: Selection into Delivery PlatformThe setup of the model follows a similar structure to the model proposed in Section2.2. The main difference respect to the baseline model is that the service sector nowis not separated in delivery and waiters but instead in a continium of service workersthat are indexed by their tasks index by a. Under this setup, the firms problem priorto adopting the platform is as follows:maxwSa,wCPjlYj â wC C ââ«awsa sa dasubject toYj = TjCαj S1âÎ±jS =[â«aÎ¸a sÏa da] 1Ïln(âµj) = Î»jâµ + β ln(wjâµ)Pjl = P0jlY â1Ïµâ«aÎ¸a = 1180Where âµ = {C, sa}. That is, each task of service workers has their own labor supplyto firm j and has a relative productivity of θa. Tasks in the service sector areassumed to have a certain degree of complementarity, such that the revenue function issupermodular.1 The first order conditions for each task are analogous to the baselinecase:wsa =β1 + β︸ ︷︷ ï¸¸MarkdownÏµâ 1ϵRSθa (1â Î±)(saS)Ïâ1ï¸¸ ︷︷ ︸MRPL(A.18)Similar to the baseline model, the adoption of delivery platforms shifts P0âtheproduct demand effect. However, under this setup the outsourcing effect has adifferent structure. Similar to Bilal and Lhuillier (2022), once adopting the platform,a restaurant can decide to outsource a task or hire it in-house. When outsourcing thetask, the firm pays a fixed price of wO per outsourced worker and an idisyncratic costof 1 â¤ εa â¤ ε¯a, which may be correlated with firm productivity. That is, the profitmaximization problem of the firm after adopting the platform is as follows:maxwSa,Sa,O={0,1},wCPjlYj â wC C ââ«a[(1âO) wsa +O PO εa] sa dasubject toYj = TjCαj S1âÎ±jS =[â«aÎ¸a sÏa da] 1Ïln(âµj) = Î»jâµ + β ln(wjâµ)Pjl = P0jlY â1Ïµâ«aÎ¸a = 1As discussed in Bilal and Lhuillier (2022), under a supermodular profit function there1Supermodularity in the revenue function imposes strictly positive cross derivatives of the revenuefunction between T and all types of service tasks, as well as between any two types of tasks. Thatis, it implies a form of complementarity between the firm level productivity and every type of task,as well as between any two types of tasks.181is a firm productivity threshold for each task Ta(εs) such that the minimum of the costfunction is attained in-house for T â¤ Ta(εs) and attained outsourced for T > Ta(εs).This is because the number of of workers hired in each task is increasing T . Giventhat firms face a positive labor supply curve for their in-house labor, wSa increases inSa, which implies that more productive firms pay higher wages to workers in a andtherefore have higher incentives to outsource and face a vertical labor supply curveinstead. This threshold, however, is increasing in εa as it is less profitable to outsourceas the idiosyncratic cost of outsourcing increases.For simplicity assume the tasks can be ordered such that all in-house tasks areaIn-house = {a, aË} and all outsourced tasks are aO = {aË, a¯}. Then the difference inin-house service workers before and after the adoption of the platform is given by:âsin-house =â« aËasappa daââ«asno-appa da (A.19)Where sappa = sno-appa +âsa and âsa is the difference in the labor hired in-house intask a before and after the adoption of the platform. This allows to decompose thedifference in in-house labor in the following way:âsin-house =â« aËaâsa daï¸¸ ︷︷ ︸Product Demand Effectââ« a¯aËsno-appa daï¸¸ ︷︷ ︸Outsourcing Effect(A.20)Where the first term reflects the change in labor for the tasks that are still hiredin-house and the second term reflects that certain tasks are now outsourced. Thepercentage change in labor demand for the tasks that are still hired in-house has thesame structure as the product demand effect under the baseline model:â ln(Sa) =Î²ÏÎ² + Ïï£®ï£¯ï£¯ï£¯ï£¯ï£° â ln(VS)ï¸¸ ︷︷ ï¸¸â Revenue p/Service Worker+ 1Ïâ ln(S)ï¸¸ ︷︷ ï¸¸â Service SectorSizeï£¹ï£ºï£ºï£ºï£ºï£» (A.21)Under this model restaurants outsource not because the relative productivity ofdelivery workers increases with the platform, but because highly productive firms findit cheaper to outsource labor to the platform given their target size. The outsourcing182effect reflects that the technology allows to switch from a positive labor supply curveto a competitive market for the tasks that are outsourced. The overall impact onin-house labor, however, will also depend on the product demand effect that dependsadditionally on the change in the revenue per worker and the size of the service sectoras in the baseline model.A.3.3 Extension: Product DemandI now extend the model to provide a micro-foundation of the product demand curve.The model builds on consumer search models that allow for demand externalitiesacross firms in the same location (Vitali, 2022).The economy consist of a set of locations l = {1, 2..., N}, and a finite number of firmsand consumers indexed by j and i, respectively. Firms produce a differentiated goodand consumers can purchase only one good. Consumers are assumed to all be locatedin some reference location lâ². The model is static, but chain of events is as follows:1. Consumers pick in which location to Search2. Within each location, consumer pick which good to buyThe utility of consumer i from consuming good j in location l is given by:Uijl =[ln (ζj) +1ϵεij â Cl â ln (Pjl) + 1{Appj > 0} ln (ηjl)]α (A.22)Where ζjl is a measure of the quality of good j, Cl is a variable that encapsulates thecost that all consumers face when searching in location l for good j, Pjl is the price ofgood j in location l, and α is a taste shifter. ηjl is a measure of the utility that orderingthrough the delivery platform provides to the consumer. This measure depends on theproduct that the restaurant sells and the location of the restaurant. Specifically, ηjlincreases as the distance between the average consumer and the restaurant increases.However, η also will depend on the good produced by restaurant j. Consumers willgain more utility from some goods as they are available on the platform while theywill gain less utilityâor even potentially negative utilityâ from others.183For the rest of this section, I will assume the scenario in which restaurants haveadopted the platform and so 1{Appj > 0} = 1. This is the direct effect of deliveryplatforms for restaurants that adopt. Lastly, similar to Vitali (2022), I assume thatεij is an idiosyncratic taste shock that is distributed as a standard type-I extremevalue, with ϵ governing its variability.The cost term can be divided into two components:Cl = lnï£«ï£¬ï£­Ï1 g1(â¥z â zlâ¥)ï¸¸ ︷︷ ︸Avg. Distanceï£¶ï£·ï£¸+ lnï£«ï£¬ï£¬ï£¬ï£¬ï£­Ï2 g2(Appl,Nlarl)ï¸¸ ︷︷ ï¸¸Spilloversï£¶ï£·ï£·ï£·ï£·ï£¸ (A.23)The first component refers to the distance between the average consumer and thelocation of firm j. The second term reflects the fact that as the density of firms inlocation l increases, the likelihood of buying from j decreases due to larger competi-tion. The function g2 governing the relation between density and search costs alsodepends positively on the number of firms in the location that adopt delivery platform.Intuitively, as more firms in location l offer services through delivery platforms, thiscrowds the market for the remaining firms in the location. This is the indirect effectof platform adoption. Finally ιjl reflects an idiosyncratic individual specific searchcost that is distributed according to a standard type-I extreme value distribution.Prior to searching, consumers observe all product and location characteristics, but donot observe the match-specific values εij. Given this information, they choose whichlocation to visit. Upon visiting a location, consumers observe the match value and buythe product that gives them the highest utility. Conditional on being in a location,consumers must buy a product.The decision to search in location l is given by:Vil = EÎµ[maxjâl{Uijl}]= Î±1Ïµlnï£®ï£° Nlâj=1exp (δlj Ïµ)ï£¹ï£» â Cl + γ α 1ϵ(A.24)With δlj = ln (ζjl) + ln (ηjl) â ln(Pjl) reflecting the mean utility of good j forconsumer i in location l and γ being the Euler constant. The share of consumers that184search for a good in location l is given by:Sl = Pr(Vil > Vâ²il, âlâ² Ì¸= l) (A.25)=[âNlj=1 exp (δjl ϵ)] 1ÏµÎ±exp(âCl)exp(u0) +âNk=1[(âNhh=1 exp (δhk ϵ)) 1ÏµÎ±exp(âCk)] (A.26)Conditional on searching in l, the share of consumers buying good j is given by:Sj|l =exp (δjl Ïµ)âNlh=1 exp (δhl ϵ)(A.27)Which gives the following unconditional share of consumers buying good j:Sjl = Sl Ã Sj|l (A.28)=exp (δjl Ïµ)[âNlj=1 exp (δjl ϵ)] 1ϵα â 1exp(âCl)exp(u0) +âNk=1[(âNhh=1 exp (δhk ϵ)) 1ÏµÎ±exp(âCk)] (A.29)Re-arranging terms we then end up with a formulation that gives a foundation to theproduct demand presented in Section 2.2:Pjl = (SÎ)1Ïµï¸¸ ︷︷ ︸Macro ContextÎ·jlï¸¸ï¸·ï¸·ï¸¸Direct Effect of AppÎ¶jl[Nlâjâ²=1exp(Ïµ Ïljâ²)ï¸¸ ︷︷ ︸Quality in l] 1ϵ(α 1Ïµâ1) [ 1Cjlï¸¸ï¸·ï¸·ï¸¸Search costs] 1ϵ︸ ︷︷ ï¸¸P0jlYâ 1ϵj(A.30)= P0jl Yâ 1ϵj (A.31)Where I renamed Sj as Yj to reflect that in equilibrium the demand equals production,and SÎ is the denominator of equation (A.28). This parametrization of the demandcurve decomposes the demand for a restaurants good in four elements: (i) an overallmacro-context that affects all restaurants in the economy, (ii) the direct effect of theadoption of the delivery platform, (iii) a positive demand externality that increases185the demand for the good produced by the restaurant as the quality of the goodsproduced by the other neighboring restaurants in the location increases (Leonardiand Moretti, 2023), and (iv) a search cost term that negatively impacts demand. Thelatter is a function of the distance between the average consumer and the location ofthe restaurantâg1(.)â, a firm specific idiosyncratic shock (ιjl), and the spillovers dueto competing restaurants adopting the platform. In summary, under this framework,the introduction of online-delivery platforms can potentially mitigate search costsfor adopting restaurants (if ηjl > 0), and increases search costs for non-adoptingrestaurants.Finally, equation (A.30) also entails the agglomeration forces that arise from searchcosts. Firms benefit from the overall quality of their neighbours and will concentrate inlocations where the distance to the average consumer is smaller. These forces howeverwill be mitigated by the overall density of restaurants in the location that increasethe firm specific search cost conditional on searching in that location. Put differently,regions that have a high density of restaurants must be either close to a large shareof consumers, must have a high a concentration of high quality restaurants, or both,such that location externalities dominate the firm-specific search costs.186A.4 Appendix: EmpiricsA.4.1 Imputation of informality: restaurantsI start by estimating the share of informal workers in the restaurant sector at eachmunicipality using the 2010 Brazilian census (latest census available). I then input toeach restaurant in my sample the share of informal workers in the restaurant sectorat the municipality level. This yields a per-restaurant share of informal workers. Iestimate that the average restaurant in my sample has 25 percent of informal workers.To recover the pre-treatment earnings of informal workers per restaurant I use theBrazilian household survey PNAD-C, which is representative at the state level. Foreach state and quarter I calculate the ratio informal wages to formal wages in therestaurant sector. I estimate that informal restaurant workers in my sample earn onaverage 70 percent of the wages of formal workers. Finally, I assume that the earningseffect (in percentage of their pre-event earnings) on informal workers is the same asfor formal workers. This inputed effect, in combination with the number of informalworkers calculated with the census and the earnings for informal workers calculatedwith PNAD-C, yields a per-restaurant earnings effect for informal workers.A.4.2 Estimation of Informal/Unemployed to EmployedWageRatioTo compute the wage ratio between informal/unemployed and employed platformworkers, I use the Brazilian household survey PNAD-C. Given the large share of menamong delivery drivers, I restrict the sample to men that are between 18 and 65.For employed workers, I keep individuals in PNAD-C with only 1 non-agriculturaljob. I define unemployed individuals as those that did not hold a job in the weekof reference of the survey but answered that they were actively searching for a job.Within employed workers, I define a formal worker if they answer having a Carteirade trabalho assinada in their job, and informal if they donât.I re-weight the sample of formal and non-formal workers in PNAD-C to match thedistribution of age, education, race and gender of my sample of delivery drivers usingthe procedure developed by DiNardo et al. (1996). I then estimate the wage ratio187between informal/unemployed and employed workers in the restaurant sector at thestate-quarter level. The average adjusted wage ratio of informal/unemployed to formalworkers in my sample is 0.32.A.4.3 Imputation of Wage on Delivery Platform per Restau-rantA limitation of my data is that I do not observe which worker delivered for eachrestaurant at each moment in time and therefore I can not compute the exact wagebill generated for platform-workers at each restaurant. I instead observe the earningsthat each app worker makes per hour at each month/municipality and the totalnumber of hours that restaurants used delivery services from the platform at eachmonth/municipality. I overcome this limitation by calculating the average per hourearnings on the platform of workers in each month and municipality (weighted by thenumber of hours each platform-worker worked in that month and municipality). Idefine the per hour wage on the platform for each worker as the total earnings theymade in a month divided by 1.15 times the total hours they worked on the platformthat month. That is, I allow for an additional 15% of time to be idle/non-productivetimeâthis is the mid point of what has been used in other studies in Brazil followingthe same type of workers (Callil and Pincaço, 2023). To recover a per-restaurantwage-bill, I then multiply the average per hour wage in the corresponding municipality-month by the number of hours each restaurant utilizes delivery platforms in thatperiod.A.4.4 Procedure to Rescale Spillover EffectsThis sections provides the details of how I rescale the worker level effects of spilloverspresented in Section 2.7.2 to reach the results presented in Column 4-6 of Table 2.5. Istart by defining a spillover event as:1881 [Spillover] = 1 [â¦jt > 95 percentile of â¦] (A.32)Where â¦jt = Ïjt â Ïjtâ1 and Ïjt is the share of restaurants within 1km that areenrolled in the platform in each quarter. I then estimate the effect of spillovers onworkers earnings, as discussed in Section 2.7.2. The effect of spillovers on workersearnings is estimated using the following regression:Yit = β0 + αi + δt +k=5âk=â7Î¸k1{t = tâ(i) + k}+k=5âk=â7Î²k1{t = tâ(i) + k} Ã 1 [Spilloveri] + ϵit(A.33)I then convert the spillover effects into units of Ïjt by applying a two-step procedure.First, I estimate the following regression:Ïjt = β0 + αj + δt +k=5âk=â7Î³k1{t = tâ(j) + k} Ã 1[Spilloverj]+ ϵjt (A.34)where γk provides the effect spillover events on the share of restaurants within 1kmthat are enrolled in the platform. The second step is to re-scale the estimated theeffect of spillover events on workers earnings into units of Ïjt:Î²rescaled{k} =βkγkI then multiply each βrescaled{k} by the average Ïjt in my sample to get the average effectof spillovers on workers earnings. Finally, I multiply the average effect of spilloverson workers earnings by the average size of restaurants in my sample to get a perrestaurant effect of spillovers which are the effects presented in Columns 4-6 of Table2.5.189A.4.5 AKM Estimation DetailsThe AKM model builds on Abowd et al. (1999b) to estimate the worker effects andfirm effects in the data. To estimate the AKM model, I start by constructing anyearly panel of workers where log earnings reflect the average monthly earnings at thedominant employer in the year. That is, each worker is assigned to one employer in ayear that reflects the employer that paid the largest total wages in the year to theworker. The model assumes that the log monthly wage paid to worker i, at firm j inyear t is given by:ln yijt = αi + Ïj(i,t) + β X â²it + ϵit (A.35)Where αi is the worker fixed effect that is fully portable to every job the worker has,Ïj(i,t) is the firm wage premium which is specific to each firm (where J(i, t) is anindex indicating the workplace for worker i at year t), Xit is a vector of time-varyingcontrols including a polynomial of age and year fixed effects, and ϵit is the error term.If we assume that the conditional expectation of the error term ϵit in equation (1)is independent of the workerâs job history (the "exogenous mobility" assumption),then estimating equation (1) using ordinary least squares (OLS) will provide unbiasedestimates of establishment wage premiums. Several specification tests introduced byCard et al. (2013) and later applied in studies such as Card et al. (2016); Macis andSchivardi (2016); Song et al. (2019a), indicate that wage changes among job moversin countries like Germany, Italy, Portugal, and the United States generally align withthe exogenous mobility assumption. Furthermore, Gerard et al. (2021) test thesespecifications in Brazil using RAIS and find that the exogenous mobility assumptionis also likely to hold in this setting.To estimate the AKM model, I follow the leave-out procedure outlined by Kline et al.(2020). As AKM highlights, in a two-way fixed effects model, establishment effects areonly identifiable across "connected sets" of workplaces that are tied together by themovement of workers between them. Therefore, I restrict my analysis to the largestconnected set in my sample to ensure the identification of these effects.Finally, if a worker did not work in the formal sector during my estimation, I imputetheir worker effects when possible. To input the worker effects, I leverage equationA.35 and compute the worker effects at each year as:190αit = ln yijt â Ïj(i,t) â β X â²itAnd then I average the worker effects across all the years in which the worker wasemployed in the formal sector such that:Î±Ëi =ât=2012,2021 αitTA.4.6 Estimation of Firm Values V ej using PageRankIn this section I present a summary of the algorithm utilized to estimate the firmvaluation V ej developed by Sorkin (2018). The starting point is to define the utilitythat a worker i receives from working at firm j as uij = V ej + εij where V ej is a firmspecific value that is common to all workers and εij is a worker specific idiosyncraticshock. I further assume that εij is distributed as a standard type-I extreme value. Ina market with 2 firms, the likelihood that a worker favors firm j over firm k is givenby: exp(Vej )exp(V ej )+exp(V ek ). When allowing for N workers and defining Mjk as the number ofworkers selecting firm j over firm k, the following relation holds between employmentchoices and firm specific employment valuations: MkjMjk= exp(Vek )exp(V ej ).When accounting for multiple firms j â J , the condition described above imposes thefollowing condition for each pair of firms:Mkjexp(V ej ) =Mjkexp(V ek ), â j â J (A.36)Summing (A.36) over all firms in the market, one arrives to the following expression:191value-weighted entryï¸· ︸︸ ï¸·âj â JMkjexp(V ej )âj â JMjkï¸¸ ︷︷ ︸exits= exp(V ek )ï¸¸ ︷︷ ︸value(A.37)which imposes a linear restriction for each firm j. As explained by Sorkin (2018), it ispossible to solve for V ej as a fixed point of a linear system and a unique solution existsfor the set of strongly connected firms. Intuitively, equation (A.37) follows the samelogic as Googleâs PageRank algorithm in website searches. While in Googleâs case agood website is linked to other good webpages, in the labor market a good employeris defined as one that receives workers from other good employers while loses fewemployees to other firms. To estimate the value of working on the delivery platform(and the rest of the establishments in Brazil), I restrict to employer-to-employertransitions in the formal sector (or delivery platform) between quarters.2. This impliesthat I only measure the valuation of the delivery platform for workers who transitionedto (from) gig work from (to) the formal sector and earn their main income throughthe platform. The tradeoff is that while this is a restrictive group of transitions, itrepresents a group of workers for whom the gig job is somewhat comparable to aformal job.To keep the analysis consistent, I consider the delivery platform in each microregionas a different establishment. Furthermore, similar to Sorkin (2018), I adjust exp(V ej )by the hours hired and by the share of unemployment-to-employment transitionsthat the establishment has. The adjustment is done to account for the fact thatlarger establishments will have more separations, while the share of unemployment-to-employment transitions adjusts for the job offer rates. 32A worker is considered to work for establishment j in quarter t if the main source of income duringt was from j.3In contrast to Sorkin (2018), I use hours instead of firm size to make it comparable between thedelivery platform and the rest of the establishments.192A.4.7 Estimation of the Effect of Delivery Platforms on Mar-ket Level OutcomesThis section describes the empirical design used to estimate the impact of deliveryplatforms on market level outcomes such as firm entry and the number of workersworking at the restaurant sector. To measure firm entry, I use data collected fromCadastro Nacional de Pessoas JurÃdicas (CNPJ), which is a database that containsinformation on the start of operations of all formal firms in Brazil. 4 Importantly, thisdataset allows to uncover firm entry even in the cases where the firm does not haveany employees or all employees are hired informally. Figure A.19 shows the histogramof the difference in quarters between firm opening and reporting employees in RAISfor the first time. Between 2015 and 2017, restaurants opened on average 3.6 quartersbefore reporting employees in RAIS for the first time. 5To study the impact of delivery platforms on market level outcomes, I leverage thestaggered entry of the platform across microregions. Specifically, I estimate thefollowing regression at the microregion:Ym,t = αm + δt +k=5âk=â7Î²k1{t = tâ(j) + k} Ã Platformm + γXm,t + εm,t (A.38)Where Ym,t is the number of restaurants (as estimated using CNPJ) that opened, orworkers working in the restuarant sector, in microregion m in quarter t and Platformmis a dummy variable that takes the value of one if the delivery platform ever enteredmicroregion m in my sample. The vector Xm,t includes the covid cases per capita thateach micoregion had in each quarter to control for the potential impact of covid. Thecoefficient of interest, βk, captures the impact of the delivery platform on firm entry ineach quarter tâ(j)+k respect to the quarter prior to the entry of the platform. Similarto the baseline regression presented in Section 2.5, the identification assumption relieson the parallel trends assumption of firm entry across regions.Restaurant entry composition In addition to the results presented in Section 2.8that suggest that platform availability affected positively the entry of new restaurants,4This data is the same as used in Feinmann et al. (2022) and was kindly shared by the authors.543 percent of all restaurants that opened between 2015 and 2017 never show up in RAIS, suggestingthat a significant set of restaurants either never have employees or only hire employees informally.193it is possible that the composition of these restaurants may have changed as well.Figure A.20 panel (c) and panel (d) show the (unweighted and weighted) estimates ofequation (A.38) when considering as an outcome the share of new restaurants that evershow up in RAIS. Both panels present similar results: there is no significant change inthe likelihood of new entrants reporting in RAIS after the platform enters a microregion.Panel (e) and (f) investigate whether the average size of the new entrants that reportemployees in RAIS is different across treated and control microregions. Both panelsshow that there is no significant change in the average size of new entrants up to fivequarters after the platform enters the microregion. In summary, the evidence suggeststhat the composition of these new restaurants in terms of their size (conditional onhaving at least one formal worker) and their likelihood of ever reporting a formalworker is unaffected by the availability of the delivery platform.A.4.8 Geolocation detailsTo geolocate restaurants in the data, I leverage the address of the restaurants andthe name of the establishments available in RAIS. Importantly, the address is onlyavailable until 2020, and so I assume that restaurants do not change addresses whenopen in 2021. Specifically, the steps I take to geolocate the restaurants are thefollowing:1. I use the Google Maps API to obtain the latitude and longitude of the addressof each restaurant.2. For restaurants that met any of the following criteria:â¢ More than one observation in the same latitude and longitude, with only apartial match for the address found in Google Maps,â¢ Latitude and longitude were missing after the first geolocation attempt,â¢ The geolocation type in Google Maps was approximate or corresponded toa geometric center.3. For these cases, I attempted a second round of geolocation. This second attemptinvolved:194â¢ Geocoding the postal code and municipalityâ¢ Running the places autocomplete API from Google Maps, using the originaladdresses but restricting the search to being within a radius of 20 kilometersof the geometric center of the postal code.â¢ Using the predicted address to geolocate the restaurant again with theGoogle Maps API.4. For those that have non-missing address after step 2 (90% of the observations),I compare the postal codes found in step 2 with the original postal code fromRAIS. I drop the cases when these two postal codes do not match.5. I finally combine the restaurants that geo-coded in step 1 with the restaurantsthat I was able to geocode in step 3, and that forms my final sample of geocodedrestaurants.Table A.4 shows the share of restaurants in RAIS that I am able to find geocoded ineach year using the procedure described above.Table A.4: Share of Restaurants that Geocode in Each YearShare of Restaurants that are Geocoded2016 90%2017 91%2018 88%2019 88%2020 88%Notes: Restaurants are defined as those that have a CNAE code equal to 56.195Appendix BAppendix to Chapter 3B.1 Appendix: Figures196Figure B.1: Industry Distribution and Hours of Work of Temp Agency and RegularWorkersFigure 1.1: Industry Distribution of Temp Agency and Regular EmploymentEducation and Health ServicesProfessional and Business ServicesFinancial activitiesUtilitiesConstructionMiningManufacturingTransportation Warehousing and comunicationOther Services (Excluding Public Administration)Leisure and HospitalityAgriculture, Foresty, Fishing and HuntingRetail TradeWholsale Trade0.1.2.3.4.5Industryâs Share of National Temp-Agency Employees0 .05 .1 .15 .2 .25Industryâs Share of National Regular EmployeesFigure 1.2: Temporary and Regular Workersâ Average Weekly Hours0.2.4.6.81Cumulative density0 20 40 60 Weekly Hours(Restrictive) ==> (employer industry code + fixed term work)(Non-Restrictive) ==> (employer industry code)Regular WorkersNote: Panel (a) plots the share of national temp agency employment enlisted in an industry againstthat industryâs share of regular employment. Panel (b) plots temporary and regular workersâ averageweekly hours, as reported in the continuous labor force survey (Encuesta Permanente de Hogares) forthe years 2011 to 2018. We draw on two definitions of temp agency work, available based on industrycodes from 2011 onward. First, we plot the CDF of weekly hours when defining temp agency workersby their 2-digit industry code (mean 34.12; SE 13.16). Second, we show the CDF of weekly hours fortemp agency workers defined by their 2-digit industry code and declaring working for a fixed periodof time (mean 36.18; SE 12.15). As a benchmark, we also plot the CDF of hours for regular workers(mean 35.61; SE 16.50). The sample is restricted to workers who declared working less than 80 hoursper week.197Figure B.2: Sorting of Regular and Temp Agency Workers: Estimated Worker EffectsAgainst Firm Effects (by Work Arrangement)Figure 2.1: Regular WorkersFigure 2.2: Temp WorkersNote: The figure shows a binned scatter plot of estimated worker effects plotted against estimatedfirm effects in regular work arrangements, ÏRJ . Panel (a) plots the worker fixed effects for workers inregular work arrangement against firm fixed effects under regular work arrangements (slope 0.27;SE 0.002). Panel (b) does so for workers in temporary agency work arrangement with firm fixedeffects under regular work arrangements (slope 0.22; SE 0.002). Two limitations of our analysis arethat using worker and firm effects to measure assortative matching leads to a downward bias duelimited mobility bias (Martyn Andrews, Len Gill, Thorsten Schank and Richard Upward, 2008), andthat our specification sidesteps the age normalization in the flat portion of the experience profilehighlighted by (David Card, Ana Rute Cardoso, Joerg Heining and Patrick Kline, 2018b).198Figure B.3: Sorting in the Temporary Agency Market: Temporary Firm Fixed EffectsAgainst Regular Firm Fixed EffectsNote: This figure shows a binned scatter plot of estimated firm effects for temporary agency firms,ξTempAgencyTAit , plotted against the estimated firm effects for regular work arrangements, ÏRJ . The slopeis -0.007 (SE 0.0001). The estimated firm effects of regular work arrangements are restricted to thosefirms in the largest connected set that, at any point in our sampling window, served as the workplaceof temp agency workers.199Figure B.4: Average Tenure vs Regular Firm Fixed EffectsFigure 4.1: Relationship Between AKM Firm FEs for Regular Workers in TwoRandom Samples (First Stage of Split-Sample IV)β = 0.97 (SE 0.0029)-.20.2.4.6Firm FE for Reg. Workers (Rand. Group 0)-.2 0 .2 .4 .6Firm FE for Reg. Workers (Rand. Group 1)Figure 4.2: Average Tenure vs. Regular Firm Fixed Effects102030405060Average Regular Worker Tenure (Months)-.2 0 .2 .4 .6Firm FE for Regular WorkersNote: Panel (a) shows a split-sample specification with AKM firm effects for regular workers estimatedbased on two different 50% samples of workers. The slope of the relationship is 0.974 (SE 0.0029,R2 = 0.9348). Panel (b) shows a binned scatter plot of estimated firm effects for firms acting inregular work arrangements, ÏRJ , plotted against the average tenure, in months, of workers underregular work arrangements at the firm (slope 49.4; SE 0.691). Estimated firm effects are restricted tothose firms in the largest connected set that, at any point in our sampling window, served as theworkplace of temp agency workers.200B.2 Appendix: TablesTable B.1: Summary Statistics: All Formal EmployeesSIPA Dataset US Survey (Katz & Krueger)Unweight. Weight. Alt. WeightYears(Average for all registered workers during each year) 2011 2014 2017 2015Median Age (years) 34 35 36 50 41 41Mean Age (years) 37 38 38 48.3 42.6 42.5Median Wage (dollars) 891 925 952Mean Wage (dollars) 1,221 1,234 1,261Female (percent) 29.7 30.4 30.9 55.5 47.1 47.1Multiple Jobholder 3.0 3.1 3.3 14.3 13.2 13.1In Labor Force (Percent of Population) 46.3 44.9 45.9 62.8 67.5 67.5Part-Time Employment 11.1 12.1 13.4 26.2 24.2 23.5Industry (percent):Agriculture, Forestry, Fishing and Hunting 5.9 5.5 5.3 1.0 1.6 1.5Mining 1.2 1.4 1.3 0.6 0.5 0.5Utilities 1.0 1.1 1.2 0.5 0.9 0.9Construction 7.2 6.8 7.0 3.1 4.1 3.9Manufacturing 20.6 20.5 19.3 7.3 8.6 8.8Wholesale Trade 5.8 5.8 5.9 2.6 2.2 2.2Retail Trade 12.1 12.4 12.7 8.7 9.6 9.6Transportation Warehousing and communication 8.6 8.9 8.9 6.4 9 9.2Financial activities 2.5 2.6 2.6 9.2 9.2 9.2Professional and Business Services 13.5 12.9 12.9 14.5 13.4 13.2Education and Health Services 10.0 10.7 11.5 26.0 22.4 22.5Leisure and Hospitality 3.9 4.1 4.3 5.4 6.0 6.0Other Services (Excluding Public Administration) 4.9 4.9 5.0 5.2 4.8 4.7Temporary work agents 1.6 1.1 0.8 1.6 1.6Avg. Workers 4,225,916 4,261,083 4,296,090Note: SIPA summary statistics are for the overall (rather than final regression) sample using SIPAadministrative data (described in the main text). The right columns report summary statistics forthe US labor market computed by Katz and Krueger (2018) based on survey data.201Table B.2: Summary Statistics: All Temporary Work Agents in User Firms (SIPA-Registro Version)SIPA Dataset US Survey (Katz & Krueger)CPS Weighted Alt. Weight(Average for workers in user firms during each year) 2011 2015 2017 2005 2015Median Age (years) 26 26 26 44 47 45Mean Age (years) 28 28 28 44.0 46.8 46.5Median Wage (dollars) 696 682 752Mean Wage (dollars) 741 745 808Female (percent) 22.6 21.2 20.6 38.6 50.4 50.8Multiple Jobholder 10.2 8.9 9.1 7.4 32.0 33.0Part-Time Employment 3.0 2.5 3.8 35.2 47.7 46.2Industry (percent):Agriculture, Forestry, Fishing and Hunting 0.5 0.3 0.5 1.5 4.4 4.1Mining 0.7 0.5 0.4 0.3 0.5 0.5Utilities 0.3 0.3 0.4 0.5 0.3 0.4Construction 0.4 0.3 0.4 18.0 7.0 6.7Manufacturing 49.7 44.6 44.0 4.7 5.9 6.2Wholsale Trade 5.2 4.6 5.3 2.3 0.6 0.7Retail Trade 12.6 11.4 12.5 7.1 6.3 6.4Transportation Warehousing and communication 11.0 11.8 16.6 6.4 9.0 9.2Financial activities 2.6 2.2 1.8 7.8 6.4 6.2Professional and Business Services 4.9 4.4 4.1 23.4 20.7 20.6Education and Health Services 0.3 0.4 0.5 13.9 21.9 22.3Leisure and Hospitality 1.7 1.9 3.0 5.1 4.7 4.6Other Services (Excluding Public Administration) 3.7 5.8 7.9 7.7 7.4 7.1Avg. Workers 40,227 20,981 21,227Note: SIPA summary statistics are for the overall (rather than final regression) sample using SIPAadministrative data (described in the main text). The right columns report summary statistics forthe US labor market computed by Katz and Krueger (2018) based on survey data.202B.3 Appendix: Sample ConstructionOur main sample consists of merging two administrative datasets: SIPA and Sim-plificacion Registral. SIPA consists of the universe of formal workers employed inall regions, industries, and types of contracts. This data is available from 1996 to2018. In order to only focus on private sector workers, we dropped all observationscorresponding to national, provincial and local public sector contracts, following a listof unique tax identifiers that were given to us by the Argentine Ministry of Labor.Simplificacion Registral consists of sworn (audited) statements by the temp agenciescollected by the Ministry of Labor. The information submitted by the temp agenciesincludes information on the worker, user companies, positions, remuneration and datesthat reference the start and end of the contract. This information is submitted with abimonthly frequency, and the data is available from 2008 to 2018.To construct our sample, we use data from SIPA and Simplificacion Registral between2009 to 2017. We avoid including 2008, as it is the first year in which temporaryagencies were required to file the information. Similarly, at the time of our analysis,the complete data for 2018 was not yet available, and therefore, we excluded this yeartoo. Furthermore, the bimonthly frequency of Simplifacion Registral requires us totake some additional precautions. Because we use SIPA to compute workers earnings,and we can only identify the temp agency (but not the user firm) that is compensatingthe worker in that dataset, when merging through the worker-temp agency identifier,we cannot associate earnings with spells when there are multiple user firms within agiven bimonthly window. Hence, we drop all observations that correspond to two ormore user firms within a given bimonthly window for each worker.Furthermore, to generate the sample that is used in our main analysis, we CPI-deflateall payments to correspond to January 2008 pesos. To avoid outliers, we winsorizeearnings at the 1% level on both sides, and drop observations that correspond toearnings lower than the average 2008 minimum wage adjusted by the average annualgrowth rate (1.5%). In order to remove ambiguity about sources of earnings, we keepone observation per worker per month using the following rule: (i) when the individualis under a regular work contract, we keep the observation that reports the highestearning, and (ii) if the worker is a temp agency worker, we keep the temp agencyobservation with the highest earnings reported. Additionally, we remove the firstand last observation of each spell, to avoid the inclusion of any extra-compensation203associated with job transitions. Lastly, to guarantee computational feasibility whenrunning our main specification, we collapse our sample to 1 observation per spell-year.204B.4 Appendix: Measurement Error CorrectionWe probe the robustness of our findings to measurement error, which may lead to adownward bias in Î³ËOLS. The effects ÏRJ are generated regressors such that the varianceof ÏRJ captures both true variation in regular workersâ pay premia across workplacesand noise due to sampling variability (Andrews et al., 2008; Kline et al., 2020).To gauge the quantitative importance of measurement error, we implement a simplesplit-sample procedure (see, e.g., Goldschmidt and Schmieder, 2017; Gerard et al.,2018, for similar resolutions). We find a corrected coefficient of Î³ËIV = 0.493 (SE0.0077). Specifically, we split the universe of workers into two randomly drawn groupsand separately estimate regular workplace effects in AKM specifications for the twosamples, which we label S1 and S0. We then regress the estimates of ÏR,S1J on thoseof ÏR,S0J .1 If there is no sampling variability or measurement error, we would expecta coefficient of one for this regression; if the workplace pay premia dispersion onlyreflects noise, then we would expect a coefficient of zero. In Appendix Figure B.4Panel (a), we plot this first stage relationship between ÏR,S1J and ÏR,S0J , and find acoefficient of 0.974 (SE 0029, R2 = 0.9348) among our sample of user firms. In thesplit-sample setting, we find a quantitatively nearly identical reduced-form slope of0.480 compared to our OLS coefficient of 0.490. Our estimates thus lead to an IVestimate of Î³ËIV = 0.480/0.974 = 0.493 (SE 0.0077) from a specification in which ÏR,S0Jserves as an instrument for ÏR,S1J (with a first-stage coefficient of 0.974). Hence, themeasurement error correction has essentially no effect on our findings.1To generate the split universe of workers, we keep the first observation of each worker and withineach firm that corresponds to a first observation unit, we split the sample in two. This way we notonly guarantee that half of the workers will be in each group but we also attempt to keep a balancebetween firms.205Appendix CAppendix to Chapter 4C.1 Appendix: Figures206Figure C.1: Selection of Outsourced Workers within the FirmFigure 1.1: Total Annual Earnings015,00030,00045,00060,00075,00090,000Outsourced Workers Earnings0 15,000 30,000 45,000 60,000 75,000 90,000Incumbents Workers Earnings45° line β = 0.67 (SE 0.02)Figure 1.2: Log Daily Wages33.544.555.56Outsourced Workers Log Wage3 3.5 4 4.5 5 5.5 6Incumbents Workers Log Wage45° line β = 0.79 (SE 0.02)Notes: This figure presents a binscatter plot of the earnings and wages of outsourced and non-outsourced workers (âincumbent workers") in the year before the outsourcing event. The black line isa linear fit, while the dotted line is the 45-degree line. Each dot represents the average earnings/wagesof outsourced workers and incumbent workers within the firm the year before the outsourcing event.207Figure C.2: Effect on Log Daily WagesFigure 2.1: Log Daily Wages: Full Sample-.04-.020.02.04Effect on Log Daily Wage-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventFigure 2.2: Log Daily Wages: Dropping Compliers-.04-.020.02.04.06Effect on Log Daily Wage-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventNotes: This figure reports event-study results on log daily wages (panel (a)) obtained after fittingequation (1) on the log daily wage of individual i in period t. Panel (b) estimates this equation afterdropping from the sample pairs where the treated worker is not employed but the control workeris. We label this control worker as a âcomplier", one who would not be employed if exposed tooutsourcing. The log daily wage corresponds to the daily wage paid by the dominant employer, i.e.,the employer that paid a given worker the most in that particular year. Both panels report 95%confidence intervals based on standard errors clustered at the worker level.208Figure C.3: Conditioning on Mother Firm SurvivalFigure 3.1: Annual Earnings-5,000-4,000-3,000-2,000-1,00001,000Effect on Total Earnings (â¬)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventBaselineMother not closing in t*+1Figure 3.2: Employment-25-20-15-10-50Effect on Employment (p.p.)-4 -3 -2 -1 0 1 2 3 4 5Year Relative to Outsourcing EventBaselineMother not closing in t*+1Notes: This figure reports event-study results on total earnings (panel (a)) and employment (panel(b)). The figure reports the baseline event-study estimates on these outcomes (see Figure 4.1) in bluealong with event-study estimates computed for outsourcing events where the mother firm remainsopen the next year after an outsourcing event. The mean of earnings at tâ â 1 for baseline is 20,566euros and 22,669 euros when conditional on the mother firm not closing. Year 0 is the year of theoutsourcing event. Both panels report 95% confidence intervals based on standard errors clustered atthe worker level.209Figure C.4: Distribution of Exposure across Outsourcing Events0.02.04.06.08Fraction0 .2 .4 .6 .8 1Exposure (Wage Bill Outsourced Workers / Total Wage Bill Mother Firm)Notes: This figure reports the exposure measure for outsourcing firms in the matched sample describedin Table 1. Exposure is calculated as the share of the wage bill paid to outsourced workers the yearbefore the outsourcing event. All statistics are person-year weighted.210Figure C.5: Effects by Exposure-60-50-40-30-20-10010Effect on Total Earnings (%)1 2 3 4 5 6 7 8 9 10Deciles of ExposureEffect 1-yr Post Outsourcing EventEffect 5-yrs Post Outsourcing EventNotes: This figure reports event-study coefficients on total earnings, obtained by fitting equation(4.1) separately for each decile of exposure of the outsourcing event. Recall that exposure is definedas the share of the wage bill paid to outsourced workers the year before the outsourcing event. Theblue line reflects the percent impact of outsourcing on total earnings in tâi + 1 (i.e., the event-studycoefficient θ1 from equation (1), rescaled by the pre-outsourcing mean of earnings observed in yeartâi â 1). The red line reflects the percent impact of outsourcing on annual earnings in tâi + 5. Thefigure displays 95% confidence intervals based on standard errors clustered at the worker level.211Figure C.6: Effect on Log Daily Wages Using the Flow-Based Approach of Goldschmidtand Schmieder (2017)-.06-.04-.020.02.04Effect on Log Daily Wage-4 -3 -2 -1 0 1 2 3 4 5Year Relative to EventNotes: This figure reports event-study estimates on the effects of outsourcing on log daily wagesfollowing the flow-based approach of Goldschmidt and Schmieder (2017), where an outsourcing eventcorresponds to a situation where a group of workers are employed in year t by a non-business firmand in year t+1 are instead employed by an FCSL business service firm (see Section 4.4.3 for details).Both panels report 95% confidence intervals based on standard errors clustered at the worker level.212C.2 Appendix: Tables213Table C.1: Outsourced Workersâ Occupations and Sector(1) (2) (3)Occupation Mother Industry WorkersShop salesperson Retail trade in non-specialized stores with a prevalence of food and drink products 9,461Waiters and bartenders Bars and other similar establishments without kitchens 3,219Waiters and bartenders Restaurants and mobile food service activities 3,163Shop salesperson Retail sale of articles of clothing in specialized stores 2,506Shop salesperson Retail trade in other non-specialized stores 2,160Other craft and related workers Other cleaning activities 2,118Domestic, hotel and office cleaners and helpers Other cleaning activities 1,934Cooks Restaurants and mobile food service activities 1,836Tellers, money collectors, and related clerks Other monetary intermediation 1,231Building frame and related trades workers Construction of residential and non-residential buildings 1,147Hairdressers, beauticians, and related workers Hairdressing and other beauty treatment services 1,089Transport and storage laborers Other business support services 994Food preparation assistants Restaurants and mobile food service activities 925Heavy truck and bus drivers Transportation of goods by road 902Manufacturing laborers Trade in electricity 901Financial and mathematical associate professionals Other monetary intermediation 877Shop salespersons Retail sale of furniture, lighting, and other household items in specialized stores 801Car, van, and motorcycle drivers Transportation of goods by road 774Other elementary workers Cargo handling 772Information and communications technology operations and user support technicians Other service activities related to information technology 720Notes: This table reports the distribution of the most common occupations and sector (of the mother firm) of outsourced workers. Occupationsare defined based on five-digit CP (âClassificazione delle professioni") codes from the INPS data, which are then cross-walked into three-digitISCO codes. Sectors are based on ATECO four-digit codes.214Table C.2: Sample ConstructionSteps to Construct Sample Share Dropped Workers RemainingStarting point 1,336,193Mother & daughter diff industry 38.9% 816,935Flows restriction 60.8% 320,241Restriction on mother closure 16.6% 267,0302-year tenure restriction 19.4% 215,337Matching algorithm 71.3% 61,849Final Sample 61,849Notes: The first row counts the number of (unique) workers registered in the INPS data who, between2005 and 2019, experienced a job separation and where the reason is âoutsourcing," as recorded byINPS records. We then remove from this sample outlier events; specifically, situations where exposureexceeds 125% (defined as the wage bill of outsourced workers relative to the mother firmâs totalwage bill in the year before the outsourcing event). This is our starting point. From this sample, weremove events where the mother and daughter firm have the same six-digit ATECO code or wherethe INPS data show a transition of workers going from the daughter firm to the mother firm. Finally,we remove events where the mother firm is outsourcing 90% or more of its workers and is dissolvedbetween year t and t+ 1. In the next-to-last row, we consider only workers who have at least twoyears of tenure with the mother firm at the moment of outsourcing. The last row counts the numberof outsourced workers for whom we can match with a similar control worker who did not experiencean outsourcing event, where the matching algorithm is defined in Section 4.3.215Table C.3: Effects by Task Content of Outsourced Jobs(1) (2)Earnings, 1 Yr after Outsourcing Earnings, 5 Yrs after OutsourcingNon-routine manual -0.2565 -0.1173(0.0589) (0.0594)Routine cognitive -0.1496 -0.0973(0.0565) (0.0717)Routine manual -0.0936 -0.0504(0.0731) (0.0788)Constant -0.1452 -0.0445(0.0439) (0.0527)Number of jobs 99 99Notes: This table presents regression results where the outcome is the average losses from outsourcingon the various task contents associated with the outsourced job. The omitted category is non-routinecognitive (non-routine analytical plus non-routine interactive). The effects on earnings are rescaledby the average level of earnings observed in a given occupation, one year before the outsourcingevent. The regression weights by the number of person-year observations associated with a given job,and robust standard errors are shown in parentheses.216
Thesis/Dissertation
eng
Economics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Lemieux, Thomas
Saggio, Raffaele
Three essays on labor economics
Text