Land and Food Systems, Faculty of
DSpace
UBCV
Litzow, Erin Lenz
Doctor of Philosophy - PhD
University of British Columbia
This dissertation presents three essays in the fields of environmental, energy, and development economics. Chapters 2 and 3 consider the effects of environmental change and associated shocks on human capital and economic outcomes. Chapter 4 documents the failure of a policy designed to address environmental degradation and its costs. Chapter 2 identifies the impact of lead (Pb) pollution on learning outcomes in Mexico. The pollution is the result of a change in the United States' environmental regulation, which caused a pollution intensive industry to shift operations to Mexico. We use a spatial difference-in-difference design to show that test scores decrease by 0.05-0.09 standard deviations in response. Effects are stronger in communities that were worse off even before the pollution increase. These results underline the importance of considering unintended consequences and cross-border spillovers when regulating toxic pollutants and highlight the need for more research on the costs of lead pollution exposure in low and middle-income countries, where most exposure occurs today. Chapter 3 considers the effects of crises, like those that can arise from global environmental change. We study how garment-making-firm owners in a Ghanaian district capital responded to the COVID-19 pandemic. All firms experienced losses during the height of lockdown policies, but female-owned firms were more likely to close. Female-owned firms that closed were already on the margin of viability, and owners of these firms lost income. Male firm owners compensated for lost income via other economic opportunities. These results show how economic shocks, which are only expected to increase as climate change accelerates, can exacerbate pre-existing gender inequalities. Chapter 4 evaluates a policy aimed at slowing environmental change and mitigating its costs. It focuses on a biogas digester promotion program, targeted at energy poverty in Senegal. Using survey data collected from participating households, this chapter documents the failure of the program to deliver clean energy due to high digester failure rates. Only 15-percent of the digesters installed in the sampled households were operating. Not surprisingly, this evaluation does not find impacts on targeted outcomes like energy use, health, or economic outcomes.
Essays in Environmental and DevelopmentEconomics: Lead-Acid Battery Recycling inMexico, Gender and Informal Labor Markets inGhana, and Domestic Biogas Adoption in SenegalbyErin Lenz LitzowB.S.F.S., Georgetown University, 2011M.E.M., Duke University, 2017A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Integrated Studies in Land and Food Systems)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)July 2024© Erin Lenz Litzow, 2024The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, the dissertation entitled:Essays in Environmental and Development Economics: Lead-Acid Battery Recy-cling in Mexico, Gender and Informal Labor Markets in Ghana, and DomesticBiogas Adoption in Senegalsubmitted by Erin Lenz Litzow in partial fulfillment of the requirements for the degree ofDoctor of Philosophy in Integrated Studies in Land and Food SystemsExamining Committee:Sumeet Gulati, Professor, Food and Resource Economics, UBCCo-SupervisorMunir Squires, Assistant Professor, Vancouver School of Economics, UBCCo-SupervisorBrian Copeland, Professor, Vancouver School of Economics, UBCUniversity ExaminerRashid Sumaila, Professor, Institute for the Oceans and Fisheries, UBCUniversity ExaminerErica Myers, Associate Professor, Department of Economics, University of CalgaryExternal ExaminerAdditional Supervisory Committee Members:Frederik Noack, Associate Professor, Food and Resource Economics, UBCSupervisory Committee MemberWerner Antweiler, Associate Professor, Sauder School of Business, UBCSupervisory Committee MemberSubhrendu Pattanayak, Professor, Sanford School of Public Policy, Duke UniversitySupervisory Committee MemberiiAbstractThis dissertation presents three essays in the fields of environmental, energy, and developmenteconomics. Chapters 2 and 3 consider the effects of environmental change and associatedshocks on human capital and economic outcomes. Chapter 4 documents the failure of a policydesigned to address environmental degradation and its costs.Chapter 2 identifies the impact of lead (Pb) pollution on learning outcomes in Mexico.The pollution is the result of a change in the United States’ environmental regulation, whichcaused a pollution intensive industry to shift operations to Mexico. We use a spatial difference-in-difference design to show that test scores decrease by 0.05-0.09 standard deviations inresponse. Effects are stronger in communities that were worse off even before the pollutionincrease. These results underline the importance of considering unintended consequencesand cross-border spillovers when regulating toxic pollutants and highlight the need for moreresearch on the costs of lead pollution exposure in low and middle-income countries, wheremost exposure occurs today.Chapter 3 considers the effects of crises, like those that can arise from global environ-mental change. We study how garment-making-firm owners in a Ghanaian district capitalresponded to the COVID-19 pandemic. All firms experienced losses during the height oflockdown policies, but female-owned firms were more likely to close. Female-owned firms thatclosed were already on the margin of viability, and owners of these firms lost income. Malefirm owners compensated for lost income via other economic opportunities. These resultsshow how economic shocks, which are only expected to increase as climate change accelerates,can exacerbate pre-existing gender inequalities.Chapter 4 evaluates a policy aimed at slowing environmental change and mitigating itscosts. It focuses on a biogas digester promotion program, targeted at energy poverty in Senegal.Using survey data collected from participating households, this chapter documents the failureof the program to deliver clean energy due to high digester failure rates. Only 15-percentof the digesters installed in the sampled households were operating. Not surprisingly, thisevaluation does not find impacts on targeted outcomes like energy use, health, or economicoutcomes.iiiLay SummaryThis dissertation presents research focused on human-environment interactions and theirconsequences in low- and middle-income settings, specifically Mexico, Ghana, and Senegal.The findings contribute to the fields of Environmental, Energy, and Development Economics.The studies rely on empirical methods to present descriptive evidence and identify causalrelationships. Chapter 2 documents learning losses that accompany increase in lead (Pb)pollution exposure in Mexico, where increase were caused by a chance in U.S. environmentalpolicy. Chapter 3 shows how economic crises, like those that are expected to accompany theacceleration in climate change, differentially affect economic outcomes of male and females,potentially exacerbating pre-existing gender inequalities. Chapter 4 evaluates a policy thatis aimed at mitigating the costs of environmental change but finds that the policy was notsuccessful given high technology failure rates.ivPrefaceChapter 1 is the general introduction of this dissertation and is an original, unpublished, andindependent work by the author of this dissertation, Erin Lenz Litzow.Chapter 2, “Toxic Recycling: The Cost of Used Lead-Acid Battery Processing inMexico,” is joint work with Bianca Cecato (University of Ottawa), Mauricio Romero (InstitutoTecnológico Autónomo de México), and Tatiana Zárate Barrera (Texas A&M University).The research was initiated by the author of this dissertation, Erin Lenz Litzow. MauricioRomero provided data access. The analysis and writing were conducted primarily by ErinLenz Litzow, with support from Tatiana Zárate Barrera and Bianca Cecato.Chapter 3, “Gender Differences in Informal Labor Market Resilience: Evidence fromGhana,” is a joint work with Morgan Hardy (New York University – Abu Dhabi), JamieMcCasland (University of British Columbia), and Gisella Kagy (University of Wisconsin –Madison). All co-authors contributed equally to all aspects of the project.Chapter 4, “When Technology Adoption Interventions Fail: The Case of DomesticBiogas Digesters in Senegal,” is a joint work with Ousmane Ndiaye (Centre de Recherchepour le Développement Economique et Social (CRDES), Sénégal, Université Gaston-Berger),Jörg Ankel-Peters (RWI – Leibniz Institute for Economic Research), and Marc Jeuland (DukeUniversity). The research was initiated by Jörg Ankel-Peters and Marc Jeuland. Erin LenzLitzow, the author of this dissertation, designed data collection instruments and OusmaneNdiaye carried out data collection. Analysis and writing were done by Erin Lenz Litzow.Chapter 5 is the general conclusion of this dissertation and is an original, unpublished,and independent work by the author of this dissertation, Erin Lenz Litzow.A version of Chapter 3 has been published under the following citation: Morgan Hardy,Erin Litzow, Jamie McCasland, Gisella Kagy, Gender Differences in Informal Labor-MarketResilience, The World Bank Economic Review, Volume 37, Issue 1, February 2023, Pages112–126. The authors’ names are in certified random order and contributions are as listedabove.The fieldwork required for data collection for Chapter 3 received ethical approvalfrom the University of British Columbia Research Ethics Committee (protocol #H20-01692).The fieldwork required for data collected for Chapter 4 received ethical approval from DukeUniversity’s Institutional Review Board (protocol #2018-0061).vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Toxic Recycling: The Cost of Used Lead-Acid Battery Processing in Mexico . . . 82.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2 Relevant Background Information . . . . . . . . . . . . . . . . . . . . . . . . 132.2.1 Lead-acid battery recycling . . . . . . . . . . . . . . . . . . . . . . . 132.2.2 Health effects of lead pollution exposure . . . . . . . . . . . . . . . . 172.2.3 The Mexican Education System . . . . . . . . . . . . . . . . . . . . . 172.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.3.1 Used Lead-Acid Battery Recycling (ULABs) Facilities . . . . . . . . . 192.3.2 Wind direction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.3.3 Test Scores from the National Assessment of Academic Achievement inSchools (ENLACE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.3.4 Location of schools . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.3.5 Locality and municipality characteristics . . . . . . . . . . . . . . . . 222.4 Estimating the Effects of Lead Exposure on Test Scores . . . . . . . . . . . . 23vi2.4.1 Defining lead exposed students . . . . . . . . . . . . . . . . . . . . . 232.4.2 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Gender Differences in Informal Labor Market Resilience: Evidence fromGhana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493.1 Context and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533.2 Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573.3 Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603.4 Conclusion and Policy Takeaways . . . . . . . . . . . . . . . . . . . . . . . . 634 When Technology Adoption Interventions Fail: The Case of Domestic BiogasDigesters in Senegal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704.2 Senegal’s Domestic Biogas Program . . . . . . . . . . . . . . . . . . . . . . . 734.3 Evaluation Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 744.3.1 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 744.3.2 Sampling and Data Collection . . . . . . . . . . . . . . . . . . . . . . 754.3.3 Analytical Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 754.4 Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764.4.1 Digester Installation and Operation . . . . . . . . . . . . . . . . . . . 764.4.2 Impact Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 784.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 804.6 Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . 815 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96A Appendix to Chapter 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110B Appendix to Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116C Appendix to Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123C.1 Detailed Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123C.1.1 Sampling and Data Collection . . . . . . . . . . . . . . . . . . . . . . 123viiC.1.2 Evaluation Design and Methods . . . . . . . . . . . . . . . . . . . . . 125C.2 Comprehensive Impact Analysis Results . . . . . . . . . . . . . . . . . . . . 130C.2.1 Descriptive Statistics and Balance Tables . . . . . . . . . . . . . . . . 130C.2.2 Fuel Use and Cooking Impacts . . . . . . . . . . . . . . . . . . . . . . 131C.2.3 Health Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132C.2.4 Agricultural and Employment Impacts . . . . . . . . . . . . . . . . . 133C.3 Cost Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145C.4 Construction of the Asset Index . . . . . . . . . . . . . . . . . . . . . . . . . 146viiiList of Tables2.1 Summary Statistics: Test Scores . . . . . . . . . . . . . . . . . . . . . . . . . 452.3 Effect of increased battery recycling activity on students attending school closeto a recycling facility, individual-level results . . . . . . . . . . . . . . . . . . 462.2 Effect of increased battery recycling activity on students attending school closeto a recycling facility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 472.4 Effect of attending school close to a battery recycling facility, by communitycharacteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483.1 Firm Sales, Before and During Crisis . . . . . . . . . . . . . . . . . . . . . . 673.2 Selection Into Persistent Closure on Pre-Crisis Firm and Owner Characteristics 683.3 Income and Labor Supply by Persistent Closure Status . . . . . . . . . . . . 694.1 Determinants of digester operation . . . . . . . . . . . . . . . . . . . . . . . 854.2 Difference in key characteristics between treatment and control groups . . . . 894.3 Impacts of biogas installation on fuel use outcomes . . . . . . . . . . . . . . 904.4 Impacts of biogas installation on time use and expenditure outcomes . . . . 914.5 Impacts of operational digesters on fuel-use, cooking and agricultural outcomes 924.6 Impacts of operational digesters on individual health and employment outcomes 93A.1 Number of students tested, by subject, grade, and year . . . . . . . . . . . . 113A.2 Effect of attending school close to a battery recycling facility, defining exposureusing prevailing wind direction . . . . . . . . . . . . . . . . . . . . . . . . . . 114A.3 Effect of attending school close to a battery recycling facility on proficiencylevels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115B.1 Self-Reported Measures of Impact of COVID-19 Crisis and Coping Mechanisms119B.2 Women Attrition Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120B.3 Men Attrition Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121B.4 Profits and Labor hours, Before and During Crisis . . . . . . . . . . . . . . . 122C.1 Difference in household-level outcomes between treatment and control . . . . 135ixC.2 Difference in individual-level outcomes between treatment and control . . . . 136C.3 Impacts of biogas installation on fuel use outcomes, outliers dropped . . . . . 137C.4 Impacts of biogas installation on fuel use outcomes, outliers dropped . . . . . 138C.5 Impacts of biogas installation on eye problems . . . . . . . . . . . . . . . . . 139C.6 Impacts of biogas installation on respiratory health in children under 12 yearsof age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140C.7 Impacts of biogas installation on respiratory health in cooks . . . . . . . . . 141C.8 Impacts of biogas installation on agricultural outcomes . . . . . . . . . . . . 142C.9 Impacts of biogas installation on agricultural outcomes (continued) . . . . . 143C.10 Impacts of biogas installation on adult employment . . . . . . . . . . . . . . 144C.11 Indicators included in wealth index principal component analysis . . . . . . . 148xList of Figures2.1 Used Lead Acid Battery Recycling Facilities in Mexico . . . . . . . . . . . . 402.2 Number of students tested, by grade and year . . . . . . . . . . . . . . . . . 402.3 Identifying lead exposed schools . . . . . . . . . . . . . . . . . . . . . . . . . 412.4 Lead concentrations near U.S. ULAB battery recycling facilities . . . . . . . 422.5 Effects of being close to a lead-acid battery recycling plant on scores, by year 422.6 Event study of math scores, by grade . . . . . . . . . . . . . . . . . . . . . . 432.7 Event study of Spanish scores, by grade . . . . . . . . . . . . . . . . . . . . . 443.1 Distribution of Feb 2020 sales by Gender . . . . . . . . . . . . . . . . . . . . 643.2 Share of Firms with Positive Sales, by Gender and Month . . . . . . . . . . . 653.3 Distribution of Feb 2020 Sales by Persistent Closure Status and Gender . . . 664.1 Sampling of treatment and control households . . . . . . . . . . . . . . . . . 834.2 Digester operation and repair from installation to interview . . . . . . . . . . 834.3 Household satisfaction with biogas digesters . . . . . . . . . . . . . . . . . . 84A.1 Varying the radius for "Near" definition . . . . . . . . . . . . . . . . . . . . 111A.2 Effects of being close to a lead-acid battery recycling plant on scores, individuallevel results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112B.1 Share of Firms with Positive Profits, by Gender and Month . . . . . . . . . . 116B.2 Share of Firm Owners Working Positive Number of Hours in Garment-MakingFirm, by Gender and Month . . . . . . . . . . . . . . . . . . . . . . . . . . . 117B.3 Distribution of Feb 2020 Sales by Outside Option and Gender . . . . . . . . 118C.1 Parallel analysis results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146C.2 Distribution of asset index values . . . . . . . . . . . . . . . . . . . . . . . . 147xiAcknowledgementsI am deeply grateful to my supervisory committee for their guidance and support. SumeetGulati welcomed me to LFS with open arms, patiently guided me on my research path, andtreated me like a peer and friend. Munir Squires made me feel so welcome in economics andgave me copious amounts of time and feedback on research, career, and life. I would nothave completed this degree without Sumeet and Munir’s constant support and compassion.Frederik and Werner were endless sources of insight and inspiration and always received meas a friend and colleague. Subhrendu, one of my longest serving mentors, welcomed me ontoexciting projects and supported me through the ups and down of my degree.I also want to thank innumerable other faculty and mentors who have supported meon my academic journey. In our small group at LFS, I benefited greatly from the support ofFrederik, Sumeet, Carol McCausland, Josephine Gantois, and Jonathon Proctor. Faculty atVSE welcomed me into their seminars and onto their projects. Thank you to Munir, JamieMcCasland, Patrick Baylis, and Katherine Wagner for the communities and mentorship youshared. Thank you especially to Hisham Zerriffi, for building a compassionate, kind, andinclusive research group and inviting me to join. Finally, to my research collaborators, JörgAnkel-Peters, Bianca Cecato, Morgan Hardy, Marc Jeuland, Gisella Kagy, Jamie, OusmaneNdiaye, Mauricio Romero, and Tatiana Zárate, thank you for your intellectual and professionalguidance. Most of all, thank you for your friendship.I would also like to thank my peers and friends, with whom I experienced the highestof highs and lowest of lows. I could not imagine life at LFS without Elyse, Bianca, Raahil,and Tatiana. I also want to thank Elise, Elyse, Tatiana, and Ieda for the laughs, pet snuggles,camping trips, and movie nights and for making my life in Vancouver so joyful.Finally, I want to thank my family for making me the person I am today and for theunwavering support on my winding journey. Mom and Dad, thank you for always lovingme no matter what. Andrea, thank you for your patience and encouragement. And Avery,you have carried me through this degree from the first days at Waterloo and 7th. I am sothankful for you, the person you are, the person you have helped me to become, and, ofcourse, our little family. I love you all.xii— For Mosi, the first and the best.xiiiChapter 1IntroductionGlobal environmental change is one of the primary concerns of the current century, largelybecause of the high costs associated with increasing pollution levels and accelerating climatechange (Lee et al., 2023; Fuller et al., 2022). The socioeconomic burdens associated withenvironmental change will reach all corners of the globe, but are unevenly distributed. Thebulk of these effects will be felt by populations in low- and middle-income countries (LMICs)(Rees & Fuller, 2020; Pattanayak, Pakhtigian, & Litzow, 2018). This dissertation focuseson these populations, estimating the human capital and economic costs that can come fromenvironmental change and evaluating an energy policy aimed at curbing environmental changeand its associated costs.Chapters 2 and 3 explore how environmental change affects populations in LMICs,going beyond the often studied health effects, e.g. Romanello et al. (2023). Through healthchannels, pollution exposure can have wider ranging effects on societal outcomes like criminalactivity (Chen & Li, 2020) and human capital development (C. Persico, Figlio, & Roth,2020). Chapter 2 documents the effects of lead pollution exposure on learning outcomesin Mexico. Environmental change can also lead to more acute economic crises, stemmingfrom, for example, natural disasters, public health crises, and drought (Rennert et al., 2022).Chapter 3 explores how firms in Ghana responded to the COVID-19 crisis, highlighting howfemale firm-owners were deferentially affected compared to their male counterparts. Togetherthese chapters shed light on the non-health costs that environmental change inflicts on alreadymarginalized populations, like women and lower-income communities.Chapter 2 investigates the costs of lead pollution exposure in Mexico. The lead1pollution we study comes from the United States, which tightened environmental regulationsgoverning ambient levels of lead pollution in 2009. In response to this policy change, usedlead-acid battery (ULAB) recycling, a lead intensive and heavily polluting industry, shiftedits operations from the United States to Mexico (Tanaka, Teshima, & Verhoogen, 2022). Leadpollution exposure has large health effects, affecting many areas of the body, and exposure isespecially detrimental for children (Rees & Fuller, 2020).This study focuses on the costs to learning that stem from lead pollution exposure andmakes three main contributions. First, it extends the economics and trade literature that looksat the cross border effects of environmental regulation, e.g. Copeland, Shapiro, and Taylor(2022), by estimating the effects of exported pollution on recipient communities. Second,despite lacking data on lead pollution and exposure levels, this study is able to estimate acausal relationship between changes in pollution levels and learning outcomes, building on aliterature that has documented these relationships largely in the Global North (Aizer, Currie,Simon, & Vivier, 2018; Shadbegian, Guignet, Klemick, & Bui, 2019; Hollingsworth, Huang,Rudik, & Sanders, 2022). This is the third contribution, extending these causal estimates tothe Global South (Rau, Urzúa, & Reyes, 2015), where the vast majority of lead pollutionexposure occurs today (Rees & Fuller, 2020).This study focuses on the 2009 policy change that tightened lead emissions standardsin the U.S. and caused ULAB recycling to shift to Mexico (Tanaka et al., 2022). Relying ona panel of standardized test scores in Spanish and math conducted in every primary schoolin Mexico between 2006 and 2013, we compare changes in students’ test scores over time,for those studying near ULAB recycling facilities and those studying slighting farther away.We find that the post-2009 increase in ULAB recycling activity led to a 0.05-0.09 standarddeviation decrease in test scores, with effects being slightly stronger for math. These effectspersist and worsen after the initial 2009 policy change and are robust to further definingexposure based on distance to ULAB facility and prevailing wind direction. In addition,given the individual-level nature of our data, we are able to account for potential selective2sorting (Chen, Oliva, & Zhang, 2022) and find that migration is not driving our results.Finally, heterogeneity analysis shows that the effects are felt more strongly in communitiesthat were worse off, e.g. lower levels of adult educational achievement and lower access toinfrastructure, even before the 2009 policy change.These effect sizes are large in magnitude. A recent review of interventions aimed atincreasing learning outcomes in LMICs found that the median intervention increases learningoutcomes by 0.1 standard deviations (Evans & Yuan, 2022), suggesting that pollution exposurecould almost fully reverse the effects of these often costly interventions and may explainsome of the disparities in educational achievement between the Global North and South(Crawfurd, Todd, Hares, Sandefur, & Bonnifield, 2023). We can also contextualize this effectsize by calculating the expected lost future earnings stemming from the test score reductionsexperienced by students studying near ULAB facilities. We estimate that this leads to annualcost of 16 to 39 million USD, compared to an estimated annual cost of reducing pollutantsfrom these industrial facilities of 10 to 19 million USD (Burr, Lazzari, & Greene, 2011).Another pathway through which environmental change can affect well-being is throughits effects on economic activity. Chapter 3 of this dissertation explores how economic crisesimpact small, informal businesses in Ghana and, specifically, explores how these effectsdiffer by gender. The economic crisis studied here is the COVID-19 pandemic and theassociated lockdown policies, but the findings here can extend to other crises brought aboutby accelerating climate change including natural disasters, like flooding, drought, storms,and extreme heat (Lee et al., 2023), and climate-linked public health crises (Jafino, Walsh,Rozenberg, & Hallegatte, 2020).The setting of this study is garment-making firms in a district capital in Ghana.These small, informal firms are important to study, as the majority of workers in LMICs areself-employed (McKenzie, 2017; McCaig & Pavcnik, 2021). The garment-making sector isunique because there is gender overlap, as opposed to more gender-differentiated sectors likehair dressing or construction. This overlap allows us to isolate gender differences in crisis3response from more sector-specific dynamics. Our analysis relies on data collected from theuniverse of garment-making firm owners in this district capital, reporting on their businessactivities and any other income generating activities for the months between February andJuly 2020. In Ghana, lockdown policies began in late March and had largely ended bymid-April (Hale et al., 2021).We observe that, during both the peak and post lockdown periods, all firms experiencelarge drops in revenue. While the level drops are mostly equal between male- and female-owned firms, the loss represents a larger share of revenue for female-owned firms due topre-pandemic gender differences in revenues. Female-owned firms are also more likely to closeduring the peak lockdown period1 but the majority of male- and female-owned firms hadresumed operations by July 2020.Digging deeper into these patterns and focusing on persistent closure2 reveals twoimportant findings regarding gendered response to crises. First, we examine how firms thatclose persistently compare to their counterparts who remain open or only close temporarily.We show that female-owned firms who persistently closed are selected based on pre-pandemic,or February 2020, sales. Female-owned firms who close have significantly lower pre-pandemicrevenue compared to those who remain open or close only temporarily. This same is not truefor male-owned firms. Male firm owners whose shops persistently close are instead more likelyto have other income sources pre-pandemic. Second, looking into total income generation,including from the garment making firm and any other sources, we find results consistentwith these patterns. The majority of female firm owners who close exit to non-employmentand these women experience large drops in total income, earning 64 percent less than womenwhose firms remained open. In contrast, men whose firms close redirect their labor to otherincome generating activities and fully compensate for lost income from garment making.During the early months of the COVID-19 pandemic, both male- and female-owned1We define closure in a given month as reporting zero sales.2We define persistently closed firms as those who reported zero revenues for at least four of the five crisismonths, March - July 2020.4garment making firms in Ghana experienced large shocks to their businesses, but theirresponses differed due to underlying disparities in occupational choice. Female-owned firmsthat were already on the low end of viability were forced into closure and owners lost incomedue to lack of other income generating opportunities. Male firm owners instead selected intoclosure based on the availability of alternative income generating opportunities and, withthese outside options, were able to make up for lost income. These findings highlight theeconomic vulnerability of female firm owners whose businesses are more likely to be on themargin of viability and emphasize the need for gender-sensitive support policies in the face ofincreasing economic crises.The final chapter of this dissertation turns its attention to a policy that has been putin place to curb an important source of pollution exposure and environmental degradation,energy poverty (Fuller et al., 2022). Households living in energy poverty rely on polluting,biomass fuels like firewood and charcoal to meet energy needs (World Health Organization,2021). Energy poverty has a number of environmental consequences, producing indoor airpollution, degrading local environments, and contributing to climate change via deforestationand climate forcing emissions (M. Jeuland et al., 2021). Through these channels andothers energy poverty is associated with negative health effects and imposes economic costs(M. A. Jeuland & Pattanayak, 2012), especially on women and girls who are often responsiblefor fuel collection and cooking (Krishnapriya, Chandrasekaran, Jeuland, & Pattanayak, 2021).Policies to address energy poverty and its costs often promote the adoption and use of lesspolluting energy sources and/or more efficient combustion technologies. In some cases, thesetechnologies have been shown to reduce the use of polluting fuels and, at times, improve healthand well-being outcomes (Bensch & Peters, 2015; Litzow, Pattanayak, & Thinley, 2019).This chapter evaluates a Senegalese program that promoted the adoption of biogas digesters,described below, which have been shown in other settings to alleviate energy poverty andhave positive effects on health and well-being (Bedi, Pellegrini, & Tasciotti, 2015; Mwirigi,Makenzi, & Ochola, 2009; Laramee & Davis, 2013).5In Senegal an estimated 71 percent of households rely on traditional, polluting fuelsto meet their energy needs (Tracking SDG 7: Senegal Country Report , 2022). Domesticbiogas digesters have been touted as a possible solution in this setting given the popularityof animal husbandry. The digesters take water and organic materials, like animal dung, asinputs and produce two main outputs: methane gas that can be used for cooking and lightingand a remaining slurry that can be used a relatively high quality fertilizer (Surendra, Takara,Hashimoto, & Khanal, 2014; Weiland, 2010). Given the potential of this technology toalleviate both energy access problems and produce a quality agriculture input, the Senegalesegovernment launched a program, the Projet National de Biogaz du Senegal (PNB-SN), or theNational Biogas Project of Senegal, to promote installation and use of biogas digesters amongstrural, agricultural households. This chapter evaluates this program, using administrative andsurvey data to track the operation of installed digesters and estimate the overall impact ofthe program on the targeted energy, health, agricultural, and economic outcomes.To evaluate digester operation, we consider households who received a biogas digestervia the PNB-SN. We find that most biogas digesters were never operating at a level sufficient toproduce gas for cooking or lighting and, even amongst those that did become fully operational,most had fallen into disrepair after at least a year. We next investigate determinants ofdigester operation and find that offering training and ensuring access to repair services maybe able to increase operability. Third, in evaluating the impacts of the PNB-SN, we comparehouseholds who received a digester with a sample of households who had been deemed eligibleto participate in the program but, because of logistical or budget constraints, had not yetreceived a digester installation at the time of our survey. Not surprisingly given the highfailure rates, we find no impact of digester installation on energy, health, or productivityoutcomes. Finally, we offer suggestive evidence that even operational digesters3 are nothaving any impacts on the targeted outcomes.43Here we define a digester as operational if it was functioning for at least half of its life since installation.4Given our relatively small sample, this analysis is limited in the effects it could detect. We may not havea large enough sample size to detect small impacts from at least partially operational digesters.6Our evaluation shows that the PNB-SN has not been successful at alleviating energypoverty and thus, has not had an impact on its targeted outcomes. Administrative data fromthe program indicates that each digester installation costs around 750 USD (ProgrammeNational De Biogaz Domestique Du Sénégal, n.d.). Given the lack of impacts identified inour study, the returns to this investment are likely very low, if not zero. Our analyses of theoperating digesters suggests that offering more training or improving access to repair servicescould improve digester operation. But these steps would likely increase program costs andmay not increase impacts by enough to cover these costs on top of the already high initialcosts. Taken together, our evaluation shows that the PNB-SN in its current form is not a costeffective way to target energy poverty and its associated environmental and socioeconomiccosts in Senegal.7Chapter 2Toxic RecyclingThe Cost of Used Lead-Acid Battery Processing in Mexico12.1 IntroductionThere is no safe level of lead pollution exposure; even small exposures can have damaging healthand developmental effects (Centers for Disease Control and Prevention, 2013).2 Children areespecially vulnerable due to greater lead absorption and developmental disruption (Rees &Fuller, 2020). Empirical evidence from rich countries highlights the exposure consequences forchildren, including reduced IQ, diminished educational achievement, and increased antisocialbehaviors (Ferrie, Rolf, & Troesken, 2012; Reyes, 2015; Aizer & Currie, 2019; Gronqvist,Nilsson, & Robling, 2020).3 Regulatory measures like removing lead from gasoline, banninglead in paint, and setting ambient pollution standards, have been successful at reducingexposure, especially in rich countries.4 However, these same measures can unintentionally shiftlead pollution to lower-income countries with weaker environmental regulations. Significantefforts have been put towards documenting when this shifting of pollution may take place,e.g. Copeland et al. (2022), but there has been little focus on the effects of this relocation onrecipient communities.1This chapter is joint work with Bianca Cecato, Mauricio Romero, and Tatiana Zárate Barrera.2Lead is a neurotoxin; it affects cardiovascular and renal health; and, in some cases, exposure can be lethal(Wani, Ara, & Usmani, 2015).3Other relevant papers include Aizer et al. (2018); Sorensen, Fox, Jun, and Martin (2019); Gazze, Persico,and Spirovska (in press); Zheng (2021); Hollingsworth et al. (2022).4For example, the U.S. reduced airborne lead pollution by about 90 percent between 1980 and 2016 (U.S.Environmental Protection Agency, 2014) and continues to make progress on reducing lead exposure.8We study the effects of one such case — the relocation of a lead-intensive and pollutingindustry, used lead-acid battery (ULAB) recycling, from the U.S. to Mexico. We estimatethe costs of this relocation of pollution, specifically those related to educational achievement,on recipient communities. In 2009, the U.S. Environmental Protection Agency tightenedregulatory standards for lead, successfully reducing airborne lead concentrations aroundbattery recycling plants in the U.S. and prompting ULAB recycling activities to shift toMexico (Tanaka et al., 2022). We identify children living near Mexican recycling plantsbetween 2006 and 2013 and estimate the effect of this influx of lead-intensive industrialactivity and its associated pollution on their learning.Previous studies have estimated the causal effects of lead exposure on learning, IQ,and other educational outcomes in rich countries like the United States, e.g. Sorensen etal. (2019), and Sweden, e.g. Gronqvist et al. (2020). These findings may underestimate thecosts of pollution exposure in low- and middle-income countries if families in lower-incomesettings are less able to counteract the effects of lead exposure via, for example, increasingeducational inputs, receiving medical treatments, or engaging in pollution averting behaviors.Heterogeneity analyses in rich country settings suggest that the effects of pollution can beworse in settings with higher poverty (Hollingsworth et al., 2022).To identify causal effects in Mexico, we use a difference-in-difference (DID) design,comparing children attending schools close to and farther away from battery recycling sites,before and after the 2009 U.S. policy change that shifted battery recycling activities toMexico.5 From our data sources we know the locations of 26 authorized battery recyclingfacilities and the locations of all schools in Mexico. We measure academic achievement usingstudent-level panel data from a nationwide standardized exam that tested math and Spanishproficiency between 2006 and 2013. We observe the universe of students tested in this exam,which, at its peak, administered tests to almost 14 million students each year.5There is no lead pollution monitoring network across Mexico. Thus, we cannot study the direct impactof the policy change in 2009 on lead exposure and lead levels. Instead, we focus on the reduced form effect ofthe policy shift in 2009 on outcomes for residents near the recycling plants in Mexico.9We find that exposure to lead-battery recycling reduces academic achievement. Thepost-2009 increase in recycling activity negatively affects learning for students attendingschools within 2-miles of a battery recycling plant. Math test scores at these schools decreasedbetween 0.05-0.09 standard deviations (σ) after 2009, relative to students in schools fartheraway but in the same state. For Spanish, we also estimate negative, though slightly smaller,effects (0.05-0.07σ). Event study results show that the pre-2009 trends in test scores donot differ by distance to a recycling plant (supporting the identification assumption of thedifference-in-difference design). Further, the event studies suggest the effects of the increasein lead pollution worsen over time.The results are robust to alternative specifications that guard against other determi-nants of learning that may also vary with distance to the recycling plant. For example, factorssuch as growth in labor demand, which could have accompanied the post-2009 increase inrecycling activity, could affect wages and local economic activity near the plants. This could,in turn, affect household incomes and children’s education. We can test for this by definingexposed schools based not just on distance but also on prevailing wind direction. The additionof wind direction helps us more precisely identify lead exposed students as environmentalstudies of lead pollution around battery recycling plants and other lead smelters show thatpollution levels correlate with prevailing winds (Ettler, 2016). Comparing test scores atschools nearby and downwind from recycling facilities to those upwind and/or farther awaywe find similar and slightly larger effect sizes (-0.09-0.11σ for math, -0.09-0.10σ for Spanish).Another concern is selective sorting, based on both the post-2009 increase in pollutionand the potential economic changes accompanying the increase in battery recycling. Theincrease in battery recycling could induce migration if families either move away from the areadue to increased pollution or move towards the area because of new economic opportunities.If movers are not randomly drawn from the ability distribution, this could bias our results.We can directly observe moving in our student-level panel: we know the school each studentattended in a given year and the location of that school. To test whether selective migration10is driving our results we assign the post-2009 exposure variable, indicating whether a studentis studying near a battery recycling facility, based on their 2008 location. In this way, thisinference method is similar to an “intent-to-treat” design. Our results are robust to thesespecifications, though slightly smaller (-0.06-0.11σ for math, -0.03-0.07σ for Spanish).Finally, we consider whether and how lead exposure affects different individuals andcommunities. Our finding that effects of exposure worsen over time could be driven byincreasing pollution levels or differences in effects based on student’s age (at first exposure).By estimating dynamic effects separately for each grade, we can isolate age effects fromincreasing or chronic pollution effects. Consistent with the medical literature (Lidsky &Schneider, 2003), effects appear to be stronger for students first exposed at younger ages (i.e.,those in kindergarten or younger as of 2009, relative to those who were older).We can also test for heterogeneity in effects across communities. A key strengthof our data and setting is that we observe the change in lead-emitting industrial activityacross many locations, as compared to other studies that focus on one polluted site (Rau etal., 2015). This allows us to explore how effects vary across different types of communities.We use census data to measure baseline socioeconomic characteristics in the communitieswhere schools are located. We find that, across multiple measures of socioeconomic welfare,the negative effects of the post-2009 increase in battery recycling are stronger in worse-offcommunities. For example, we can compare effects in communities with different levels ofmarginalization, defined by the Mexican government as a composite measure of literacyrates, educational attainment, housing quality, infrastructure access, and incomes. In moremarginalized communities, with above median values of the marginalization index, we estimatea -0.09σ effect on math scores (t-statistic of 3.70) but, in below median communities, a lesssignificant -0.03σ effect (t-statistic of 1.75). We find qualitatively similar results comparingeffects across communities with above and below median adult education levels and formalemployment rates. These results suggest that lead exposure may exacerbate inequality ineducational achievement.11What can be done, from a policy perspective, to address these effects? One option isto increase regulatory stringency and/or enforcement to reduce lead emissions from batteryrecycling facilities. In section 2.5, we estimate the cost of the education effects we identifyhere. In terms of lost lifetime earnings for exposed students, the costs fall in the range of 16to 39 million USD annually. This estimate ignores the other health costs associated withlead pollution and likely outweighs the expected costs of lead pollution control technology.This study offers two main contributions to the existing social science literature onthe effects of lead pollution exposure. First, few papers have assessed the causal effects oflead exposure in low- to middle-income countries. A notable exception is a study by Rau etal. (2015) who use data from the city of Arica in northern Chile to study the relationshipbetween school proximity to toxic lead waste and students’ scores in a standardized test forfourth graders and a college entrance exam.6 They find that exposure to this hazardouswaste strongly and negatively affects test scores and estimate resulting lost lifetime earningsof 60,000 USD. Findings specific to contexts outside of the Global North are important,as that is where the vast majority of lead pollution exposure and associated costs occurtoday (Larsen & Sánchez-Triana, 2023). Effects could differ in these settings, relative to richcountries, for multiple reasons, including higher overall emissions and weaker health andeducational institutions to mitigate the effects of exposure. Additionally, location-specificestimates of pollution costs are important for regulatory design. Our study allows us toobserve the total effects of one lead pollution exposure channel in a middle-income countryand estimate heterogeneity across community-level characteristics. Even within Mexico,effects are concentrated in more marginalized communities, suggesting underlying levels ofdevelopment matter when estimating the costs of lead pollution exposure and that estimatesfrom rich countries may underestimate costs in less developed settings.Second, from a methodological perspective, previous studies have primarily reliedon cross-sectional data from a single grade or educational data aggreagted at the grade or6Tanaka et al. (2022) study the effects of ULAB recycling activity in Mexico on maternal and infant healthoutcomes.12school level to study the effects of lead exposure on educational outcomes (Rau et al., 2015;Aizer et al., 2018; Zheng, 2021; Gronqvist et al., 2020; C. Persico et al., 2020; Jacqz, 2022;Hollingsworth et al., 2022). In contrast, our paper tracks students’ academic performanceover several years to estimate the impact of lead pollution on cognitive development, asproxied by math and Spanish test scores. Hence, the longitudinal structure of our data allowsus to address the issue of student sorting as a confounding factor, a common challenge inthe existing literature (Rau et al., 2015).7 We also expand on this literature by focusing ontest scores for grades 3-6 and grade 9 from 2006 to 2013, providing a more robust measure ofcognitive development by capturing changes in individual students’ academic achievementover time.2.2 Relevant Background Information2.2.1 Lead-acid battery recyclingThe production of lead-acid batteries accounts for over 80 percent of the total demand for leadworldwide (International Lead and Zinc Study Group, 2019), reflecting the growing marketdriven by the rise in vehicle ownership in low- to middle-income countries and the globaldemand for energy storage solutions (Rees & Fuller, 2020). At the same time, lead demand isprimarily met through secondary lead, or lead that comes from recycling, instead of primarylead that is mined. Used lead acid batteries (ULABs) have one of the highest end-of-liferecycling rates (Alistair, Binks, & Gediga, 2016), and almost all lead found in them can bereclaimed.8 Still, recycling lead is dangerous and can be heavily polluting if strict protocolsand advanced technology are not used to control lead emissions during recycling (Ballantyne,Hallett, Riley, Shah, & Payne, 2018). Lead from recycling can enter the environment through7Student sorting can occur when there is selection into schools subject to environmental toxicants fromlead-recycling plants.8Besides, recycling the lead in used lead-acid batteries is a cost-effective alternative to mining it, furthercontributing to high recycling rates.13a number of pathways. Lead laden gases and dust can escape during handling, separation,smelting, and refining (U.S. Environmental Protection Agency, 1995). Lead can also runinto the environment through water discharges or dumping of solid wastes. Moreover, whereworker safety protocols are non-existent or not enforced, workers can expose themselves andtheir families to lead contamination by bringing lead dust home on their clothes and skin(WHO, 2017). Thus, improper management of even small-scale lead recycling operations cancause environmental contamination and severely threaten human health (Commission forEnvironmental Cooperation, 2013; WHO, 2017).Pollution and toxicology researchers have long documented the contamination that isemitted from used lead acid battery recycling sites. At two sites in Indonesia, both formaland informal, surface soil concentrations of lead were found to be as much as ten times higherthan background levels, with concentrations decreasing with distance (Adventini, Santoso,Lestiani, Syahfitri, & Rixson, 2017). Airborne concentrations of lead in these areas wasprimarily attributed to industrial activity (Santoso et al., 2011). Samples of dust aroundthree heavy metal processing (smelting and refining) sites in Mexico found concentrations oflead to be in excess of U.S. standards with concentrations also associated with distance tothe facility’s emissions stack and prevailing wind direction (Benin, Sargent, Dalton, & Roda,1999).9 Studies have also focused on more direct exposure measures, investigating lead levelsin blood and teeth of those living around these sites. At a battery recycling facility in China,the median child had a blood lead level of 8.9 micrograms per deciliter, compared to thecurrent U.S. standard of 3.5 micrograms per deciliter (Zhang et al., 2016).10 Elevated bloodlead levels were more likely among younger children, male children, those living closer tothe recycling facility, and those who had at least one parent working in the facility. A 20119The author’s of this study use Superfund cleanup site goals of 200-500 parts per million (ppm) as thestandard of comparison.10This U.S. non-regulatory standard set by the Center for Disease Control is known as the blood leadreference value. Children with blood lead levels above this reference value are recommended to receiveintervention such as reporting the result to local health authorities, conducting an exposure assessment of thechild’s home, and monitoring child’s nutrition and development milestones (Centers for Disease Control andPrevention, 2022).14review of studies documenting lead exposure around lead acid battery manufacturing andrecycling plants in developing countries found that the average child living near a facility hada blood lead level of 29 micrograms per deciliter, significantly higher than the U.S. referencevalue of 3.5 (Gottesfeld & Pokhrel, 2011). Tested children living around an informal batteryrecycling facility in Bihar, India were all found to have lead in their blood, with the meanconcentration at 24 micrograms per deciliter (Ansari, Mahdi, Malik, & Jafar, 2020). Even inmore regulated settings like the U.S., battery recycling facilities are associated with elevatedlead in the body. A 1977 study of children with a parent working in a battery recycling plantin Tennessee found that almost half had blood lead levels above 25 micrograms per deciliter(Baker Jr et al., 1977). Lead exposure was more recently detected around a lead smelter thatprocessed secondary lead from used batteries in Los Angeles, California (Johnston, Franklin,Roh, Austin, & Arora, 2019). Pre- and post-natal lead exposure was detected in shed babyteeth from children who lived within two miles of the smelter during it’s time of operation.11In our study we focus on used lead acid battery recycling in Mexico and study theperiod between 2006 and 2013. The battery recycling industry is Mexico is made up of bothformal and informal firms, though due to the increasingly competitive nature of sourcing usedlead acid batteries, it is expected that the majority of the small, informal operations haveclosed (Commission for Environmental Cooperation, 2013). As of 2014, 26 known formalrecycling facilities were in operation (see Figure 2.1) and were estimated to be producing401,151 tons of recycled lead annually (Tanaka et al., 2022; Commission for EnvironmentalCooperation, 2013). In Mexico, the Secretary of the Environment and Natural Resources(Secretaria del Medio Ambiente Recursos Naturales or SEMARNAT) regulates airbornelead concentrations; the standard during our study period was 1.5 micrograms per cubicmeter. Companies are also required to report emissions of toxic materials via the Registryof Emissions and Transfers of Contaminants (Registro de Emisiones y Transferencia de11Lead concentrations in teeth is not a commonly taken measure, so more difficult than blood concentrationto compare to population levels or standards. The authors of this study cite that they find higher concentrationsin their sample than those documented in a similar study in Sweden.15Contaminantes or RETC) program). Enforcement of these regulations has been noted to beincomplete and inconsistent (Gottesfeld, Chavez Arce, & Macias Raya, 2023). More thanhalf of the registered recycling facilities in Mexico did not report emissions during our studyperiod and air quality monitoring outside of Mexico City is almost non-existent (Commissionfor Environmental Cooperation, 2013). In addition, the country does not have standardsaround the construction and operation of battery recycling plants, including emissions fromstacks (Commission for Environmental Cooperation, 2013). These loopholes in regulationare exacerbated because technologies to reduce lead emissions from battery recycling plantsare costly, and, without regulation, it is unlikely recycling plants in Mexico will adopt them.Consequently, emissions from Mexican recycling plants are estimated to be 20 times higherthan those of their US counterparts (OK International & Fronteras Comunes, 2011). Further,labor protection regulations in Mexico permit exposure levels that are three times higherthan in the US (OK International & Fronteras Comunes, 2011).To study the effects of the pollution from the 26 facilities, we rely on an increase intheir operations that was caused by a 2009 U.S. regulatory change. In January 2009, theU.S. Environmental Protection Agency reduced the air quality standard for lead from 1.5µg/m3 to 0.15 µg/m3.12 This regulation made it more difficult and expensive to recycle usedlead-acid batteries domestically, pushing U.S. recycling facilities to close or shift activities toMexico. Tanaka et al. (2022) document this shift, showing that the U.S. regulatory changedrastically increased used battery exports from the U.S. to Mexico and increased the valueof the lead battery recycling industry in Mexico, relative to other, similar industrial sectors.It’s estimated that, as of 2013, 30-60 percent of all used batteries processed in Mexico camefrom the U.S. (Commission for Environmental Cooperation, 2013).12While signed on May 2008, it became effective on January 2009.162.2.2 Health effects of lead pollution exposureLead poses a toxic threat regardless of how it enters the body. According to the U.S. Centerfor Disease Control Centers for Disease Control and Prevention (2013), there is no safe bloodlead level, making any exposure hazardous, especially for fetuses, infants, and children. Athigh levels, lead can be lethal (WHO, 2017). At low levels, it can harm human health, actingas a cumulative toxic substance that impacts various organs and systems. The nervoussystem is the most susceptible as the brain’s prefrontal cortex is particularly vulnerable tolead exposure. Hence, lead exposure can hinder a child’s brain development resulting inlearning disabilities, lower educational achievement, and even antisocial behaviors later inlife (Ferrie et al., 2012; Reyes, 2015; Rau et al., 2015; Billings & Schnepel, 2018; Aizer et al.,2018; Aizer & Currie, 2019; Sorensen et al., 2019; Gronqvist et al., 2020; C. Persico et al.,2020; C. Persico, 2022; Gazze et al., in press). In response, most countries have banned leadfrom everyday products such as gasoline, toys, and paint. Between 1970 and 2000, this led toa significant reduction in lead exposure and pollution.2.2.3 The Mexican Education SystemThe Mexican education system is based on a federal model, regulated by the Ministry ofEducation (Secreataria de Educacion Publica or SEP), while individual states are responsiblefor administration and implementation. School days are shorter than in other countries;there are two sessions each day to accommodate numerous students. These are divided intomorning (8:00 am to 12:30 pm) and afternoon (2:00 pm to 6:30 pm) sessions. While thesetwo sessions take place within the same physical school, there are essentially two differentoperations, with different teachers, principals, etc.13The system is divided into three main levels: basic, upper secondary, and higher13Starting in 2007, Mexico began rolling out the Full-time Schools Program (Programa Escuelas de TiempoCompleto or PETC) which increased the school day from 4.5 to 6-8 hours (Cabrera-Hernández, Padilla-Romo,& Peluffo, 2023). The program eventually reached more than 25,000 schools before being terminated in 2022.We do not expect this program to correlate with battery recycling plant locations, so it is not a threat to ourcausal estimates.17education. Basic education, which includes preschool, primary, and lower secondary education,is compulsory and free for all students. Preschool lasts three years, primary lasts six years,from grades 1 to 6, and lower secondary lasts from grades 7 to 9. Upper secondary education(grades 9-12) provides students with specialized academic and vocational training. It wasnot compulsory during the majority of our study period but is available to all students whohave completed lower secondary education. Upper secondary became compulsory in 2012(Santiago, McGregor, Nusche, Ravela, & Toledo, 2012), but it is the parents responsibility tomake sure their children complete grade 12 and enforcement is variable across states.Public schools account for about 89 percent of the enrollment.14 By 2013, schoolenrollment accounted for more than 25 million students across 243,000 schools. This includedabout 4 million in preschool, 14.1 million in primary education, and 7.3 million in lowersecondary education.The Mexican education system has undergone significant reforms in the last twodecades, focusing on improving the quality of teaching and learning and increasing accessto education. These reforms have included changes to teacher training and evaluation,introducing new curricula and teaching materials, and developing of standardized tests toassess student learning, such as the National Assessment of Academic Achievement in School(ENLACE) whose data we use here.2.3 DataFor our analyses, we rely on the following sources of data.14When attending public schools, students usually enroll in the closest one to their homes. Students’parents send a list of three desired schools for preschool and primary school; for secondary education, theysend five schools. Then, the schools are assigned based on two criteria: 1) siblings attending the school and2) household address.182.3.1 Used Lead-Acid Battery Recycling (ULABs) FacilitiesTo identify the effects of lead exposure from battery recycling we rely on a list of 26 usedlead-acid battery recycling facilities in Mexico (see Figure 2.1 for the location of the plants).These facilities were identified and geo-referenced by Tanaka et al. (2022). The authorsidentified facilities from three main sources: (1) a report published by the Commission forEnvironmental Cooperation (Commission for Environmental Cooperation, 2013); (2) fromMexico’s Registry of Emission and Transfer of Contaminants, with which plants in Mexicanindustries that emit select pollutants are required to register;15 and (3) from a 2014 listof approved battery recyclers from Mexico’s Secretariat of the Environment and NaturalResources. Given this search method, we include only authorized battery-recycling plants inthe sample and our analyses will only address lead pollution from these sources and ignoreany lead pollution emitted from informal battery recyclers.162.3.2 Wind directionWe accessed wind direction data from the Global Wind Atlas.17 The Global Wind Atlasreports microscale modeled wind climate data at a 3 kilometer resolution grid. For each gridcell the atlas reports average windspeed and direction. For each of the 26 battery recyclingfacilities on the list described above, we extracted the 100 meter wind frequency rose data forthe grid cell in which the facility lies. These wind frequency roses report how often the windis blowing from a given direction at 100 meters above ground level. The 360 degrees around15Unfortunately, we cannot use the reported emissions from Registry of Emission and Transfer of Contami-nants as a reliable measure of lead emissions from ULAB facilities in Mexico. Environmental organizationshave reported that many large recycling facilities do not report to the Registry of Emission and Transferof Contaminants Gottesfeld et al. (2023). We can confirm this with our checking of the data, finding thatmany facilities do not report to the registry and, for those that do, the reported emissions data has manyirreconcilable errors.16In other countries informal recycling, done without regulatory oversight and often located in residentialareas, is common and can drastically increase lead exposure (Haefliger et al., 2009; Ericson et al., 2016).17Global Wind Atlas 3.0 is a free, web-based application developed, owned and operated by the TechnicalUniversity of Denmark (DTU). The Global Wind Atlas 3.0 is released in partnership with the World BankGroup, utilizing data provided by Vortex, using funding provided by the Energy Sector Management AssistanceProgram (ESMAP). For additional information visit https://globalwindatlas.info.19a point is divided into 12, 30-degree sectors, and the share of time the wind is blowing agiven sector is reported.2.3.3 Test Scores from the National Assessment of AcademicAchievement in Schools (ENLACE)Our primary outcomes of interest, math and Spanish test scores, come from a nationalstandardized test, the National Assessment of Academic Achievement in Schools (EvaluaciónNacional del Logro Académico en Centros Escolares or ENLACE). This test was a nationwidetest designed to evaluate students’ academic performance in public and private schools. It wasa low-stakes test for students, having no influence on GPA, graduation, or admissions to anyschool. The exam was developed and administered nationwide by the Ministry of Education(Secreataria de Educacion Publica or SEP), with oversight from the National Institute ofEducation Evaluation (Instituto Nacional para la Evaluacion de la Educacion or INEE). Itaimed to provide information on the quality of education in Mexico, measure the effectivenessof educational policies and practices, and offer parents and educators information about theperformance of students and schools.ENLACE was implemented for the first time in 2006 and was administered annuallyuntil 2013 to all public and private elementary school students in grades 3 to 6, as wellas to students in grade 9.18 From 2009 to 2013 it was also administered to students ingrades 7 and 8 and, from 2008 to 2013, to students in grade 12. The exam tested studentsin Spanish, mathematics, and a rotating subject. It consisted of 50 to 70 multiple-choicequestions per subject applied in 45-min sessions over two days. Questions were designed toevaluate different levels of cognitive complexity, ranging from simple recall of information toanalysis and synthesis. The exam was usually administered in April, and the results weredisseminated to parents, educators, and schools in August of the same year.The test itself was designed in the following way. To ensure comparability of scores18The exam was discontinued in 2013 as part of the education reform undertaken by the 2012-18 government.20across different exam versions, SEP first applied a set of questions to all students and a controlgroup. Then, they used a calibration process called Test Equating based on Item ResponseTheory, which determined scores by the number of correct answers and by which questionswere answered correctly.19 After calibration, the student’s scores were standardized on a scaleof 200 to 800, with a mean of 500 and a standard deviation of 100 for each subject and grade.This allowed for an accurate comparison of scores across different exam administrations foreach subject and grade.We observe math and Spanish scores for 30,358,688 individual students in 151,884schools across the country from 2006 to 2013 (a total of 91,707,458 observations). These datacorrespond to the universe (after removing inconsistent entries) of students who sat for theENLACE test during this time. See Figure 2.2 for a plot of the number of students tested ineach grade and year. These data, apart from a few exceptions where the exam could not takeplace for various reasons, corresponds to all students in Mexico who were eligible for the testin a given year.For each student-year observation the data contains:• An identifier of the school in which the student was enrolled and took the test;• A student identifier that is a scrambled version of the student’s national identificationnumber, the Unique Population Registry Code (Clave Única de Registro de Poblaciónor CURP), so we are able to track individual students over time;• An indicator of the grade in which the student was enrolled at the time of the test; and• An indicator of the school session, AM or PM, in which the student was enrolled at thetime of the test.19Test equating is a statistical method that adjusts for differences in the difficulty level of different versionsof each exam so that scores are comparable across years. The model considers the level of difficulty, guessing,and discrimination assigning a score for each student.212.3.4 Location of schoolsWe retrieved the location of all schools in Mexico from the National Institute of EducationEvaluation (Instituto Nacional para la Evaluacion de la Educacion or INEE).20 Specifically wedownloaded the Comprehensive Assessment Results System (Sistema Integral de Resultadosde las Evaluaciones or SIRE) database for each state and extracted the GPS coordinates foreach school in the database. This database was last updated for the 2016-17 school year, somay not contain schools that were operating during the ENLACE testing period but havesince closed.212.3.5 Locality and municipality characteristicsFor the heterogeneity analysis described in section 2.4.3, we use locality and municipalitycharacteristics from various sources. Localities are the second smallest administrative unitin Mexico, just above block, and correspond roughly to a neighborhood. Municipalities arelarger administrative units, between localities and states. From the school location datadescribed above, we can link each school to it’s corresponding locality and municipality. Weget locality and municipality level demographic and socioeconomic information from variousstatistical archives.22 The variables we use include adult education levels (years of completededucation for individuals older than 15), access to infrastructure (percent of households inlocality with access to sewerage), rates of social security access, malnutrition rates, localincome inequality (Gini index), and overall marginalization.2320We downloaded the SIRE database for each state from this url:https://www.inee.edu.mx/evaluaciones/sire/sire-bases-de-datos/. We last downloaded it in July2023.21The downloaded SIRE database contained location data for 88 percent of the schools that are present inthe test score, or ENLACE, data.22Locality-level measures come from a 2005 population enumeration from Mexico’s National PopulationCouncil (Consejo Nacional de Poblaci ón or CONAPO) (Instituto Nacional de Estadística y Geografía, 2005).Municipality characteristics come from the National Council for Evaluation of Social Development Policy(Consejo Nacional de Evaluación de la Política de Desarrollo Social or CONEVAL) and the National Popula-tion Council (Consejo Nacional de Población or CONAPO) (Consejo Nacional de Poblaci ón (CONAPO),2005; Consejo Nacional de Evaluación de la Política de Desarrollo Social (CONEVAL), 2005).23The marginalization index is calculated by Mexico’s Consejo Nacional de Población, or the NationalPopulation Council, and uses principal component analysis to create an index of marginalization from measures222.4 Estimating the Effects of Lead Exposure on TestScoresWe now come to estimating the average impact of lead-acid battery recycling on students’academic performance. Tanaka et al. (2022) showed that after the US EPA strengthenedtheir air quality standard for lead in 2009, used lead-acid battery exports from the US toMexico sharply increased relative to previous years. The value-added and output in Mexicanbattery-recycling plants also increased discontinuously relative to other, similar industries.We investigate the potential effects of this increased battery recycling activity on academicperformance in schools close to these recycling plants, where students can be directly exposedto lead contamination primarily through the air and top soil.2.4.1 Defining lead exposed studentsThe gold standard for measuring lead pollution exposure is a biological measure of lead inthe body. This can be done via testing blood to measure lead concentrations from recentexposure, e.g. Gazze et al. (in press), or testing bones or teeth, for example shed babyteeth a la Johnston et al. (2019), to measure longer term lead exposure. These tests provideindividual measures of lead exposure. When these measures aren’t available, researchers canalso use environmental pollution data as a proxy for lead exposure, e.g. Hollingsworth andRudik (2021). Lead in the air, soil, or water is a likely indicator that populations living,studying, or working in the polluted area have been exposed. Unfortunately, Mexico did nothave a lead pollution monitoring network nor did they systematically collect bio-markers forlead exposure during our study period.24 To overcome this, we rely on the battery recyclingfacility location, school location, and wind direction data described in Section 2.3.related to education and illiteracy, housing characteristics (like access to electricity and overcrowding), size ofpopulation, and income.24The 2018 National Health and Nutrition Survey (Encuesta Nacional de Salud y Nutrición or ENSANUT)did include blood lead measures for a subset of the population. We are currently in the process of accessingthese geocoded exposure measures to validate the location based lead exposure measures we describe below.23The first measure we use to proxy for lead exposure is the distance between batteryrecycling facilities and the primary and lower secondary schools (i.e. schools where theENLACE math and Spanish testing took place). We define students as exposed to leadpollution from ULAB recycling in a given year if they attend a school within 2 miles of abattery recycling facility in that year. Figure 2.3 illustrates this process. In each panel abattery recycling facility is shown as a red point and primary and lower secondary schools areshown as black points. The red circle around the recycling facility has a two mile radius. Wedefine all schools within this red circle as "near" the recycling facility and, therefore, exposedto lead pollution from the facility. This two mile radius is supported by an analysis of batteryrecycling facilities in the United States, where a network of lead pollution monitors allows usto measure how far airborne lead pollution travels. In Figure 2.4, recreated from Tanaka etal. (2022), we plot in blue the airborne lead concentrations around more heavily pollutingplants in the U.S., i.e. those plants who were required to reduce their pollution to complywith the 2009 updated NAAQS for lead. The figure shows how airborne lead concentrationsdrop steeply at monitors 3 or more miles from a ULAB facility. The two mile cut-off is alsosupported by other U.S.-based studies which identify contamination within a 1.5 to a 2-mileradius around airports, where pollution comes from leaded aviation fuel, and lead smelters(Miranda, Anthopolos, & Hastings, 2011; Currie, Lucas, Greenstone, & Walker, 2015). Thisdistance from a pollution source as a proxy for exposure has been used by other researchers,e.g. C. L. Persico and Venator (2021), to study many types of toxic pollutant exposure.We can additionally proxy for lead pollution exposure using wind direction. Envi-ronmental scientists have shown that environmental lead concentrations, often tested in topsoil, and individual exposure around known lead pollution sites are correlated with prevailingwinds (Karali, Stavridis, Loupa, & Rapsomanikis, 2020; Johnston et al., 2019; Zhang et al.,2016; Lidsky & Schneider, 2003). Winds blow the pollution downwind of the source, where itsettles in the top soil. Given this, we can further refine our definition of lead exposed studentsby identifying schools that are both near to a battery recycling facility, as defined above, and24downwind of the prevailing winds. We define the prevailing wind direction as the 30-degreesector from which the wind blows the highest percentage of the time. Downwind schools arethen considered as those that fall within a 30 degree cone that is directly opposite, plus orminus 180 degrees from, the prevailing wind cone. For example, if the wind most frequentlyblows directly from the North, the downwind cone would be defined from 165 degrees (15degrees East of South) to 195 degrees (15 degrees West of South). All schools in this cone arethen considered downwind schools. We also test the robustness of this definition to wideningthe downwind cone. This method focuses on lead pollution that is emitted into the air anddispersed by the wind. It ignores other potential pathways of lead exposure, for examplethrough transport routes where lead materials are brought in and out of the facilities, viathe clothing of battery recycling facility staff who can bring lead pollution home on the skin,hair, or clothing, or via waterways if waste is dumped or leeches into surface or ground water.Therefore, our comparison of students who attend downwind versus more upwind schools mayresult in a lower bound estimate of the effect of lead exposure from the post-2009 increasein battery recycling activity, as even students at upwind schools may be exposed to leadpollution from the facility via non-air pathways.2.4.2 Empirical StrategyMain specificationsFor our primary analysis, we construct a yearly panel of school-session-grade observations.We calculate the mean standardized Spanish and math test scores as well as proficiency levels,described below, in each school-session-grade. We adopt a difference in difference strategy,where we compare the test score trends over time between session-grades in schools nearby,fewer than 2 miles from, a battery recycling facility and those more than 2 miles from afacility but within the same state. To provide clarity, imagine two primary schools. Eachhave a morning session where they teach students in grades 3 to 6. School A is 1 mile fromthe nearest recycling facility. School B is 5 miles away from the same facility. Both schools25are small enough to only have one classroom per grade. In this method we would identifythe effect of the post-2009 increase in lead pollution from the recycling facility by comparingthe 3rd grade classroom’s test scores at School A with those at School B before and after the2009 U.S. policy change.Empirically, this method translates to the following specification:ysegt = αseg + δt + γp(s)t + β(Nears × Postt) + εsegt (2.1)where ysegt represents a test score outcome for school s - session e - grade g in yeart. We evaluate two outcomes for math and Spanish: the average standardized ENLACEtest score25 and the percentage of students proficient in each subject.26 Nears is a dummyvariable that equals one if school s is located within two miles from the nearest lead-recyclingplant p(s), and zero if the school s is farther away from it – 502 schools are within this twomile distance from a recycling facility. The Postt indicator takes the value 0 before 2009and the value 1 in 2009 and thereafter. The terms αseg and δt represent school-session-gradeand year fixed effects respectively, making this a two-way fixed effects specification thataccounts for common shocks in any year and time-invariant differences across classrooms.Additionally, in our preferred specification, we include plant-by-year fixed effects, γp(s)t. Thesefixed effects account for differential trends in school-session-grades’ achievement around eachplant, essentially accounting for the fact that schools around, for example, a plant in an urbanarea may be fundamentally different and evolve differently over time than schools aroundplants in a more rural area. In the results below we also test the robustness of our resultsto varying fixed effects. For example, we can include a grade g by year t fixed effect thatallows test scores trends to vary by grade. Our coefficient of interest is β, which captures thedifferential effect of increased lead exposure due to the U.S. environmental regulation on test25We first standardized student’s test scores by grade and year, then we take the average of these testscores in each school-session-grade-year.26Proficiency is evaluated in four achievement levels. Level 4 contains the highest-achieving students, whilelevel 1 contains the lowest. We define students in levels 3 and 4 as proficient.26scores at schools within 0–2 miles from the closest battery-recycling plant. We restrict ouranalysis to school-session-grades that we observe at least once before 2009 and at least oncein 2009 or later. Standard errors are clustered at the school level, as this is the level at whichthe lead exposure indicator is defined.The identification assumption is that in the absence of this 2009 policy change andconditional on the fixed effects included, classrooms near and far from their closest plantwould have exhibited parallel trends in test scores. We test how plausible this assumption isby running event studies of the form:ysegt = αseg + δt + γp(s)t +2008∑τ=2006βτ (Nears × 1(t = τ))+2013∑τ=2010βτ (Nears × 1(t = τ)) + εsegt(2.2)where βτ captures the effect of being close to a battery-recycling plant on students’scores τ periods before or after the US environmental reform. This specification allows us totest directly if there are any differential trends before 2009, which would suggest that theparallel trends assumption may be violated. We expect all the βτ ’s in the pre-2009 years to beindistinguishable from zero, which would be consistent with the parallel trends assumption.Addressing other identification concernsThe primary threat to our identification comes from time varying changes other than leadpollution that may be affecting schools close to battery recycling facilities deferentially relativeto those farther away. One such concern is increased economic activity. The ramp up ofULAB recycling activity after 2009 likely also caused an increase in employment at theseplants, not just an increase in lead pollution. These economic effects are likely strongerin areas closer to plants. While we do not observe employment at the facilities, so cannotdirectly test for this, we do see that schools within two miles of a recycling facility grow by5-10 students after 2009, compared to schools farther away. This could reflect families moving27into the area to take advantage of job opportunities, and this change in student compositioncould be driving the effects we estimate. We can address this concern in two ways.First, we can use wind direction to differentiate between upwind and down windschools. To do this we define a specification that is similar to equation 2.1, but interactingthe Near indicator with an indicator equal to 1 if a school is downwind from, as described inSection 2.4.1, a battery recycling facility. The environmental science literature has shownthat lead pollution moves with prevailing winds, but we do not expect the economic effects ofa post-2009 ramp up at a recycling facility to varying based on bearing from a plant relativeto prevailing winds. In this specification the exposed group is made up of those schools wherestudents are exposed to both the increased pollution and economic activity, whereas thenon-exposed group, including nearby upwind schools, is made up of students who are exposedto the change in economic activity but not, or at least relatively less, exposed to the leadpollution.We can also take advantage of the student-level data to account for migration thatmay be happening in response to the 2009 U.S. policy change. This could be both familiesmoving towards battery recycling facilities because of increased labor demand or familiesmoving away in response to increased pollution. To test if this migration is driving our results,we use a similar specification in equation 2.1, but our unit of observation here is an individual.We compare changes in test scores before and after 2009 between students studying in schoolsnear versus farther from recycling facilities. We specify the following equation:yit = θi + δt + κg + γp(s)×t + χ[(Nearist × Postt × 1(t < 2009))+(Nearis2008 × Postt × 1(t ≥ 2009))] + eit(2.3)where yit is individual i’s standardized math or Spanish test score in year t.27 Insimilar fashion to the main specification, we include individual, θi, and year δt fixed effects.27Test scores are standardized at the year-grade level.28To account for learning as students move up in grade and differences in the ENLACE testbetween grades we include a set of grade fixed effects, κg. We again include a set of nearestplant by year fixed effects to account for areas around battery recycling facilities changingdifferently over time. Our coefficient of interest is χ. In this specification, the Near indicatorcan vary over time as students, unlike schools, can move locations. This set up also allows usto include grade 7 and 8 test results in our estimation, as we only need to observe students,not specific school-session-grades, both before and after 2009.We use this specification to account for migration by "freezing" the value of Nearistfor each student in 2008, the year before the U.S. policy change was enacted. In other words,whatever the value of the indicator was for a student in 2008, we force to the indicator tomaintain that value for all subsequent years. To illustrate, imagine a student attendinga school greater than 2 miles from a battery recycling facility as of 2008. If that studentmoves to a school near a recycling facility as of 2009, the Nearis2008 indicator for this studentwill continue to take the value 0 in this specification. This is similar to an intent to treatset up where, no matter what families may endogenously choose to do, they remain in thecomparison group that they were in prior to the policy change.2.4.3 ResultsEstimates of the effect of post-2009 used lead acid battery recycling on testscoresWe will first discuss the findings of our main specification, equation 2.1, presented in Table2.2. In Panel A, columns 1 and 2, we estimate that the 2009 U.S.-induced increase inbattery recycling activity decreased math test scores by about 0.09 standard deviations (σ) insession-grades at schools within 2 miles of a used lead acid battery recycling activity, relativeto session-grades at schools farther away but in the same states. Panel B of the table presentsthe results of similar specifications, with mean standardized Spanish scores as the outcome.Here we see that the estimated effect is still negative, -0.07σ, though slightly smaller for29Spanish than math.In columns 4 and 5 of each panel we include the plant-, or facility-, by-year fixedeffects, and results are qualitatively similar though the magnitude of our estimates decreases.These estimates provide evidence that the negative effects are not driven by differential timetrends in school-session-grade’s achievement around each plant.A causal interpretation of these estimates primarily relies on the parallel trendsassumption that, in the absence of the 2009 increase in recycling activity, test scores atsession-grades near a recycling plant would have evolved parallel to those farther away. Tosupport this assumption we estimate the event study specified in equation 2.2, with resultspresented in Figure 2.5. For both the math and Spanish results, we do not estimate anydifferences, conditional on the fixed effects specified in 2.2, between trends in test scores atschool-session-grades within 2 miles of a facility versus those farther. This lack of pre-trendsis consistent with the parallel trends assumption; lending support to our causal interpretationof the coefficients in Table 2.2.Finally, we analyze the effects on students’ proficiency in both subjects. Proficiencyis evaluated in four achievement levels. Level 4 contains the highest-achieving students,while level 1 contains the lowest. We define students in levels 3 and 4 as proficient students.Columns 1 and 2 of Table A.3 show the percentage of students in each school-grade who areproficient in math and Spanish, respectively. The coefficients in these columns suggest thatthe percentage of students who are proficient decreases by about 2 points in both math andSpanish, from a baseline of 34 and 32 percent, respectively.Sensitivity checksAny remaining potential threats to the causal interpretation of the estimates reported inTable 2.2 must mimic the variation caused by the 2009 U.S. environmental regulation anddifferentially affect schools located two miles from a lead recycling plant compared to thosefarther away. As discussed in section 2.4.2, the increased industrial activity after 2009 at these30battery recycling plants could have caused other changes besides increases in pollution. Oneconcern is that the ramp up in recycling activity also increased labor demand and, potentially,had other effects on local economies. It is not unreasonable to think that these effects mayalso be correlated with proximity to the plants, felt more strongly in neighborhoods that arecloser to the facilities. While we would expect that increases in local economic activity wouldincrease test scores, we do want to confirm that the results we report above are not beingdriven by these potential changes. To do this we further refine our group of exposed schoolsto only include those that are near and downwind of a recycling facility, as described in 2.4.2.Table A.2 presents the results of this estimation. Focusing on “Panel A: Math scores”we can see that the estimated coefficients are again negative and fall between -0.08 and -0.11standard deviations. When we define downwind schools as those that fall within a 30◦cone ofthe downwind direction, results are not statistically significant, likely due to the fact that thisgreatly reduces the number of schools in the “near” group. Expanding the cone to 90◦increasesthe number of schools and the significance of our estimates without greatly changing thepoint estimate. We see the same consistency in results with the Spanish test scores.Another, similar concern is that these local changes in pollution and/or economicactivity could induce migration. The school-session-grade level estimates we’ve presentedso far do not account for this migration. Given that academic achievement can be partiallydetermined by wealth, e.g. if wealthier families can afford tutoring or their children do notneed to work after school to earn income, the negative effects that we’ve present so far couldbe driven by migration. This could be the case if high income families move away from batteryrecycling plants after 2009 to escape increased pollution levels or if low income families mayin for job opportunities. We can account for this migration by doing our analyses at theindividual level, where can observe students moving schools over time. The results are verysimilar to the school-session-grade estimates (see Table 2.3) and do not change when we takethe intent to treat approach outline in equation 2.3, not allowing for potentially endogenousmovement of students in response to the 2009 increase in battery recycling.31Dynamic and heterogeneous effectsThe event study results that we presented earlier not only support the parallel trendsassumption but can also shed light on how the effects of the point-2009 increase in recyclingactivity change over time. Figure 2.5 illustrates the negative effects we’ve presented. Thesefigures also shed light on how effects evolve, with the negative estimated effect of the batteryrecycling plants getting worse over time for both math and Spanish test scores. What couldbe driving this worsening of effects? One explanation is that the battery recycling industrycontinued to experience growth after 2009. This is supported by Tanaka et al. (2022), wherethey show that used lead acid battery exports from the U.S. to Mexico increased in 2009 andcontinued to grow in the following years. It could be the case then that lead pollution levelsaround Mexico’s battery recycling plants are getting worse each year after 2009.A second explanation is that the age at first exposure matters. The younger a child isthe weaker their blood-brain barrier, which allows ingested lead to more easily enter theirbrains (Saunders, Liddelow, & Dziegielewska, 2012). In addition, smaller children are morelikely to ingest lead from top soil when they are playing outside (Rees & Fuller, 2020). Wecan use our school-session-grade level analysis to investigate this, and compare how effectschange with age at first exposure, while holding duration of exposure constant. Figure 2.6illustrates the results of this analysis on math test scores. Consider Panel A, which plots theresults of the event study described in equation 2.2 but only for school-sessions in grade 3.Here we can see that negative effects start to appear in 2012. Third graders in 2012 wouldhave been in kindergarten in 2009 and, if studying in a school near a recycling plant, havebeen exposed to the post-2009 pollution for 3 years. Compare this to grade 4’s results inPanel B, focusing again on 2012. These fourth graders had also been exposed to three yearsof post-2009 pollution, but were slightly older, in grade 1, in 2009. For fourth graders weestimate a smaller, less negative, effect of being near a battery recycling plant in 2012. Wesee a stronger effect for fourth graders in 2013. These fourth graders were in kindergartenin 2009, the same age of exposure at which we saw effects among the third graders. Since32the ENLACE test was ended in 2013, we cannot observe younger cohorts, but the grade 5and 6 event studies, where all children were in grade 1 or later as of 2009, continue the asimilar pattern. These grade-specific event studies, while not conclusive given large confidenceintervals, suggest that age at first exposure matters with respect to lead exposure’s effect onlearning.The dynamic and age-based heterogeneity discussed above consider how effects maychanged based on the amount and severity of lead exposure. We are also interested inexploring how family, community, and institutional factors mitigate the lead exposure -learning relationship. To test for heterogeneity along these lines, we use the locality andmunicipality level data described in section 2.3.5. We consider how our estimated effectsvary based on pre-2009 characteristics, including adult educational achievement, access toinfrastructure, malnutrition rates, formal employment status, regional income inequality,and marginalization. To do this we first define a set of indicator variables equal to one if aclassroom is in a community with an above median value of each characteristic. The medianwe consider is the 50-th percentile of each measure among all classrooms within a 2-mileradius of a battery recycling facility. To estimate if effect sizes vary for classrooms in theabove versus below median groups for a given characteristic, we fully interact the aboveindicator with the specification in equation 2.1.The results of these analyses are presented in Table 2.4. For ease of interpretationwe first estimate equation 2.1 separately for the above and below median classrooms. Forexample, Panel A Column 1 shows that the post-2009 increase in recycling negatively affectedmath test scores by -.12 standard deviations for students attending schools in relatively lesseducated communities, measured as average years of schooling completed by adults in thecommunity. We estimate a null effect in schools with above median educational attainment.The row "Difference (high - low)" presents the estimated coefficient on the interaction between"Near facility X Post-2009" and the above median indicator, showing the difference betweenthe coefficients presented in columns 1 and 2 and testing the statistical significance of that33difference.In Panel A of table 2.4 we consider characteristics that are signs of more developed,wealthier, or generally better off communities. In each case we find that the negative effectsof lead are stronger in communities with relatively low values of these characteristics. Thedifferences are statistically significant in the case of education achievement and formalemployment, proxied for here by the share of the community that is covered by a socialsecurity agency. In communities with lower quality infrastructure, which we measure as thepercentage of households who are connected to a sewer, effects are again strong relative tohigher access communities, but there is not a statistically detectable difference in the effectsizes. In Panel B we see the opposite trend, as the measures we consider here take highervalues in worse off communities. In each case we find that, qualitatively, the effects of the leadpollution we study are higher in communities above the median of these characteristics. Thedifferences in effects are strongest and statistically different when comparing communitiesabove and below the median value of the marginalization index. Taken together these resultstell a consistent story, that the negative learning effects of pollution exposure are concentratedin communities that were already worse off before the 2009 increase in recycling activities.2.5 DiscussionHow can we contextualize the magnitude of the effects that we estimate? One way is tocompare our estimates to the effects of policies aimed at increasingly educational attainment.A recent review of education intervention impact evaluations in low- and middle-incomecountries found that, at the median, these interventions are shown to increase learningoutcomes by 0.10 standard deviations. These interventions include programs aimed at bothincreasing access to education and in-classroom learning. An education intervention withthis effect size could reverse the effects of lead pollution in our setting (Evans & Yuan, 2022).From Mexico, a study examining the effect of a program to increase the time students spend34in the classroom, found that full-time school increased test scores by 0.11 standard deviations(Cabrera-Hernández, 2020). Effects of this program were concentrated among poorer students,who may be similar in ways to those in our study living in marginalized communities aroundlead acid battery recycling facilities. Unfortunately, this program was shut down in 2022(Mexico News Daily, 2022).We can also place a monetary value on our estimates by calculating how the lower testscores that result from the post-2009 increase in recycling activity will affect students’ futureearnings. For this exercise we consider the total costs in terms of lifetime earnings to a cohortof students living near a ULAB recycling plant in the years after 2009. Consider the cohort ofstudents who were in 3rd grade in 2009. In 2009 average annual wages in Mexico was 18,806USD in 2016 dollars (OECD 2023). Using a discount rate of 5 percent, the 2009 presentvalue of future earnings for a 3rd grader in Mexico is 208,705 USD.28 We can next applyestimates from Mexico and other countries to link test scores to future earnings. De Hoyos,Estrada, and Vargas (2021) link grade 12 test scores to young adult earnings for secondaryschool graduates in Mexico and find that a one standard deviation change in test scores inassociated with a 6% change in earnings. Chetty et al. (2014) and Lindqvist and Vestman(2011) do similar analyses in the U.S. and Sweden and estimate relationships between 6and 15%, relative to a 1 standard deviation change in test scores. If we apply our mostconservative estimates of the effect on math scores from Table 2.2, -0.08 standard deviations,we estimate that the post-2009 growth in ULAB recycling reduced lifetime earnings of each3rd grader by between 1,002 and 2,504 USD.29 Given that 15,553 3rd graders studied withintwo miles of a ULAB recycling plant in 2009, the total estimated cost to them in terms oflifetime earnings is 15.6 to 38.9 million USD. Each year a new cohort of students is born28We apply the 5% discount rate following Chetty, Friedman, and Rockoff (2014). We do not consider wagegrowth here, as the average wage growth in Mexico between 1991 and 2022 was very small, on average 0.3%(OECD, 2023). If we consider this then lifetime expected earnings are 210,257 USD.29We use the range of test score to future earning relationship estimates here given that the Mexico estimateis based only on secondary school graduates and considers their earnings at ages 18-20. We believe this canunderestimate the relationship as it ignores students who have not completed secondary school, which is alarge share in Mexico, and thus are likely earning significantly less.35into the communities surrounding ULAB recycling plants, and these children additionallybear this cost. Thus we consider this cost an annual cost of pollution from the post-2009increase in ULAB recycling. We consider this estimate to be a lower bound of the cost giventhat lead pollution exposure is especially harmful to young children. In this case we expectthat the lost lifetime earnings per cohort would increase past the time span we observe inour data. This cost estimate is additionally a lower bound given that it ignores other healthrelated effects of lead pollution exposure, including negative effects on cardiovascular andrenal health.Estimates of the costs of reducing lead pollution emissions from ULAB recyclingactivities comes from Burr et al. (2011), who estimated that the cost of bringing the 14 ULABrecycling plants in the U.S. into attainment with the updated 2009 air quality standards was10.33 million USD per year in 2016 dollars. If we assume it would cost the same amountto clean up pollution from processing the exported batteries in Mexico, then the annualbenefits of reducing emissions clearly outweigh the costs. If we increase this cost estimate toaccount for the 26 recycling facilities in Mexico, assuming that costs are more a function ofthe number of facilities as opposed to the number of batteries processed, the costs increasesto around 19.2 million USD, still well within range of the total value of lost lifetime earningsestimated above.2.6 ConclusionIn this paper we estimate the impact of increased lead acid battery recycling on the academicperformance of students in Mexico. This increased activity of a heavily polluting industry inMexico is a result of a 2009 U.S. policy change that tightened environmental standards forlead and caused the industry to shift activities to Mexico, where regulation remained lessstringent.For our analyses we use data from ENLACE, a census-based standardized exam in36Mexico, and a difference-in-difference approach comparing students at schools close to versusfarther away from Mexican battery recycling plants before and after 2009. We find thatexposure to the post-2009 lead-battery recycling reduces academic achievement in mathby 0.05-0.09 standard deviations. Effects on Spanish scores are also negative and slightlysmaller. We complement these results with event study estimates, which provide support forthe parallel trends assumption necessary for identification of causal effects. The event studyestimates also show that the negative effect of lead exposure on test scores persists for atleast 4 years after students were first exposed to the 2009 increase in lead pollution. In fact,the effects seem to become even more negative over time. We also show suggestive evidencethat age at first exposure matters, with students who were in kindergarten or younger asof 2009 experiencing stronger effects than those who were older. These results are robustto school- and student-level specifications and are not driven by other local changes thatcould have accompanied the post-2009 growth in Mexico’s battery recycling industry such asavailability of jobs or migration.We take advantage of a seldom available comprehensive dataset on national standard-ized test scores in a developing country and implement a quasi-experimental research design.Our findings highlight the harmful impact of lead exposure on academic performance in thecontext of limited regulatory oversight. Understanding the effects of exposure to pollutionon children’s cognitive development is especially important in developing countries, wherecitizens have fewer resources to adapt to worsening environmental quality and where parentshave fewer resources to make compensatory investments. It is difficult to directly compareour results to other studies from rich settings, as we do not know the magnitude of leadpollution exposure changes that are driving our results. Still, our heterogeneity analyses showthat the negative results of this post-2009 increase in recycling activity are concentrated incommunities that are already worse off before the policy change. For example, effects arestronger in communities with lower levels of adult education, less access to infrastructure,and higher malnutrition rates. These findings suggest that cost of exposure estimates from37rich settings many underestimate effects in less developed settings, where the vast majorityof lead pollution exposure today occurs. Our findings underscore the importance of con-tinued research on the impact of environmental factors on child development in low- andmiddle-income countries.Our findings also have important implications for environmental and education policy.First, our analyses build on Tanaka et al. (2022), further quantifying the size of the environ-mental externality from ULAB recycling shifting from the U.S. to Mexico and underscoringthe importance of transboundary coordination of environmental policy. From an educationperspective, because we study the impact of lead exposure on academic outcomes duringschooling years (rather than during early childhood or gestational periods), the results of thisstudy underscore the importance of investing in compensatory measures to protect studentsfrom exposure to pollution in schools.Our estimated effect sizes are largely in line with the literature linking pollution andtest scores. Compared to studies that consider air pollution, usually defined as PM10 orPM2.5, our estimated effects are slightly larger. Studies that estimate the effects of day-of-testparticulate matter pollution on test score outcomes find a negative effect of 0.03 to 0.05standard deviations, with effects being stronger for more analytical subjects like mathematics(Amanzadeh, Vesal, & Ardestani, 2020; Ebenstein, Lavy, & Roth, 2016). Looking at theeffects of longer-term, at-school exposure to air pollution from vehicle traffic, Heissel, Persico,and Simon (2019) find that attending schools downwind of a major highway is associatedwith a 0.04 standard deviation decrease in test scores. In a U.S.-based study linking leadexposure and education, where the authors were able to use blood lead level (BLL) testingdata to measure lead exposure precisely, the authors found that interventions to decreaseBLLs resulted in a 0.12 standard deviation increase in educational performance, comparedto those students who were not eligible for the intervention (Billings & Schnepel, 2018). Ifimplemented in our settings, these same interventions could potentially reverse the negativeimpacts of lead pollution exposure that we estimate here.38Despite decades of research showing the negative health effects of lead pollution, globalexposure rates remain unbelievably high (Rees & Fuller, 2020). And the vast majority ofthe costs of this lead pollution exposure falls on children living in low- and middle-incomecountries (Larsen & Sánchez-Triana, 2023; Crawfurd et al., 2023). Rich countries have andcontinue to work hard to clean lead out of their environments, but, like in the setting westudied here, these environmental policies have at times worsened the problem in LMICs byshifting pollution sources to areas with weaker or less enforced regulatory standards. Thereis still much work to be done to combat this environmental challenge and reduce the globalcosts of lead pollution exposure.39Figure 2.1: Used Lead Acid Battery Recycling Facilities in Mexico1000 kmNNote: The 26 facilities locations plotted here as black points were compiled by Tanaka et al. (2022) fromthe CEC and SEMARNAT. Some facilities are located very close to each other so difficult to visuallydifferentiate at this scale. This map was produced in R using ggplot.Figure 2.2: Number of students tested, by grade and year0.51.01.52.0Number of students tested (millions)2006 2008 2010 2012 2014YearGrade 3Grade 4Grade 5Grade 6Grade 7Grade 8Grade 9Grade 12Note: Here we plot the number of students for whom we observe ENLACE test results, by grade and year.Note that tests for grades 7 and 8 did not start until 2009 and testing in grade 12 did not start until 2008.40Figure 2.3: Identifying lead exposed schools(a) Panel A: Used lead acid battery recycling facility andnearby schools in Greater Mexico City(b) Panel B: Used lead acid battery recycling facility andnearby schools in a rural part of Michoacán StateNote: Used lead acid battery recycling facilities are indicated by a red point. Black points indicate schools atwhich the ENLACE test was conducted between 2006 and 2013. The red circle represents a two mile radiusaround the recycling facility. Maps were generated in R using ggmap.41Figure 2.4: Lead concentrations near U.S. ULAB battery recycling facilities0.1.2.3.4.5Lead concentration (µg/m3 )1 2 3 4 5 7 8 9 10Distance from Monitor to "Binding" Plant2007 2015Note: This figure, replicated from Tanaka et al. (2022), plots the average ambient lead concentration atmonitors within 10 miles of a U.S. lead acid battery plant, in 1 mile bins. The figure only includes monitorsaround more heavily polluting plants, which are defined as those around which ambient lead concentrationsare greater than 0.15 micrograms per cubic meter before the 2009 regulatory change, i.e. for whom theupdated standard is "binding." The data on battery recycling plant location comes from the U.S. ToxicRelease Inventory and the lead concentration data come from the U.S. Environmental Protection Agency.Figure 2.5: Effects of being close to a lead-acid battery recycling plant on scores,by yearPanel A: Math Scores Panel B: Spanish Scores-.2-.10.12006 2007 2008 2009 2010 2011 2012 2013-.2-.10.12006 2007 2008 2009 2010 2011 2012 2013Note: Figure displays the coefficients and 95% confidence intervals from the event-study of being near a recycling plant when looking at mathand Spanish scores as outcomes. These coefficients correspond to the βτ parameters in equation 2.2. Standard errors are clustered at the schoollevel. The reference period is 2009 when the US changed its lead air quality standard.42Figure 2.6: Event study of math scores, by grade-.2-.10.12006 2007 2008 2009 2010 2011 2012 2013(a) Grade 3-.2-.10.12006 2007 2008 2009 2010 2011 2012 2013(b) Grade 4-.2-.10.12006 2007 2008 2009 2010 2011 2012 2013(c) Grade 5-.2-.10.12006 2007 2008 2009 2010 2011 2012 2013(d) Grade 6Note: This figure plots the results of four event studies, estimated on standardized math test score resultsseparately for school-grade-sessions in grade 3-6. Each panel plots the point estimates and 95% confidenceintervals of the βτ parameters from equation 2.2, estimated separately for each grade.43Figure 2.7: Event study of Spanish scores, by grade-.2-.10.12006 2007 2008 2009 2010 2011 2012 2013(a) Grade 3-.2-.10.12006 2007 2008 2009 2010 2011 2012 2013(b) Grade 4-.2-.10.12006 2007 2008 2009 2010 2011 2012 2013(c) Grade 5-.2-.10.12006 2007 2008 2009 2010 2011 2012 2013(d) Grade 6Note: This figure plots the results of four event studies, estimated on standardized Spanish test score resultsseparately for school-grade-sessions in grade 3-6. Each panel plots the point estimates and 95% confidenceintervals of the βτ parameters from equation 2.2, estimated separately for each grade.44Table 2.1: Summary Statistics: Test ScoresGrade 3 Grade 4 Grade 5 Grade 6 Grade 9Panel A: MathMean 539.26 534.84 535.61 543.03 526.23(122.71) (90.24) (122.81) (124.44) (115.62)Proficiency Rate (%)Non-satisfactory 17.94 19.40 18.81 17.61 18.81Basic 41.24 42.10 42.99 40.69 47.89Good 26.78 24.46 24.21 25.98 22.31Excellent 14.04 14.04 13.99 15.72 10.99Observations 14,039,726 14,270,661 14,361,452 14,305,880 11,833,328Panel B: SpanishMean 538.10 526.96 526.19 531.28 505.17(112.90) (117.93) (110.10) (111.57) (105.65)Proficiency Rate (%)Non-satisfactory 15.79 19.99 17.96 17.22 21.59Basic 41.57 41.94 44.15 42.60 47.64Good 33.34 28.88 30.58 32.18 26.87Excelent 9.31 9.19 7.31 8.00 3.90Observations 14,047,767 14,256,445 14,343,142 14,283,435 11,834,961Note: Mean non-standardized ENLACE test scores for each subject and grade arereported. Test scores represent exams taken between 2006 and 2013. Standarddeviations are in parenthesis. The total number of students tested in each gradebetween 2006 and 2013, by subject, are reported in the "Observations" rows. Inaddition we report the share of students in the same range of years that fall into eachproficiency category, by grade and subject.45Table 2.3: Effect of increased battery recycling activity on students attendingschool close to a recycling facility, individual-level resultsIntent-to-treat(1) (2) (3) (4) (5)Panel A: Math scoresNear facility X Post 2009 -0.0815*** -0.0892*** -0.0821*** -0.0813*** -0.0772***(0.0119) (0.0126) (0.0125) (0.0110) (0.0114)Observations 42,003,229 42,003,229 42,003,229 21,570,694 21,570,689R2 0.704 0.707 0.708 0.692 0.696N clusters 55,770 55,770 55,770 50,981 50,978Panel B: Spanish scoresNear facility X Post 2009 -0.0541*** -0.0682*** -0.0604*** -0.0567*** -0.0591***(0.0110) (0.0109) (0.0108) (0.00975) (0.0100)Observations 41,984,199 41,984,199 41,984,199 21,557,200 21,557,195R2 0.718 0.720 0.720 0.707 0.709N_clust 55,771 55,771 55,771 50,985 50,982Fixed effects:Individual ✓ ✓ ✓ ✓ ✓Year ✓ ✓ ✓ ✓ ✓Grade ✓ ✓ ✓ ✓ ✓Grade-by-year ✓ ✓ ✓ ✓ ✓Municipality ✓ ✓ ✓State-by-year ✓ ✓ ✓Plant-by-year ✓ ✓Note: The unit of observation is the student-year. Near is a dummy variable that equals one if the studentattends a school s located within two miles from the nearest lead-recycling plant p(s), and zero if theirschool s is farther away from it but in the same state. In columns 4 and 5, Near is defined using the 2008value for each student, as discussed in section 2.4.2. The Postt indicator takes the value 1 in 2009 andthereafter. Outcomes are students’ standardized test scores, standardized at the grade-year level. Panel Areports estimates with math scores as the outcome; Panel B reports estimates with Spanish test scores asthe outcome. In each column the two specifications, with math and Spanish as outcomes, include thesame levels of fixed effects; only the outcomes are changed. Standard errors, in parentheses, are clusteredat the school level. * p<0.1, ** p<0.05, *** p<0.01.46Table 2.2: Effect of increased battery recycling activity on students attendingschool close to a recycling facility(1) (2) (3) (4) (5)Panel A: Math scoresNear facility X Post 2009 -0.0936*** -0.0890*** -0.0881*** -0.0707*** -0.0663***(0.0136) (0.0137) (0.0140) (0.0141) (0.0142)Number of students -0.000889***(0.0000655)Observations 1,141,093 1,141,093 1,141,093 1,141,093 1,141,093R2 0.516 0.517 0.523 0.525 0.526Num. of clusters 48,830 48,830 48,830 48,830 48,830Panel B: Spanish scoresNear facility X Post 2009 -0.0674*** -0.0637*** -0.0639*** -0.0559*** -0.0539***(0.0126) (0.0127) (0.0127) (0.0126) (0.0127)Number of students -0.000409***(0.0000599)Observations 1,141,007 1,141,007 1,141,007 1,141,007 1,141,007R2 0.560 0.561 0.565 0.567 0.567Num. of clusters 48,829 48,829 48,829 48,829 48,829Fixed effects:School-session-grade ✓ ✓ ✓ ✓ ✓Year ✓ ✓ ✓ ✓ ✓Grade-by-year ✓ ✓ ✓ ✓Session-by-year ✓ ✓ ✓ ✓State-by-year ✓ ✓ ✓Plant-by-year ✓ ✓Note: The unit of observation is school-session-grade-year. Near is a dummy variable that equals one ifschool s is located within two miles from the nearest lead-recycling plant p(s), and zero if the school s isfarther away from it but in the same state. The Postt indicator takes the value 1 in 2009 and thereafter.The sample includes all school-sessions in grades 3, 4, 5, 6, and 9 which we observe at least once in thepre-2009 and at least once in the post-2009 period. Outcomes are school-session-grade means of students’standardized test scores, standardized at the grade-year level. Panel A reports estimates with math scores asthe outcome; Panel B reports estimates with Spanish test scores as the outcome. In each column the twospecifications, with math and Spanish as outcomes, include the same levels of fixed effects (as indicated inthe bottom panel); only the outcomes are changed. Standard errors, in parentheses, are clustered at theschool level. * p<0.1, ** p<0.05, *** p<0.01.47Table 2.4: Effect of attending school close to a battery recycling facility, by communitycharacteristics(1) (2) (3) (4) (5) (6)Low High Low High Low HighPanel A Education (years) Access to sewer(%) Social security (%)Near facility X Post 2009 -0.124*** 0.0117 -0.0613*** -0.0313* -0.119*** -0.00783(0.0223) (0.0157) (0.0221) (0.0173) (0.0220) (0.0187)Difference (high - low) 0.136*** 0.030 0.111***0.0273 0.0281 0.0289Observations 835,657 286,623 603,431 519,001 773,619 348,813R2 0.488 0.587 0.474 0.547 0.512 0.555Num. of clusters 36,241 11,714 26,350 21,612 33,918 14,044Panel B Marginalization index Gini index Malnutrition rateNear facility X Post 2009 -0.0272* -0.0906*** -0.0680*** -0.0802*** -0.0302* -0.0660***(0.0155) (0.0245) (0.0218) (0.0182) (0.0177) (0.0205)Difference (high - low) -0.0634** -0.012 -0.0360.0291 0.0284 0.0271Observations 282,578 857,479 426,471 713,586 218,510 921,547R2 0.579 0.497 0.514 0.533 0.561 0.510Num. of clusters 11,341 37,449 18,552 30,238 8,638 40,152Note: The estimates in this table are produced using the same specification as the estimates presented in Table 2.2,column 4, with the mean standardized math scores as outcomes. The specification is run separately on schools incommunities with above and below median values of the given characteristics, with medians calculated from amongschools within two miles of a battery recycling facility. The "Difference" row is estimated by fully interacting theTable 2.2, column 4 specification with an above median indicator for each characteristic. This row reports estimates ofthe coefficient on the interaction between this indicator and the “Near X Post” term. Standard errors are clustered atthe school level. * p<0.1, ** p<0.05, *** p<0.01.48Chapter 3Gender Differences in Informal LaborMarket ResilienceEvidence from Ghana1Economists have long appreciated the importance of volatility in labor markets, atopic of increasing relevance as public health crises and climate change continue to exacerbateexposure to economic shocks around the world. Labor markets in low- and middle-incomecountries are arguably more volatile than those in high-income countries; economic crises dueto periods of rapid inflation, cycles of election violence, energy shortages, disease outbreaks,and extreme weather events are shockingly common. Underpinning this volatility is widespreadacknowledgment and documentation of gender gaps in labor markets. These gender gaps havebeen well documented and explored in high-income countries (Blau & Kahn, 2017), and thereis a more nascent literature providing evidence for similar gaps in low- and middle-incomecountries (Nix, Gamberoni, & Heath, 2016; Hardy & Kagy, 2018; World Bank Group, 2019).Understanding the gender dimensions of resiliency to economic shocks is critical to supportingwomen’s economic empowerment.Labor market outcomes in low- and middle-income countries are inextricably tiedto small firm performance, as the vast majority of workers are self-employed and the vastmajority of households depend on self-employment income (Gindling & Newhouse, 2014;McKenzie, 2017; McCaig & Pavcnik, 2021). Small firm outcomes are also of independentimportance to understanding economic volatility and resiliency because small firms are the1This chapter is joint work with Morgan Hardy, Jamie McCasland, and Gisella Kagy.49modal firm in low- and middle-income countries (Hsieh & Olken, 2014). Understanding firmexit and selection patterns into exit during volatile periods thus has implications both foraggregate productivity via reallocation and individual and household welfare via effects onthe incomes of self-employed individuals.In this paper, we document gender differences in informal labor market resilience thatstem from differential selection into firm survival during an economic crisis. We analyzepanel data from February 2020 through to July 2020. The early COVID-19 crisis experiencein Ghana mirrored that of many other countries in Sub-Saharan Africa, including a shortlockdown that began in late March and ended in mid-April. Our six-month retrospectivepanel thus includes one pre-pandemic observation and five crisis months, where about 80% ofboth male- and female-owned firms are operational by the end of the panel.Our data overcomes several key challenges in understanding differential informal labormarket resiliency by gender. In almost all labor markets there is some degree of gendersegmentation; in Sub-Saharan Africa in particular, Bardasi, Sabarwal, and Terrell (2011)show that small firm ownership within an industry is nearly always heavily dominated byone gender. Prior influential work on gender differences in response to economic shocks fromthe United States has shown that while men experienced more job loss during the GreatRecession, this difference is largely explained by variation in industry-occupation (Hoynes,Miller, & Schaller, 2012). Garment-making is one of very few industries in Ghana wherelarge shares of both men and women can be found in self-employment, allowing us to isolatedifferences by gender of the firm owner from industry-occupation shocks. Secondly, we observethe universe of firms in a single labor market (a district capital and its outlying suburbs)pre-crisis, allowing us to track changes in the overall distribution of surviving firms andstudy survival selection by pre-crisis characteristics. Importantly, firm owners in our contextexhibit the same gender earnings gap documented pre-pandemic that has been documentedmore broadly in the literature (Hardy & Kagy, 2018; Nix et al., 2016). Finally, small firmsin low-income countries are both firms and individual workers in a labor market; our data50includes earnings from all sources, providing a rich understanding of livelihood vulnerabilityand occupational choice under duress.We begin by characterizing the nature of the COVID-19 economic shock to the firmsin our sample. In our six-month retrospective panel, firm owners of both genders experiencelarge level drops in revenues across the five crisis months we observe, relative to February2020. This level shift in sales is nearly identical on average for male- and female-owned firmsand not detectably different in the full sample, conditional on being operational in a givenmonth, or conditional on the measure of survivorship we use in this paper (positive salesin at least two of five crisis months). In contrast, we define persistently-closed firms as theconverse, those firms with zero revenues for at least four of the five crisis months we observe.While female-owned firms are more likely than male-owned firms to close at the peak ofthe formal lockdown in April 2020, we observe no statistically significant difference in theprobability of persistent closure at 16% of male-owned firms and 19% of female-owned firms.Our first main finding documents gender differences in selection into persistent closure,leveraging the unique strengths of our data. Female-owned firms exiting the market comefrom the left-tail of the pre-crisis sales distribution. The same trend does not hold true formale-owned firms that are persistently closed, which are not selected on firm characteristics.Instead, male owners with alternative sources of pre-crisis income are those that are mostlikely to close their garment-making businesses for at least four of the five crisis months weobserve.Consistent with these sources of selection, we show that 53% of female owners ofpersistently-closed firms exit to non-employment (having zero market hours of work in July2020), 26 percentage points more than male owners of persistently-closed firms. These womenexperience large drops in total income, earning 64% less market income than women whosebusinesses remained operational. Men who close their firms appear to be directing their laborto other income-generating activities, fully offsetting garment-making sales losses with othersources of income.51Our evidence is most consistent with male- and female-owned firms experiencing asimilarly-sized negative economic shock, but responding quite differently, due to underlyingstatic differences in occupational opportunities in this context. We argue that the pre-pandemic presence of marginal female-owned firms near the zero revenue viability thresholdis likely due to underlying occupational choice constraints (relative to men) and that female-owned firm closures appear to be driven by proximity to the zero revenue viability threshold.In contrast, there are relatively few male-owned firms near zero revenues pre-pandemic.Instead, men with lucrative outside options are more likely to persistently close, consistentwith differential underlying occupational choice fundamentals for men and women.Our work complements a recent literature on firm exit in low- and middle-incomecountries. McCaig and Pavcnik (2021) show that informal firms at the margin of viabilityare those that are both more likely to exit and more likely to enter, implying that churnhas no net effect on aggregate (revenue) productivity in Vietnam. McKenzie and Paffhausen(2019) show across 12 low- and middle-income countries that female-owned firms are slightlymore likely to close than male-owned firms and that female owners are more likely to reportviability as a cause of firm death. This paper provides evidence of a within-industry settingin which female-owned firm closures come from the left-tail of the revenue distribution andmale-owned firm closures are most consistent with an occupational choice model, in thepresence of a major negative shock. Our findings also echo a theoretical and empiricalliterature in high-income countries on the “cleansing effect” of recessions,2 put a gender lenson intersecting stylized facts around productivity and firm size distributions in low-incomecountries,3 and highlight the economic vulnerability of women who run businesses at themargin of viability.In addition, the findings of this paper have potentially important implications for the2See for example Caballero and Hammour (1994) for a seminal theoretical model, Foster, Grim, andHaltiwanger (2016) for evidence from the Great Recession, and Morikawa (2021) for evidence that reliefpolicies muted any cleansing effect during COVID among Japanese firms.3Hsieh and Klenow (2009) documented larger productivity dispersion in low-income countries than inhigh-income countries; Hsieh and Olken (2014) documented an extremely right-skewed firm size distribution.52gender wage gap literature, complementing recent work on level differences in outcomes formale- and female-owned firms.4. We also add to the growing body of empirical work on theconsequences of the COVID-19 pandemic for the developing world.5 Finally, we contributeto a larger body of literature that examines how individuals and firms respond to economicshocks; while the COVID-19 crisis and ensuing lockdown measures are notable in terms ofhow they impacted enterprises and individuals all over the world, enterprises and individualsin low-income countries are frequently subjected to similarly large economic disruptions andthus lessons learned from the context of COVID-19 extend to other settings.63.1 Context and DataGarment Making in Ghana Our study focuses on garment-making microenterprisesin the mid-sized district capital of Hohoe, Ghana. Like many other industries in low- andmiddle-income country contexts, the majority of these enterprises are small in size and notformally registered. Although the primary input of bespoke garments is the garment makingfirm owner’s (and any workers’) labor, thread and fabric inputs are also important. Ghanadoes not have a domestic textile industry for these products and, instead, imports the bulkof them from the Netherlands, other European Union countries, and China (Ibrahim et al.,2017).Garment making in Ghana is overwhelmingly bespoke, with customers that almostexclusively live within miles of a garment makers shop (Quartey, 2006). Demand for thesemade-to-order garments, in contrast with the ready-made imported alternatives, is primarilyfor special events, religious gatherings, or office wear (Quartey, Abor, et al., 2011). Itis exceedingly common practice to have bespoke (often matching) garments hand-made4See World Bank Group (2019) for a recent discussion of the gender profit gap in Africa.5See for example von Carnap, Almås, Bold, Ghisolfi, and Sandefur (2020); Miguel and Mobarak (2021);Eichenbaum, Rebelo, and Trabandt (2021), and (Alfaro, Becerra, & Eslava, 2020) for recent work.6For example, the 2014 Ebola outbreak in West Africa (Huber, Finelli, & Stevens, 2018), election violencein Kenya (Ksoll, Macchiavello, & Morjaria, 2021), climate disasters (Cavallo, Galiani, Noy, & Pantano, 2013),and electricity crises in Ghana (Hardy & McCasland, 2021).53for funerals (which often have hundreds of people), graduations, local community groupcelebrations, religious revival events (also with hundreds of people), and weddings.Importantly, both men and women own these enterprises. Male- and female-ownedenterprises use the same production function. Male-owned firms tend to be slightly larger,whether measured by non-owner workers or total revenues. Other studies on gender fromthis context document an economically significant gender profit gap for these enterprises,and a lower market-to-firm ratio for female versus male owners, which leaves women demandconstrained (Hardy & Kagy, 2018). Figure 3.1 shows the distribution of February 2020 salesfor female- and male-owned businesses in our sample, immediately prior to the impacts ofthe pandemic. Notably, we observe a large bulge of female-owned firms in the left part of thedistribution, clustering close to zero, and overlap between male- and female-owned firms inother parts of the distribution.The 2020 COVID-19 Crisis in Ghana Similar to many countries around the world,in late March 2020, the Government of Ghana instituted a nation-wide lockdown on allnon-essential businesses, closed in-person schools and restricted the movement of individualswithin Ghana in response to the global spread of COVID-19. The country formally went intolockdown on March 30, 2020 and the lockdown was lifted in some areas on April 20, 2020when less-restrictive social distancing mandates were put in place. Schools were closed onMarch 16, 2020 and partially reopened on June 15, 2020 (Hale et al., 2021).While mandated lockdowns were quite short-lived, large gatherings and events remainedrare for the duration of our data collection period. Anecdotes from firm owners and membersof the research team who were in Ghana in 2020 suggest that the absence of these typesof occasions created a lasting negative demand shock for garment-makers throughout theperiod of our data. In addition, imports of inputs to Ghana were severely disrupted for theduration of our data collection period (Amponsah, Frimpong, et al., 2020), putting pricingand scarcity pressure on key inputs for garment-making firm owners. In appendix Table B.154we present self-reported impacts and coping strategies from this period. Notably, 98% and99% of female and male owners, respectively, reported loss of demand/customers. About twothirds of both owner genders reported difficulties with accessing supplies.Sampling We began our sampling process with 569 firm owners identified during a censuslisting activity in September of 2019.7 We followed up with these firms in person in January2020, and identified 557 that remained operational. Implicitly, this means our sample excludesnew entrants that entered between September 2019 and January 2020. Our targeted samplingframe for this study is thus operational firms as of January 2020 that had been open for atleast six months. We interviewed 518 of these 557 firm owners for our primary phone survey,launched in August 2020. Of the 518, 28 owners reported having zero sales in February 2020.Our primary analysis excludes these firms, categorizing them as closed in the pre-crisis period.The remaining 490 firms (102 male and 388 female) are our main analysis sample of interest.Appendix Tables B.2 and B.3 show tests for differences between the 557 target sample, the518 surveyed firms, and our final 490 firm analysis sample separately for female and maleowners. We find no significant differences on observables, strong evidence that our sample isrepresentative of our sampling frame.Generalizability The type of enterprise we study is ubiquitous in low- and middle-incomecountries, the town we study is similar to small towns around the continent, and the COVID-19 policies pursued in Ghana in the early period of the pandemic mirror those pursuedin many other African countries (Hale et al., 2021). Hardy and Kagy (2020) show thatpre-COVID differences in earnings by owner gender observed in our setting are echoed innationally representative data (the Ghana Living Standards Survey, GLSS).With respect specifically to firm exit, previous census and survey data collected in thiscontext in February 2014 and February 2015 suggest an average yearly exit rate of ∼ 11%7Surveyors canvassed the town identifying any commercial store fronts and inquired with locals about anyless visible enterprises. A list of enterprises from the local trade association was also cross-checked. Surveyorsalso collected basic information on listed firms and owners, including owner gender.55for female-owned firms and ∼ 7% for male-owned firms. This difference of ∼ 4% is notstatistically significant. These pre-COVID annual exit rates in our context are comparable topre-COVID annual exit rates documented across low- and middle-income countries in theliterature which range from 8% to 18% (McCaig & Pavcnik, 2021; McKenzie & Paffhausen,2019), and smaller than the half-yearly COVID-period exit rates we report below.As we noted above, economic crises of various magnitudes are pervasive in low- andmiddle-income countries. This February 2014 to February 2015 time period happens tocoincide with an energy crisis in Ghana, where extreme weather events interacted withhydropower systems and aging infrastructure to generate frequent planned and unplannedblackouts over the course of several years (Hardy & McCasland, 2021). The lights crisiswas less acute than early COVID, but serves as a useful reminder that volatility is quiteimportant to understanding the private sector in low- and middle-income countries and mayindeed be the default circumstance.Key Variables The phone survey that constitutes the bulk of our data included a ret-rospective panel on enterprise sales, profits, hours worked, and wages paid for each monthfrom February 2020 through July 2020, generating a six-month panel. In addition to theretrospective panel, we asked detailed information about owners’ alternative income sources,generating measures of total income for each owner for February 2020 and July 2020. Wedefine a firm as being closed in a specific month if sales that month are zero. Persistentclosure is defined as having zero sales for at least four of the five observed crisis months. Thisnaming convention is for expositional ease and is not meant to imply that we have data onclosure beyond the six-month period.We augment descriptive statistics on selection into closure in Table 3.2 with somemeasures from the September 2019 census data. We also present information on sampleattrition from the September 2019 census in Appendix Tables B.2 and B.3563.2 FindingsCharacterizing the Shock We plot the monthly share of closed firms by gender betweenFebruary 2020 and July 2020 in Figure 3.2. By construction, all enterprises in our samplewere open in February. The lockdown measures are associated with increases in closure forboth male- and female-owned enterprises. Only 36% of male-owned enterprises, and 21% offemale-owned enterprises were open in April. This 15% difference is significant at the onepercent level. Both male- and female-owned firms began to re-open as the lockdown measureslifted. By July 2020, 86% of male owned enterprises and 80% of female-owned enterprises areopen, a statistically insignificant difference.8 Visually, Figure 3.2 shows that firm closures aremost common in March, April and May, consistent with the lockdown timeline described inSection 2.Table 3.1 presents a quantitative exploration of sales changes over the six monthsusing the following specification separately for male- and female-owned firms:Yi,t = α + β1Postt + ϵi,t (3.1)where Yi,t is the monthly sales outcome variable of interest for enterprise i and Posttis a binary indicator for the months of March, April, May, June, or July. February is thecomparison month. To test the differences in β1 between men and women, we run a pooledregression with both men and women where a binary indicator for if an owner is male is fullyinteracted with equation 3.1. Standard errors are clustered at the enterprise level. Leveldrops in sales are large and meaningful at about 200 Ghana Cedis (GhC) for both men andwomen and not statistically significantly different by gender. Relative to February sales levelsfor female-owned firms, this drop amounts of about a 67% drop in revenues. Relative toFebruary sales levels for male-owned firms, this drop amounts to something closer to a 60%8Appendix Figures B.1 and B.2 explore this same time period using different measures of closure: (1)monthly enterprise profits of zero and (2) monthly owner labor hours of zero. Both figures follow the sametrend in terms of magnitude and significance.57drop in revenues, where the difference is due to pre-pandemic differences in average firmrevenues by gender.Both female- and male-owned businesses see economically and statistically significantincreases in closure during the crisis (46.8 percentage points and 37.6 percentage points,respectively), with female-owned businesses closing 9.1 percentage points more. Note thatthis difference in monthly closure is driven by peak-crisis months (as seen in Figure 3.2).Taking all five crisis months together, as we document in Column (9), there is no statisticaldifference in the share of male- and female-owned firms that persistently close.We also estimate level sales drops in two sub-samples: firm-months with any positivesales and surviving (not persistently-closing) firms. In these conditional estimates, the equallevel drops pattern remains the same. We fail to reject any difference in the level revenueslosses of male- and female-owned firms over the crisis months we observe.9Differential Selection into Persistent Closure by Gender We now turn our attentionto firm exit selection patterns. Figure 3.3(a) and 3.3(b) plot the distribution of pre-crisissales by persistent closure status for female- and male-owned firms, respectively. Persistentlyclosing female-owned firms have a pre-crisis sales distribution that is visibly shifted to the leftof female-owned firms that do not persistently close. These two distributions are significantlydifferent with a Kolmogorov–Smirnov test p-value of 0.00. The pre-crisis sales distributionfor enterprises owned by men that experience persistent closure and those that do not arevisually and statistically overlapping (Kolmogorov–Smirnov test p-value of 0.95).Table 3.2 tests for mean differences in pre-pandemic sales and other characteristicsbetween enterprises and owners by persistent closure status, separately for men and women.Female-owned firms that persistently close are significantly negatively selected relative tofemale-owned firms that do not on all three of our proxies of pre-pandemic firm size (and pro-ductivity): monthly sales, monthly total wage bill, and number of workers. Importantly, Panel9To corroborate these findings, we also consider how enterprise profits and owner hours worked responseto the crisis. Results are reported in Appendix Table B.4. Consistent with our revenue findings, we fail toreject any difference in profit level losses between male- and female-owned firms.58B shows no similar pattern of selection by pre-pandemic owner or household characteristicsfor women. Persistently closed male-owned firms exhibit no significant selection on enterprisecharacteristics. Instead, male owners who persistently close are positively selected on theexistence of a pre-crisis income source outside of the garment-making firm. Male owners whopersistently close are 36 percentage points more likely to have another income-generatingactivity outside of the garment firm pre-crisis.Total Labor Earnings and Total Labor Supply In Table 3.3 we present evidence thatdifferential closure selection by gender is associated with differential impacts on labor supplyand total market earnings, using the following ANCOVA specification:Yi,post = α + β1PCi + β2 ∗ Yi,pre + ϵi (3.2)where Yi,post is the outcome of interest for owner i in July 2020, Yi,pre is the February2020 value of the outcome of interest, and PCi is an indicator for if the firm owner’s businesspersistently closed during the observed period. The specification is run separately for male-and female- owned firms, and to test the difference in β1 between men and women, we run apooled regression with both men and women where a binary indicator for if an owner is maleis fully interacted with equation 3.2. The table reports robust standard errors.Women whose businesses persistently close have a 57 GhC lower total income inJuly 2020 compared to women whose businesses do not, significant at the one percent level.We detect no significant decrease in total income for male owners of persistently closedbusinesses, relative to male owners whose firms reopen. Women whose firms persistently closeare 26 percentage points more likely than their male counterparts to exit to non-employment,working zero hours outside the home in July 2020. While both male and female ownersexperience large and statistically significant drops in firm profits (their income from thebusiness), only men experience large and statistically significant increases in income fromother sources.593.3 InterpretationWhat drives these gender differences in informal labor market resilience in our context? Weargue that female-owned firms are simply forced to exit to non-employment by the economicrealities of a large negative shock, while the patterns observed for male entrepreneurs aremore consistent with an occupational choice model of outside options relative to self-employedgarment-making during crisis. These two disparate explanations are consistent with sharedunderlying fundamentals: marginal female-owned firms (near the viability threshold) existwhere marginal male-owned do not precisely because of static underlying differences inoccupational choice.Observing the density of sales across male- and female-owned firms in the pre-pandemicperiod in Figure 3.1, it is immediately clear that an equal level shift to the left should resultin more female- than male-owned businesses reaching a zero sales threshold. This simplevisual construction would suggest that it is precisely those firms clustered close to zero priorto the pandemic that tip into closure in the presence of a large level shock. The averagesize of the monthly level shift relative to February 2020 is about 200 GhC, which visuallycorresponds to the left-tail female-owned firms we observe exiting in response to the shock.While not every firm with sales below this level shift threshold is observed closing, the simpledifference in mass in proximity to zero revenues by gender can explain a large number ofmarginal female-owned firms closing where male-owned firms in the left-tail of the male salesdistribution do not.Prior work from this setting (and other work on gender differences around the world)provides some insight into this pre-pandemic equilibrium. Why is there a cluster of marginalnear-zero earnings female-owned firms prior to the pandemic? Hardy and Kagy (2020)document more crowding in female dominant self-employed occupations, pondering whetherthis difference in crowding may result from cultural constraints in occupational choice,relative to male counterparts. This crowding could lead to lower average occupational matchquality for women, with respect to the craft itself or to entrepreneurship. In other data60from Ghana (collected for Hardy, Mbiti, Mccasland, and Salcher (2019) and Hardy andMcCasland (in press)), applicants to study garment-making in informal firms were asked toreport their motivations for pursuing the craft. Male applicants to study garment-makingwere 11 percentage points more likely than their female counterparts to report a passionfor the craft and 6 percentage points more likely to report that training in the trade wouldcomplement their existing skillset, both statistically significant at the one percent level.It is precisely these underlying differences in occupational choice that we argue alsoexplain the closure selection pattern we observe for male-owned firms. As documented byHallward-Driemeier (2013), Gindling and Newhouse (2014), and Calderon, Iacovone, andJuarez (2017), male firm owners across a variety of settings likely have more access to wageemployment and other opportunities outside small firm self-employment. In addition, Hardyand Kagy (2020), Ashraf, Delfino, and Glaeser (2019), and Delecourt and Fitzpatrick (2021)suggest that within self-employment, men face fewer occupational choice constraints about theindustry or sector in which to pursue entrepreneurship. In the context of agriculture, seminalwork by Goldstein and Udry (2008) documents that men benefit from differential agriculturalland property rights, suggesting men face fewer constraints in integrating farming into theirbasket of income-generating activities. Taken together, the literature supports both theabsence of a cluster of marginal male entrepreneurs in the pre-pandemic equilibrium (becausethose men would have already selected out) and the presence of economically lucrativeoutside options for male entrepreneurs who experience shocks to their core garment-makingbusinesses.Considering the context of the garment-making industry described in Section 2,we believe it is reasonable to suggest that garment-making may have been harder hit bythe pandemic, relative to the modal outside option for men in our context, self-employedagriculture. Although the lockdown in Ghana was quite time-limited, large events likefunerals, weddings, church revivals, and community meetings were limited for the durationof our data. Anecdotally, a large share of bespoke garment purchases correspond with61these culturally important large events. This industry-specific shock may have shifted thecost-benefit calculus for men whose suite of pre-pandemic income generating opportunitiesincluded those in industries less exposed to the level shock of the COVID-19 crisis (likefarming). Note that men’s access to outside opportunities is orthogonal to pre-pandemicrevenues in the core garment-making business, as shown in Appendix Figure B.3(b). Thisorthogonality is consistent with our finding that selection into closure for men is not predictedby pre-pandemic sales.Finally, our findings on total labor supply and total earnings (including both thecore garment-making business and all other income-generating activities) are consistent withunderlying occupational choice being the key mechanism through which differential closureselection patterns function. Women who close their firms are 26 percentage points morelikely than men to exit to non-employment, suggesting that these women close their firmsbecause the firms are not viable under the current economic conditions and not becausebetter income-generating opportunities await.In Table 3.2, we also explore care-taking responsibilities as a possible source of selectioninto firm exit for women (and men). Although we lack detailed time use data, we find norelationship between the presence of children age 15 or under in the household and firm exitfor women (or men), in contrast to work that finds shocks to care-taking responsibilities drovefemale exit from the labor force during the COVID-19 crisis in high-income countries (Fairlie,Couch, & Xu, 2021; Albanesi & Kim, 2021). While it is quite likely that women carry largerstatus quo care-taking burdens in our context and that women experienced larger shocksto care-taking burdens in response to the crisis, we have no evidence that these differentialcare-taking burdens drove differential firm exit.623.4 Conclusion and Policy TakeawaysThe findings from this paper have implications for research and policy. We provide someof the first evidence on informal labor market resilience by gender during the COVID-19crisis, documenting differing dynamics and predictors of earnings loss for male and femalefirm owners. Vulnerable women appear to be falling off of the economic ladder and shiftinginto non-employment rather than other income generating opportunities. Men in this contextappear more resilient to economic shocks. These findings are occurring in a context thatalready has existing gender inequities, as women-owned enterprises are less profitable thantheir male-owned counterparts in general. As policy makers and governments continue torespond to the ongoing COVID-19 pandemic and begin to prepare for future disruptions,incorporating gender specific evidence and approaches is necessary.63Figure 3.1: Distribution of Feb 2020 sales by GenderNote: This figure shows the distribution of February 2020 sales by gender for the 490 firmowners that make up our analysis sample, winsorizing the top earning female-owned firmfor visual clarity. Data comes from the retrospective six-month panel of firm outcomescollected via phone survey. Male-owned firms are indicated in the dashed line (102 firms)and female-owned in the solid line (388 firms).64Figure 3.2: Share of Firms with Positive Sales, by Gender and MonthNote: This figure shows the share of firms reporting positive monthly sales (>0), for Februarythrough July 2020. Data comes from the retrospective six-month panel of firm outcomescollected via phone survey for the 490 firm owners that constitute our analysis sample.Male-owned firms are indicated with a triangle (102 firms) and female-owned with a circle(388 firms). Bars represent the 95% confidence interval.65(a) Female firm owners(b) Male firm ownersFigure 3.3: Distribution of Feb 2020 Sales by Persistent Closure Status andGenderNote: This figure shows the distribution of February 2020 sales by persistent closure statusfor the 490 firm owners that make up our analysis sample, winsorizing the top earningfemale-owned firm for visual clarity. Persistent closure is defined as having zero sales forat least four of the five observed crisis months. Female-owners are shown in panel A, andmale-owners are shown in panel B. Data comes from the retrospective six-month panel offirm outcomes collected via phone survey.66Table 3.1: Firm Sales, Before and During Crisis(1) (2) (3) (4) (5) (6) (7) (8) (9)Sales, GHC Sales > 0 Sales, GHC Sales, GHC Persistent(Conditional on (Conditional on CloserSales > 0) Survivor) (0/1)Females Males Females Males Females Males Females MalesCrisis period (Mar-Jul) -201.7*** -209.1*** -0.468*** -0.376*** -116.0*** -126.8*** -207.1*** -194.9***(18.73) (28.28) (0.0134) (0.0286) (18.71) (30.09) (22.85) (32.27)=1 if Male -0.036(0.041)Difference (male-female) -7.445 0.0911*** -10.71 12.15(33.82) (0.0315) (35.33) (39.41)Constant 299.2*** 345.5*** 1.00 1.00 299.2*** 345.5*** 323.4*** 352.7*** 0.193***(19.51) (29.48) (.) (.) (19.51) (29.50) (23.61) (33.91) (0.020)Observations 2328 612 2328 612 1421 420 1878 516 490R2 0.119 0.114 0.128 0.091 0.045 0.051 0.110 0.092 0.001Num. of clusters 388 102 388 102 388 102 313 86 490Note: Columns 1-8 report changes in monthly revenues between the pre-crisis period (February) and the crisis period (March-July). Columns 5-6 onlyconsider owners who had positive sales in a given month. Columns 7-8 only consider owners who reported positive sales for two or more of the crisismonths, i.e. not persistent closers/survivors. The difference row presents the coefficient on the interaction term between male and the crisis periodindicator in a pooled regression with both female and male owners. The final column considers an indicator for whether or not the firm owner reportedzero sales for at least four of the five crisis months, i.e. the firm is a persistent closer. Data comes from the retrospective six-month panel of firm outcomescollected via phone survey. Standard errors are clustered at the firm level. * p<0.1, ** p<0.05, *** p<0.01.67Table 3.2: Selection Into Persistent Closure on Pre-Crisis Firm and Owner Characteristics(1) (2) (3) (4) (5) (6)Females MalesNot NotPersistent Persistent Persistent PersistentCloser Closer Diff Closer Closer DiffPanel A: Firm Characteristics, February 2020Sales (GHC) 323.38 198.13 -125.24*** 352.67 306.88 -45.80(417.53) (154.81) (29.58) (314.15) (186.97) (56.97)Wage Bill (GHC) 6.05 1.73 -4.32* 14.07 21.88 7.81(32.11) (13.89) (2.42) (44.73) (69.88) (17.76)Non-Owner Workers 1.10 0.73 -0.37* 0.97 0.62 -0.34(2.10) (1.51) (0.21) (1.66) (0.81) (0.27)Firm Age 14.38 14.29 -0.09 18.01 18.31 0.30(8.74) (9.26) (1.17) (12.53) (12.83) (3.42)Panel B: Owner Characteristics, February 2020Age 38.61 38.66 0.05 41.31 39.88 -1.43(8.34) (8.57) (1.11) (11.33) (11.62) (3.10)Years Schooling 10.02 10.74 0.72 10.53 11.69 1.16(4.58) (4.33) (0.57) (4.89) (4.17) (1.15)Married (0/1) 0.76 0.72 -0.04 0.88 0.81 -0.07(0.43) (0.45) (0.06) (0.32) (0.40) (0.10)Children in the Home (0/1) 0.81 0.73 -0.08 0.84 0.75 -0.09(0.39) (0.45) (0.06) (0.37) (0.45) (0.12)Other Income-Generating Activity (0/1) 0.18 0.17 -0.01 0.27 0.62 0.36***(0.38) (0.38) (0.05) (0.45) (0.50) (0.13)Observations 313 75 86 16Share of firms, by gender 0.81 0.19 0.84 0.16Note: Sales, wage bill, firm size, married, children in the house, and presence of other income-generating activity come fromthe retrospective phone survey and correspond with February 2020. Firm age, age, and years of schooling were measured in theSeptember 2019 census. Persistent closure is defined as having zero sales for at least four of the five observed crisis months. Sales,wage bill, and non-owner workers have not been winsorized. Children in the home are defined as people age 15 and below who eatfrom the same pot. Columns 3 and 6 report results of a t-test of difference in means by persistent closure status, by gender of firmowner. In columns 3 and 6, standard errors are in parenthesis. * p<0.1, ** p<0.05, *** p<0.01.68Table 3.3: Income and Labor Supply by Persistent Closure Status(1) (2) (3) (4) (5) (6) (7) (8)Total income, GHC Total hours >0 Firm profits, GHC Other Income, GHCFemales Males Females Males Females Males Females Males=1 if Persistent Closer -57.7*** 159 -0.53*** -0.28** -88.9*** -117*** 24.0 275*(21.2) (138) (0.058) (0.12) (8.40) (17.9) (19.5) (141)Difference (male - female) 223 0.26* -25.2 244*(138) (0.13) (21.5) (141)Constant 91.3*** 214*** 0.77*** 1*** 77.1*** 106*** 17.9** 101***(12.9) (45.5) (0.17) (6.7e-08) (7.95) (22.1) (7.66) (27.1)Observations 388 102 388 102 388 102 388 102R2 0.18 0.05 0.37 0.14 0.36 0.21 0.19 0.11Note: This table reports how income and labor supply of hours changed for firm owners between the pre-crisis period (February) andJuly by persistent closure status and gender. All columns report the results from an ANCOVA specification where the Februarylevel of the outcome is controlled for. The outcomes considered are: firm owner’s total reported income in July 2020 (includingfrom the firm of interest, other self-employment, wage earnings, and farm profits); if the firm owner reported working >0 hours perweek across all income generating activities in July 2020; profits from the garment-making firm; and other income in GhC (fromother self-employment, wage earnings and farm profits). Persistent closure is defined as having zero sales for at least four of the fiveobserved crisis months. The difference row presents the coefficient on the interaction term between male and persistently closed in apooled regression with both female and male owners. Data comes from the retrospective six-month panel of firm outcomes collectedvia phone survey. Robust standard errors are reported in parentheses. * p<0.1, ** p<0.05, *** p<0.01.69Chapter 4When Technology Adoption InterventionsFailThe Case of Domestic Biogas Digesters in Senegal14.1 IntroductionEnergy poverty remains a critical global challenge, with significant implications for health,environment, and socioeconomic development. As of 2016, only 60% of the global populationhad access to clean cooking technologies and fuels (IEA, 2023). The remaining 3 billionpeople, primarily in developing countries, rely on traditional solid fuels like firewood, charcoal,and dung for cooking, often using inefficient stoves or open fires. This reliance on traditionalfuels and technologies has severe consequences. Indoor air pollution from cooking with solidfuels contributes to approximately 4 million deaths annually, disproportionately affectingwomen and children (World Health Organization, 2016). The environmental impact is alsosubstantial, with emissions from woodfuel combustion accounting for up to 11% of totalgreenhouse gas emissions in sub-Saharan Africa (Bailis, Drigo, Ghilardi, & Masera, 2015).Biogas technology has emerged as a potential solution to address energy poverty,particularly in rural areas with prevalent animal husbandry. Biogas digesters convert organicmaterials like cow dung into methane gas for cooking and lighting, as well as nutrient-richslurry for fertilizer. This technology offers multiple potential benefits, including reduced1This chapter is joint work with Ousmane Ndiaye, Jörg Ankel-Peters, and Marc Jeuland.70reliance on traditional fuels, improved indoor air quality, reduced greenhouse gas emissions,and potential agricultural productivity gains.The adoption and impact of biogas and other improved cooking technologies havebeen the subject of extensive research. Theoretical treatments of adoption have consideredhousehold decision-making in a utility maximization framework, where households choose howmuch averting behavior to adopt based on the costs and benefits of the technology (Pattanayaket al., 2018). Empirical studies have identified various factors influencing adoption, includingincome, education, urban status, and fuel prices (Lewis & Pattanayak, 2012). More recentresearch has highlighted the importance of program and policy characteristics, technicalaspects of the technology, and market development (Puzzolo, Pope, Stanistreet, Rehfuess, &Bruce, 2016).Studies specifically focused on biogas adoption in sub-Saharan Africa have revealedcharacteristics that may be associated with successful digester operation. In Kenya, Mwirigiet al. (2009) found that socioeconomic status was correlated with initial adoption but notwith maintenance and operation of digesters. In Uganda, Walekhwa, Mugisha, and Drake(2009) identified household size, wealth, and cattle ownership as positive predictors of biogasadoption, while remoteness was negatively associated.Impact evaluations of biogas and other improved cooking interventions have shownmixed results. In Rwanda, Bedi et al. (2015) found that biogas digesters reduced firewoodconsumption by about 30% and were associated with a 3-tonne annual reduction in CO2equivalent emissions per household. However, they found no impacts on overall time use. InSenegal, evaluations of solar stoves and improved wood and charcoal stoves have shown somereductions in traditional fuel use (Beltramo & Levine, 2013; Bensch & Peters, 2013), withone intervention reducing fuel use by enough to also produce positive health impacts (Bensch& Peters, 2015).This paper evaluates Senegal’s domestic biogas program, known as the Projet Nationalde Biogaz du Senegal (National Biogas Project of Senegal, PNB-SN). In Senegal, 76% of71households lack access to clean cooking technologies and fuels, with this figure rising to 95%in rural areas (Tracking SDG 7: Senegal Country Report , 2022). The reliance on firewoodis particularly problematic, contributing to deforestation and desertification in the Sahelregion (Brandt, Romankiewicz, Spiekermann, & Samimi, 2014). PNB-SN aims to promotethe development of a national, commercially viable biogas sector by addressing both supplyand demand aspects of the market (Programme National De Biogaz Domestique Du Sénégal,n.d.).Our evaluation of this program reveals a key finding: widespread failure of installeddigesters. We find that 50% of the digesters in our sample never produced enough gas forcooking, and only 15% were fully operational at the time of our survey. This high failure ratehas significant implications for the program’s effectiveness and raises important questionsabout the suitability of the technology in the local context.To evaluate the program, we employed a mixed-methods approach combining quantita-tive and qualitative data. Our sample included 97 households that received biogas installations(treatment group) and 99 households selected for future installations but not yet treated(control group). We conducted household surveys covering demographics, energy use, healthoutcomes, agricultural practices, and perceptions of the biogas program. We used econometrictechniques to analyze the determinants of digester operation and to estimate the impactof the program on various outcomes. Our main results indicate that the program has notachieved its desired impacts of transitioning households to cleaner energy sources. We findno statistically significant effects on traditional fuel use, health outcomes, employment rates,or agricultural productivity. The high failure rate of digesters appears to be the primarydriver of these null effects. However, we do identify factors correlated with successful digesteroperation, including participation in training programs, proximity to markets, and motivationfor fertilizer production.These findings have important implications for energy access interventions in Senegaland similar contexts. They highlight the challenges of implementing complex technologies72in resource-constrained environments and underscore the need for robust support systems,appropriate technology design, and careful consideration of local contexts in program planningand implementation.4.2 Senegal’s Domestic Biogas ProgramThe Projet National de Biogaz du Senegal (PNB-SN) operates under the Ministry of Energyand is one of three programs aimed at increasing access to improved cooking technologiesin Senegal. The other two programs focus on improved charcoal and firewood cookstoves,primarily targeting households in urban areas.PNB-SN aims to promote the development of a national, commercially viable biogassector by addressing both supply and demand aspects of the market (Programme NationalDe Biogaz Domestique Du Sénégal, n.d.). The program partners with local implementingpartners to promote biogas technology and identify eligible households, collaborates withlocal masons and companies for digester installations and maintenance, and offers financingmechanisms and subsidies to increase affordability. It targets rural households in regionswith prevalent cattle rearing and provides training on digester operation, maintenance, andassociated agricultural practices.The program’s implementation involves a selection process for eligible householdsbased on criteria such as ownership of at least 10 cows, access to water, presence of a brickkitchen, sufficient space for digester installation, and ability to make in-kind contributions forconstruction. PNB-SN states that they subsidized 50% of installation costs, with householdscontributing the remainder through in-kind materials or cash payments.2 At it’s outset theprogram aimed to negotiate with local financial institutions to offer credit with favorableterms to biogas applicants.2According to program documents, an 18 cubic meter digester would cost 728,300 CFA (1,200 USD) toinstall (Programme National De Biogaz Domestique Du Sénégal, n.d.). Half of this, 364,150 CFA, is supposedto be paid by PNB-SN directly to the construction companies in charge of installation, while the remaining ispaid by the household as an in-kind contribution of construction materials or through cash contributions,which can also be financed via credit.73The program’s history can be divided into two main phases: the Pilot Phase (2009-2013) and the Large-Scale Dissemination Phase (2015-2019). During the pilot phase, 600digesters were installed across twelve regions, but the program faced several challenges,including failure to meet installation goals, high costs limiting affordability, unfavorable loanterms, lack of experience among personnel, and issues with feedstock availability. Based onlessons from the pilot phase, PNB-SN adapted the program for the full-scale rollout. Theprimary objective was to construct 10,000 digesters by 2019 and achieve a 90% operation rate.However, by 2019, only 2,652 digesters had been installed, of which 2,611 were at householdsand 41 at schools. Of these, only 967 were reported to be operational.The program’s broader development goals included improving access to modernenergy services, enhancing soil fertility through organic fertilizers, improving herd andmanure management systems, and reducing extreme poverty by improving living conditions,decreasing respiratory health problems, and promoting gender equity.4.3 Evaluation Methodology4.3.1 Research QuestionsOur evaluation addresses two main categories of research questions. The first category focuseson program implementation and digester operation, investigating the digester installationprocess, maintenance after after installation, what factors are related to household operationand maintenance, and how households perceived the program and installed digesters. Thesecond category examines program impacts, including the effect of biogas installations onhousehold energy use, respiratory health of children and cooks, agricultural productivity, andemployment outcomes.744.3.2 Sampling and Data CollectionOur evaluation focuses on installations initiated between 2015 and 2017 during the first halfof PNB-SN’s Large-Scale Dissemination Phase. We sampled two groups: a treatment groupof 97 households that received biogas installations and a control group of 99 householdsselected for future installations but not yet treated. The sampling process is illustrated inFigure 4.1 and discussed in more detail in Appendix Section C.1.Data collection occurred in February 2019, covering 196 households across 50 villages.Our survey included modules on household demographics, education, health, employment,daily time use, energy use, agricultural and livestock practices, biogas installation andoperation, and knowledge and perception of PNB-SN and biogas digesters.4.3.3 Analytical ApproachWe employ two main analytical approaches, which are presented briefly here and discussed indetail in Appendix Section C.1. First, we analyze determinants of digester operation usinglogistic and linear probability models. We consider three binary outcome variables: whetherthe digester was operating on the survey day, whether it had operated for at least half of itslife, and whether it had ever operated. We also use a continuous outcome variable measuringthe share of time since installation that the digester had been at least partially operational.Second, for our impact evaluation, we estimate the effect of receiving a digesterinstallation on key outcomes using the following equation:Yij = α + βBij + γXij + δZij + θVj + ϵij (4.1)where Yij is the outcome of interest, Bij is a binary measure indicating biogas installation,Xij are demographic controls, Zij and Vj are household and village characteristics that mayinfluence installation timing, and ϵij is the error term. We cluster standard errors at thevillage level to account for intra-village correlation.754.4 Key Findings4.4.1 Digester Installation and OperationWe have detailed data on the installation process from the treatment households. The majorityreceived fixed dome digesters, while a few plastic tubular digesters have been installed inTambacounda region.3 According to PNB-SN documents, the exact size of digester that ahousehold receives is determined by household size and the number of cows that the householdmanages. The majority of households in our sample report installing an 18 cubic meterdigester and, on average, the digesters took a little over a month to install.4 Anywherebetween one and 15 masons were involved in the installation processes, with a median of fouramongst our sample of 97 households who received installations. Two-thirds of the householdswe sampled received some sort of training or trainings on how to operate and maintain thedigester. At the time of our data collection in February 2019, digesters in our sample wereon average 2 years old.Our analysis reveals widespread failure of installed digesters, as illustrated in Figure4.2. Half of the digesters (48 out of 97) never produced enough gas for cooking, and only15% (15 out of 97) were fully operational at the time of the survey. Of the 50% that did atsome point produce enough gas to cook, 44 of them broke at least once. On average, thedigesters had been broken so badly as to be totally inoperable for 70 percent of the timesince installation; 15 percent of the time they had been partially broken, i.e. they were notworking to their full capacity. This means that, on average, digesters had only operated wellfor 15 percent of their usable life to date.Despite this high breakage rate, 90 percent of households whose digesters were notoperating on the day of the survey reported that they had never sought to repair their digester.Thirteen households attempted to repair their digester at least once. Of these 13, only fourwere successful in all repair attempts. Of the 13 households who reported doing repairs, they3See Surendra et al. (2014) for illustrations of the fixed dome and plastic tubular digesters.4Only 52 out of 100 households who had biogas installations could report the size of the installation.76report spending between 0 and 40,000 CFA on all repairs, with an average total cost of 8,000CFA across these 13 households.In the end, 15 digesters were operating on the day of the survey. Five of these werethose that had never broken; four had broken but were successfully repaired; six had partiallybroken and had not been repaired but were still operating in some capacity. The remaining82 digesters were not operating at the time of our survey. They had been broken for at mostthree years, at the shortest, two weeks. Forty-eight of these were the digesters that hadnever functioned. Twenty-six of the 82 had been operational at some point, but they hadbroken and were never repaired. The remaining eight digesters had broken at least once, thehousehold had tried to repair them, and may even have been successful at some point, butthe last repair was not successful as the digester was not operating on the day of our visit.Of the 15 digesters that were operating on the day of the survey, only eleven householdsreported using the digester to cook at some point in the preceding week. Five householdsreport that their main cooking stove is the biogas stove. Two of these households owneddigesters that had operated fully since installation, one had a digester that broke but wassuccessfully repaired, and two use digesters that had partially broken, never been repaired,but were still usable. Interestingly, of the five households whose digesters had functionedoptimally since installation, two report using biogas as their main stove, one reports usingLPG, and two report that a traditional, three-stone stove is their main cooking stove.5Despite these high breakdown rates, 56% percent of recipient households said they wereoverall somewhat or very satisfied with their biogas digester (Figure 4.3).6 When specificallyasked about cooking and lighting, households were more likely to report being dissatisfied,with fifty percent of households reporting that they were very dissatisfied with the digester’s5Only six households total report ever using biogas as a lighting source.6The satisfaction measures reported may be subject to a degree of yea saying bias, or a tendency torespond positively to questions that are presented. This could help to explain the seemingly high satisfactionrate, especially as compared to the digesters’ low operation rate. It also may be that households are hesitantto criticize a government program or service. The most common response to “What do you dislike aboutthe digester?” was “Nothing.” But, among those who stated they had no dislikes, almost half reported beingdissatisfied with the digester. This inconsistency indicates that some households are hesitant to discussnegative aspects of a program and may lead some households to overstate their satisfaction with the digesters.77performance with respect to lighting.Determinants of Digester OperationTable 4.1 presents the results of our analysis of factors associated with digester operation.Having a flexible balloon digester (as opposed to a fixed dome digester) is positively associatedwith operation across all measures, though not statistically significant. Receiving training isstrongly and positively related to operation, particularly for ever operating (47 percentagepoint increase in likelihood). Having a toilet connected to the digester is a strong predictorof ever-functioning digesters. Household characteristics also play a role in digester operation.Household size and the share of female members are negatively associated with operation,while proximity to markets is positively associated, particularly for ever operating. Householdwealth and liquidity are positive predictors of operation. Interestingly, motivation for fertilizerproduction is significantly and positively related to operation (about 20 percentage pointincrease across all specifications). The number of cattle owned is positively associated withoperation, though not statistically significant.4.4.2 Impact AnalysisHere we present a brief discussion of the impact analysis results. Not surprisingly, given thehigh rates of digester failure, we do not find effects of the program on any of the targetedoutcomes. A more detailed presentation of these results can be found in Appendix SectionC.2.Balance and Descriptive StatisticsTable 4.2 reports descriptive statistics for our sample and tests for differences betweentreatment and control groups. The households in our sample are predominantly rural, witha median size of 13 individuals. Most are male-headed households (80% with no schoolingoutside Koranic education), predominantly Muslim, and members of the majority ethnic78group in their village. Most households cook indoors. The treatment and control groups aresimilar across most characteristics, with the main significant difference being distance to thenearest bus stop, with treatment households being closer.Fuel Use and Cooking ImpactsOur analysis of fuel use outcomes, presented in Table 4.3, shows a 26% decrease in thegeometric mean of daily firewood use (about 1.4 kg) associated with biogas installation.However, this effect is not statistically significant when controls are added. We find verysmall and non-significant decreases in charcoal use and increases in LPG use.Table 4.4 shows impacts on other fuel and cooking outcomes. Biogas installationis associated with a reduction in cook time by 14 minutes per day and a decrease in fuelexpenditure by 35% or 2,300 CFA/month, though these effects are not statistically significant.We find no significant impact on fuel collection time.Health ImpactsOur analysis reveals no statistically significant effects of biogas installation on the incidenceof cough or ALRI symptoms in children under 12, respiratory health outcomes for cooks(ages 12 and above), or eye problems for children or cooks.Agricultural and Employment ImpactsWe find no significant effects of biogas installation on agricultural outcomes such as thenumber of crops grown, crop revenue, use of improved fertilizers (including bioslurry), orharvests of commonly grown crops (peanuts, millet, rice). Similarly, we observe no significantimpact on employment rates.79Impacts of Operational DigestersEven when considering only households with digesters that operated for at least half oftheir usable life, we do not find statistically significant impacts on fuel use, health, orproductivity outcomes (Tables 4.5 and 4.6). This analysis is limited by the small number ofoperating digesters. To attempt to overcome this, we defined operating digesters as thosewho had operated for half of their usable life, as opposed to more restrictive definitions.One interpretation of these null findings may be then that digesters have to reach a higherthreshold of operation before producing impacts on energy, health, or economic outcomes.4.5 DiscussionThe widespread failure of biogas digesters in Senegal’s program has significant implications forprogress towards reducing energy poverty, missed opportunities for health and environmentalbenefits, and inefficient use of resources and investments. Our study documents even higherfailure rates than past evaluations. The impact findings contrast with evaluations of otherbiogas programs, such as in Rwanda, where digesters helped substitute some fuelwood andcharcoal use (Bedi et al., 2015). However, even in these cases where the program has impact,the payback period for households can be long given the high costs of installation (seeAppendix Section C.3 for data on the installation costs of PNB-SN), raising questions aboutthe cost-effectiveness of such interventions.Several factors may contribute to the PNB-SN program’s lack of success, includinginadequate training and support for digester operation and maintenance, limited access toaffordable credit and spare parts, weak supply chains and maintenance networks, and apossible mismatch between the technology and local contexts or user needs. The strongand consistent association between fertilizer production motivation and digester operationsuggests that emphasizing this benefit in program marketing and targeting could improveoutcomes.80It’s important to note the limitations of our study, including potential selection biasin the timing of installations, limited statistical power for detecting small effects, and thecross-sectional, ex-post design limiting our ability to capture details of digester operationover time.4.6 Conclusions and RecommendationsThis evaluation reveals that Senegal’s domestic biogas program has failed to achieve itsobjectives due to widespread digester failure. Our analysis provides suggestive evidence thatfuture interventions could be improved by ensuring comprehensive training for all recipienthouseholds. Additionally, improving access to maintenance and spare parts could improvedigester operation. This could involve training local technicians and establishing partnershipswith suppliers.The program could also focus on motivated households, particularly those interestedin fertilizer production, as this was a significant predictor of successful operation. This mayinvolve adjusting marketing strategies and selection criteria. Addressing gender dynamicsis important, given the negative association between number of female household membersand digester operation. This may involve tailoring training programs or technology design tobetter suit women’s needs and constraints. Strengthening the supply chain by developinga more robust network for biogas technology, including diversifying product offerings andengaging more installation companies, could also improve outcomes.Before further program expansion, it is essential to conduct thorough feasibility studiesto assess local contexts, including availability of feedstock, water access, and household energyneeds. The program should also consider exploring alternative technologies that might bemore appropriate or cost-effective in the Senegalese context.Implementing rigorous monitoring and evaluation systems to track digester perfor-mance and household usage patterns over time is crucial for ongoing program improvement.81Finally, revising program goals to set realistic targets based on the challenges identified andthe program’s capacity to address them is necessary.It is important to note that these suggestions are largely based on correlationalevidence, so evaluations of program changes are needed to ensure impact. Given the highcosts associated with biogas installations, it’s crucial that any program modifications arerigorously evaluated to ensure they produce benefits commensurate with costs. The currentlow rate of successful digester operation and lack of measurable impacts suggest a very low,possibly zero, rate of return on investment for the program in its current form.Future research should focus on understanding the long-term sustainability of biogastechnologies in rural contexts, investigating the gender dynamics of adoption and use, exploringalternative approaches to reducing energy poverty in sub-Saharan Africa, conducting cost-benefit analyses of different clean cooking interventions, including different technologies andscales, and examining the potential for creating markets for biogas by-products, such asorganic fertilizer.In conclusion, while biogas technology may hold promise for addressing multipledevelopment challenges, our evaluation of Senegal’s program highlights the significant hurdlesin implementing such interventions effectively. The lessons learned from this experience caninform the design and implementation of future energy access programs, not only in Senegalbut in similar contexts across sub-Saharan Africa. Ultimately, addressing energy poverty willlikely require a diverse portfolio of technologies and interventions, tailored to local contextsand continuously adapted based on rigorous evidence.82Figure 4.1: Sampling of treatment and control householdsNote: Items in bold indicate the number of households and/or villages from which the sample was selected.The blue and orange boxes indicate the final study sample, with blue indicating treatment households andorange indicating control.Figure 4.2: Digester operation and repair from installation to interviewNote: The numbers indicate the number of households that fall into each response category. The colorsindicate good “green” vs. bad “red” outcomes. For example, 48 of the digesters never started working, whichis considered a bad outcome.83Figure 4.3: Household satisfaction with biogas digesters(a) Never functioning digester (b) Ever functioning digesterNote: These figures plot household satisfaction with digester operation for lighting services (green), cookingservices (yellow), and overall (orange). The bars indicate the percentage of households that reported eachlevel of satisfaction, on the x-axes. Satisfaction levels are plotted separately for households whose digestersnever functioned and those whose digesters functioned for at least a short period of time.84Table 4.1: Determinants of digester operationCurrentlyOperated at least Ever Share ofoperating half the time operated time working(1) (2) (3) (4)Digester characteristicsDigester type (=0 if fixed dome, 0.16 0.13 0.13 0.097=1 if flexible balloon) (0.11) (0.14) (0.18) (0.12)Training (=1 if household received 0.13 0.38** 0.47*** 0.31**a training) (0.15) (0.15) (0.10) (0.13)Months since completion of 0.003 0.004 0.011* 0.004installation (0.007) (0.007) (0.006) (0.006)Toilet (=1 if toilet connected -0.018 0.25 0.80*** 0.20to digester) (0.099) (0.27) (0.100) (0.15)PricesPrice of firewood, avg CFA/kg -0.001 0.00002 0.0002 -0.000in village (0.001) (0.001) (0.002) (0.001)Price of charcoal, avg CFA/kg 0.001 0.001 0.000 -0.000in village (0.001) (0.001) (0.001) (0.001)Continued on next page85Table 4.1 – continued from previous pageCurrentlyOperated at least Ever Share ofoperating half the time operated time working(1) (2) (3) (4)Household characteristicsLog household size -0.14 -0.19* -0.15 -0.17*(0.097) (0.10) (0.11) (0.093)Share of household who is female -0.65** -0.38 -0.68* -0.38(0.30) (0.39) (0.38) (0.30)Education (=1 if household head has -0.015 -0.059 -0.047 -0.072any school above Koranic school) (0.12) (0.13) (0.10) (0.10)Log number of employed adults 0.040 0.056 -0.093 0.028in household (0.075) (0.083) (0.064) (0.068)Cubic-root distance to market, km1/3 -0.011 -0.11* -0.15* -0.084*(0.030) (0.062) (0.080) (0.045)Asset index 0.036 0.025 0.062** 0.032(0.028) (0.033) (0.028) (0.030)Access to a loan (=1 if can access -0.13 -0.11 -0.34*** -0.24***Continued on next page86Table 4.1 – continued from previous pageCurrentlyOperated at least Ever Share ofoperating half the time operated time working(1) (2) (3) (4)emergency funds via loan) (0.100) (0.11) (0.099) (0.082)Liquidity (=1 if can access 250,000 CFA -0.10 -0.030 0.19** 0.040in emergency funds) (0.081) (0.12) (0.090) (0.087)Perceptions=1 if energy savings is reason 0.011 0.016 -0.044 -0.010for installation (0.10) (0.12) (0.11) (0.10)=1 if fertilizer production is 0.20*** 0.20** 0.18* 0.18**reason for installation (0.044) (0.084) (0.095) (0.066)=1 if health is reason for installation 0.026 -0.015 -0.046 -0.067(0.090) (0.12) (0.079) (0.10)LivestockLog number of cows household 0.008 0.034 0.050 0.002looks after (0.044) (0.040) (0.040) (0.033)Minimal pasturing (=1 if cows out to -0.048 -0.15 -0.087 -0.14Continued on next page87Table 4.1 – continued from previous pageCurrentlyOperated at least Ever Share ofoperating half the time operated time working(1) (2) (3) (4)pasture <8 hrs/day) (0.082) (0.12) (0.11) (0.092)Manure as fertilizer (=1 if household 0.020 -0.087 -0.068 0.021applies manure directly to fields) (0.090) (0.11) (0.092) (0.084)Manure for cooking (=1 if household 0.46 0.18 0.14 0.29uses manure directly for cooking) (0.34) (0.28) (0.29) (0.24)Constant 0.62 0.62 0.75 0.83**(0.43) (0.47) (0.53) (0.39)Observations 93 93 93 93R-squared 0.233 0.322 0.513 0.364Note: Dependent variables are equal to 1 if digester operated on the day of survey (column 1), at leasthalf the time since installation (column 2), or if it ever operated (column 3); Dependent variable incolumn 4 is a continuous measure of the share of time since installation that the digester was workingfully or partially, equal to 1 if it was never fully broken; Standard errors are clustered at the village level(24 clusters); Robust standard errors in parentheses; * p<0.1, ** p<0.05, *** p<0.01.88Table 4.2: Difference in key characteristics between treatment and controlgroupsControl Treatment Difference NRural (=1 if rural) 0.99 1.00 0.01 196(0.10) (0.00) (0.01)Household size+ 14 13 -1 196(10) (7)Household size, male+ 7 6 -1 196(5) (4)Household size, female+ 7 6 -1 196(7) (5)Gender of household head (=1 if male) 0.93 0.96 0.03 196(0.26) (0.20) (0.04)Age of household head 55.06 51.66 -3.40 196(13.17) (13.16) (2.51)Education of HH head (=1 if any schooling 0.16 0.22 0.05 196above Koranic school) (0.37) (0.41) (0.07)Cooking location (=1 if inside, =0 if outside) 0.72 0.68 -0.04 196(0.45) (0.47) (0.08)Islamic (=1 if HH is Islamic) 0.97 0.95 -0.02 196(0.17) (0.22) (0.03)Ethnicity (=1 if ethnicity is the same as 0.90 0.88 -0.02 196village majority) (0.30) (0.33) (0.06)Political leadership (=1 if any HH member 0.10 0.07 -0.03 196has held political position at any time) (0.30) (0.26) (0.04)Religious leadership (=1 if any HH member 0.10 0.13 0.03 196has held religious position at any time) (0.30) (0.34) (0.05)CSO leadership (=1 if any HH member has 0.04 0.06 0.02 196held CSO position at any time) (0.20) (0.24) (0.03)Land owned or leased, hectares+ 2.75 3 0.25 194(5) (5.58)Asset Index -0.34 0.34 0.69 196(2.26) (2.09) (0.51)Access to a loan (=1 if can access 0.16 0.13 -0.03 196emergency funds via loan) (0.37) (0.34) (0.06)Liquidity (=1 if can access 250,000 CFA in 0.84 0.80 -0.03 196emergency funds) (0.37) (0.40) (0.06)Distance to nearest weekly market, km+ 10 5 -5 196(11) (7.33)Distance to nearest bus stop, km+ 2 1 -1*** 184(4.5) (2)Note: Means and standard deviations (in parentheses) are reported for each variable, for the treatmentand control groups; The distributions of variables marked with + are right-skewed, so the medianand interquartile range is reported; In column 3, the difference between treatment and controlmean or median is reported; Stars indicate the result of a t-test of the difference in means betweentreatment and control; For variables marked with ∧, the t-test is performed after a log or cubic-roottransformation to reduce skewness; * p<0.1, ** p<0.05, *** p<0.01; For all tests, standard errors areclustered at the village level.89Table 4.3: Impacts of biogas installation on fuel use outcomesLog firewood use, Cubic-root charcoal Cubic-root LPG use, 6ln(kg/day) use, kg/day1/3 kg cannisters/month1/3(1) (2) (3) (4) (5) (6)Biogas installation -0.31* -0.17 0.063 -0.097 0.060 0.020(0.16) (0.18) (0.097) (0.12) (0.087) (0.061)Cubic-root distance to -0.080 0.0038 0.070nearest weekly market, km1/3 (0.11) (0.067) (0.051)Cubic-root distance to 0.21 -0.15** 0.075*nearest bus stop, km1/3 (0.16) (0.072) (0.039)Log land owned 0.22** -0.00014 0.042or leased, ln(hectares) (0.082) (0.051) (0.031)Asset Index -0.097** 0.068*** 0.12***(0.040) (0.025) (0.018)Liquidity (=1 if can access 250,000 0.016 0.032 -0.0028CFA in emergency funds) (0.20) (0.094) (0.090)Constant 1.66*** 0.66 0.30*** 0.70 0.28*** -0.20(0.11) (0.81) (0.067) (0.46) (0.056) (0.28)Demographic controls Y Y Y Y Y YObservations 193 179 196 182 196 182R-squared 0.018 0.133 0.002 0.105 0.004 0.325Note: Robust standard errors in parentheses; standard errors clustered at the village level with 49 clusters; *** p<0.01,** p<0.05, * p<0.190Table 4.4: Impacts of biogas installation on time use and expenditure outcomesCook time, min/day Fuel collection time Total fuel expenditure(min/wk)1/3 ln(CFA/month)(1) (2) (3) (4) (5) (6)Biogas installation -25.1 -14.0 -0.53 -0.24 0.20 -0.44(19.3) (21.6) (0.60) (0.52) (0.83) (0.69)Cubic-root distance to nearest -15.9 0.12 0.73weekly market, km1/3 (11.0) (0.51) (0.48)Cubic-root distance to nearest 17.2 0.30 0.097bus stop, km1/3 (11.0) (0.27) (0.44)Log land owned or leased, 5.14 0.49** 0.21ln(hectares) (7.40) (0.23) (0.28)Asset Index -9.85** -0.52*** 0.98***(4.14) (0.12) (0.17)Liquidity (=1 if can access 250,000 17.7 0.32 -1.29CFA in emergency funds) (23.0) (0.73) (0.80)Constant 328*** 238*** 5.76*** 9.11*** 4.54*** 3.69(13.0) (73.4) (0.47) (2.27) (0.55) (3.06)Demographic controls N Y N Y N YObservations 182 168 196 182 196 182R-squared 0.014 0.142 0.006 0.217 0.000 0.248Note: Robust standard errors in parentheses; standard errors clustered at the village level with 49 clusters; *** p<0.01, **p<0.05, * p<0.191Table 4.5: Impacts of operational digesters on fuel-use, cooking and agricultural outcomesLog Cubic-root Cubic-root LPG Cook Fuel Total fuel Number Cropfirewood charcoal use, (6 kg time, collection expenditure, of crops revenue,use use cannisters/ min/day time, ln(CFA/ grown ln(CFA)ln(kg/day) (kg/day)1/3 month)1/3 (min/wk)1/3 month)(1) (2) (3) (4) (5) (6) (7) (8)Digester operated for at -0.0024 0.20 0.074 -21.6 -1.01 0.18 -0.24 0.67least half of it’s usable (0.51) (0.27) (0.14) (29.8) (1.10) (1.26) (0.45) (2.12)life (=1 if yes)Predicted probability of 0.25 0.33 -0.094 -3.48 0.72 1.30 0.85 1.90digester operating at least (0.77) (0.29) (0.28) (36.1) (2.01) (1.67) (0.88) (3.41)half of it’s usable lifeCubic-root distance to nearest -0.079 0.0096 0.070 -17.7 -0.20 1.29* -0.38** 1.11weekly market, km1/3 (0.18) (0.097) (0.070) (11.3) (0.71) (0.69) (0.18) (0.71)Cubic-root distance to 0.23 -0.16 0.12* 3.03 0.079 0.24 0.091 -1.62nearest bus stop, km1/3 (0.31) (0.13) (0.068) (22.4) (0.45) (0.74) (0.24) (1.30)Log land owned or 0.25 -0.027 0.028 9.62 0.069 0.52 0.28** 0.88leased, ln(hectares) (0.15) (0.057) (0.052) (11.1) (0.40) (0.54) (0.13) (0.57)Asset Index -0.15** 0.091** 0.15*** -7.94 -0.55*** 1.23*** -0.096 -0.87**(0.061) (0.034) (0.031) (7.51) (0.15) (0.23) (0.070) (0.39)Liquidity (=1 if can access -0.019 0.0067 -0.11 -20.3 1.45 -1.39 0.14 1.81250,000 CFA in (0.34) (0.20) (0.13) (35.2) (0.88) (1.25) (0.30) (2.22)emergency funds)Constant 1.68 -0.11 0.37 295*** 11.7*** 4.17 1.81* 0.32(1.25) (0.61) (0.34) (85.1) (3.54) (3.48) (0.98) (5.34)Demographic controls Y Y Y Y Y Y Y YObservations 83 85 85 78 85 85 85 85R-squared 0.191 0.154 0.473 0.168 0.292 0.453 0.258 0.197Note: Standard errors are clustered at the village level; Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.192Table 4.6: Impacts of operational digesters on individual health and employment outcomesChildren under 12 CooksCough ALRI Cough ALRI Employmentsymptoms symptoms status(1) (2) (3) (4) (5)Digester operated for at least half of it’s usable -0.19 -0.84 0.47 -0.57 0.012life (=1 if yes) (0.62) (1.31) (0.72) (0.85) (0.45)Predicted probability of digester operating at least -0.93 2.38 -0.80 -0.28 1.77**half of it’s usable life (1.45) (3.13) (1.25) (2.13) (0.69)Age, in years -0.13** -0.075 0.031* 0.039* 0.0032(0.057) (0.11) (0.017) (0.022) (0.0047)Sex (=1 if female) 0.58* 0.88 -1.82***(0.34) (0.56) (0.26)Cubic-root distance to nearest weekly market, km1/3 0.027 0.97* -0.15 -0.35 0.28(0.33) (0.58) (0.39) (0.46) (0.18)Cubic-root distance to nearest bus stop, km1/3 -0.81** -5.59*** -0.30 -1.55 -0.28(0.41) (1.29) (0.46) (0.99) (0.26)Log land owned or leased, ln(hectares) -0.45 -2.99** -0.16 -1.22 -0.051(0.36) (1.46) (0.28) (0.80) (0.14)Asset Index -0.050 0.56 -0.22 -0.12 0.093(0.15) (0.36) (0.15) (0.16) (0.076)Liquidity (=1 if can access 250,000 CFA 1.26* 1.31 0.77 1.13*in emergency funds) (0.76) (0.95) (0.95) (0.59)Constant -1.35 3.03 -3.44 -0.92 -2.45**(2.01) (2.65) (2.50) (3.00) (1.18)Demographic controls Y Y Y Y YPseudo-R2 0.18 0.46 0.15 0.22 0.19Observations 412 316 193 193 574Note: Models estimated using logistic regression; Standard errors clustered at the household level; Robust standard errors inparentheses; *** p<0.01, ** p<0.05, * p<0.193Chapter 5ConclusionThis dissertation has presented research in the areas of Environmental, Energy, and De-velopment Economics, contributing to the growing literature that sits at the intersectionof these important fields. The first two chapters examine how environmental change, likerising pollution levels and accelerating climate change and associated crises, can affect humancapital development and labor market outcomes. The final chapter turns its attention to anenergy policy aimed at mitigating environmental change and its associated costs, documentinghow this policy has failed to achieve its targeted impacts.Chapter 2 demonstrates how environmental regulation in one country can have crossborder effects and cause environmental degradation in another. The shift in used lead-acidbattery recycling from the United States to Mexico and the associated lead pollution havenegative effects on the human capital development of school-age children living aroundrecycling plants in Mexico, decreasing their test scores by 0.05-0.09 standard deviations. Dataon the location of schools and recycling plants allows for the identification of effects evenwithout data on lead pollution or exposure levels. The negative effects persist past the initialinflux of pollution and are especially strong in communities that were already worse off beforethe increase in lead pollution.Chapter 3 turns its attention to another consequence of environmental change, eco-nomic crises. The chapter focuses on the effects of the crisis associated with the COVID-19pandemic. These types of crises are already common in lower income settings and are onlyexpected to increase as climate change accelerates. The analysis of garment-making firmsand their owners in Ghana shed light on how small, informal firms respond to crises. All94firms are negatively affected, with many closing down during the peak crisis months, but thedeterminants and consequences of closure differ by firm-owner gender. Female-owned firmsthat close are those already on the margin of viability, and their owners experience substantialdrops in income. Male-owned firms are instead more likely to close if the firm-owner, evenbefore the pandemic, has other income generating options, and, thus, the owners of thesefirms are able to compensate for lost earnings. While all firms are exposed to the same shock,the pre-existing differences in occupational choice mean male firm owners are better able torespond and smooth income generation.These chapters together show how environmental degradation can affect populations inlow- and middle-income settings, outside of the often studied health effects. More importantly,the heterogeneity of effects across different communities and genders shows how environmentaldegradation may serve to exacerbate existing inequalities. These effects are part of the socialcosts of environmental degradation and are often, like the case of lead pollution in Mexico,of large enough magnitude to justify policy interventions. Chapter 4 considers one suchpolicy, aimed at mitigating the environmental and socioeconomic costs associated with energypoverty in Senegal.Energy poverty, or reliance on polluting fuels and technologies for energy services, isassociated with a range of consequences including indoor air pollution, deforestation, drudgery,and health costs. In recognition of these costs, governments have adopted policies to promotethe adoption of more efficient, cleaner fuels and technologies. One such example is Senegal’sprogram to subsidize the adoption of household biogas digesters, a renewable source of energythat is clean at the point of combustion. Chapter 4 evaluates this policy, using primary datacollected from participating households. Overall, the policy was not successful at targetingenergy poverty, given the high failure rate of installed digesters. 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Lead Exposure, Human Capital Formation, and Inequality: The Impactsof Drinking Water Lead on Short-run and Long-run Educational Outcomes (WorkingPaper).109Appendix AAppendix to Chapter 2110Figure A.1: Varying the radius for "Near" definition-.2-.10.1[0,1] [0,2] [0,3] [0,4] [0,5] [0,6] [0,7] [0,8] [0,9] [0,10](a) Math scores-.2-.10.1[0,1] [0,2] [0,3] [0,4] [0,5] [0,6] [0,7] [0,8] [0,9] [0,10](b) Spanish scoresNote: Point estimate and 95% confidence intervals for the β term in equation 2.1 plotted. Each point isthe result of a separate estimation. The definition of the Nears variable was varied in each specification,taking the value of 1 for any school within 1-10 miles of the nearest battery recycling facility and zero for anyschools farther away but in the same states as the ULAB recycling facilities.111Figure A.2: Effects of being close to a lead-acid battery recycling plant on scores,individual level results-.3-.2-.10.1.22006 2007 2008 2009 2010 2011 2012 2013(a) Math scores-.3-.2-.10.1.22006 2007 2008 2009 2010 2011 2012 2013(b) Spanish scoresNote: Figure displays the coefficients and 95% confidence intervals from the individual-level event-study ofbeing near a recycling plant when looking at math and Spanish scores as outcomes. Standard errors areclustered at the school level. The reference period is 2009 when the US changed its lead air quality standard.112Table A.1: Number of students tested, by subject, grade, and yearPanel A: Primary school gradesGrade 3 Grade 4 Grade 5 Grade 6Year Spanish Math Spanish Math Spanish Math Spanish Math2006 964,764 956,950 1,098,962 1,113,318 1,148,475 1,166,842 1,182,469 1,204,9552007 1,414,599 1,414,372 1,457,865 1,457,725 1,608,936 1,608,879 1,674,748 1,674,7072008 1,847,773 1,847,773 1,876,372 1,876,372 1,889,260 1,889,260 1,926,968 1,926,9682009 1,889,749 1,889,749 1,798,896 1,798,896 1,766,137 1,766,137 1,769,359 1,769,3592010 2,023,136 2,023,136 1,977,838 1,977,838 1,873,472 1,873,472 1,863,794 1,863,7942011 2,096,655 2,096,655 2,068,812 2,068,812 2,007,179 2,007,179 1,894,190 1,894,1902012 1,871,962 1,871,962 1,997,152 1,997,152 1,958,716 1,958,716 1,902,874 1,902,8742013 1,939,129 1,939,129 1,980,548 1,980,548 2,090,967 2,090,967 2,069,033 2,069,033Panel B: Lower and upper secondary school gradesGrade 7 Grade 8 Grade 9 Grade 12Year Spanish Math Spanish Math Spanish Math Spanish Math2006 900,177 898,6452007 1,393,534 1,393,4332008 1,583,080 1,583,080 616,711 616,7112009 1,665,348 1,665,348 1,657,853 1,657,853 1,552,949 1,552,949 655,276 655,2762010 1,697,046 1,697,046 1,708,842 1,708,842 1,613,114 1,613,114 752,686 752,6862011 1,758,378 1,758,378 1,685,339 1,685,339 1,612,146 1,612,146 698,118 698,1182012 1,741,882 1,741,882 1,661,375 1,661,375 1,546,018 1,546,018 841,210 841,2102013 1,919,870 1,919,870 1,733,528 1,733,528 1,633,943 1,633,943 973,871 973,871Note: Each cell reports the number of students tested as part of ENLACE in a given grade, subject, and year.Tests for grades 7 and 8 did not start until 2009 and testing in grade 12 did not start until 2008.113Table A.2: Effect of attending school close to a battery recyclingfacility, defining exposure using prevailing wind direction(1) (2) (3) (4)30◦downwind cone 90◦downwind conePanel A: Math scoresNear & downwind of -0.0794 -0.0848 -0.106*** -0.0868***facility X Post 2009 (0.0588) (0.0612) (0.0290) (0.0293)Number of students -0.000896*** -0.000895***(0.0000655) (0.0000655)Observations 1,141,093 1,141,093 1,141,093 1,141,093R2 0.516 0.526 0.516 0.526Num. of clusters 48,830 48,830 48,830 48,830Panel B: Spanish scoresNear & downwind of -0.0512 -0.0611 -0.0975*** -0.0878***facility X Post 2009 (0.0562) (0.0565) (0.0284) (0.0276)Number of students -0.000415*** -0.000414***(0.0000599) (0.0000599)Observations 1,141,007 1,141,007 1,141,007 1,141,007R2 0.560 0.567 0.560 0.567Num. of clusters 48,829 48,829 48,829 48,829Fixed effects:School-session-grade ✓ ✓ ✓ ✓Year ✓ ✓ ✓ ✓Grade-by-year ✓ ✓Session-by-year ✓ ✓State-by-year ✓ ✓Plant-by-year ✓ ✓Note: The unit of observation is school-session-grade-year. "Near & downwind of a facility"is a dummy variable that equals one if school s is located within two miles from the nearestlead-recycling plant p(s) and downwind of that facility, and zero if the school s is fartheraway from it or not downwind. The Postt indicator takes the value 1 in 2009 and thereafter.The sample includes all school-sessions in grades 3, 4, 5, 6, and 9 which we observe at leastonce in the pre-2009 and at least once in the post-2009 period. Outcomes are school-session-grade means of students’ standardized test scores, standardized at the grade-year level.Panel A reports estimates with math scores as the outcome; Panel B reports estimateswith Spanish test scores as the outcome. In each column the two specifications, with mathand Spanish as outcomes, include the same levels of fixed effects; only the outcomes arechanged. Columns 1 and 2 define downwind using a 30 ◦cone and columns 3 and 4 use a90◦cone. Standard errors, in parentheses, are clustered at the school level. * p<0.1, **p<0.05, *** p<0.01.114Table A.3: Effect of attending school close to a battery recycling facility onproficiency levels(1) (2) (3) (4) (5)Panel A: Math scoresNear facility X Post 2009 -0.0250*** -0.0240*** -0.0261*** -0.0215*** -0.0200***(0.00538) (0.00533) (0.00539) (0.00544) (0.00547)Number of students -0.000311***(0.0000285)Observations 1,141,093 1,141,093 1,141,093 1,141,093 1,141,093R2 0.501 0.503 0.508 0.509 0.509Num. of clusters 48,830 48,830 48,830 48,830 48,830Panel B: Spanish scoresNear facility X Post 2009 -0.0205*** -0.0195*** -0.0224*** -0.0208*** -0.0203***(0.00546) (0.00528) (0.00531) (0.00529) (0.00530)Number of students -0.0000989***(0.0000250)Observations 1,141,093 1,141,093 1,141,093 1,141,093 1,141,093R2 0.528 0.536 0.539 0.540 0.540Num. of clusters 48,830 48,830 48,830 48,830 48,830Fixed effects:School-session-grade ✓ ✓ ✓ ✓ ✓Year ✓ ✓ ✓ ✓ ✓Grade-by-year ✓ ✓ ✓ ✓Session-by-year ✓ ✓ ✓ ✓State-by-year ✓ ✓ ✓Plant-by-year ✓ ✓Note: The unit of observation is school-session-grade-year. Near is a dummy variable that equals one if schools is located within two miles from the nearest lead-recycling plant p(s), and zero if the school s is farther awayfrom it. The Postt indicator takes the value 1 in 2009 and thereafter. The sample includes all school-sessionsin grades 3, 4, 5, 6, and 9 which we observe at least once in the pre-2009 and at least once in the post-2009period. Outcomes are the share of students in a school-session-grade that are considered proficient (score aproficiency level of 3 or 4) in a given subject. Panel A reports estimates with math proficiency as the outcome;Panel B reports estimates with Spanish proficiency as the outcome. In each column the two specifications,with math and Spanish as outcomes, include the same levels of fixed effects; only the outcomes are changed.Standard errors, in parentheses, are clustered at the school level. * p<0.1, ** p<0.05, *** p<0.01.115Appendix BAppendix to Chapter 3Figure B.1: Share of Firms with Positive Profits, by Gender and MonthNote: This figure shows the share of firms reporting positive monthly profits (>0), forFebruary through July 2020. The sample is limited to those reporting positive profits inFeb 2020. Data comes from the retrospective six-month panel of firm outcomes collected viaphone survey for the 490 firm owners that constitute our analysis sample. Male-owned firmsare indicated with a triangle (102 firms) and female-owned with a circle (386 firms). Barsrepresent the 95% confidence interval.116Figure B.2: Share of Firm Owners Working Positive Number of Hours inGarment-Making Firm, by Gender and MonthNote: This figure shows the share of firm owners who report working positive hours per week(>0) at the firm of interest, for February through July 2020. The sample is limited to thosereporting positive hours worked per week in Feb 2020. Data comes from the retrospectivesix-month panel of firm outcomes collected via phone survey for the 490 firm owners thatconstitute our analysis sample. Male-owned firms are indicated with a triangle (101 firms)and female-owned with a circle (388 firms). Bars represent the 95% confidence interval.117(a) Female firm owners(b) Male firm ownersFigure B.3: Distribution of Feb 2020 Sales by Outside Option and GenderNote: This figure shows the distribution of February 2020 sales by if the owner has anoutside option to generate income for the 490 firm owners that make up our analysis sample,winsorizing the top earning female-owned firm for visual clarity. An owner is defined ashaving an outside option if, besides the garment firm they own, they have another form ofself-employment, work in small-holder agriculture, and/or have wage-employment (whetheragricultural or non-agricultural). Female owners are shown in panel A, and male ownersare shown in panel B. Data comes from the retrospective six-month panel of firm outcomescollected via phone survey.118Table B.1: Self-Reported Measures of Impact of COVID-19 Crisis andCoping Mechanisms(1) (2) (3)Female owners Male owners DifferenceLoss in demand/customers 0.98 0.99 0.01(0.14) (0.10) (0.01)Difficulty accessing inputs 0.66 0.70 0.03(0.47) (0.46) (0.05)Worker absenteeism 0.02 0.08 0.06**(0.15) (0.28) (0.03)Applied for and/or received any 0.23 0.29 0.07gov’t assistance during COVID (0.42) (0.46) (0.05)Temporarily migrated back to my 0.08 0.09 0.01original home place (0.27) (0.28) (0.03)Found another job/earning 0.05 0.11 0.06occupation (0.22) (0.31) (0.03)Spent savings to cover living 0.46 0.44 -0.02expenses (0.50) (0.50) (0.06)Relied on lenders or family 0.12 0.05 -0.07**for money (0.33) (0.23) (0.03)Observations 388 102 490Note: This table reports the mean and standard deviation of self-reported measures ofthe impact of the COVID-19 crisis and coping mechanisms by gender of the firm owner.Column 3 reports results of a t-test of difference in means by gender of firm owner. Incolumn 3 standard errors are in parenthesis. * p<0.1, ** p<0.05, *** p<0.01. The surveyquestions used to construct this table, excluding that related to government assistance, hadan additional consent accompanying them in the survey. Some firm owners opted out ofanswering these questions. The number of observations for the first three rows is 470 andfor the final four rows is 466.119Table B.2: Women Attrition Balance(1) (2) (3) (4) (5) (6)Mean DifferenceOpen, Jan 2020 Surveyed >0 Sales, Feb 2020 (Open - Surv.) (Surv - Sample) (Open - Sample)Yrs. Exp, Garment- 13.28 13.21 13.36 0.06 -0.15 -0.09making (8.88) (8.72) (8.83) (0.61) (0.62) (0.62)Yrs. Exp, Hohoe 11.71 11.59 11.74 0.12 -0.15 -0.03Garment-making (8.43) (8.26) (8.39) (0.57) (0.59) (0.59)Apprentices 1.31 1.32 1.35 -0.01 -0.03 -0.04(2.15) (2.17) (2.21) (0.15) (0.15) (0.15)Paid Workers 0.07 0.07 0.07 0.00 -0.00 -0.00(0.32) (0.32) (0.32) (0.02) (0.02) (0.02)Unpaid Workers 0.15 0.15 0.15 0.00 -0.01 -0.00(0.51) (0.52) (0.53) (0.04) (0.04) (0.04)Reason Self-Employed: 0.34 0.35 0.33 -0.00 0.02 0.01Money (0.48) (0.48) (0.47) (0.03) (0.03) (0.03)Reason Self-Employed: 0.17 0.17 0.17 0.00 -0.00 0.00Interest (0.38) (0.38) (0.38) (0.03) (0.03) (0.03)Reason Self-Employed: 0.48 0.48 0.50 -0.00 -0.01 -0.02Own Boss (0.50) (0.50) (0.50) (0.03) (0.04) (0.03)Garment-Making 19.72 19.89 19.59 -0.16 0.30 0.14Contacts, Hohoe (16.94) (17.14) (16.64) (1.17) (1.20) (1.17)Garment-Making 4.42 4.50 4.52 -0.08 -0.02 -0.10Contacts, Other (5.91) (5.98) (5.91) (0.41) (0.42) (0.41)Observations 435 412 388Note: This table reports the balance in observables, collected during the July 2019 census, between the female firm owners in the full sampling frame(Open Jan, 2020), those in the surveyed sample (Surveyed) and those in the analysis sample that had positive sales in February 2020 (>0 Sales, Feb2020). In columns 1-3, the mean is presented, with the standard deviation in parentheses. Columns 4-6 test for differences between these samples.Standard errors, in parentheses, are clustered at the firm owner level. * p<0.1, ** p<0.05, *** p<0.01.120Table B.3: Men Attrition Balance(1) (2) (3) (4) (5) (6)Mean DifferenceOpen, Jan 2020 Surveyed >0 Sales, Feb 2020 (Open - Surv.) (Surv - Sample) (Open - Sample)Yrs. Exp, Garment- 16.99 16.63 17.06 0.36 -0.43 -0.07making (12.60) (12.46) (12.51) (1.66) (1.73) (1.68)Yrs. Exp, Hohoe 14.88 14.74 15.12 0.14 -0.38 -0.24Garment-making (11.88) (11.30) (11.34) (1.54) (1.57) (1.55)Apprentices 0.91 0.92 0.96 -0.01 -0.04 -0.05(1.77) (1.80) (1.83) (0.24) (0.25) (0.24)Paid Workers 0.13 0.14 0.13 -0.01 0.01 0.00(0.51) (0.54) (0.54) (0.07) (0.07) (0.07)Unpaid Workers 0.19 0.21 0.21 -0.02 0.00 -0.02(0.54) (0.56) (0.57) (0.07) (0.08) (0.07)Reason Self-Employed: 0.24 0.23 0.24 0.01 -0.01 0.00Money (0.43) (0.42) (0.43) (0.06) (0.06) (0.06)Reason Self-Employed: 0.12 0.14 0.13 -0.02 0.01 -0.00Interest (0.33) (0.35) (0.34) (0.05) (0.05) (0.04)Reason Self-Employed: 0.64 0.63 0.64 0.01 -0.01 0.00Own Boss (0.48) (0.48) (0.48) (0.06) (0.07) (0.06)Garment-Making 23.95 23.64 23.96 0.31 -0.32 -0.01Contacts, Hohoe (20.13) (19.70) (19.91) (2.64) (2.75) (2.69)Garment-Making 6.68 6.24 6.16 0.44 0.08 0.52Contacts, Other (6.63) (6.08) (6.00) (0.84) (0.84) (0.84)Observations 122 106 102Note: This table reports the balance in observables, collected during the July 2019 census, between the male firm owners in the full sampling frame(Open Jan, 2020), those in the surveyed sample (Surveyed) and those in the analysis sample that had positive sales in February 2020 (>0 Sales, Feb2020). In columns 1-3, the mean is presented, with the standard deviation in parentheses. Columns 4-6 test for differences between these samples.Standard errors, in parentheses, are clustered at the firm owner level. * p<0.1, ** p<0.05, *** p<0.01.121Table B.4: Profits and Labor hours, Before and During Crisis(1) (2) (3) (4) (5) (6) (7) (8)Profits, GHC Profits > 0 Hours per week Hours per week > 0Females Males Females Males Females Males Females MalesCrisis period (Mar-Jul) -131.0*** -138.5*** -0.467*** -0.380*** -23.36*** -20.35*** -0.403*** -0.329***(7.877) (17.93) (0.0134) (0.0292) (0.706) (1.428) (0.0153) (0.0308)Difference (male-female) -7.506 0.0870*** 3.014* 0.0744**(19.52) (0.0320) (1.587) (0.0343)Constant 190.8*** 219.8*** 1.00 1.00 47.22*** 48.47*** 1.00 1.00(8.649) (19.08) (.) (.) (0.458) (0.733) (.) (.)Observations 2316 612 2316 612 2328 606 2328 606R2 0.172 0.126 0.128 0.093 0.165 0.131 0.101 0.075Num. of clusters 386 102 386 102 388 101 388 101Note: This table reports how monthly firm profits and hours of labor worked changed between the pre-crisis period (February) and thecrisis period (March-July), conditional on positive profits or hours in the pre-crisis period. The level of profits in GhC, an indicator forpositive profits, the average weekly hours worked in a given month, and an indicator for whether the owner reported to work positive hoursin the average week in a given month are considered. The difference row presents the coefficient on the interaction term between male andthe crisis period indicator in a pooled regression with both female and male owners. Data comes from the retrospective six-month panel offirm outcomes collected via phone survey. Standard errors are clustered at the firm owner level. * p<0.1, ** p<0.05, *** p<0.01.122Appendix CAppendix to Chapter 4C.1 Detailed MethodologyC.1.1 Sampling and Data CollectionThe focus of this study is the first half of the Large-Scale Dissemination Phase of PNB-SN.This phase followed the pilot phase (which we did not evaluate) and was planned for 2015-2019.Our study planning began in late-2017 and data collection occurred in early 2019,1 so we focuson installations that were initiated between 2015 and 2017. Specifically, we are interested intwo groups of households, one treated by the program, and the second acting as controls,who were planned to receive the intervention but had not received it at the time of datacollection. The methodology that we used to select our sample is explained below and shownin Figure 4.1.Our treatment group is made up of 100 households who received biogas installationsfrom the PNB-SN. To draw this sample, we used a list of the 901 households that had receivedinstallations between 2015 and 2017. Because the main cost of data collection comes fromtraveling to remote villages, we aimed to interview four households per village. Thus, wedropped recipient households that were in villages where 2 or fewer other households hadreceived an installation. While this restriction does affect the generalizability of our findings,we do not think it is a problem, from a policy perspective. If the government continues toscale up the biogas program, there will likely be numerous installations per village. The 368remaining households were distributed across 47 villages. From these villages we randomlyselected 25, and, within each village, randomly selected four households. This gives us asample of 100 treatment households. If one of our sampled households was not available1We obtained ethical approval for this study from Duke University’s Institutional Review Board. Allparticipants gave verbal, informed consent prior to conducting the survey. Participants were free to refuse toanswer any questions and to end the interview at any time. Participants were informed about the aims of theresearch, the data to be collected, and were assured that data would be stored securely and they would remainanonymous in all reports. They received a local telephone number to use in case of any questions or concernsregarding the survey questions or research project. The survey did not involve any medical treatments nordid we ask any sensitive or potentially upsetting questions. We collected a minimum amount of personallyidentifying information. We gave each respondent a small gift at the end of the survey to compensate for thetime we used for the interview.123to be interviewed, we randomly selected another recipient household from that village asa replacement. In the end, we have a sample of 97 treatment households, as replacementhouseholds for unavailable respondents were not available in three of the villages we visited.Similar to the sampling methodology used by Bedi et al. (2015), our control group ismade up of households who had been selected to receive biogas installations following thesame selection process as the treatment group, but who, because of budgetary reasons, hadyet to receive these installations as of December 2018. From PNB-SN we received a list ofmore than 3,000 households who had been identified as eligible and scheduled to receiveinstallations in this phase of the program. Again, we consider only villages where four or morehouseholds appear on the list of eligible households. Instead of randomly selecting villages,we asked the PNB regional coordinators and installation companies, who are responsible forselecting the households that will receive installations, to identify 25 villages that were similarto our treatment villages. While this method is less systematic than, for example, statisticalmatching on observable characteristics, we believe it more closely mirrors the process throughwhich treatment villages were selected. We additionally asked the regional coordinators toselect villages where four or more households appeared on the list of those who were deemedeligible but who had not yet received an installation and where no biogas digesters had yetbeen installed. In each of these 25 control villages, we again randomly selected four householdsand, when necessary due to lack of their availability, selected replacement households. In theend, we have a sample of 99 control households, as replacement households were not availablein one of the villages we visited.As explained above, we selected treatment and control households from differentvillages. While there are households in treatment villages that do not have a biogas digester,we believe that these households are likely very different than those, from the same village,who adopted a digester when PNB-SN came to their community. Therefore, as control, weselected households whose villages had not yet received installations from PNB-SN but whowere selected to participate in the program once it reached their community. In additionto supporting the internal validity of our results, this design also reduces concern aboutspillovers between treatment and control households. There are a number of channels throughwhich functioning biogas digesters could affect non-recipient households. It is possible that,if installations reduce the amount of fuelwood demanded by recipient households, fuelwoodbecomes less scarce for non-recipient households and they may increase their use. If recipienthouseholds sell their bioslurry as fertilizer, non-recipient households may shift their agriculturalinvestments in response to this change in input availability.Data collection occurred in February 2019, with 8 interviewers. Each interviewerspent one day in a village, interviewing all four households in that village. In one village,124one household refused to be interviewed and there were no replacement households available,thus only 3 interviews were conducted. In another, two households refused to be interviewedand no replacements were available, so only 2 interviews took place. In a third village, onesampled household was traveling, and no replacements were available. We were limited in theavailability of replacements in some villages because we only interviewed households whosenames appeared on the PNB-SN lists of households who had received digesters or who hadhad their eligibility confirmed.Of the originally sampled 200 households, 65 were not available or refused, 61 of thesewere successfully replaced with other households from PNB-SN’s lists, for a final sample of196 households across 50 villages. Sampled households were not available for interview andhad to be replaced for a few reasons. Fifty-five percent of the households who were replacedwere simply not available on the day our team was in the village. Either they had moved orwere traveling. For budget reasons, we were only able to spend one day in each village, sowe were not able to return to look for these households on another day. Thirteen percentrefused to be interviewed because they were upset with the biogas program. Four percentwere replaced because the name listed belonged to another household that had already beeninterviewed, i.e. the household had been listed twice in PNB-SN’s lists. Twenty-eight percentwere control group households that we discovered did not meet the eligibility criteria toreceive biogas, despite their names being listed by PNB-SN on the demand lists. We do nothave demographic data on the replaced households to test for potential sources of bias fromthe high refusal rate. We believe we are likely overestimating impacts and operation rateswith our final sample, as households who moved away can no longer use the biogas and thosewho are upset with the program likely have non-functioning digesters.C.1.2 Evaluation Design and MethodsDeterminants of Digester operationAs a first step, we explore the operation of the digesters that were installed by PNB-SN.Specifically, we investigate what determines the varying levels of digester operability that weobserve in our survey data. For this analysis we use only the households in the treatmentsample. We first divide the sample according to three binary variables that define importantthresholds in digester operation. We test for determinants of reaching these three thresholdsusing both logistic and linear probability models. First, we divide the sample into householdswhose digesters had ever operated since installation and those whose never operated. Second,we divide the households based on whether or not their digester had been operating for atleast half of its life, i.e. the time since installation was complete. Third, we split the sample125based on whether or not the household’s digester was operating on the day of the survey.Finally, we use linear regression to test for determinants of operation using a continuousoutcome variable that measures the share of time since installation that a household’s digesterhad been at least partially operational. For this outcome variable a value of, for example,0.6 indicates that the digester had been usable for 60 percent of its life to date and totallybroken down for 40 percent.With respect to our covariates of interest, we rely on the rich literature studying thedeterminants of household energy choices. Specifically, we draw from reviews of the literatureto identify factors that we expect to influence digester operability (Puzzolo et al., 2016; Muller& Yan, 2018; Lewis & Pattanayak, 2012). The covariates we test fall into five categories. Thefirst includes digester and program characteristics, including the type of digester installed,the age of the digester, i.e. how much time has passed since installation was completed,whether or not the household has connected a toilet to the digester, and whether or not thehousehold received any trainings regarding digester operation and maintenance. For manyof these variables it is not ex-ante clear what direction we should expect its relationshipwith operability to be. For example, older digesters may be more likely to break down. Buthouseholds who have had digesters longer may also be more experienced and successful intheir operation. It’s also not clear whether the fixed dome or floating balloon digester has abetter operating history in the Senegalese context. We do expect that trainings are positivelyrelated to operation, as households without trainings are less likely to have information abouthow to operate the digester. We also expect households with a toilet connected to have amore consistent feed stock going into the digester, which means better operation.The second category includes prices of alternative fuels. The higher the price of fuelslike charcoal, the more likely households are to maintain an operating digester for the methanegas. We test for the cost of firewood and charcoal. Unfortunately, we were not able to collectsufficient data to test for the relationship between LPG price and digester operation. Ifwealthy households are both more likely to adopt biogas and LPG, we could expect that, inthe absence of biogas, many treatment households would have used LPG for cooking.The third category of covariates includes household demographic and socioeconomiccharacteristics. Specifically, we test for the role of household size and the share that is female.It may be that larger households have more labor available to successfully maintain thedigester. Households with more females may have more labor available to both maintain thedigester and to collect firewood, a task often done by women (World Health Organization,2016), so it is not ex ante clear whether they will be more or less likely to maintain anoperating digester. With respect to employed adults in the household, it may be that themore members who are employed the less likely operation because of labor constraints at126home. But it also could be that households with more employed individuals can better affordany digester maintenance that is required. We also test whether the age of the householdhead is associated with operation, hypothesizing that older heads are less willing to changetheir behavior, so less likely to invest in maintaining a new technology. We test for therole of relative wealth and liquidity, using an asset index and binary variable equal to oneif a household can access at least 250,000 CFA in emergency funds. (See Appendix C.4for information about how we construct the asset index.) We expect wealthier and lessliquidity constrained households to be more likely to maintain their digester. We also includea measure of credit access, equal to one if a household could access a loan to get emergencyfunds. This variable may be positively correlated with operation, as households who canget access to funds via credit are more likely to invest in maintenance. It also may be thathouseholds who can access emergency funds through their own income or savings are lesslikely to report accessing a loan, are wealthier, and therefore more likely to maintain thedigester. Finally, we test for the role of market access, as we expect households who are moreconnected to markets are also more likely to have access to digester appliances, replacementparts, and maintenance services.Fourth, we test for the role of household perceptions, including covariates that indicatethe motivation for installing a digester, e.g. to reduce energy expenses, improve health,or produce fertilizer. These variables are coded based on an open-ended question askinghouseholds to report their motivation for installing the digester. They do not capture theintensity of motivation. We expect all motivations to be positively related with operation,but it is not clear ex-ante which motivation should have the strongest relationship.Finally, as cow manure is the primary digester input, we test for the role of livestockownership and rearing patterns, including stabling and pasturing practices and the collectionand use of cow manure. We expect that households with more cows are more likely tomaintain the digester, as they are more likely to have sufficient feedstock. We also predictthat households who stable their livestock are more likely to operate the digester, as they donot need to collect dung from the fields and bring it back to the digester. We also test forother uses of livestock dung, such as applying it directly as fertilizer or using it as cookingfuel. If households use the dung for other purposes, they may be less willing to put it intothe digester and less likely to maintain an operable digester.Impact Evaluation MethodologyUnbiased identification of the impacts of biogas installations requires that treatment andcontrol households are similar across both observed and unobserved baseline characteristics.Our sampling strategy, described above, ensures that treatment and control households have127gone through the same selection process, i.e. being included in PNB-SN’s lists of eligiblehouseholds and then being selected by the regional coordinators for installation. This helpsto balance the two groups on both the observed and unobserved characteristics that drivethe selection process. There are some drivers of selection that we cannot observe, suchas what drives households to approach PNB-SN and be included on the eligibility lists orwhat determines where the regional coordinators decide to implement the program. Weassume that, by ensuring that all households have been listed on the eligibility lists and gonethrough the regional coordinator selection process, we have reduced differences that arisefrom these relatively opaque selection processes. We expect that, in the future, all of thecontrol households will have also received the biogas program.The remaining threat to identification comes from the fact that treatment villageswere selected to receive installations before control villages. As discussed above this maybe because they are located in a larger village with more biogas eligible households. It maybe because they are wealthier, so able to more quickly buy the in-kind inputs or make thenecessary payments. Or it may be because these villages are the easiest to access, such thatinstallation costs, including transport of labor and material, are lower. These factors, likewealth and accessibility, may not only affect timing of biogas installation but could also affecthousehold access to host of other goods and services, which in turn could drive differences inour outcomes of interest between treatment and control groups.We account for this timing selection in our impact analysis by controlling for covariatesthat may drive the order in which villages and households received installations. We first testwhether these variables are balanced between the treatment and control groups and thencontrol for them in our impact estimation. For the treatment households, we are not able toobserve the levels of these characteristics from before they received the installation, but webelieve, because so many digesters were not operating, that they have likely not been affectedby the program. They are therefore the best approximation we have of pre-installation factorsthat drove the treatment households’ earlier selection into the program.To measure the impact of the biogas installations, we estimate the following equation:Yij = α + βBij + γXij + δZij + θVj + ϵij (C.1)where Yij is our outcome of interest, Bij is a dummy variable equal to one if household i invillage j received a biogas installation from PNB-SN. Xij is a vector of observable demographiccontrols, including household size, age, gender, education, religion, and ethnicity. Zij and Vjare vectors of household and village characteristics, respectively, that may drive the orderin which the installations are delivered, including village j’s accessibility and household i’srelative wealth and liquidity, and ϵij is an error term. We cluster standard errors at the village128level to account for intra-village correlation. Thus, to identify our parameter of interest, β,we rely on the assumption that, conditional on the set of controls, Xij , Zij , and Vj , treatmentstatus, Bij, is independent of the error term.Because we are not able to observe every factor that influenced whether or not aneligible household received an installation initiated between 2015 and 2017, there may still beselection bias in the estimates from equation C.1. Because villages that were targeted first arelikely larger, more connected, and possibly wealthier, as they could make the required in-kindcontribution of construction materials quickly, any unobserved selection would likely result ina positive bias. Therefore, if we estimate positive impacts, it may be that the true impact islower than our estimates, or even zero. Given that we find null impacts of the installations,we do not believe that this potential source of bias threatens our overall policy conclusions.The estimates from equation C.1 are estimates of the local average treatment effect ofreceiving a digester installation from PNB-SN. It is a local effect because we only considerthe population of Senegalese households who are eligible to receive the digester according tothe eligibility criteria discussed previously. It also can be interpreted as an intent to treat(ITT) estimate, as it considers all installed digesters, not just those that were successfullyoperated. There were many households who received an installation, but whose digestersnever produced enough gas for cooking.We then estimate the impact of having an operational digester on the same set ofoutcomes, Yij . For this analysis we use only the sample of recipient, or treatment households.This estimate can be interpreted as the effect of treatment on the treated (ToT) households.The challenge here is that digester operation is highly endogenous. For example, we predictthat wealthier households are more likely to have an operational digester, but wealth alsoaffects our outcomes of interest, like cooking fuel choice, through channels other than thebiogas digester. To account for this endogeneity we predict each household’s likelihood ofhaving a functioning digester using the observable characteristics and models described above.We then regress our outcome of interest on a binary variable equal to 1 if a household’sdigester adheres to our definition of operation, the predicted likelihood of a household havinga digester that fits this definition of operation, and the Xij, Zij, and Vj control vectors fromequation C.1. The coefficient on the binary variable of operation will then constitute theeffect of an operational digester on Yij. This identification is based on the assumption thatconditional on the predict probability of operation which is a function of observables, theaverage likelihood of having an operating digester is equal between those households withand without operating digesters.129C.2 Comprehensive Impact Analysis ResultsC.2.1 Descriptive Statistics and Balance TablesTable C.1 reports describes important outcome variables for our sample and tests for differencesbetween the treatment and control groups. For skewed outcomes, medians and interquartileranges are reported, instead of the mean and standard deviation. For these skewed variables,tests of differences in means were conducted using a transformed variable to reduce skewness.For all tests, standard errors were clustered at the village level.With respect to cooking patterns, the average household has two different types ofstoves. The most common type is the three-stone stove, which about 80 percent of householdshave in their home. Almost all of these households report that the three-stove stone is theirmain cooking stove. The second most common stove is LPG, which about 40 percent ofhouseholds have, but only five percent of households report that this is their main stove. Only18 percent of households report having a biogas stove, which is indicative of the fact thatmany of the installed digesters never operated or were broken down by the time of our visit.With respect to cooking fuels, the median household uses 5 kilograms of firewoodper day (mean = 9) and zero kilograms of charcoal per day (mean = 1.6). One third ofhouseholds report that they use agricultural residue to cook, but, on average, they onlyreport using about one kilogram per week. Of the households who have an LPG stove,about seventy percent report that they use LPG fuel. This indicates that the other thirtypercent of the LPG stoves are present in the household but not being used. The averagehousehold burns a 6kg cannister of LPG every two months (median = 0), using about halfa cannister per month. The only significantly difference in fuel use between treatment andcontrol is firewood use, with control households using significantly more. As shown in TableC.1, treatment households spend more money on purchasing fuel, including the cost of travel,while control households spent more time collecting fuel. The median household spends aboutthree person-hours per week collecting fuel. In the average household, the main cook spendsabout five hours a day cooking, and this is lower in the treatment households. All main cooksin our sample are women.We also consider the health of two different groups in Table C.2. First, we look atchildren under 12, as they are physiologically more susceptible to health damage from airpollution. About ten percent of the children in our sample were reported to have a cough atsome point in the two weeks preceding the survey and four percent reported to have troublebreathing as well. These two indicators together are indicative of an acute lower respiratoryinfection. Interestingly, rates of both coughs and possible ALRI cases are higher in thetreatment group. We also consider eye problems, as these can be associated with exposure to130indoor air pollution. Again, children in the treatment group are more likely to experience eyeproblems, thought none of these differences are significant at the ten percent level or below.The other individuals we consider are those over the age of twelve who are responsible forcooking in the household, including both main and not main cooks. Respiratory problemsamong these individuals occur at about the same rate as children, and there is no significantdifference between treatment and control. Cooks in the treatment group experience eyeproblems at about twice the rate of those in the control.Households in our sample are mainly rural, agricultural households. The three maincrops reported to be grown are peanuts (grown by 122 households), millet (grown by 119households), and corn (grown by 64 households). Other, less commonly grown crops arecowpea, rice, and onion. Across all households the average number of different crops grown is2.7, with control households slightly more diversified across crops than treatment (Table C.1).Control households also tend to harvest more and generate more revenue from crop sales,despite fewer control households using any sort of improved fertilizer, defined as bioslurry orany type of chemical fertilizer. This may indicate that control households are more remoteand more reliant on agriculture for their livelihoods.In our sample, about one-third of adults are employed in a wage-earning activityor personal business (Table C.2). Men are much more likely to be employed than woman,but there is no significant difference in employment status between adults in treatment andcontrol households.C.2.2 Fuel Use and Cooking ImpactsHere we present estimates of the effect of PNB-SN’s biogas installations on our outcomes ofinterest. In our sample, the only households that report having a biogas digester are treatmenthouseholds, and all digesters were installed through PNB-SN. Table 4.3 presents estimatesof the impact of digester installation on fuel use outcomes. In Table 4.3 all regressions areestimated using ordinary least squares and standard errors are clustered at the village-level.Our preferred specification controls for both demographic characteristics and factors that webelieve determine whether a household gets treated earlier or later in the program, specificallydistance to the nearest market and bus stop, land ownership, an asset index, and a measureof liquidity.The magnitude of the estimates indicate that biogas installations led to a 26 percentdecrease in the geometric mean of daily firewood use, or about 1.4 kilos. Without controls,the estimated effect is larger and statistically significant. When the controls are added, theestimated effect size decreases, indicating that, without controls, we would likely over-estimate131the treatment effect. If any unobserved differences are still upwardly biasing our estimates,the true effect would be even closer to zero.The coefficients in columns 4 and 6 indicate that installations led to a very smalldecrease in charcoal use and similarly small increase in LPG use. Additionally, none of theseestimates are found to be statistically significant at the ten percent level or lower. We believewe can conclude that, for the treatment households in our sample, the biogas installationscarried out by PNB-SN and their partner organizations did not have an economically significanteffect on cooking fuel.The null effects from Table 4.3 are mirrored in Table 4.4, where we consider otherfuel and cooking outcomes. The coefficients in columns 2 and 6 indicate that biogas reducedcook time by fourteen minutes per day and fuel expenditure by thirty-five percent, or 2,300CFA/month. The coefficient in column 4 is more difficult to interpret because of the cubicroot transformation, but the estimated effect is small and not statistically significant. Incolumns 2, 4, and 6 we can see that asset index, a relative measure of household wealth, issignificantly associated with lower cook times and fuel collection times as well as higher fuelexpenditure. Wealthier households are more to likely purchase, rather than collect their fuelsand may use more modern stoves that reduce cooking times. Appendix Tables C.3 and C.4present estimates after dropping outliers for firewood, charcoal, and LPG use, as well as fuelcollection time and fuel expenditure. Results are robust to dropping outliers.C.2.3 Health ImpactsAs discussed previously, we consider health outcomes for two groups of household members,children under 12 and individuals responsible for cooking. To estimate the impact of biogasinstallations on incidence of a cough and of ALRI, we use logistic regression, controlling forindividual’s age and gender as well as the household and village characteristics from theprevious estimations. We cluster standard errors at the village level.Appendix Table C.6 shows the results for children under 12. Columns 2 and 6 areour preferred specifications, controlling for demographic characteristics as well as variablesthat drive earlier installations within PNB-SN. The negative coefficient on biogas installationin both of those columns reverses the pattern in Table C.2, where children in treatmenthouseholds were slightly more likely to experience a cough or ALRI symptoms. The oddsratios associated with these coefficients estimate that biogas causes a 5 percent reductionin the likelihood of a child having a cough in the two weeks preceding the survey. Themagnitude of the estimated effect is even larger in the case of ALRI symptoms, estimating a16 percent reduction in likelihood. Still these estimated effect sizes are quite small when we132consider that nine percent of children in the control group experienced a cough and 3 percentexperienced symptoms consistent with ALRI. In addition, none of these estimated effectsare significant at the ten percent level. Heterogenous impact estimates for girls versus boysare displayed in columns 3 and 7. Installation is associated with fewer respiratory healthproblems in boys and more in girls. For girls, this result is significant in the case of ALRIsymptoms, though only at the ten percent level.2The results for cooks ages 12 and above are displayed in Appendix Table C.7. Hereagain, there is no significant effect of biogas installation on respiratory health outcomes, asindicated by the coefficients on biogas installation in columns 2 and 6. Though, the coefficientwith respect to coughs is negative and indicates a reduction in likelihood of 16 percent. ForALRI symptoms, the coefficient estimate indicates an increase in likelihood of 12 percent. Allindividuals responsible for cooking are females, so instead we test for heterogenous impactsby age and find that treatment effects do not significantly vary across ages. We also considerthe impact of biogas installations on the incidence of eye problems but find no effects foreither children or cooks, as illustrated in Appendix Table C.5.C.2.4 Agricultural and Employment ImpactsFinally, we turn to agricultural and employment impacts. Impacts on binary outcomes likeemployment and fertilizer use are estimated using logistic regression. All others are estimatedusing ordinary least squares.As shown in Appendix Table C.8, we do not estimate biogas to have any significanteffects on agricultural patterns. The magnitude of the estimated effect suggests householdsspecialize more after biogas, i.e. reduce the number of crops they grow, but the effect size issmall and not significant. The coefficient in column 4 is associated with a 75 decrease in croprevenue, or 250,000 CFA. This is a large change, though not statistically significant. It couldbe that households are shifting away from agriculture to other income generating activities,but this is not supported by the employment results in Appendix Table C.10. Biogas digestersmay improve agricultural outcomes, most directly through the use of bioslurry as a high-quality fertilizer. Alternatively, the bioslurry could be sold to other farmers and the incomeused to buy chemical fertilizer or a variety of other productivity increasing expenditures.As shown in Appendix Table C.8, column 5, treatment households are more likely to usebioslurry or chemical fertilizers, but this difference is no longer significant after controlling2Interestingly, distance to the nearest bus stop and nearest market is negatively and significantly related torespiratory problems across all specifications for children (Appendix Table C.6) and cooks (Appendix TableC.7), respectively. It may be that more isolated areas experience less ambient air pollution from industry ortraffic.133for demographic characteristics and determinants of early treatment.In Appendix Table C.9 we estimate the effects of biogas on harvests, considering thethree most commonly grown crops, peanuts, millet, and rice. Again we find no statisticallysignificant effects, though the magnitude of the estimates are positive. Unfortunately, we didnot have data on the acreage cultivated for each crop, so were unable to consider agriculturalproductivity outcomes.Digesters could also affect employed outcomes. Time shifted away from cooking orfuel collection could be put into a personal business or a wage-earning activity. Increasedagricultural productivity may mean that fewer acres need to be cultivated to feed thefamily, so some agricultural laborers can switch into wage-earning employment. While biogasinstallation is associated with higher employment, especially among men, the effect is notsignificant at the ten percent level (Appendix Table C.10). We do not find statisticallysignificant heterogeneous treatment effects across men and women (Appendix Table C.10,columns 3 and 4). Across all specifications, women are significantly less likely to be employedthan men.134Table C.1: Difference in household-level outcomes between treatment andcontrolControl Treatment Difference NSelf reported daily firewood use, kg+ 7 3 -4** 193(9.9) (9.00)Self reported daily charcoal use, kg+ 0 0 0 196(0.14) (0.36)Self reported monthly LPG use, 6kg 0 0 0 196cannister+ (0.5) (0.5)Total minutes spent collecting fuel across 173.75 150 -23.75 196all household members, per week+ (422.75) (323.75)Total amount spent on fuel (including 1,200 1,500 300 196travel cost) per month, CFA+ (6,363) (12,000)Minutes that the main cook spends 328.01 302.92 -25.10 182cooking, per day (111.12) (99.02) (19.35)Number of crops grown 2.89 2.48 -0.40* 196(1.46) (1.26) (0.23)Corn harvested in past 12 months, kg+ 400 275 -125 64(800) (1,300)Peanuts harvested in past 12 1,200 1,000 -200 122months, kg+ (2,400) (1,500)Millet harvested in past 12 months, kg+ 1,000 1,000 0 119(1,700) (1,150)Revenue from crop sales in past 12 115,000 84,000 -31,000 196months, CFA+ (575,000) (350,000)=1 if household uses fertilizer other than 0.57 0.75 0.19** 196direct application of animal manure (0.50) (0.43) (0.08)Note: Means and standard deviations (in parentheses) are reported for each variable, for thetreatment and control groups; The distributions of variables marked with + are right-skewed, sothe median and interquartile range is reported; In column 3, the difference between treatment andcontrol mean or median is reported; Stars indicate the result of a t-test of the difference in meansbetween treatment and control; For variables marked with ∧, the t-test is performed after a logor cubic-root transformation to reduce skewness; * p<0.1, ** p<0.05, *** p<0.01; For all tests,standard errors are clustered at the village level.135Table C.2: Difference in individual-level outcomes betweentreatment and controlControl Treatment Difference N=1 if individual experienced a cough in the past 2 weeksCooks (>12 y.o.) 0.11 0.12 0.01 473(0.31) (0.33) (0.04)Boys (<12 y.o.) 0.09 0.11 0.02 529(0.29) (0.31) (0.04)Girls (<12 y.o.) 0.09 0.16 0.07 520(0.28) (0.36) (0.04)=1 if individual had ALRI symptoms in the past 2 weeksCooks (>12 y.o.) 0.03 0.07 0.04 473(0.18) (0.25) (0.02)Boys (<12 y.o.) 0.04 0.04 0.00 529(0.19) (0.20) (0.02)Girls (<12 y.o.) 0.03 0.07 0.04 518(0.16) (0.25) (0.03)=1 if individual had any eye issue in the past 2 weeksCooks (>12 y.o.) 0.06 0.06 -0.00 471(0.25) (0.24) (0.02)Boys (<12 y.o.) 0.02 0.03 0.00 527(0.16) (0.17) (0.02)Girls (<12 y.o.) 0.01 0.03 0.02 519(0.08) (0.16) (0.01)=1 if the adult (18 y.o. or older) is employedFull Sample 0.27 0.30 0.03 1,453(0.45) (0.46) (0.04)Males 0.42 0.48 0.06 679(0.49) (0.50) (0.06)Females 0.14 0.14 0.00 756(0.35) (0.35) (0.04)Note: Means and standard deviations (in parentheses) are reported for each variable,for the treatment and control groups; Employment means being employed in a personalbusiness or wage-earning activity; In column 3, the difference between treatment andcontrol means is reported; Stars indicate the result of a t-test of the difference in meansbetween treatment and control; * p<0.1, ** p<0.05, *** p<0.01; For all tests, standarderrors are clustered at the village level.136Table C.3: Impacts of biogas installation on fuel use outcomes, outliers droppedLog firewood use, Cubic-root charcoal Cubic-root LPG use, 6ln(kg/day) use, kg/day1/3 kg cannisters/month1/3(1) (2) (3) (4) (5) (6)Biogas installation -0.31** -0.21 0.070 -0.029 0.042 0.0048(0.15) (0.15) (0.072) (0.072) (0.085) (0.063)Cubic-root distance to -0.033 0.0047 0.066nearest weekly market, km1/3 (0.099) (0.066) (0.052)Cubic-root distance to 0.079 -0.060 0.066*nearest bus stop, km1/3 (0.099) (0.043) (0.038)Log land owned 0.27*** 0.0049 0.046or leased, ln(hectares) (0.075) (0.035) (0.029)Asset Index -0.11*** 0.044** 0.11***(0.040) (0.022) (0.016)Liquidity (=1 if can access 250,000 0.041 -0.012 0.044CFA in emergency funds) (0.18) (0.088) (0.077)Constant 1.58*** 1.29* 0.25*** 0.69* 0.28*** -0.12(0.098) (0.69) (0.052) (0.39) (0.056) (0.30)Demographic controls N Y N Y N YObservations 189 175 194 180 195 181R-squared 0.021 0.171 0.005 0.117 0.002 0.312Note: Robust standard errors in parentheses; standard errors clustered at the village level with 49 clusters; *** p<0.01,** p<0.05, * p<0.1137Table C.4: Impacts of biogas installation on fuel use outcomes, outliers droppedCook time, min/day Fuel collection time Total fuel expenditure(min/wk)1/3 ln(CFA/month)(1) (2) (3) (4) (5) (6)Biogas installation -25.1 -14.0 -0.50 -0.22 0.12 -0.48(19.3) (21.6) (0.58) (0.53) (0.81) (0.69)Cubic-root distance to nearest -15.9 0.29 0.72weekly market, km1/3 (11.0) (0.38) (0.49)Cubic-root distance to nearest 17.2 0.34 0.077bus stop, km1/3 (11.0) (0.24) (0.44)Log land owned or leased, 5.14 0.62** 0.22ln(hectares) (7.40) (0.21) (0.28)Asset Index -9.85** -0.49*** 0.96***(4.14) (0.11) (0.17)Liquidity (=1 if can access 250,000 17.7 0.55 -1.18CFA in emergency funds) (23.0) (0.62) (0.82)Constant 328*** 238*** 5.62*** 8.51*** 4.54*** 3.87(13.0) (73.4) (0.43) (1.89) (0.55) (3.01)Demographic controls N Y N Y N YObservations 182 168 194 180 195 181R-squared 0.014 0.142 0.006 0.257 0.000 0.238Note: Robust standard errors in parentheses; standard errors clustered at the village level with 49 clusters; *** p<0.01, **p<0.05, * p<0.1138Table C.5: Impacts of biogas installation on eye problemsChildren under 12 Individuals responsible for cooking(1) (2) (3) (4) (5) (6) (7) (8)Odds Ratio Odds RatioBiogas installation 0.55 0.081 -0.43 0.65 -0.081 -0.62 -0.71 0.49(0.58) (0.53) (0.53) (0.34) (0.37) (0.44) (1.08) (0.53)Age, years 0.00059 0.032 0.032 1.03 0.056*** 0.071*** 0.070*** 1.07***(0.045) (0.054) (0.055) (0.057) (0.012) (0.016) (0.023) (0.025)Female (=1 if female) -0.55 -0.61 -1.46* 0.23*(0.40) (0.44) (0.88) (0.20)Treatment, female interaction 1.39 4.01(0.94) (3.76)Treatment, age interaction 0.0026 1.00(0.026) (0.026)Cubic-root distance to nearest weekly -0.12 -0.097 0.91 0.010 0.0085 1.01market, km1/3 (0.37) (0.36) (0.33) (0.32) (0.33) (0.33)Cubic-root distance to nearest bus -0.76* -0.78* 0.46* -0.16 -0.16 0.85stop, km1/3 (0.43) (0.43) (0.20) (0.21) (0.20) (0.17)Log land owned or leased, ln(hectares) -0.41 -0.44 0.64 -0.23 -0.23 0.80(0.27) (0.28) (0.18) (0.21) (0.21) (0.16)Asset Index 0.25 0.27 1.31 0.058 0.058 1.06(0.18) (0.18) (0.23) (0.12) (0.12) (0.13)Liquidity (=1 if can access 250,000 CFA 0.67 0.66 1.94 -0.70 -0.70 0.50in emergency funds) (0.84) (0.83) (1.61) (0.56) (0.57) (0.28)Constant -3.90*** -1.83 -1.49 0.22 -4.54*** -1.86 -1.82 0.16(0.48) (2.27) (2.23) (0.50) (0.54) (2.35) (2.42) (0.39)Demographic controls N Y Y Y N Y Y YObservations 1,046 762 762 762 471 405 405 405Pseudo R-squared 0.015 0.12 0.13 0.13 0.049 0.11 0.11 0.11Note: Columns 1-3 and 4-7 report logistic regression coefficients; Column 4 (8) reports the odds ratio of the logistic regression shown in column 3 (7);Robust standard errors are in parentheses; standard errors clustered at the household level with 181 clusters; *** p<0.01, ** p<0.05, * p<0.1139Table C.6: Impacts of biogas installation on respiratory health in children under 12 years of ageCough ALRI symptoms(1) (2) (3) (4) (5) (6) (7) (8)Odds Ratio Odds RatioBiogas installation 1.55 -0.050 -0.38 0.68 1.62 -0.18 -0.86 0.42(0.55) (0.44) (0.48) (0.32) (0.85) (0.59) (0.59) (0.25)Age, years 0.93** -0.092*** -0.094*** 0.91*** 0.98 -0.027 -0.028 0.97(0.029) (0.031) (0.031) (0.028) (0.044) (0.050) (0.050) (0.049)Female (=1 if female) 1.20 0.24 -0.078 0.92 1.13 0.20 -0.51 0.60(0.24) (0.23) (0.35) (0.32) (0.37) (0.37) (0.62) (0.37)Treatment, female interaction 0.62 1.87 1.29* 3.62*(0.43) (0.80) (0.68) (2.45)Cubic-root distance to nearest weekly -0.39 -0.39 0.68 -0.039 -0.029 0.97market, km1/3 (0.39) (0.38) (0.26) (0.35) (0.34) (0.33)Cubic-root distance to nearest bus -0.44** -0.44** 0.65** -0.94*** -0.96*** 0.38***stop, km1/3 (0.21) (0.21) (0.13) (0.35) (0.35) (0.13)Log land owned or leased, ln(hectares) -0.26 -0.27 0.76 -0.30 -0.33 0.72(0.19) (0.19) (0.15) (0.27) (0.27) (0.19)Asset Index 0.086 0.090 1.09 0.25 0.27* 1.31*(0.093) (0.092) (0.10) (0.16) (0.15) (0.20)Liquidity (=1 if can access 250,000 CFA -0.011 0.00063 1.00 -0.19 -0.20 0.82in emergency funds) (0.50) (0.50) (0.50) (0.79) (0.76) (0.62)Constant 0.13*** 0.94 1.11 3.02 0.035*** 2.35 2.74 15.5(0.038) (1.82) (1.77) (5.35) (0.016) (2.37) (2.29) (35.4)Demographic controls N Y Y Y N Y Y YObservations 1,049 971 971 971 1,047 969 969 969Pseudo R-squared 0.015 0.094 0.097 0.097 0.0075 0.14 0.15 0.15Note: Columns 1-3 and 4-7 report logistic regression coefficients; Columns 4 (8) report the odds ratio of the logistic regression shown in column 3 (7); Robuststandard errors in parentheses; standard errors clustered at the village level with 49 clusters; *** p<0.01, ** p<0.05, * p<0.1140Table C.7: Impacts of biogas installation on respiratory health in cooksCough ALRI symptoms(1) (2) (3) (4) (5) (6) (7) (8)Odds Ratio Odds RatioBiogas installation 1.12 -0.17 -1.32 0.27 2.15 0.12 -1.82 0.16(0.40) (0.44) (1.06) (0.28) (1.17) (0.59) (1.51) (0.24)Age, years 1.02* 0.019 -0.00087 1.00 1.03* 0.023 -0.019 0.98(0.013) (0.013) (0.024) (0.024) (0.017) (0.016) (0.035) (0.034)Treatment, age interaction 0.036 1.04 0.061 1.06(0.029) (0.030) (0.040) (0.043)Cubic-root distance to nearest weekly -0.55** -0.57* 0.57* -0.59* -0.62* 0.54*market, km1/3 (0.28) (0.29) (0.16) (0.34) (0.36) (0.19)Cubic-root distance to nearest bus -0.21 -0.17 0.84 -0.69 -0.60 0.55stop, km1/3 (0.20) (0.21) (0.18) (0.47) (0.49) (0.27)Log land owned or leased, ln(hectares) -0.023 -0.039 0.96 -0.86*** -0.92*** 0.40***(0.16) (0.16) (0.16) (0.25) (0.24) (0.097)Asset Index -0.12 -0.11 0.90 0.097 0.11 1.12(0.098) (0.099) (0.088) (0.16) (0.16) (0.18)Liquidity (=1 if can access 250,000 CFA -0.049 0.019 1.02 -0.70 -0.62 0.54in emergency funds) (0.50) (0.51) (0.52) (0.72) (0.72) (0.39)Constant 0.059*** -0.048 0.50 1.64 0.014*** 1.89 3.12 22.7(0.033) (2.00) (2.06) (3.38) (0.011) (2.81) (2.98) (67.6)Demographic controls N Y Y Y N Y Y YObservations 473 427 427 427 473 427 427 427Pseudo R-squared 0.0093 0.061 0.066 0.066 0.029 0.15 0.16 0.16Note: Columns 1-3 and 4-7 report logistic regression coefficients; Column 4 (8) reports the odds ratio of the logistic regression shown in column 3 (7);Robust standard errors in parentheses; standard errors clustered at the household level with 181 clusters;*** p<0.01, ** p<0.05, * p<0.1141Table C.8: Impacts of biogas installation on agricultural outcomesNumber of crops Revenue from selling Use of improvedgrown crops in the last 12 fertilizermonths, ln(CFA)(1) (2) (3) (4) (5) (6)Biogas installation -0.40* -0.35 -0.32 -1.51 0.85** 0.64(0.23) (0.24) (1.39) (1.20) (0.40) (0.43)Cubic-root distance to nearest -0.052 0.24 0.30weekly market, km1/3 (0.16) (0.74) (0.27)Cubic-root distance to nearest 0.068 -1.26 -0.057bus stop, km1/3 (0.13) (0.80) (0.21)Log land owned or leased, 0.28*** 0.83* -0.032ln(hectares) (0.088) (0.45) (0.15)Asset Index -0.054 -0.24 0.16(0.052) (0.30) (0.12)Liquidity (=1 if can access 250,000 0.049 0.00065 -0.30CFA in emergency funds) (0.23) (1.32) (0.50)Constant 2.89*** 1.70* 8.02*** 11.8** 0.26 0.10(0.17) (0.96) (1.09) (4.83) (0.29) (2.17)Demographic controls N Y N Y N YObservations 196 182 196 182 196 182R-squared 0.022 0.153 0.001 0.156 0.031 0.076Note: Robust standard errors in parentheses; standard errors clustered at the village level with 49 clusters;Columns 5 and 6 report logistic regression coefficients and pseudo r-squared; *** p<0.01, ** p<0.05, * p<0.1142Table C.9: Impacts of biogas installation on agricultural outcomes (continued)Crops harvested in the last 12 months, kg1/3Corn Peanuts Millet(1) (2) (3) (4) (5) (6)Biogas installation 0.75 0.73 0.22 0.59 -0.64 0.63(1.41) (1.42) (1.18) (1.37) (1.22) (1.29)Cubic-root distance to nearest 1.97** 1.04 0.87weekly market, km1/3 (0.93) (0.87) (0.75)Cubic-root distance to nearest 0.053 -1.99** -0.95bus stop, km1/3 (0.75) (0.75) (0.67)Log land owned or leased, -0.48 0.60* -0.20ln(hectares) (0.48) (0.34) (0.40)Asset Index 0.43 0.070 -0.26(0.34) (0.29) (0.28)Liquidity (=1 if can access 250,000 1.95* -0.25 0.067CFA in emergency funds) (1.00) (1.10) (1.05)Constant 7.68*** 5.39 10.6*** 6.00 9.90*** 1.72(0.40) (6.49) (0.95) (4.25) (0.85) (4.45)Demographic controls N Y N Y N YObservations 64 58 122 114 119 114R-squared 0.008 0.203 0.001 0.204 0.004 0.155Note: Robust standard errors in parentheses; standard errors clustered at the village level with 49 clusters;*** p<0.01, ** p<0.05, * p<0.1143Table C.10: Impacts of biogas installation on adult employment(1) (2) (3) (4)Odds RatioBiogas installation 0.16 -0.23 -0.088 0.92(0.23) (0.23) (0.26) (0.24)Age, years -0.00011 -0.00079 -0.0010 1.00(0.0030) (0.0033) (0.0033) (0.0033)Female (=1 if female) -1.59*** -1.59*** -1.41*** 0.24***(0.17) (0.18) (0.25) (0.062)Treatment, female interaction -0.37 0.69(0.35) (0.24)Cubic-root distance to nearest weekly 0.062 0.065 1.07market, km1/3 (0.13) (0.13) (0.13)Cubic-root distance to nearest bus -0.26* -0.25* 0.78*stop, km1/3 (0.14) (0.14) (0.11)Log land owned or leased, -0.11 -0.11 0.89ln(hectares) (0.12) (0.12) (0.11)Asset Index 0.11* 0.11* 1.12*(0.061) (0.061) (0.068)Liquidity (=1 if can access 250,000 0.16 0.16 1.17CFA in emergency funds) (0.34) (0.34) (0.40)Constant -0.29 -0.73 -0.82 0.44(0.21) (1.28) (1.25) (0.55)Demographic controls N Y Y YObservations 1,435 1,301 1,301 1,301Pseudo R-squared 0.098 0.12 0.12 0.12Note: Columns 1-3 report logistic regression coefficients; Column 4 reports the odds ratio of thelogistic regression shown in column 3; Robust standard errors are in parentheses; standard errorsare clustered at the household level with 184 clusters; *** p<0.01, ** p<0.05, * p<0.1144C.3 Cost AnalysisBecause our research team did not implement the intervention, the biogas installations,the majority of the information we have regarding costs comes from PNB-SN documents.Program documents report the cost of installing the digester, which varies by size. The costof the smallest digester, 8 cubic meters, was reported at around 450,000 CFA, or 750 USD(Programme National De Biogaz Domestique Du Sénégal, n.d.). The largest digester, 18cubic meters, was reported to cost 728,300 CFA, around 1,200 USD. As discussed previously,installation costs were paid by both the government and the recipient households. Only 52out of 97 households were able to report the size of their digester, and many of these reportsdid not match the sizes available from the government documents. Therefore, we do not havereliable data on the size of digesters installed in our sample.We additionally asked households how much they had spent on associated appliances(like a stove or gas-powered lamps) as well as any repairs since installation. Seventy-nineof the recipient households, around eighty percent, reported that they had not spent anymoney on appliances or repairs. The average expenditure on appliances and repairs amongstthe other twenty percent of households was 37,000 CFA, or 60 USD. We do not have anyinformation regarding appliances or repairs that were paid for by PNB-SN.If we assume every household received an 8 cubic meter digester and did not spendany money on appliances or repairs, the total cost of the program for all 97 households in oursample was 72,750 USD. Under these same assumptions, the cost for the 901 installationsinitiated by PNB-SN between 2015 and 2017 would be estimated at 675,750 USD. This isclearly an underestimate as it is very unlikely that repair and appliance costs, whether borneby households or the government, were zero. In addition, we are not considering the value ofthe time that households spend on operating the digester, which involves collecting biomassand water to feed into the tank.145C.4 Construction of the Asset IndexThe asset index was computed using principal component analysis (PCA). Assets were chosenbased on those used by the Demographic and Health Surveys to conduct their wealth index forrural Senegal (https://dhsprogram.com/topics/wealth-index/Wealth-Index-Construction.cfm,n.d.). Table C.11 shows the variables that were included, summary statistics and thecomponent score for each. Variables are set equal to 1 if a household reports owning thelisted asset. In the case of drinking water and construction materials, each household iscoded based on their main water source or construction material. Parallel analysis was usedto determine the number of factors to include. As shown by the results in Figure C.1, 8components were included.Figure C.1: Parallel analysis results146Figure C.2: Distribution of asset index values147Table C.11: Indicators included in wealth index principal component analysisAnalysis Missing N Component 1Asset Mean Std. Dev. N Missing N ScoreBank account 0.38 0.487 196 0 0.138Car 0.09 0.282 196 0 0.169Motorcycle 0.21 0.408 196 0 0.069Bicycle 0.14 0.346 196 0 -0.059Refrigerator 0.19 0.396 196 0 0.274Television 0.41 0.494 196 0 0.320DVD/CD/VCR Player 0.02 0.142 196 0 0.007Radio 0.72 0.450 196 0 0.080Sewing Machine 0.06 0.231 196 0 -0.039Cassette player 0.01 0.071 196 0 0.052Iron 0.05 0.210 196 0 0.076Fan 0.24 0.428 196 0 0.310Mobile Phone 0.98 0.142 196 0 0.010Tractor 0.03 0.158 196 0 -0.073Computer 0.12 0.329 196 0 0.187Mosquito net 0.95 0.210 196 0 0.053Private toilet 0.70 0.458 196 0 0.149Drinking water from:Well 0.27 0.445 196 0 -0.249Drilling 0.09 0.290 196 0 -0.008Public 0.09 0.290 196 0 -0.079Private tap 0.49 0.501 196 0 0.267Shared tap 0.05 0.210 196 0 -0.015Other tap 0.01 0.101 196 0 0.055Wall material:Mud brick 0.16 0.366 196 0 -0.257Metal 0.01 0.071 196 0 -0.011Straw 0.05 0.221 196 0 -0.030Wood 0.03 0.158 196 0 -0.046Stone 0.06 0.240 196 0 -0.104Cement brick 0.69 0.462 196 0 0.292No wall 0.01 0.071 196 0 -0.019Floor material:Mud 0.11 0.316 196 0 -0.211Tiles 0.12 0.323 196 0 0.227Stone 0.19 0.396 196 0 -0.143Cement 0.48 0.501 196 0 0.077Sand 0.09 0.290 196 0 0.039Roof material:Metal 0.40 0.492 196 0 0.084Straw 0.23 0.422 196 0 -0.324Slate 0.13 0.334 196 0 0.091Cement 0.12 0.329 196 0 0.190Zinc 0.12 0.323 196 0 0.007Note: Variables are set equal to 1 if a household reports owning the listed asset. In the case of drinkingwater and construction materials, each household is coded based on their main water source or constructionmaterial.148
Thesis/Dissertation
eng
Integrated Studies in Land and Food Systems
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Gulati, Sumeet
Squires, Munir
Essays in environmental and development economics : lead-acid battery recycling in Mexico, gender and informal labor markets in Ghana, and domestic biogas adoption in Senegal
Text