Abstract: The incidence of redistributive policies is central to whether they meet their stated goals. We study this in the context of one of the world's largest programs to improve social equity in schooling: a 25% quota in all Indian private schools for students from disadvantaged groups. We use lottery-based estimates to show that, although students admitted under the quota attend more expensive and preferred schools on average, the distribution of program benefits is very regressive. Program applicants are concentrated among more-educated and better-off households. Consequently, 7.4% of the program spending accrues to the bottom socioeconomic quintile, compared to 24.3% to the top quintile. We use rich survey data to show that low application rates for poorer children are not driven by preferences and beliefs. Instead, information constraints and application frictions appear to be key. Finally, we use a randomized intervention to confirm the importance of these frictions and further demonstrate that alleviating a single constraint (e.g., information) may not reduce regressive selection, even if it boosts application rates substantially. Our results demonstrate how constraints facing potential applicants can make redistributive policies regressive in practice. Appropriate policy interventions must consider the joint incidence of these constraints to reduce regressivity.

Abstract: Millions of migrants in developing countries move to urban areas in search of better prospects, but access to public services varies widely within cities. Yet, we know little about spatial inequalities within cities in low- and middle-income countries. This paper investigates the spatial distribution of socio-economic status and public goods access within Brazilian cities, using high-resolution Census maps. I consider spatial metrics of "distance segregation", capturing the physical proximity between neighborhoods of different socio-economic status. I document descriptive patterns of segregation by income, race, and informality and disparities in access to public goods within cities. To make progress on the identification of the impacts of residential patterns on public goods provision, I develop an instrumental variables strategy that leverages within-city geography to predict where the poor and rich live. I find that cities with greater distance between rich and poor have fewer households connected to sewerage and water, worse neighborhood quality, and lower access to public amenities. Leveraging spatial variation in public goods provision within cities, I highlight competing mechanisms that shape the allocation of urban services, including externalities across neighborhoods, strategic underprovision to deter the poor, and preferences over public goods provision. These findings help inform the debate on policies such as slum clearance and relocations, social housing, and the spatial targeting of public goods.

Abstract This paper proposes novel natural language methods to measure worker rights from collective bargaining agreements (CBAs) for use in empirical economic analysis. Applying unsupervised text-as-data algorithms to a new collection of 30,000 CBAs from Canada in the period 1986-2015, we parse legal obligations (e.g. “the employer shall provide...”) and legal rights (e.g. “workers shall receive...”) from the contract text. We validate that contract clauses provide worker rights, which include both amenities and control over the work environment. Companies that provide more worker rights score highly on a survey indicating pro-worker management practices. Using time-varying province-level variation in labor income tax rates, we find that higher taxes increase the share of worker-rights clauses while reducing pre-tax wages in unionized firms, consistent with a substitution effect away from taxed compensation (wages) toward untaxed amenities (worker rights). Further, an exogenous increase in the value of outside options (from a Bartik instrument for labor demand) increases the share of worker rights clauses in CBAs. Combining the regression estimates, we infer that a one-standard deviation increase in worker rights is valued at about 5.7% of wages.

Abstract: The incidence of redistributive policies is central to whether they meet their stated goals. We study this in the context of one of the world's largest programs to improve social equity in schooling: a 25% quota in all Indian private schools for students from disadvantaged groups. We use lottery-based estimates to show that, although students admitted under the quota attend more expensive and preferred schools on average, the distribution of program benefits is very regressive. Program applicants are concentrated among more-educated and better-off households. Consequently, 7.4% of the program spending accrues to the bottom socioeconomic quintile, compared to 24.3% to the top quintile. We use rich survey data to show that low application rates for poorer children are not driven by preferences and beliefs. Instead, information constraints and application frictions appear to be key. Finally, we use a randomized intervention to confirm the importance of these frictions and further demonstrate that alleviating a single constraint (e.g., information) may not reduce regressive selection, even if it boosts application rates substantially. Our results demonstrate how constraints facing potential applicants can make redistributive policies regressive in practice. Appropriate policy interventions must consider the joint incidence of these constraints to reduce regressivity.

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Abstract: The incidence of redistributive policies is central to whether they meet their stated goals. We study this in the context of one of the world's largest programs to improve social equity in schooling: a 25% quota in all Indian private schools for students from disadvantaged groups. We use lottery-based estimates to show that, although students admitted under the quota attend more expensive and preferred schools on average, the distribution of program benefits is very regressive. Program applicants are concentrated among more-educated and better-off households. Consequently, 7.4% of the program spending accrues to the bottom socioeconomic quintile, compared to 24.3% to the top quintile. We use rich survey data to show that low application rates for poorer children are not driven by preferences and beliefs. Instead, information constraints and application frictions appear to be key. Finally, we use a randomized intervention to confirm the importance of these frictions and further demonstrate that alleviating a single constraint (e.g., information) may not reduce regressive selection, even if it boosts application rates substantially. Our results demonstrate how constraints facing potential applicants can make redistributive policies regressive in practice. Appropriate policy interventions must consider the joint incidence of these constraints to reduce regressivity.

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