Developing country cities are characterized by informal housing—slums—but growing urban populations and incomes will lead governments to pursue a host of policies that promote the construction of modern, formal sector housing. This has the potential to affect entire neighborhoods since the effects are likely to spillover beyond directly targeted locations. In this paper, we ask how large are the spillovers from formal development, and what do these imply for the welfare consequences of pro-formalization policies in developing country cities? We address this question in three steps. First, we exploit a unique natural experiment in Mumbai that led 15% of central city land occupied by the city’s defunct textile mills to come onto the market for redevelopment in the 2000s. Second, we use a “deep learning” approach to measure slums from satellite images, and combine this with administrative sources to construct a uniquely spatially disaggregated dataset spanning the period. Third, we develop a quantitative general equilibrium model of a city featuring formal and informal housing supply to guide our empirical analysis. We find evidence of substantial housing and agglomeration externalities, and provide reduced-form evidence suggestive of both efficiency gains (through increased employment density in central areas) and potential equity losses (through the conversion of slums and gentrification near redeveloped mill sites).
We derive a formal, decision-based method for comparing the performance of counterfactual treatment regime predictions using the results of experiments that give relevant information on the distribution of treated outcomes. Our approach allows us to quantify and assess the statistical significance of differential performance for optimal treatment regimes estimated from structural models, extrapolated treatment effects, expert opinion, and other methods. We apply our method to evaluate optimal treatment regimes for conditional cash transfer programs across countries where predictions are generated using data from experimental evaluations in other countries and pre-program data in the country of interest.
To what extent are causal effects estimated in one region or time period informative about another region or time? In this paper, I derive bounds on the average causal effect in a context of interest using experimental evidence from another context. I use differences in outcome distributions for individuals with the same characteristics and treatment status in the original study and the context of interest to learn about unobserved differences across contexts. Greater differences in outcome distributions generate wider bounds. Empirically, I explore using experimental results on the return to cash transfers to male microentrepreneurs in one Mexican city in 2006 to predict the returns among male microentrepreneurs in urban Mexico in 2012. I show that existing methods would lead us to be overconfident in extrapolating from the small experiment to all of urban Mexico in 2012. Using data from a pair of remedial education experiments carried out in urban India, I show that the methods suggested in this paper are able to recover average causal effects in one city using results from the other where existing methods are unsuccessful.
Indian Labor Regulations and the Cost of Corruption: Evidence from the Firm Size Distribution
(with Amrit Amirapu)
Accepted at the Review of Economics and Statistics
In this paper, we estimate the costs associated with a suite of labor regulations in India whose components have gone largely unstudied in developing countries. We take advantage of the fact that these regulations only apply to firms above a size threshold. Using distortions in the firm size distribution at the threshold together with a structural model of firm size choice, we estimate that the regulations increase firms’ unit labor costs by 35%. We document a robust positive association between regulatory costs and exposure to corruption, which may explain why regulations appear to be so costly in developing countries.
Selected work in progress
Combining Experimental and Observational Studies in Meta-Analysis: Leveraging Experimental Structures to Eliminate Selection Bias
(with Rachael Meager)