We propose a method for aggregating evidence from observational studies, which may be subject to bias in identifying causal effects, and randomized controlled trials (RCTs) which may be subject to site selection bias. We show that an instrument for research design choice nonparametrically identifies average observational and site selection bias. We develop a parametric hierarchical Bayesian approach to estimation, and use entry of RCT-facilitating organisations as a differences-in-differences instrumental variable. Applications to the conditional cash transfer (CCT) and microcredit literatures show bias in observational studies of CCTs but not microcredit. Neither application shows evidence of site selection bias.
We consider the problem of using a sample in which treatment assignment is unconfounded to learn a treatment-targeting policy that accounts for costs arising when implementing the policy in the broader population. The result is a “practical” treatment policy that, by construction, will trade potential treatment-effect gains from highly granular targeting for reductions in implementation costs from simplifying the targeting. We define a penalized welfare criterion that incorporates measurement and complexity costs and solve for the optimal coarsened policy. We derive a bound on the welfare loss for the coarse policy. We develop a revealed preference method for bounding complexity costs on a scale that is commensurate with a welfare measure and illustrate using the PROGRESA conditional cash transfer experiment in Mexico
How informative are treatment effects estimated in one region or time period for another region or time? In this paper, I derive bounds on the average treatment effect in a context of interest using experimental evidence from another context. The bounds are based on (1) the information identified about treatment effect heterogeneity due to unobservables in the experiment and (2) using differences in outcome distributions across contexts to learn about differences in distributions of unobservables. Empirically, using data from a pair of remedial education experiments carried out in India, I show the bounds are able to recover average treatment effects in one location using results from the other while the benchmark method cannot.
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.
Developing country cities are characterized by informal housing–slums–but as incomes grow their governments will pursue a host of urban renewal policies that promote the construction of modern, formal sector housing. This paper examines spatial spillovers from urban renewal using a unique policy experiment in Mumbai that led 15% of central city land occupied by the city’s defunct textile mills to be redeveloped during the 2000s. We digitize a host of new spatially disaggregated datasets on population, employment and house prices, and provide the first application of a deep convolutional neural network to measure changing slum cover from daytime satellite imagery. We find reduced form evidence of sizable spatial spillovers that impact surrounding locations by (i) increasing formal sector house prices and reducing slum cover, (ii) reducing informal employment density with no increase from the formal sector and (iii) increasing the share of high-skill residents and reducing population density. We disentangle the source of these spillovers by developing a quantitative urban model with formal and informal land and labor markets, and use it to quantify the equity-efficiency trade-off associated with slums and urban renewal policies.
Indian Labor Regulations and the Cost of Corruption: Evidence from the Firm Size Distribution
(with Amrit Amirapu)
Review of Economics and Statistics, March 2020.
Final draft, VoxDev article, Code and data
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
How Do Environmental Firm Location Policies Affect Workers, Firms, and Environmental Quality?
(with Namrata Kala)
(with Keisuke Hirano)