We propose a method for aggregating evidence from observational studies, which may be subject to internal selection bias, and randomized controlled trials (RCTs) which may be subject to site selection bias. We show that it is possible to nonparametrically debias both types of studies using an Instrumental Variables (IV) strategy, uncovering the true distribution of treatment effects for complier studies. As we often have a small number of studies, imperfect instruments, and observe study results with error, parametric hierarchical Bayesian models work well in practice. Our specific implementation uses the presence of a facilitating organisation such as the Jameel Poverty Action Lab (JPAL) or Innovations for Poverty Action (IPA) as a novel Differences in Differences “Plausibly Exogenous” IV. This model point-identifies the internal selection bias for studies switching to the experimental design due to JPAL-IPA entry which is the negative local average treatment effect (LATE) in our setting. Applying this strategy to Conditional Cash Transfers (CCTs) and Microcredit shows substantial internal selection bias in observational studies of CCTs and much less for Microcredit; neither application shows evidence of RCT site selection bias, though credible intervals are wide with minimal updating.
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.
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)
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)