Publications
Indoor Air Quality, Information, and Socio-Economic Status: Evidence from Delhi
with Kenneth Lee and Michael Greenstone
American Economic Association: Papers and Proceedings
May 2021
Coverage: BBC, Hindustan Times (Op-Ed)
[Paper] [Appendix]
Abstract: In Delhi, one of the world's most polluted cities, there is relatively little information on indoor air pollution and how it varies by socioeconomic status (SES). Using indoor air quality monitors (IAQMs), we find that winter levels of household air pollution exceed World Health Organization standards by more than 20 times in both high- and low-SES households. We then evaluate a field experiment that randomly assigned monthlong IAQM user trials across medium- and high-SES households but suffered from significant survey non-response. Among respondents, IAQMs did not affect take-up of subsidized air purifier rentals or other defensive behavior.
working papers
School Voucher Design and Strategic Pricing: Evidence from India
Winner: National Academy of Education / Spencer Dissertation Fellowship
Coverage: World Bank Blog, VoxDev
[Working Paper]
Abstract: This paper uses detailed government records from India to study the world’s largest voucher system for primary education, in which voucher levels are linked to private school tuition. Lottery results show that voucher recipients benefit from improved achievement measures and lower tuition payments. However, private schools respond to the tuition-linked voucher design by raising tuition fees up to 15%, with only modest improvements in quality. These strategic responses affect the 95% of children who do not receive vouchers. Using an empirical model of the education market, welfare estimates based on revealed preference suggest the program’s benefits exceed its costs. Failing to account for school responses, however, would have overstated the benefit-cost ratio by a factor of two. An alternative voucher design – which pays a fixed amount regardless of tuition charged – would eliminate the distortionary price incentives and increase the benefit-cost ratio by 40%.
Long-range Forecasts as Climate Adaptation:
Experimental Evidence from Developing-Country Agriculture
with Fiona Burlig, Amir Jina, Erin Kelley, and Greg Lane
Accepted Pre-Results Review at Journal of Development Economics
Coverage: NBER Digest, Media Summary
[Working Paper]
Abstract: Climate change increases weather variability, preventing farmers from tailoring investments to the upcoming monsoon. In theory, accurate, seasonal forecasts overcome this challenge. We experimentally evaluate monsoon onset forecasts in India, randomizing 250 villages into control, forecast, and benchmark insurance groups. Forecast farmers update their beliefs and behavior: receiving “good news” relative to a farmer’s prior increases cultivation, farm inputs, farm profits (for those unaffected by flooding) and reduces business; receiving “bad news” reduces cultivated land and farm profits but increases business. Overall, forecasts raise a welfare index by 0.06 SD. Unlike insurance, forecasts reduce climate risk by enabling tailoring.
Social Networks and Internal Migration: Evidence from Facebook in India
with Michael Bailey
[Working Paper]
Abstract: Despite potentially large economic returns, rates of internal migration remain low in many developing countries. This paper uses new, de-identified data from Facebook to quantify the role of social networks in explaining this development puzzle. We study this question in India, a country that exhibits substantial wage dispersion across regions but remains relatively under-urbanized. Detailed records of nearly 20 million individuals on the evolution of social connections and residential choice reveal that networks and migration are strongly linked. Across several identification strategies, a model of migration suggests that social networks account for roughly 20% of the relationship between migration and distance. We develop a simple, static model of spatial equilibrium, which suggests that equalizing social connections across locations increases average wages by 3% (24% for the bottom wage-quartile) through increased migration. This impact is larger than fully removing the marginal effect of distance in migration decisions, akin to building rapid transport infrastructure. Taken together, our data suggest that - by reducing migration frictions - increasing social connections across space may have considerable economic gains. We provide suggestive evidence for economic and emotional support mechanisms underlying network effects and show that college attendance can boost the size and diversity of social networks by 20%.
Is the Demand for Clean Air Too Low? Experimental Evidence from Delhi
with Patrick Baylis, Kenneth Lee, and Michael Greenstone
Coverage: Washington Post, Indian Express (Op-Ed)
[Working Paper]
Abstract: Do hazardous levels of air pollution in developing countries reflect low demand for air quality or imperfect information about its benefits? This paper implements an experiment to estimate the demand for clean air in a low-income country and tests for several possible market failures in information that may affect it. Combining randomized price variation for low-cost pollution masks with day-to-day variation in ambient air quality, we estimate an average marginal willingness-to-pay (MWTP) for an annual 10 unit reduction in PM2.5 of $1.14 (USD) among low-income residents of Delhi, India. This estimate is low in global terms, but increases more than five times for respondents who are treated with a description of the health effects of air pollution prior to demand elicitation. These findings suggest limited demand for clean air may partly reflect limited information about its benefits.
An Evaluation of Historically-Trained Statistical Models in Projecting Climate Impacts
with Haynes Stephens, Katherine Dixon, Maria Hernandez Limon, James Franke, Christoph Muller, Jonas Jagermeyr, Alex Ruane, Jonathan Proctor, and Elisabeth Moyer
Abstract: A common approach for estimating climate change impacts is to use historical responses to year-over-year weather fluctuations as analogues of responses to future, warmer conditions. For agricultural impacts in particular, it is standard to rely on statistical models trained on observed crop yields, temperature, and precipitation. In this work we show that widely-used statistical models overestimate climate-driven yield losses by conducting “simulated data experiments” using simulations of maize in the U.S. corn belt from the Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 exercise. These simulations involve independent process-based models varying widely in construction and assumptions, but statistical fits to their historical yields overstate future damages by nearly a factor of two on average. The underlying cause is the sensitivity of plants to moisture stress: because relative humidity stays roughly constant under climate change, while it is typically high in warm years in the historical period, the moisture stress associated with a given heat exposure is lower in a warmer future. We show that statistical models based on vapor pressure deficit or soil moisture instead better reproduce both present-day yield fluctuations and future climatological changes. These results highlight that goodness-of-fit tests on present-day data do not ensure that a statistical model can accurately project climate damages. The lessons learned are general to all climate impacts studies based on historical data: simulated data experiments are critical for ensuring that the variables used reflect the primary drivers of future changes.