Household labor reallocation provides a potentially important channel for rural households to adapt to changing weather patterns. Exploiting the temporal and spatial variation of drought occurrence in India, I find that drought reduces the share of agriculture labor hours by 110 hours (3% at the mean). This reduction is driven by households that do not own land. Motivated by these facts, I develop a model of labor allocation across the agriculture and non-agriculture sectors to analyze how droughts may affect structural transformation. My results imply that projected increase in the spatial extent of droughts over the coming years will induce landowning households to allocate 2% more labor to agriculture and induce landless households to reduce their agricultural labor. The net effect is a 1% reduction in agricultural labor. While small in percentage terms, this implies that 2.5 million individuals would leave agriculture. I use the model to analyze how projected climate change would affect the cost to the government of achieving its stated target of increasing the manufacturing share of GDP to 25% by 2035. Under climate change, I find that the government would need to subsidize non-agriculture wages by a higher margin due to the presence of land market frictions.

Droughts are becoming increasingly common in India, where 50\% of the labor force works in agriculture, and most agricultural production is rainfall dependent.  This paper investigates the extent to which rural households adapt to drought by reallocating labor from agriculture to other sectors of the economy. I combine high-resolution data on drought with panel data on about 8,000 Indian households collected over a 20-year period in rural areas in 184 districts. I use household-level fixed effects regressions to estimate how drought affects household consumption and diversification from agriculture, and to investigate the mechanisms underlying these effects.  I find that household consumption declines by 5.9% in response to drought and agricultural jobs decline by 2.9%  in the year following a drought. Further, I find that these effects are mediated by job skills and land ownership. Specifically, I find that households with working members who have completed primary education account for most of the workers who exit the agricultural sector. In contrast, I find that households that own land increase their agricultural labor share after experiencing a drought. Thus, while I find that drought causes households to diversify away from agriculture on aggregate, the extent of this structural change is mitigated by the behavior of landowners. Cultural norms, relative prices, and land market transaction costs provide potential explanations for this behavior.

Resource booms are often associated with adverse distributional effects across different demographic groups. We exploit time and spatial variation generated by the copper boom in 2000s to measure the effect of natural resources on human capital investment in Zambia. Combining repeated cross-section of household data with mining data, we find that there has been reduction in school attendance and increased paid work for adolescent girls. Even though working age men increase their labor market participation, we identify substitution in labor market participation of working age women by younger adolescent girls. These effects are stronger for wealthier households, suggesting that growing inequality is associated with the copper boom.

We study how the effects of “Superfund” hazardous waste sites’ listing and deletion during the 2000’s differed across race, income, and health among US seniors. Using a random 20% sample of the US Medicare population from 1999 through 2013 and a spatial difference–in–difference regression method, we find the probability to move out is 5-7% higher for seniors who live within 3.5km of a site in response to the designation of a Superfund site compared to seniors living within 3.5-7km. On average, seniors who move away from sites reduce their exposure to PM2.5 and move further from other sites, but the reductions in pollution are smaller for poorer, sicker, and non-white movers. In addition, we find that Black, Hispanic, and poorer people are 5-6% more likely to move within 3.5km of a site that is not yet proposed to be cleaned up compared to 3.5-7km of a site. We find nearly symmetric results upon the completion of cleanup of a Superfund site. These findings add to the environmental justice literature by providing new evidence on pollution- exposure and sorting for the Medicare population who are defined by the EPA as a vulnerable group to pollution based on age, income, and health.

Can we achieve economic growth while reducing carbon emissions? We investigate the unintended impacts of India’s Special Economic Zones (SEZs), a place-based policy designed to foster economic development, on firms’ energy usage and carbon emissions. Using extensive firm data and a spatial RD-DID design, we find that SEZs significantly reduce firms’ carbon emissions by 30%. This substantial decline in emissions is predominantly driven by larger firms and those located in regions with access to cleaner energy.

It is well-documented that Indian cities are segregated along caste and religious lines (Adukia et al, 2019; Bharathi et al, 2021). In the recent past, major Indian cities have witnessed an expansion of metro rail which is intended to reduce commuting times in congested cities. Neighborhoods with improved public goods such as new metro rail terminals may influence housing market dynamics that could affect residential sorting. This could lead to segregation which affects economic outcomes (Chetty et al, 2020). This is the first paper to investigate how urban commuting infrastructure affects segregation in India. In this paper, we document how the expansion of metro rail in Bangalore impacts residential segregation along caste and religious lines. We use a simple model of residential sorting incorporating preference for commuting facilities and demographic composition of neighborhoods to motivate our empirical specifications. We use a novel dataset of the universe of property sales in Bangalore between 2014-2018. Our empirical strategy relies on the distance between new metro rail terminals and property locations. We assign treatment and control neighborhoods based on their distance to the nearest metro rail terminal. Exploiting the caste identity of buyers and sellers, we calculate inflows and outflows of various caste and religious groups in different neighborhoods. We identify the effect of metro rail expansion on the composition of neighborhoods using the temporal and spatial variation in the opening of metro links. This paper would add to the urban economics literature, particularly the branch studying how place-based investment policies affect residential segregation.