This dissertation explores how local circumstances affect local

The SHRUG is an open data platform describing multidimensional socioeconomic development across 600,000 villages and towns in India. This paper presents three illustrative analyses only possible with high-resolution data. First, it confirms that nighttime lights are highly significant proxies for population, employment, per-capita consumption, and electrification at very local levels. However, elasticities between night lights and these variables are far lower in time series than in cross section, and vary widely across context and level of aggregation. Next, this study shows that the distribution of manufacturing employment across villages follows a power law: the majority of rural Indians have considerably less access to manufacturing employment than is suggested by aggregate data. Third, a poverty mapping exercise explores local heterogeneity in living standards and estimates the potential targeting improvement from allocating programs at the village- rather than at the district-level. The SHRUG can serve as a model for open high-resolution data in developing countries.

Nearly one billion people worldwide live in rural areas without access to the paved road network. This paper measures the impacts of India's $40 billion national rural road construction program using regression discontinuity and data covering every individual and firm in rural India. The main effect of new feeder roads is to allow workers to obtain nonfarm work. However, there are no major changes in consumption, assets or agricultural outcomes. Nonfarm employment in the village expands only slightly, suggesting the new work is found outside of the village. Even with better market connections, remote areas may continue to lack economic opportunities.

National governments frequently pull strings to get their citizens appointed to senior positions in international institutions. We examine, over a 60 year period, the nationalities of the most senior positions in the United Nations Secretariat, ostensibly the world's most representative international institution. The results indicate which nations are successful in this zero-sum game, and what national characteristics correlate with power in international institutions. The most overrepresented countries are small, rich democracies like the Nordic countries. Statistically, democracy, investment in diplomacy, and economic/military power are predictors of senior positions -- even after controlling for the U.N. staffing mandate of competence and integrity. National control over the United Nations is remarkably sticky; however the influence of the United States has diminished as U.S. ideology has shifted away from its early allies. In spite of the decline in U.S. influence, the Secretariat remains pro-American relative to the world at large.

How do investments in agricultural productivity translate into development and structural transformation? We estimate the long-run impacts of India's irrigation canals, which span 300,000+ km and deliver water to 130,000+ villages. Drawing on high-resolution data on every household, firm, village, and town in India, we use three empirical strategies to characterize the direct and spillover effects of large increases in agricultural productivity. Our findings are consistent with a spatial equilibrium model in which labor is mobile, and urban areas have non-farm productivity advantages. In the long run, areas directly treated by canal irrigation have sharply higher agricultural productivity and population density, but similar non-farm employment shares to non-canal areas. Persistent consumption gains accrue only to landowners and structural transformation occurs almost exclusively through the concentrated growth of regional towns. In the long run, the substantial productivity effects of canals were equilibrated through the movement of labor across space rather than within locations across sectors.

Managing the outbreak of COVID-19 in India constitutes an unprecedented health emergency in one of the largest and most diverse nations in the world. On May 4, 2020, India started the process of releasing its population from a national lockdown during which extreme social distancing was implemented. We describe and simulate an adaptive control approach to exit this situation, while maintaining the epidemic under control. Adaptive control is a flexible counter-cyclical policy approach, whereby different areas release from lockdown in potentially different gradual ways, dependent on the local progre of the dis- ease. Because of these features, adaptive control requires the ability to decrease or increase social distancing in response to observed and projected dynamics of the disease outbreak. We show via simulation of a stochastic Susceptible-Infected-Recovered (SIR) model and of a synthetic intervention (SI) model that adaptive control performs at least as well as immediate and full release from lockdown starting May 4 and as full release from lockdown after a month (i.e., after May 31). The key insight is that adaptive response provides the option to increase or decrease socioeconomic activity depending on how it affects disease progression and this freedom allows it to do at least as well as most other policy alternatives. We also discuss the central challenge to any nuanced release policy, including adaptive control, specifically learning how specific policies translate into changes in contact rates and thus COVID-19's reproductive rate in real time.

A basic problem in applied settings is that different parameters may apply to the same model in different populations. We address this problem by proposing a method using moment trees; leveraging the basic intuition of a classification tree, our method partitions the covariate space into disjoint subsets and fits a set of moments within each subspace. We prove the consistency of this estimator and show standard rates of convergence apply post-model selection. Monte Carlo evidence demonstrates the excellent small sample performance and faster-than-parametric convergence rates of the model selection step in two common empirical contexts. Finally, we showcase the usefulness of our approach by estimating heterogeneous treatment effects in a regression discontinuity design in a development setting.

There is a long-standing debate over whether new roads unavoidably lead to environmental damage, especially forest loss, but causal identification has been elusive. Using multiple causal identification strategies, this paper studies the construction of new rural roads to over 100,000 villages and the upgrading of 10,000 kilometers of national highways in India. The new rural roads had precise zero effects on local deforestation. In contrast, the highway upgrades caused substantial forest loss, which appears to be driven by increased timber demand along the transportation corridors. In terms of forests, last mile connectivity had a negligible environmental cost, while expansion of major corridors had important environmental impacts.