We show that labor market transaction costs explain why the smallest farms are more efficient than slightly larger farms in most low-income countries and that increases in machine capacity with operational scale result in the globally observed rising upper tail of productivity. We find evidence consistent with these mechanisms using Indian data, and we show that if all Indian farms were at the minimum scale required to maximize the return on land, the number of farms would be reduced by 82% and income per farm worker would rise by 68%.
We show that labor market transaction costs explain why the smallest farms are more efficient than slightly larger farms in most low-income countries and that increases in machine capacity with operational scale result in the globally observed rising upper tail of productivity. We find evidence consistent with these mechanisms using Indian data, and we show that if all Indian farms were at the minimum scale required to maximize the return on land, the number of farms would be reduced by 82% and income per farm worker would rise by 68%.
We show using a theoretical framework that embeds a voting model in a general-equilibrium model of a rural economy with two interest groups defined by land ownership that the effects of democratization – a shift from control of public resources by the landed elite to a democratic regime with universal suffrage – on the portfolio of public goods is heterogeneous, depending the population landless. In accord with the model and empirical findings from micro data on the differing material interests of the two land classes, we find, based on 30-year panel data describing the democratization of Indian villages, that democratization in villages with a larger landless population share shifted resources away from public irrigation, secondary schools, and electrification and towards programs that increase employment. When the landed farmers have a large population share, public resources were shifted towards irrigation, secondary schools and electrification and away from employment programs.
We exploit recent molecular genetics evidence on the genetic basis of arsenic excretion and unique information on family links among respondents living in different environments from a large panel survey to uncover the hidden costs of arsenic poisoning in Bangladesh. We provide for the first time estimates of the effects of the ingestion and retention of inorganic arsenic on direct measures of cognitive and physical capabilities as well as on the schooling attainment, occupational structure, entrepreneurship, and incomes of the rural Bangladesh population. We also provide new estimates of the effects of the consumption of foods grown and cooked in arsenic-contaminated water on individual arsenic concentrations. The estimates are based on arsenic biomarkers obtained from a sample of members of rural households in Bangladesh who are participants in a long-term panel survey following respondents and their coresident household members over a period of 26 years.
We examine empirically the generalizability of internally valid micro-estimates of causal effects in a fixed population over time when that population is subject to aggregate shocks. Using panel data, we show that the returns to investments in agriculture in India and Ghana, small and medium non-farm enterprises in Sri Lanka, and schooling in Indonesia fluctuate significantly across time periods. We show how the returns to these investments interact with specific, measurable, and economically relevant aggregate shocks, focusing on rainfall and price fluctuations. We also obtain lower-bound estimates of confidence intervals of the returns based on estimates of the parameters of the distributions of rainfall shocks in our two agricultural samples. We find that even these lower-bound confidence intervals are substantially wider than those based solely on sampling error that are commonly provided in studies, most of which are based on single-year samples. We also find that cross-sectional variation in rainfall cannot be confidently used to replicate within-population rainfall variability. Based on our findings, we discuss methods for incorporating information on external shocks into evaluations of the returns to policy.