1,720,988 research outputs found
The Importance of Specification Choices When Analyzing Sectoral Productivity Gaps
No full textA consistent finding in the development literature is that average non-farm labor productivity is higher than average farm labor productivity. These differences in average productivity are sometimes used to promote policies which advance the non-farm sector. In this paper, we analyze the importance of two specification choices when comparing productivity gaps, using detailed household panel data from Malawi. Importantly, we are able to calculate both average revenue products (ARPLs) – similar to most of the sectoral productivity gap literature – as well as marginal revenue products (MRPLs). We show that the choice of productivity measure combined with the choice of production function specification can lead to different sectoral productivity rankings. MRPLs from translog production functions suggest the household farm sector is more productive than the household non-farm sector, while MRPLs from a Cobb-Douglas and ARPLs from both a translog and a Cobb-Douglas find the opposite ranking
Smallholders, Market Failures, and Agricultural Production: Evidence from India
No full textMarket completeness has important implications for household behavior. I firmly reject complete markets for smallholders but am unable to do so for non-smallholders. This leads to important differences in production behavior: smallholders reallocate labor across activities less in response to intra-seasonal crop price changes than do non-smallholders. A counterfactual exercise indicates smallholders could increase revenue by almost nine percent if they were to reallocate labor similarly to non-smallholders. The overall pattern of results is consistent with small-holders lacking sufficient wage employment opportunities. Since non-smallholders have to hire in for agricultural production, this lack of opportunities does not affect their decisions
Sectoral Wage Gaps and Gender in Rural India
No full textUsing detailed monthly panel data from rural India, this paper analyzes sectoral wage gaps for men and women. I document three important findings. First, there is clear evidence of sorting into sectors, with very large differences in worker human capital across the farm and non-farm sectors and much higher wages in the latter. Second, while these wage gaps are substantial in the cross-section, the wage gap within individuals is decidedly smaller, consistent with worker sorting. Third, the wage gap for women is much larger than it is for men, with the latter exhibiting almost no within-individual gap in wages across sectors. Women work fewer hours and are less likely to work outside of their own village in the non-farm sector, yet the wage gap is driven by higher-caste and married women. I find no evidence of non-pecuniary benefits of agricultural employment relative to non-farm employment being responsible for this gap. These results are consistent with a lack of local non-farm employment opportunities interacting with barriers to labor mobility for women but not men
Should farmers farm more? Comparing marginal products within Malawian households
No full textAccording to standard economic theory, households should equate the marginal revenue product of an input across activities within the household. However, this prediction may not hold in the presence of risk. Using data on farm plots and non-farm enterprises in Malawi, we examine the impact of risk on the allocation decisions of agricultural households as they allocate labor across farm and non-farm production. We control for many household and production characteristics, including household fixed effects, and find farm marginal revenue product of labor (MRPL) to be consistently higher than non-farm MRPL. These results are consistent with farm production being riskier than non-farm production for most households in Malawi. These findings suggest that improved access to insurance of farming activities and wage employment opportunities could increase total household income.1
Protecting girls from droughts with social safety nets
No full textThis paper revisits the relationship between agricultural productivity shocks and the infant sex ratio in India and investigates how this relationship changes when households have access to government-provided employment opportunities outside of agriculture. When a household's preference for sons coin-cides with adverse agricultural productivity shocks, previous research shows that households tend to dis-proportionately reduce investments (prenatal and postnatal) in their female children. This behavior leads to a relatively more balanced sex ratio in good rainfall years and a more skewed sex ratio (in favor of boys) in low rainfall years. In a deviation from past work, we find evidence of this effect primarily through prenatal channels in modern India. More importantly, we show that a workfare program that decouples both wages and consumption from rainfall attenuates the relationship between rainfall and the infant sex ratio. Using a back-of-the-envelope calculation, we find that the program could have saved at least 0.7 million girls - relative to boys - if the government had implemented it from 2001 to 2005. Suggestive evidence also indicates that the program may have attenuated the positive effect of birth year productiv-ity shocks on girls' longer-term height-for-age. (c) 2021 Elsevier Ltd. All rights reserved.1
Agricultural Plots, Labor Allocation, and Income Hiding in Ethiopia
No full textPast research has found that pressures to share income with family members and relatives can incentivize individuals to hide income, possibly distorting labor allocation and leading to inefficiencies. Due to the difficulty of observing hidden income, the majority of this research has used lab-in-the-field experiments, which raises concerns of external validity. In this paper, I present a novel way to observe hidden income on agricultural plots using household data from Ethiopia. To the best of my knowledge, this is the first paper to test income hiding hypotheses on agricultural plots. I develop a simple model which predicts income hiding is quadratic in distance from the plot to the dwelling, first increasing in distance and then decreasing. I construct a proxy for hidden income based on the difference between output per hectare constructed using enumerator-measured (crop-cut) yields and output per hectare constructed using self-reported yields. Analyses confirm the predictions of the model. Additional analyses suggest these findings are not driven by unobserved heterogeneity in plots. Ease of hiding income and the ability to translate output into cash are also important predictors of income hiding: plots with a single decision-maker and plots located in areas with easy access to markets are most likely to see the quadratic pattern predicted by the model. Finally, women appear to be more likely to hide income than men
Spatially Heterogeneous Effects of a Public Works Program
No full textMost research on labor market effects of the Mahatma Gandhi National Rural Employment Guarantee Scheme focuses on outcomes at the district level. This paper shows that such a focus masks substantial spatial heterogeneity: treated villages located near untreated areas see smaller increases in casual wages than treated villages located farther from untreated areas. Spatial differences in implementation or program leakages do not appear to drive this spatial heterogeneity. The effects of the program on private-sector employment display similar intra-district heterogeneity and these effects on employment are highly correlated with the effect on wages. Overall, these results suggest that worker mobility leads a district-level focus to underestimate the true effect of the program on wages. Quantifying this underestimate using two separate methods produces very similar results; the overall effect on wages appears to be approximately twice as large as district-level estimates suggest.1
Improving Estimates of Mean Welfare and Uncertainty in Developing Countries
No full textReliable estimates of economic welfare for small areas are valuable inputs into the design and evaluation of development policies. This paper compares the accuracy of point estimates and confidence intervals for small area estimates of wealth and poverty derived from four different prediction methods: linear mixed models, Cubist regression, extreme gradient boosting, and boosted regression forests. The evaluation draws samples from unit-level household census data from four developing countries, combines them with publicly and globally available geospatial indicators to generate small area estimates, and evaluates these estimates against aggregates calculated using the full census. Predictions of wealth are evaluated in four countries and poverty in one. All three machine learning methods outperform the traditional linear mixed model, with extreme gradient boosting and boosted regression forests generally outperforming the other alternatives. The proposed residual bootstrap procedure reliably estimates confidence intervals for the machine learning estimators, with estimated coverage rates across simulations falling between 94 and 97 percent. These results demonstrate that predictions obtained using tree-based gradient boosting with a random effect block bootstrap generate more accurate point and uncertainty estimates than prevailing methods for generating small area welfare estimates
Protecting Girls from Droughts with Social Safety Nets
No full textThis paper revisits the relationship between agricultural productivity shocks and the infant sex ratio in India and investigates how this relationship changes when households have access to government-provided employment opportunities outside of agriculture. When a household's preference for sons coincides with adverse agricultural productivity shocks, previous research has shown households tend to disproportionately reduce investments (prenatal and postnatal) in their female children. This behavior leads to a relatively more balanced sex ratio in good rainfall years and a more skewed sex ratio (in favor of boys) in bad rainfall years. We find evidence of both prenatal and postnatal channels in India and show that a workfare program, which decouples both wages and consumption from rainfall, attenuates the relationship between rainfall and the infant sex ratio. Using a back-of-the-envelope calculation and the assumption that females should never significantly outnumber males, the program could have saved around 550 girls per district per year -- relative to boys -- if the government had implemented it in the years 2001 to 2005. Additional results on postnatal channels show substantial impacts on long-run health outcomes of surviving girls, as rainfall no longer differentially affects girls' height-for-age, relative to boys', following the implementation of the program. In an important deviation from previous research, the entirety of the relationship between the sex ratio and rainfall is apparently driven by sex-selective abortions, not infant mortality
Poverty at Higher Frequency
No full textPoverty is typically measured as insufficient yearly income or consumption. In practice, however, poverty is marked by seasonality, economic instability, and illiquidity across months. To capture within-year variability, we extend traditional poverty measures to include a temporal dimension. Using panel data from rural India, we show how conventional poverty measures can distort understandings of poverty: exposure to poverty is wider and more common than typically measured, and poverty entry and exit are not sharp transitions. Accounting for within-year variability improves predictions of anthropometrics, and targeting transfers to challenging periods can reduce poverty most effectively by compensating for imperfect consumption smoothing
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