Working Papers
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Urban Labor Supply Responses to Weather Shocks for Ugandan Uber Drivers
This paper examines the effect of flexible labor arrangements on insuring against rural weather shocks in developing countries, using data from the recently formalized taxi industry. I use machine learning classification to match Uber drivers in Kampala to rural weather events. A one standard deviation increase in the intensity of a shock is associated with working 20% more in the week of a shock and then hours decline steeply the following week. Dynamics are driven by extensive margin changes and are stronger for poorer parts of Uganda. Driver behavior changes based on the type of shock; drivers tend to drive more intensively and increase income if a drought event occurs, but leave the platform in the case of a flood.
Gendered Impacts of Reforming Night-Shift Work in India
Genetic Dilution erodes productivity: Exploring farmers’ low adoption levels of improved maize in Ethiopia
with Cristina Chiarella, Juan Sebastian Correa and Oscar Barriga Cabanillas
Submitted
Despite large investments in developing new maize varieties, evidence shows that few farmers consistently adopt this new technology over time. We use an innovative methodology that studies the adoption trajectories of farmers over a three-round panel data set to explain low adoption rates with heterogeneous returns to adoption. By relying on self-reported use of improved seeds, we find a puzzling answer for why farmers do not use new seed varieties: adoption leads to seemingly small and negative returns. Heterogeneity analysis across different levels of comparative advantage show that farmers who adopt don’t actually have a comparative advantage in adopting improved seed, nor do they exhibit high returns to adopting. To reconcile this result with the expected higher yields for improved seeds, we exploit unique information on maize DNA-fingerprinting collected over the same areas in 2018. We find that the negative returns to adoption mask heterogeneity in the actual quality of the genetic material of the seeds farmers categorized as improved varieties in the self-reported data. Positive returns to adoption are found for those farmers that use higher-purity germplasm, drought-tolerant maize, and newly released varieties. When we account for misclassification in our heterogeneity analysis, we find that the puzzle is resolved. Our findings point to the wide dispersion of older and genetically diluted varieties, for which poorer farmers may be paying a premium. The implications of our findings speak to the need for policies to better target context and geography, expand accessibility of improved seeds, and make higher yields varieties more inclusive.
Livelihood Strategies in a Climate-change Vulnerable Region
with Miki Doan, Heng Zhu, Anubhab Gupta and Binoy Majumder
Climate change has caused a disproportionate burden on the poor in developing coun- tries, who are primarily reliant on subsistence and small-scale agriculture for their liveli- hood. This paper contributes to our understanding of the livelihood diversification strategies of a vulnerable coastal population living in the Sundarbans region of India, an archipelago in the Bay of Bengal in India and Bangladesh that has been predicted to be on the brink of the largest exodus of the human populace. Using novel high- frequency weekly data collected over a year on a representative sample of households, this paper utilizes k-means clustering and unsupervised time-series clustering tech- niques to provide new insights into how poor households adapt to climate change. Our analysis highlights that households rely on multiple income sources and cope from week to week through mechanisms such as local borrowing, spousal income, or changes in expenditure. Households that rear livestock have volatile yet relatively high female income.
Measuring Ethnicity and Estimating its Effects on Voting at Scale: Evidence from Uganda
With Samuel S. Bird
A large literature studies the effect of ethnicity on voting in developing countries. Yet empirically it is difficult to measure ethnicity at a scale necessary to disentangle the effect of ethnicity on voting from shared political goals that stem from living in the same location. This paper differentiates between the effects of a voter’s ethnicity and their location on voting behavior. We estimate these effects by pairing voter registration data with election outcomes from polling stations throughout Uganda. We overcome the challenge of measuring ethnicity by using a machine learning algorithm that exploits variation in surnames across ethnic and linguistic groups. We use these ethnicity measures to estimate the effect of ethnicity on support for the incumbent president in the 2016 general election. We find that differences in voting by ethnicity are significant and do not vary between regions, suggesting the importance of measuring and studying ethnicity at scale.
Classification into ethnic groups in Burkina Faso using names and localities
with Michael Kevane
This paper reports on the geographic distribution of ethnic groups in Burkina Faso using approximately six million first and last names listed in electoral registries of 2012 and 2015. We construct, from credible ethnographic sources, an expert classification list of last names and first names associated with geographic places (villages, communes, provinces, and regions) and ethnic groups in Burkina Faso. Supervised machine learning used the alphabetic structure of names as well as geographic locations to assign ethnic labels to the unclassified names. The classification permits representation of ethnic diversity and polarisation at fine geographic scales, and concordance of the actual ethnic distribution of people with standard ethnic maps. We find substantial variance from the static picture conveyed by the standard ethnic maps. We use the classification to address an important contemporary social phenomenon in Burkina Faso, Mossi migration throughout the country. In many localities, the Mossi population is now larger than the population of the “autochtonous” ethnic group. Using the ethnically classified names in the voter registry data, we show that significant autochthonous ethnic discontinuities across borders appear to be associated with significant Mossi settlement discontinuities. That is, when borders sharply separate autochtonous ethnic groups, Mossi settlement patterns also exhibit larger discontinuities across borders. it may be that Mossi settlements are correlated with “host” conceptualizations of inter-ethnic accommodation. The possible endogenity of Mossi settlement patterns is a relevant consideration in evaluating popular discourses that an inevitable consequences of Mossi settlement will be rising inter-ethnic tension over political power and resource allocation.
Evaluating a Systems Approach to Suppressive Crop Rotations in Strawberry Production
With Rachael Goodhue, Daniel Chellemi, and Krishna Subbarao
We evaluate a new, dynamic approach to suppressive crop rotations, which incorporate flexibility in cropping decisions. This “systems” approach allows a grower to change their cropping decisions mid-rotation and plant higher value crops to address cash constraints while recognizing the effect of doing so on V. dahliae populations in the soil. A grower can plant broccoli to suppress the amount of microsclerotia (\(\mu s\)) of disease in the soil, or plant lettuce if they need more cash in the current period. This flexibility represents a dynamic tradeoff whose intention is for suppressive crop rotations, but more broadly, it is relevant for any multi-crop production system. These production schemes must take streams of profits into account (not just profits from each crop separately), as well as any dynamic spillovers that crops may have on each other. We use the results of a field study to calibrate a dynamic model to investigate how much this flexibility can change grower net returns, as well as how different market conditions, specifically expected prices, and levels of vertical coordination can change growers’ rotation decisions in this systems context.
Comment on Suri (2011) “Selection and Comparative Advantage in Technology Adoption”
With Emilia Tjernstrom, Dalia Ghanem, Oscar Barriga Cabanillas, Travis J. Lybbert, and Jeffrey D. Michler
Revise and Resubmit, Econometrica
This paper illustrates and addresses weak identification concerns in the correlated random coefficient (CRC) model that Suri (2011) uses to study agricultural technology adoption. Using the publicly available version of the dataset used in Suri (2011), cleaned per the author’s instructions, we are unable to replicate the paper’s main CRC model results. To understand why, we recast the CRC model as a more general random coefficient model in which the returns to hybrid adoption are restricted to be linear in comparative advantage. This reveals that the key structural parameter in the CRC model (\(\phi\)) is prone to a weak identification problem. We then propose a procedure to conduct weak-identification robust inference on \(\phi\) using test inversion. Only with this robust procedure accounting for weak identification are we able to reconcile the original Suri (2011) results.
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