Work in progress

Upgrade or Migrate: The Consequences of Input Subsidies on Household Labor Allocation.

[Twitter thread], [Mastodon thread]

Abstract: Rural development programs often focus on increasing agricultural investment. Yet, many farmers can benefit from investing in a different technology: outmigration. I explore how one common class of policies — input subsidy programs (ISPs) — allows households to sort based on the relative returns of these two technologies. First, I exploit the roll-out of a large-scale Zambian ISP and use a difference-in-differences strategy. I show that the ISP fosters specialization by farmers based on their comparative advantage, resulting in increases in both agricultural yields and outmigration. Second, I estimate a structural model that incorporates a positive learning externality related to fertilizer adoption. With this externality, the ISP offers advantages relative to alternative revenue-neutral policy counterfactuals. Compared to an untargeted cash transfer, I find that an ISP that allows for re-selling of fertilizer would increase migration out of agriculture. A more targeted cash transfer, or an ISP without resale markets, would reduce migration. All three counterfactual policies reduce fertilizer use relative to the ISP and hinder the process of specialization.

Talks: 2022: NEUDC* (New Haven), EEA Congress (Milan), ICDE (Clermont-Ferrand), RES Easter Training School (Bristol) 2021: UEA’s PhD Workshop, University of Johannesburg, European Meeting of the UEA, Africa Meeting of the ES, MWIEDC

“The Productivity and Allocation of Labor across Ghana’s Health Facilities”

Binta Zahra Diop, Koku Awoonor-Williams, Hamza Ismaila, Anthony Ofosu, Martin J. Williams

Summary: We use never-used-before administrative data covering all Ghana’s healthcare staff and facilities. We measure potential gains from reallocating labor across facilities while accounting for administrative constraints. We provide the first comprehensive estimate of a healthcare system production functions. We further explores the allocation of medical labor across vacancies and geographic areas. Finally, we identify complementarities across staff and assignments that contribute to better outcomes.

Talks: University of Johannesburg (2022), ODI PFI (2020), WGAPE (2019).

“An experimental task to elicit preferences over definitions of algorithmic fairness”

Binta Zahra Diop, Amma Panin, Moustapha Cissé


“The relatively young and rural population may limit the spread and severity of Covid-19 in Africa: a modelling study” (2020), BMJ Global Health 2020;5:e002699 [paper]

Binta Zahra Diop, Marieme Ngom, Clémence Pougué Biyong, John N. Pougué Biyong
Media Citations: CNN Business, The Conversation, Quartz, allAfrica, The Independent, Le Point
Talks: World Health Organization (WHO) TC Modeling Series (June 2020)

Comparison between predictions and actual COVID19 progressions (click to uncover)

Predictions of the model:

The actual progression of infections:


Policy Reports (Pre-PhD)

“Using Behavioral Science to Improve Criminal Justice Outcomes” [paper].

Brice Cooke, Binta Zahra Diop, Alissa Fishbane, Jonathan Hayes, Aurélie Ouss, Anuj K. Shah
Media Coverage: Boston Globe, FastCompany,The American Bar Association Journal (ABA Journal),The Behavioral Scientist , NYDaily News, Metro, Courthouse News Service, CityLab.