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Agricultural and Food Policy

Paper Session

Saturday, Jan. 3, 2026 8:00 AM - 10:00 AM (EST)

Philadelphia Convention Center
Hosted By: American Economic Association
  • Chair: Sushant Singh, University of Oklahoma

Structural Adjustments and Food Sovereignty: The Effects of IMF Programs and Loan Conditions on Food Self-Sufficiency

Sushant Singh
,
University of Oklahoma
Firat Demir
,
University of Oklahoma
Jayash Paudel
,
University of Oklahoma
Jonathan McFadden
,
United States Department of Agriculture

Abstract

With a growing body of evidence highlighting unintended negative consequences of International Monetary Fund (IMF) interventions in borrowing countries, I study the impact of IMF policy reforms, known as ‘conditionalities,’ on food self-sufficiency. Using the most comprehensive dataset on IMF conditionality, covering all IMF member countries from 1980 to 2019, I employ a compound instrumental variable design within a system of three equations estimated via maximum likelihood to examine the impact of IMF program participation and policy conditions on food self-sufficiency. After adjusting for confounding economic and climatic factors and addressing selection bias, I find that each additional binding IMF policy reform reduces the cereals self-sufficiency ratio by 0.003 percentage points. Disaggregating IMF conditionality by issue area, the findings suggest that IMF conditionality hinders food self-sufficiency, with policy reforms in trade and exchange systems, financial sectors, monetary policy, and central banking playing a particularly significant role. Outside of the conditionality channel (e.g., credit injections, technical assistance and policy advice, catalytic effect on aid and investment or moral hazard), the IMF affects food self-sufficiency in the opposite direction.

Weather Stations and Agricultural Productivity: Evidence from Historical Data in the U.S.

Vaibhav Anand
,
St. John's University
Honglin Li
,
University of Wisconsin-Madison

Abstract

In this paper, we examine the effect of access to forecasts on local economic productivity in the context of agriculture and weather risk. Weather significantly affects agriculture, with forecasts shaping farmers’ input decisions and adaptation to adverse shocks. Despite substantial public investment in meteorological infrastructure, limited empirical evidence exists on whether better forecasts translate into higher agricultural output. Using historical U.S. data from 1870 to 2012, we leverage the staggered establishment of over 30,000 weather stations to investigate the causal impact of forecast access on agricultural productivity.

We construct county-level measures of farm productivity using data on farm size, crop output, and revenue from the Agricultural Census (Haines, Fishback, and Rhode, 2019). Weather station data from the Master Station History Report (NCEI) include detailed station locations, operation dates, and institutional affiliations. Our methodology exploits variation in counties’ proximity to the nearest weather station, employing a stacked difference-in-difference design following Kantor and Whalley (2019). To address potential bias from station placement, we utilize two identification strategies: first, comparing counties before and after gaining station access, and second, restricting analysis to a sub-sample of weather stations initially established to support U.S. Army activities, reducing placement endogeneity.

Preliminary results indicate that counties closer to weather stations experience significant increases in agricultural productivity. This proximity effect is robust over time but shows important temporal variations, particularly strengthening during technological advancements in forecasting in the early 20th century and declining thereafter with improvements in communication technologies. We also examine heterogeneity across crop types and periods.

Our research provides empirical evidence on the economic value of forecast access and contributes to the literature on the role of weather forecasts on economic productivity (Burlig et al, 2024; Downey, Lind, and Shrader, 2022; Shrader, 2021; Chambers and Pieralli, 2020; Rosenzweig and Udry, 2019).

Unveiling Effectiveness of Unconditional Cash Transfer on Farm Productivity: Evidence from India

Debdatta Pal
,
Indian Institute of Management-Lucknow
Neeraj Katewa
,
Mahindra University

Abstract

Governments worldwide implement farm support policies to assist farmers, yet universalising such measures may lead to inefficient resource allocation. We use a difference-in-differences framework on National Sample Survey data to examine the impact of India’s first unconditional cash transfer scheme to support landowning farmers in their initial farming investments. We find a strong land productivity response to transfers with a 25% increase for medium and large farmers and an 11% rise for small and marginal farmers. This heterogeneous outcome of transfer is also evident across the conditional quantiles. We conjecture that more fund allocation towards agricultural machinery and equipment upgrades by medium and large farmers from higher transfer has served as a potential mechanism for this heterogeneous effect. We validate our results through triple difference estimation based on rural bank branch penetration and various robustness checks.

Discussant(s)
Debdatta Pal
,
Indian Institute of Management-Lucknow
Vaibhav Anand
,
St. John's University
Sushant Singh
,
University of Oklahoma
JEL Classifications
  • Q1 - Agriculture
  • O1 - Economic Development