Ji-sang Yoo

Crop insurance premium subsidies affect patterns of crop acreage for two reasons. First, holding insurance coverage constant, premium subsidies directly increase expected profit, which encourages more acreage of insured crops (direct profit effect). Second, premium subsidies encourage farms to increase crop insurance coverage. With more insurance coverage, farms obtain more subsidies, and farm revenue becomes less variable as indemnities offset revenue shortfalls, so acreage of insured crops lik

Abstract This study focuses on how subsidized crop insurance affects crop choices. Crop insurance may change farm investments by reducing risks and providing subsidies. First, actuarially fair insurance reduces risks in crop production and marketing, holding the expected return constant. Second, insurance subsidies encourage farms to purchase crop insurance, which increases the expected return to insured risky crops. Farms also have many self‐insurance mechanisms such as crop diversification or

Abstract Over the last two decades, the US federal crop insurance programme expanded rapidly. Despite growing importance of crop insurance programmes, little is known about the relationship between crop insurance and disinvestment and exit decisions of farms. Using a farm-level panel dataset, we parametrically and semi-parametrically estimate the effects of crop insurance on farm disinvestment and farm exits with carefully developed identification strategies. Our estimation results indicate that

Using historical yield and rainfall data from three university-managed ranches in Kansas and Nebraska, we measure basis risk of Rainfall Index Insurance for Pasture, Rangeland, and Forage (PRF-RI). We investigate the relationship between forage yield and monthly precipitation and estimate the relationship between forage yield and PRF-RI indices. Finally, we estimate basis risk of PRF-RI. Our estimates suggest that using actual site-level precipitation values would reduce basis risk by only 5%–9%

Emerging precision agriculture technologies allow farms to make input decisions with greater information on crop conditions. This greater information occurs by providing improved predictions of crop yields using remote sensing and crop simulation models and by allowing farms to apply inputs within the growing season when some crop conditions are already realized. We use a stylized model with uncertainty in yield and price to examine how greater information on crop conditions (i.e., a “forecast”)

We estimate the impact of tariffs faced by US agricultural exports on farmland rents. The localized tariff is determined by the average of tariffs across trading partners for the crops produced within the county. We utilize shift-share designs to avoid endogeneity concerns that arise because factors affecting rents could also affect trade flows and cropping patterns. Using the county-level data from 2008 to 2017, we find that a one percentage point decrease in the localized tariff increases rent

Detrimental impacts of extreme heats on the U.S. crop yields have been well-documented by a number of empirical studies. However, less have focused on within-growing season weather variation and the interaction between temperature and precipitation. The objective of this study is to emphasize the importance of disaggregating temperature exposures within growing season. To achieve our objective, we estimate the impact of within-season monthly temperature and precipitation variations on maize yiel

Abstract Government‐supported farm programs can provide farm operators with different economic incentives regarding the use of their land. We study two US farm programs relevant to hay and pastureland, the Conservation Reserve Program (CRP) and Pasture, Rangeland, and Forage (PRF) Index Insurance. Using county‐level enrollment and availability data, we find that the introduction of PRF reduces enrollment in CRP. This interaction potentially returns environmentally sensitive farmland back into pr

Abstract This study provides a novel empirical framework for estimating the effects of temperature on the production of several tree crops in California by using underused insurance data and a variable selection technique. We utilize a Bayesian variable selection technique to select relevant temperature variables. We then use the selected temperature variables to assess the temperature effects on crop losses. We find that a greater length to freeze exposure increases crop losses and relatively s

Abstract Understanding how subsidies affect the selection of farms with different risk exposure into insurance products is key to evaluating the efficiency of government‐supported insurance programs. We study the US crop insurance program, which is a major federally supported insurance program, to assess the impact of premium subsidies on the riskiness of the insured. By exploiting two waves of policy changes, we find that the average loss per insured liability falls as premium subsidies increas

The U.S. federal crop insurance program experienced periodic policy changes over the past three decades that increased premium subsidies. These premium subsidies encourage changes in crop acreage for two reasons. First, holding insurance coverage constant, premium subsidies directly increase the expected return, which may encourage more acreage of the insured crop (profit effect). Second, premium subsidies encourage farms to increase crop insurance coverage. With more insurance coverage, farm re

Abstract Using a large‐scale, individual‐level administrative data set for 2008–2021, we document the inequality in farm program payments across all recipients in the U.S. By examining the relationship between within‐county inequality and demographic characteristics of farmers in a county, we find that there is a positive association between the share of Black operators and within‐county inequality. We also provide suggestive evidence that a substantial portion of racial and gender disparities i

Summary Synthetic control methods have gained popularity among causal studies with observational data, particularly when estimating the impacts of the interventions implemented to a small number of large units. The synthetic control methods face two major challenges: (a) estimating weights for each donor to create a synthetic control and (b) providing statistical inferences. To overcome these challenges, we propose a Bayesian framework that implements the synthetic control method with the parall