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 likely increases (indirect coverage effect). By exploiting exogenous policy changes and using approximately 180,000 county‐crop‐year observations, we estimate the sum of these two effects of premium subsidies on the pattern of U.S. acreage across seven major field crops. We estimate that a 10% increase in the premium subsidy causes a 0.43% increase in the acreage of a crop in a county holding the premium subsidy of its competing crop constant. Taking into account the small share of premium subsidies in expected crop revenue, this subsidy impact is analogous to an own‐subsidy acreage elasticity of 1.24, which exceeds own‐price acreage elasticity estimates in the literature. One explanation for the larger acreage response to premium subsidies is that insurance causes an indirect coverage effect in addition to a direct profit effect.

Previous research predicts significant negative yield impacts from warming temperatures, but estimating the effects on yield risk and disentangling the relative causes of these losses remains challenging. Here we present new evidence on these issues by leveraging a unique publicly available dataset consisting of roughly 30,000 county-by-year observations on insurance-based measures of yield risk from 1989–2014 for U.S. corn and soybeans. Our results suggest that yield risk will increase in response to warmer temperatures, with a 1 °C increase associated with yield risk increases of approximately 32% and 11% for corn and soybeans, respectively. Using cause of loss information, we also find that additional losses under warming temperatures primarily result from additional reported occurrences of drought, with reported losses due to heat stress playing a smaller role. An implication of our findings is that the cost of purchasing crop insurance will increase for producers as a result of warming temperatures.

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 working off the farm. We derive conditions under which (1) unsubsidized and actuarially fair crop insurance or (2) insurance premium subsidies lead to more investment in a risky higher return crop. We then examine the role of self-insurance for these conditions. The impact of premium subsidies is decomposed into a direct profit effect and an indirect coverage effect. These effects are explained by substitutions between market insurance and self-insurance and between a risky crop and a safe crop. We discuss each effect as a combination of subsidy and risk effects. Numerical illustrations show that an insurance subsidy has a larger impact on risky crop investments compared to that of an input subsidy when farms are more risk-averse and have high costs of self-insurance. The framework provides a novel way to evaluate subsidized crop insurance programs. JEL classifications: Q18, Q12, O12, O13 (Jisang Yu). 1 Morduch (1995) shows early empirical examples of how risk deters accumulation of human and material capital. The examples emphasize the importance of risk management for economic growth and development.

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 (i) crop insurance reduces the likelihood of farm exits and (ii) lowers the magnitude of farm disinvestment. The positive and significant effects of crop insurance on farm survival and disinvestment remain robust across different specifications.

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Using a novel policyholder-level data set, we analyse participants’ choices of 2-month index intervals in the Rainfall Index for Pasture, Rangeland and Forage (RI-PRF) insurance programme. We first provide a conceptual model that illustrates participation patterns of the rainfall index insurance. We then connect these predicted patterns to some empirical evidence from the policyholder-level data set, which is a subset of data provided by the USDA Risk Management Agency for all RI-PRF participants in Nebraska and Kansas during the years 2013–2017. Because the correlations between forage yield and precipitation and the expected premium subsidy vary by month, different degrees of risk aversion may predict distinctively different choices of the 2-month intervals. Using cluster analysis, we group the participants with similar allocation patterns across the 2-month intervals. We observe that the number of participants displaying relatively low levels of risk aversion increase over time. We connect this to the fact that premium subsidies and producer returns associated with non-growing season (risk-increasing) months are often greater than those for growing season (risk-reducing) months, and this has important implications for policy design. Our findings suggest that more research in this area could assist policymakers in keeping the RI-PRF programme in line with its objective of reducing risk for livestock producers.

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”) affects input use for insured and uninsured farms. We show that moral hazard decreases—farms apply more inputs—as the forecast accuracy improves when the forecast indicates good yields, and vice versa when the forecast indicates bad yields. In the long run, moral hazard decreases in response to an improvement in forecast accuracy. Even though moral hazard decreases in the long run, indemnity payments are likely to increase in the long run—driven by the increase in moral hazard when the forecast indicates bad crop conditions. We use the results of our model to discuss the potential impact of different technologies and types of inputs on the federal crop insurance program and the environment.

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 yields in the U.S. corn belt region. We provide a discussion on variable selection methods in the context of estimating crop yield responses to climate variables. We find that the models that utilize within-growing season monthly variations performs better compared to the models with growing season aggregated weather variables and show the strength of Bayesian estimations. We also find that the warming impacts predicted by the models that utilize within-growing season variations are smaller than the predicted impacts of the models with aggregated weather variables. The findings indicate that the temperature effects are not additive across months within growing season.