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Crop insurance premium subsidy and irrigation water withdrawals in the western United States

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Abstract

We estimate the effects of the federal crop insurance premium subsidy on freshwater withdrawals for irrigation among U.S. counties to the west of the 100th meridian. Our results indicate that a 1% increase in premium subsidy leads to a 0.446% (about 475,901 acre-feet/year) and 0.673% (about 474,026 acre-feet/year) increase in total freshwater withdrawals for irrigation and fresh surface water withdrawals for irrigation, respectively. The elasticity of total freshwater withdrawals for irrigation and fresh surface water withdrawals for irrigation with respect to revenue insurance premium subsidy is more than twice as large as those with respect to yield insurance premium subsidy. Groundwater withdrawals for irrigation are not found to be responsive to crop insurance premium subsidy. Because the elasticities are all non-negative, moral hazard should not be a dominant factor in the relationship between crop insurance subsidies and freshwater withdrawals for irrigation. Thus, exploring the causal relationship between crop insurance premium subsidy and agricultural input uses, this study underscores the unintended effect of the federal crop insurance programme on water resource sustainability in the U.S.

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Fig. 1

Source Compiled by the authors using RMA’s Summary of Business Reports data. The vertical lines denote farm bills or other legislative changes that caused major changes in the crop insurance premium subsidy. From left to right, they stand for: (1) the Federal Crop Insurance Reform Act of 1994, which introduced a catastrophic (CAT) coverage and increased premium subsidy rate in 1995 and after; (2) the Agricultural Risk Protection Act of 2000, which again significantly increased the premium subsidy rate beginning in the 2001 crop year; (3) the Food, Conservation and Energy Act of 2008, which also changed subsidy rates beginning in the 2009 crop year; and (4) the Agricultural Act of 2014, which expanded existing coverage and authorised reimbursement of ‘shallow losses’ beginning in the 2015 crop year

Fig. 2

Source Compiled by the authors using data from the USGS National Water Information System

Fig. 3

Source Compiled by the authors using data from RMA’s Summary of Business Reports Data

Fig. 4

Source Created by the authors based on RMA’s Summary of Business Reports data

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Notes

  1. To address the moral hazard problem, crop insurance policies have a good management practice clause, which asks farmers to provide “adequate water” to the insured crop lands. The good management practice clause, however, does not eliminate the moral hazard problem (see e.g., Annan and Schlenker 2015; Deryugina and Konar 2017; Yu and Hendricks 2019).

  2. This substitutional relationship between crop insurance and some input uses has been discussed in detail in the literature. Recent examples include Woodard et al. (2012), Yu and Sumner (2018), and Miao (2020).

  3. Systematic measurement errors biased toward a particular direction can be problematic, as Millimet and Parmeter (2021) point out that it will bias the estimate of the intercept and may bias the estimate of slope parameters. Due to the limited information that we have regarding how USGS approximated part of the data, however, we know little about the expectation or specific direction of the potential measurement error. Fully addressing this issue requires availability of water use data with higher quality or more advanced econometric techniques, which can be a direction for future research. In addition to employing the instrumental variable estimation, we include a set of robustness checks based on a sub-sample of our data that less likely includes imputed water-use values (see Robustness and Appendix for details). Nevertheless, our results should be interpreted with caution due to this potential measurement error issue.

  4. Available online at: https://www.rma.usda.gov/SummaryOfBusiness (accessed July 4, 2020).

  5. Note that coefficient β1 measures the aggregate effect of PSPDL on freshwater withdrawals for irrigation. Due to data limitations, we cannot separate partial effects of the three potential channels discussed in the conceptual framework.

  6. With an assumption of monotonicity (i.e., the IV only causes the endogenous variable to change in one direction), Angrist et al. (1996) show that the IV estimator identifies the local average treatment effect (LATE) of compliers, individuals who positively respond to the IV. In the present study, the monotonicity assumption is reasonable because an increase in subsidy rate caused by legislation changes will only increase the subsidy amount per dollar of liability. Therefore, the impact of PSPDL estimated in the present study is the impact within the group of farmers who take up crop insurance responding to the increase of premium subsidy rate. We believe that this group of farmers (i.e., the compliers) are quite large and hence the IV estimates are of policy relevance because before the series of increases in premium subsidy rate the insurance take-up rate was about 20% and after that it was more than 80% (Deryugina and Konar 2017). In the Robustness section of the supplementary information we also provide suggestive evidence for the assumption of monotonicity of the IV by re-running the analysis based on subsamples of the dataset.

  7. When evaluated at the sample means, the formula to calculate the elasticity is: \(\beta_{{{\text{PSPDL}}}} \times \overline{{{\text{PSPDL}}}} /\overline{{\text{Water Use}}} ,\) where \(\beta_{{{\text{PSPDL}}}}\) is the coefficient of PSPDL, \(\overline{{{\text{PSPDL}}}}\) the sample mean of PSPDL, and \(\overline{{\text{Water Use}}}\) the sample mean of water use. Because the analysis is based on data available every five years, we believe that the estimated elasticity is closer to a “long-run” elasticity than is an annual-data-based estimate.

  8. Here 0.673% is calculated by using (1175.79 × 0.069/120.46)%, where 120.46 is the sample mean of fresh surface water withdrawals in Mgal/day/county. In addition, 0.811 is calculated by using 0.673% × 120.46.

  9. Note that the estimation cannot separate the partial effects of crop insurance from the intensive margin, extensive margin, and moral hazard channels. It could be the case that the effect of moral hazard is significant but is dominated by effects from the intensive or extensive margin.

  10. We thank an anonymous reviewer for helpful comments that led to this inclusion.

  11. To conserve space, here we only present the results from fixed effects regression models with the instrumental variable (FE-IV), which are our preferred models.

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Acknowledgements

This research was supported by the Alabama Agricultural Experiment Station and the Hatch program of the National Institute of Food and Agriculture, U.S. Department of Agriculture.

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Authors and Affiliations

  1. Department of Economics and Marketing, Romain College of Business, University of Southern Indiana, BEC-2071, 8600 University Boulevard, Evansville, IN, 47712, USA

    Prasenjit N. Ghosh

  2. Department of Agricultural Economics and Rural Sociology, Auburn University, 208B Comer Hall, Auburn, AL, 36849, USA

    Ruiqing Miao

  3. Department of Economics, Lee Business School, University of Nevada, Las Vegas, 4505 S. Maryland Pkwy, Las Vegas, NV, 89154-6005, USA

    Emir Malikov

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  1. Prasenjit N. Ghosh
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Correspondence to Ruiqing Miao.

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Ghosh, P.N., Miao, R. & Malikov, E. Crop insurance premium subsidy and irrigation water withdrawals in the western United States. Geneva Pap Risk Insur Issues Pract 48, 968–992 (2023). https://doi.org/10.1057/s41288-021-00252-4

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