Skip to main content Accessibility help
×
Home

When Less Is More: On the Use of Historical Yield Data with Application to Rating Area Crop Insurance Contracts

  • Yong Liu (a1) and Alan P. Ker

Abstract

Crop insurance is the cornerstone program of domestic farm policy in most developed countries. Although most countries’ rating methodology corrects for time-varying movements in the first two moments, it is unclear whether using the entire yield series remains appropriate.  We use distributional tests and an out-of-sample retain-cede rating game to answer whether governments/insurers should historically trim yields in estimating their premium rates. Despite small sample sizes and the need to estimate tail probabilities, the historical data appear to be sufficiently different such that trimming is justified.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      When Less Is More: On the Use of Historical Yield Data with Application to Rating Area Crop Insurance Contracts
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      When Less Is More: On the Use of Historical Yield Data with Application to Rating Area Crop Insurance Contracts
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      When Less Is More: On the Use of Historical Yield Data with Application to Rating Area Crop Insurance Contracts
      Available formats
      ×

Copyright

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Corresponding author

*Corresponding author. Email: aker@uoguelph.ca

References

Hide All
Annan, F., Tack, J., Harri, A., and Coble, K.. “Spatial Pattern of Yield Distributions: Implications for Crop Insurance.” American Journal of Agricultural Economics 96, 1(2013):253–68.
Assefa, Y., Prasad, P., Carter, P., Hinds, M., Bhalla, G., Schon, R., Jeschke, M., Paszkiewicz, S., and Ciampitti, I.A.. “A New Insight into Corn Yield: Trends from 1987 through 2015.” Crop Science 57, 5(2017):2799–811.
Challinor, A.J., Watson, J., Lobell, D., Howden, S., Smith, D., and Chhetri, N.. “A Meta-analysis of Crop Yield under Climate Change and Adaptation.” Nature Climate Change 4(March 2014):287–91.
Claassen, R., and Just, R.E.. “Heterogeneity and Distributional Form of Farm-Level Yields.” American Journal of Agricultural Economics 93, 1(2011):144–60.
Duvick, D.N.The Contribution of Breeding to Yield Advances in Maize (Zea mays L.).Advances in Agronomy 86(2005):83145.
Egli, D.Comparison of Corn and Soybean Yields in the United States: Historical Trends and Future Prospects.” Agronomy Journal 100, S3(2008):S-7988.
Egli, D.B.Seed Biology and Yield of Grain Crops. Wallingford, UK: CABI, 2017.
Fernandez-Cornejo, J. “The Seed Industry in US Agriculture: An Exploration of Data and Information on Crop Seed Markets, Regulation, Industry Structure, and Research and Development.” Working paper, Washington, DC: U.S. Department of Agriculture, Economic Research Service, 2004.
Fernandez-Cornejo, J., Wechsler, S., Livingston, M., and Mitchell, L.. Genetically Engineered Crops in the United States. Washington, DC: U.S. Department of Agriculture, Economic Research Service, Economic Research Report No. 162, 2014.
Goodwin, B.K., and Hungerford, A.. “Copula-Based Models of Systemic Risk in US Agriculture: Implications for Crop Insurance and Reinsurance Contracts.” American Journal of Agricultural Economics 97, 3(2015):879–96.
Goodwin, B.K., and Ker, A.P.. “Nonparametric Estimation of Crop Yield Distributions: Implications for Rating Group-Risk Crop Insurance Contracts.” American Journal of Agricultural Economics 80, 1(1998):139–53.
Harri, A., Coble, K.H., Ker, A.P., and Goodwin, B.J.. “Relaxing Heteroscedasticity Assumptions in Area-Yield Crop Insurance Rating.” American Journal of Agricultural Economics 93, 3(2011):707–17.
Ker, A.P., and McGowan, P.. “Weather-Based Adverse Selection and the US Crop Insurance Program: The Private Insurance Company Perspective.” Journal of Agricultural and Resource Economics 25, 2(2000):386410.
Ker, A.P., Tolhurst, T.N., and Liu, Y.. “Bayesian Estimation of Possibly Similar Yield Densities: Implications for Rating Crop Insurance Contracts.” American Journal of Agricultural Economics 98, 2(2016):360–82.
Kucharik, C.J., and Ramankutty, N.. “Trends and Variability in U.S. Corn Yields over the Twentieth Century.” Earth Interactions 9, 1(2005):129.
Leng, G.Recent Changes in County-Level Corn Yield Variability in the United States from Observations and Crop Models.” Science of the Total Environment 607–608(December 2017):683–90.
Li, Q.Nonparametric Testing of Closeness between Two Unknown Distribution Functions.” Econometric Reviews 15, 3(1996):261–74.
Li, Q., Maasoumi, E., and Racine, J.S.. “A Nonparametric Test for Equality of Distributions with Mixed Categorical and Continuous Data.” Journal of Econometrics 148, 2(2009):186200.
Liu, Y., and Ker, A.P.. “Simultaneous Borrowing of Extraneous Information across Space and Time for Estimating Crop Insurance Premium Rates.” Working Paper 2019.9, Institute for the Advanced Study of Food and Agricultural Policy, University of Guelph, Guelph, Ontario, Canada, 2019.
Miranda, M.J., and Glauber, J.W.. “Systemic Risk, Reinsurance, and the Failure of Crop Insurance Markets.” American Journal of Agricultural Economics 79, 1(1997):206–15.
Naylor, R., Falcon, W., and Zavaleta, E.. “Variability and Growth in Grain Yields, 1950–94: Does the Record Point to Greater Instability?Population and Development Review 23, 1(1997):4158.
Park, E., Brorsen, B.W., and Harri, A.. “Using Bayesian Kriging for Spatial Smoothing in Crop Insurance Rating.” American Journal of Agricultural Economics 101, 1(2019):330–51.
Racine, J., and Ker, A.P.. “Rating Crop Insurance Policies with Efficient Nonparametric Estimators That Admit Mixed Data Types.” Journal of Agricultural and Resource Economics 31, 1(2006):2739.
Reilly, J.M., and Fuglie, K.O.. “Future Yield Growth in Field Crops: What Evidence Exists?Soil and Tillage Research 47, 3–4(1998):275–90.
Shen, Z., Odening, M., and Okhrin, O.. “Adaptive Local Parametric Estimation of Crop Yields: Implications for Crop Insurance Rate Making.” European Review of Agricultural Economics 45, 2(2018):173203.
Tack, J., Harri, A., and Coble, K.. “More Than Mean Effects: Modeling the Effect of Climate on the Higher Order Moments of Crop Yields.” American Journal of Agricultural Economics 94, 5(2012):1037–54.
Tack, J.B., and Ubilava, D.. “Climate and Agricultural Risk: Measuring the Effect of ENSO on U.S. Crop Insurance.” Agricultural Economics 46, 2(2015):245–57.
Wilcox, R.R.Some Practical Reasons for Reconsidering the Kolmogorov-Smirnov Test.” British Journal of Mathematical and Statistical Psychology 50, 1(1997):920.
Zhang, Y.Y.A Density-Ratio Model of Crop Yield Distributions.” American Journal of Agricultural Economics 99, 5(2017):1327–43.
Zhu, Y., Goodwin, B.K., and Ghosh, S.K.. “Modeling Yield Risk under Technological Change: Dynamic Yield Distributions and the U.S. Crop Insurance Program.” Journal of Agricultural and Resource Economics 36, 1(2011):192210.

Keywords

When Less Is More: On the Use of Historical Yield Data with Application to Rating Area Crop Insurance Contracts

  • Yong Liu (a1) and Alan P. Ker

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed