Recency effects and participation at the extensive and intensive margins in the U.S. Federal Crop Insurance Program
- 52 Downloads
Participation in the U.S. Federal Crop Insurance Program (FCIP) has increased over time at both extensive (insured acres) and intensive (coverage level) margins, but there are clear spatio-temporal variations in these trends. Farmers’ decisions are likely influenced by recent indemnity or weather experiences (i.e., recency effects). We develop a model to identify two channels through which recent adverse weather experiences may affect participation, one where the weather shocks directly affect participation and the other where they affect participation through indemnity payouts. With historic FCIP data over the period 2001–2017, we use parametric and non-parametric methods to estimate these effects. At both extensive and intensive margins, higher past indemnities are found to encourage participation. This provides evidence that prior adverse weather shocks work indirectly. Less evidence is found in favour of direct weather effects. We also find that the increase in participation due to indemnities peaks in the year following a loss.
KeywordsCoverage level Direct and indirect responses Event study Recency bias Weather shocks
This research was partly funded by Michigan State University’s Elton R. Smith Chair of Food and Agricultural Policy. Without implication, the authors benefitted from comments made by Scott Swinton and seminar participants at AAEA Meetings—Chicago 2017, Michigan State University 2019, and SCC-76 Kansas City 2019—as well as comments from the journal editor and referees.
- Cai, J., A. De Janvry, and E. Sadoulet. 2015. Social networks and the decision to insure. American Economic Journal: Applied Economics. 7(2): 81–108. https://www.aeaweb.org/articles?id=10.1257/app.20130442. Accessed 26 November 2018.Google Scholar
- Cai, J., A. De Janvry, and E. Sadoulet. 2016. Subsidy policies and insurance demand (No. w22702). National Bureau of Economic Research. https://doi.org/10.3386/w22702.
- Chong, H., and J. Ifft. 2016. (Over)reacting to bad luck: Low yields increase crop insurance participation. In Presentation at SCC-76 2016 annual meeting, “Economics and Management of Risks in Agriculture and Natural Resources”, March 17–19, 2016, Pensacola Beach, FL. http://docplayer.net/1710422-Over-reacting-to-bad-luck-low-yields-increase-crop-insurance-participation-howard-chong-jennifer-ifft.html. Accessed 7 September 2016.
- Geruso, M., T.J. Layton, G. McCormack, and M. Shepard. 2019. The two margin problem in insurance markets. Unpublished working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3385492. Accessed 1 June 2019.
- Hanaoka, C., H. Shigeoka, and Y. Watanabe. 2018. Do risk preferences change? Evidence from the Great East Japan Earthquake. American Economic Journal: Applied Economics 10(2): 298–330. https://www.aeaweb.org/articles?id=10.1257/app.20170048. Accessed 28 May 2019.Google Scholar
- Kramer, R.A. 1983. Federal crop insurance, 1938–1982. Agricultural History 57(2): 181–200. https://www.jstor.org/stable/3743155. Accessed 11 May 2019.
- Kunreuther, H.C., M.V. Pauly, and S. McMorrow. 2013. Insurance and behavioral economics: Improving decisions in the most misunderstood industry. Cambridge: Cambridge University Press.Google Scholar
- Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston. 2012. Global Historical Climatology Network - Daily (GHCN-Daily), Version 3. NOAA National Climatic Data Center. https://doi.org/10.7289/V5D21VHZ. Accessed 6 Oct 2018.
- O’Donoghue, E. 2014. The effects of premium subsidies on demand for crop insurance (No. 178405). U.S. Department of Agriculture (USDA), Economic Research Service (ERS). http://doi.org/10.2139/ssrn.2502908.
- Papke, L.E., and J.M. Wooldridge. 1996. Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics 11 (6): 619–632. https://doi.org/10.1002/(SICI)1099-1255(199611)11:6%3C619:AID-JAE418%3E3.0.CO;2-1.CrossRefGoogle Scholar
- Pétraud, J., S. Boucher, and M. Carter. 2015. Competing theories of risk preferences and the demand for crop insurance: Experimental evidence from Peru. In Paper presented at the 2015 conference, August 9–14, 2015, Milan, Italy. International Association of Agricultural Economists. https://ageconsearch.umn.edu/record/211383. Accessed 11 January 2017.
- Ramirez, O.A., and J.S. Shonkwiler. 2017. A probabilistic model of the crop insurance purchase decision. Journal of Agricultural and Resource Economics 42 (1): 10–26.Google Scholar
- Shields, D.A. 2015. Federal crop insurance: Background. Congressional Research Service, 7-5700.Google Scholar
- Stein, D. 2016. Dynamics of demand for rainfall index insurance: Evidence from a commercial product in India. The World Bank Economic Review 32(3): 692–708. https://ssrn.com/abstract=2497235. Accessed 26 November 2018.
- Sutton, R.S., and A.G. Barto. 2018. Reinforcement learning: An introduction, 2nd ed. Cambridge: MIT Press.Google Scholar