Mitigation of Short-Term Climate Risks and Uncertainties through Insurance and Self-Protection: Theory and Application
Downloads
This paper investigates the interplay between short-term insurance and self-protection strategies in mitigating weather-related risks and uncertainties amidst rising global temperatures. We explore the decision-making process behind these strategies, focus- ing on whether the choice between insurance and self-protection depends on the type of stochastic loss encountered, distinguishing between risk and uncertainty.
Existing research highlights the context-specific nature of the relationship be- tween insurance and self-protection. While a significant portion of the literature has concentrated on understanding long-term dynamics, the sudden occurrence of weather-related events requires a closer examination of short-term decision-making processes. This paper contributes by providing a theoretical framework for analyzing how weather stochastics influence producers’ decisions regarding insurance and self- protection in the short term.
Simulation outcomes reveal distinct responses of farmers to risk and uncertainty. Under risk, farmers without irrigation systems tend to increase their reliance on crop insurance as precipitation risks heighten, while those with irrigation systems adopt a nuanced approach, adjusting their insurance purchases based on the severity of precip- itation risks. This suggests that irrigation serves as both a substitute and complement to crop insurance, depending on the level of risk. Conversely, under uncertainty, farm- ers exhibit a general trend of decreased crop insurance purchases regardless of their self-protection measures. Addressing uncertainty within agricultural loss mitigation frameworks is crucial for safeguarding against potential food insecurity and increasing investment to mitigate climate-related disasters.
Policy implications underscore the need to consider producers’ level self-protection and the type of stochastics faced in climate policy design. Additionally, reducing un- certainty in weather forecasts is imperative to mitigate farmers’ vulnerability and promote agricultural resilience.
Downloads
Abdellaoui, M., & Wakker, P. P. (2005). The likelihood method for decision under uncertainty. Theory and Decision, 58 . doi: 10.1007/s11238-005-8320-4
Aimin, H. (2010). Uncertainty, risk aversion and risk management in agriculture.
Agriculture and Agricultural Science Procedia, 1 . doi: 10.1016/j.aaspro.2010
.09.018
Allais, M. (1953). Le comportement de l’homme rationnel devant le risque: Critique des postulats et axiomes de l’ecole americaine. Econometrica, 21 . doi: 10.2307/ 1907921
Aref, S., & Wander, M. M. (1997). Long-term trends of corn yield and soil organic matter in different crop sequences and soil fertility treatments on the morrow plots. Advances in Agronomy, 62 . doi: 10.1016/S0065-2113(08)60568-4
Baillon, A., Bleichrodt, H., Keskin, U., & ... (2013). Learning under ambiguity: An experiment using initial public offerings on a stock market.
Barberis, N. C. (2013). Thirty years of prospect theory in economics: A review and assessment (Vol. 27). doi: 10.1257/jep.27.1.173
Bard, S. K., & Barry, P. J. (2001). Assessing farmers’ attitudes toward risk using the ”closing-in” method. Journal of Agricultural and Resource Economics, 26 .
Batchelor, W. D., Basso, B., & Paz, J. O. (2002). Examples of strategies to analyze spatial and temporal yield variability using crop models. In (Vol. 18). doi: 10.1016/S1161-0301(02)00101-6
Ben-Tal, A., & Hochman, E. (1985). Approximation of expected returns and op- timal decisions under uncertainty using mean and mean absolute deviation. Zeitschrift fu¨r Operations Research, 29 . doi: 10.1007/BF01918761
Ben-Tal, A., & Teboulle, M. (1986). Expected utility, penalty functions, and duality in stochastic nonlinear programming. Management Science, 32 . doi: 10.1287/ mnsc.32.11.1445
Bert, F. E., Laciana, C. E., Podest´a, G. P., Satorre, E. H., & Men´endez, A. N. (2007). Sensitivity of ceres-maize simulated yields to uncertainty in soil properties and daily solar radiation. Agricultural Systems, 94 . doi: 10.1016/j.agsy.2006.08
.003
Boyer, C. N., Larson, J. A., Roberts, R. K., McClure, A. T., Tyler, D. D., & Zhou,
V. (2013). Stochastic corn yield response functions to nitrogen for corn after corn, corn after cotton, and corn after soybeans. Journal of Agricultural and Applied Economics, 45 . doi: 10.1017/s1074070800005198
Bullock, D. G., & Bullock, D. S. (1994). Quadratic and quadratic-plus-plateau models for predicting optimal nitrogen rate of corn: A comparison. Agronomy Journal,
86 . doi: 10.2134/agronj1994.00021962008600010033x
Cabas, J. H., Leiva, A. J., & Weersink, A. (2008). Modeling exit and entry of farmers in a crop insurance program. In (Vol. 37). doi: 10.1017/S1068280500002173
Cameron, T. A., & Quiggin, J. (1994). Estimation using contingent valuation data from a dichotomous choice with follow-up questionnaire. Journal of Environ- mental Economics and Management, 27 . doi: 10.1006/jeem.1994.1035
Cao, R., Carpentier, A., & Gohin, A. (2011). Measuring farmers’ risk aversion: the unknown properties of the value function. 2011 International Congress, . . . .
Cerrato, M. E., & Blackmer, A. M. (1990). Comparison of models for describing; corn yield response to nitrogen fertilizer. Agronomy Journal, 82 . doi: 10.2134/ agronj1990.00021962008200010030x
Chambers, R. G., Chung, Y., & Fa¨re, R. (1996). Benefit and distance functions.
Journal of Economic Theory, 70 . doi: 10.1006/jeth.1996.0096
Chung, Y. H., F¨are, R., & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51 . doi: 10.1006/jema.1997.0146
Coble, K. H., Knight, T. O., Patrick, G. F., & Baquet, A. E. (2002). Understanding the economic factors influencing farm policy preferences. Review of Agricultural Economics, 24 . doi: 10.1111/1467-9353.00021
Dalhaus, T., Barnett, B. J., & Finger, R. (2020). Behavioral weather insurance: Applying cumulative prospect theory to agricultural insurance design under narrow framing. PLoS ONE , 15 . doi: 10.1371/journal.pone.0232267
Dinar, A., & Yaron, D. (1992). Adoption and abandonment of irrigation technologies.
Agricultural Economics, 6 . doi: 10.1016/0169-5150(92)90008-M
Dogan, E., Copur, O., Kahraman, A., Kirnak, H., & Guldur, M. E. (2011). Sup- plemental irrigation effect on canola yield components under semiarid climatic conditions. Agricultural Water Management, 98 . doi: 10.1016/j.agwat.2011.04
.006
Dowling, J. A., Rinaldi, K. Z., Ruggles, T. H., Davis, S. J., Yuan, M., Tong, F., . . . Caldeira, K. (2020). Role of long-duration energy storage in variable renewable electricity systems. Joule, 4 . doi: 10.1016/j.joule.2020.07.007
Ellsberg, D. (1961). Risk, ambiguity, and the savage axioms. Quarterly Journal of Economics, 75 . doi: 10.2307/1884324
Eurosif. (2018, 6). Eurosif 2018 sri study. Retrieved from https://
www.eurosif.org/wp-content/uploads/2022/06/Eurosif-Report-June
-22-SFDR-Policy-Recommendations.pdf
Feder, G., Just, R. E., & Zilberman, D. (1985). Adoption of agricultural innovations in developing countries: a survey. Economic Development Cultural Change,
33 . doi: 10.1086/451461
Fern´andez, C., Koop, G., & Steel, M. F. (2002). Multiple-output production with undesirable outputs: An application to nitrogen surplus in agriculture. Journal of the American Statistical Association, 97 . doi: 10.1198/016214502760046989
Fersund, F. R. (2009). Good modelling of bad outputs: Pollution and multiple-output production. International Review of Environmental and Resource Economics,
3 . doi: 10.1561/101.00000021
Flaten, O., Lien, G., Koesling, M., Valle, P. S., & Ebbesvik, M. (2005). Compar- ing risk perceptions and risk management in organic and conventional dairy farming: Empirical results from norway. Livestock Production Science, 95 . doi: 10.1016/j.livprodsci.2004.10.014
Foster, A. D., & Rosenzweig, M. R. (1995). Learning by doing and learning from others: human capital and technical change in agriculture. Journal of Political Economy, 103 . doi: 10.1086/601447
Foster, A. D., & Rosenzweig, M. R. (2010). Microeconomics of technology adop- tion. Annual Review of Economics, 2 . doi: 10.1146/annurev.economics.102308
.124433
Foudi, S., & Erdlenbruch, K. (2012). The role of irrigation in farmers’ risk manage- ment strategies in france (Vol. 39). doi: 10.1093/erae/jbr024
Friedman, M., & Savage, L. J. (1948). The utility analysis of choices involving risk.
Journal of Political Economy, 56 . doi: 10.1086/256692
Frittelli, M., & Gianin, E. R. (2002). Putting order in risk measures. Journal of Banking and Finance, 26 . doi: 10.1016/S0378-4266(02)00270-4
Fuentes-Arderiu, X., & Dot-Bach, D. (2009). Measurement uncertainty in manual differential leukocyte counting. Clinical Chemistry and Laboratory Medicine,
47 . doi: 10.1515/CCLM.2009.014
Fa¨re, R., Grosskopf, S., Noh, D. W., & Weber, W. (2005). Characteristics of a polluting technology: Theory and practice. Journal of Econometrics, 126 . doi: 10.1016/j.jeconom.2004.05.010
Fa¨re, R., Grosskopf, S., & Weber, W. L. (2006). Shadow prices and pollution costs in
u.s. agriculture. Ecological Economics, 56 . doi: 10.1016/j.ecolecon.2004.12.022 Fo¨llmer, H., & Schied, A. (2002). Convex measures of risk and trading constraints.
Finance and Stochastics, 6 . doi: 10.1007/s007800200072
Goodwin, B. K. (1993). An empirical analysis of the demand for multiple peril crop insurance. American Journal of Agricultural Economics, 75 . doi: 10.2307/ 1242927
Han, X., Zhang, G., Xie, Y., Yin, J., Zhou, H., Yang, Y., . . . Bai, W. (2019).
Weather index insurance for wind energy. Global Energy Interconnection, 2 . doi: 10.1016/j.gloei.2020.01.008
Hasenkamp, G. (1976). A study of multiple-output production functions. klein’s rail- road study revisited. Journal of Econometrics, 4 . doi: 10.1016/0304-4076(76) 90036-1
Hayhoe, K., Wuebbles, D., Easterling, D., Fahey, D., Doherty, S., Kossin, J., . . . Wehner, M. (2018). Our changing climate. in impacts, risks, and adaptation in the united states: Fourth national climate assessment, volume ii. Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assess- ment, Volume II , II .
Heath, C., & Tversky, A. (1991). Preference and belief: Ambiguity and competence in choice under uncertainty. Journal of Risk and Uncertainty, 4 . doi: 10.1007/ BF00057884
Hertwig, R., Barron, G., Weber, E. U., & Erev, I. (2004). Decisions from experience and the effect of rare events in risky choice. Psychological Science, 15 . doi: 10.1111/j.0956-7976.2004.00715.x
Huettel, S. A., Stowe, C. J., Gordon, E. M., Warner, B. T., & Platt, M. L. (2006). Neural signatures of economic preferences for risk and ambiguity. Neuron, 49 . doi: 10.1016/j.neuron.2006.01.024
Ipcc. (2013). Working group i contribution to the ipcc fifth assessment report, climate change 2013: The physical science basis. Ipcc, AR5 .
Ipcc. (2022). Ar6 synthesis report outline: Climate change 2022. Re-
trieved from https://www.ipcc.ch/site/assets/uploads/2021/12/IPCC
-52 decisions-adopted-by-the-Panel.pdf
Iyer, P., Bozzola, M., Hirsch, S., Meraner, M., & Finger, R. (2020). Measuring farmer risk preferences in europe: A systematic review. Journal of Agricultural Economics, 71 . doi: 10.1111/1477-9552.12325
Kahn, B. E., & Sarin, R. K. (1988). Modeling ambiguity in decisions under uncer- tainty. Journal of Consumer Research, 15 . doi: 10.1086/209163
Kahneman, D., & Tversky, A. (1979). Kahneman tversky (1979) - prospect theory - an analysis of decision under risk.pdf (Vol. 47).
Kessler, R. (2021, 3). Texas wind farms face billion-dollar losses from blackouts in ’illegal wealth transfer’. Retrieved from https://www.windaction.org/posts/ 52234
Kilka, M., & Weber, M. (2001). What determines the shape of the probability weighting function under uncertainty? Management Science, 47 . doi: 10.1287/ mnsc.47.12.1712.10239
Knight, F. H. (1921). Risk uncertainty and profit knight (Vol. 36).
Kooperman, Chen, Hoffman, Koven, Lindsay, Pritchard, . . . Randerson (2018). Forest response to rising co2 drives zonally asymmetric rainfall change over tropical land. Nature Climate Change. doi: https://doi.org/10.1038/s41558-018-0144-7
Koundouri, P., Nauges, C., & Tzouvelekas, V. (2006). Technology adoption under pro- duction uncertainty: Theory and application to irrigation technology. American Journal of Agricultural Economics, 88 . doi: 10.1111/j.1467-8276.2006.00886.x
Kumbhakar, S. C. (2002). Specification and estimation of production risk, risk pref- erences and technical efficiency. American Journal of Agricultural Economics,
84 . doi: 10.1111/1467-8276.00239
Kumbhakar, S. C., & Lovell, C. A. K. (2000). Stochastic frontier analysis. doi: 10.1017/cbo9781139174411
Kweilin Ellingrud, B. Q., Alex Kimura, & Ralph, J. (2022). Five steps to improve in- novation in the insurance industry. McKinsey & Co. Retrieved from https:// www.mckinsey.com/industries/financial-services/our-insights/
five-steps-to-improve-innovation-in-the-insurance-industry
Laeven, R. J., & Stadje, M. (2014). Robust portfolio choice and indifference valuation.
Mathematics of Operations Research, 39 . doi: 10.1287/moor.2014.0646
Lee, D. (2005). Agricultural sustainability and technology adoption: Issues and policies for developing countries. American Journal of Agricultural Economics,
87 . doi: 10.1111/j.1467-8276.2005.00826.x
Lempert, R., Popper, S., & Bankes, S. (2019). Shaping the next one hundred years: New methods for quantitative, long-term policy analysis. doi: 10.7249/mr1626
Levy, I., Snell, J., Nelson, A. J., Rustichini, A., & Glimcher, P. W. (2010). Neu- ral representation of subjective value under risk and ambiguity. Journal of Neurophysiology, 103 . doi: 10.1152/jn.00853.2009
Link, J., Graeff, S., Batchelor, W. D., & Claupein, W. (2006). Evaluating the economic and environmental impact of environmental compensation payment policy under uniform and variable-rate nitrogen management. Agricultural Sys- tems, 91 . doi: 10.1016/j.agsy.2006.02.003
Llewelyn, R. V., & Featherstone, A. M. (1997). A comparison of crop production functions using simulated data for irrigated corn in western kansas. Agricultural Systems, 54 . doi: 10.1016/S0308-521X(96)00080-7
Lyu, K., & Barr´e, T. J. (2017). Risk aversion in crop insurance program purchase decisions evidence from maize production areas in china. China Agricultural Economic Review, 9 . doi: 10.1108/CAER-04-2015-0036
Maharjan, B., Das, S., Nielsen, R., & Hergert, G. W. (2021). Maize yields from manure and mineral fertilizers in the 100-year-old knorr–holden plot. Agronomy Journal, 113 . doi: 10.1002/agj2.20713
Mahul, O. (2002). Hedging in futures and options markets with basis risk (Vol. 22).
doi: 10.1002/fut.2207
Miao, Y., Mulla, D. J., Batchelor, W. D., Paz, J. O., Robert, P. C., & Wiebers,
M. (2006). Evaluating management zone optimal nitrogen rates with a crop growth model. Agronomy Journal, 98 . doi: 10.2134/agronj2005.0153
Murty, S., Russell, R. R., & Levkoff, S. B. (2012). On modeling pollution-generating technologies. Journal of Environmental Economics and Management, 64 . doi: 10.1016/j.jeem.2012.02.005
of Sciences, N. A., Council, N. R., of Mathematical, A., & Sciences, P. (1979). Carbon dioxide and climate: a scientific assessment. Re- trieved from https://nap.nationalacademies.org/catalog/12181/carbon
-dioxide-and-climate-a-scientific-assessment
Paz, J. O., Batchelor, W. D., Babcock, B. A., Colvin, T. S., Logsdon, S. D., Kaspar,
T. C., & Karlen, D. L. (1999). Model-based technique to determine variable rate nitrogen for corn. Agricultural Systems, 61 . doi: 10.1016/S0308-521X(99) 00035-9
Piet, L., & Bougherara, D. (2016, 3). The impact of farmers’ risk preferences on the design of an individual yield crop insurance. WORKING PAPER SMART, INARE UMR SMART .
Platt, M. L., & Huettel, S. A. (2008). Risky business: The neuroeconomics of decision making under uncertainty (Vol. 11). doi: 10.1038/nn2062
Pope, R. D. (1982). Expected profit, price change, and risk aversion. American Journal of Agricultural Economics, 64 . doi: 10.2307/1240655
Program, U. G. C. R. (2018). Climate science special report: Fourth national climate assessment, volume i (Vol. 1). doi: 10.7930/J0J964J6
Puntel, L. A., Sawyer, J. E., Barker, D. W., Dietzel, R., Poffenbarger, H., Castellano,
M. J., . . . Archontoulis, S. V. (2016). Modeling long-term corn yield response to nitrogen rate and crop rotation. Frontiers in Plant Science, 7 . doi: 10.3389/ fpls.2016.01630
Raiffa, H. (1993). Decision analysis: introductory lectures on choices under un- certainty. 1968. M.D. computing : computers in medical practice, 10 . doi: 10.2307/2987280
Ruszczy’ski, A. (2006, 8). Stochastic programming. John Wiley Sons, Inc. doi: 10.1002/0471667196.ess3225
Schahczenski, J. (2021, 9). Crop insurance rules challenge organic and sustainable farming practices. Retrieved from https://sustainableagriculture.net/ blog/crop-insurance-rules-challenge-organic-and-sustainable
-farming-practices
Schnitkey, G., Batts, R., Swanson, K., Paulson, N., & Zulauf, C. (2021). Crop insurance tools. Farmdoc.
Schultz, W., Preuschoff, K., Camerer, C., Hsu, M., Fiorillo, C. D., Tobler, P. N., & Bossaerts, P. (2008). Review. explicit neural signals reflecting reward uncer- tainty (Vol. 363). doi: 10.1098/rstb.2008.0152
Scofield, C. (1927). Irrigated crop rotations in western nebraska. United States Department of Agriculture, Technical Bulletin, 02 .
Shapiro, A., Tekaya, W., Soares, M. P., & Costa, J. P. D. (2013). Worst-case- expectation approach to optimization under uncertainty. Operations Research,
61 . doi: 10.1287/opre.2013.1229
Shephard, R. W. (1970). Theory of cost and production functions. doi: 10.2307/ 2230285
Sherrick, B. J., Zanini, F. C., Schnitkey, G. D., & Irwin, S. H. (2004). Crop in- surance valuation under alternative yield distributions. American Journal of Agricultural Economics, 86 . doi: 10.1111/j.0092-5853.2004.00587.x
Steiger, R., Damm, A., Prettenthaler, F., & Pro¨bstl-Haider, U. (2021). Climate change and winter outdoor activities in austria. Journal of Outdoor Recreation and Tourism, 34 . doi: 10.1016/j.jort.2020.100330
Strupczewski, G. (2019). What characterizes farmers who purchase crop insurance in poland? Problems of Agricultural Economics, 1 . doi: 10.30858/zer/103596
Sulewski, P., & K-loczko-Gajewska, A. (2014). Farmers’ risk perception, risk aversion and strategies to cope with production risk: An empirical study from poland. Studies in Agricultural Economics, 116 . doi: 10.7896/j.1414
Thorp, K. R., DeJonge, K. C., Kaleita, A. L., Batchelor, W. D., & Paz, J. O. (2008). Methodology for the use of dssat models for precision agriculture de- cision support. Computers and Electronics in Agriculture, 64 . doi: 10.1016/ j.compag.2008.05.022
Ullah, R., Shivakoti, G. P., & Ali, G. (2015). Factors effecting farmers’ risk attitude and risk perceptions: The case of khyber pakhtunkhwa, pakistan. International Journal of Disaster Risk Reduction, 13 . doi: 10.1016/j.ijdrr.2015.05.005
Vajda, S., Luce, R. D., & Raiffa, H. (1958). Games and decisions: Introduction and critical survey. Journal of the Royal Statistical Society. Series A (General),
121 . doi: 10.2307/2342906
Valone.T. (2021). Linear global temperature correlation to carbon dioxide level, sea level, and innovative solutions to a projected 6°c warming by 2100. Journal of Geoscience and Environment Protection. Retrieved from https://www.scirp
.org/journal/paperinformation.aspx?paperid=107789
Vollmer, E., Hermann, D., & Mußhoff, O. (2017). Is the risk attitude measured with the holt and laury task reflected in farmers’ production risk? European Review of Agricultural Economics, 44 . doi: 10.1093/erae/jbx004
Weaver, R. (1977). The theory and measurement of provisional agricultural production decisions .
Weber, E. U. (1994). From subjective probabilities to decision weights: The effect of asymmetric loss functions on the evaluation of uncertain outcomes and events. Psychological Bulletin, 115 . doi: 10.1037//0033-2909.115.2.228
Yaari, M. E. (1987). The dual theory of choice under risk. Econometrica, 55 . doi:
10.2307/1911158
Copyright (c) 2024 Abdelmoumine Traore
This work is licensed under a Creative Commons Attribution 4.0 International License.
