Agricultural insurance in Indonesia is focused specifically on rice farming and is locally known as Asuransi Usahatani Padi (AUTP). To encourage farmer participation, the government subsidises farmers’ cost of insurance (premium) by 80%. Despite high subsidy, AUTP is still unable to reach the coverage target. The objectives of this study are to investigate farmers’ Risk Aversion Level (RAL), its influence on farmers’ decision to participate in AUTP, and the effect of farmers’ participation in AUTP on their income. The result of this study can contribute to enriching agriculture insurance literature from the point of view of developing countries and catalyse other studies on this matter especially in Indonesia. The analysis methods used in this study were multiple pricelist designs and propensity score matching with a logistic regression model. 130 farmers were interviewed. The results showed that farmers tend to have a high level of risk aversion (82.3% of farmers insure almost all of their land). RAL has a significant effect on farmers’ decision to purchase AUTP (< 0.01). A positive value of Average Treatment on the Treated (ATT) indicated that participation in AUTP has a positive impact on farmers’ income. AUTP is able to absorb production risks and encourage use of high input in farming.
Afroz R., Akhtar R., Farhana P. (2017): Willingness to pay for crop insurance to adapt flood risk by Malaysian farmers: an empirical investigation of Kedah. International Journal of Economics and Financial Issues, 7: 1–9. Available at www.econjournals.com (accessed Aug 8, 2018).
Ali A. (2013): Farmers’ willingness to pay for index based crop insurance in Pakistan: a case study on food and cash crops of rain-fed areas. Agricultural Economics Research Review, 26: 241–248. Available at https://ageconsearch.umn.edu (accessed Feb 3, 2019).
Becker S.O., Ichino A. (2002): Estimation of average treatment effects based on propensity scores. The Stata Journal, 2: 358–377. Available at https://www.stata-journal.com (accessed Jan 20, 2019).
Binswanger H.P. (1980): Attitudes toward risk: experimental measurement in Rural India. American Journal of Agricultural Economics, 62: 395–407. Available at https://academic.oup.com (accessed Oct 9, 2018).
Caliendo M., Kopeinig S. (2008): Some practical guidance for the implementation of propensity score matching. Discussion Paper No. 1588. Journal of Economic Surveys, 22: 31–72. Available at http://onlinelibrary.wiley.com/ (accessed Feb 3, 2019).
Coble K.H., Knight T.O., Pope R.D., Williams J.R. (1997): An expected-indemnity approach to the measurement of moral hazard in crop insurance. American Agricultural Economics Asociations, 79: 216–226. Available at http://agris.fao.org/ (accessed Oct 20, 2018).
Pan D. (2014): The impact of agricultural extension on farmer nutrient management behavior in Chinese rice production: A household-level analysis. Sustainability, 6: 6644–6665. Available at https://www.mdpi.com (accessed Apr 11, 2019).
Harwood J., Heifner R., Coble K., Perry J., Somwaru A. (1999): Managing Risk in Farming: Concepts, Research, and Analysis. Agricultural Economic Report No. 774, Economic Research Service, US Department of Agriculture (USDA), Washington, DC. Available at https://ageconsearch.umn.edu/ (accessed Feb 22, 2019).
Indonesian Ministry of Agriculture (2018): Implementation of AUTP Report Interview. Jakarta, Indonesia.
Lyu K., Barré T.J. (2017): Risk aversion in crop insurance program purchase decisions. China Agricultural Economic Review, 9: 62–80. Available at https://www.emeraldinsight.com/loi/caer (accessed Oct 18, 2018).
Nahvi A., Kohansal M.R., Ghorbani M., Shahnoushi N. (2014): Factors affecting rice farmers to participate in agricultural insurance. Journal of Applied Science and Agriculture, 9: 1525–1529. Available at http://www.aensiweb.com (accessed Feb 2, 2019).
Rosenbaum P.R., Rubin D.B. (1983): The central role of the propensity score in observational studies for causal effects. Biometrika, 70: 41–55. Available at http://www.stat.cmu.edu (accessed Dec 1, 2018).
Rosenbaum P.R., Rubin D.B. (1985): Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician, 39: 33–38. Available at https://www.jstor.org/ (accessed Jan 3, 2019).
Sianesi B. (2002): An evaluation of the Swedish system of active labour network programmes in the 1990s. Review of Economics and Statistics, 86: 133–155. Available at https://www.jstor.org/journal/revieconstat (accessed Feb 2, 2019).
Varadan R.J., Kumar P. (2012): Impact of crop insurance on rice farming in Tamil Nadu. Agricultural Economics Research Review, 25: 291–298. Available at https://ageconsearch.umn.edu (accessed Feb 3, 2019)
Vassalos M., Li Y. (2016): Assessing the impact of fresh vegetable growers’ risk aversion levels and risk preferences on the probability of adopting marketing contracts: A Bayesian approach. International Food and Agribusiness Management Review, 19: 25–42. Available at https://www.ifama.org (accessed Oct 15, 2018).
Zhao Y., Chai Z., Delgado M.S., Preckel P.V. (2016): An empirical analysis of the effect of crop insurance on farmers’ income results from Inner Mongolia in China. China Agricultural Economic Review, 8: 299–313. Available at https://www.emeraldinsight.com/loi/caer (accessed Oct 10, 2018).
Zhao Y., Chai Z., Delgado M.S., Preckel P.V. (2017): A test on adverse selection of farmers in crop insurance : Results from Inner Mongolia, China. Journal of Integrative Agriculture, 16: 478–485. https://doi.org/10.1016/S2095-3119(16)61440-5