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Demand for temperature and rainfall index insurance in India

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  • Ayako Matsuda
  • Takashi Kurosaki

Abstract

Weather index insurance has been attracting considerable attention from academics and policymakers. This study investigates the demand for two types of index insurance sold in India: temperature index insurance for dry season and rainfall index insurance for subsequent monsoon season. Using data from randomized subsidy experiments, we separate purchase and quantity decisions and investigate how demand for rainfall insurance is correlated with demand for temperature insurance sold in the previous season. We find that the price (premium) does not influence purchase decisions per se but does significantly influence quantity decisions. The quantity demanded is less price‐sensitive for subsequent rainfall insurance than it is for temperature insurance. We also find that purchasers of temperature insurance tend to buy rainfall insurance more often than nonpurchasers do. However, a one‐time subsidy does not influence on subsequent demand, suggesting no price‐anchoring effect.

Suggested Citation

  • Ayako Matsuda & Takashi Kurosaki, 2019. "Demand for temperature and rainfall index insurance in India," Agricultural Economics, International Association of Agricultural Economists, vol. 50(3), pages 353-366, May.
    • Handle: RePEc:bla:agecon:v:50:y:2019:i:3:p:353-366
    DOI: 10.1111/agec.12489
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    Cited by:

    1. Qingxia Wang & Yim Soksophors & Angelica Barlis & Shahbaz Mushtaq & Khieng Phanna & Cornelis Swaans & Danny Rodulfo, 2022. "Willingness to Pay for Weather-Indexed Insurance: Evidence from Cambodian Rice Farmers," Sustainability, MDPI, vol. 14(21), pages 1-17, November.
    2. Takahashi, Kazushi & Noritomo, Yuma & Ikegami, Munenobu & Jensen, Nathaniel D., 2020. "Understanding pastoralists’ dynamic insurance uptake decisions: Evidence from four-year panel data in Ethiopia," Food Policy, Elsevier, vol. 95(C).
    3. Huajin Li & Yusen He & He Yang & Yong Wei & Songlin Li & Jianqiang Xu, 2021. "Rainfall prediction using optimally pruned extreme learning machines," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 799-817, August.
    4. Jeetendra Prakash Aryal & Tek B. Sapkota & Ritika Khurana & Arun Khatri-Chhetri & Dil Bahadur Rahut & M. L. Jat, 2020. "Climate change and agriculture in South Asia: adaptation options in smallholder production systems," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(6), pages 5045-5075, August.
    5. Amogh Prakasha Kumar & Laura Meriluoto & Richard Watt, 2024. "Too Cold for Comfort: A Theoretical Analysis of Index-Based Insurance for Frost Damage to Crops," Working Papers in Economics 24/02, University of Canterbury, Department of Economics and Finance.
    6. Sakketa, Tekalign G. & Kornher, Lukas, 2021. "Unintended Consequences or a Glimmer of Hope? Comparative Impact Analysis of Cash Transfers and Index Insurance on Pastoralists’ Labor Allocation Decisions," 2021 Conference, August 17-31, 2021, Virtual 315113, International Association of Agricultural Economists.
    7. Rajesh Tiwari & Khem Chand & Bimal Anjum, 2020. "Crop Insurance in India: A Review of Pradhan Mantri Fasal Bima Yojana (PMFBY)," FIIB Business Review, , vol. 9(4), pages 249-255, December.
    8. Ayako Matsuda & Kazushi Takahashi & Munenobu Ikegami, 2019. "Direct and indirect impact of index-based livestock insurance in Southern Ethiopia," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 44(3), pages 481-502, July.
    9. Pengfei Liu & Lingling Hou & Dongqing Li & Shi Min & Yueying Mu, 2021. "Determinants of Livestock Insurance Demand: Experimental Evidence from Chinese Herders," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(2), pages 430-451, June.

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