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Weather index‐based insurances for farmers in the North China Plain

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  • Leif Erec Heimfarth
  • Oliver Musshoff

Abstract

Purpose - The purpose of this paper is to analyze the extent to which weather index‐based insurances can contribute to reducing shortfall risks of revenues of a representative average farm that produces corn or wheat in the North China Plain (NCP). The geographical basis risk is quantified to analyze the spatial dependency of weather patterns between established weather stations in the area and locations where the local weather patterns are unknown. Design/methodology/approach - Data are based on theStatistical Yearbook of Chinaand the Chinese Meteorological Administration. Methods of insurance valuation are burn analysis and index value simulation. Risk reduction is measured non‐parametrically and parametrically by the change of the standard deviation and the value at risk of revenues. The geographical basis risk is quantified by setting up a decorrelation function. Findings - Results suggest significant differences in the potential risk reduction between corn and wheat when using insurance based on a precipitation index. The spatial analysis suggests a potential to expand the insurance around a reference weather station up to community level. Research limitations/implications - Findings are limited by a weak database in China and, in particular, by the unavailability of individual farm data. Moreover, the low density of weather stations currently limits the examination of the approach in a broader context. Practical implications - The risk reduction potential of the proposed insurance is encouraging. From a policy point of view, the approach used here can support the adjustment of insurers towards different crops. Originality/value - This paper is believed to be the first that investigates a weather index‐based insurance designed for an average farm in the NCP and the quantification of geographical basis risk.

Suggested Citation

  • Leif Erec Heimfarth & Oliver Musshoff, 2011. "Weather index‐based insurances for farmers in the North China Plain," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 71(2), pages 218-239, August.
    • Handle: RePEc:eme:afrpps:v:71:y:2011:i:2:p:218-239
    DOI: 10.1108/00021461111152582
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    Cited by:

    1. Lu Zong & Manuela Ender, 2016. "Spatially-Aggregated Temperature Derivatives: Agricultural Risk Management in China," IJFS, MDPI, vol. 4(3), pages 1-17, September.
    2. Ho, Shuay-Tsyr & Ifft, Jennifer E. & Rickard, Bradley J. & Turvey, Calum G., 2018. "Alternative Strategies to Manage Weather Risk in Perennial Fruit Crop Production," Agricultural and Resource Economics Review, Cambridge University Press, vol. 47(3), pages 452-476, December.
    3. Zhiwei Shen & Martin Odening, 2013. "Coping with systemic risk in index-based crop insurance," Agricultural Economics, International Association of Agricultural Economists, vol. 44(1), pages 1-13, January.
    4. Tappi, Marco & Carucci, Federica & Gatta, Giuseppe & Giuliani, Marcella Michela & Lamonaca, Emilia & Santeramo, Fabio Gaetano, 2023. "Temporal and design approaches and yield-weather relationships," MPRA Paper 117488, University Library of Munich, Germany.
    5. Weber, Ron & Musshoff, Oliver, 2012. "Microfinance for Agricultural Firms- Credit Access and Loan Repayment in Tanzania," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122552, European Association of Agricultural Economists.
    6. Qing Sun & Zaiqiang Yang & Xianghong Che & Wei Han & Fangmin Zhang & Fang Xiao, 2018. "Pricing weather index insurance based on artificial controlled experiment: a case study of cold temperature for early rice in Jiangxi, China," 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. 91(1), pages 69-88, March.
    7. Hering, I. & Mußhoff, O., 2016. "Dynamic Incentives in Microfinance – What about the Farmers?," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 51, March.
    8. Bell, Peter Newton, 2014. "Design of Financial Derivatives: Statistical Power does not Ensure Risk Management Power," MPRA Paper 57438, University Library of Munich, Germany.
    9. Lu Zong & Manuela Ender, 2018. "Comparison of Stochastic and Spline Models for Temperature‐based Derivatives in China," Pacific Economic Review, Wiley Blackwell, vol. 23(4), pages 547-589, October.
    10. Lim, Sung Soo & Bone, Matthew, 2022. "Optimism, debt accumulation, and business growth," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 97(C).
    11. Sibiko, Kenneth W. & Veettil, Prakashan C. & Qaim, Matin, 2016. "Small Farmers’ Preferences for Weather Index Insurance: Insights from Kenya," 2016 Fifth International Conference, September 23-26, 2016, Addis Ababa, Ethiopia 246399, African Association of Agricultural Economists (AAAE).
    12. Nordmeyer, Eike Florenz, 2023. "German farmers' perceived usefulness of satellite-based index insurance - Insights from a transtheoretical model," 97th Annual Conference, March 27-29, 2023, Warwick University, Coventry, UK 334557, Agricultural Economics Society - AES.
    13. Zed Zulkafli & Farrah Melissa Muharam & Nurfarhana Raffar & Amirparsa Jajarmizadeh & Mukhtar Jibril Abdi & Balqis Mohamed Rehan & Khairudin Nurulhuda, 2021. "Contrasting Influences of Seasonal and Intra-Seasonal Hydroclimatic Variabilities on the Irrigated Rice Paddies of Northern Peninsular Malaysia for Weather Index Insurance Design," Sustainability, MDPI, vol. 13(9), pages 1-23, May.
    14. Weber, Ron & Musshoff, Oliver, 2012. "Microfinance for agricultural firms - What can we learn from bank data?," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126708, International Association of Agricultural Economists.

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