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An empirical analysis of the factors influencing farmer demand for forest insurance: Based on surveys from Lin’an County in Zhejiang Province of China

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  • Qin, Tao
  • Gu, Xuesong
  • Tian, Zhiwei
  • Pan, Huanxue
  • Deng, Jing
  • Wan, Li

Abstract

The lack of effective farmer demand is a major factor that restricts the development of China's forest insurance. To solve this problem, this study uses a Logit model to conduct an empirical analysis of relevant factors in the farmers’ demand for forest insurance, based on field survey data of Lin’an County, Zhejiang Province. The results show that the farmers’ understanding of forest insurance, the proportion of forestry revenues in the total household income, forest size, forest disaster frequency, forest insurance liability, insurance amount setup, and the farmers’ satisfaction regarding the premium subsidy policy, are the main factors that affect the farmers’ demand for forest insurance. Therefore, we propose to expand forest insurance promotion, raise the farmers’ income, rationally design insurance products, and optimize the forest premium subsidy policy to enhance the farmers’ willingness to participate in forest insurance.

Suggested Citation

  • Qin, Tao & Gu, Xuesong & Tian, Zhiwei & Pan, Huanxue & Deng, Jing & Wan, Li, 2016. "An empirical analysis of the factors influencing farmer demand for forest insurance: Based on surveys from Lin’an County in Zhejiang Province of China," Journal of Forest Economics, Elsevier, vol. 24(C), pages 37-51.
  • Handle: RePEc:eee:foreco:v:24:y:2016:i:c:p:37-51
    DOI: 10.1016/j.jfe.2016.04.001
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    References listed on IDEAS

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    1. Dai, Yongwu & Chang, Hung-Hao & Liu, Weiping, 2015. "Do forest producers benefit from the forest disaster insurance program? Empirical evidence in Fujian Province of China," Forest Policy and Economics, Elsevier, vol. 50(C), pages 127-133.
    2. Brunette, M. & Holecy, J. & Sedliak, M. & Tucek, J. & Hanewinkel, M., 2015. "An actuarial model of forest insurance against multiple natural hazards in fir (Abies Alba Mill.) stands in Slovakia," Forest Policy and Economics, Elsevier, vol. 55(C), pages 46-57.
    3. Barry K. Goodwin & Monte L. Vandeveer & John L. Deal, 2004. "An Empirical Analysis of Acreage Effects of Participation in the Federal Crop Insurance Program," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1058-1077.
    4. Brunette, Marielle & Couture, Stéphane, 2008. "Public compensation for windstorm damage reduces incentives for risk management investments," Forest Policy and Economics, Elsevier, vol. 10(7-8), pages 491-499, October.
    5. Marielle Brunette & Laure Cabantous & Stéphane Couture & Anne Stenger, 2013. "The impact of governmental assistance on insurance demand under ambiguity: a theoretical model and an experimental test," Theory and Decision, Springer, vol. 75(2), pages 153-174, August.
    6. Barreal, Jesús & Loureiro, Maria L. & Picos, Juan, 2014. "On insurance as a tool for securing forest restoration after wildfires," Forest Policy and Economics, Elsevier, vol. 42(C), pages 15-23.
    7. Barry K. Goodwin, 2001. "Problems with Market Insurance in Agriculture," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 643-649.
    8. Thomas O. Knight & Keith H. Coble, 1997. "Survey of U.S. Multiple Peril Crop Insurance Literature Since 1980," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 19(1), pages 128-156.
    9. Brunette, Marielle & Couture, Stéphane, 2008. "Public compensation for windstorm damage reduces incentives for risk management investments," Forest Policy and Economics, Elsevier, vol. 10(7-8), pages 491-499, October.
    10. Holecy, Jan & Hanewinkel, Marc, 2006. "A forest management risk insurance model and its application to coniferous stands in southwest Germany," Forest Policy and Economics, Elsevier, vol. 8(2), pages 161-174, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Feng, Xin & Dai, Yongwu, 2019. "An innovative type of forest insurance in China based on the robust approach," Forest Policy and Economics, Elsevier, vol. 104(C), pages 23-32.
    2. Juan Wu & Wenjing Yu & Xiaobing Liu & Yali Wen, 2022. "Analysis of Influencing Factors and Income Effect of Heterogeneous Agricultural Households’ Forestland Transfer," Land, MDPI, vol. 11(9), pages 1-17, September.
    3. Ye Song & Hongjun Peng, 2019. "Strategies of Forestry Carbon Sink under Forest Insurance and Subsidies," Sustainability, MDPI, vol. 11(17), pages 1-13, August.
    4. M. Brunette & S. Couture & J. Foncel & S. Garcia, 2020. "The decision to insure against forest fire risk: an econometric analysis combining hypothetical real data," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 45(1), pages 111-133, January.
    5. Cipollaro, Maria & Sacchelli, Sandro, 2018. "Demand and potential subsidy level for forest insurance market in Demand and potential subsidy level for forest insurance market in Italy," 2018 Seventh AIEAA Conference, June 14-15, Conegliano, Italy 275647, Italian Association of Agricultural and Applied Economics (AIEAA).
    6. Sauter, Philipp A. & Möllmann, Torsten B. & Anastassiadis, Friederike & Mußhoff, Oliver & Möhring, Bernhard, 2016. "To insure or not to insure? Analysis of foresters' willingness-to-pay for fire and storm insurance," Forest Policy and Economics, Elsevier, vol. 73(C), pages 78-89.
    7. Qi Cai & Yushi Cai & Yali Wen, 2018. "Spatially Differentiated Trends between Forest Pest-Induced Losses and Measures for Their Control in China," Sustainability, MDPI, vol. 11(1), pages 1-16, December.
    8. Sacchelli, Sandro & Cipollaro, Maria & Fabbrizzi, Sara, 2018. "A GIS-based model for multiscale forest insurance analysis: The Italian case study," Forest Policy and Economics, Elsevier, vol. 92(C), pages 106-118.
    9. Félix Bastit & Marielle Brunette & Claire Montagne-Huck, 2021. "Earth, wind and fire: A multi-hazard risk review for natural disturbances in forests," Working Papers of BETA 2021-25, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.

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    More about this item

    Keywords

    Forest insurance; Insurance demand; Influencing factors; Logit model;
    All these keywords.

    JEL classification:

    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance

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