Residents’ Preferences for Rural Housing Disaster Insurance Attributes in Central and Western Tibet

Abstract

Understanding the heterogeneous preferences of individuals for disaster insurance attributes is critical for product improvement and policy design. In an era of global environmental change, the Qinghai-Tibet Plateau is a hotspot of natural hazards. Improving the capability of rural housing disaster insurance to foster local residents’ disaster resilience is of great significance but remains under addressed. We used a discrete choice experiment approach to provide the first estimates of rural residents’ preferences for rural housing disaster insurance attributes in central and western Tibet. We estimated residents’ preferences and willingness-to-pay for the sum insured, subsidy rate, insured object, and perils covered. The potential impacts of increasing the sum insured, expanding the insured object, and lowering subsidy rates were evaluated. Our results suggest that residents prefer products with a high sum insured, high subsidy rate, and a complete list of insured objects. Residents who have experienced specific hazards tend to prefer the corresponding perils covered. Females and residents who have a closer social network are more likely to purchase insurance. Product improvement and policy simulation results suggest that, while lowering the subsidy rate, increasing the sum insured and expanding the insured object could promote participation and improve residents’ welfare. Our results could improve the understanding of the preferences of households in remote regions and support policy implementations.

Keywords

Discrete choice experiment / Preference for insurance attributes / Qinghai-Tibet Plateau / Rural housing disaster insurance / Willingness-to-pay

References

[1] Aizaki H. Basic functions for supporting an implementation of choice experiments in R. Journal of Statistical Software, 2012

[2] Ayanlade A, Radeny M, Morton JF. Comparing smallholder farmers’ perception of climate change with meteorological data: A case study from southwestern Nigeria. Weather and Climate Extremes, 2017, 15: 24-33

[3] Botzen, W.J.W., and J.C.J.M. van den Bergh. 2012. Monetary valuation of insurance against flood risk under climate change. International Economic Review 53(3): 1005–1026.

[4] Brouwer R, Akter S. Informing micro insurance contract design to mitigate climate change catastrophe risks using choice experiments. Environmental Hazards, 2010, 9(1): 74-88

[5] Brouwer R, Tinh BD, Tuan TH, Magnussen K, Navrud S. Modeling demand for catastrophic flood risk insurance in coastal zones in Vietnam using choice experiments. Environment and Development Economics, 2014, 19(2): 228-249

[6] Cai J, De Janvry A, Sadoulet E. Social networks and the decision to insure. American Economic Journal: Applied Economics, 2015, 7(2): 81-108.

[7] Campbell D. Willingness to pay for rural landscape improvements: Combining mixed logit and random-effects models. Journal of Agricultural Economics, 2007, 58(3): 467-483

[8] Carson RT, Groves T, List JA. Consequentiality: A theoretical and experimental exploration of a single binary choice. Journal of the Association of Environmental and Resource Economists, 2014, 1(1/2): 171-207

[9] Chantarat S, Mude AG, Barrett CB, Turvey CG. Welfare impacts of index insurance in the presence of a poverty trap. World Development, 2017, 94: 119-138

[10] Darlington JC, Yiannakoulias N. Experimental evidence for coverage preferences in flood insurance. International Journal of Disaster Risk Science, 2022, 13(2): 178-189

[11] Feng, X., H. Qiu, J. Pan, and J. Tang. 2021. The impact of climate change on livestock production in pastoral areas of China. Science of the Total Environment 770: Article 144838.

[12] Fonta, W.M., S. Sanfo, A.M. Kedir, and D.R. Thiam. 2018. Estimating farmers’ willingness to pay for weather index-based crop insurance uptake in West Africa: Insight from a pilot initiative in Southwestern Burkina Faso. Agricultural and Food Economics 6(1): Article 11.

[13] Ghosh RK, Gupta S, Singh V, Ward PS. Demand for crop insurance in developing countries: New evidence from India. Journal of Agricultural Economics, 2021, 72(1): 293-320

[14] Gonçalves T, Costa Pinto LM, Lourenço-Gomes L. Attribute non-attendance in wine choice: Contrasts between stated and inferred approaches. Economic Analysis and Policy, 2020, 66: 262-275

[15] Greene WH, Hensher DA, Rose J. Accounting for heterogeneity in the variance of unobserved effects in mixed logit models. Transportation Research Part B: Methodological, 2006, 40(1): 75-92

[16] Greene WH, Hensher DA, Rose JM. Scarpa R, Alberini A. Using classical simulation-based estimators to estimate individual WTP values. Applications of simulation methods in environmental and resource economics, 2005, Dordrecht: Springer 17-33

[17] Hanemann WM. Welfare evaluations in contingent valuation experiments with discrete responses. American Journal of Agricultural Economics, 1984, 66(3): 332-341

[18] Hanley N, MacMillan D, Wright RE, Bullock C, Simpsom I, Parsisson D, Crabtree B. Contingent valuation versus choice experiments: Estimating the benefits of environmentally sensitive areas in Scotland. Journal of Agricultural Economics, 1998, 49(1): 1-15

[19] Hensher DA, Greene WH. The mixed logit model: The state of practice. Transportation, 2003, 30(2): 133-176

[20] Hess S, Hensher DA. Using conditioning on observed choices to retrieve individual-specific attribute processing strategies. Transportation Research Part B: Methodological, 2010, 44(6): 781-790

[21] Immerzeel WW, van Beek LPH, Bierkens MFP. Climate change will affect the Asian water towers. Science, 2010, 328(5984): 1382-1385

[22] Ladenburg J, Bonnichsen O, Dahlgaard J. Testing the effect of a short cheap talk script in choice experiments. Nationaløkonomisk Tidsskrif, 2011, 149: 25-54.

[23] Lancaster KJ. A new approach to consumer theory. The Journal of Political Economy, 1966, 74(2): 132-157

[24] Liesivaara P, Myyrä S. Willingness to pay for agricultural crop insurance in the northern EU. Agricultural Finance Review, 2014, 74(4): 539-554

[25] Liu P, Hou L, Li D, Min S, Mu Y. Determinants of livestock insurance demand: Experimental evidence from Chinese herders. Journal of Agricultural Economics, 2021, 72(2): 430-451

[26] Luo, J., G. Yin, F. Niu, Z. Lin, and M. Liu. 2019. High spatial resolution modeling of climate change impacts on permafrost thermal conditions for the Beiluhe basin, Qinghai-Tibet Plateau. Remote Sensing 11(11): Article 1294.

[27] McFadden D. Zarembka P. Conditional logit analysis of qualitative choice behavior. Frontiers in econometrics, 1973, New York: Academic Press 105-142.

[28] McFadden D, Train K. Mixed MNL models for discrete response. Journal of Applied Econometrics, 2000, 15(5): 447-470

[29] Murphy JJ, Allen PG, Stevens TH, Weatherhead D. A meta-analysis of hypothetical bias in stated preference valuation. Environmental and Resource Economics, 2005, 30(3): 313-325

[30] Reynaud A, Nguyen MH, Aubert C. Is there a demand for flood insurance in Vietnam? Results from a choice experiment. Environmental Economics and Policy Studies, 2018, 20(3): 593-617

[31] Smith VL. Optimal insurance coverage. Journal of Political Economy, 1986, 76(1): 68-77

[32] Takahashi, K., Y. Noritomo, M. Ikegami, and D.J. Nathaniel. 2020. Understanding pastoralists’ dynamic insurance uptake decisions: Evidence from four-year panel data in Ethiopia. Food Policy 95: Article 101910.

[33] Tian L, Yao P. Preferences for earthquake insurance in rural China: Factors influencing individuals’ willingness to pay. Natural Hazards, 2015, 79(1): 93-110

[34] Tian T, Zhang J, Jiang W, Tian Y. Quantitative study of crustal structure spatial variation based on gravity anomalies in the Eastern Tibetan Plateau: Implication for earthquake susceptibility assessment. Earth and Space Science, 2020, 7(3): e2019EA000943

[35] Tibet Autonomous Region Provincial Bureau of Statistics. 2021. Tibet statistical yearbook 2021. Beijing: China Statistics Press (in Chinese).

[36] Train KE. Recreation demand models with taste differences over people. Land Economics, 1998, 74(2): 230-239

[37] Train KE. Discrete choice methods with simulation, 2009 2 New York: Cambridge University Press

[38] Train K, Weeks M. Scarpa R, Alberini A. Discrete choice models in preference space and willingness-to-pay space. Applications of simulation methods in environmental and resource economics, 2005, Dordrecht: Springer 1-16.

[39] Wang HH, Liu L, Ortega DL, Jiang Y, Zheng Q. Are smallholder farmers willing to pay for different types of crop insurance? An application of labelled choice experiments to Chinese corn growers. The Geneva Papers on Risk and Insurance—Issues and Practice, 2020, 45(1): 86-110

[40] Wang M, Liao C, Yang S, Zhao W, Liu M, Shi P. Are people willing to buy natural disaster insurance in China? Risk awareness, insurance acceptance, and willingness to pay. Risk Analysis, 2012, 32(10): 1717-1740

[41] Yang, F., J. Tan, and L. Peng. 2020. The effect of risk perception on the willingness to purchase hazard insurance—A case study in the Three Gorges Reservoir region, China. International Journal of Disaster Risk Reduction 45: Article 101379.

[42] Yao T, Thompson LG, Mosbrugger V, Zhang F, Ma Y, Luo T, Xu B, Yang X Third Pole Environment (TPE). Environmental Development, 2012, 3: 52-64

[43] Ye T, Wang M. Exploring risk attitude by a comparative experimental approach and its implication to disaster insurance practice in China. Journal of Risk Research, 2013, 16(7): 861-878

[44] Ye, T., W. Liu, S. Chen, D. Chen, P. Shi, A. Wang, and Y. Li. 2022. Reducing livestock snow disaster risk in the Qinghai–Tibetan Plateau due to warming and socioeconomic development. Science of the Total Environment 813: Article 151869.

[45] Ye T, Wang M, Hu W, Liu Y, Shi P. High liabilities or heavy subsidies: Farmers’ preferences for crop insurance attributes in Hunan. China. China Agricultural Economic Review, 2017, 9(4): 588-606

[46] Yi J, Bryant HL, Richardson JW. How do premium subsidies affect crop insurance demand at different coverage levels: The case of corn. Geneva Papers on Risk and Insurance——Issues and Practice, 2020, 45(1): 5-28

[47] Zou, Q., P. Cui, H. Jiang, J. Wang, C. Li, and B. Zhou. 2020. Analysis of regional river blocking by debris flows in response to climate change. Science of the Total Environment 741: Article 140262.