A choice experiment is used to estimate how Vietnamese households value a flood risk reduction. The empirical analysis is conducted on a sample of households located in the Nghe An Province, one of the provinces which is the most affected by floods in Vietnam. The results reveal that there is a high level of heterogeneity in preferences across households. We compute the willingness to pay (WTP) for a flood risk reduction, and we identify how it relates to different attributes of flood management policies (reduction of economic losses, reduction of human losses, political level in charge of implementing the flood management policy). In particular, the marginal WTP for reducing the flood fatality rate, which can be interpreted as the value of statistical life (VSL), varies from 2 517 million VND (approximately 120,818 USD) to 3 590 million VND (approximately 172,323 USD) depending on the model considered. The VSL represents between 77 and 111 times the annual household average income in our sample, a result in line with previous estimates in similar countries.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
For a country at high flood risk such as the Netherlands, flood defense expenses were in 2005 approximately equal to 1.3 billion euro, representing around 0.25 % of the country-level GDP .
This is also the case for studies assessing the WTP for a catastrophic flood risk insurance such as the recent work conducted in Vietnam by .
The multi-sectoral objectives of the flood policy in Vietnam are summarized in the national strategy for natural disaster prevention, response and mitigation to 2020 which states that the main goals of flood management policy are to “mobilize resources to effectively implement disaster prevention, response and mitigation from now up to 2020 in order to minimize the losses of human life and properties, the damage of natural resources and cultural heritages, and the degradation of environment, contributing significantly to ensure the country sustainable development, national defense and security.”
Largest dikes (category 1) are managed at the state level by the CCFSC, whereas smaller dikes (categories 2 and 3) are operated at provincial, district, or village level. Province CFSCs are, according to the Law, required to store necessary materials for dyke protection such as bags of sand, rock stones, or bamboo trunks. Broadcasting of flood-related information (warnings, evacuation, etc.) is processed by commune CFSCs which usually manage the system of loudspeakers.
While agriculture’s share of GDP has fallen significantly over the years and now accounts for about 20 %, the primary sector still employs more than half of the labor force.
Intentionally, we did not use the term probability which may have been difficult to understand for some of the households we have interviewed. See the Supplementary Material available online.
Damage to home has been also used by  in the context of flood insurance valuation in Netherlands.
The fatality rate due to floods has also been used by  in the Japanese context.
Largest dikes (category 1) are managed at the state level by the CCFSC whereas smaller dikes (categories 2 and 3) are operated at provincial, district or village level. Province CFSCs are, according to the Law, required to store necessary materials for dyke protection such as bags of sand, rock stones, or bamboo trunks. Broadcasting of flood-related information (warnings, evacuation, etc.) is processed by commune CFSCs which usually manage the system of loudspeakers.
The official conversion rate is 1 USD for 20 833 VND on May 14th 2013.
The last section, not discussed here, is a CE for assessing the WTP for a flood insurance, see .
Due to missing answers, flood costs have been computed on a sub-sample of 407 households.
Since respondent characteristics do not vary over the repeated choices of a respondent, they have to be interacted with the ASC or at least with one of the five attributes of flood risk reduction programs.
See Appendix for the definition of each variable.
We have in fact four modalities for this variable namely “more floods,” “less floods,” “the same number of floods,” and “I don’t know if there will be more or less floods.”
In addition to the socio-economic variables presented in Table 4, we have considered some other potential determinants including household’ age, number of children, professional occupation, housing characteristics, risk, and time preferences. These variables were never significant.
Estimates of the main parameters of interest considering log-normal distributions are quite similar.
Bin, O., & Polasky, S. (2004). Effects of flood hazards on property values: evidence before and after hurricane Floyd. Land Economics, 80(4), 490–500.
Botzen, W.J.W., Aerts, J.C.J.H., & Bergh, J.C.J.M. (2013). Individual preferences for reducing flood risk to near zero through elevation. Mitigation and Adaptation Strategies for Global Change, 18(2), 229–244.
Botzen, W.J.W., Aerts, J.C.J.H., & van den Bergh, J. C.J.M. (2009). Willingness of homeowners to mitigate climate risk through insurance. Ecological Economics, 68(8–9), 2265–2277.
Botzen, W.J.W., & van den Bergh, J.C.J.M. (2012). Monetary valuation of insurance against climate change risk. International Economic Review, 53(3), 1005–1026.
Brouwer, R., & Akter, S. (2010). Informing micro insurance contract design to mitigate climate change catastrophe risks using choice experiments. Environmental Hazards, 9(1), 74–88.
Brouwer, R., Akter, S., Brander, L., & Haque, E. (2009). Economic valuation of flood risk exposure and reduction in a severely flood prone developing country. Environment and Development Economics, 14(03), 397–417.
Brouwer, R., Tinh, B.D., Tuan, T.H., Magnussen, K., & Navrud, S. (2014). Modeling demand for catastrophic flood risk insurance in coastal zones in Vietnam using choice experiments. Environment and Development Economics, 19, 228–249. 4.
Bubeck, P., Botzen, W.J.W., & Aerts, J.C.J.H. (2012). A review of risk perceptions and other factors that influence flood mitigation behavior. Risk Analysis, 32(9), 1481–1495.
Cameron, M., Gibson, J., Helmers, K., Lim, S., Tressler, J., & Vaddanak, K. (2010). The value of statistical life and cost-benefit evaluations of landmine clearance in Cambodia. Environment and Development Economics, 15, 395–416. 7.
Daniel, V.E., Florax, R.J.G.M., & Rietveld, P. (2009). Flooding risk and housing values: an economic assessment of environmental hazard. Ecological Economics, 69(2), 355–365.
Eckel, C.C., El-Gamal, M.A., & Wilson, R.K. (2009). Risk loving after the storm: a bayesian-network study of hurricane Katrina evacuees. Journal of Economic Behavior & Organization, 69(2), 110–124.
Fuks, M., & Chatterjee, L. (2008). Estimating the willingness to pay for a flood control project in Brazil using the contingent valuation method. Journal of Urban Planning and Development, 134(1), 42–52.
Greenstone, M., & Jack, B.K. (2015). Envirodevonomics: a research agenda for an emerging field. Journal of Economic Literature, 53(1), 5–42.
Grothmann, T., & Reusswig, F. (2006). People at risk of flooding: why some residents take precautionary action while others do not. Natural Hazards, 38, 101–120.
Jha, A.K., Bloch, R., & Lamond, J. (2010). Cities and flooding. A guide to integrated urban flood risk management for the 21st century. A summary for policy makers. Washington DC: World Bank.
Kousky, C. (2010). Learning from extreme events: risk perceptions after the flood. Land Economics, 86(3), 395–422.
Krinsky, I., & Robb, A.L. (1986). On approximating the statistical properties of elasticities. The Review of Economics and Statistics, 68(4), 715–719.
Kundzewicz, Z.W., & Stakhiv, E.Z. (2010). Are climate models “ready for prime time” in water resources management applications, or is more research needed? Hydrological Sciences Journal, 55(7), 1085–1089.
Michel-Kerjan, E.O., & Kousky, C. (2010). Come rain or shine: evidence on flood insurance purchases in Florida. Journal of Risk and Insurance, 77(2), 369–397.
Navrud, S., Tuan, T.H., & Tinh, B.D. (2012). Estimating the welfare loss to households from natural disasters in developing countries: a contingent valuation study of flooding in Vietnam. Global Health Action, 5, 17609.
Norris, F.H., & Murrell, S.A. (1988). Prior experience as a moderator of disaster impact on anxiety symptoms in older adults. American Journal of Community Psychology, 16, 665–683. doi:10.1007/BF00930020.
Ohno, E., Morisugi, M., Kyophilavong, P., & Sao, H. (2012). Measurement of value of statistical life by evaluating diarrhea mortality risk due to water pollution in Laos and Vietnam. Unpublished manuscript, Faculty of Urban Science, Meijo University, Japan.
Petrolia, D.R., Landry, C.E., & Coble, K.H. (2013). Risk preferences, risk perceptions, and flood insurance. Land Economics, 89(2), 227–245.
Pilarczyk, K.W., & Nuoi, N.S. (2005). Experience and practices on flood control in Vietnam. Water International, 30(1), 114– 122.
Revelt, D., & Train, K. (1998). Mixed logit with repeated choices: households’ choice of appliance efficiency level. Review of Economics and Statistics, 80(4), 647–657.
Reynaud, A., & Nguyen, M.-H. (2013). Monetary valuation of a flood insurance in Vietnam. Unpublished manuscript.
Rijkswaterstaat (2006). Lessons learned from flood defence in the Netherlands. Irrigation and Drainage, 55 (S1), S121–S132.
Salanié, F., & Treich, N. (2009). Regulation in happyville. The Economic Journal, 119(537), 665–679.
Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of Risk and Uncertainty, 1(1), 7–59.
Train, K.E. (2009). Discrete choice methods with simulation. Cambridge: Cambridge University Press.
Vassanadumrongdee, S., & Matsuoka, S. (2005). Risk perceptions and value of a statistical life for air pollution and traffic accidents: evidence from Bangkok, Thailand. Journal of Risk and Uncertainty, 30, 261–287. doi:10.1007/s11166-005-1155-0.
Whittington, D. (2010). What have we learned from 20 years of stated preference research in less-developed countries? Annual Review of Resource Economics, 2(1), 209–236.
WorldBank (2005). Natural disaster hotspots: a global risk analysis. Disaster risk management series No. 5, Hazard Management Unit, The World Bank.
WorldBank (2010). Natural Hazards, UnNatural Disasters. The economics of effective prevention. Washington DC: World Bank.
WorldBank (2010). Weathering the storm: options for disaster risk financing in Vietnam. Washington DC: World Bank.
Zhai, G., Fukuzono, T., & Ikeda, S. (2007). Multi-attribute evaluation of flood management in Japan: a choice experiment approach. Water and Environment Journal, 21(4), 265– 274.
Zhai, G., Sato, T., Fukuzono, T., Ikeda, S., & Yoshida, K. (2006). Willingness to pay for flood risk reduction and its determinant in Japan. Journal of the American Water Resources Association, 42(4), 927–940.
Zhai, G., & Suzuki, T. (2008). Effects of risk representation and scope on willingness to pay for reduced risks: evidence from Tokyo bay, Japan. Risk Analysis, 28(2), 513–522.
The authors would like to thank Mr. Nhung Nguyen from the Vietnamese Ministry of Agriculture and Rural Development for his patience when explaining the organization of flood protection in Vietnam and in the Nghe An Province. We thank Thanh Duy Nguyen for his very efficient assistance during the field work and we are also grateful to Christoph Rheinberger and Henrik Andersson for very useful comments on a preliminary version of the choice experiment survey. This paper has also benefitted from very useful comments at the Cost-Benefit Analysis workshop of the Toulouse School of Economics (TSE) and at the fifth Vietnam Economist Annual Meeting (VEAM) in Hanoi. The usual disclaimer applies. Financial support from the Nghe An province in Vietnam is recognized as part of the VIETFLOOD project.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Appendix: Definition of Variables
Appendix: Definition of Variables
About this article
Cite this article
Reynaud, A., Nguyen, MH. Valuing Flood Risk Reductions. Environ Model Assess 21, 603–617 (2016). https://doi.org/10.1007/s10666-016-9500-z
- Choice experiment
- Environmental valuation
- Flood risk
- Value of statistical life