Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Estimating what US residential customers are willing to pay for resilience to large electricity outages of long duration


Climate-induced extreme weather events, as well as other natural and human-caused disasters, have the potential to increase the duration and frequency of large power outages. Resilience, in the form of supplying a small amount of power to homes and communities, can mitigate outage consequences by sustaining critical electricity-dependent services. Public decisions about investing in resilience depend, in part, on how much residential customers value those critical services. Here we develop a method to estimate residential willingness-to-pay for back-up electricity services in the event of a large 10-day blackout during very cold winter weather, and then survey a sample of 483 residential customers across northeast USA using that method. Respondents were willing to pay US$1.7–2.3 kWh–1 to sustain private demands and US$19–29 day–1 to support their communities. Previous experience with long-duration outages and the framing of the cause of the outage (natural or human-caused) did not affect willingness-to-pay.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Overview of the web-based elicitation survey.
Fig. 2: Distributions of the respondents’ preferences to sustain their critical electricity-dependent demands and their preference uncertainty, separated by their previous longest outage experiences.
Fig. 3: Heat-map representation of the respondents’ WTP-per-day preferences for enhanced grid resilience.
Fig. 4: Pyramid diagram showing the respondents’ preferences against LLD outages that occurred through two different causes.

Data availability

The data that is directly used for the statistical tests in the results section and for generating Figs. 24 and Tables 13 can be found in Supplementary Data. The complete datasets that support the plots and other findings of this study are available in the Open Science Framework project page,

Code availability

The R code file written for the data analysis is available in the Open Science Framework project page,


  1. National Academies of Sciences, Engineering, and Medicine Enhancing the Resilience of the Nation’s Electricity System (National Academies Press, 2017).

  2. Narayanan, A. & Morgan, M. G. Sustaining critical social services during extended regional power blackouts. Risk Anal. 32, 1183–1193 (2012).

    Article  Google Scholar 

  3. Baik, S., Morgan, M. G. & Davis, A. L. Providing limited local electric service during a major grid outage: a first assessment based on customer willingness to pay. Risk Anal. 38, 272–282 (2018).

    Article  Google Scholar 

  4. Sullivan, M., Schellenberg, J. & Blundell, M. Updated Value of Service Reliability Estimates for Electric Utility Customers in the United States Report LBNL-6941E (Lawrence Berkeley National Lab, 2015).

  5. Sullivan, M. J. & Keane, D. M. Outage Cost Estimation Guidebook Report EPRI-TR-106082 (Electric Power Research Inst., 1995).

  6. Baik, S., Davis, A. L. & Morgan, M. G. Assessing the cost of large-scale power outages to residential customers. Risk Anal. 38, 283–296 (2018).

    Article  Google Scholar 

  7. Schulze, W., McClelland, G., Waldman, D. & Lazo, J. in The Contingent Valuation of Environmental Resources: Methodological Issues and Research Needs (eds Bjornstad, D. J. & Kahn, J. R.) 97–116 (Edward Elgar, 1996).

  8. Fischhoff, B. (1991). Value elicitation: is there anything in there? Am. Psychol. 46, 835–847 (1991).

    Article  Google Scholar 

  9. Mishra, S. & Suar, D. Do lessons people learn determine disaster cognition and preparedness? Psychol. Dev. Societies 19, 143–159 (2007).

    Article  Google Scholar 

  10. Mulilis, J. P., Duval, T. S. & Rogers, R. The effect of a swarm of local tornados on tornado preparedness: a quasi-comparable cohort investigation. J. Appl. Soc. Psychol. 33, 1716–1725 (2003).

    Article  Google Scholar 

  11. Heller, K., Alexander, D. B., Gatz, M., Knight, B. G. & Rose, T. Social and personal factors as predictors of earthquake preparation: the role of support provision, network discussion, negative affect, age, and education. J. Appl. Soc. Psychol. 35, 399–422 (2005).

    Article  Google Scholar 

  12. Sorensen, J. H. Knowing how to behave under the threat of disaster: can it be explained? Environ. Behav. 15, 438–457 (1983).

    Article  Google Scholar 

  13. Palm, R. & Hodgson, M. Earthquake insurance: mandated disclosure and homeowner response in California. Ann. Assoc. Am. Geogr. 82, 207–222 (1992).

    Article  Google Scholar 

  14. Paton, D., Smith, L. & Johnston, D. M. Volcanic hazards: risk perception and preparedness. New Zeal. J. Psychol. 29, 86–91 (2000).

    Google Scholar 

  15. Paton, D. & Johnston, D. Disasters and communities: vulnerability, resilience and preparedness. Disaster Prev. Manag. 10, 270–277 (2001).

    Article  Google Scholar 

  16. Zhai, G., Sato, T., Fukuzono, T., Ikeda, S. & Yoshida, K. Willingness to pay for flood risk reduction and its determinants in Japan. J. Am. Water Resour. 42, 927–940 (2006).

    Article  Google Scholar 

  17. Hoffmann, R. & Muttarak, R. Learn from the past, prepare for the future: impacts of education and experience on disaster preparedness in the Philippines and Thailand. World Dev. 96, 32–51 (2017).

    Article  Google Scholar 

  18. Schultz, P. W. in New Tools for Environmental Protection: Education, Information, and Voluntary Measures (eds Dietz, T. and Stern, P.C.) 67–82 (National Academies Press, 2002).

  19. Monroe, M. C., Pennisi, L., McCaffrey, S. & Mileti, D. Social Science to Improve Fuels Management: A Synthesis of Research Relevant to Communicating with Homeowners About Fuels Management (US Department of Agriculture, 2006).

  20. Jepson, R. G., Harris, F. M., Platt, S. & Tannahill, C. The effectiveness of interventions to change six health behaviours: a review of reviews. BMC Public Health 10, 538–553 (2010).

    Article  Google Scholar 

  21. Murphy, J. J., Allen, P. G., Stevens, T. H. & Weatherhead, D. A meta-analysis of hypothetical bias in stated preference valuation. Environ. Resour. Econ. 30, 313–325 (2005).

    Article  Google Scholar 

  22. Schmidt, J. & Bijmolt, T. H. Accurately measuring willingness to pay for consumer goods: a meta-analysis of the hypothetical bias. J. Acad. Mark. Sci. 0, (2019).

  23. Curtis, J. A. & McConnell, K. E. The citizen versus consumer hypothesis: evidence from a contingent valuation survey. Aust. J. Agr. Resour. Econ. 46, 69–83 (2002).

    Article  Google Scholar 

  24. Camerer, C. F. & Fehr, E. in Foundations of Human Sociality: Economic Experiments and Ethnographic Evidence from Fifteen Small-Scale Societies (eds Henrich, J. et al.) 55–95 (Oxford Univ. Press, 2004).

  25. Levitt, S. D. & List, J. A. What do laboratory experiments measuring social preferences reveal about the real world? J. Econ. Perspect. 21, 153–174 (2007).

    Article  Google Scholar 

  26. Donahue, A. K. Risky business: willingness to pay for disaster preparedness. Public Budg. Finance 34, 100–119 (2014).

    Article  Google Scholar 

  27. Fischhoff, B., Slovic, P., Lichtenstein, S., Read, S. & Combs, B. How safe is safe enough? A psychometric study of attitudes towards technological risks and benefits. Policy Sci. 9, 127–152 (1978).

    Article  Google Scholar 

  28. Slovic, P., Fischhoff, B. & Lichtenstein, S. Behavioral decision theory perspectives on risk and safety. Acta Psychol. 56, 183–203 (1984).

    Article  Google Scholar 

  29. Englander, T., Farago, K., Slovic, P. & Fischhoff, B. A comparative analysis of risk perception in Hungary and the United States. Soc. Behav. 1, 55–66 (1986).

    Google Scholar 

  30. Brun, W. Cognitive components in risk perception: natural versus manmade risks. J. Behav. Decis. Making 5, 117–132 (1992).

    Article  Google Scholar 

  31. Dziegielewski, S. F. & Sumner, K. An examination of the American response to terrorism: handling the aftermath through crisis intervention. Brief Treat. Crisis Intervention 2, 287–300 (2002).

    Article  Google Scholar 

  32. Beutler, L. E., Reyes, G., Franco, Z. & Housley, J. in Psychology of Terrorism (eds Bongar, B., Brown, L. M., Beutler, L. E., Breckenridge, J. N. & Zimbardo P. G.) 32–55 (Oxford Univ. Press, 2007).

  33. Renn, O. & Rohrmann, B. Cross-Cultural Risk Perception: A Survey of Empirical Studies (Springer Science & Business Media, 2000).

  34. Baik, S., Davis, A. L. & Morgan, M. G. Illustration of a method to incorporate preference uncertainty in benefit–cost analysis. Risk Anal. 39, 2359–2368 (2019).

    Article  Google Scholar 

  35. Sullivan, M. & Schellenberg, J. Downtown San Francisco Long Duration Outage Cost Study (Freeman, Sullivan & Company, 2013).

  36. Corwin, J. L. & Miles, W. T. Impact Assessment of the 1977 New York City Blackout. Final Report HCP/T5103-01. (System Control, Inc., 1978).

  37. Valuation of Energy Security for The United States (US Department of Energy, 2017);

  38. Tversky, A. & Kahneman, D. Judgment under uncertainty: heuristics and biases. Science 185, 1124–1131 (1974).

    Article  Google Scholar 

  39. Desvousges, W. H, Reed Johnson, F, Dunford, R. W, Nicole, W. K. & Boyle, K. J. in Contingent Valuation: A Critical Assessment (ed. Hausmann, J. A.) 91–164 (Emerald Group, 1993).

  40. Hensher, D. A. Hypothetical bias, choice experiments and willingness to pay. Transp. Res. B 44, 735–752 (2010).

    Article  Google Scholar 

  41. Morrissey, K., Plater, A. & Dean, M. The cost of electric power outages in the residential sector: a willingness to pay approach. Appl. Energ. 212, 141–150 (2018).

    Article  Google Scholar 

  42. Bhatia, S. Associations and the accumulation of preference. Psychol. Rev. 120, 522–543 (2013).

    Article  Google Scholar 

  43. Payne, J. W., Bettman, J. R. & Johnson, E. J. Adaptive strategy selection in decision making. J. Exp. Psychol. Learn. 14, 534–552 (1988).

    Article  Google Scholar 

  44. Payne, J. W., Bettman, J. R., Schkade, D. A., Schwarz, N. & Gregory, R. in Elicitation of Preferences (eds Fischhoff, B. & Manski, C. F.) 243–275 (Springer, 1999).

  45. Luce, R. D. & Suppes, P. in Handbook of Mathematical Psychology Vol. 3 (eds Luce, R. D. et al.) 249–409 (Wiley, 1965).

  46. Davis-Stober, C. P. Analysis of multinomial models under inequality constraints: applications to measurement theory. J. Math. Psychol. 53, 1–13 (2009).

    MathSciNet  MATH  Article  Google Scholar 

  47. De La Maza, C., Davis, A., Gonzalez, C. & Azevedo, I. A graph-based model to discover preference structure from choice data. In Proc. 40th Annual Meeting of the Cognitive Science Society 25–28 (Cognitive Science Society, 2018).

  48. Boxall, P. C., Adamowicz, W. L., Swait, J., Williams, M. & Louviere, J. A comparison of stated preference methods for environmental valuation. Ecol. Econ. 18, 243–253 (1996).

    Article  Google Scholar 

  49. Mogas, J., Riera, P. & Bennett, J. A comparison of contingent valuation and choice modeling with second-order interactions. J. For. Econ. 12, 5–30 (2006).

    Google Scholar 

  50. Foster, V. & Mourato, S. Elicitation format and sensitivity to scope. Environ. Resour. Econ. 24, 141–160 (2003).

    Article  Google Scholar 

  51. Jin, J., Wang, Z. & Ran, S. Comparison of contingent valuation and choice experiment in solid waste management programs in Macao. Ecol. Econ. 57, 430–441 (2006).

    Article  Google Scholar 

  52. Arrow, K. et al. Report of the NOAA panel on contingent valuation. Fed. Register 58, 4601–4614 (1993).

    Google Scholar 

  53. List, J. A. & Gallet, C. A. What experimental protocol influence disparities between actual and hypothetical stated values? Environ. Resour. Econ. 20, 241–254 (2001).

    Article  Google Scholar 

  54. Cubitt, R. P., Navarro-Martinez, D. & Starmer, C. On preference imprecision. J Risk Uncertainty. 50, 1–34 (2015).

    Article  Google Scholar 

  55. Håkansson, C. A new valuation question: analysis of and insights from interval open-ended data in contingent valuation. Environ. Resour. Econ. 39, 175–188 (2008).

    Article  Google Scholar 

  56. Bayrak, O. & Kriström, B. Is there a valuation gap? The case of interval valuations. Econ. Bull. 36, 218–236 (2015).

    Google Scholar 

  57. Dillman, D. A. & Smyth, J. D. Design effects in the transition to web-based surveys. Am. J. Prev. Med. 32, S90–S96 (2007).

    Article  Google Scholar 

  58. Baik, S., Sirinterlikci, S., Park, J. W., Davis, A. & Morgan, M. G. in Frontiers in the Economics of Widespread, Long-duration Power Interruptions: Proceedings from an Expert Workshop (eds Larsen, P. H. et al.) Topic II (Lawrence Berkeley National Lab, 2019).

  59. Messer, B. L. & Dillman, D. A. Surveying the general public over the internet using address-based sampling and mail contact procedures. Public Opin. Quart. 75, 429–457 (2011).

    Article  Google Scholar 

  60. S. Siegel, Nonparametric Statistics for the Behavioral Sciences (McGraw-Hill, 1956).

Download references


The work was funded by the Center for Climate and Energy Decision Making through a cooperative agreement between the National Science Foundation (SES-0949710 and SES-1463492) and Carnegie Mellon University, Transmission Planning and Technical Assistance Division of the US Department of Energy’s Office of Electricity Delivery and Energy Reliability under Lawrence Berkeley National Laboratory contract no. DE-AC02–05CH1123, and by the Thomas Lord Chair, the Hamerschlag Chair and other academic funds from Carnegie Mellon University.

Author information

Authors and Affiliations



M.G.M. and A.L.D. secured project funding; S.B., A.L.D. and M.G.M. designed the study; J.P. and S.S. developed and demonstrated the online survey framework; S.B., J.P. and S.S. conducted pilot tests; S.B. and J.P. conducted online surveys; S.B. analysed the data and created the figures and tables with iterative feedback from A.L.D. and M.G.M.; S.B., A.L.D. and M.G.M. drafted and edited the manuscript.

Corresponding author

Correspondence to Sunhee Baik.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2, Tables 1–10, Discussion, Method, Notes 1 and 2 and refs. 1–11.

Reporting Summary

Supplementary Data

The data that are directly used for the statistical tests in the results section and were used to generate Figs. 24 and Tables 13 can be found in this file.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Baik, S., Davis, A.L., Park, J.W. et al. Estimating what US residential customers are willing to pay for resilience to large electricity outages of long duration. Nat Energy 5, 250–258 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing