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Estimating Family Preference for Home Elderly-care Services: Large-scale Conjoint Survey Experiment in Japan

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  • KANEKO Shinji
  • KAWATA Keisuke
  • YIN Ting

Abstract

Elderly care services have attracted attention in many aging countries. However, the relative importance of service attributes has not been well evaluated. This paper estimates the consumer surplus of multi-attribute elderly care services at home, which allows us to evaluate them from the social welfare perspective. We propose a new empirical approach combining a fully-randomized conjoint survey experiment and the non-parametric rational choice model. Our survey is for Japanese respondents aged 40-59 and shows that expansions of service contents significantly increases the consumer surplus. Additionally, the surplus gain is completely heterogeneous; while many respondents have no surplus gains, the average surpluses are of significant size among remaining respondents.

Suggested Citation

  • KANEKO Shinji & KAWATA Keisuke & YIN Ting, 2019. "Estimating Family Preference for Home Elderly-care Services: Large-scale Conjoint Survey Experiment in Japan," Discussion papers 19092, Research Institute of Economy, Trade and Industry (RIETI).
    • Handle: RePEc:eti:dpaper:19092
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    References listed on IDEAS

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    1. Flynn, Terry N. & Louviere, Jordan J. & Peters, Tim J. & Coast, Joanna, 2007. "Best-worst scaling: What it can do for health care research and how to do it," Journal of Health Economics, Elsevier, vol. 26(1), pages 171-189, January.
    2. Jerry A. Hausman & Whitney K. Newey, 2016. "Individual Heterogeneity and Average Welfare," Econometrica, Econometric Society, vol. 84, pages 1225-1248, May.
    3. Jerry A. Hausman & Whitney K. Newey, 2017. "Nonparametric Welfare Analysis," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 521-546, September.
    4. Hainmueller, Jens & Hopkins, Daniel J. & Yamamoto, Teppei, 2014. "Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments," Political Analysis, Cambridge University Press, vol. 22(1), pages 1-30, January.
    5. Debopam Bhattacharya, 2015. "Nonparametric Welfare Analysis for Discrete Choice," Econometrica, Econometric Society, vol. 83, pages 617-649, March.
    6. Office of Health Economics, 2007. "The Economics of Health Care," For School 001490, Office of Health Economics.
    7. KANEKO Shinji & KAWATA Keisuke & YIN Ting, 2018. "Estimating Family Preferences for Elder-care Services: A conjoint-survey experiment in Japan," Discussion papers 18082, Research Institute of Economy, Trade and Industry (RIETI).
    8. Debopam Bhattacharya, 2018. "Empirical welfare analysis for discrete choice: Some general results," Quantitative Economics, Econometric Society, vol. 9(2), pages 571-615, July.
    9. Colin Green & Karen Gerard, 2009. "Exploring the social value of health‐care interventions: a stated preference discrete choice experiment," Health Economics, John Wiley & Sons, Ltd., vol. 18(8), pages 951-976, August.
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