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Challenges in Constructing a Survey-Based Well-Being Index

Author

Listed:
  • Daniel J. Benjamin
  • Kristen B. Cooper
  • Ori Heffetz
  • Miles Kimball

Abstract

How should a survey-based measure of well-being be implemented? How could it be constructed in a systematic and politically neutral way? These questions should be approached by economists with the same level of care that has been taken in the theoretical and practical development of GDP. We focus on two essential requirements for implementation: formulating a list of different aspects of well-being that is theoretically valid and can be measured accurately via surveys, and choosing and interpreting the survey response scales. We discuss progress to date on these issues, remaining challenges, and some possible approaches to overcoming them.

Suggested Citation

  • Daniel J. Benjamin & Kristen B. Cooper & Ori Heffetz & Miles Kimball, 2017. "Challenges in Constructing a Survey-Based Well-Being Index," American Economic Review, American Economic Association, vol. 107(5), pages 81-85, May.
    • Handle: RePEc:aea:aecrev:v:107:y:2017:i:5:p:81-85
    Note: DOI: 10.1257/aer.p20171099
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    References listed on IDEAS

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    1. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74(2), pages 132-132.
    2. Fleurbaey, Marc & Blanchet, Didier, 2013. "Beyond GDP: Measuring Welfare and Assessing Sustainability," OUP Catalogue, Oxford University Press, number 9780199767199.
    3. Angus Deaton & Arthur A. Stone, 2016. "Understanding context effects for a measure of life evaluation: how responses matter," Oxford Economic Papers, Oxford University Press, vol. 68(4), pages 861-870.
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    Cited by:

    1. Adam Okulicz-Kozaryn & Rubia R. Valente, 2019. "Livability and Subjective Well-Being Across European Cities," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 14(1), pages 197-220, March.
    2. Rosalia Castellano & Gabriella Bernardo & Gennaro Punzo, 2023. "Well-being in OECD countries: an assessment of technical and social efficiency using data envelopment analysis," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 70(2), pages 141-176, June.
    3. Clemens Hetschko & Louisa von Reumont & Ronnie Schöb, 2019. "Embedding as a pitfall for survey‐based welfare indicators: evidence from an experiment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 517-539, February.
    4. Daniel J. Benjamin & Kristen Cooper & Ori Heffetz & Miles S. Kimball, 2023. "From Happiness Data to Economic Conclusions," NBER Working Papers 31727, National Bureau of Economic Research, Inc.
    5. Daniel J. Benjamin & Kristen Cooper & Ori Heffetz & Miles S. Kimball & Jiannan Zhou, 2023. "Adjusting for Scale-Use Heterogeneity in Self-Reported Well-Being," NBER Working Papers 31728, National Bureau of Economic Research, Inc.
    6. Akter, Sonia & Fauzia, Talitha & Pundit, Madhavi & Schroder, Marcel, 2022. "A Gender-Specific Earthquake Recovery Assessment Using Administrative and Satellite Data: The Case of Indonesia’s 2016 Aceh Earthquake," ADB Economics Working Paper Series 674, Asian Development Bank, revised 07 Feb 2023.
    7. David Steinmayr & Doris Weichselbaumer & Rudolf Winter-Ebmer, 2020. "Gender Differences in Active Ageing: Findings from a New Individual-Level Index for European Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(2), pages 691-721, September.
    8. Adi Arad & Steven Laufer & Zohar Or Sharvit & Yaniv Reingewertz & Michael Hartal, 2024. "Preference heterogeneity over the aspects of individual well-being: towards the construction of an applied well-being index," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 63(2), pages 437-468, September.
    9. Ge, Shuang & Liu, Xielin, 2022. "The role of knowledge creation, absorption and acquisition in determining national competitive advantage," Technovation, Elsevier, vol. 112(C).

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    More about this item

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being

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