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"If You Were Me": Proxy Respondents' Biases in Population Health Surveys

Author

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  • Bérengère Davin

    (SESSTIM - U1252 INSERM - Aix Marseille Univ - UMR 259 IRD - Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale - IRD - Institut de Recherche pour le Développement - AMU - Aix Marseille Université - INSERM - Institut National de la Santé et de la Recherche Médicale)

  • Xavier Joutard

    (LEST - Laboratoire d'Economie et de Sociologie du Travail - AMU - Aix Marseille Université - CNRS - Centre National de la Recherche Scientifique, OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po)

  • Alain Paraponaris

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

Proxy respondents are widely used in population health surveys to maximize response rates. When surveys target frail elderly, the measurement error is expected to be smaller than selection or participation biases. However, in the literature on elderly needs for care, proxy use is most often considered with a dummy variable in which endogeneity with subjects' health status is rarely scrutinised in a robust way. Pitfalls of this choice extend beyond methodological issues. Indeed, the mismeasurement of needs for care with daily activities might lead to irrelevant social policies or to private initiatives that try to address those needs. This paper proposes a comprehensive and tractable strategy supported by various robustness checks to cope with the suspected endogeneity of proxy use to the unobserved health status of subjects in reports of needs for care with activities of daily living. Proxy respondents' subjectivity is found to inflate the needs of the elderly who are replaced or assisted in answering the questionnaire and to deflate the probability of unmet or undermet needs.

Suggested Citation

  • Bérengère Davin & Xavier Joutard & Alain Paraponaris, 2019. ""If You Were Me": Proxy Respondents' Biases in Population Health Surveys," Sciences Po Economics Publications (main) halshs-02036434, HAL.
  • Handle: RePEc:hal:spmain:halshs-02036434
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-02036434v1
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    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination

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