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I feel good! Gender differences and reporting heterogeneity in self-assessed health

Author

Listed:
  • Pfarr, Christian
  • Schneider, Brit S.
  • Schneider, Udo
  • Ulrich, Volker

Abstract

For empirical analysis and policy-oriented recommendation, the precise measurement of individual health or well-being is essential. The problem with variables based on questionnaires such as self-assessed health is that the answer may depend on individual reporting behaviour. Moreover, if individual‟s health perception varies with certain attitudes of the respondent reporting heterogenei-ty may lead to index or cut-point shifts of the health distribution, causing estimation problems. We analyse the reporting behaviour of individuals on their self-assessed health status, a five-point categorical variable. We explore observed heterogeneity in categorical variables and include unob-served individual heterogeneity using German panel data. Estimation results show different im-pacts of socioeconomic and health related variables on the five subscales of self-assessed health. Moreover, the answering behaviour varies between female and male respondents, pointing to gen-der specific perception and assessment of diseases. Reporting behaviour on self-assessed health questions in surveys is problematic due to a possible heterogeneity. Hence, in case of reporting heterogeneity, using self-assessed measures in empirical studies may be misleading or at least ambiguous.

Suggested Citation

  • Pfarr, Christian & Schneider, Brit S. & Schneider, Udo & Ulrich, Volker, 2010. "I feel good! Gender differences and reporting heterogeneity in self-assessed health," MPRA Paper 24231, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:24231
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    References listed on IDEAS

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    More about this item

    Keywords

    reporting heterogeneity; generalized ordered probit; self-assessed health;
    All these keywords.

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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