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Risky Sexual Behaviours: Biological Markers and Self‐reported Data

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  • Lucia Corno
  • Áureo de Paula

Abstract

High‐risk sexual behaviours are generally unobserved and difficult to identify. In this paper, we investigate the accuracy of two risky‐behaviour measures: biomarkers for sexually transmitted infections (STIs) and self‐reported data. We build an epidemiological model to assess the relative performance of biomarkers versus self‐reported data. We then suggest an econometric strategy that combines both types of measures to estimate actual unobserved risky sexual behaviours. Using data from the Demographic and Health Survey in 28 countries, we calibrate the model and provide conditions under which self‐reported data are a better proxy for risky sexual behaviours than biomarkers. In countries with low STI prevalence, biomarkers have a higher probability of misclassification than self‐reported answers. We apply our econometric strategy to the data and show that the probability of actual risky behaviour is much higher than the probability of self‐reported risky behaviour and of testing positive for an STI.

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  • Lucia Corno & Áureo de Paula, 2019. "Risky Sexual Behaviours: Biological Markers and Self‐reported Data," Economica, London School of Economics and Political Science, vol. 86(342), pages 229-261, April.
    • Handle: RePEc:bla:econom:v:86:y:2019:i:342:p:229-261
    DOI: 10.1111/ecca.12286
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    Cited by:

    1. Oriana Bandiera & Niklas Buehren & Robin Burgess & Markus Goldstein & Selim Gulesci & Imran Rasul & Munshi Sulaiman, 2020. "Women's Empowerment in Action: Evidence from a Randomized Control Trial in Africa," American Economic Journal: Applied Economics, American Economic Association, vol. 12(1), pages 210-259, January.
    2. Martina Björkman Nyqvist & Lucia Corno & Damien de Walque & Jakob Svensson, 2018. "Incentivizing Safer Sexual Behavior: Evidence from a Lottery Experiment on HIV Prevention," American Economic Journal: Applied Economics, American Economic Association, vol. 10(3), pages 287-314, July.
    3. Wilson, Nicholas, 2016. "Antiretroviral therapy and demand for HIV testing: Evidence from Zambia," Economics & Human Biology, Elsevier, vol. 21(C), pages 221-240.
    4. Kibrom A. Abay & Leah E. M. Bevis & Christopher B. Barrett, 2021. "Measurement Error Mechanisms Matter: Agricultural Intensification with Farmer Misperceptions and Misreporting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 498-522, March.
    5. Gong, Erick & de Walque, Damien & Dow, William H., 2019. "Coping with risk: Negative shocks, transactional sex, and the limitations of conditional cash transfers," Journal of Health Economics, Elsevier, vol. 67(C).
    6. Lépine, Aurélia & Treibich, Carole, 2020. "Risk aversion and HIV/AIDS: Evidence from Senegalese female sex workers," Social Science & Medicine, Elsevier, vol. 256(C).
    7. David Mmopelwa & Oliver Morrissey & Trudy Owens, 2021. "HIV/AIDS and sexual behaviour in Botswana," Discussion Papers 2021-03, University of Nottingham, CREDIT.

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    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development

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