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Counting unreported abortions: A binomial-thinned zero-inflated Poisson model

Author

Listed:
  • Vidhura Tennekoon

    (Indiana University – Purdue University Indianapolis)

Abstract

Background: Self-reported counts of intentional abortions in demographic surveys are significantly lower than the actual counts. To estimate the extent of misreporting, previous research has required either a gold standard or a validation sample. However, in most cases, a gold standard or a validation sample is not available. Objective: Our main intention here is to show that a researcher has an alternative tool to estimate the extent of underreporting in a given dataset, particularly when neither a valid gold standard nor a validation sample is available. Methods: We adopt a binomial-thinned zero-inflated Poisson model and apply it to a sample dataset, the National Survey of Family Growth (NSFG), for which an alternative estimate of the average reporting rate (38%) is available. We show how this model could be used to estimate the reporting probabilities of intentional abortions by each individual in addition to the overall average reporting rate. Results: Our model estimates the average reporting rate in the NSFG during 2006‒2013 as 35.3% (SE 8.2%). Individual reporting probabilities vary significantly. Conclusions: Our estimate of the average reporting rate of the dataset used is qualitatively and statistically similar to the available alternative estimate. Contribution: The model we propose can be used to predict the reporting probability of abortions of each individual, which in turn can be used to correct the bias due to underreporting in any model in which the number of abortions is used as the dependent variable or as one of the covariates.

Suggested Citation

  • Vidhura Tennekoon, 2017. "Counting unreported abortions: A binomial-thinned zero-inflated Poisson model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 36(2), pages 41-72.
    • Handle: RePEc:dem:demres:v:36:y:2017:i:2
    DOI: 10.4054/DemRes.2017.36.2
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    References listed on IDEAS

    as
    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273.
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    Cited by:

    1. Katherine I. Tierney, 2019. "Abortion Underreporting in Add Health: Findings and Implications," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 38(3), pages 417-428, June.
    2. Keith Kranker & Sarah Bardin & Dara Lee Luca & So O’Neil, 2020. "Estimating the incidence of unintended births and pregnancies at the sub-state level to inform program design," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
    3. Isaac Maddow-Zimet & Laura D. Lindberg & Kate Castle, 2021. "State-Level Variation in Abortion Stigma and Women and Men’s Abortion Underreporting in the USA," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 40(6), pages 1149-1161, December.
    4. Laura Lindberg & Kathryn Kost & Isaac Maddow-Zimet & Sheila Desai & Mia Zolna, 2020. "Abortion Reporting in the United States: An Assessment of Three National Fertility Surveys," Demography, Springer;Population Association of America (PAA), vol. 57(3), pages 899-925, June.

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    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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