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


  • Vidhura Tennekoon

    (Indiana University – Purdue University Indianapolis)


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

    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273.
    2. Papadopoulos, Georgios & Santos Silva, Joao M C, 2008. "Identification issues in models for underreported counts," Economics Discussion Papers 3552, University of Essex, Department of Economics.
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    5. Adimora, A.A. & Schoenbach, V.J. & Doherty, I.A., 2007. "Concurrent sexual partnerships among men in the United States," American Journal of Public Health, American Public Health Association, vol. 97(12), pages 2230-2237.
    6. Daniel Goodkind, 2011. "Child Underreporting, Fertility, and Sex Ratio Imbalance in China," Demography, Springer;Population Association of America (PAA), vol. 48(1), pages 291-316, February.
    7. Georgios Papadopoulos, 2014. "Immigration status and property crime: an application of estimators for underreported outcomes," IZA Journal of Migration and Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 3(1), pages 1-30, December.
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    9. Jagannathan, R., 2001. "Relying on surveys to understand abortion behavior: Some cautionary evidence," American Journal of Public Health, American Public Health Association, vol. 91(11), pages 1825-1831.
    10. Athena Tapales & Lawrence Finer, 2015. "Unintended pregnancy and the changing demography of American women, 1987-2008," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 33(45), pages 1257-1270.
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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.

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    JEL classification:

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


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