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Comparative Analysis of Count Data Regression Models on Factors Affecting Infant Mortality

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  • Sibel Selim

    (Manisa Celal Bayar Universitesi, Iktisadi ve Idari Bilimler Fakultesi, Ekonometri Bolumu, Manisa, Turkiye)

  • Gizem Kilinc

    (Manisa, Turkiye)

Abstract

Infant mortality is one of the most important indicators of development and varies across countries. It is thus important to measure this difference statistically and econometrically. This study examines the factors affecting the number of infant deaths among married women between the ages of 15 and 49 through different count data regression models using 2018 Turkey Demographic and Health Survey (TDHS) Syrian Migrant and Turkey Sample data—implemented by the Hacettepe University Institute of Population Studies. Various count data regression models were compared to determine the best analysis method for this study where infant deaths were expressed as a count variable. In the presence of overdispersion, the Negative Binomial regression model was determined to be the most suitable model after comparing the Poisson, Negative Binomial, Zero-Inflated Poisson, and Zero-Inflated Negative Binomial regression models. The results of this study showed that the most important variables affecting infant mortality were the size of the household, the duration of marriage, the desire for more children, single risk factors, the use of birth control methods, the total number of stillbirths, multiple births, and migration numbers.

Suggested Citation

  • Sibel Selim & Gizem Kilinc, 2021. "Comparative Analysis of Count Data Regression Models on Factors Affecting Infant Mortality," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(35), pages 147-179, December.
    • Handle: RePEc:ist:ekoist:v:0:y:2021:i:35:p:147-179
    DOI: 10.26650/ekoist.2021.35.972604
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