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Socio-Economic Factors Affecting Early Childhood Health: the Case of Turkey

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Abstract

In this study we examine the association between parents’ socioeconomic status (SES) and childhood health in Turkey, a middle income, developing country using the 2013 round of Demographic Health Survey (DHS) data set. In our investigation, we focus on 7-to-59-month-old children and as a measure of health status, we use the height-for-age z-score, which is the measure of stunting and wasting. In order to overcome the biases with respect to age and gender, we calculate the child’s standardized height measure. Using classical regression techniques, after controlling for the child’s birth order, birth weight, mother’s height, mother’s breastfeeding, nutrition status and pre-school attendance, the impact of parents’ SES on child’s health measures is assessed, and the SES indicators include region of residence, number of household members, father’s presence, parents’ education and work status, and a wealth index based on the household’s asset holdings. Our results indicate that urban residence is a dominant factor that enhances the child’s health status in Turkey: convenient access to health care services as well as better living conditions and infrastructure such as sanitation, access to clean water, availability of electricity, which are more readily available in urban areas, consistently improve the child’s health outcomes, regardless of other SES factors. Additionally, we find that mother’s education level is also crucial in determining the child’s health status: it is more likely that educated mothers have access to information for better health, and they make more efficient use of available health care services for their children.

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Notes

  1. However one must still be cautious to consider the endogeneity in income and health, or the direction of causality: relationship also may run from income/wealth and higher socioeconomic status to better health (Adler et al. 1994; Meer et al. 2003).

  2. Kazeem and Musalia (2017) show that children who are malnourished during early childhood and hence have poorer health status tend to have delayed entry into primary school compared to their peers who are better nourished, for the case of Nigeria.

  3. Nevertheless, Thomas (1994) using cross-country data from the US, Brazil and Ghana, demonstrates that mother’s education has a bigger effect on daugther’s height, while father’s education has a bigger impact on son’s height as a status of health measure.

  4. The sample sizes may get smaller in different estimations because we may not observe some control or dependent variables in the selected sample. It is explained in detail in the following sections.

  5. We do not use standardized anthropometric measures related to weight (such as weight-for-age) due to three reasons: first, higher weight does not imply better health. Second, thinness does not necessarily imply health risk. Third, previous research shows that the models using household or demographic surveys better explain height than weight (Pradhan et al. 2003).

  6. We calculate the height-for age z-scores by using zscore06 command in STATA, version 13.0.

  7. A recent study explores the changes in associations between birthweight and height over different life stages (Krishna et al. 2016).

  8. The p-values for the differences between the means of the variable at different quantiles of child’s standardized height are given in Table 2, columns (5) to (10).

  9. Arimond and Ruel (2004) also show that dietary diversity is strongly associated with child’s height for age z-score after controlling for selected indicators of parents’s socioeconomic status, using DHS data from 11 developing countries.

  10. As of 2013, overall pre-school attendance in Turkey is at 28%, while the pre-school attendance rate for the OECD countries average is 82%. In Turkey, among the 3-year-olds, 7% attend pre-school or creches, while the rate is 36% for 4-year-olds, and 74% for 5-year-olds (Saracoğlu and Karaoğlan 2017).

  11. We also apply quantile regression techniques in order to test the effect of mother’s completed years of schooling on child’s health. Quantile regression results provide robust evidence that mother’s education level has significant impact on children with approximately median value of the standardized height (i.e., between the 40th and 50th quantiles), in other words, on children with average health outcomes. This significant effect is maintained even if we control for the household’s wealth index. Quantile regression results are available upon request.

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Acknowledgements

Authors would like to thank Meltem Dayıoğlu-Tayfur for invaluable suggestions and comments.

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Authors and Affiliations

  1. Department of Economics, Bahçeşehir University, Yıldız Mahallesi, Çırağan Caddesi No.4, 34349, İstanbul, Turkey

    Deniz Karaoğlan

  2. Department of Economics, Middle East Technical University, Üniversiteler Mah. Dumlupınar Bulv. No.1, 06800, Ankara, Turkey

    Dürdane Şirin Saraçoğlu

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  1. Deniz Karaoğlan
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Karaoğlan, D., Saraçoğlu, D.Ş. Socio-Economic Factors Affecting Early Childhood Health: the Case of Turkey. Child Ind Res 11, 1051–1075 (2018). https://doi.org/10.1007/s12187-017-9501-8

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