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Rural–Urban Child Height for Age Trajectories and Their Heterogeneous Determinants in Four Developing Countries

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Abstract

The large literature on health differentials between rural and urban areas relies almost exclusively on cross-sectional data. Bringing together the demographic literature on area-level health inequalities with the bio-physiological literature on children’s catch-up growth over time, this paper uses panel data to investigate the stability and origins of rural–urban health differentials. Using data from the Young Lives longitudinal study of child poverty, I present evidence of large level differences but similar trends in rural versus urban children’s height for age in four developing countries. Further, observable characteristics of children’s environment such as their household wealth, mother’s education, and epidemiological environment explain these differentials in most contexts. In Peru, where they do not, children’s birthweight and mothers’ health and other characteristics suggest that initial endowments—even before birth—may play an important role in explaining "residual" rural–urban child height inequalities. These latter results imply that prioritizing maternal nutrition and health is essential—particularly where rural–urban height inequalities are large. Interventions to reduce area-level health inequalities should begin even before birth.

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Notes

  1. The initial older cohort sample was significantly smaller in Peru than it was in the three other countries. While the number of children enrolled in wave 1 in Ethiopia, India, and Vietnam, was well over 900 in each country, it was 714 in Peru.

  2. The additional regression results reported in Peru are based on a slightly smaller sample for which none of the "additional" covariates were missing (n = 1498).

  3. I investigated father’s education in early regression models but never found it to be statistically significantly predictive of child height for age and thus do not include it in the models presented here.

  4. Stunting is defined as more than two standard deviations below the mean of the WHO reference population.

  5. The traditional proxy for socioeconomic status in survey data from developing countries is using either a linear or principal components index using a variety of dummy variables reflecting household assets, quality of housing, and access to amenities, including but not limited to number of people per room, consumer durables such as radio, fridge, TV, bike, motor vehicle, etc., whether the dwelling has electricity, cement walls, and a sturdy roof, as well as the material of the floor, the main source of drinking water, the type of toilet facility, and the type of fuel used for cooking.

  6. It is important to note that the level of aggregation of this community-level information is different in each Young Lives country. While there were 20 sentinel sites selected for sampling purposes in each country, the community data were collected at various levels of detail in these sentinel sites across the countries. The number of "communities" for which there is information on health facilities is 22 in Ethiopia, 98 in India, 82 in Peru, and 31 in Vietnam.

  7. Birthweight is missing for 81 and 56 % of Young Lives children in Ethiopia and India, respectively; it is missing only for 12 % of the children in Peru, another reason that only data from this country are used in these further analyses.

  8. It is not entirely clear how to interpret the association between the number of antenatal care visits and birth outcomes; a large number of visits could actually indicate a health problem. For this reason, the indicator I use is whether the woman did not have any antenatal care visits—rather than their number—as a dichotomous yes (1) or no (0) variable.

  9. Proportion stunted over time displays very similar—inverse—patterns; see Fig. 4 in the Appendix section. The confidence intervals on stunting are larger due to the reduction in information resulting from using a dichotomous, rather than a continuous, variable.

  10. In mothers with very high BMI (>25kg/m2), however, the coefficient remains statistically significant only at the 10 % level. Obesity has been increasingly prevalent in urban areas in Peru, and the health condition may be reducing the net health "benefits" associated with living in an urban area. Further, the consumer durables index remains statistically significant and positive in this model; it appears that among mothers with high BMI, higher socioeconomic status is still better for child health.

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Acknowledgements

This work was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health (Award Numbers P32CHD04879 and T32HD007163 at Princeton University, and Award Number P2CHD058486 at the Columbia Population Research Center). The content is solely the responsibility of the author and does not necessarily represent the official views of the Eunice Kennedy Shriver National Institute of Child Health & Human Development or the National Institutes of Health. Many thanks to Janet Currie, Doug Massey, Noreen Goldman, and Germán Rodriguez.

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  1. Columbia Population Research Center, Columbia University School of Social Work, 1255 Amsterdam Ave., New York, NY, 10027, USA

    Laura B. Nolan

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Appendix

Appendix

See Figs. 4, 5, and 6

Fig. 4
figure 4

Proportion of Young Lives children who are stunted by country and cohort over time

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Fig. 5
figure 5

Rural/urban height for age z-score trajectories among children in the poorest wealth index quintile

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Fig. 6
figure 6

Rural/urban height for age z-score trajectories among children in the richest wealth index quintile

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Nolan, L.B. Rural–Urban Child Height for Age Trajectories and Their Heterogeneous Determinants in Four Developing Countries. Popul Res Policy Rev 35, 599–629 (2016). https://doi.org/10.1007/s11113-016-9399-8

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