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Differences in Child Health Across Rural, Urban, and Slum Areas: Evidence From India

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Demography

Abstract

The developing world is rapidly urbanizing, but an understanding of how child health differs across urban and rural areas is lacking. We examine the association between area of residence and child health in India, focusing on composition and selection effects. Simple height-for-age averages show that rural Indian children have the poorest health and urban children have the best, with slum children in between. With wealth or observed health environment held constant, the urban height-for-age advantage disappears, and slum children fare significantly worse than their rural counterparts. Hence, differences in composition across areas mask a substantial negative association between living in slums and height-for-age. This association is more negative for girls than boys. Furthermore, a large number of girls are “missing” in slums; we argue that this implies that the negative association between living in slums and health is even stronger than our estimate. The missing girls also help explain why slum girls appear to have a substantially lower mortality than rural girls, whereas slum boys have a higher mortality risk than rural boys. We estimate that slum conditions (such as overcrowding and open sewers), which the survey does not adequately capture, are associated with 20 % to 37 % of slum children’s stunting risk.

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Notes

  1. Other studies have examined rural-urban differences in specific countries but without allowing for the potential differences between slum and nonslum urban areas. For rural-urban mortality differences in India and Brazil, see Sastry (1997, 2004), Pradhan and Arokiasamy (2010), and Saikia et al. (2013). Studies that focus solely on slum areas include Subbaraman et al. (2012).

  2. Günther and Harttgen (2012) and Fink et al. (2014) used the number of people per room as a proxy when defining slums, which may be more likely to pick up poverty than whether a household lives in a slum.

  3. Other potential selection effects, such as migration, cannot be addressed because of data limitations.

  4. This approach does assume that the underlying health process is the same for all three outcomes and that the models are correctly specified. Pitt (1997) discussed estimating determinants of child health when there is potentially selection in fertility and mortality.

  5. For an early discussion of son preference in India, see Sen (1990). See Pörtner (2016) for references on these different outcomes and an analysis of the relationships among fertility, birth spacing, and the use of sex selection.

  6. Empirical evidence exists for this mechanism in Taiwan, where access to sex-selective abortion reduced relative neonatal female mortality rates for higher-parity births (Lin et al. 2014).

  7. The absence of sisters and/or the expense of sex selection might affect the resources available to boys and therefore their health; even then, boys would be much less affected than girls.

  8. In the quotation, “UT” refers to union territory, which is an administrative unit in India, governed directly by the central government.

  9. In urban areas, PSUs follow the 2001 census enumeration blocks, which contain 150–200 households. See IIPS and Macro International (2007: appendix C) for the selection process.

  10. We do not use the information on diarrhea, cough, and fever because of the noisiness of these self-reported variables.

  11. Following IIPS and Macro International (2007), the estimations use a set of dummy variables to capture parental education: 1–4, 5–7, 8–9, 10–11, and 12 or more years. Father’s height is not included because the information is missing for more than one-half of the children in our sample.

  12. This does assume that the seasonal pattern is similar across states, but the loss of degrees of freedom if we were to interact month of survey with state would be large, and we would fail to capture the seasonal variation in health because no state survey covered the entire year.

  13. See http://dhsprogram.com/topics/wealth-index/ for an in-depth discussion of the wealth index.

  14. In addition to water piped into the dwelling, yard, or plot, an improved drinking water source includes water available from a public tap or standpipe, a tube well or borehole, a protected dug well, a protected spring, rainwater, and bottled water (IIPS and Macro International 2007). We also tried splitting into four main safe water sources, but none were statistically significantly different from unsafe/unimproved water sources. All showed coefficients close to 0, and the changes in the association between area dummy variable and height-for-age z score were minimal.

  15. Online Resource 1 shows results by religion and caste affiliation.

  16. There are 384 female deaths of 4,261 female births in rural areas. In urban areas, there are 120 missing girls, 98 female deaths, and 2,122 observed female births. To calculate the percentage, we add the number of missing girls to the number of observed births to yield (98 + 120) / (2,122 + 120) = 0.10. Finally, for slum areas there are 161 missing girls, 72 female deaths, and 1,374 observed female births, yielding (72 + 161) / (1,374 + 161) = 0.15.

  17. The caveat to this argument is that supervisors might have been too stringent and therefore also failed to classify areas as slums.

  18. It would also make urban nonslum areas appear less healthy relative to rural areas because any newer, missed slums would be classified as urban areas.

  19. These numbers are based on the question asked of all women: “How long have you been living continuously in (NAME OF CURRENT PLACE OF RESIDENCE)?,” where name of current place of residence is the village’s name in rural areas and the neighborhood in urban areas. Hence, we can reasonably expect that a woman surveyed in a slum who responds that she was born in the same neighborhood would have spent her entire life in the slum. For more on this question, see the discussion on DHS user forum on India ( https://userforum.dhsprogram.com/index.php?t=tree&goto=11187&&t=tree&goto=11187&##msg_11187 ). Results by group are available upon request.

  20. Consider, for example, using the number of people per room as an indicator for crowding. The number of people per room fails to capture that dwellings in slums are located much closer together than in either urban or rural areas, and the average number of people per room varies little across areas.

  21. The main caveat is that we cannot rule out selective migration.

  22. See also Bhan and Jana (2013). This may also explain why the positive relationship between mother’s education and child health found in rural areas diminishes, or even disappears, in slum and urban areas, as shown in Online Resource 1. One interpretation is that slums’ broader health environment is so bad that more education does little to counter the negative effects. That a mother knows to wash her hands, boil water before use, and take a sick child to the doctor matters little for child health if the local playground is an open sewer, or if diseases spread quickly and easily due to overcrowding.

  23. These numbers should be taken as suggestive at best and not as causal estimates, and are conditional on correctly specifying the underlying model with the aforementioned caveats. Furthermore, although we do employ the weights provided in NFHS-3, we use only the subset of states that have slum information in NFHS-3, and the composition of the population in NFHS-3 may vary from India as a whole. Our total predicted number of stunted does not match that of, for example, UNICEF (2013) because the census count for slums included children aged 5 and 6. Scaling our estimate by 5 / 7 to get an approximation for those under 5 leads to a total number of 59.8 million stunted children below age 5, which is slightly lower than the 61.7 million quoted in the UNICEF report.

  24. See Levine (2007) on calculation and interpretation of the population attributable fraction.

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Acknowledgments

We thank Seik Kim, Robert Plotnick, Judith Thornton, five anonymous referees, and participants at the Population Association of America annual meetings, Pacific Conference for Development Economics, Annual Conference of the European Society for Population Economics, DIAL Development Conference, and the Labor and Development Seminar at the University of Washington for their helpful comments. Partial support for this research came from a research grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R24 HD042828) to the Center for Studies in Demography & Ecology at the University of Washington, from the Office of Research and Development of National Chengchi University, and from the Ministry of Science and Technology of Taiwan Government (104-2914-I-004-009-A1).

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Authors and Affiliations

  1. Department of Economics, Albers School of Business and Economics, Seattle University, 901 12th Avenue, Seattle, WA, 98122, USA

    Claus C. Pörtner

  2. Center for Studies in Demography and Ecology, University of Washington, Seattle, WA, 98195, USA

    Claus C. Pörtner

  3. Graduate Institute of Development Studies, National Chengchi University, Taipei City, 11605, Taiwan

    Yu-hsuan Su

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Correspondence to Claus C. Pörtner.

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Pörtner, C.C., Su, Yh. Differences in Child Health Across Rural, Urban, and Slum Areas: Evidence From India. Demography 55, 223–247 (2018). https://doi.org/10.1007/s13524-017-0634-7

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