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Local Social Inequality, Economic Inequality, and Disparities in Child Height in India

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Demography

Abstract

This study investigates disparities in child height—an important marker of population-level health—among population groups in rural India. India is an informative context in which to study processes of health disparities because of wide heterogeneity in the degree of local segregation or integration among caste groups. Building on a literature that identifies discrimination by quantifying whether differences in socioeconomic status (SES) can account for differences in health, we decompose height differences between rural children from higher castes and rural children from three disadvantaged groups. We find that socioeconomic differences can explain the height gap for children from Scheduled Tribes (STs), who tend to live in geographically isolated places. However, SES does not fully explain height gaps for children from the Scheduled Castes (SC) and Other Backward Classes (OBCs). Among SC and OBC children, local processes of discrimination also matter: the fraction of households in a child’s locality that outrank her household in the caste hierarchy predicts her height. SC and OBC children who are surrounded by other lower-caste households are no shorter than higher-caste children of the same SES. Our results contrast with studies from other populations where segregation or apartheid are negatively associated with health.

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Notes

  1. In this study, as in the relational approach described by Cummins et al. (2007), place has a different relationship with health for higher caste children than it does for lower caste children.

  2. The literature on health disparities by caste and tribe has largely focused on disparities in the use of health services (Acharya 2012; Baru et al. 2010; Borooah 2012). A larger literature explores gender disparities in health outcomes (Arnold et al. 1998; Barcellos et al. 2014; Das Gupta 1987; Murthi et al. 1995; Pande 2003). Some examples of research on health disparities by religion include Bhalotra et al. (2010), Brainerd and Menon (2015), Desai and Temsah (2014), Geruso and Spears (2018), and Guillot and Allendorf (2010).

  3. The government of India categorizes caste and tribal groups in this way for the purpose of affirmative action programs. We discuss these programs in the Background section.

  4. We classify households as they report themselves to the DHS surveyor. These categories are reported for both Hindus and non-Hindus. Table A1 in the online appendix tabulates SC, ST, OBC, and general caste status separately for Hindus, Muslims, and other religions.

  5. The caste system is traditionally associated with India, but aspects of caste also operate in other South Asian countries, including Nepal, Pakistan, and Bangladesh (International Dalit Solidarity Network 2009; Jodhka and Shah 2010).

  6. Although the caste system has its origins in Hinduism, many non-Hindus—including Muslims and Christians whose ancestors converted to these religions—also have a jati identity.

  7. Many occupations in the modern economy do not have a caste counterpart; that is, these are not hereditary caste-specific occupations. Thus, in an obvious sense, the caste-occupation overlap has weakened. However, the overlap between caste and occupational status continues in that higher-ranked castes are disproportionately represented in more prestigious and better-paying occupations.

  8. One important affirmative action program that is not related to caste or tribe is the reservation of some seats in public office for women.

  9. As the WHO recommends, we exclude children more than 6 standard deviations from the mean of the reference population.

  10. Table A1 in the online appendix shows the fraction of children with measured heights for whom social group data are missing. Muslims are more likely than Hindus to have missing caste data: 17 % of rural Muslim children do not have a caste designation in the NFHS-3.

  11. Table A2 in the online appendix presents the results of OLS regressions showing that floor type is associated with height among children in rural India.

  12. Hathi et al. (2017) also examined the effect of PSU-level variables on child health. They used the fraction of households that do not use a toilet or latrine in a PSU as a measure of disease externalities.

  13. According to the Office of the Registrar General and the Census Commissioner of India, for the purposes of the 2001 census, a village was defined as “the smallest area of habitation, viz., the village generally follows the limits of a revenue village that is recognized by the normal district administration” (Government of India 2001). This geographic unit is different from a gram panchayat, which is a village or cluster of villages represented by an elected local leader.

  14. To further elaborate on the NFHS-3 study design, the survey manual explains that PSUs and households are selected as follows: “A uniform sample design was adopted in all states. In each state, the rural sample was selected in two stages, with the selection of Primary Sampling Units (PSUs), which are villages, with probability proportional to population size (PPS) at the first stage, followed by the random selection of households within each PSU in the second stage” (IIPS and Macro International 2007:12–23).

  15. Because not all households in a PSU are surveyed, any constructed PSU mean (such as these fractions) is an unbiased estimate with sampling error of the true PSU mean. This sampling error will tend to attenuate estimated effects of PSU means, with the consequence being that the importance of local caste composition for child health outcomes could be even greater than we document.

  16. For more on the construction of the NFHS wealth index, see IIPS and Macro International (2007).

  17. See Allendorf (2013) for an investigation of the association between living in a nuclear or joint family on Indian women’s health.

  18. See Filmer and Pritchett (2001) for more on creating an index with asset variables in DHS data.

  19. For example, several states, including Delhi and Bihar, have a department of “SC and ST welfare.” Employing a standard practice in the literature, both Burgess et al. (2005) and Azam and Bhatt (2015) (selected as examples of high-quality research) used a single combined categorical variable for “SC or ST.” Of course, whether such a combined indicator is inappropriate for studying height would not necessarily imply it is inappropriate for studying other outcomes.

  20. If we use father’s education instead of mother’s education in the reweighting decomposition, the results are qualitatively similar. Figure A2 in the online appendix shows the same comparisons as the ones shown in Fig. 3 using father’s education instead of mother’s education.

  21. The SC line ends before the OBC line because very few SC children live in localities with no higher-ranking households; beyond this point, there are too few SC children to reweight on a set of SES bins.

  22. This figure, drawn only for the disadvantaged SC and OBC groups, includes mainly negative averages of height for age residuals because the residualizing regression includes general caste children, who all have a fraction higher ranking of 0.

  23. Motivated by the concern that the caste rank and composition of OBC categorization varies throughout India but that SCs are always of the lowest-ranking castes, Table A3 (online appendix) verifies that our regression results are robust—quantitatively and in their qualitative pattern—to excluding OBC children and focusing only on the SC–general caste height gap.

  24. Such a concern regarding omitted variable bias would be misguided because the fraction higher ranking is an interaction between neighborhood composition and the population group membership of the child: increasing the fraction of a village that is OBC at the expense of SCs and general castes, for example, would increase the fraction higher ranking for SC children and decrease it for OBC children.

  25. We do not mean to suggest that there are not important differences within SC and ST populations. Indeed, documenting health disparities by subcaste within SCs, or by tribe within STs, would be useful in further elucidating both the extent of and processes behind health inequality in India.

  26. Literature on ethnic and immigrant enclaves, a different form of residential segregation, has found mixed associations between living in segregated neighborhoods and health outcomes (Osypuk et al. 2010; Xie and Gough 2011).

  27. Om Prakash Valmiki opens his autobiography, Joothan: A Dalit’s Life, by describing how, when he was growing up, his neighbors would defecate on the shores of a pond next to his and other SC people’s houses (Valmiki 2003:1).

  28. Anderson (2011) found that agricultural yields of lower caste households are higher in villages with only SCs, or with SCs and OBCs, than in villages where general castes live as well. This difference can be explained by the fact that lower caste households in homogenous villages are better able to negotiate irrigation for their crops than those living in villages with general caste households. Although this study suggests that the economic variables measured by the DHS may not be able to paint a full picture of differences in households’ economic situations across village types—the DHS does not measure, for example, agricultural yields—research documenting tenuous links between agriculture and child anthropometry (Gillespie et al. 2012) suggests that if we had data on agricultural production, Anderson’s (2011) finding about caste-heterogenous villages would be unlikely to explain our results.

  29. Singh et al. (2008) considered the relationship between village size and village-level indicators of development.

  30. Local processes of caste discrimination may limit the effectiveness of existing programs intended to promote health and nutrition, such as India’s public distribution of food or its rural sanitation programs (Lamba and Spears 2013; Thorat and Lee 2005).

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Acknowledgments

We appreciate helpful suggestions from Sarah Burgard, Kenzie Lantham, Aashish Gupta, Mark Hayward, Narayan Sastry, and participants at the 2017 annual meeting of the Population Association of America (session on Economic Inequality and Health). This research was supported by Grant P2CHD042849, Population Research Center, awarded to the Population Research Center at The University of Texas at Austin by the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. All errors are our own.

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Authors and Affiliations

  1. Department of Sociology, University of Texas, Austin, TX, USA

    Diane Coffey

  2. The Population Research Center, University of Texas, Austin, TX, USA

    Diane Coffey & Dean Spears

  3. Indian Statistical Institute, Delhi, India

    Diane Coffey & Dean Spears

  4. R.I.C.E., Hyderabad, India

    Diane Coffey & Dean Spears

  5. Delhi School of Economics, Delhi University, Delhi, India

    Ashwini Deshpande

  6. Woodrow Wilson School of Public & International Affairs, Princeton University, Princeton, NJ, USA

    Jeffrey Hammer

  7. Department of Economics, University of Texas, Austin, TX, USA

    Dean Spears

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Coffey, D., Deshpande, A., Hammer, J. et al. Local Social Inequality, Economic Inequality, and Disparities in Child Height in India. Demography 56, 1427–1452 (2019). https://doi.org/10.1007/s13524-019-00794-2

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