Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-29T21:16:28.678Z Has data issue: false hasContentIssue false
  • English
  • Français

CASTE DIFFERENTIALS IN DEATH CLUSTERING IN CENTRAL AND EASTERN INDIAN STATES

Published online by Cambridge University Press:  07 June 2017

Mukesh Ranjan ,
Laxmi Kant Dwivedi  and
Rahul Mishra
Mukesh Ranjan*
Affiliation:
International Institute for Population Sciences, Deonar, Mumbai, India
Laxmi Kant Dwivedi
Affiliation:
International Institute for Population Sciences, Deonar, Mumbai, India
Rahul Mishra
Affiliation:
International Institute for Population Sciences, Deonar, Mumbai, India
*
1Corresponding author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This study assessed caste differentials in family-level death clustering, linked survival prospects of siblings (scarring) and mother-level unobserved heterogeneity affecting infant mortality risk in the central and eastern Indian states of Jharkhand, Madhya Pradesh, Odisha and Chhattisgarh. Family-level infant death clustering was examined using bivariate analysis, and the linkages between the survival prospects of siblings and mother-specific unobserved heterogeneity were captured by applying a random effects logit model in the selected Indian states using micro-data from the National Family Health Survey-III (2005–06). The raw data clustering analysis showed the existence of clustering in all four states and among all caste groups with the highest clustering found in the Scheduled Castes of Jharkhand. The important factor from the model that increased the risk of infant deaths in all four states was the causal effect of a previous infant death on the risk of infant death of the subsequent sibling, after controlling for mother-level heterogeneity and unobserved factors. The results show that among the Scheduled Castes and Scheduled Tribes, infant death clustering is mainly affected by the scarring factor in Jharkhand and Madhya Pradesh, while mother-level unobserved factors were important in Odisha and both (scarring and mother-level unobserved factors) were key factors in Chhattisgarh. Similarly, the Other Caste Group was mainly influenced by the scarring factor only in Odisha, mother-level unobserved factors in Jharkhand and Chhattisgarh and both (scarring and mother-level unobserved factors) in Madhya Pradesh. From a government policy perspective, these results would help in identifying high-risk clusters of women among all caste groups in the four central and eastern Indian states that should be targeted to address maternal and child health related indicators.

Type
Research Article
Information
Journal of Biosocial Science , Volume 50 , Issue 2 , March 2018 , pp. 254 - 274
Copyright
Copyright © Cambridge University Press, 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arulampalam, W. (2006) Sibling death clustering in India: state dependence versus unobserved heterogeneity. Journal of the Royal Statistical Society: Series A 169(4), 829–848.Google Scholar
Arulampalam, W. & Bhalotra, S. (2008) The linked survival prospects of siblings: evidence for the Indian states. Population Studies 62(2), 171–190.Google Scholar
Bolstad, W. M. & Manda, S. O. (2001) Investigating child mortality in Malawi using family and community random effects: a Bayesian analysis. Journal of the American Statistical Association 96(453), 12–19.Google Scholar
Chitnis, S. (1997) Definition of the terms scheduled castes and scheduled tribes: a crisis of ambivalence. In Panandiker, V. A. P. (ed.) The Politics of Backwardness: Reservation Policy in India. Centre for Policy Research, New Delhi, p. 104.Google Scholar
Claeson, M., Bos, E. R., Mawji, T. & Pathmanathan, I. (2000) Reducing child mortality in India in the new millennium. Bulletin of the World Health Organization 78(10), 1192–1199.Google Scholar
Curtis, S. L., Diamond, I. & McDonald, J. W. (1993) Birth interval and family effects on post-neonatal mortality in Brazil. Demography 30(1), 33–43.Google Scholar
Curtis, S. L. & Steele, F. (1996) Variations in familial neonatal mortality risks in four countries. Journal of Biosocial Science 28(2), 141–159.Google Scholar
Das, M. B., Kapoor, S. & Nikitin, D. (2010) A Closer Look at Child Mortality among Adivasis in India. Policy Research Working Paper WPS5231. Human Development Department, World Bank South Asia Region. URL: http://documents.worldbank.org/curated/en/955711468044086021/pdf/WPS5231.pd (accessed 23rd March 2015).Google Scholar
Das Gupta, M. (1990) Death clustering, mothers’ education and the determinants of child mortality in rural Punjab, India. Population Studies 44(3), 489–505.CrossRefGoogle Scholar
Das Gupta, M. (1997) Socio-economic status and clustering of child deaths in rural Punjab. Population Studies 51(2), 191–202.Google Scholar
Deaton, A. & Drèze, J. (2009) Food and nutrition in India: facts and interpretations. Economic and Political Weekly 44(7), 42–65.Google Scholar
Dommaraju, P., Agadjanian, V. & Yabiku, S. (2008) The pervasive and persistent influence of caste on child mortality in India. Population Research and Policy Review 27(4), 477–495.Google Scholar
Dumont, L. & Sainsbury, M. (1970) Homo Hierachius: The Caste System and its Implications. Weidenfeld & Nicolson.Google Scholar
Dyson, T. & Moore, M. (1983) On kinship structure, female autonomy, and demographic behavior in India. Population and Development Review 9(1), 35–60.Google Scholar
Gazdar, H. & Drèze, J. (1996) Uttar Pradesh: the burden of inertia. In Drèze J. & Sen, A. (eds) Indian Development: Selected Regional Perspectives. Oxford University Press, Oxford and New Delhi, pp. 33–128.Google Scholar
Guo, G. (1993) Use of sibling data to estimate family mortality effects in Guatemala. Demography 30(1), 15–32.CrossRefGoogle ScholarPubMed
Gwatkin, D. R. (2000) Health inequalities and the health of the poor: What do we know? What can we do? Bulletin of the World Health Organization 78(1), 3–18.Google Scholar
Heckman, J. J. (1987) The Incidental Parameters Problem and the Problem of Initial Conditions in Estimating a Discrete Time-Discrete Data Stochastic Process and Some Monte Carlo Evidence. University of Chicago Center for Mathematical Studies in Business and Economics.Google Scholar
Hobcraft, J., McDonald, J. W. & Rutstein, S. (1983) Child-spacing effects on infant and early child mortality. Population Index 49(4), 585–618.Google Scholar
IIPS & Macro International (2007) National Family Health Survey (NFHS-3), 2005–06: India Volume I. IIPS, Mumbai.Google Scholar
Larsen, K. & Merlo, J. (2005) Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression. American Journal of Epidemiology 161(1), 81–88.Google Scholar
Madise, N. J. & Diamond, I. (1995) Determinants of infant mortality in Malawi: an analysis to control for death clustering within families. Journal of Biosocial Science 27(1), 95–106.Google Scholar
Maharatna, A. (1998) On Tribal Fertility in Late Nineteenth and Early Twentieth Century India. Harvard Center for Population and Development Studies, Harvard University, Cambridge, MA, USA.Google Scholar
Maharatna, A. (2000) Fertility, mortality and gender bias among tribal population: an Indian perspective. Social Science & Medicine 50(10), 1333–1351.CrossRefGoogle ScholarPubMed
Maharatna, A. (2005) Demographic Perspectives on India’s Tribes. Oxford University Press.Google Scholar
Mahadevan, K., Murthy, M. S. R., Reddy, P. R., Reddy, P. J., Gowri, V. & Sivaraju, S. (1985) Socio-demographic correlates of infant and childhood mortality. Rural Demography 12(1–2), 21–40.Google Scholar
Manski, C. F. (1999) Identification Problems in the Social Sciences. Harvard University Press.Google Scholar
Merlo, J., Chaix, B., Ohlsson, H., Beckman, A., Johnell, K., Hjerpe, P. & Larsen, K. (2006) A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena. Journal of Epidemiology and Community Health 60(4), 290–297.Google Scholar
Miller, J. E., Trussell, J., Pebley, A. R. & Vaughan, B. (1992) Birth spacing and child mortality in Bangladesh and the Philippines. Demography 29(2), 305–318.Google Scholar
Murthi, M., Guio, A. C. & Drèze, J. (1995) Mortality, fertility, and gender bias in India: a district-level analysis. Population and Development Review 21(4), 745–782.Google Scholar
Navarro, V. (1990) Race or class versus race and class: mortality differentials in the United States. The Lancet 336(8725), 1238–1240.Google Scholar
Pandey, G. & Tiwary, R. (1993) Demographic characteristics in a tribal block of Madhya Pradesh. Social Change 23(2/3), 124–131.Google Scholar
Pinker, S. (2003) The Blank Slate: The Modern Denial of Human Nature. Penguin.Google Scholar
Ranjan, M., Dwivedi, L. K., Mishra, R. & Brajesh (2017) Infant mortality differentials among the tribal and non-tribal populations of Central and Eastern India. International Journal of Population Studies 2(2), 26–43.CrossRefGoogle Scholar
Reddy, S. (2008) Health of tribal women and children: an interdisciplinary approach. Indian Anthropologist 38(2), 61–74.Google Scholar
Rogers, R. G. (1989) Ethnic differences in infant mortality: fact or artifact? Social Science Quarterly 70(3), 642.Google Scholar
Registrar General and Census Commissioner. (2013) Annual Health Survey (AHS). 2nd Update Bulletins 2012–2013. Ministry of Home affairs, Government of India, New Delhi. URL: http://www.censusindia.gov.in/vital_statistics/AHSBulletins/AHS_2nd_Updation_Bulletins.html (accessed 10th July 2015).Google Scholar
Registrar General and Census Commissioner (2016) Sample Registration Bulletin (SRS) 2016. Ministry of Home Affairs, Government of India, New Delhi. URL: http://www.censusindia.gov.in/vital_statistics/SRS_Bulletin_2015.pdf (accessed 3rd August 2016).Google Scholar
Ronsmans, C. (1995) Patterns of clustering of child mortality in a rural area of Senegal. Population Studies 49(3), 443–461.Google Scholar
Rosenzweig, M. R. & Wolpin, K. I. (1986) Evaluating the effects of optimally distributed public programs: child health and family planning interventions. American Economic Review 76(3), 470–482.Google Scholar
Sastry, N. (1997) Family-level clustering of childhood mortality risk in Northeast Brazil. Population Studies 51(3), 245–261.Google Scholar
Scalone, F., Agati, P., Angeli, A. & Donno, A. (2016) Exploring unobserved heterogeneity in perinatal and neonatal mortality risks: the case of an Italian sharecropping community, 1900–39. Population Studies 71(1), 1–19.Google Scholar
Shah, B. D. & Dwivedi, L. K. (2011) Causes of neonatal deaths among tribal women in Gujarat, India. Population Research and Policy Review 30(4), 517–536.Google Scholar
Subramanian, S. V., Ackerson, L. K., Subramanyam, M. A. & Sivaramakrishnan, K. (2008) Health inequalities in India: the axes of stratification. Brown Journal of World Affairs 14(2), 127–138.Google Scholar
WHO (2015) World Health Statistics. URL: http://www.who.int/gho/child_health/mortality/neonatal_infant_text/en/ (accessed 7th August 2015).Google Scholar
Zaba, B. & David, P. H. (1996) Fertility and the distribution of child mortality risk among women: an illustrative analysis. Population Studies 50(2), 263–278.Google Scholar
Zenger, E. (1993) Siblings’ neonatal mortality risks and birth spacing in Bangladesh. Demography 30(3), 477–488.CrossRefGoogle ScholarPubMed