Multidimensional Human Opportunity Index

Abstract

One fact that emerges from the evaluation of the Millennium Development Goals is that not all countries met all the goals and there are significant complementaries among failing on specific goals. This paper proposes the Multidimensional Human Opportunity Index (MHOI) that focuses on the complementaries among access to multiple services. We focus on access to services for children, with the aim of capturing equality in opportunity for children from diverse socio-economic backgrounds. This index builds on the Human Opportunity Index of the World Bank that measures children’s access to a basic service, such as access to clean water. However, the MHOI differs from the parent index in that we measure joint access to multiple services or access to a bundle of services. We apply the MHOI on two Himalayan states of South Asia, Nepal and Bhutan, and show that although each basic service is available to a large proportion of the population, only two-thirds in Bhutan and one half in Nepal have access to the bundle of basic services in 2011–2012.

This is a preview of subscription content, access via your institution.

Notes

  1. 1.

    This literature shows that the correlation of income between fathers and sons is very high in the US (Lee and Solon 2009; Mazumder 2005; Solon 1992; Zimmerman 1992), which can reduce upward mobility of an individual born to poor parents. The evidence from other countries is mixed; in countries like Britain and Germany, intergenerational mobility is low Wiegand (1997), while it is high in Finland and Sweden (Osterbacka 2001; Osterberg 2000). Evidence from Latin American countries and China show that mobility is much lower in those countries when compared to that seen in the US (Behrman et al. 2001; Ferreira and Veloso 2006; Gong et al. 2012). Currie (2009) show that children who are born into poor families do have a lower birth weight, worse health and learning outcomes than children born into richer families. Access to proper healthcare and education could improve the chances of a child having a favorable adult life.

  2. 2.

    Although some papers do not find improvements in a community leading to improvements of individual outcomes in the short run (Kling et al. 2005; Katz et al. 2001), recent research by Chetty et al. (2015) shows that neighborhood quality can have an effect on individual outcomes. Neighborhood school quality can increase returns to schooling of an individual (Altonji and Dunn 1996), which shows the importance of proper and quality services in improving the future outcomes of a child.

  3. 3.

    For each of the services we assume that the metric we use to quantify access has a lower and upper bound signifying no access and complete access, respectively.

  4. 4.

    A permutation matrix \(\Pi\) is a square matrix with a single ‘1’ in each row and each column, and the rest ‘0’s.

  5. 5.

    The full explanation of how each variable has been calculated is presented in Table 5.

  6. 6.

    See the Table 5 for the specific definitions of each service.

References

  1. Aaronson, D. (1998). Using sibling data to estimate the impact of neighborhoods on children’s educational outcomes. Journal of Human Resources, 33(4), 915–946.

    Article  Google Scholar 

  2. Alkire, S., & Foster, J. E. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7–8), 476–487.

    Article  Google Scholar 

  3. Altonji, J. G., & Dunn, T. A. (1996). The effects of family characteristics on the return to education. The Review of Economics and Statistics, 78(4), 692–704.

    Article  Google Scholar 

  4. Barros, R., Ferreira, F., Vega, J. M., & Chanduvi, J. S. (2009). Measuring inequality of opportunities in Latin America and the Caribbean. Washington DC: World Bank Publications.

    Google Scholar 

  5. Becker, G. S., & Tomes, N. (1986). Human capital and the rise and fall of families. Journal of Labor Economics, 4(3), S1–S39.

    Article  Google Scholar 

  6. Behrman, J. R., Gaviria, A., & Szekely, M. (2001). Intergenerational mobility in latin America. Economia, 2(1), 1–31.

    Google Scholar 

  7. Bhutan Living Standards Survey 2012 Report. (2013). National Statistics Bureau (Bhutan) and Asian Development Bank.

  8. Chetty, R., Hendren, N., & Katz, L. F. (2015). The effects of exposure to better neighborhoods on children: New evidence from the moving to opportunity experiment. Technical report, National Bureau of Economic Research.

  9. Currie, J. (2009). Healthy, wealthy, and wise? Socioeconomic status, poor health in childhood, and human capital development. Journal of Economic Literature, 47(1), 87–122.

    Article  Google Scholar 

  10. Datcher, L. (1982). Effects of community and family background on achievement. The Review of Economics and Statistics, 64(1), 32–41.

    Article  Google Scholar 

  11. Durlauf, S. N. (1996). A theory of persistent income inequality. Journal of Economic Growth, 1(1), 75–93.

    Article  Google Scholar 

  12. Ferreira, S. G., & Veloso, F. A. (2006). Intergenerational mobility of wages in Brazil. Brazilian Review of Econometrics, 26(2), 181.

    Article  Google Scholar 

  13. Gong, H., Leigh, A., & Meng, X. (2012). Intergenerational income mobility in urban China. The Review of Income and Wealth, 58(3), 481–503.

    Article  Google Scholar 

  14. Islam, T. M. Tonmoy. (2013). Childhood neighborhood conditions and the persistence of adult income. Regional Science and Urban Economics, 43(4), 684–693.

    Article  Google Scholar 

  15. Katz, L. F., Kling, J. R., & Liebman, J. B. (2001). Moving to opportunity in boston: Early results of a randomized mobility experiment. Quarterly Journal of Economics, 116(2), 607–654.

    Article  Google Scholar 

  16. Kling, J. R., Ludwig, J., & Katz, L. F. (2005). Neighborhood effects on crime for female and male youth: Evidence from a randomized housing voucher experiment. The Quarterly Journal of Economics, 120(1), 87–130.

    Google Scholar 

  17. Lee, C. I., & Solon, G. (2009). Trends in intergenerational income mobility. The Review of Economics and Statistics, 91(4), 766–772.

    Article  Google Scholar 

  18. Mazumder, B. (2005). Fortunate sons: New estimates of intergenerational mobility in the united states using social security earnings data. The Review of Economics and Statistics, 87(2), 235–255.

    Article  Google Scholar 

  19. Nepal—Nepal Living Standards Survey 2010–2011. (2012). Central Bureau of Statistics—Government of Nepal.

  20. Osterbacka, E. (2001). Family background and economic status in Finland. The Scandinavian Journal of Economics, 103(3), 467–484.

    Article  Google Scholar 

  21. Osterberg, T. (2000). Intergenerational income mobility in Sweden: What does tax data show? The Review of Income and Wealth, 46(4), 421–436.

    Article  Google Scholar 

  22. Sen, A. (1980). Equality of what? The Tanner Lecture on Human Values.

  23. Solon, G. (1992). Intergenerational income mobility in the united states. The American Economic Review, 82(3), 393–408.

    Google Scholar 

  24. Wiegand, J. (1997). Intergenerational earnings mobility in Germany. Unpublished manuscript.

  25. Zimmerman, D. J. (1992). Regression twoard mediocrity in economic stature. The American Economic Review, 82(3), 409–429.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to T. M. Tonmoy Islam.

Additional information

We would like to thank Jose Molinas Vega, Ambar Narayan and James Foster for helpful discussions. Of course, all remaining errors are solely ours.

Appendix

Appendix

See Table 5.

Table 5 Service list and definitions

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Islam, T.M.T., Mitra, S. Multidimensional Human Opportunity Index. Soc Indic Res 130, 523–535 (2017). https://doi.org/10.1007/s11205-015-1202-4

Download citation

Keywords

  • Multidimensional
  • Equality of opportunity
  • Poverty

JEL Classification

  • D72
  • D78
  • O20