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Measuring Equitable MDG Progress

Abstract

How do we identify and highlight countries achieving equitable Millennium Development Goal (MDG) progress? The answer lies in how we measure MDG progress. The current approach focuses solely on scale. Most policymakers have focused on the size, rather than the shape of change. Measuring equitable progress will make policymakers work harder towards large-scale and equitable progress. This article proposes an approach to combine scale with equity of progress into a single indicator – it can also be applied to national measurements of progress and provides a concrete manner in which to include equity in the post-2015 agenda. The article applies the methodology to 62 countries, and compares standard average progress with equity-adjusted progress. It then isolates the equity component of progress, and highlights the strengths and challenges of this approach. Finally, the article illustrates that when indicators incorporate both the size and shape of progress valuable new policy objectives emerge that would otherwise be missed.

Abstract

Comment identifier et mettre en lumière les pays progressant vers la réalisation des OMD de manière équitable? La réponse réside dans la manière dont nous mesurons le progrès vers cette réalisation. L’approche actuelle ne considère que l’échelle. La plupart des responsables politiques s’intéressent donc à la taille plutôt qu’aux formes du changement. La mesure du progrès équitablement réalisé incitera les décideurs politiques à œuvrer en faveur d’un progrès équitable et à grande échelle. Cet article propose une approche intégrant l’échelle et l’équité des progrès en un seul indicateur, lequel peut également être appliqué aux mesures du progrès au niveau national et offre une manière concrète d’intégrer l’équité dans l’agenda post-2015. Il applique cette méthodologie à 62 pays et compare les moyennes standards du progrès aux moyennes ajustées en fonction de l’équité. Il isole ensuite la composante équité du progrès et met en évidence les forces et faiblesses de cette approche. Enfin, il illustre que l’utilisation d’indicateurs intégrant à la fois la taille et la forme du progrès fait émerger de nouveaux objectifs stratégiques clés qui nous échapperaient autrement.

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Notes

  1. Equality and equity, although often used interchangeably, are distinct concepts. Essentially, equity represents the overarching concept of fairness and avoidable disparities in society, whereas equality does not allow for differences in outcomes not even those that are earned fairly based on a level playing field.

  2. The inequity adjustment, distinct from the adjustment presented in this article, is based on a distribution-sensitive class of composite indices proposed by Foster, Lopez-Calva and Szekely and is computed as a geometric mean of geometric means, calculated across the population for each dimension separately (UNDP, 2010, p. 217).

  3. This article builds on the ODI report by producing league tables, analysing the differences between equity-adjusted and average progress, analysing the relationship between the scale and degree of equity in progress, and discussing the challenges of this analytical approach.

  4. See Appendix A for further details.

  5. Data downloaded from DHS Statcompiler (www.statcompiler.com/) in January 2010.

  6. Data taken directly from the annexes of MICS reports (www.unicef.org/statistics/index_24302.html) in January 2010.

  7. Vandemoortele and Delamonica (2010) explore various methods to adjust standard statistics for inequality, using U5MR data for 63 countries such as the ‘bottom quintile’ approach, the ‘vast majority’ approach, ‘ratio-gap’ analysis, ‘excess’ mortality, geometric mean and concentration ratio.

  8. Vandemoortele and Delamonica (2010) highlight that ‘[t]he unadjusted weights of 20 per cent per quintile for U5MR would assume that fertility rate is uniform across quintiles. In reality, families in the lower quintiles tend to have more children so that their share in the under-five population will exceed 20 per cent’ (p. 68). The same underrepresentation of poor households is relevant for other indicators analysed in this article: 1-year-olds immunised against measles, underweight prevalence among children under 5 years of age and the number of births attended by skilled health personnel.

  9. Appendix A describes the detailed methodology and full set of tests carried out.

  10. To ensure consistency across results, and facilitate interpretation across positive and negative indicators, we calculate the difference between the standard average and the equity-adjusted indicator. That is, for negative indicators we subtract the standard average from the equity-adjusted indicator for positive results; for positive indicators we subtract the equity-adjusted indicator from the standard average.

  11. Ghana does not appear in Table 2 for practical reasons; only results for the top 20 are reported. Appendix B reports the whole league table.

  12. U5MR in Ghana declined by a fifth for the richest and by a third for the poorest quintile. These changes are substantial. In Guatemala, the richest quintile does not show any progress, and the poorest quintile falls by about one-eighth. The relative gap between the wealthiest and poorest quintiles (as in Minujin and Delamonica, 2003) falls from 2.1 to 1.7 in Ghana and from 2.3 to 2.0 in Guatemala.

  13. Analysis on the distribution of progress indicates that Guinea-Bissau saw the most inequitable progress of all countries examined (the section ‘Measuring progress’) – explaining the 25 place drop in its ranking from ninth to thirty-fourth.

  14. 2000 (Poorest 6; Q2 19; Q3 23; Q4 32; Wealthiest 24). 2006 (Poorest 3; Q2 9; Q3 14; Q4 33; Wealthiest 81).

  15. 1996 (Poorest 32; Q2 25; Q3 24; Q4 18; Wealthiest 13). 2007 (Poorest 21; Q2 21; Q3 21; Q4 16; Wealthiest 14).

  16. 1995 (Poorest 48; Q2 47; Q3 49; Q4 50; Wealthiest 40). 1999 (Poorest 82; Q2 73; Q3 72; Q4 36; Wealthiest 45).

  17. 1998 (Poorest 69; Q2 67; Q3 53; Q4 49; Wealthiest 30). 2001 (Poorest 64; Q2 52; Q3 39; Q4 32; Wealthiest 19).

  18. 1998 (Poorest 37; Q2 36; Q3 28; Q4 27; Wealthiest 30). 2001 (Poorest 19; Q2 26; Q3 31; Q4 54; Wealthiest 79).

  19. 2000 (Poorest 20; Q2 26; Q3 31; Q4 34; Wealthiest 33). 2006 (Poorest 33; Q2 32; Q3 46; Q4 45; Wealthiest 60).

  20. 1996 (Poorest 13; Q2 7; Q3 3; Q4 2; Wealthiest 1). 2007 (Poorest 7; Q2 5; Q3 3; Q4 2; Wealthiest 2).

  21. The quadratic effect of scale on progress is statistically significant at the 1 per cent level (P<0.01).

  22. In the remaining countries, the immunisation rate in the wealthiest quintile ranges between 95 and 90 per cent for 13 countries; the remaining countries represent a range from 38.1 up to 90 per cent immunisation coverage.

  23. As data improve, such atypical distributions are likely to become less frequent. Additional research, beyond the scope of this article, may also perform country-specific investigations to control data quality and identify country-specific factors that may explain counterintuitive distributions. If data are found to be noisy or inadequate, we suggest excluding countries from analysis.

  24. This would require a large team and a significant amount of time to calculate, beyond the scope of this paper.

References

  • Alkire, S. and Foster, J. (2010) Designing the Inequality-Adjusted Human Development Index (HDI). New York. Human Development Research Paper 2010/28.

  • Booysen, F., van der Berg, S., Burger, R., von Maltitz, M. and du Rand, G. (2008) Using an asset index to assess trends in poverty in seven Sub-Saharan African countries. World Development 36 (6): 1113–1130.

    Article  Google Scholar 

  • Ferguson, B.D., Tandon, A., Gakidou, E. and Murray, C.J.L. (2003) Estimating Permanent Income Using Indicator Variables Evidence and Information for Policy Cluster. Geneva, Switzerland: World Health Organization.

    Google Scholar 

  • Filmer, D. and Pritchett, L. (2001) Estimating wealth effects without expenditure data – Or tears: With an application to educational enrollments in states of India. Demography 38 (1): 115–132.

    Google Scholar 

  • Gasparini, L., Horenstein, M., Molina, E. and Olivieri, S. (2006) Economic Polarisation in Latin America and the Caribbean: What do Household Surveys Tell Us? CEDLAS. Working Paper 38.

  • Grimm, M., Harttgen, K., Klasen, S., Misselhorn, M., Munzi, T. and Smeeding, T. (2010) Inequality in human development: An empirical assessment of 32 countries. Social Indicators Research 97 (2): 191–211.

    Article  Google Scholar 

  • Harttgen, K. and Klasen, S. (2010) A Household-Based Human Development Index. New York. Human Development Research Paper 2010/22.

  • Houweling, T., Kunst, A.E., Huisman, M. and Mackenbach, J.P. (2007) Using relative and absolute measures for monitoring health inequalities: Experiences from cross-national analyses on maternal and child health. International Journal for Equity in Health 6 (15), http://download.springer.com/static/pdf/136/art%253A10.1186%252F1475-9276-6-15.pdf?auth66=1387920277_9a6ef82c25b7b9a52d98a6063993d7e2&ext=.pdf.

  • Kabeer, N. (2010) MDGs, Social Justice and Challenge of Intersecting Inequalities. London: CDPR. Policy Brief.

  • Minujin, A. and Delamonica, E. (2003) Mind the gap! Widening child mortality disparities. Journal of Human Development 4 (3): 396–418.

    Article  Google Scholar 

  • Moser, K., Leon, D. and Gwatkin, D. (2005) How does progress towards the child mortality millennium development goal affect inequalities between the poorest and the least poor? Analysis of demographic and health survey data. British Medical Journal 331 (7526): 1180–1183.

    Article  Google Scholar 

  • ODI (2011) MDG Report Card: Measuring Progress across Countries. London: ODI.

  • Ortiz, I. and Cummins, M. (2011) Global Inequality: Beyond the Bottom Billion – A Rapid Review of Income Distribution in 141 Countries, available at SSRN, http://ssrn.com/abstract=1805046.

  • Reidpath, D., Morel, C., Mecaskey, J. and Allotey, P. (2009) The Millennium Development Goals fail poor children: The case for equity-adjusted measures. PLoS Med 6 (4): 1–3.

    Article  Google Scholar 

  • Sahn, D.E. and Stifel, D. (2003) Exploring alternative measures of welfare in the absence of expenditure data. Review of Income and Wealth 49 (4): 463–489.

    Article  Google Scholar 

  • Save the Children (2010a) A Fair Chance at Life: Why Equity Matters for Child Mortality. London: Save the Children.

  • Save the Children (2010b) Inequalities in Child Survival. London: Save the Children.

  • United Nations (UN) (2000) Millennium Declaration. New York: UN. Report No. 55/2.

  • United Nations Development Programme (UNDP) (2010) The Real Wealth of Nations: Pathways to Human Development. New York: UNDP. Human Development Report.

  • United Nations Development Programme (UNDP) (2005) International Cooperation at a Crossroads: Aid, Trade and Security in an Unequal World. New York: UNDP. Human Development Report.

  • Vandemoortele, J. (2010) Poverty, growth and children: What’s the right sequence? In: A. Minujin and L. Daniels (eds.) Child Poverty. New York: Policy Press.

    Google Scholar 

  • Vandemoortele, J. (2011) The MDG story: Intention denied. Development and Change 42 (1): 1–21.

    Article  Google Scholar 

  • Vandemoortele, J. and Delamonica, E. (2010) Taking the MDGs beyond 2015: Hasten slowly. IDS Bulletin 41 (1): 60–69.

    Article  Google Scholar 

  • Wilkinson, R. and Pickett, K. (2009) The Spirit Level: Why More Equal Societies Almost Always Do Better. London: Allen Lane.

    Google Scholar 

  • Wirth, M., Delamonica, E., Sacks, E., Minujin, A., Storeygard, A. and Balk, D. (2008) Delivering on the MDGs? Equity and maternal health in Ghana, Ethiopia and Kenya. Paper presented at the UNU-WIDER Conference on Advancing Health Equity, Helsinki.

  • World Health Organization (2008) Closing the Gap in a Generation: Health Equity through Action on the Social Determinants of Health. Geneva, Switzerland: Commission on Social Determinants of Health.

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Acknowledgements

The authors would like to thank Liesbet Steer, lead of the Progress in Development Project, for her support and guidance; Dan Wu and Lauren Walshe-Roussel for support in data collection and analysis; Claire Melamed, Sabina Alkire and two anonymous referees for their comments on the article. Preliminary data analysis was funded by the Bill & Melinda Gates Foundation under the Development Progress Story project. The writing of this article and additional data analysis, however, was done on the author’s initiative. All errors remain the authors’ own. The views presented in this article do not reflect those of the LSE, Bill & Melinda Gates Foundation, UNICEF or ODI.

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Correspondence to Milo Vandemoortele.

Appendices

Appendix A

Sensitivity analysis, confidence testing, data quality

Sensitivity analysis tests how changes in the weights affect the equity-adjusted indicators. If the adjusted indicators change proportionately to the change in the weights then the weight distribution is justified. Given the distinct nature of indicators this test was performed for all indicators. To ensure consistency, as a starting point we used the same quintile weights allocated to U5MR by Vandemoortele and Delamonica (2010), which are a 30 per cent weighting to the bottom quintile, declining by increments of 5 until the top quintile is weighted by 10 per cent. We conducted the same sensitivity analysis as the Vandemoortele and Delamonica (2010) on the database weighting the bottom quintile 25 and 35 per cent, respectively. This sensitivity analysis on all indicators supports the use of the same weighting scheme as Vandemoortele and Delamonica (2010).

Confidence intervals provided in DHS and MICS surveys were examined to test whether temporal change was statistically significant. If 2005’s confidence interval overlaps with 2003’s confidence interval, for example, the change from 2003 to 2005 cannot be considered statistically significant. When temporal change is statistically significant, we cannot assume that the change over time of the specific quintiles was statistically significant. Confidence intervals of quintile values are not available from DHS or MICS reports, and therefore we cannot assess statistical differences between quintile values. We therefore discuss all results and interpret them with caution.

Appendix B

Table B1

Table B1 Equity-adjusted and unadjusted progress league table for U5MR

Table B2

Table B2 Equity-adjusted and unadjusted progress league table for measles immunisation

Table B3

Table B3 Equity-adjusted and unadjusted progress league table for births attended by skilled health personnel

Table B4

Table B4 Equity-adjusted and unadjusted progress league table for underweight prevalence

Table B5

Table B5 Equity in change league table (U5MR)a

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Vandemoortele, M., Natali, L. & Geddes, M. Measuring Equitable MDG Progress. Eur J Dev Res 26, 651–675 (2014). https://doi.org/10.1057/ejdr.2013.61

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Keywords

  • equity
  • inequality
  • post-2015
  • MDGs
  • multi-dimensional poverty
  • development indicators
  • progress