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Mathematical Performance among the Poor: Comparative Performance across Developing Countries

  • Janeli KotzéEmail author
  • Servaas van der Berg
Chapter

Abstract

This chapter provides a perspective on the interplay between low education and poverty among different education systems by comparing data from 7 sub-Saharan countries and 14 Latin-American countries. A new method for comparing socio-economic status across different educational evaluations is used to compare the mathematics performance of children in equally impoverished circumstances across developing countries. More specifically this measure is applied to the SACMEQ (sub-Saharan Africa) and SERCE (Latin America) education datasets to compare the educational outcomes of students living under the $3.10-a-day poverty line. Most strikingly, the comparison shows that Ugandan and Mozambican children living under the $3.10-a-day poverty line achieve much higher educational outcomes than similarly poor children in middle-income countries such as South Africa and the Dominican Republic.

JEL Classification

D63 I24 I32 

Keywords

Income distribution Asset index Inequality in Education Social gradients 

References

  1. Angrist, N., Patrinos, H. A., & Schlotter, M. (2013). An expansion of a global data set on educational quality: A focus on achievement in developing countries. Policy Research Working Paper 6536. The World Bank.Google Scholar
  2. Barro, R., & Lee, J. (2001). International data on educational attainment: Updates and implications. Center for International Development Working Paper no. 45. Harvard University. Google Scholar
  3. Bollen, K., Glanville, J., & Stecklov, G. (2002). Economic status proxies in studies of fertility in developing countries: Does the measure matter? Population Studies, 56(1), 81–96.CrossRefGoogle Scholar
  4. Caro, D., & Cortes, D. (2012). Measuring family socioeconomic status: An illustation using data from PIRLS 2006. IERI Monograph Series: Issues and Methodologies in Large-Scale Assessments, 5, 9–33.Google Scholar
  5. Case, A., Paxson, C., & Ableidinger, J. (2004). Orphans in Africa: Parental death, poverty, and school enrollment. Demography, 41(3), 483–508.CrossRefGoogle Scholar
  6. Chudgar, A., Luschei, T. F., Fagioli, L. P., & Lee, C. (2012). Socio-economic status (SES) measures using the Trends in International Mathematics and Science Study data. In annual meeting of the American Educational Research Association, Vancouver, Canada.Google Scholar
  7. Chuma, J., & Molyneux, C. (2009). Estimating inequalities in ownership of insecticide treated nets: Dose the choice of socio-economic status measure matter? Health Polocy and Planning, 24, 83–93.CrossRefGoogle Scholar
  8. Coleman, J. (1966). Equality of Educational Opportunities. Washington, DC: U.S. Office of Education.Google Scholar
  9. Cruces, G., Domenech, C., & Gasparini, L. (2014). Inequality in education: Evidence for Latin America. In Falling inequality in Latin America: Policy changes and lessons (pp. 318–339). Oxford University Press, OxfordCrossRefGoogle Scholar
  10. Das, J. D., Habyarimana, J., & Krishnan, P. (2004). Public and private finding of basic education in Zaimbia: Implications of budgetary allocations for service delivery. Washington, DC: The World Bank.Google Scholar
  11. Fay, M., Leipziger, D., Wodon, Q., & Yepes, T. (2005). Achieving child-health-related millennium development goals: The role of infrastructure. World Development, 33(8), 1267–1284.CrossRefGoogle Scholar
  12. Filmer, D. (2005). Fever and its treatment among the more and less poor in suc-Saharan Africa. Health Policy and Planning, 20(6), 337–346.CrossRefGoogle Scholar
  13. Filmer, D., & Pritchett, L. (2001). Estimating the wealth effects without expenditure data – or tears: An application to educational enrollments in states of India. Demography, 38(1), 115–132.Google Scholar
  14. Ghuman, S., Behrman, J. R., Borja, J. B., Gultiano, S., & King, E. M. (2005). Family background, service providers, and early childhood development in the Philippines: Proxies and interactions. Economic Development and Cultural Change, 54(1), 129–164.CrossRefGoogle Scholar
  15. Gregorio, J., & Lee, J. (2002). Education and income inequality: New evidence from cross-country data. Review of Income and Wealth, 48(3), 395–416.CrossRefGoogle Scholar
  16. Gustafsson, M. (2012). More countries, similar results: A nonlinear programming approach to normalising the scores needed for growth regressions. Stellenbosch Working Paper Series: 12/12. Google Scholar
  17. Gwatkon, D., Rustein, S., Johnson, K., Pande, K., & Wagstaff, A. (2000). Socio-Eocnomic differences in Brazil. Washington, DC: HNP/Poverty Thematic Group of the World Bank.Google Scholar
  18. Hanushek, E., & Woessman, L. (2009). Do better schools lead to more growth? Cognitive skills, economic outcomes and causation. Washington, DC: National Bureau of Economic Research.CrossRefGoogle Scholar
  19. Hanushek, E. A., & Woessman, L. (2012). Do better schools lead to more growth? Cognitive skills, economic outcomes, and causation. Journal of Economic Growth, 17(4), 267–321.CrossRefGoogle Scholar
  20. Harttgen, K., & Vollmer, S. (2011). Inequality decomposition without income or expenditure data: Using an asset index to simulate houehold income., s.l.: Human Development Research Paper 2011/13. Human Development Reports. United Nations Development Programme.Google Scholar
  21. Hungi, N., Makuwa, D., Ross, K., Saito, M., Dolata, S., & Cappelle, F. V. (2010). SACMEQIII project result: Pupil achievement levels in reading and mathematics. Working Document Number 1. Paris: SACMEQ.Google Scholar
  22. Kotzé, J., & Van der Berg, S. (in press). A new methodology for investigating cognitive performance differentials by socio-economic status across international assessments. Stellenbosch Working Paper Series. Google Scholar
  23. Lindelow, M. (2006). Sometimes more equal than other: How health inequalities depend on the choice of welfare indicator. Health Economics, 15(3), 263–279.CrossRefGoogle Scholar
  24. Moloi, M., & Strauss, J. (2005). The SACMEQ II project in South Africa: A study of the conditions of schooling and the quality of education. Harare, Zimbabwe: SACMEQ Montgomery.Google Scholar
  25. Montgomery, M. R., Gragnolati, M., Burke, K., & Paredes, E. (2000). Measuring living standards with proxy variables. Demography, 37(2), 155–174.CrossRefGoogle Scholar
  26. Njau, J., Goodman, C., Kachur, S. P., Palmer, N., Khatib, R. A., Abdulla, S., et al. (2006). Fever reatment and household welath: The challenve posed for rolling out combination therapy for malaria. Tropical Medicine & International Health., 11(3), 299–313.CrossRefGoogle Scholar
  27. OECD. (2001). Knowledge and skills for life. In First results form PISA 2000. Paris: OECD.Google Scholar
  28. Paxson, C., & Schady, N. (2005). Cognitive development among young children in Ecuador: The roles of wealth, health and parenting. Washington, DC: The World Bank.CrossRefGoogle Scholar
  29. Reardon, S. (2011). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In R. Murnane & G. Duncan (Eds.), Whither opportunity? Rising inequality and the uncertain life chances of low-income children. New York: Russell Sage Foundation Press.Google Scholar
  30. Rolleston, C., James, Z., & Aurino, E. (2013). Exploring the effect of educational opportunity and inequality on learning outcomes in Ethiopia, Peru, India and Vietnam. Background Paper for the UNESCO Education for All Global Monitoring Report. Google Scholar
  31. Ross, K., Saito, M., Dolata, S., Ikeda, M., Zuze, L., Murimba, S., et al. (2005). The conduct of the SACMEQ III project. In E. Onsomu, J. Nzomo, & C. Obiero (Eds.), The SACMEQ II project in Kenya: A study of the conditions of schooling and the quality of education. Harare, Zimbabwe: SACMEQ.Google Scholar
  32. SACMEQ. (2014). SACMEQ [Online]. Available at: http://www.sacmeq.org. Accessed 22 Oct 2014.
  33. Sahn, D., & Stifel, D. (2003). Exploring alternative measures of welfare in the absence of expenditure data. Review of Income and Wealth, 49(4), 463–489.CrossRefGoogle Scholar
  34. Sastry, N. (2004). Trends in socioeconomic inequalities in mortality in developing countries: The case of child survival in Sao Paulo, Brazil. Demography, 41, 443–464.CrossRefGoogle Scholar
  35. Schellenberg, J., Victora, C. G., Mushi, A., De Savigny, D., Schellenberg, D., Mshinda, H., et al. (2003). Inequalities among the very poor: Health care for children in rural southern Tanzania. The Lancet, 361, 561–566.CrossRefGoogle Scholar
  36. Tarozzi, A., & Mahajan, A. (2005). Child nutrition in India in the Nineties: A story of increased gender inequality?. Discussion Paper No. 04-29. Stanford Institute for Economic Policy Research. Google Scholar
  37. Taylor, S. & Yu, D., 2009. The importance of socioeconomic status in determining educational achievement in South Africa, Stellenbosch: Stellenbosch Economic Working Papers: 01/09.Google Scholar
  38. UNESCO. (2008). Los aprendizajes de los estudiantes de América Latina y el Caribe: Resumen Ejecutivo del Primer Reporte de Resultados del Segundo Estudio Regional Comparativo y Explicativo, Santiago: la Oficina Regional de Educación de la UNESCO para América Latina y el Caribe OREALC/UNESCO.Google Scholar
  39. Van der Berg, S. (2015). Brookings education: Future development blog [Online]. Available at: https://www.brookings.edu/blog/future-development/2015/03/09/how-does-the-rich-poor-learning-gap-vary-across-countries/. Accessed 27 Aug 2016.
  40. Wagstaff, A., & Watanabe, N. (2003). What difference does the choice of SES make in health Inquality measurement. Health Economics, 12(10), 885–890.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of EconomicsStellenbosch UniversityStellenboschSouth Africa

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