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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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.
Barro, R., & Lee, J. (2001). International data on educational attainment: Updates and implications. Center for International Development Working Paper no. 45. Harvard University.
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.
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.
Case, A., Paxson, C., & Ableidinger, J. (2004). Orphans in Africa: Parental death, poverty, and school enrollment. Demography, 41(3), 483–508.
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.
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.
Coleman, J. (1966). Equality of Educational Opportunities. Washington, DC: U.S. Office of Education.
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, Oxford
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.
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.
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.
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.
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.
Gregorio, J., & Lee, J. (2002). Education and income inequality: New evidence from cross-country data. Review of Income and Wealth, 48(3), 395–416.
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.
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.
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.
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.
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.
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.
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.
Lindelow, M. (2006). Sometimes more equal than other: How health inequalities depend on the choice of welfare indicator. Health Economics, 15(3), 263–279.
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.
Montgomery, M. R., Gragnolati, M., Burke, K., & Paredes, E. (2000). Measuring living standards with proxy variables. Demography, 37(2), 155–174.
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.
OECD. (2001). Knowledge and skills for life. In First results form PISA 2000. Paris: OECD.
Paxson, C., & Schady, N. (2005). Cognitive development among young children in Ecuador: The roles of wealth, health and parenting. Washington, DC: The World Bank.
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.
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.
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.
SACMEQ. (2014). SACMEQ [Online]. Available at: http://www.sacmeq.org. Accessed 22 Oct 2014.
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.
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.
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.
SERCE. (2006). [Online]. Available at: http://www.unesco.org/new/en/santiago/education/education-assessment-llece/second-regional-comparative-and-explanatory-study-serce/. Accessed 22 Jan 2018.
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.
Taylor, S. & Yu, D., 2009. The importance of socioeconomic status in determining educational achievement in South Africa, Stellenbosch: Stellenbosch Economic Working Papers: 01/09.
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.
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.
Wagstaff, A., & Watanabe, N. (2003). What difference does the choice of SES make in health Inquality measurement. Health Economics, 12(10), 885–890.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Kotzé, J., van der Berg, S. (2019). Mathematical Performance among the Poor: Comparative Performance across Developing Countries. In: Fritz, A., Haase, V.G., Räsänen, P. (eds) International Handbook of Mathematical Learning Difficulties. Springer, Cham. https://doi.org/10.1007/978-3-319-97148-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-97148-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97147-6
Online ISBN: 978-3-319-97148-3
eBook Packages: EducationEducation (R0)