This paper examines the mathematical abilities of 15-year olds in a range of countries which participated in the 2003 cycle of the OCED’s Programme for International Student Assessment (PISA). Utilising information on the scores obtained by individual students in the mathematical part of the PISA assessment, we use a range of indicators from the literature on inequality and poverty to evaluate the “mathematical performance” of participating countries. Since data from PISA contained a wealth of information on the circumstances of the students, in terms of their home and school environment, we identify, and examine the relative influence of, factors which serve to enhance the mathematical performance of students in the PISA assessment.
Bibliographical noteReference text: Anand, S. and Sen, A.K. (1997). “Concepts of Human Development and Poverty: A Multidimensional Perspective.” In Poverty and Human Development. Human Development Papers 1997. New York: Human Development Report Office, the United Nations Development Programme.
Atkinson, A.B. (1970), "On the Measurement of Inequality," Journal of Economic Theory, 2, 244-63.
Bee, M. and Dolton, P. (1985), “Costs and Economies of Scale in UK Private Schools”, Applied Economics, vol. 17, pp. 281-90.
Brown, G., Micklewright, J., Schnepf, S. and Waldmann, R. (2007), “International Surveys of Educational Achievement: How Robust are the Findings?”, Journal of the Royal Statistical Society Series A, 170(1), pp. 1 – 24.
Cowell, F.A. and S.P. Jenkins, 1995, How Much Inequality Can We Explain? A Methodology and an Application to the USA, Economic Journal, 105, 421-30.
Dolton, P. (2000), “The Economics of University Computer Provision”, Applied Economics, vol. 23, pp. 353-66.
Dolton, P. and Vignoles, A (2000), “The pay-off to Mathematics A level”, in The Maths We Need Now: Demand Deficits and Remedies (ed. C. Tikly & A. Wolf), London: Institute of Education.
Dolton, P. and Vignoles, A (2002) ‘The Return to Post-Compulsory School Mathematics Study’, Economica, Vol. 69, No. 273, pp.113-142.
Gordon, I. (1996), “Family Structure, Educational Achievement, and the Inner City”, Urban Studies, vol. 33, pp. 407-23.
Galindo-Rueda, F., Marcenaro-Gutierrez, O., and Vignoles, A. (2004), “The Widening Gap in UK Higher Education”, National Institute Economic Review, No. 190, pp.70-82.
Greenspan, A. (2004), Testimony of Chairman Alan Greenspan before the Committee on Education and the Workforce, U.S. House of Representatives: March 11,2004 http://www.federalreserve.gov/boarddocs/testimony/2004/20040311/default.htm
Hansen, K. and Vignoles, A. (2005), “The United Kingdom Education System in a Comparative Context”, What’s the Good of Education: The Economics of Education in the UK (ed. S. Machin & A. Vignoles), London: Princeton University Press.
Hanushek, E. A. and D. D. Kimko (2000), “Schooling, Labor-Force Quality, and the Growth of Nations”, American Economic Review, 90(5), pp.1184 - 1208.
Ingram, B., and G. Neumann (2006), “The Returns to Skill,” Labour Economics, 13(1), pp. 35-59
Jenkins A., Vignoles A., Wolf A. and Galindo-Rueda, F. (2003), “The determinants and labour market effects of lifelong learning”, Applied Economics, 35(16), pp. 1711 - 1721
Jenkins, S. P. (1995), “Accounting for Inequality Trends: Decomposition Analyses for the UK, 1971-86”, Economica, 62, 29–63.
Jenkins, S. (2008), “Maths? I breakfasted on quadratic equations, but it was a waste of time”, The Guardian, June 6th 2008.
Kenny, L.W., Lee, L., Maddala, G.S., and Trost, R.P. (1979), “Returns to College Education: an Investigation of Self-selection Bias Based on the Project Talent Data”, International Economic Review, 20 (3), pp.775 - 789.
Kounine, L., Marks, J., and Truss, E. (2008), The Value of Mathematics, Reform: London.
McIntosh, S. and Vignoles, A. (2000), Measuring and Assessing the Impact of Basic Skills on Labour Market Outcomes, London: Centre for the Economics of Education, London School of Economics and Political Science.
Machin, S. and Vignoles, A. (2004), “Educational inequality: the widening socio-economic gap”, Fiscal Studies, vol. 25, pp. 107-28.
Mass, J. v L. and Criel, C. (1982), Distribution of Primary School Enrolments in East Africa, World Bank Staffing Paper 511, Washington DC: The World Bank.
Murnane, R.J., Willett, J.B. and Levy, F. (1995), “The Growing Importance of Cognitive Skills in Wage Determination”, Review of Economics and Statistics, 77, pp.251 - 266.
Okpala, P. and Onocha, C. (1988), “Student Factors as Correlates of Acievement in Physics”, Physics Education, vol. 23, pp. 361-64.
Sen, A. (1976), Poverty: An Ordinal Approach to Measurement, Econometrica, 44, 219 - 231.
Sen, A.K. (1999), “Economic Policy and Equity: An Overview”, in Tanzi, V., Chu, K-E. and Gupta, S. (editors), Economic Policy & Equity: Conference Proceedings, Washington DC: The International Monetary Fund (p. 28-43).
Shorrocks, A F, (1980), The Class of Additively Decomposable Inequality Measures, Econometrica, 48, 613 - 625.
Theil, H. (1967), Economics and Information Theory, Amsterdam: North Holland.
Thomas, V., Wang, Y., and Fan, X. (2001), Measuring Education Inequality: Gini Coefficients of Education, Washington DC: The World Bank
Thomas, V., Wang, Y., and Fan, X. (2002), A New Dataset on Inequality in Education: Gini and Theil Indices of Schooling for 140 countries, 1960-2000, Washington DC: The World Bank
Wolf, A. (2002), Does Education Matter?, London: Penguin Books