A New Extension of Bourguignon and Chakravarty Index to Measure Educational Poverty and Its Application to the OECD Countries
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The consequences that educational underperformance has on both individuals and society as a whole lead policy makers and planners to focus on how to measure it properly. The aim of this paper is to propose an index to measure educational poverty which, taking as a starting point the economic literature on multidimensional poverty measurement, turns out to be appropriate in the educational context. With this purpose, the following two features are demanded: (1) an individual should be identified as poor whenever they do not reach the basic level of knowledge in at least one of the relevant subjects; (2) the degree of poverty of individuals who present the same level of insufficiency in some subjects but have different scores in others should be different. Based on these premises, we introduce a multidimensional adjusted poverty index, called BCa index, which is an extension of Bourguignon and Chakravarty index, and we apply it to measure educational poverty in the OECD countries by using data from PISA 2012 and 2015 reports.
KeywordsMultidimensional adjusted poverty measurement Educational poverty PISA 2012 and 2015
JEL ClassificationI24 I32 D31 D63
This work was supported by the Ministerio de Economía y Competitividad (Spain) under Grant Numbers: ECO2013-43119-P; ECO2016-77200-P.
- Kelly, D., Nord, C. W., Jenkins, F., Chan, J. Y., & Kastberg, D. (2013). Performance of US 15-year-old students in mathematics, science, and reading literacy in an international context. first look at PISA 2012. NCES 2014-024. National Center for Education Statistics.Google Scholar
- Lasso de la Vega, C., Urrutia, A., & Diez, H. (2009). The Bourguignon and Chakravarty multidimensional poverty family: A characterization. Working Papers No 109, ECINEQ, Society of the Study of Economic Inequality (109).Google Scholar
- Lohmann, H., & Ferger, F. (2014). Educational poverty in a comparative perspective: Theoretical and empirical implications. SFB 882 Working Paper Series, no 26, DFG Research Center (SFB) from Heterogeneities to Inequalities. http://www.sfb882.uni-bilefeld.de/. Accessed 21 Dec 2018.
- Minzyuk, L., & Russo, F. (2016). La misurazione multidimensionale della povertà in istruzione in Italia multidimensional measurement of educational poverty in Italy. Politica economica, 1, 65–122.Google Scholar
- OECD. (2014b). PISA 2012 technical report. Paris: OECD Publishing.Google Scholar
- OECD. (2016b). PISA 2015 results in focus. https://www.oecd.org/pisa/pisa-2015-results-in-focus.pdf. Accessed 21 Dec 2018.
- Thomas, V., Wang, Y., & Fan, X. (2000) Measuring educational inequality: Gini coefficients of education. Working Paper 2525. World Bank, Washington DC: World Bank.Google Scholar