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The relative and absolute risks of disadvantaged family background and low levels of school resources on student literacy

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  • Nonoyama-Tarumi, Yuko
  • Willms, J. Douglas

Abstract

There has been a long-lasting debate of whether the effects of family background are larger than those of school resources, and whether these effects are a function of national income level. In this study, we bring a new perspective to the debate by using the concepts of relative risk and population attributable risk in estimating family and school effects. The study uses data from the Programme of International Student Assessment (PISA), a large international comparative study of the skills of 15-year-old students in 43 countries. The study finds that: (1) there is a curvilinear association between family effects, measured by both relative and attributable risk, and national income level; (2) there is no association between school effects and national income level; (3) the risk associated with low levels of family background is larger than that of low levels of school resources, regardless of national income level.

Suggested Citation

  • Nonoyama-Tarumi, Yuko & Willms, J. Douglas, 2010. "The relative and absolute risks of disadvantaged family background and low levels of school resources on student literacy," Economics of Education Review, Elsevier, vol. 29(2), pages 214-224, April.
    • Handle: RePEc:eee:ecoedu:v:29:y:2010:i:2:p:214-224
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    1. repec:fth:prinin:357 is not listed on IDEAS
    2. David Card & Alan Krueger, 1996. "Labor Market Effects of School Quality: Theory and Evidence," Working Papers 736, Princeton University, Department of Economics, Industrial Relations Section..
    3. David Card & Alan B. Krueger, 1996. "Labor Market Effects of School Quality: Theory and Evidence," NBER Working Papers 5450, National Bureau of Economic Research, Inc.
    4. Samuel Bowles & Henry M. Levin, 1968. "The Determinants of Scholastic Achievement-An Appraisal of Some Recent Evidence," Journal of Human Resources, University of Wisconsin Press, vol. 3(1), pages 3-24.
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    1. Chowa, Gina A.N. & Masa, Rainier D. & Ramos, Yalitza & Ansong, David, 2015. "How do student and school characteristics influence youth academic achievement in Ghana? A hierarchical linear modeling of Ghana YouthSave baseline data," International Journal of Educational Development, Elsevier, vol. 45(C), pages 129-140.
    2. Bouhlila, Donia Smaali, 2015. "The Heyneman–Loxley effect revisited in the Middle East and North Africa: Analysis using TIMSS 2007 database," International Journal of Educational Development, Elsevier, vol. 42(C), pages 85-95.
    3. Jerrim, John & Lopez-Agudo, Luis Alejandro & Marcenaro-Gutierrez, Oscar D. & Shure, Nikki, 2017. "What happens when econometrics and psychometrics collide? An example using the PISA data," Economics of Education Review, Elsevier, vol. 61(C), pages 51-58.
    4. Destin, Mesmin, 2013. "Integrating resource-based and person-based approaches to understanding wealth effects on school achievement," Economics of Education Review, Elsevier, vol. 33(C), pages 171-178.
    5. Antonio Villar, 2016. "Educational poverty as a welfare loss: Low performance in the OECD according to PISA 2012," Working Papers 16.04, Universidad Pablo de Olavide, Department of Economics.
    6. Donia Smaali Bouhlila, 2013. "Students’ Achievement in the MENA Countries: The Heyneman-Loxley Effect Revisited Using TIMSS 2007 Data," Working Papers 779, Economic Research Forum, revised Oct 2013.

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