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When can school inputs improve test scores?

Listed author(s):
  • Das, Jishnu
  • Dercon, Stefan
  • Habyarimana, James
  • Krishnan, Pramila

The relationship between school inputs and educational outcomes is critical for educational policy. The authors recognize that households will respond optimally to changes in school inputs and study how such responses affect the link between school inputs and cognitive achievement. To incorporate the forward-looking behavior of households, the authors present a household optimization model relating household resources and cognitive achievement to school inputs. In this framework, if household and school inputs are technical substitutes in the production function for cognitive achievement, the impact of unanticipated inputs is larger than that of anticipated inputs. The authors test the predictions of the model for nonsalary cash grants to schools using a unique data set from Zambia. They find that household educational expenditures and school cash grants are substitutes with a coefficient of elasticity between -0.35 and -0.52. Consistent with the optimization model, anticipated funds have no impact on cognitive achievement, but unanticipated funds lead to significant improvements in learning. This methodology has important implications for educational research and policy.

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Paper provided by The World Bank in its series Policy Research Working Paper Series with number 3217.

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Date of creation: 01 Feb 2004
Handle: RePEc:wbk:wbrwps:3217
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  1. Abhijit Banerjee & Shawn Cole & Esther Duflo & Leigh Linden, 2005. "Remedying education: Evidence from two randomized experiments in india," Framed Field Experiments 00122, The Field Experiments Website.
  2. Stefan Dercon & Pramila Krishnan, 2000. "In Sickness and in Health: Risk Sharing within Households in Rural Ethiopia," Journal of Political Economy, University of Chicago Press, vol. 108(4), pages 688-727, August.
  3. Hanan G. Jacoby & Emmanuel Skoufias, 1997. "Risk, Financial Markets, and Human Capital in a Developing Country," Review of Economic Studies, Oxford University Press, vol. 64(3), pages 311-335.
  4. Gary S. Becker & Nigel Tomes, 1994. "Human Capital and the Rise and Fall of Families," NBER Chapters, in: Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education (3rd Edition), pages 257-298 National Bureau of Economic Research, Inc.
  5. Smith, Richard J & Blundell, Richard W, 1986. "An Exogeneity Test for a Simultaneous Equation Tobit Model with an Application to Labor Supply," Econometrica, Econometric Society, vol. 54(3), pages 679-685, May.
  6. Ablo, Emmanuel & Reinikka, Ritva, 1998. "Do budgets really matter? - evidence from public spending on education and health in Uganda," Policy Research Working Paper Series 1926, The World Bank.
  7. Urquiola, Miguel, 2001. "Identifying class size effects in developing countries : evidence from rural schools in Bolivia," Policy Research Working Paper Series 2711, The World Bank.
  8. Abhijit V. Banerjee & Shawn Cole & Esther Duflo & Leigh Linden, 2007. "Remedying Education: Evidence from Two Randomized Experiments in India," The Quarterly Journal of Economics, Oxford University Press, vol. 122(3), pages 1235-1264.
  9. Foster, Andrew D, 1995. "Prices, Credit Markets and Child Growth in Low-Income Rural Areas," Economic Journal, Royal Economic Society, vol. 105(430), pages 551-570, May.
  10. Deon Filmer & Lant Pritchett, 1999. "The Effect of Household Wealth on Educational Attainment: Evidence from 35 Countries," Population and Development Review, The Population Council, Inc., vol. 25(1), pages 85-120.
  11. Paul Glewwe & Hanan Jacoby, 1994. "Student Achievement and Schooling Choice in Low-Income Countries: Evidence from Ghana," Journal of Human Resources, University of Wisconsin Press, vol. 29(3), pages 843-864.
  12. Joshua D. Angrist & Victor Lavy, 1997. "Using Maimonides' Rule to Estimate the Effect of Class Size on Student Achievement," NBER Working Papers 5888, National Bureau of Economic Research, Inc.
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