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CHILDREN’S LEARNING ENVIRONMENT AND GROWTH-PROMOTING INCOME REDISTRIBUTION

Published online by Cambridge University Press:  09 July 2021

Debasis Bandyopadhyay  and
Xueli Tang
Debasis Bandyopadhyay*
Affiliation:
University of Auckland
Xueli Tang
Affiliation:
Deakin University
*
Address correspondence to: Debasis Bandyopadhyay, 12 Grafton Road, OGGB (Level 6), Department of Economics, The University of Auckland, Auckland 1142, New Zealand. e-mail: d.bandyopadhyay@auckland.ac.nz. Phone: 64 9 923 7439.
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Abstract

Bénabou [(2002) Econometrica 70, 481–517.] demonstrated how progressive redistribution mitigates unequal opportunity for education to promote growth. We ask how effective that policy is when it affects the average quality of education. So, we add a learning externality in his model of human capital accumulation. It captures how knowledge diffusion through global channels—such as a national educational curriculum—influences children’s learning outcomes and, hence, growth. Whether inequality is a curse or a blessing for economic growth depends on how efficiently an economy designs these channels and to what extent they influence children’s education. The greater this efficiency, the lower is the optimal progressiveness of income redistribution for maximizing the long-run output growth rate. Interestingly, if poorly designed global channels exert greater influence in education, the optimal progressivity may increase. Our quantitative analysis reveals new implications for redistributive policies, including a surprisingly low optimal progressivity (approximately 6–7%) for the calibrated US economy.

Keywords

Efficiency of the knowledge economyEndogenous growthProgressivityDynamic macroeconomic cost–benefit trade-off
Type
Articles
Information
Macroeconomic Dynamics , Volume 26 , Issue 8 , December 2022 , pp. 2029 - 2085
Copyright
© Cambridge University Press 2021

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Footnotes

We gratefully acknowledge two anonymous referees and the coeditor Oded Galor for their valuable comments and helpful suggestions, which were critical for reconstructing this paper. We also thank Roland Bénabou, Dirk Krueger, Roberto Foa, Kaushik Basu, Debraj Ray, Dilip Mookherjee, and Alexandre Dmitriev for their valuable comments on earlier drafts of this paper. The usual disclaimer applies.

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