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Equalizing Outcomes vs. Equalizing Opportunities: Optimal Taxation When Children'’s Abilities Depend on Parents' ’Resources


Alexander M. Gelber


Wharton School, University of Pennsylvania; National Bureau of Economic Research

Matthew Weinzierl


Harvard Business School - Business, Government and the International Economy Unit

August 1, 2012

Harvard Business School BGIE Unit Working Paper No. 13-014

Abstract:     
Empirical research suggests that parents’ economic resources affect their children’s future earnings abilities. Optimal tax policy therefore will treat future ability distributions as endogenous to current taxes. We model this endogeneity, calibrate the model to match estimates of the intergenerational transmission of earnings ability in the United States, and use the model to simulate optimal policy numerically. Optimal policy is more redistributive toward low-income parents than existing U.S. tax policy. The optimal policy increases the probability that low-income children move up the economic ladder, generating a present-value welfare gain of 1.28% of consumption in our baseline case.

Number of Pages in PDF File: 51

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Date posted: August 7, 2012  

Suggested Citation

Gelber, Alexander M. and Weinzierl, Matthew, Equalizing Outcomes vs. Equalizing Opportunities: Optimal Taxation When Children'’s Abilities Depend on Parents' ’Resources (August 1, 2012). Harvard Business School BGIE Unit Working Paper No. 13-014. Available at SSRN: http://ssrn.com/abstract=2125435 or http://dx.doi.org/10.2139/ssrn.2125435

Contact Information

Alexander M. Gelber
Wharton School, University of Pennsylvania ( email )
1403 Steinberg-Dietrich Hall
3620 Locust Walk
Philadelphia, PA 19104-6372
United States
HOME PAGE: http://www.nber.org/~agelber

National Bureau of Economic Research ( email )
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
Matthew Weinzierl (Contact Author)
Harvard Business School - Business, Government and the International Economy Unit ( email )
Soldiers Field Road
Morgan 270C
Boston, MA 02163
United States
Feedback to SSRN (Beta)


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