The Return to Knowledge Hierarchies

67 Pages Posted: 29 Jun 2007

See all articles by Luis Garicano

Luis Garicano

Centre for Economic Policy Research (CEPR); IE Business School

Thomas N. Hubbard

Northwestern University - Department of Management & Strategy; National Bureau of Economic Research (NBER)

Multiple version iconThere are 3 versions of this paper

Date Written: February 2007

Abstract

Hierarchies allow individuals to leverage their knowledge through others' time. This mechanism increases productivity and amplifies the impact of skill heterogeneity on earnings inequality. To quantify this effect, we analyze the earnings and organization of U.S. lawyers and use the equilibrium model of knowledge hierarchies in Garicano and Rossi-Hansberg (2006) to assess how much lawyers' productivity and the distribution of earnings across lawyers reflects lawyers' ability to organize problem-solving hierarchically. We analyze earnings, organizational, and assignment patterns and show that they are generally consistent with the main predictions of the model. We then use these data to estimate the model. Our estimates imply that hierarchical production leads to at least a 30% increase in production in this industry, relative to a situation where lawyers within the same office do not.

Keywords: Hedonics, hierarchy, matching, scale of operations effects, sorting, Structural Estimation

JEL Classification: D31, J31, J41, L22, L23, L84

Suggested Citation

Garicano, Luis and Garicano, Luis and Hubbard, Thomas N., The Return to Knowledge Hierarchies (February 2007). CEPR Discussion Paper No. 6077, Available at SSRN: https://ssrn.com/abstract=997389

Luis Garicano (Contact Author)

Centre for Economic Policy Research (CEPR)

London
United Kingdom

IE Business School ( email )

Calle María de Molina, 11
Madrid, 28006
Spain

Thomas N. Hubbard

Northwestern University - Department of Management & Strategy ( email )

Kellogg School of Management
2001 Sheridan Road
Evanston, IL 60208
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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