Shadow Wages, Allocative Inefficiency, and Labor Supply in Smallholder Agriculture

35 Pages Posted: 28 Jun 2007  

Christopher B. Barrett

Cornell University - Charles H. Dyson School of Applied Economics & Management

Shane M. Sherlund

Federal Reserve Board of Governors

Akinwumi A. Adesina

The Rockefeller Foundation

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Abstract

This paper introduces a method for estimating structural labor supply models in the presence of unobservable wages and deviations of households' marginal revenue product of self-employed labor from their shadow wage. This method is therefore robust to a wide range of assumptions about labor allocation decisions in the presence of uncertainty, market frictions, locational preferences, etc. We illustrate the method using data from rice producers in Côte d'Ivoire. These data, like previous studies, reveal significant, systematic differences between shadow wages and the marginal revenue product of family farm labor. We demonstrate how one can exploit systematic deviations, in the present case related to household characteristics such as the land/labor endowment ratio, to control for both unobservable wages and prospective allocative inefficiency in labor allocation in structural household labor supply estimation.

JEL Classification: O1, J0, C3, Q1

Suggested Citation

Barrett, Christopher B. and Sherlund, Shane M. and Adesina, Akinwumi A., Shadow Wages, Allocative Inefficiency, and Labor Supply in Smallholder Agriculture. Agricultural Economics, Forthcoming. Available at SSRN: https://ssrn.com/abstract=997105

Christopher B. Barrett (Contact Author)

Cornell University - Charles H. Dyson School of Applied Economics & Management ( email )

315 Warren Hall
Ithaca, NY 14853-7801
United States
607-255-4489 (Phone)
607-255-9984 (Fax)

HOME PAGE: http://aem.cornell.edu/faculty_sites/cbb2/

Shane M. Sherlund

Federal Reserve Board of Governors ( email )

20th and C Streets, NW
Mailstop 93
Washington, DC 20551
United States
202-452-3589 (Phone)
202-728-5887 (Fax)

Akinwumi A. Adesina

The Rockefeller Foundation ( email )

420 Fifth Avenue
New York, NY 10018-2702
United States

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