Testing for Labor Rationing: Revealed Preference Estimates from Demand Shocks
Project Outcomes Statement
This research project provides a test for and quantification of the existence of surplus labor---individuals who are involuntarily rationed out of the wage labor market---in low income communities. In both the U.S. and in developing countries, many social programs are predicated on the idea of surplus labor; yet there has been no empirical test of its existence.
This research utilized a field experiment to obtain revealed preference estimates of labor rationing, using rural labor markets in India as the test bed. The field experiment involved generating factory jobs in rural labor markets to test the labor surplus theory across different times of the year. More specifically, a large transitory labor demand shock is generated by recruiting up to 24 percent of workers in surrounding villages to work in nearby factory jobs for one month. To test the labor surplus theory, we examine how this external demand shock affects wages and employment in the labor market.
In response to the hiring shocks, we find that in peak months, wages increase instantaneously and local aggregate employment declines. In lean months, consistent with severe labor rationing, wages and aggregate employment are unchanged, with positive employment spillovers on remaining workers---indicating that over a quarter of labor supply is rationed. We also find that at least 24% of lean self-employment among casual workers occurs because they cannot find jobs. Consequently, because self-employment disguises labor rationing, traditional survey approaches mis-measure labor market slack.
The results of this research shed light on the functioning of labor markets in low income and high unemployment communities, and provide guidance on how to craft policies to reduce unemployment or to combat the effects of such high unemployment rates. In addition, by revealing which types of self-employed small entrepreneurs prefer wage work, the findings have implications for how to better target programs to enable small business growth in high unemployment settings.
Supported by the National Science Foundation grant #1658924
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