Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Gibbons and Katz's asymmetric information model of the labormarket predicts that wage losses following displacement should belarger for layoffs than for plant closings. This was borne out intheir empirical work. In this article, we examine how the differencein wage losses across plant closing and layoff varies with race andgender. We find that the basic prediction by Gibbons and Katz holdsonly for white males. We augment their asymmetric information modelwith heterogeneous human capital and show that this augmented modelcan match the data. (c) 2011 by The University of Chicago. Allrights reserved.