In a seminal paper, Gibbons and Katz (1991) develop and empirically test an asymmetric information model of the labor market. The model predicts that wage losses following displacement should be larger for layoffs than for plant closings, which was borne out by data from the Displaced Workers Survey (DWS). In this paper, we take advantage of many more years of DWS data to examine how the difference in wage losses across plant closings and layoffs varies with race and gender. We find that the differences between white males and the other groups are striking and complex. The “lemons effect” of layoffs holds for white males, as in the Gibbons-Katz model, but not for the other three demographic groups (white females, black females, and black males). These three all experience a greater decline in earnings at plant closings than at layoffs. This results from two reinforcing effects. First, plant closings have substantially more negative effects on minorities than on whites. Second, layoffs seem to have more negative consequences for white men than the other groups. These findings suggest that the Gibbons-Katz asymmetric information model is not sufficient to explain all of the data. We augment the model with heterogeneous human capital and show that this model can explain the findings. We also provide some additional evidence suggestive that both asymmetric information and heterogeneous human capital are important. In support of both explanations, we demonstrate that the racial and gender effects are surprisingly robust to region, industry, and occupation controls. To look at the asymmetric information, we make use of the Civil Rights Act of 1991, which induced employers to lay off “protected” workers in mass layoffs rather than fire them for cause. As a result, relative to whites, a layoff would be a more negative signal for blacks after 1991 than before. If information is important, this would in turn imply that blacks experience a relatively larger loss in earnings at layoffs after 1991 than prior; and that’s what we find in the data. In addition, as further evidence for heterogeneous human capital, we document for the first time in the literature that the two types of layoffs reported in the DWS data, namely layoffs due to “slack work” and “position abolished,” have very different features when compared to plant closings. Finally, we simulate our model and show that it can match the data.