This paper shows how record-value theory, a branch of statistics that deals with the timing and magnitude of extreme values in sequences of random variables, can be used to nonparametrically identify the offer distribution of wages workers face.Using NLSY wage data, I show that the data supports the hypothesis that the wage offer distribution is Pareto but rejects that it is lognormal.In addition, I show that my approach can be used to construct a bound on the return to job-specific human capital.Using the same NLSY data, I find that job-specific human capital plays only a minor role in the wage growth of the workers in my sample.Instead, wage growth among the young workers in my sample appears to be driven primarily by the accumulation of general human capital as well as on-the-job search.

Previous work has established that recessions involve a “cleansing” effect, so that in downturns, only high productivity jobs remain. But empirical evidence suggests job quality is procyclical: jobs created in recessions are likely to be low-paying and temporary. This paper modifies previous models by adding on-the-job search, which leads to an additional “sullying” effect. Calibration of the model suggests this offsetting sullying effect is likely to be much larger than the cleansing effect, and can account for the procyclical match quality we observe in the data.

Previous work on information and financial markets has focused on a special set of assumptions: agents have exponential utility, and random variables are normally distributed. These assumptions are often necessary to obtain closed-form solutions. We present an example with alternative assumptions, and demonstrate that some of the conclusions from previous literature fail to hold. In particular, we show that in our example, as more agents acquire information, prices do not necessarily become more informative, and agents may have greater incentive to acquire information. Learning can therefore be a strategic complement, allowing for the possibility of multiple equilibria.