Assortative Learning

Abstract

Because of sorting, more skilled workers are more productive in higher type firms. They also learn at different rates about their productivity and therefore have different wage paths across firms. We show that under supermodularity there is always Positive Assortative Matching: differential learning is always dominated by the productivity. Surprisingly, this holds even if learning is faster in the low type firm. The key assumption driving this result is Bayesian updating and that this is a pure learning model. The model provides realistic predictions about wage variance, turnover and the wage distribution. We also derive a new equilibrium condition in this class of continuous time models in addition to the common smooth-pasting and valuematching conditions. This no-deviation condition captures sequential rationality and results in a restriction on the second derivative of the value function. Keywords. Sorting. Learning. Labor Market Turnover. Matching. Diffusion Process. Continuous Time Games. Supermodularity. Experimentation.

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