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Industry Effects and Appropriability Measures in the Stock Market’s Valuation

The mechanisms present in these models however, cannot generate the empirical patterns documented here. These models will predict that workers move to better matches over time and stay there. This will thus produce a downward sloping profile for over-education over the lifecycle instead of a U-shape. To generate these patterns in a model, I borrow insights from the literature on lifecycle wage growth and human capital (see Rubinstein and Weiss (2006) and Sanders and Taber (2012)). This literature has successfully explained different moments of lifecycle wages such as mean and variance. In these models, workers make active human capital investments over their career where the opportunity cost of investment is forgone earnings. Human capital investments decline with age and worker productivity is thus hump-shaped over the life-cycle. On a theoretical level I combine vertical sorting into occupations with human capital investment. Most matching models have assumed that the distribution of attributes on both sides of the market is exogenous and fixed. Recently some dynamic matching papers have started to relax this assumption and analyze cases where the attributes change based upon the match (see for example Anderson and Smith (2010)). In my setup the attributes of the occupations stay fixed but the productivity of the workers evolves over time based on their human capital investments. Human capital investment in turn depend not only on the occupation that the worker is currently matched with but also upon his chances of moving up the occupation ladder.