We examine the content of newly emerging job categories over an 80-year period and the countervailing roles of labor-augmenting and automating innovations in generating demand for new work. The distribution of new work emergence polarized from middle-paid production and clerical occupations over 1940–1980, to high-paid professional and, secondarily, low-paid services since 1980. While the demand-eroding effects of automation innovations have intensified in the last four decades, the demand-increasing effects of augmentation innovations have not.

We propose a simple and computationally tractable methodology for computing similarity between two documents along with economic applications of the method.

Public firms with high labor productivity have a large and expanding labor market competitive advantage. Using firm-specific stock returns to estimate heterogeneous labor supply elasticities by labor productivity and across time, calibrated to a dynamic wage posting model featuring costly hiring, I estimate wage markdowns which largely explain: a wide cross-sectional labor share spread by productivity; the public firm aggregate labor share decline from 1991-2014; and productive firms’ high valuations given their modest investment rates. Cashflows from wage markdowns are worth two-fifths of aggregate capital income. Market power over skilled workers may play an important role in these patterns.

The returns and risk premia of stocks with similar characteristics but different levels of ownership comove far more with shocks to the risk-bearing capacity of financial intermediaries. This implies that intermediaries are not a veil, even within the least-intermediated asset class.

We develop measures of workers’ exposure to labor saving and labor augmenting technologies. Labor-saving technologies uniformly predict earnings declines for individual incumbent workers; labor-augmenting innovations only do so for skilled incumbents, but they also predict higher aggregate occupational labor demand. We interpret our findings through a model with automation and skill displacement, and use it to make predictions about AI and worker earnings. Previously titled ‘Technology, Vintage-Specific Human Capital, and Labor Displacement: Evidence from Linking Patents with Occupations.’