We present evidence showing that more expensive cities – measured by rental costs – have not only invested proportionately more in automation but also have seen a higher decrease in the share of routine abstract jobs (clerical workers and routine white collar workers). We propose an equilibrium model of location choice by heterogeneously skilled workers where each location is a small open economy in the market for computers and software. We show that if computers are substitutes to middle skill workers – commonly known as the automation hypothesis – in equilibrium large and expensive cities invest more in computers and software, substituting middle skill workers with computers. Intuitively, in expensive cities, the relative benefit of substituting computers for routine abstract workers is higher, since workers must be compensated for the high local housing prices. Moreover, if the curvature of the production function is the same across skills, the model also delivers the thick tails in large cities’ skill distributions presented by Eeckhout et al. (2014).

Over the last two decades labor market dynamism, measured by flows of workers between employers, declined substantially in the US. During the same period employment polarized into low and high skill jobs. This paper shows that the two trends are linked. First, I provide a framework to study employment and worker flows, where skill intensity of jobs and workers’ skills are complements. I analyze within this framework the effects of routine-biased technological change and the increasing supply of college graduates on labor market flows. When routine-biased technological change displaces mid-skill jobs, it lowers the opportunity to move up to better jobs for low-skilled workers. Similarly, high skilled workers have less opportunity to take stepping stone jobs and are more likely to start employment further up the job ladder, reducing the frequency of transitions between employers. The rising share of college graduates puts further pressure on labor markets by increasing competition for jobs from top to bottom. In equilibrium workers trade down to jobs with lower skill intensity to gain employment, but find it harder to move up as they are competing with more highly educated workers. I quantitatively assess whether such mechanisms contribute to the fall in labor market dynamism, by estimating the model using data on labor market flows. I find that routine-biased technological change accounts for 40% of the decline in job-to-job mobility.


Introduction to Python for Social Scientists

The course gives a short introduction to programming in Python. We also went through several applications: web scraping, web API use and data analysis. For resources and links see the Class Materials