Ömer Faruk Koru
Over the last 50 years, the share of wealth held by the richest 1% of individuals in the US has increased by 30%. This paper analyzes the effects of improvements in automation technology on the rise of the top wealth share. I build an incomplete market model with entrepreneurs and a collateral constraint. Automation impacts wealth concentration through two channels. First, it decreases the severity of diseconomies of scale in the entrepreneurial sector, and, hence, it increases income concentration. Since the wealth distribution follows the income distribution, it affects wealth concentration. Second, it raises capital demand, which tightens the collateral constraint and, in turn, increases the dispersion of the return to capital. I calibrate the model to the 1968 US economy to quantitatively analyze the impact of an improvement in automation. I analyze the impact of an unexpected increase in automation technology to the 2016 level. In the model, the capital share of income equals the automation level; hence, I measure the increase in automation by the change in the capital share. In the new steady-state, the top wealth share increases by 8%. In other words, the model can explain one-fourth of the rise in the wealth share of the top 1%. In consumption equivalence terms, workers’ welfare increased by 4% and entrepreneurs’ welfare increased by 8%.
Top income inequality has been increasing in the US. Hence, the Pareto parameter associated with the top income distribution is decreasing. In this paper, we provide a theory that links automation technology to the Pareto parameter of the top income distribution. We construct a model in which the span of control is defined by the measure of labor used in production. We model this as a convex cost of labor. This convex cost generates a decreasing returns to scale production function. Improvements in automation enables entrepreneurs to substitute labor with capital and decreases the severity of diseconomies of scale. This leads to higher returns to entrepreneurial skills, a decrease in the Pareto parameter, and an increase in top income inequality. We rationalize the convex cost of labor using a theory of efficiency wages. Using cross-industry and cross-country data, we show that there is a significant correlation between automation and top income inequality.
Recent empirical work shows a strong positive correlation between job-to-job transition rates and nominal wage growth in the U.S. First, using time series regressions, structural monetary policy shocks, and survey data on search effort we provide evidence that inflationary shocks cause higher job-to-job transitions in the subsequent years. Second, to understand the aggregate implications, we build a structural model with aggregate shocks and competitive on-the-job search in which wages react sluggishly to inflation. In periods with high inflation, the decline in real wages incentivizes the employees to search on-the-job more actively, to negotiate a new contract, but also to be less selective in their search behavior. This creates a fundamental trade-off: increased search effort leads to more job-to-job transitions while being less selective reduces the expected efficiency gain in each transition. Therefore, the effect on output becomes ambiguous. Third, we calibrate the model to the U.S. economy and confirm that the output response to inflation shock is non-monotonic. Importantly, our paper highlights a novel role for inflation: the monetary authority can stimulate productivity with an inflationary shock through job-to-job transitions.
Ph. D., Economics, 2021
University of Pennsylvania, Philadelphia, PA, USA.
MA, Economics, 2015
Sabanci University, Istanbul, Turkey.
BA, Economics (with minor in Mathematics), 2013
Sabanci University, Istanbul, Turkey.