Job market paper

Scale-Biased Technical Change and Inequality

Scale bias in technical change is the degree to which technical change increases the productivity of large relative to small firms. I propose that this dimension of technical change is important for inequality. I first develop a tractable framework with heterogeneous households choosing to work for wages or earn profits as entrepreneurs. Entrepreneurs choose from a set of available production technologies, defined by a fixed and a marginal cost. Large-scale-biased technical change lowers entrepreneurship rates and leads to larger firms on average. With fewer and larger firms, top entrepreneurs are capturing a larger share of the profits which increases top income inequality. Small-scale-biased technical change has the opposite effects. I test the theory by comparing the effects of two technologies that vary in scale bias, but are similar in purpose: steam engines (large-scale-biased) and electric motors (small-scale-biased). Using newly collected data from the United States and the Netherlands, I verify that these two technologies had opposite effects on firm sizes and inequality. Steam engines increased firm sizes, while electric motors decreased them. Steam engines led to increased inequality, electric motors did not. Consistent with scale bias (rather than skill bias), I find that adopting entrepreneurs were the main drivers of inequality increases after steam engine adoption

Winner of the Best Job Market Paper Award, European Economic Association and UniCredit Foundation

Work in progress

Jim Crow and Black Economic Progress After Slavery 

with Lukas Althoff

Quarterly Journal of Economics, Revise and Resubmit

Winner of the 2023 Erik Olin Wright Prize, 2022 Urban Economics Association Prize, 2021 IPUMS USA Research Award

Media: Marginal Revolution, Frankfurter Allgemeine Zeitung, Helsingin Sanomat 


The Missing Link(s): Women and Intergenerational Mobility 

with Lukas Althoff and Harriet Brookes Gray 


Publication

A multi-step kernel-based regression estimator that adapts to error distributions of unknown form

with Jan de Gooijer

Communications in Statistics-Theory and Methods, 50(24), 6211-6230