The Geography of Business Dynamism and Skill-Biased ... The Geography of Business Dynamism and...
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The Geography of Business Dynamism and Skill-Biased Technical Change⇤
Hannah Rubinton †
December 4, 2019
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This paper seeks to explain three key components of the growing regional disparities in the
U.S. since 1980, referred to as the Great Divergence by Moretti (2012). Namely, big cities saw
a larger increase in the relative wages of skilled workers, a larger increase in the relative supply
of skilled workers, and a smaller decline in business dynamism. These trends can be explained
by differences across cities in the extent to which firms adopt new skill-biased technologies.
In response to the introduction of a new skill-biased, high fixed cost but low marginal cost
technology, firms endogenously adopt more in big cities, cities that offer abundant amenities
for high-skilled workers and cities that are more productive in using high-skilled labor. The
differences in adoption can account for the increasing relationship between skill intensity and
city size, the divergence of the city size wage premium by skill group and changing cross-sectional
patterns of business dynamism. I document a new fact that firms in big cities invest more in
Information and Communication Technology per employee than firms in small cities, consistent
with patterns of adoption in the model.
⇤I am extremely grateful to my advisor Richard Rogerson and to my committe, Ezra Oberfield, Teresa Fort and Stephen Redding, for their invaluable help and guidance. For helpful comments, I thank Esteban Rossi-Hansberg, Gene Grossman, Eduardo Morales, Kamran Bilir, Mark Aguiar, Gianluca Violante, Oleg Itskhoki, Nobu Kiyotaki, Gregor Jarosch, Jonathan Dingel, Rebecca Diamond, Chris Tonetti, Benjamin Pugsley, Andrew Bernard, David Chor, Peter Schott, Ethan Lewis, Bob Staiger, Nina Pavcnik, Treb Allen, Laura Castillo-Martinez, Elisa Giannone, Fabian Eckert, Gideon Bornstein, and other visitors to and participants of the IES Student Seminar Series. Thank you to my classmates for their continuous support and encouragement. This research benefited from financial support from the International Economics Section (IES) and the Simpson Center for the Study of Macroeconomics. Any opinions and conclusions expressed herein are those of the author(s) and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed.
†Department of Economics, Princeton University; Email: email@example.com
Since 1980, economic growth in the United States has been concentrated in large, urban areas while small rural areas have fallen further behind. Moretti (2012) refers to this growing gap as the Great Divergence. The Great Divergence has become an increasing concern for policy makers as they have learned of its implications for gaps in health outcomes, disintegrating social connections, economic mobility and political tension1. For policies to effectively address these trends, it is necessary to understand the underlying causes.
This paper seeks to explain several key components of the Great Divergence: big cities saw a larger increase in the relative wages of skilled workers and big cities saw a larger increase in the relative supply of skilled workers. I also introduce a new fact to the literature on the Great Divergence: big cities saw a smaller decline in business dynamism, as measured by the rates of establishment entry and exit and the rates of job creation and destruction. While in 1980 big and small cities had similar rates of business dynamism, today big cities are more dynamic than small cities. The simultaneous increase in high-skilled wage growth and the abundance of high-skilled workers is typically explained at the aggregate level by skill-biased technical change (SBTC) (Katz and Murphy, 1992). But, this aggregate story does not address the question of why SBTC should have occurred differently across cities.
To explain these facts I develop a spatial model in which the amount of skill-biased technical change will endogenously vary across cities. The key insight from my model is that firms face different incentives to adopt a new technology based on the characteristics of the city in which they are located. I consider the introduction of a new technology that lowers the marginal cost for a firm, but has a higher fixed cost and uses high-skilled labor more intensively. Firms will adopt more in cities that are big, cities with amenities attractive to high-skilled workers, and cities that are more productive in using high-skilled labor. The extent of adoption and therefore, skill-biased technical change, endogenously varies across cities, explaining the changing relationships between wages and skill intensity with city size.
Differences in adoption also affect rates of business dynamism across cities. In cities with high adoption rates, small and unproductive firms are less profitable, increasing their probability of exit (a selection effect similar to Melitz (2003)), which increases the equilibrium rate of turnover. Since these less productive firms are using the old technology which places more weight on low-skilled labor, the increase in selection amplifies the differences across cities in the share of firms adopting the new technology, and therefore, skill-biased technical change.
A key feature of the response to the new technology is that adoption is higher in big cities. The literature has emphasized Information and Communication Technologies (ICT) as being intimately related to skill-biased technologies (Krueger, 1993; Autor et al., 1998). Thus, as evidence for this
1For papers that discuss these implications see Austin et al. (2018), Chetty and Hendren (2018), Chetty et al. (2016), Autor et al. (2019), and Autor et al. (2016).
mechanism, I use data on investment in ICT to establish a novel empirical fact: firms in big cities invest more intensively in ICT.
The changing relationship of these economic variables with respect to city size has important implications for welfare inequality, economic mobility, and declining regional convergence (Diamond (2016), Moretti (2013), Giannone (2017), and Autor (2019)). Understanding the drivers of these trends is key to policy makers who wish to influence them. I make progress by showing that differences across cities in technology adoption will amplify existing geographic inequalities along the dimensions for which a new technology is most suitable. Policies that attempt to reallocate economic activity across cities should target the incentives firms face to adopt new technologies, but they should also consider how such policies will affect the aggregate rate of technology adoption and, therefore, aggregate growth.
I do three things in this project. My first contribution is to document empirical changes in the distribution of economic activity across cities since 1980. I focus on three cross-sectional rela- tionships of interest. First, I introduce a new fact to the literature on the Great Divergence, the changing relationship between business dynamism and city size since 1980. In 1980, these measures were similar in big and small cities. By 2014, big cities exhibited much faster rates of dynamism than small cities. The second relationship is the correlation between average wages and city size, referred to as the city size wage premium, by skill group. While the city size wage premium was similar for high- and low-skilled workers in 1980, by 2014, the city size wage premium for high- skilled workers was almost twice that of low-skilled workers. This divergence was driven by both an increasing city size wage premium for high-skilled workers and a decreasing city size wage premium for low-skilled workers. Third, I document that the relationship between skill intensity, or the ratio of high- to low-skilled workers, and city size has increased since 1980.
My second contribution is to build a model that matches the salient features of the data in 1980. I embed a rich model of firm dynamics into an otherwise standard spatial equilibrium model with high- and low-skilled workers, allowing a joint consideration of the geographic distribution of relative wage inequality and firm dynamics. The model features two types of workers, high- and low- skilled, who are freely mobile across space. Workers pay rent to live and work in a city and receive an amenity specific to the city where they live and their skill level. Workers have idiosyncratic preferences for each city and choose to live in the city that gives them the highest utility. Within each city there is a continuum of monopolistically competitive firms that use high- and low-skilled labor to produce non-tradable intermediate goods. Firms pay a fixed cost in units of high- and low-skilled labor, and they pay rent on a unit of floor space in the city where they produce. Firms receive idiosyncratic productivity shocks and, therefore, make dynamic entry and exit decisions. Entrants choose the city where they want to enter and produce. In equilibrium, firms are indifferent between entering in different cities. I calibrate the model steady state to the data in 1980 and show that it can match the key features of the data.
My third contribution is to use the model to analyze the diffusion of a new technology that favors skilled workers. I consider the introduction of a new technology that has an absolute productivity
advantage, but is more skill-biased in that the marginal productivity of high-skilled labor is higher than that of the old technology. Firms can choose to adopt the n