Research

Innovation Through Labor Mobility: Evidence from Non-Compete Agreements, May 2023 (revised: April 2024)

(with Kate Reinmuth) 

Much of the United States workforce is subject to non-compete agreements. Proponents argue that non-competes provide innovation incentives that outweigh negative worker outcomes like suppressed wages. In reality, the causal effect of non-competes on innovation is an open empirical question. To answer it, we leverage plausibly exogenous state-level changes in enforceability, finding that non-competes have a significant negative effect on innovation—a 13% drop in patenting for an average-sized increase in enforceability. Further analysis shows that this effect manifests primarily for incumbents rather than entrants. Moreover, our work suggests a central role for labor mobility as a channel of idea diffusion that increases overall innovation, with inventor mobility expected to fall alongside patenting by 22% for an increase in enforceability of the mean size in our sample.

AI Adoption and Inequality, February 2024

(with Carlo Pizzinelli and Marina M. Tavares) 

Using household microdata, we document how occupational exposure to AI varies across the income distribution and how it compares to the previous wave of routine-biased automation. We show that high-income workers are more exposed to AI displacement than low-income workers whereas automation was more concentrated at the bottom of the income distribution. However, high-income workers in exposed jobs also possess substantial sources of non-labor income and may thus benefit from higher returns to capital from AI. As such, the net effect of AI on total income inequality, factoring in capital income, depends on several competing channels which must be disciplined structurally. To this end, we calibrate a task-based model of production with heterogeneous workers to the UK. The model shows that, unlike the fall in wages at the bottom of the income distribution induced by automation, AI exposure poses a greater risk of declining wages for middle- and high-income workers. This result, however, is sensitive to assumptions about potential AI-labor complementarity and aggregate productivity increases. Meanwhile, for the highest-income workers this negative shock is more than offset by increased capital income. As a result, wage inequality may or may not fall in response to AI, while wealth inequality is likely to increase. Finally, we show how endogenizing firms' technology adoption decisions allows for questions of optimal capital taxation to balance the trade-off between redistribution and productivity growth.

Gen-AI: Artificial Intelligence and the Future of Work, January 2024, IMF Staff Discussion Note 2024/001

(with Mauro Cazzaniga, Florence Jaumotte, Longji Li, Giovanni Melina, Augustus Panton, Carlo Pizzinelli and Marina M. Tavares) 

Artificial Intelligence (AI) has the potential to reshape the global economy, especially in the realm of labor markets. Advanced economies will experience the benefits and pitfalls of AI sooner than emerging market and developing economies, largely due to their employment structure focused on cognitive-intensive roles. There are some consistent patterns concerning AI exposure, with women and college-educated individuals more exposed but also better poised to reap AI benefits, and older workers potentially less able to adapt to the new technology. Labor income inequality may increase if the complementarity between AI and high-income workers is strong, while capital returns will increase wealth inequality. However, if productivity gains are sufficiently large, income levels could surge for most workers. In this evolving landscape, advanced economies and more developed emerging markets need to focus on upgrading regulatory frameworks and supporting labor reallocation, while safeguarding those adversely affected. Emerging market and developing economies should prioritize developing digital infrastructure and digital skills.

Markups, Firm Scale and Distorted Economic Growth, October 2023

(with Jean-Felix Brouilette and Mohamad Adhami)

We study the consequences of markups for long-run economic growth in a model of firm-driven endogenous technological change. In this framework, differentiated firms engage in monopolistic competition, charge heterogeneous markups, and make forward-looking investments in R&D to improve their process efficiency. Markups distort the scale at which these firms operate and therefore affect their incentives to invest in R&D. With dispersion in markups, the introduction of size-dependent subsidies inducing firms to price at marginal cost can alter both the aggregate and cross-firm allocations of such investments. Using firm-level administrative data from France to discipline our model, we find that this intervention increases the long-run growth rate of productivity by 1.2 percentage points per year. Nearly 75% of this faster productivity growth can be achieved by simply reallocating R&D resources across firms, revealing that it is the dispersion in markups, rather than their average level, that is more detrimental to economic growth.

Household Debt and Labour Supply, September 2021, Bank of England Working Paper No. 941

(with Phil Bunn, Jagjit Chadha, Thomas Lazarowicz and Stephen Millard)

In this paper, we first develop a theoretical framework with three types of household: outright homeowners, mortgagors and renters. We then examine empirically how household debt affects the response of labour supply to shocks to income, mortgage interest rates and house prices for each type of household. In line with our framework, we find that negative income shocks lead to lower participation among outright homeowners while increasing mortgagors’ desired hours; surprise rises in interest rates lead to increases in desired hours that are larger the higher is the household’s debt level; and falls in house prices increase mortgagors’ desired hours.

Exposure to Artificial Intelligence and Occupational Mobility: a Cross-Country Analysis, (draft forthcoming)

(with Mauro Cazzaniga, Carlo Pizzinelli and Marina M. Tavares) 

We document historical patterns of workers' transitions across occupations and over the life-cycle for different exposure and complementarity to Artificial Intelligence (AI) in Brazil and the UK. In both countries, college-educated workers frequently move from high-exposure, low-complementarity occupations - those more likely to be negatively affected by AI ("upward transition") - to high-exposure, high-complementarity ones - those more likely to be positively affected by AI. This transition is especially common for young college-educated workers and is associated with an increase in average salaries. Young highly educated workers thus represent the demographic group for which AI-driven structural change could most expand opportunities for career progressions but also highly disrupt entry into the labor market by removing stepping-stone jobs that can be performed by AI. The patterns of upward labor market transitions for college-educated workers look broadly alike in the UK and Brazil, suggesting that the impact of AI adoption on the highly educated labor force could be similar across advanced economies and emerging markets. Meanwhile, workers without a college degree in Brazil face markedly higher chances of moving from better-paid high-exposure and low-complementarity occupations to low-exposure ones, suggesting a higher risk of income loss if AI were to reduce labor demand for the former type of jobs. This research contributes to advancing our understanding of the potential impact of AI on labor markets by providing valuable insights on workers' potential reallocation across occupations.

Misallocation and Inequality

(with Pete Klenow and Emmanuella Kyei Manu)