Research

Innovation Through Inventor Mobility: Evidence from Non-Compete Agreements

(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. Yet, the causal effect of non-competes on innovation is an open empirical question. Leveraging plausibly exogenous state-level changes in the enforceability of non-compete agreements, we find a significant negative effect on innovation: a 14% decrease in patenting for an average-sized increase in enforceability. This effect is even larger for the most novel and innovative patents and firms and cannot be explained by patent-trade secret substitution or cross-state spillovers. Further analysis shows that these negative effects on innovation (i) cannot be explained by entry alone; and (ii) may instead operate through reduced labor mobility and knowledge flows between firms, with inventor moves declining and firms becoming more technologically isolated in response to stronger NCAs.

AI Adoption and Inequality

(with Carlo Pizzinelli and Marina M. Tavares) 

There are competing narratives about artificial intelligence's impact on inequality. Some argue AI will exacerbate economic disparities, while others suggest it could reduce inequality by primarily disrupting high-income jobs. Using household microdata and a calibrated task-based model, we show these narratives reflect different channels through which AI affects the economy. Unlike previous waves of automation that increased both wage and wealth inequality, AI may actually decrease wage inequality by disproportionately displacing high-income workers. However, these same workers are better positioned to benefit from higher capital returns, likely leading to increased wealth inequality. When AI adoption is endogenized, the latter effect is particularly pronounced, as the potential cost savings from automating high-wage tasks may drive significantly higher adoption rates. Models that ignore the endogeneity of adoption risk understating the trade-off policymakers face between inequality and efficiency: measures to mitigate growing wealth inequality risk dampening productivity gains by reducing AI adoption.

Variable Markups, Incomplete Pass-Throughs, and R&D Misallocation

(with Jean-Felix Brouilette and Mohamad Adhami)

Assumptions about demand influence the positive and normative implications of growth models. In light of the growing evidence of variable markups and positive yet incomplete pass-throughs, we develop an endogenous growth model with a Kimball (1995) demand system. It features differentiated firms engaging in monopolistic competition and making forward-looking investments in R&D to improve their process efficiency. The model succeeds in matching the evidence on markups and pass-throughs by featuring a lower elasticity of demand at lower prices. A novel implication of our model is that market power does not only distort the overall level of innovation, but also the cross-firm allocation of R&D resources. Using firm-level administrative data from France to discipline our model, we find that this R&D misallocation slows down aggregate growth by 0.92 percentage points. 

Exposure to Artificial Intelligence and Occupational Mobility: A Cross-Country Analysis, June 2024, IMF Working Paper No. 2024/116

(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 levels of 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) 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 progression but also highly disrupt entry into the labor market by removing stepping-stone jobs. These 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, non-college workers 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.

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.

Misallocation versus Inequality

(with Chang-Tai Hsieh, Pete Klenow and Emmanuella Kyei Manu)

Policies that constrain firm growth are common in developing countries, despite their negative effects on productivity. This prevalence may reflect concerns that the reallocation of resources toward larger, more productive firms could exacerbate inequality. Using detailed Indian household and manufacturing microdata, we document that larger firms have higher revenue productivity but also employ more skilled workers and pay higher wages, suggesting a fundamental trade-off between efficiency and equality. We find that removing size-dependent distortions could substantially increase manufacturing output through better resource allocation. However, this reallocation would increase both wage and consumption inequality due to imperfect substitutability between skilled and unskilled workers. We develop a model with heterogeneous firms and non-homothetic skill intensity in production to quantify these effects and evaluate potential redistributive policies.

Designing an AI Bond for Growth and Shared Prosperity in the UK, July 2024, UK Day One Policy Brief

(with Emma Casey and Helena Roy) 

Capitalising on the growth potential of AI and associated technologies provides a vital opportunity to improve UK economic growth. This is an urgent issue: UK real productivity growth has fallen below peer economies since 2008, driven in part by chronic underinvestment. Policymakers face two primary economic challenges with respect to integrating AI into the economy: 1) Under-investment from the private market given a lack of coordination and inability to capture excess returns from agglomeration and spillovers. 2) Exacerbated inequality from a heightened return on capital and regional concentration. We propose the UK government design and operationalise an AI bond to link and solve these challenges. Connecting the two missions — boost innovation and distribute returns — via a financial instrument could coordinate and increase investment in innovation while distributing the returns more equitably, without distortionary measures. Purchases of the bond would be used to build on the UK’s head start in AI, particularly in London. As a centralised, public investment vehicle, the resulting fund could design an investment strategy that targets all components of the AI innovation pipeline, creating excess returns from agglomeration and spillovers. Proceeds from the bond — bolstered by the excess return — would be used to distribute the gains across the UK as a whole, including to those who are not directly involved or invested in the AI industry.

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.

The financial position of British households: evidence from the 2018 NMG Consulting survey, December 2018, Bank of England QB

(with Lisa Panigrahi and Harry Rigg)

Could knowledge about Central banks impact households’ expectations?, October 2018, Bank Underground

The financial position of British households: evidence from the 2017 NMG Consulting survey, December 2017, Bank of England QB

(with Philippe Bracke, Hasdeep Sethi and Catherine Shaw)