Downward Rigidity in the Wage for New Hires, with Bledi Taska [Job Market Paper]
Abstract: Downward wage rigidity is central to many explanations of unemployment fluctuations. In benchmark models, the wage for new hires is particularly important, but there is limited evidence of downward rigidity on this margin. We introduce a dataset that tracks the wage for new hires at the job level—across successive vacancies posted by the same job title and establishment. We show that the wage for new hires is rigid downward but flexible upward, in two steps. First, the nominal wage rarely changes at the job level. When wages do change, they fall infrequently, suggesting a constraint from below. Second, when unemployment rises, wages do not fall—but wages do rise strongly as unemployment falls. We show that prior strategies, which study the average wage for new hires, cannot detect downward rigidity due to changing job composition. We then develop a tractable dynamic wage bargaining model with downward rigidity. We fit the model to our findings, and uncover state dependent asymmetry in unemployment dynamics. When there has been a contraction in the recent past, unemployment responds symmetrically to subsequent labor demand shocks; when there has recently been an expansion, unemployment is subsequently twice as sensitive to negative as to positive shocks.
The Falling Labor Share Dampens Unemployment Fluctuations [slides] [draft coming soon!]
The labor share fell in the US and worldwide after the 1980s. This paper argues that the falling labor share dampens unemployment fluctuations. First, I study a class of labor search models with capital. The falling labor share lowers the sensitivity of unemployment to labor demand shocks, regardless of whether rising capital or rising rents govern the labor share. Second, I show empirically that low labor shares dampen employment fluctuations. I exploit labor share variation within industries and between regions, to show that low labor share markets are less sensitive to the aggregate business cycle. The result holds in tradable industries—thereby sweeping away industry product demand—and instrumenting for the labor share with state unionization. Third, I test the secular predictions of the model. The model predicts that the falling labor share increases vacancy posting. I document this pattern at the industry-state level, using proprietary data on the near-universe of online vacancies.
Systemic Risk Shifting in Financial Networks, with Matthew Elliott and Co-Pierre Georg
Revise and Resubmit at Journal of Economic Theory
Abstract: Banks face different but potentially correlated risks from outside the financial system. Financial connections can help hedge these risks, but also create the means by which shocks can propagate. We examine this tradeoff in the context of a new stylised fact we present: German banks are more likely to have financial connections when they face more similar risks—potentially undermining the hedging role of financial connections and contributing to systemic risk. We find that such patterns are socially suboptimal, but can be explained by risk-shifting. Risk-shifting motivates banks to correlate their failures with their counterparties even though it creates systemic risk.
AI and Jobs: Evidence from Online Vacancies [slides], with Daron Acemoglu, David Autor and Pascual Restrepo
Abstract: Artificial intelligence (AI) technologies are developing rapidly, yet there is limited evidence on how AI is affecting hiring in job categories most likely to be either substituted or complemented by AI. We study the impact of AI on US hiring from 2010 onwards, using establishment level data on vacancies with detailed occupation information comprising the near-universe of online vacancies in the US. We classify establishments as “AI exposed” — that is, likely to replace workers with AI — based on their detailed skill mix garnered from their job postings 2010. We offer three sets of findings. First, we document rapid growth in AI related vacancies over 2010-2018 that is not limited to the Information Technology sector and is greater in AI-exposed establishments. Second, AI-exposed establishments reduce vacancy postings in occupations that are “at risk” of AI-replacement and increase vacancy postings in occupations that are not at risk of AI-replacement. These countervailing effects are essentially fully offsetting: exposed establishments do not significantly alter total vacancy postings. Finally, we find suggestive evidence that AI adoption has non-neutral effects on aggregate vacancy postings at the local labor market level. When an establishment posts more AI vacancies, other establishments in the same labor market post fewer overall vacancies. These “spillovers” are confirmed when we apply a novel identification strategy that leverages the occupation mix in establishments' non-local headquarters.
The Slope of the Phillips Curve: Evidence from US States [draft coming soon], with Juan Herreño, Emi Nakamura and Jón Steinsson
We estimate the slope of the Phillips curve in the cross section of U.S. states using newly constructed state-level price indexes for non-tradable goods back to 1978. We develop a panel-data identification approach based on tradeable demand spillovers. In contrast to recent research, we find that the Phillips curve has been if anything steeper since 1985 than it was during the Volcker disinflation. We use a multiple region model to infer the slope of the aggregate Phillips Curve from our regional estimates. We show how our findings are consistent with the behavior of aggregate inflation in the early 1980's, once aggregate inflation is measured in a consistent way going back in time. Our results suggest that the sharp drop in inflation in the early 1980s was due to shifting expectations about long-run monetary policy as opposed to a steep Phillips curve.
Research in Progress
The Labor Share, Superstar Firms and Business Cycles, with Christina Patterson
National Chains and National Wage Setting, with Christina Patterson