Research

This paper investigates the fundamental trade-off between firm specialization and diversification. While management theories emphasize core competency to maximize efficiency, finance favors diversification to mitigate cash flow volatility. We develop a heterogeneous-firm general equilibrium model to quantify these forces. We devise a novel empirical approach, grounded in theory, to measuring them. We first extract granular revenue shares from 10-K filings using a large language model. Next, we use the co-movement between sectoral aggregates as an inverse measure of the economic distance between them. The observed relationship between scope and markups at the firm level pins down the price of risk. These sufficient statistics also recover productivity at the firm-sector level. The results reveal that risk considerations significantly reduce aggregate output by 10%. We also find that firms are more likely to operate in related sectors, where they tend to be more productive on average.

We investigate how households adjust consumption to aggregate and idiosyncratic persistent income shocks using PSID data from 1999 to 2021. We document that households respond almost equally to aggregate and idiosyncratic income shocks, even though the aggregate component of income is much more persistent. This pattern is difficult to reconcile with the standard full-information permanent-income logic, which predicts that more persistent shocks should induce larger consumption responses. A quantitative full-information Huggett model with aggregate risk, disciplined by the estimated income process, predicts far greater pass-through of aggregate shocks than is observed in the data. We show that incomplete information about the source of income fluctuations provides a natural resolution: households observe total income as a signal to extract the aggregate component, and therefore under-react to aggregate shocks. Embedding this inference problem in a heterogeneous-agent model aligns the model-implied estimates more closely with the empirical pass-through estimates and implies substantially weaker aggregate consumption responses to aggregate shocks.

In this paper, I study how changes in the productivity distribution affect the division of labor income between skilled and unskilled workers, as well as the skill premium. The literature on the rise of superstar firms links the emergence of highly productive firms to a decline in the aggregate labor share. I show that the same phenomenon also widens the gap between skilled and unskilled workers. I develop a Burdett–Mortensen search model with heterogeneous labor types and capital-skill complementarity in production and calibrate it to data on US job flows. The calibrated model shows that a thickening of the right tail of the firm productivity distribution disproportionately reduces unskilled labor’s share and increases the skill premium.

Why does fiscal policy become less effective at stimulating private investment precisely when intervention is most valuable? We develop a general-equilibrium theory in which ambiguity-averse firms act as if adverse outcomes are more likely. This generates endogenous required-return wedges that weaken their response to improved investment opportunities. This mechanism creates an ambiguity trap: as pessimistic belief distortions increase, the social value of additional capital rises while the private investment response to conventional fiscal stimulus falls. A central implication is that ambiguity changes the ranking of fiscal instruments—policies whose benefits depend on uncertain future profits lose effectiveness as ambiguity rises, while front-loaded investment incentives and policies that insure downside risk are more effective.

In this paper, I examine how firms' pricing decisions are shaped by their time-varying beliefs about marginal costs. I develop a sticky-price general equilibrium model in which firms' sentiments evolve dynamically in response to cost shocks, and use it to study how monetary shocks propagate through the economy. The model highlights that the dynamic evolution of firms' sentiments is essential to reproduce the empirical responses of prices and output to monetary policy shocks.

In this paper, we investigate how AI reshapes career ladders across occupations. Combining task-level AI usage measures from Anthropic with career histories and job postings from Revelio Labs, we examine whether AI weakens entry-level jobs that serve as pathways to skill accumulation and higher earnings, while also potentially lowering barriers to more advanced work at the same time. Preliminary evidence suggests that occupations with higher AI exposure have experienced larger declines in junior-level hiring than less-exposed occupations. These occupations have also historically offered greater mobility from junior to senior positions, suggesting that AI may be disproportionately affecting important pathways for career advancement and social mobility. At the same time, we find evidence of lower entry requirements for senior positions. We use a general equilibrium model to study the implications of these competing forces for career progression, social mobility, and inequality.