Stephan Hobler

The spread of new, transformative technologies often relies on specialized knowledge among workers and managers. When the required expertise is scarce, human capital and worker mobility can become bottlenecks to technology diffusion. To study these dynamics, I develop a theory in which firms and workers accumulate technology-specific expertise through mutual learning, and worker mobility is subject to search frictions. I calibrate the model to the diffusion of predictive AI among U.S. firms, matching empirical patterns of technology adoption and worker flows using comprehensive microdata from LinkedIn. Worker mobility emerges as a key driver of diffusion. When a new technology is introduced, adoption is initially slow due to the lack of experienced workers and concentrated only among the most productive firm-worker pairs. Diffusion then accelerates through the poaching of workers from early adopters, generating an S-shaped diffusion curve. Aggregate output follows a J-curve, with productivity gains taking time to materialize. A counterfactual matched to European-style labor markets with low mobility and long job tenures reveals a trade-off: the European economy delivers modestly higher steady-state output, but slows adoption by two-thirds and reduces welfare gains from the new technology by one-third—potentially contributing to transatlantic productivity gaps in technology-intensive sectors.

We measure the effect of deficits on inflation using a “high frequency narrative approach”. We identify an event that released news about the 2021 deficits—the Georgia Senate election runoff—and size the shock using new narrative data from investment banks. We then study the high frequency response of inflation forecasts from asset prices. We estimate that the price level was expected to increase by 22–38 basis points over 2021–22, meaning the 2021 deficits caused a significant share of the 2021–22 inflation. Standard models—such as the Fiscal Theory of the Price Level and the heterogeneous agent New Keynesian model—match the inflation response.

The large spike at zero in the distribution of year-to-year nominal wage changes in household surveys is often seen as evidence of nominal wage rigidity. But measurement error—especially from workers rounding their reported wages—can exaggerate this spike. Using U.S. Current Population Survey data, we adjust for potential rounding behavior and find that the zero-change spike falls from 15–20 percent to 7–12 percent, aligning closely with recent estimates from administrative data.

This paper studies the implications of the emergence of dominant firms for aggregate market power and the growth of small fringe firms. The greater visibility of large sellers endows them with a matching advantage in the product market which they use to charge high markups. Long-run competitive forces discipline the behaviour of a granular seller and in a natural benchmark model firm dynamics of fringe firms are neutral under the presence of a large seller. I then present an extension in which the presence of the granular seller raises aggregate markups, lowers the growth of the most productive firms and slows down the reallocation of customers from unproductive to productive sellers. This sheds light on a new mechanism of how dominant firms could shape the aggregate behaviour of the macroeconomy.

This paper proposes a robust and efficient method of solving models with endogenous borrowing constraints and strategic default in continuous-time. Using a change of variable the free-boundary problem is transformed into a boundary value problem on a fixed domain in which default thresholds enter explicitly as endogenous variables. Default thresholds can then be solved using algorithms that are analogous to solving for the fixed point of prices in standard general equilibrium models. I demonstrate the approach using two sovereign default models featuring short-term and long-term debt, respectively. At last, I show how the method can be applied to time-dependent problems featuring transition dynamics.