Working Papers

Super-Robust Endogenous Growth: Theory and Estimation with Pietro Peretto [Online presentation]
We propose an endogenous growth model that accommodates increasing, constant, or decreasing aggregate returns to scale with respect to the growth-driving factor: quality-improving knowledge accumulated by firms in-house. When aggregate production is non-linear in firm knowledge, the profitability of firms reflects that property, and entry (new product creation) responds accordingly. The consequent changes in market share offset the non-constant aggregate returns to scale and deliver constant firm-level returns to innovation in steady state. Because returns to innovation are constant, the steady-state growth rate of income per capita is constant and fully endogenous (i.e., dependent on policy parameters). The non-linearity with respect to the growth driving factor has testable implications for convergence dynamics. Specifically, the speed of convergence is decreasing (increasing) in the distance from the steady state when aggregate returns to firm knowledge are increasing (decreasing), causing asymmetric convergence dynamics. This propagation mechanism ensures that, subject to symmetric shocks, the average growth rate across shocks differs from the steady state rate, implying that these shocks’ frequency and magnitude are a determinant of long-run growth. The model reduces the growth dynamics to a single quadratic differential equation in the growth rate of GDP per capita. The quadratic term captures the non-linearity in convergence and uniquely identifies the aggregate returns to firm knowledge. We estimate this equation on a panel of countries in the post-industrial revolution era finding evidence of increasing aggregate returns to firm knowledge.

Turbulent Growth: Business Dynamism and Aggregate Productivity, [Updated: December 2024] R&R at European Economic Review
Turbulence is the process of endogenous reallocation of resources (e.g., jobs) across firms due to entry, exit, and churning (movements within the firm-size distribution). This paper formulates a model of turbulent endogenous growth built on the insight that the forces that drive aggregate productivity growth also drive turbulence because the two are manifestations of a single underlying process: profit-driven competition for the market share through innovation. When firms increase their technological knowledge, they gain market share by lowering their price. This reduces the marginal value of further gains in market share. Therefore, the central force in the model is that the incentive to perform cost-reducing innovation in house decreases in firms’ relative size. Allowing for firm-specific idiosyncratic shocks, this mechanism generates churning and a stationary firm-size distribution. These outcomes are robust to introducing entry and exit. A counterfactual exercise studies the effects of a smaller right tail of the R&D productivity distribution of startups. If the decline in job reallocation rates across incumbents observed in the early 2000 is attributable exclusively to that change, the model can explain almost all of the decline in productivity growth in the same period, without invoking a commensurate reduction in R&D.

Business Cycles, R&D, and Hysteresis: An Empirical Analysis with Hedieh Shadmani [Updated March 2025], R&R at Macroeconomic Dynamics
This paper investigates the permanent effect on total factor productivity (TFP) of temporary shocks. We estimate a structural vector
autoregression to test the predictions of endogenous growth models over the business cycle. According to theory, the stock of technological knowledge promotes its flow as researchers “stand on the shoulders of giants.” Therefore, if R&D investment is pro- cyclical — as data show and theory predicts — a recession leads to a temporary deviation of the R&D level from its trend, thus reducing new knowledge creation. The lost technological advancements cause the economy to follow a parallel but permanently lower growth path. Our findings align with the primary theoretical prediction. Quantitatively, the US economy forgoes approximately 1.3% in TFP following an increase in cyclical unemployment that peaks at 1 percentage point above the mean. The historical variance decomposition shows a strong positive effect during the boom of the late ‘60s, and strong negative effects around the Volcker disinflation period and the Great Recession. Finally, we estimate the effects on R&D of a TFP shock to differentiate between different explanations on how the R&D pro-cyclicality arises. Our results align with models where financial frictions or nominal rigidities drive it.