I examine the impact of patent term on R&D and innovation in the presence of policy anticipation, common in real-world settings. Using a difference-in-difference design, I exploit quasi-experimental variation in U.S. patent term across technological fields due to the ratification of TRIPs agreements in 1995. Despite a general increase in average patent term, in most fields innovators faced a considerable probability of patent term reduction for future innovations. Three key findings emerge: (1) R&D and innovation accelerate more in fields with a higher probability of patent term reduction, i.e., a shorter average patent term extension, before implementation. (2) This heightened activity persists for at least five years post-implementation, driven by indirect effects where the news-related acceleration fosters further innovation through technological externalities linked to cumulative knowledge creation. (3) Conversely, the direct effect of a shorter extension in patent term would stimulate relatively less innovation, absent the indirect effects of anticipation.

We analyze the role of endogenous capital-embodied innovation for macroeconomic dynamics. We provide empirical evidence that new-product introduction by capital-goods producers is an engine of long-run technological progress, but drops during recessions, when investment demand is weak. We then develop a model of investment with heterogeneous firms and endogenous capital-embodied innovation. The model features two endogenous state variables: (i) the cross-sectional distribution of firms and (ii) the quality of new capital. The fraction of firms replacing their capital determines the incentives for capital-goods producers to innovate through a market-size effect. In turn, growth in new-capital quality induces final-good producers to replace their old capital. This feedback provides a propagation mechanism for macroeconomic shocks and generates fluctuations in productivity. We analyze efficiency in the model and show that capital-replacement subsidies have stronger stimulus effects than innovation subsidies in the short run, but are less desirable in the long run.

We develop a model of capital accumulation in an economy that sources investment goods from large firms with market power. We model investment-goods producers as a dynamic oligopoly with increasing marginal cost and characterize the equilibrium with a dynamic markup rule. We use this characterization to analyze the dynamics of investment and prices. The markup on investment goods acts as an endogenous adjustment cost, which decreases as the economy grows but permanently distorts the steady state. We calibrate the model to simulate the post-2020 shocks to demand for equipment and semiconductors. The calibrated model attributes the observed increase in the price of equipment mainly to increasing marginal costs and to a smaller extent to increasing markups. We then analyze the effects of policy interventions to expand capacity and address market power. Finally, we extend the model to investment-specific technological progress due to learning by doing.

We analyze optimal subsidies for the replacement of durable assets. We first develop a tractable model of capital replacement with heterogeneous producers, endogenous capital-embodied innovation, and environmental externalities that depend on capital vintages. We characterize the decentralized equilibrium, the first-best allocation, and the constrained-efficient allocation that arises if a planner chooses capital replacement subject to the equilibrium evolution of innovation. We formalize a decentralization of this allocation with subsidies for capital replacement and obtain a formula for optimal subsidies. We also generalize our analysis to the case of heterogeneous technologies. Finally, we quantify the model using empirical evidence on several types of capital, including aircraft and vehicles. We use the calibrated model to simulate the transitional dynamics induced by the optimal policy and quantify the associated subsidies.

We develop a method to estimate producers' productivity beliefs in settings where output quantities and input prices are unobservable, and we use it to evaluate allocative efficiency in the market for science. Our model of researchers' labor supply shows that their willingness to pay for their two key inputs, funding and time, reveals their underlying productivity beliefs. We estimate the model's parameters using data from a nationally representative survey of research-active professors from all major fields of science. We find that the distribution of research productivity is highly skewed. Using these estimates, we assess the market's allocative efficiency by comparing actual input allocations to optimal allocations given various objectives. Overall, the market for science is moderately efficient at maximizing output and researchers' utility: actual input levels are positively correlated with the optimal levels implied by the model. However, the wedge between researchers' actual and optimal input levels is often significant and difficult to predict. Our estimates imply that total budgets would need to increase by roughly 40% under actual allocations in order to achieve the same growth in scientific output that we predict under alternative allocations of the current budget. Scaling to the population level, this equates to billions of dollars in funding---there are substantial gains from developing new ways of identifying and supporting productive scientists.

This paper investigates the relationship between the disclosure of standard-essential patent (SEPs)-which represent the content of technological standards like 5G-and sectoral productivity growth. I develop a Schumpeterian growth model where innovation requires combining technological components  (complementarity) owned by different firms, which may disclose their patents to standards. Disclosures enhance innovation and growth by helping technological coordination, but they can also raise implementation costs through higher royalties. The first positive effect dominates if, conditional on complementarity, the bargaining power of disclosing firms is low enough. Using sectoral data from 2000–2012, I find that, on average, more SEP disclosures are negatively associated with total factor productivity (TFP) growth. However, the effect varies across sectors. Consistent with the model, sectors with the largest combined effect of technological complementarity and disclosing firms' bargaining power drive the average negative effect.

We document that new-product quality largely accounts for the dynamics of durable-goods expenditures around the Great Recession. To this end, we assemble a rich dataset on US new-car markets during 2004-2012, combining transaction-level data on prices with detailed information about vehicles' technical characteristics. During the recession, car manufacturers introduced new models of low quality. In turn, a reallocation of expenditures away from high-quality new models accounts for a significant decline in the dispersion of expenditures. The drop in new-model quality induced a persistent decline in the technology embodied in vehicles, and likely contributed to the slow recovery of expenditures.

We assess whether the effects of fiscal policy depend on the extent uncertainty in the economy. Focusing on tax shocks, identified by the narrative series by Romer and Romer (2010), and various measures of uncertainty, we use a Threshold VAR model to allow for dependence of the effects of the tax shocks on both the level of uncertainty and the sign of the shock. We find that the economy responds more positively to tax cuts during periods of low uncertainty, while, in response to tax increases, the response of main aggregates is more negative in more uncertain times. We argue that controlling for monetary policy in fiscal VARs is important to avoid omitted variable bias. We interpret our empirical evidence in light of existing theoretical contributions.