Essays on the guidance of the direction of innovation
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Abstract
My dissertation studies how policy should guide, and respond to, technological change. The first chapter addresses the "guide" side of the question, while chapters two and three address the "respond to" side of the question.In the first chapter, I argue that cross-technology knowledge spillovers are critical for understanding policy's role in the transition to clean technology. I develop an endogenous growth model with clean and dirty technologies and a network of cross-technology spillovers. I derive formulas for the size and speed of technological transition, following a policy reform, which show that greater spillovers across technologies induce a faster transition but at the expense of a smaller long-run impact of policy. Such spillovers also prevent the lock-in of dirty technology. The economy's spillover structure can be summarized by a sufficient statistic matrix, which I estimate using patent citation data. Applying my model to US transportation and electricity generation, I find that cross-technology spillovers are mid-sized: they prevent lock-in but imply a slow transition with a high long-run impact of policy. I conclude by examining how cross-technology spillovers affect optimal clean innovation subsidies, deriving an innovation subsidy formula that holds for arbitrary carbon prices. Quantitatively, I find that optimal clean innovation subsidies are small, reflecting the low centrality of clean technologies in the spillover network. Hence, a "big push" of temporary clean innovation subsidies is not warranted.
In the second chapter, I start with the observation that the standard reaction to the problem of automation, by both lay people and the economics literature, follows a Pigouvian intuition: robots harm workers, so they should be taxed. I argue that this Pigouvian intuition is misguided, or at least oversimplified. As shown by the recent literature modeling automation within the task framework, capital only exerts a negative pecuniary externality on labor at the extensive margin of automation. At the intensive margin, more capital producing a task that has already been automated raises wages for everyone via capital deepening. To formalize this point, I present a model with heterogeneous agents where the Planner can tax income from capital and labor as well as target the extensive margin of automation by stipulating how much more expensive labor must be than capital before automation can occur. I show, via an envelope argument, that capital taxation should ignore automation when the extensive margin tool is set optimally. In a quantitative application to the US economy, I find that labor should be 3.4% more expensive than capital before automation can occur.
In the third chapter, Masao Fukui, Yuhei Miyauchi, and I study optimal transfer policy in dynamic spatial equilibrium models with frictional migration and incomplete financial markets. A key policy trade-off is to provide consumption insurance while minimizing the distortion of migration flows. We derive a recursive formula for optimal spatial transfers that strikes this balance. We calibrate our model to U.S. states and find that the U.S. economy would benefit from increased transfers to low-income-growth states. Welfare gains from optimal transfers are substantial but smaller than in a framework abstracting from slow migration adjustment.
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2024