Innovation, Growth, and Policy
Monday, Jan. 4, 2021 3:45 PM - 5:45 PM (EST)
- Chair: Stefanie Stantcheva, Harvard University
Patents to Products: Product Innovation and Firm Dynamics
AbstractWe study the relationship between patents and actual product innovation in the market, and how this relationship varies with firms' market share. We use textual analysis to create a unique data set that links patents to products in the consumer goods sector. We document that while more than half of innovation comes from never-patenting firms, patents on average reflect product innovation, but this relationship crucially depends on a firm's size. We show empirically and theoretically that as firm size increases, patent filings are less reflective of innovation in the market and are more likely to be used to deter competition.
Recipes and Economic Growth: A Combinatorial March Down an Exponential Tail
AbstractNew ideas are often combinations of existing goods or ideas, a point emphasized by Romer (1993) and Weitzman (1998). A separate literature highlights the links between exponential growth and Pareto distributions: Gabaix (1999) shows how exponential growth generates Pareto distributions, while Kortum (1997) shows how Pareto distributions generate exponential growth. But this raises a "chicken and egg" problem: which came first, the exponential growth or the Pareto distribution? And regardless, what happened to the Romer and Weitzman insight that combinatorics should be key to understanding growth? This paper answers these questions by showing that combinatorial growth based on draws from standard thin-tailed distributions leads to exponential economic growth; no Pareto assumption is required. More generally, it provides a theorem linking the behavior of the max extreme value to the number of draws and the shape of the tail for any continuous probability distribution.
Optimal Taxation and R&D policies
AbstractWe study the optimal design of corporate taxation and R&D policies as a dynamic mechanism design problem with spillovers. Firms are heterogeneous in their research productivity, i.e., in the efficiency with which they convert a given set of R&D inputs into successful innovations and that research productivity is private information. There are non-internalized technological spillovers across firms, but the asymmetric information prevents correcting them in the first best way. We highlight that key parameters for the optimal policies are i) the relative complementarities between observable R&D investments, unobservable R&D inputs, and firm research productivity, ii) the dispersion and persistence of firms' research productivities, and iii) the magnitude of technological spillovers across firms. We estimate our model using firm-level data matched to patent data and quantify the optimal policies. In the data, high research productivity firms get disproportionately higher returns to R&D investments than lower productivity firms. Very simple innovation policies, such as linear corporate taxes combined with a nonlinear R&D subsidy -- that provides lower marginal subsidies at higher R&D levels -- can do almost as well as the full unrestricted optimal policies. Our formulas and theoretical and numerical methods are more broadly applicable to the provision of firm incentives in dynamic settings with asymmetric information and spillovers and to firm taxation more generally.
- G3 - Corporate Finance and Governance
- O4 - Economic Growth and Aggregate Productivity