We show that much of the heterogeneity in patent outcomes, such as patent value, citations and litigation, is caused by the way patent rights are crafted, rather than by heterogeneity in idea quality. We obtain variation in patent rights from the quasi-random allocation of patents to examiners: a one standard deviation change in examiner effects leads stock market capitalization to increase by 3 million dollars, citations by 24%, and litigation by 64%. Patent Assertion Entities, which are very active in litigation and licensing, overwhelmingly purchase and assert patents granted by “lenient” examiners, who craft patents with higher litigation and invalidity risks.
Motivated by historical evidence on the relation between military threats and expansions of primary education, we assemble a novel panel dataset from the last 150 years in European countries and from the postwar period in a large set of countries. We find empirically that (i) investments in education increase in response to military threats, (ii) democracy has a negative direct effect on education investments, and, (iii) education investments in better democracies respond more to military threats. These empirical results are robust and continue to hold when we instead exploit rivalries in a certain country’s immediate neighborhood as an alternative source of variation. To help us interpret these patterns in the data, we develop a theoretical model which is consistent with the three empirical findings. The model has an additional prediction about investments in physical infrastructures, which we also take to the data.
We establish the importance of team-specific capital in the typical inventor's career. Using administrative tax and patent data for the population of US patent inventors from 1996 to 2012 and the premature deaths of 4,714 inventors, we find that an inventor's premature death causes a large and long-lasting decline in their co-inventor's earnings and citation-weighted patents (-4% and - 15% after 8 years, respectively). We rule out rm disruption, network effects and top-down spillovers as primary drivers of this result. Consistent with the team-specic capital interpretation, the effect is larger for more closely-knit teams and primarily applies to co-invention activities.
This paper shows theoretically and empirically that, in the context of economic growth and rising income inequality, product innovations disproportionately benefit high-income households due to the supply response to market size effects. Using detailed barcode-level scanner data in the US retail sector from 2004 to 2015, higher-income households are found to systematically experience a larger increase in product variety and a lower inflation rate for continuing products. Annual retail inflation was 0.65 percentage points lower for households earning above $100,000 a year, relative to households making less than $30,000 a year. This finding can be quantitatively explained by the supply response to market size effects: (A) the relative demand for products consumed by high-income households increased because of growth and rising inequality; (B) in response, firms introduced more new products catering to such households; (C) as a result, the prices of continuing products in these market segments were lowered due to increased competitive pressure. Changes in demand plausibly exogenous to supply factors — from shifts in the national income and age distributions over time — are used to provide causal evidence that increasing relative demand leads to more new products and lower inflation for continuing products, implying that the long-term supply curve is downward-sloping. Based on this channel, a model is developed and predicts a secular trend of lower inflation for higher-income households, which is tested and validated using Consumer Price Index and Consumer Expenditure Survey data on the full consumption basket going back to 1953.
We characterize the lives of 1.2 million inventors in the United States by linking patent records to tax data. Tracking these inventors from birth through their careers, we establish three empirical results that shed light on the key factors that determine who becomes an inventor. First, rates of innovation vary substantially by parent income, race, and gender. Differences in ability account for relatively little of these gaps and inventors from under-represented groups do not have higher quality patents on average, contrary to existing models of selection into innovation. Second, exposure to innovation during childhood plays a critical role in determining children’s propensities to innovate. Growing up in an area or in a family with a high innovation rate in a particular technology class leads to a higher probability of patenting in exactly that technology class. Third, the private returns to innovation are highly skewed and are typically earned many years after career choices are made. Using a simple model that matches these facts, we show that providing children from under-represented backgrounds greater exposure to innovation have more potential to increase innovation rates than increasing the private returns to innovation.
A broad empirical literature uses “event study” research designs for treatment effect estimation, a setting in which all units in the panel receive treatment but at random times. We make four novel points about identification and estimation of causal effects in this setting and show their practical relevance. First, we show that in the presence of unit and time fixed effects, it is impossible to identify the linear component of the path of pre-trends and dynamic treatment effects. Second, we propose graphical and statistical tests for pretrends. Third, we consider commonly-used “static” regressions, with a treatment dummy instead of a full set of leads and lags around the treatment event, and we show that OLS does not recover a weighted average of the treatment effects: long-term effects are weighted negatively, and we introduce a different estimator that is robust to this issue. Fourth, we show that equivalent problems of under-identification and negative weighting arise in difference-in-differences settings when the control group is allowed to be on a different time trend or in the presence of unit-specific time trends. Finally, we show the practical relevance of these issues in a series of examples from the existing literature, with a focus on the estimation of the marginal propensity to consume out of tax rebates.
Cohen and Levinthal (1989) introduced the notion of absorptive capacity and demonstrated that knowledge spillovers can induce complementarities in R&D efforts. We show that this idea has rich implications when analysing important aspects of the growth process such as cross-country convergence and divergence, the international co-ordination of climate change policies, and the role of openness in the production of ideas. We also show that the notion of absorptive capacity sets an agenda for new empirical and theoretical analyses of the role of R&D spillovers in innovation and growth.