with Aiday Sikhova and Bruce Weinberg
We use unique, linked data on biomedical researchers and an extended AKM-style design to study the role of leaders in engaging and providing opportunities to researchers. Specifically, we study how women principal investigators (PIs) affect gender disparities among the faculty on their projects. We find that research teams with more women PIs are significantly more supportive of female faculty in three major ways. First, teams with more female leaders employ a larger share of women on their teams. Second, using researcher-year and paper fixed effects to account for unobserved characteristics, we find that teams with women leaders give significantly more credit---in terms of authorship---to female faculty who work on those teams, with the largest effect on high impact papers and in heavily-male fields. Third, the impact of female PIs extends beyond the time female faculty are part of the teams, leading to an increase in the number of co-authored papers with female PIs after female faculty leave the teams. Our estimates are strongest for the most productive women, suggesting that, although women leaders tend to narrow the gender gap in authored papers, they seem to perpetuate within-group inequality in authorship for women.
R&R at Research Policy
with Matthew Lee Chen and Matteo Tranchero
The study of innovation depends heavily on high-quality patent data. Yet, datasets containing complete patent documents focus only on recent decades, while historical patent datasets with broader temporal coverage typically lack detailed information. Therefore, our ability to leverage advances in textual analyses to study long-run innovation dynamics remains limited. To this end, we introduce a large-scale dataset of the universe of technical specifications of British patents granted between 1617--1899. Our data consists of the full specification texts alongside linked information about inventors, including their disambiguated names, occupations, and addresses. We use our data to document changes over time in total inventive activity, the geography of innovation, inventor occupations, and patent novelty and impact. Finally, we discuss use cases and avenues for subsequent research.
with Mike Andrews and Ruveyda Gozen
Patents do not report inventors' gender, requiring researchers to infer the gender of inventors. To conduct these inferences, researchers must make several choices. We show how these researcher choices can affect conclusions about the role of women inventors in the U.S. from 1845 to 1924. More specifically, we compare two automated methods to determine inventor gender for the universe of U.S. patents: inferring gender from inventors' first names and linking inventors to census data. These methods paint similar pictures about aggregate patterns of patenting by women, but often give different predictions about the gender of particular inventors. Both automated methods identify a larger number of patents by women inventors than have previously been identified in the literature. Using the gender inferred by these two methods, we study how the characteristics of patents and inventors differ by gender.
Patents are commonly used as the main source of data for empirical studies related to innovation and technological change. The large amount of information about the underlying innovative process contained in each patent has certainly contributed to their popularity. Nevertheless, due to the lack of reliable data, historical analysis has focused on relatively small time frames or on specific dimensions of patents data. The goal of this paper is to fill this gap. I build and release a comprehensive time series of the universe of U.S. patents. The data set contains all the variables commonly used in the literature and, importantly, geolocates every inventor and assignee reported in each grant over the period 1836-2016.
with Kristina Manysheva and Martí Mestieri
We use a panel of historical patent data covering a large range of countries over the past century to study the evolution of innovation across time and space and its effect on productivity. We document a substantial rise of international knowledge spillovers as measured by patent citations since the 1990s. This rise is mostly accounted for by an increase in citations to US and Japanese patents in fields of knowledge related to computation, information processing, and medicine. We estimate the causal effect of innovation induced by international spillovers on sectoral output per worker and TFP growth in a panel of country-sectors from 2000 to 2014, and on aggregate income per capita since 1960. To assess causality, we develop a shift-share instrument that leverages pre-existing citation linkages across countries and fields of knowledge, and heterogeneous countries' exposure to technology waves. On average, an increase of one standard deviation in log-patenting activity increases sectoral output per worker growth by 1.1 percentage points. We find results of similar magnitude for sectoral TFP growth and long-run aggregate income per capita growth.
with Davide Coluccia, Gaia Dossi, and Mara P. Squicciarini
How do societies respond to adversity? After a negative shock, separate strands of research document either an increase in religiosity or a boost in scientific progress. In this paper, we show that both reactions can occur at the same time, driven by different individuals within society. The setting of our study is the 1918--1919 influenza pandemic in the United States. To measure religiosity, we construct a novel indicator based on the naming patterns of newborns. We measure scientific progress through the share of people in STEM occupations and the universe of granted patents. Exploiting plausibly exogenous county-level variation in exposure to the pandemic, we provide evidence that more affected counties become both more religious and more scientific. Within counties, we uncover heterogeneous responses: individuals from more religious backgrounds further embrace religion, while those from less religious backgrounds become more likely to choose a scientific occupation. Facing adversity widens the distance in religiosity between science-oriented individuals and the rest of the population and increases the polarization of religious beliefs.
with Ruben Gaetani
We study the relationship between local corruption and income misreporting, in the context of the EITC program. Using a newly assembled dataset of corruption at an MSA level, we find that public officials’ corruption predicts more than 70% of the variation in bunching observed in the distribution of EITC eligible self-employed workers’ reported income. We employ a research design that exploits exogenous variation in institutional accountability and find that a raise in our corruption measure by one standard deviation causes sharp bunching among self-employed workers to increase by 0.60 to 0.83 standard deviations. We argue and show suggestive evidence that information via news coverage is a relevant channel through which corruption affects tax evasion. The results are consistent with a framework in which individuals weigh their tax evasion decision against the cost of social stigma which is decreasing in the (perceived) level of institutional corruption.
with Paul Mohnen and Bledi Taska
Early-career returns to college majors vary substantially across cities and over time, even after accounting for differences in the cost of living. In this paper, we show that mismatch between college majors and the skills demanded by local employers in the years following graduation is an important contributing factor. Exploiting data on the near-universe of online job postings in the U.S. between 2010 and 2015, we build a novel measure of skill mismatch capturing the “distance” between individual college majors and local labor demand. Intuitively, skill mismatch is low for a particular college graduate when a large fraction of job postings in her city are suitable for her major. We find that a one standard deviation increase in our mismatch measure is associated with a 1.5% decline in hourly wages among recent college graduates, controlling for individual characteristics and two-way interactions of college major, city and year fixed effects. College graduates are also more likely to be unemployed and more likely to hold non-college jobs conditional on employment when their major is less in demand. In ongoing work, we quantify how much of the cross-sectional and time variation in the returns to college majors can be explained by skill mismatch.