Research
Publications
Gendered Citations at Top Economic Journals, AEA Papers and Proceedings, Vol.111 , May 2021.
Racial Justice from Within? Diversity and Inclusion in Economics, (with Leonard Wantchekon) Forthcoming, Econometric Society World Congress Monograph.
Working Papers
******* Economics of Innovation and Science
"Innovative Ideas and Gender Inequality", R&R, American Economic Review
This paper analyzes the recognition of women's innovative ideas compared to men, using bibliometric data from research. Based on machine learning, I establish the similarities between papers and link them to construct relevant counterfactual citations. I uncover striking heterogeneity in terms of authorship. On average, papers authored solely by women are 10% less cited than those by men, a disparity significantly influenced by endogenous regressors. This results in female scholars being 0%, 3%, and 8% less cited in economics, sociology, and mathematics, respectively. However, substantial variations arise with the citing authors' gender, raising concerns about fairness, idea recognition, and dissemination.
Coverage: The Washington Center for Equitable Growth
CLEF Best Young-Researcher Paper Award (second prize)
Cassatts in the Attic, 2023 (with Matt Marx), R&R, American Economic Journal: Applied Economics
We analyze 70 million scientific articles to document a double-digit gender gap in the commercialization of science. The gap is explained neither by the quality of science nor ex-ante commercial potential nor by traditional explanations such as networks or female representativeness. The gap is widest among high-quality papers with female last authors (i.e., lab head). A natural experiment based on the Obama administration's staggered open-access requirement for federally-funded research increases commercialization, but mostly among men, suggesting demand-side bias. Indeed, firms pay more attention to self-promoting papers—men self-promote more—and gender homophily exists between scientific authors and commercializing inventors.
Pricing Innovation: Evidence from Canadian Pharmaceuticals (with Vasia Panousi)
This paper uses a new panel dataset constructed from information provided by the Canadian Intellectual Property Office to study the relationship between patents, innovation and growth in the Canadian pharmaceutical industry. First, using advanced machine learning method, we perform textual analysis on patent documents to create an indicator of patent quality. Our indicator assigns higher quality to patents or innovations that are novel. Second, matching the firms in our patent dataset to their balance-sheet information, we are then able to validate our patent-quality measure by relating it to various measures of firm value and performance. The results indicate that the anticipation of the granting of a breakthrough patent increases firm profitability, on average, for up to five years before the grant. This increase in profitability is reflected in increased markups, as opposed to increased employment or investment. Third, we construct firm- and aggregate-level TFP measures and find that significant innovations increase firm productivity as captured by measured TFP. Finally, our quality index is used for policy purposes in the pharmaceutical sector. In fact, the quality index shows a positive and significant relationship with the prices of the patented medicines at the federal level. Surprisingly the positive relationship disappears at the provincial level and becomes even negative for some cases. Pharmaceutical innovation in Canada is therefore captured differently at the provincial level. The economic policy implication is a standardization of pharmaceutical industries in Canada by the adoption of a ``Pharmacare".
Racial inequality and Inclusion in Economics Research (with Roland Pongou and Leonard Wantchekon)
******* Broader Applications of Machine Learning to Economics
High Risk Workers and High Risk Firms (with Serdar Ozkan, Sergio Salgado, and Marco Weißler )
Recent literature documented large heterogeneity in earnings dynamics individuals experience, in particular, in average income profiles and higher order moments of income shocks as well as in unemployment risk and job finding rates. Using administrative social security data from Germany, we decompose heterogeneity in earnings dynamics into observable and unobservable worker and firm components. First, we document salient features of earnings risk conditional on observable worker and firm characteristics. Next, in order to identify unobservable heterogeneity we employ machine learning algorithms to cluster workers and firms by features of their earnings dynamics. Finally, we estimate an individual income process that allows for ex-ante worker and firm heterogeneity. We find that workers in smaller and shrinking firms experience lower earnings growth with more volatile and left skewed earnings changes as well as higher unemployment risk. When we control for unobservable worker and firm types jointly, we conclude that person effects explain majority of differences in earnings dynamics while firm effects explain relatively little. These findings have important implications for public policy such as unemployment insurance..
Predicting the Effectiveness of Teachers: a Machine Learning Approach (with William Arbour and Phil Oreopoulos)
Other topics (doctoral works)
Idiosyncratic risk, financial integration and growth (with Vasia Panousi)
We investigate the role of financial integration in insuring idiosyncratic investment risk. On the empirical front, using a panel of 79 countries over 1985-2018, we find that an increase in idiosyncratic risk decreases economic growth. However, this decline is 40 percent smaller for more integrated countries, compared to less integrated ones. Furthermore, this mitigating effect of integration is somewhat larger for developing economies. On the theoretical front, we provide a two-country stochastic general-equilibrium model with countercyclical investment risk that explains our empirical findings. The results from an empirically relevant calibration of the theoretical model match the empirical results.
Assessing Debt Sustainability: An Enhanced Signal Extraction Approach (with Nadeem Sanaa) [IMF staff internal paper August 2018, Draft available upon request]
For early and effective policy responses that minimize economic costs, it is vital to have a reliable framework for predicting the likelihood of a sovereign debt crisis. In doing so, this paper investigates several ways of enhancing the current debt sustainability framework. We estimate a non-parametric model based on signal extraction and find three elements, key in leading the predictive power of a given estimation: the choice of the objective function, the choice of the variables and their aggregation into a composite index, the heterogeneity among countries. In addition, we explore a multivariate signalling approach which appears to be a parsimonious and promising avenue in predicting debt distress event. Finally, we apply our methodology on the new crisis database and find substantial improvements both in-sample and out-of-sample compared to the existing framework.