Publications
Articles
Lockdowns and Innovation: Evidence from the 1918 Flu Pandemic
with Olivier Deschenes, Ruben Gaetani, Jeffrey Lin, and Christopher Severen
The Review of Economics and Statistics, forthcoming
Does social distancing harm innovation? We estimate the effect of non-pharmaceutical interventions (NPIs)—policies that restrict interactions in an attempt to slow the spread of disease—on local invention. We construct a panel of issued patents and NPIs adopted by 50 large US cities during the 1918 flu pandemic. Difference-in-differences estimates show that cities adopting longer NPIs did not experience a decline in patenting during the pandemic relative to short-NPI cities, and recorded higher patenting afterward. Rather than reduce local invention by restricting localized knowledge spillovers, NPIs adopted during the pandemic may have better preserved other inventive factors.
NBER Working Paper 28152
Income Segregation and the Rise of the Knowledge Economy
with Ruben Gaetani
American Economic Journal: Applied Economics, 15 (2): 69-102, 2023
We analyze the effect of the rise of knowledge-based activities on spatial inequality within U.S. cities, exploiting the predetermined network of patent citations to instrument for local trends in innovation. Innovation intensity explains 33.5% of the cross-sectional change in income segregation between 1990 and 2010. This effect is mainly driven by the geographical sorting of residents in terms of income, occupation, and education. To shed light on the mechanism, we show that shocks to local innovation induce a clustering of knowledge-intensive employment and a relocation of knowledge residents, amplified by the response of local rents and consumption amenities.
Preview version: [link] Working paper (2019): [link]
Non-technical discussion on: The Atlantic, Fast Company, Quartz
The Census Place Project: A Method for Geolocating Unstructured Place Names
with Ezra Karger and Peter Nencka
Explorations in Economic History, 87:101477, 2023
Awarded the 2023 Larry Neal Prize
Researchers use historical microdata to study the economic development of the United States and the causal effects of historical policies. Much of this research focuses on county- and state-level patterns and policies because comprehensive sub-county data is not consistently available. We describe a new method that geocodes and standardizes the towns and cities of residence for individuals and households in decennial census microdata from 1790-1940. We release public crosswalks linking individuals and households to consistently-defined place names, longitude-latitude pairs, counties, and states. Our method dramatically increases the number of individuals and households assigned to a sub-county location relative to standard publicly available data: we geocode an average of 83% of the individuals and households in 1790-1940 census microdata, compared to 23% in currently widely-used crosswalks. In years with individual-level microdata (1850-1940), our average match rate is 94% relative to 33% in widely-used crosswalks. To illustrate the value of our crosswalks, we measure place-level population growth across the United States between 1870 and 1940 at a sub-county level, confirming predictions of Zipf's Law and Gibrat's Law for large cities but rejecting similar predictions for small towns. We describe how our approach can be used to accurately geocode other historical datasets.
Preview version: [link]
Data available here
Women are Credited Less in Science than are Men
with Matthew B. Ross, Britta M. Glennon, Raviv Muricano-Goroff, Bruce A. Weinberg, Julia I. Lane
Nature, 608(7921): 135-145, 2022
There is a well-documented gap in the observed number of scientific works produced by women and men in science, with clear consequences for the retention and promotion of women in science1. The gap might be a result of productivity differences2-5, or it might be due to women’s contributions not being acknowledged6,7. This paper finds that at least part of this gap is due to the latter: women in research teams are significantly less likely to be credited with authorship than are men. The findings are consistent across three very different sources of data. Analysis of the first source - large scale administrative data on research teams, team scientific output, and attribution of credit - show that women are significantly less likely to be named on any given article or patent produced by their team relative to their peers. The gender gap in attribution is found across almost all scientific fields and career stages. The second source – an extensive survey of authors – similarly shows that women’s scientific contributions are systematically less likely to be recognized. The third source – qualitative responses – suggests that the reason is that their work is often not known, not appreciated, or ignored. At least some of the observed gender gap in scientific output may not be due to differences in scientific contribution, but to differences in attribution.
Published version: [link]
The Ripple Effects of Funding on Researchers and Output
with Reza Sattari, Jung Bae, and Bruce Weinberg
Science Advances, 8(16), 2022
Using unique, new, matched UMETRICS data on people employed on research projects and Author-ity data on biomedical publications, this paper shows that National Institutes of Health funding stimulates research by supporting the teams that conduct it. While faculty—both principal investigators (PIs) and other faculty—and their productivity are heavily affected by funding, so are trainees and staff. The largest effects of funding on research output are ripple effects on publications that do not include PIs. While funders focus on research output from projects, they would be well advised to consider how funding ripples through the wide range of people, including trainees and staff, employed on projects.
Published version: [link]
The Geography of Unconventional Innovation
with Ruben Gaetani
The Economic Journal, 131(636): 1466-1514, 2021
Awarded the 2021 Austin Robinson Memorial Prize
Using a newly assembled dataset of U.S. patents, we show that overall innovation activity is less concentrated in high-density urban areas than commonly believed, but inventions based on atypical combinations of knowledge are indeed more prevalent in high-density cities. To interpret this relation, we propose that informal interactions in densely populated areas help knowledge flows between distant fields, but are less relevant for flows between close fields. We build a model of innovation in a spatial economy that endogenously generates the pattern observed in the data: specialized clusters emerge in low-density areas, whereas high-density cities diversify and produce unconventional ideas.
Published version: [link]
Non-technical discussion on Citylab: The Geography of Innovation
Too Much Finance?
With Jean-Louis Arcand and Ugo Panizza
Journal of Economic Growth, 20(115), 2015
This paper examines whether there is a threshold above which financial depth no longer has a positive effect on economic growth. We use different empirical approaches to show that financial depth starts having a negative effect on output growth when credit to the private sector reaches 100 % of GDP. Our results are consistent with the “vanishing effect” of financial depth and are not driven by endogeneity, output volatility, banking crises, low institutional quality, or by differences in bank regulation and supervision.
Published version: [link] Working paper (2012): [link]
Non-technical summary on Voxeu: Has Finance Gone Too Far?
Media mentions and discussions of the paper on: FT, Time, The Economist, Forbes, Mother Jones
Afghanistan: Balancing Social and Security Spending in the Context of a Shrinking Resource Envelope
With Aqib Aslam, Martin Fukac, Jeta Menkulasi and Axel Schimmelpfennig
Asian Development Review, 31(2): 165-197, 2014
For Afghanistan, the dual prospect of declining donor support and high ongoing security spending over the medium term keeps the government budget tight. This paper uses a general equilibrium model to capture the security-development tradeoff facing the government in its effort to rehabilitate macroeconomic stability and welfare. In particular, it considers strategic policy options for counteracting and minimizing the negative macroeconomic impact of possible aid and revenue shortfalls. We find that the mobilization of domestic revenues through changes in tax policy is the preferred policy response for Afghan central government. Such a response helps to place its finances on a sustainable path and preserve most of the growth potential. Cutting expenditures balances public finances, but causes the economy to permanently shrink. Debt financing helps to preserve much of the economy size but can jeopardize the sustainability of public finances.
Published version: [link] Working paper (2013): [link]
Book Chapters
Finance and Economic Development in a Model with Credit Rationing
With Jean-Louis Arcand and Ugo Panizza
in V. Acharya, T. Beck, D. Evanoff, G. Kaufman, and R. Portes (Eds.), The Social Value of The Financial Sector: Too Big to Fail or Just Too Big?, pp. 67-80, 2013
This paper develops a simple model with credit rationing and endogenous default risk in which the expectation of a bailout may lead to a financial sector which is too large with respect to the the social optimum. The paper concludes with a short discussion of how this model could be used as a building block for models aimed at endogenizing the probability of a bailout, and discussing the relationship between the size of the financial sector and economic growth in the presence of default risk.
Published version: [link] Working paper (2013): [link]
Do Money Targets Matter for Monetary Policy in Kenya?
With Michal Andrle, Andrew Berg, R. Armando Morales, Rafael Portillo, and Jan Vlcek
in A. Berg and R. Portillo (Eds.), Monetary Policy in Sub-Saharan Africa, Chapter 16, 2018
The framework in Chapter 15 is extended to incorporate an explicit role for money aggregates, with an application to Kenya. The chapter provides a general specification that can nest various types of money targeting (ranging from targets based on optimal money demand forecasts to those derived from simple money growth rules), interest-rate based frameworks, and intermediate cases. A novel interpretation of target misses in terms of structural shocks (aggregate demand, policy, shocks to money demand, etc.) is presented. In the case of Kenya, the authors find that: (i) the setting of money targets is consistent with money demand forecasting, (ii) targets have not played a systematic role in monetary policy, and (iii) target misses mainly reflect shocks to money demand. Simulations of the model under alternative policy specifications show that the stronger the ex post target adherence, the greater the macroeconomic volatility.
Published version: [link] Working paper (2013): [link]
Working Papers and Comments
Assessing Bias and Accuracy in the World Bank-IMF’s Debt Sustainability Framework for Low-Income Countries
with Andrew Berg, Catherine A. Pattillo, Andrea Presbitero and Yorbol Yakhshilikov
The World Bank and the IMF have adopted a debt sustainability framework (DSF) to evaluate the risk of debt distress in Low Income Countries (LICs). At the core of the DSF are empirically-based thresholds for each of five different measures of the debt burden (the “debt threshold approach” DTA). The DSF contains a rule for aggregating the information contained in these five different variables which we label the “worst-case aggregator” (WCA) in view of the fact that the DSF considers a breach of any one of the thresholds sufficient to indicate a high risk of debt distress. However, neither the DTA nor the WCA has heretofore been subject to empirical testing. We find that: (1) the DTA loses information relative to a simple proposed alternative; (2) the WCA is too conservative (predicting crises too often) in terms of the loss function used in the DSF; and (3) the WCA is less accurate than some simple proposed alternative aggregators as a predictor of debt distress.
Working paper: [link]
Too Much Finance or Statistical Illusion: A comment
With Jean-Louis Arcand and Ugo Panizza
A recent policy brief from Peterson Institute suggests that “Too Much Finance” result may be an artifact of spurious attribution of causality. While more work needs to be done to understand the links between finance and growth and explore the drivers of possible non-monotonicities, this note shows that the too much finance result is robust.
Working paper: [link]