Research

We uncover political dynamics that reward and reinforce increases in US health spending by studying the passage of the 2003 Medicare Modernization (MMA). We focus on a provision added to the MMA, which allowed hospitals to apply for temporary Medicare payment increases. Hospitals represented by members of Congress who voted ‘Yea’ to the MMA were more likely to receive payment increases. The payment increases raised local health spending and led to suggestive increases in health sector employment. Members of Congress representing hospitals that got a payment increase received large increases in campaign contributions before and after the program was extended.

Using city-level crime data for six major U.S. cities from Jan 21 to May 30 2020, we document an approximately 20% average reduction in reported crimes during March, simultaneous with sharp economic downturn and heightened social distancing restrictions. We also decompose trends by crime type and location. Our key findings are:

• Since the steep 20% crime drop in March, overall rates have steadily risen but remain below pre-pandemic levels on average.
• Crimes committed in commercial and street settings (as opposed to residential areas) account for most of the drop in crimes.
• Violent crimes decline in similar proportion to nonviolent crimes.
• Though larcenies fall by one-third, other kinds of theft like burglary and auto theft rise.

Caveats to our findings include the possibility of simultaneous changes in reporting and policing activities.

Recently in economics there has been discussion of how to increase diversity in the profession and how to improve the work life of diverse peoples. We conducted surveys and interviews with Black, Latinx and Native American people. These groups have long been underrepresented in the economics profession. Participants were at various stages along the economics career trajectory, or on the trajectory no longer, and used their lived experience to reflect on what helps and hurts underrepresented minorities in economics. We heard a few consistent themes: bias, hostile climate, and the lack of information and good mentoring among them. Respondents' insights and experience point toward action steps that you can take today to increase the presence and improve the work life of underrepresented minorities in the economics profession.

We propose a method for identifying exposure to changes in trade policy based on asset prices that has several advantages over standard measures: it encompasses all avenues of exposure, it is natively firm-level, it yields estimates for both goods and service producers, and it can be used to study reductions in difficult-to-quantify non-tariff barriers in a way that controls naturally for broader macroeconomic shocks. Applying our method to two well-studied US trade liberalizations provides new insight into service sector responses to trade liberalizations as well as dramatically different responses among small versus large firms, even within narrow industries.

Climate policies vary widely across countries, with some countries imposing stringent emissions policies and others doing very little. When climate policies vary across countries, energy- intensive industries have an incentive to relocate to places with few or no emissions restrictions, an effect known as leakage. Relocated industries would continue to pollute but would be operating in a less desirable location. We consider solutions to the leakage problem in a simple setting where one region of the world imposes a climate policy and the rest of the world is passive. We solve the model analytically and also calibrate and simulate the model. Our model and analysis imply: (1) optimal climate policies tax both the supply of fossil fuels and the demand for fossil fuels; (2) on the demand side, absent administrative costs, optimal policies would tax both the use of fossil fuels in domestic production and the domestic consumption of goods created with fossil fuels, but with the tax rate on production lower due to leakage; (3) taxing only production (on the demand side), however, would be substantially simpler, and almost as effective as taxing both production and consumption, because it would avoid the need for border adjustments on imports of goods; (4) the effectiveness of the latter strategy depends on a low foreign elasticity of energy supply, which means that forming a taxing coalition to ensure a low foreign elasticity of energy supply can act as a substitute for border adjustments on goods

Nursing homes and other long-term care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in U.S. nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, Washington to other skilled nursing facilities. The full extent of staff connections between nursing homes—and the role these connections serve in spreading a highly contagious respiratory infection—is currently unknown given the lack of centralized data on cross-facility employment. We perform the first large-scale analysis of nursing home connections via shared staff and contractors using device-level geolocation data from 50 million smartphones, and find that 5.1 percent of smartphone users who visit a nursing home for at least one hour also visit another facility during our 11-week study period—even after visitor restrictions were imposed. We construct network measures of connectedness and estimate that nursing homes, on average, share connections with 7 other facilities. Controlling for demographic and other factors, a home’s staff-network connections and its centrality within the greater network strongly predict COVID-19 cases. Traditional federal regulatory metrics of nursing home quality are unimportant in predicting outbreaks, consistent with recent research. Multivariate regressions comparing demographically and geographically similar nursing homes suggest that 49 percent of COVID cases among nursing home residents are attributable to staff movement between facilities.

The onset of the Covid-19 pandemic has led to a dramatic reduction in employment and hours worked in the US economy. The decline can be measured using conventional data sources such as the Current Population Survey and in the number of individuals filing for unemployment. However, given the unprecedented pace of the ongoing changes to labor market conditions, detailed, up-to-date, high frequency data on wages, employment, and hours of work is needed. Such data can provide insights into how firms and workers have been affected by the pandemic so far, and how those effects differ by type of firm and worker wage level. It can also be used to detail – in real time – the state of the labor market.

We study the role of European Immigration on local and aggregate economic growth in the United States between 1880 and 1920. We employ a big data approach and link, at the individual-level, information from the Population Census, the universe of patents and millions of historical immigration records. We find that immigrants were more prolific innovators than natives, and document large differences in innovation potential across nationalities and regions in the United States. To measure the importance of immigrants for the creation of new ideas and economic growth, we develop a new spatial model of growth through dissemination of knowledge and workers’ mobility. The model allows us to use our micro and regional empirical findings to measure immigrants’ innovation human capital and the degree of knowledge diffusion which regulates scale effects. We quantitatively analyze the effects of imposing major immigration restrictions on American economic growth in the 19th and early 20th century. We find large, accumulating, losses from these restrictions. Both the scale effects and the exclusion of high-human capital immigrants contribute significantly to these losses.