Research Database

How does wealth taxation differ from capital income taxation? When the return on investment is equal across individuals, a well-known result is that the two tax systems are equivalent. Motivated by recent empirical evidence documenting persistent return heterogeneity, we revisit this question. With heterogeneity, the two tax systems typically have opposite implications for both efficiency and inequality. Under capital income taxation, entrepreneurs who are more productive and therefore generate more income pay higher taxes. Under wealth taxation, entrepreneurs who have similar wealth levels pay similar taxes regardless of their productivity, which expands the tax base, shifts the tax burden toward unproductive entrepreneurs, and raises the savings rate of productive ones. This reallocation increases aggregate productivity and output. In the simulated model parameterized to match the US data, replacing the capital income tax with a wealth tax in a revenue-neutral fashion delivers a significantly higher average welfare. Turning to optimal taxation, the optimal wealth tax (OWT) is positive and yields large welfare gains by raising efficiency and lowering inequality. In contrast, the optimal capital income tax (OKIT) is negative—a subsidy—and delivers lower welfare gains than OWT, owing to the welfare losses from higher inequality. Furthermore, when the transition path is considered, the gains from OKIT turn into significant welfare losses for existing cohorts, whereas OWT continues to deliver robust welfare gains. These results suggest that moderate wealth taxation may be a more appealing alternative than capital income taxation, which can be significantly more distorting under return heterogeneity than under the equal-returns assumption.

Applying the Foster, Haltiwanger, and Krizan (FHK) (2001) decomposition to plant-level manufacturing data from Chile and Korea, we find that the entry and exit of plants account for a larger fraction of aggregate productivity growth during periods of fast GDP growth. To analyze this relationship, we develop a model of firm entry and exit based on Hopenhayn (1992). When we introduce reforms that reduce entry costs or reduce barriers to technology adoption into a calibrated model, we find that the entry and exit terms in the FHK decomposition become more important as GDP grows rapidly, just as they do in the data from Chile and Korea.

The epidemiological literature suggests that virus transmission occurs only when individuals are in relatively close contact. We show that if society can control the extent to which economic agents are exposed to the virus and agents can commit to contracts, virus externalities are local, and competitive equilibria are efficient. The Second Welfare Theorem also holds. These results still apply when infection status is imperfectly observed and when agents are privately informed about their infection status. If society cannot control virus exposure, then virus externalities are global and competitive equilibria are inefficient, but the policy implications are very different from those in the literature. Economic activity in this version of our model can be inefficiently low, in contrast to the conventional wisdom that viruses create global externalities and result in inefficiently high economic activity. If agents cannot commit, competitive equilibria are inefficient because of a novel pecuniary externality.

We measure the importance of increasing returns to scale and trade in medical services. Using Medicare claims data, we document that “imported” medical care—services produced by a medical provider in a different region—constitute about one-fifth of US healthcare consumption. Larger regions specialize in producing less common procedures, which are traded more. These patterns reflect economies of scale: larger regions produce higher-quality services because they serve more patients. Because of increasing returns and trade costs, policies to improve access to care face a proximity-concentration tradeoff. Production subsidies and travel subsidies can impose contrasting spillovers on neighboring regions.

This paper develops a quantitative framework to study the impact of Unemployment Insurance (UI) expansions to workers earning below eligibility thresholds. A model of how UI affects welfare and labor supply is developed and calibrated with microeconomic data, including consumption. The model predicts that the current ineligible would choose to stay on UI longer than the current eligible and the margins of why this is the case are quantified. The model is applied to the Great Recession by identifying ineligible workers in the data using machine learning and to an actual expansion during COVID-19 using administrative data. The UI duration for newly eligible under the expansion was 1.7 times longer than the previous eligible but is one-third shorter than the model's economic incentives predict. This suggests caution in extrapolating from the COVID-19 data and the model is used to predict impacts of smaller scale expansions during non-pandemic times.

Many central banks whose exchange rate regimes are classified as flexible are reluctant to let the exchange rate fluctuate. This phenomenon is known as “fear of floating”. We present a simple theory in which fear of floating emerges as an optimal policy outcome. The key feature of the model is an occasionally binding borrowing constraint linked to the exchange rate that introduces a feedback loop between aggregate demand and credit conditions. Contrary to the Mundellian paradigm, we show that a depreciation can be contractionary, and letting the exchange rate float can expose the economy to self-fulfilling crises.

We develop a theory of labor markets in a monetary economy with four realistic features: search frictions, worker productivity shocks, wage rigidity, and two-sided lack of commitment. Due to the non-Coasean nature of labor contracts, inefficient job separations occur in the form of endogenous quits and layoffs that are unilaterally initiated whenever a worker’s wage-to-productivity ratio moves outside an inaction region. We derive sufficient statistics for the aggregate labor market response to a monetary shock based on the distribution of workers’ wage-to-productivity ratios. These statistics crucially depend on the incidence of inefficient job separations, which we show how to identify using readily available microdata on wage changes and worker flows between jobs.

We re-examine recent trends in regional income convergence, considering the full distribution of income rather than focusing on the mean. Measuring similarity by comparing each percentile of state distributions to the corresponding percentile of the national distribution, we find that state incomes have become less similar (i.e. they have diverged) within the top 20 percent of the income distribution since 1969. The top percentile alone accounts for more than half of aggregate divergence across states over this period by our measure, and the top five percentiles combine to account for 93 percent. Divergence in top incomes across states appears to be driven largely by changes in top incomes among White people, while top incomes among Black people have experienced relatively little divergence.

We study the COVID-19 epidemic in emerging markets that face financial frictions and its mitigation through social distancing and vaccination. We find that restricted vaccine availability in emerging markets, as captured by limited quantities and high prices, renders the pandemic exceptionally costly in these countries, compared with economies without financial frictions. Improved access to financial markets enables a better response to the delay in vaccine supplies, as it supports more stringent social distancing measures before wider vaccine availability. We show that financial assistance programs to such financially constrained countries can increase vaccinations and lower fatalities, at no present-value cost to the international community.

We develop a model of banking industry dynamics to study the quantitative impact of regulatory policies on bank risk taking and market structure. Since our model is matched to U.S. data, we propose a market structure where big banks with market power interact with small, competitive fringe banks as well as non-bank lenders. Banks face idiosyncratic funding shocks in addition to aggregate shocks which affect the fraction of performing loans in their portfolio. A nontrivial bank size distribution arises out of endogenous entry and exit, as well as banks' buffer stock of capital. We show the model predictions are consistent with untargeted business cycle properties, the bank lending channel, and empirical studies of the role of concentration on financial stability. We find that regulatory policies can have an important impact on banking market structure, which, along with selection effects, can generate changes in allocative efficiency and stability.