Research Database

A bivariate Granger-causality test on money and output finds statistically significant causality when data are measured in log levels, but not when they are measured in first differences of the logs. Which of these results is right? The answer to that question matters because a finding of no Granger-causality from money to output would substantially embarrass existing business cycle models in which money plays an important role [Eichenbaum and Singleton (1986)]. Monte Carlo simulation experiments indicate that, most probably, the first difference results reflect lack of power, whereas the level results reflect Granger-causality that is actually in the data.

This paper analyses the effects of open market operations on interest rates in a model in which agents must pay a fixed cost to exchange assets and cash. Asset markets are endogenously segmented in that some agents choose to pay the fixed cost and some do not. When the fixed cost is zero, the model reduces to the standard one in which persistent money injections increase nominal interest rates, flatten the yield curve, and lead to a downward-sloping yield curve on average. In contrast, if markets are sufficiently segmented, then persistent money injections decrease interest rates, steepen or even twist the yield curve, and lead to an upward-sloping yield curve on average.

In this paper, we study the optimal design of financial safety nets under limited private credit. We ask when it is optimal to restrict ex ante the set of investors that can receive public liquidity support ex post. When the government can commit, the optimal safety net covers all investors. Introducing a wedge between identical investors is inefficient. Without commitment, an optimally designed financial safety net covers only a subset of investors. Compared to an economy where all investors are protected, this results in more liquid portfolios, better social insurance, and higher ex ante welfare. Our result can rationalize the prevalent limited coverage of safety nets, such as the lender of last resort facilities.

This paper studies lifetime aggregate labor supply with endogenous workweek length. Such a theory is needed to evaluate various government policies. A key feature of our model is a nonlinear mapping from hours worked to labor services. This gives rise to an endogenous workweek that can differ across occupations. The theory determines what fraction of the lifetime an individual works, not when. We find that constraints on workweek length have different consequences for total hours than total labor services. Also, we find that policies designed to increase the length of the working life may not increase aggregate lifetime labor supply.

In the early 1980’s, the world steel market collapsed. Since the almost exclusive use of iron-ore is in steel production, many iron-ore mines had to be shut down. We divide the major iron-ore producing countries into groups based on the threat of closure faced by iron-ore mines in the respective country. In countries where mines faced no threat of closure, the iron-ore industry had little or no productivity gain over the decade. In countries where mines faced a large threat of closure, the industry typically had productivity gains ranging from 50 to 100 percent, gains that were unprecedented. We then argue that these productivity increases were not driven by new technology or by the closing of low productivity mines. Hence, the productivity gains were driven by continuing mines, using existing technology, increasing their productivity in order to stay in operation.

This paper develops a methodology for predicting the impact of trade liberalization on exports by industry (3-digit ISIC) based on the pre-liberalization distribution of exports by product (5-digit SITC). Using the results of Kehoe and Ruhl (2013) that much of the growth in trade after trade liberalization is in products that are traded very little or not at all, we predict that industries with a higher share of exports generated by least traded products will experience more growth. Using our methodology, we develop predictions for industry-level changes in trade for the United States and Korea following the U.S.-Korea Free Trade Agreement (KORUS). As a test for our methodology, we show that it performs significantly better than the applied general equilibrium models originally used for the policy evaluation of the North American Free Trade Agreement (NAFTA).

This paper is a primer on the great depressions methodology developed by Cole and Ohanian (1999, 2007) and Kehoe and Prescott (2002, 2007). We use growth accounting and simple dynamic general equilibrium models to study the depression that occurred in Finland in the early 1990s. We find that the sharp drop in real GDP over the period 1990–93 was driven by a combination of a drop in total factor productivity (TFP) during 1990–92 and of increases in taxes on labor and consumption and increases in government consumption during 1989–94, which drove down hours worked in Finland. We attempt to endogenize the drop in TFP in variants of the model with an investment sector and with terms-of-trade shocks but are unsuccessful.

We address this question in a heterogeneous-agent incomplete-markets model featuring exogenous idiosyncratic risk, endogenous skill investment, and flexible labor supply. The tax and transfer schedule is restricted to be log-linear in income, a good description of the US system. Rising inequality is modeled as a combination of skill-biased technical change and growth in residual wage dispersion. When facing shifts in the income distribution like those observed in the US, a utilitarian planner chooses higher progressivity in response to larger residual inequality but lower progressivity in response to widening skill price dispersion reflecting technical change. Overall, optimal progressivity is approximately unchanged between 1980 and 2016. We document that the progressivity of the actual US tax and transfer system has similarly changed little since 1980, in line with the model prescription.

This paper studies the incidence and efficiency of a progressive income tax in a spatial equilibrium. We use US census data to estimate an empirical spatial equilibrium with heterogeneous workers, landowners, and firms. The US income tax shifts skilled workers out of high-productivity cities, leading to a deadweight loss of 2% of tax revenue. Flattening the tax schedule significantly increases welfare inequality between skilled and unskilled workers and does not increase overall worker welfare, as the efficiency gains are captured by landowners. This suggests that progressive income taxes reduce welfare inequality without reducing total worker welfare.

We show that cities with higher population density specialize in high-skill service jobs that can be done remotely. The urban and industry bias of remote work potential shaped the recent pandemic’s economic impact. Many high-skill service workers started to work remotely, withdrawing spending from big-city consumer service industries dependent on their demand. As a result, low-skill service workers in big cities bore most of the recent pandemic’s economic impact. Our findings have broader implications for the distributional consequences of the U.S. economy’s transition to more remote work.