Research Database

Financial integration generates macroeconomic spillovers that may require international monetary policy coordination. We show that individual central banks may set nominal interest rates too low or too high relative to the cooperative outcome. We identify three sufficient statistics that determine whether the Nash equilibrium exhibits under-tightening or over-tightening: the output gap, sectoral differences in labor intensity, and the trade balance response to changes in nominal rates. Independently of the shocks hitting the economy, we find that under-tightening is possible during economic expansions or contractions. For large shocks, the gains from coordination can be substantial.

This paper explores the role of restrictions on the use of international reserves as economic sanctions. We develop a simple model of the strategic game between a sanctioning (creditor) country and a sanctioned (debtor) country. We show how the sanctioning country should impose restrictions optimally, internalizing the geopolitical benefits and the financial costs of a potential default from the sanctioned country.

We characterize the little wage information contained in online job posts. Wage information is rare: only 14% of posts contain any information. Of these, wage ranges are more common than point wages, and are wide on average, spanning 28% of the midpoint (e.g. $32,000-$42,000/yr). Posted wages are highly selected in low income occupations: 40% higher than wages of employed workers. High wage firms are more opaque, with more and wider ranges. We find zero correlation between wage information and local labor market tightness. We provide an example of bias in econometric inference that worsens as wage information falls.

In the 1960 cohort, American men and women graduated from college at the same rate, and this was true for Whites, Blacks and Hispanics. But in more recent cohorts, women graduate at much higher rates than men. To understand the emerging gender education gap, we formulate and estimate a model of individual and family decision-making where education, labor supply, marriage and fertility are all endogenous. Assuming preferences that are common across ethnic groups and fixed over cohorts, our model explains differences in all endogenous variables by gender/ethnicity for the ‘60-‘80 cohorts based on three exogenous factors: family background, labor market and marriage market constraints. Changes in parental background are a key factor driving the growing gender education gap: Women with college educated mothers get greater utility from college, and are much more likely to graduate themselves. The marriage market also contributes: Women’s chance of getting marriage offers at older ages has increased, enabling them to defer marriage. The labor market is the largest factor: Improvement in women’s labor market return to college in recent cohorts accounts for 50% of the increase in their graduation rate. But the labor market returns to college are still greater for men. Women go to college more because their overall return is greater, after factoring in marriage market returns and their greater utility from college attendance. We predict the recent large increases in women’s graduation rates will cause their children’s graduation rates to increase further. But growth in the aggregate graduation rate will slow substantially, due to significant increases in the share of Hispanics – a group with a low graduation rate – in recent birth cohorts.

We analytically characterize the aggregate productivity loss from allocative distortions in a setting that accounts for the sectoral linkages of production. We show that the effects of distortions and the role of sectoral linkages depend crucially on how substitutable inputs are. We find that the productivity loss is smaller if input substitutability is low. Moreover, with low input substitutability, sectoral linkages do not systematically amplify the effects of distortions. In addition, the impact of the sectors that supply intermediate inputs becomes smaller. We quantify these effects in the context of the distortions caused by market power, using industry-level data for 35 countries. With our benchmark calibration, which accounts for low input substitutability, the median aggregate productivity loss from industry-level markups is 1.3%. To assume instead unit elasticities of substitution (i.e., to use a Cobb-Douglas production function) would lead to overestimating the productivity loss by a factor of 1.8. Sectoral linkages do amplify the cost of markups, but the amplification factor is considerably weaker than with unit elasticities.

We derive an optimal labor income tax rate formula for urban models in which tax rates are determined by traditional forces plus a new term arising from urban forces: house price, migration and agglomeration effects. Based on the earnings distributions and housing costs in large and small US cities, we find that in a benchmark model (i) optimal income tax rates are U-shaped, (ii) urban forces serve to raise optimal tax rates at all income levels and (iii) adopting an optimal tax system induces agents with low skills to leave large, productive cities. While agglomeration effects enter the optimal tax formula, they play almost no quantitative role in shaping optimal labor income tax rates.

We benchmark six global optimization algorithms by comparing their performance on challenging multidimensional test functions as well as on a method of simulated moments estimation of a panel data model of earnings dynamics. Five of the algorithms are from the popular NLopt open-source library: (i) Controlled Random Search with local mutation (CRS), (ii) Improved Stochastic Ranking Evolution Strategy (ISRES), (iii) Multi-Level Single-Linkage (MLSL), (iv) Stochastic Global Optimization (StoGo), and (v) Evolutionary Strategy with Cauchy distribution (ESCH). The sixth algorithm is TikTak, which is a multistart global optimization algorithm used in some recent economic applications. For completeness, we add three popular local algorithms to the comparison—the Nelder-Mead downhill simplex algorithm, the Derivative-Free Nonlinear Least Squares (DFNLS) algorithm, and a popular variant of the Davidon-Fletcher-Powell (DFPMIN) algorithm. To give a detailed comparison of algorithms, we use benchmarking tools recently developed in the optimization literature. We find that the success rate of many optimizers varies dramatically with the characteristics of each problem and the computational budget that is available. Overall, TikTak is the strongest performer both on the test functions and the economic application. The next-best performing optimizers are StoGo for the test functions and MLSL and ISRES for the economic application.