Research Database

Occupational licensure, one of the most significant labor market regulations in the United States, may restrict the interstate movement of workers. We analyze the interstate migration of 22 licensed occupations. Using an empirical strategy that controls for unobservable characteristics that drive long-distance moves, we find that the between-state migration rate for individuals in occupations with state-specific licensing exam requirements is 36 percent lower relative to members of other occupations. Members of licensed occupations with national licensing exams show no evidence of limited interstate migration. The size of this effect varies across occupations and appears to be tied to the state specificity of licensing requirements. We also provide evidence that the adoption of reciprocity agreements, which lower re-licensure costs, increases the interstate migration rate of lawyers. Based on our results, we estimate that the rise in occupational licensing can explain part of the documented decline in interstate migration and job transitions in the United States.

This paper describes a Bayesian specification procedure used to generate a vector autoregressive model for forecasting macroeconomic variables. The specification search is over parameters of a prior. This quasi-Bayesian approach is viewed as a flexible tool for constructing a filter which optimally extracts information about the future from a set of macroeconomic data. The procedure is applied to a set of data and a consistent improvement in forecasting performance is documented.

The statistical significance of variance decompositions and impulse response functions for unrestricted vector autoregressions is questionable. Most previous studies are suspect because they have not provided confidence intervals for variance decompositions and impulse response functions. Here two methods of computing such intervals are developed, one using a normal approximation, the other using bootstrapped resampling. An example from Sims’ work illustrates the importance of computing these confidence intervals. In the example, the 95 percent confidence intervals for variance decompositions span up to 66 percentage points at that usual forecasting horizon.

On our interpretation, real bills advocates favor unfettered intermediation, while their critics, who we call quantity theorists, favor legal restrictions on intermediation geared to separate “money” from “credit.” We display examples of economies in which quantity-theory assertions about “money-supply” and price-level behavior under the real bills regime are valid. In particular, both the price level and an asset total that quantity theorists would identify as money fluctuate more under a real bills regime than under a regime with restrictions like those favored by quantity theorists. Despite this, the Pareto criterion does not support the quantity-theory position.

This paper provides an exact Bayesian framework for analyzing the arbitrage pricing theory (APT). Based on the Gibbs sampler, we show how to obtain the exact posterior distributions for functions of interest in the factor model. In particular, we propose a measure of the APT pricing deviations and obtain its exact posterior distribution. Using monthly portfolio returns grouped by industry and market capitalization, we find that there is little improvement in reducing the pricing errors by including more factors beyond the first one.

In the early 1970s, hours worked per working-age person in Spain were higher than in the United States. Starting in 1975, however, hours worked in Spain fell by 40 percent. We find that 80 percent of the decline in hours worked can be accounted for by the evolution of taxes in an otherwise standard neoclassical growth model. Although taxes play a crucial role, we cannot argue that taxes drive all of the movements in hours worked. In particular, the model underpredicts the large decrease in hours in 1975–1986 and the large increase in hours in 1994–2007. The lack of productivity growth in Spain during 1994–2015 has little impact on the model’s prediction for hours worked.

We develop a search-theoretic model of financial intermediation and use it to study how trading frictions affect the distribution of asset holdings, asset prices, efficiency, and standard measures of liquidity. A distinctive feature of our theory is that it allows for unrestricted asset holdings, so market participants can accommodate trading frictions by adjusting their asset positions. We show that these individual responses of asset demands constitute a fundamental feature of illiquid markets: they are a key determinant of bid-ask spreads, trade volume, and trading delays—all the dimensions of market liquidity that search-based theories seek to explain.

This paper is an extension of Ricardo Lagos’s work while he was in the Research Department of the Federal Reserve Bank of Minneapolis.

The central puzzle in international business cycles is that fluctuations in real exchange rates are volatile and persistent. We quantity the popular story for real exchange rate fluctuations: they are generated by monetary shocks interacting with sticky goods prices. If prices are held fixed for at least one year, risk aversion is high, and preferences are separable in leisure, then real exchange rates generated by the model are as volatile as in the data and quite persistent, but less so than in the data. The main discrepancy between the model and the data, the consumption—real exchange rate anomaly, is that the model generates a high correlation between real exchange rates and the ratio of consumption across countries, while the data show no clear pattern between these variables.

Statistical inference in multinomial multiperiod probit models has been hindered in the past by the high dimensional numerical integrations necessary to form the likelihood functions, posterior distributions, or moment conditions in these models. We describe three alternative approaches to inference that circumvent the integration problem: Bayesian inference using Gibbs sampling and data augmentation to compute posterior moments, simulated maximum likelihood (SML) estimation using the GHK recursive probability simulator, and method of simulated moment (MSM) estimation using the GHK simulator. We perform a set of Monte-Carlo experiments to compare the performance of these approaches. Although all the methods perform reasonably well, some important differences emerge. The root mean square errors (RMSEs) of the SML parameter estimates around the data generating values exceed those of the MSM estimates by 21 percent on average, while the RMSEs of the MSM estimates exceed those of the posterior parameter means obtained via agreement via Gibbs sampling by 18 percent on average. While MSM produces a good agreement between empirical RMSEs and asymptotic standard errors, the RMSEs of the SML estimates exceed the asymptotic standard errors by 28 percent on average. Also, the SML estimates of serial correlation parameters exhibit significant downward bias.