Using confidential firm-level panel data from the Bureau of Economic Analysis, we examine how the bilateral trade flows of U.S. multinational corporations (MNCs) and their Canadian affiliates responded to U.S.-Canadian tariff reductions from 1983 to 1992. We find that Canadian affiliate sales to the United States are negatively correlated with Canadian tariffs, but U.S. parent sales to Canadian affiliates have little association with Canadian tariffs. These results contradict the notion that Canadian tariff reductions would lead to a ‘hollowing out’ of Canadian manufacturing. We also find substantial heterogeneity in MNC responses to tariff changes within narrowly defined manufacturing industries. Overall, bilateral trade liberalization is trade-creating, as U.S. MNCs integrated their North American production such that Canadian affiliates increased sales to the United States and reduced domestic sales.

Over the past decade, a substantial literature on methods for the estimation of discrete choice dynamic programming (DDP) models of behavior has developed. However, the implementation of these methods can impose major computational burdens because solving for agents' decision rules often involves high dimensional integrations that must be performed at each point in the state space. In this paper we develop an approximate solution method that consists of: (1) using Monte Carlo integration to stimulate the required multiple integrals at a subset of the state points, and (2) interpolating the non-simulated values using a regression function. The overall performance of this approximation method appears to be excellent.

This research compares several approaches to inference in the multinomial probit model, based on two Monte Carlo experiments for a seven choice model.The methods compared are the simulated maximum likelihood estimator using the GHK recursive probability,simulator, the method of simulated moments estimator using the GHK recursive simulator and kernel-smoothed frequency simulators, and posterior means using a Gibbs sampling-data augmentation algorithm.Overall, the Gibbs sampling algorithm has a slight edge, with the relative performance of MSM and SML based on the GHK simulator being difficult to evaluate.The MSM estimator with the kernel-smoothed frequency simulator is clearly inferior.I. Introduction T HE multinomial probit is an appealing model of choice behavior because it allows a flexible pattern of conditional covariance among the latent utilities of alternatives.Nevertheless, multinomial probit applications have been limited because the required integrations of the multivariate normal density over subsets of Euclidean space are computationally burdensome.The computational simplicity of the multinomial logit has made it the model of choice for applied work.However, because the multinomial probit model relaxes the assumption of independence of irrelevant alternatives, it is generally preferred in principle to the multinomial logit model (McFadden, 1984, pp.1395-1458).Recently the method of simulated moments (McFadden, 1989; Pakes and Pollard, 1989) and Gibbs sampling with data augmentation (Albert and Chib, 1993; McCulloch and Rossi, 1994) have shown promise of making the required computations in the multinomial probit model practical.The development of the highly accurate GHK probability simulator (see Geweke, 1991; Hajivassiliou and McFadden, 1990; and Keane, 1990, 1994a) has also led to renewed interest in simulated maximum likelihood (Albright, Lerman, and Manski, 1977) as a method for estimating multinomial probit models.The objective of the research reported here is to provide a systematic comparison of the numerical properties of different simulation-based methods of inference in the multinomial probit model.Rather than considering the performance of these methods on a single model for a single data set, we attempt to control for a number of features of the inference problem, such as the number and nature of the unknown parameters of interest and the information content of the data on which inference is based.Also, we investigate for the first time how the performance of MSM estimation is affected by the type of probability simulator employed (i.e., GHK vs. kernel smoothing).While some investigators have examined the performance of particular estimators and computational techniques, Borsch-Supan and Hajivassiliou (1993), Hajivassiliou (1992), and Hajivassiliou, McFadden, and Ruud (1992) have made systematic comparison of alternative probability simulators, we are aware of only one systematic comparison of different estimators per se: Keane (1994a) compares method of simulated moments and simulated maximum likelihood estimators for an eight period binomial probit model in a Monte Carlo study.This paper is the first to compare performance of alternative methods of inference for the multinomial probit model and the first to examine how the relative performance of alternative methods differs across model specifications and across different data sets.In addition to addressing this main objective, this work introduces a new factor structure for the disturbances that may help to alleviate the proliferation of covariance matrix parameter problems in MNP models.We also illustrate Bayesian inference in a multinomial probit model with a factor structure for the first time.(See Elrod and Keane (forthcoming) for a discussion of factor structures for probit models.)

This paper analyzes the evolution of inequality in Poland during the economic transition that began in 1989-1990. Using microdata from the Household Budget Surveys, we find that, after a brief spike in 1989, income and consumption inequality actually declined to below pretransition levels during 1990-1992 and then increased gradually, rising only moderately above pretransition levels by 1997. In sharp contrast, inequality in labor earnings increased markedly and consistently throughout the 1990-1997 period. We find that social transfer mechanisms, including pensions, played an important role in mitigating increases in both overall inequality and poverty. We argue that, from a political economy perspective, transfer mechanisms were well designed to reduce political resistance to market-oriented reforms in the early years of transition, paving the way for rapid growth. Finally, we provide cross-country evidence from the transition economies that is consistent with our interpretation of the Polish experience and is also consistent with recent work in growth theory suggesting that redistribution that reduces inequality can enhance growth.

In this paper, we use micro panel data to examine the effects of oil price changes on employment and real wages, at the aggregate and industry levels.We also measure differences in the employment and wage responses for workers differentiated on the basis of skill level.We find that oil price increases result in a substantial decline in real wages for all workers, but raise the relative wage of skilled workers.The use of panel data econometric techniques to control for unobserved heterogeneity is essential to uncover this result, which is completely hidden in OLS estimates.We find that changes in oil prices induce changes in employment shares and relative wages across industries.However, we find little evidence that oil price changes cause labor to consistently flow into those sectors with relative wage increases.