Drug testing in the transportation industry: The maritime case

The maritime industry provides an interesting case study of the design and implementation of drug-testing programs in the transportation industry during the 1980s. It is clear that such programs were designed and implemented less because of empirical evidence of safety problems than for political reasons. The results in the maritime industry are indicative of a program that is expensive to operate, intrusive with regard to employee privacy, and which will have little or no impact on safety. Focusing such programs on the issue of impairment rather than on the issue of drug usage per se is likely to improve the outcome of the programs. Although, despite common belief, there is little evidence that drug usage or the “drug problem” in U.S. society as a whole had gotten worse in the preceding years (Schonsheck, 1989, 250–251).     

Temporary controls on capital inflows

During the past decade a number of countries imposed capital controls that had two distinguishing features: they were asymmetric, in that they were designed principally to discourage capital inflows, and they were temporary. This paper studies formally the consequences of these policies, calibrates their potential effectiveness, and assesses their welfare implications in an environment in which the level of capital inflows can be sub-optimal. In addition, motivated by the fact that these types of controls have often been left in place after the dissipation of the shock that lead to the controls being implemented, the paper evaluates the welfare cost of procrastination in removing these types of controls.     

The dynamics of positive action in UK trade unions: the case of women and black members

This paper considers positive action strategies amongst UK trade unions, aimed at increasing membership and levels of participation and representation among women and black workers. It provides an overview of women’s, black members’ and race structures within large Trades Union Congress unions and a detailed case study of one large UK trade union. We find that there are salient differences in the way that unions approach issues of gender equality, compared with the approach adopted towards race equality. The paper explores possible explanations, justifications and implications of these differences.     

General‐to‐Specific Model Selection Procedures for Structural Vector Autoregressions*

Structural vector autoregressive (SVAR) models have emerged as a dominant research strategy in empirical macroeconomics, but suffer from the large number of parameters employed and the resulting estimation uncertainty associated with their impulse responses. In this paper, we propose general‐to‐specific (Gets) model selection procedures to overcome these limitations. It is shown that single‐equation procedures are generally efficient for the reduction of recursive SVAR models. The small‐sample properties of the proposed reduction procedure (as implemented using PcGets) are evaluated in a realistic Monte Carlo experiment. The impulse responses generated by the selected SVAR are found to be more precise and accurate than those of the unrestricted VAR. The proposed reduction strategy is then applied to the US monetary system considered by Christiano, Eichenbaum and Evans (Review of Economics and Statistics, Vol. 78, pp. 16–34, 1996) . The results are consistent with the Monte Carlo and question the validity of the impulse responses generated by the full system.     

"Flexible" Work Practices and Occupational Safety and Health: Exploring the Relationship Between Cumulative Trauma Disorders and Workplace Transformation

This article matches establishment-level data on workplace transformation (e.g., quality circles, work teams, and just-in-time production) with measures of cumulative trauma disorders at these same establishments to explore the relationship between "flexible" workplace practices and workplace health and safety. The results reveal a positive, statistically significant, and quantitatively sizable relationship between cumulative trauma disorders and the use of quality circles and just-in-time production.     

Inferential Causal Models: Integrating Quality & Quantity

In-depth data analysis plus statistical modeling can produce inferentialcausal models. Their creation thus combines aspects of analysis by close inspection,that is, reason analysis and cross-tabular analysis, with statistical analysis procedures,especially those that are special cases of the generalized linear model (McCullaghand Nelder, 1989; Agresti, 1996; Lindsey, 1997). This paper explores some of the roots of this combined method and suggests some new directions. An exercise clarifies some limitations of classic reason analysis by showing how the cross tabulation of variables with controls for test factors may produce better inferences. Then, given the cross tabulation of several variables, by explicating Coleman effect parameters, logistic regressions, and Poisson log-linear models, it shows how generalized linear models provide appropriate measures of effects and tests of statistical significance. Finally, to address a weakness of reason analysis, a case-control design is proposed and an example is developed.     

Risk and the miller equilibrium: Capital structure choice with risk-averse investors

Merton Miller's (1977) tax model of equilibrium capital structure choice results in capital structure irrelevance and the existence of tax clienteles, assuming the restrictive case of risk-neutrality. Relaxation of the assumption of risk-neutrality in Miller's tax framework, allowing utility-maximizing risk-averse investors, indicates that capital structure irrelevance continues to hold under reasonable assumptions about utility. Evaluation of resulting tax clienteles shows that marginal tax rates do not restrict investors from investing in equities but do affect the tax status of purchased bonds.     

Estimating nonlinear time-series models using simulated vector autoregressions

This paper develops two new methods for conducting formal statistical inference in nonlinear dynamic economic models. The two methods require very little analytical tractability, relying instead on numerical simulation of the model's dynamic behaviour. Although one of the estimators is asymptotically more efficient than the other, a Monte Carlo study shows that, for a specific application, the less efficient estimator has smaller mean squared error in samples of the size typically encountered in macroeconomics. The estimator with superior small sample performance is used to estimate the parameters of a real business cycle model using observed US time-series data.