Air pollution-mortality models: A demonstration of the effects of random measurement error

Errors of measurement have long been recognized as a chronic problem in statistical analysis. Although there is a vast statistical literature of multiple regression models estimating the air pollution-mortality relationship, this problem has been largely ignored. It is well known that pollution measures contain error, but the consequences of this error for regression estimates is not known. We use Lave and Seskin's air pollution model to demonstrate the consequences of random measurement error. We assume a range of 0% to 50% of the variance of the pollution measures is due to error. We find large differences in the estimated effects on mortality of the pollution variables as well as the other explanatory variables once this measurement error is taken into account. These results cast doubt on the usual regression estimates of the mortality effects of air pollution. More generally our results demonstrate the consequences of random measurement error in the explanatory variable of a multiple regression analysis and the misleading conclusions that may result in policy research if this error is ignored.     

Proxies for legal firearm prevalence

Quantitative Marketing and Economics - Product acquisition policies define legal markets. Policy evaluations require data but prevalence data are not always available. We introduce Legal Firearm...     

A Review of The Empirical Ethical Decision-Making Literature: 1996–2003

This review summarizes and critiques the empirical ethical decision-making literature from 1996–2003. One hundred and seventy-four articles were published in top business journals during this period. Tables are included that summarize the findings by dependent variable – awareness, judgment, intent, and behavior. We compare this review with past reviews in order to draw conclusions regarding trends in the ethical decision-making literature and to surface directions for future research.Michael J. O’Fallon is a graduate student in the Department of Management and Operations at Washington State University. His primary research interest is individual ethical decision-making and behavior in organizations. Recently, he has explored issues in organizational behavior, human resource management, and consumer behavior toward genetically modified foods.Kenneth D. Butterfield is an associate professor of management at Washington State University. He received his Ph.D. in Business Administration from The Pennsylvania State University. His research has been published in a variety of academic journals, including Academy of Management Journal, Academy of Management Review, Business and Society, Business Ethics Quarterly, Human Relations, Journal of Higher Education, Journal of Managerial Issues, and Research in Higher Education. His current research interests involve organizational behavior issues such as managing ethical decision-making and behavior in organizations, examining why people fail to recognize moral issues, understanding academic dishonesty and promoting academic integrity, and examining organizational punishment from the manager’s perspective.     

A Temporary Federal Discount Program to Stimulate Consumer Spending

In this paper, we evaluate a new proposal to stimulate recovery from the current recession: a temporary federal price discount on consumer goods. An attractive feature of the temporary federal discount program is that it gives consumers a price incentive to purchase more rather than simply giving consumers more disposable income, which they might choose to either spend or to save. According to our simulations with the Fair macro-econometric model, a temporary 20 percent federal discount on all consumer goods in a severe recession would significantly reduce the unemployment rate while causing only a small increase in federal debt as a percentage of GDP.     

THE POLITICAL ECONOMY OF CONTINGENT PROTECTION

In recent years, several empirical studies have attempted to investigate the key influences, or determinants, of the anti-dumping and countervailing duty (AD/CVD) decision-making process. As a consequence, several different empirical modelling approaches have been developed using alternative, and in some cases, competing theoretical frameworks. Each approach tests empirically aspects of the AD/CVD decision-making process for evidence of particular forms of pressure that contribute to protectionist outcomes. However, the results of these studies have been somewhat inconsistent and inconclusive, possibly because the analytical scope of these studies appears to be too narrow. Hence, the current article sets for itself the important objective of developing a more general framework for modelling contingent protection empirically. A taxonomy is introduced that suggests that the types of bias affecting AD/CVD decision-makers may be separated into three broad categories: political supply pressure, industry demand pressure, and regulatory process pressure. This approach is then used to analyse recent Australian anti-dumping outcomes. The empirical results generated from the Australian data, using the taxonomy, suggest that from the supply side, the Australian government appears to be biased against the provision of AD/CVD protection. On the other hand, the results suggest that a demand-side bias appears to be present. That is, the Australian AD/CVD process appears to be weighted towards a demand, rather than supply, orientation contrary to political economy suggestions that AD/CVD policy is used as a politically driven trade policy device.