Midlife and older displaced workers: A comparison of predisplacement and new earnings

The economic consequences of business closings and worker layoffs are of great concern to today's work force. The study compares earnings of 755 workers previous to displacement to 422 displaced workers with new earnings. Variables are identified using the data supplement of the 1988Current Populations Survey: Displaced Workers. Displaced workers employed in professional and white-collar positions have more favorable predisplacement earnings and new earnings than workers from other job sectors, but displaced workers who are homeowners and those who have health care coverage before and following job displacement have a decline in earnings. Displaced workers with some high school education and workers with 30 to 39 years of job tenure also experience decreased wages. Overall, displacement of American workers stifles the economy and negatively influences the human capital of the nation's work force. Her major research interests include displaced worker issues, work and family policy, and the economics of aging. Her major research interests include labor force participation of midlife and older persons.     

Performance measurement: Some conceptual issues

Measures of how well a system is operating are clearly of interest to a wide range of users, from organizational researchers to system managers. The current literatures in such areas as “performance measurement, ” “organizational effectiveness, ” and the like show little consistency in their definitions of terms or in their methods for generating measures. The present paper attempts a clarification of these issues in the form of a conceptual minimalist position which requires only three basic definitions, and leaves, as far as possible, all remaining issues open to empirical investigation.We first review the literature on “organizational effectiveness” contrasting the organizational goals and systems paradigms, and note the lack of either theoretical or empirical convergence between the two. An examination of the nature of effectiveness statements suggests that this failure of convergence flows mainly from the different criterion sets generated by the two paradigms — and, importantly, that one should not expect convergence on a single measure or set of measures which uniquely define how well a system is performing. One's view of how well a given system is performing is a function of where one stands (either theoretically or in relationship to the system), and pursuit of the one true set of performance measures is a futile exercise. Instead, we propose to redirect attention to the identification of the various individuals and groups (“constituencies”) with an interest in system performance, and to the investigation of those items of system relevant information (their “performance measure sets”) which do, in fact, change their evaluations of how well the system is performing. This perspective will, we hope, redirect effort from futile theoretical debate to empirical investigations of what measures are used, by whom, and to what effect, in specific settings.     

Misunderstandings between experimentalists and observationalists about causal inference

Summary.  We attempt to clarify, and suggest how to avoid, several serious misunderstandings about and fallacies of causal inference. These issues concern some of the most fundamental advantages and disadvantages of each basic research design. Problems include improper use of hypothesis tests for covariate balance between the treated and control groups, and the consequences of using randomization, blocking before randomization and matching after assignment of treatment to achieve covariate balance. Applied researchers in a wide range of scientific disciplines seem to fall prey to one or more of these fallacies and as a result make suboptimal design or analysis choices. To clarify these points, we derive a new four-part decomposition of the key estimation errors in making causal inferences. We then show how this decomposition can help scholars from different experimental and observational research traditions to understand better each other's inferential problems and attempted solutions.     

When not to rely on the central limit theorem - an example from absenteeism data

When sampling from a skew distribution, one often assumes that the robustness of the one- and two-sample t-tests ensures their accuracy for a 'reasonably large' sample size. An example from absenteeism (days lost per annum) serves to illustrate the fallacy of this assumption even for quite large samples (e.g. 500) especially for single sample estimation and testing. The two-sample t-test appears somewhat more robust provided the two samples are of approximately equal size.