Effect of Health on Retirement of Older Americans: a Competing Risks Study

Retirement is an important event in the life of an individual. The decision to retire or exit from full-time employment may be motivated by several factors, including health. This paper explores the effect of both subjective and relatively more objective physical and mental health conditions on the probability of exit from full-time employment. Using longitudinal data on older Americans from ten waves of the Health and Retirement Study (1992–2010), eight health indices are created from a wide range of health measures by principal component analysis. The effect of these health indices on the time until exit from full-time employment is empirically examined in a proportional hazard model. Single and competing risk specifications are estimated that allow for multiple spells of full-time employment and control for unobserved heterogeneity. The main results suggest that better self-reported health decreases the likelihood of exit from full- time employment, while poor physical health (functional limitations factor) increases the likelihood of exit from full-time employment via complete retirement and disability. For mental health, I find that depression increases the likelihood of exit via complete retirement, part-time work and unemployment while cognitive disorders lead to an increase in likelihood of exit via the disability exit route. Hence, physical and mental health problems are both impediments to continued work. These results have implications for public policies targeted towards retaining older workers within the labor market.     

A likelihood based estimator for vector autoregressive processes

A onestep estimator, which is an approximation to the unconditional maximum likelihood estimator (MLE) of the coefficient matrices of a Gaussian vector autoregressive process is presented. The onestep estimator is easy to compute and can be computed using standard software. Unlike the computation of the unconditional MLE, the computation of the onestep estimator does not require any iterative optimization and the computation is numerically stable. In finite samples the onestep estimator generally has smaller mean square error than the ordinary least squares estimator. In a simple model, where the unconditional MLE can be computed, numerical investigation shows that the onestep estimator is slightly worse than the unconditional MLE in terms of mean square error but superior to the ordinary least squares estimator. The limiting distribution of the onestep estimator for processes with some unit roots is derived.     

Classification rules for triply multivariate data with an AR(1) correlation structure on the repeated measures over time

In this article we study the problem of classification of three-level multivariate data, where multiple q$q$-variate observations are measured on u$u$-sites and over p$p$-time points, under the assumption of multivariate normality. The new classification rules with certain structured and unstructured mean vectors and covariance structures are very efficient in small sample scenario, when the number of observations is not adequate to estimate the unknown variance–covariance matrix. These classification rules successfully model the correlation structure on successive repeated measurements over time. Computation algorithms for maximum likelihood estimates of the unknown population parameters are presented. Simulation results show that the introduction of sites in the classification rules improves their performance over the existing classification rules without the sites.     

Optimal and lead-in adaptive allocation for binary outcomes: A comparison of Bayesian methods

We compare posterior and predictive estimators and probabilities in response-adaptive randomization designs for two- and three-group clinical trials with binary outcomes. Adaptation based upon posterior estimates are discussed, as are two predictive probability algorithms: one using the traditional definition, the other using a skeptical distribution. Optimal and natural lead-in designs are covered. Simulation studies show that efficacy comparisons lead to more adaptation than center comparisons, though at some power loss, skeptically predictive efficacy comparisons and natural lead-in approaches lead to less adaptation but offer reduced allocation variability. Though nuanced, these results help clarify the power-adaptation trade-off in adaptive randomization.     

A New Kuznetsian Dynamic: The Knowledge Economy and Income Inequality in the United States, 1917–2008

The rise of the knowledge economy resulted in higher levels of income inequality in the United States and forced many to question the Kuznets Inverted‐U hypothesis. However, this study argues that the establishment of a knowledge economy does not negate the importance of employment shifts for income inequality. Instead, the expansion of knowledge employment alters the major sectors that are responsible for the overall distribution of income. To this end, this article presents the key argument that the current service–knowledge transition impacts income inequality trends, of today, in a way that is similar to the agricultural–industrial transition, of the past. According to the autoregressive conditional heteroskedasticity regressions, the agricultural–industrial transition returns stronger associations with income inequality in the United States before 1950. The agricultural–industrial transition's impact diminishes thereafter as the service–knowledge transition shares a more robust association with income inequality after 1980.     

On evaluating interactive statistical program packages

Most statistical computing for data analysis has come to depend upon statistical program packages. In recent years, interactive computing has become widespread both on large time-sharing systems and on mini-computers. This paper identifies ways in which interactive statistical software packages differ from batchoriented software and discusses evaluation considerations pertaining specifically to interactive packages.     

Bridging the awful gap between strategy and action

This article describes the lessons learned from a study carried out within British Telecommunications as part of the author's doctoral research programme. It was found that strategy was formulated and implemented, with direct action taken and resources commited, by Divisional managers at all three organizational levels in BT so that implementation did not always follow the lines intended by the HQ strategic planners. Implementation was found to be an interactive rather than a rational/sequential process. Marked variations in practice were observed and explanations for these are offered. The relative success of the strategies differed widely, both overall and within the divisional field units. The manner of implementation and factors managers perceived to help and hinder it were studied. Success or failure was felt to hinge on getting a few basics right: resources, organizational ‘fit’, historical performance and the expectations it generated (track record), information and support, market acceptance, technical competence, consistent goals and top management support.     

Character-Based Determinants of Trust in Leaders

Trust is a prominent determinant of effective interpersonal relationships, group process, and organizational development. However, for leaders and managers, trust building is often problematic. The aim of this article is to contribute to a growing understanding of the way in which individual managers can develop trust in organizations, particularly those defined by medium to high risk. The article presents a theoretical and empirical analysis of the nature of trust at work. Building on the results of an earlier study, use is made of the facet-theoretical approach to generate a definitional framework of trust, which focuses on the characteristics of the trusted person that contribute to the development of trust. Using items developed on the basis of a mapping sentence, hypotheses regarding the relations between the definitional framework and empirical observations were tested through smallest space analysis (SSA) of data collected from a sample of 398 bank and hospital employees. The results demonstrate strong support for the definitional system and show a clear association with results of prior research that identify perceptions of ability, integrity, fairness, and openness as key determinants of trust. The empirical structure clearly reflects the attitudinal nature of interpersonal trust at work and the items empirically distinguish distinct character-based components of trust.