Natural Resource Management in Pastoral Africa

Current research on rangeland ecology suggests that we have less to fear from pastoral land stewardship than was previously thought. On the one hand, the natural environments exploited by pastoralists are generally robust and resilient. And on the other hand, pastoral techniques of land management are not as dysfunctional as was once widely assumed. While regulation of pastoral activity may be necessary in specific circumstances, there no longer exists a broad scientific mandate to control or modify almost every aspect of pastoral land use in order to preserve the environment.     

Dynamics of Asset Poverty in South Korea

Studies of economic insecurity tend to focus on changes in incomes and transitions in and out of income poverty. Yet family economic conditions are shaped by more than just income. Levels of assets and wealth, and changes in these over time also play a role. To identify which groups of the poor have been structurally trapped in poverty over time, using the Korean Welfare Panel Study, we examined the dynamics of asset poverty from 2005 to 2014. We defined three asset poverty lines by operationalizing assets as resources for either future consumption or development. Findings show that asset poverty experience in a previous year significantly increased the probability to incur asset poverty by 5–12% for all analysis samples. In addition, the probability of incurring asset poverty decreased with home ownership, higher disposable income, and greater diversification of the household portfolio. Our findings suggest that the asset poor are likely to fall into structural and persistent poverty over time, and an asset-building approach is needed to improve the asset poverty status of households in South Korea.

     

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.     

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.     

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.     

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.