Liberalization, FDI, and Growth in Developing Countries: A Panel Cointegration Approach

Using a panel cointegration framework, the article explores the two-way link between FDI and growth for a panel of 23 developing countries. In addition, it investigates the impact of liberalization on the dynamics of the FDI and GDP relationship. A long-run cointegrating relationship is found between FDI and GDP after allowing for heterogeneous country effects. The cointegrating vectors reveal a bidirectional causality between GDP and FDI for more open economies. For relatively closed economies, long-run causality appears unidirectional and runs from GDP to FDI, implying that growth and FDI are not mutually reinforcing under restrictive trade and investment regimes.     

Bargaining with set-valued disagreement

It is arguable that in many-two person bargaining situations disagreement leads to a set of possible payoffs with no probabilities attached to the elements of the set. Axioms are developed for bargaining games of this kind and solution concepts are derived from these axioms. Particular attention is paid to what are here called the max-max and rectangular general solutions. The latter can be applied to an important sub-class of bargaining games where the disagreement set is equal to the feasible set.A part of this work was done while I was a visitor at the University of Stockholm. For valuable discussions and comments I am grateful to T.C.A. Anant, Salvador Barbera, Bhaskar Dutta, Efe Ok, Jorgen Weibull and an anonymous referee of this journal. The paper also benefited from a presentation at the Indian Statistical Institute, New Delhi.     

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.     

Statistical modeling for Bayesian extrapolation of adult clinical trial information in pediatric drug evaluation

Children represent a large underserved population of “therapeutic orphans,” as an estimated 80% of children are treated off‐label. However, pediatric drug development often faces substantial challenges, including economic, logistical, technical, and ethical barriers, among others. Among many efforts trying to remove these barriers, increased recent attention has been paid to extrapolation; that is, the leveraging of available data from adults or older age groups to draw conclusions for the pediatric population. The Bayesian statistical paradigm is natural in this setting, as it permits the combining (or “borrowing”) of information across disparate sources, such as the adult and pediatric data. In this paper, authored by the pediatric subteam of the Drug Information Association Bayesian Scientific Working Group and Adaptive Design Working Group, we develop, illustrate, and provide suggestions on Bayesian statistical methods that could be used to design improved pediatric development programs that use all available information in the most efficient manner. A variety of relevant Bayesian approaches are described, several of which are illustrated through 2 case studies: extrapolating adult efficacy data to expand the labeling for Remicade to include pediatric ulcerative colitis and extrapolating adult exposure‐response information for antiepileptic drugs to pediatrics.     

Letter to the editor comments on Groparu-Cojocaru and Doray (2013)

Although estimating the five parameters of an unknown Generalized Normal Laplace (GNL) density by minimizing the distance between the empirical and true characteristic functions seems appealing, the approach cannot be advocated in practice. This conclusion is based on extensive numerical simulations in which a fast minimization procedure delivers deceiving estimators with values that are quite far away from the truth. These findings can be predicted by the very large values obtained for the true asymptotic variances of the estimators of the five parameters of the true GNL density.     

Developing an undue influence screening tool for Adult Protective Services

The study purpose was to develop and pilot an undue influence screening tool for California’s Adult Protective Services (APS) personnel based on the definition of undue influence enacted into California law January 1, 2014. Methods included four focus groups with APS providers (n = 33), piloting the preliminary tool by APS personnel (n = 15), and interviews with four elder abuse experts and two APS administrators. Social service literature—including existing undue influence models—was reviewed, as were existing screening and assessment tools. Using the information from these various sources, the California Undue Influence Screening Tool (CUIST) was developed. It can be applied to APS cases and potentially adapted for use by other professionals and for use in other states. Implementation of the tool into APS practice, policy, procedures, and training of personnel will depend on the initiative of APS management. Future work will need to address the reliability and validity of CUIST.