Refining Population Health Comparisons: A Multidimensional First Order Dominance Approach

Protest parties are on the rise in several European countries. This development is commonly attributed to a growing dissatisfaction with life and associated with declining quality of life in modern society of the lowest social strata. This explanation is tested in a cross-sectional analysis of voting and life-satisfaction in 63 districts of the city of Rotterdam in the Netherlands, where the share of protest voters increased from 10 % in 1994 to 31 % in 2009. Contrary to this explanation protest voting appeared not to be the most frequent in the least happy districts of Rotterdam, but in the medium happy segment. Also divergent from this explanation was that average happiness in city districts is largely independent of local living conditions, but is rather a matter of personal vulnerability in terms of education, income and health. These results fit alternative explanations in terms of middle class status anxiety.     

Market structure,bank conduct and bank performance: Evidence from ASEAN

Whether banks in a concentrated market increase their profits through monopoly pricing is a question of prime concern for antitrust policies. We explore this question by introducing the role of bank conduct into the structure–performance relationship. We apply Two-step System GMM dynamic panel model to commercial banks in the Association of South East Asian Nations over the period of 1999–2014. The results indicate that the higher profits in concentrated banking industries are partially attributable to the anti-competitive conduct of banks. These findings are robust across alternative measures of market structure and bank conduct, and different time horizons. The implications of these findings require regulators to make sure that the consolidation policy for ASEAN is achieving its purpose – i.e. financial stability – and not allowing the banks to earn monopoly rents.     

Growth Effects of Religion Dependent Social Capital: An Empirical Evidence

Social Indicators Research - There is an imperceptible laxity in literature in finding socio economic effects of social capital by integrating role of religion. However, international surveys data...     

Connecting the Dots: Do Financial Analysts Help Corporate Boards Improve Corporate Social Responsibility?

This paper presents an examination of the joint impact of board structural elements at firm level and financial analysts as market-level corporate governance (CG) on corporate social responsibility (CSR) performance. Our study contributes to the CG–CSR literature by adopting the bundling approach, a perspective that has recently attracted researchers’ attention as an answer to any heterogeneity and fragmentation in existing findings. It is based on an extensive sample consisting of 7,739 firm-year observations of US firms for the 2006–2015 period. The findings suggest that financial analysts complement the corporate board with more independence, gender diversity and a specialized CSR committee to realize a certain level of CSR performance of a firm. The findings also indicate that analysts substitute for those internal governance factors that are associated with weaker boards – larger sizes and dual-role CEOs. We also draw implications for research and practice from our findings.     

Selection of suitable prior for the Bayesian mixture of a class of lifetime distributions under type-I censored datasets

This paper explores the study on mixture of a class of probability density functions under type-I censoring scheme. In this paper, we mold a heterogeneous population by means of a two-component mixture of the class of probability density functions. The parameters of the class of mixture density functions are estimated and compared using the Bayes estimates under the squared-error and precautionary loss functions. A censored mixture dataset is simulated by probabilistic mixing for the computational purpose considering particular case of the Maxwell distribution. Closed-form expressions for the Bayes estimators along with their posterior risks are derived for censored as well as complete samples. Some stimulating comparisons and properties of the estimates are presented here. A factual dataset has also been for illustration.     

Performance Evaluation Based on the Robust Mahalanobis Distance and Multilevel Modeling Using Two New Strategies

In this article, we propose a general framework for performance evaluation of organizations and individuals over time using routinely collected performance variables or indicators. Such variables or indicators are often correlated over time, with missing observations, and often come from heavy-tailed distributions shaped by outliers. Two new double robust and model-free strategies are used for evaluation (ranking) of sampling units. Strategy 1 can handle missing data using residual maximum likelihood (RML) at stage two, while strategy two handles missing data at stage one. Strategy 2 has the advantage that overcomes the problem of multicollinearity. Strategy one requires independent indicators for the construction of the distances, where strategy two does not. Two different domain examples are used to illustrate the application of the two strategies. Example one considers performance monitoring of gynecologists and example two considers the performance of industrial firms.     

A Simple Method to Ensure Plausible Multiple Imputation for Continuous Multivariate Data

Multiple Imputation (MI) is an established approach for handling missing values. We show that MI for continuous data under the multivariate normal assumption is susceptible to generating implausible values. Our proposed remedy, is to: (1) transform the observed data into quantiles of the standard normal distribution; (2) obtain a functional relationship between the observed data and it's corresponding standard normal quantiles; (3) undertake MI using the quantiles produced in step 1; and finally, (4) use the functional relationship to transform the imputations into their original domain. In conclusion, our approach safeguards MI from imputing implausible values.