What Moves the Highly Skilled and Why? Comparing Turkish Nationals in Canada and Germany

Based on in‐depth interviews with highly skilled and business Turkish nationals (HSBTN) in Canada and Germany, this study aims to explore why HSBTN decide to move and whether migration policy differences among the countries of destination affect recent migration motivations of HSBTN. It mainly focuses on the reasons and rationale of HSBTN and their explanations. This study argues that the high skilled and business migrants in general and HSBTN in particular move internationally as a consequence of individual‐level gain beyond economic prospects.     

Refugees and Elections: The Effects of Syrians on Voting Behavior in Turkey

In the wake of the Syrian civil war, more than three million people have fled to Turkey, which now hosts the largest refugee population on earth. Making up 4.42 per cent of the total population in Turkey as of February 2018, Syrian refugees are nevertheless spread unevenly within its borders. The ratio of refugees to the local population ranges from as high as 99per cent in the city of Kilis to as low as 0.05per cent in Sinop. This article presents findings from the empirical study of the effect of this geographical variation on election outcomes in Turkey, after the arrival of major refugee populations in 2012. Drawing on a unique subnational dataset and ordinary least squares (OLS), generalized least squares (GLS), and difference‐in‐differences (DiD) regressions, the study compares cities hosting few refugees (control group) with cities with large refugee populations (treatment group) to determine whether significant differences in voting patterns emerged. Our findings show a negative, but insignificant, impact on the incumbent party. The findings have policy implications for Turkey as well as any country that experiences a considerable flow of refugees.     

How Poor is Poor? A novel look at multidimensional poverty in the UK

Social Indicators Research - The integrated nature of well-being produces difficulty in evaluating poverty levels and one issue is the neglected of human life aspects, multiple feedback loops, and...     

Particle swarm optimization based Liu-type estimator

In this study, a new method for the estimation of the shrinkage and biasing parameters of Liu-type estimator is proposed. Because k is kept constant and d is optimized in Liu’s method, a (k, d) pair is not guaranteed to be the optimal point in terms of the mean square error of the parameters. The optimum (k, d) pair that minimizes the mean square error, which is a function of the parameters k and d, should be estimated through a simultaneous optimization process rather than through a two-stage process. In this study, by utilizing a different objective function, the parameters k and d are optimized simultaneously with the particle swarm optimization technique.     

Joint GEEs for multivariate correlated data with incomplete binary outcomes

This study considers a fully-parametric but uncongenial multiple imputation (MI) inference to jointly analyze incomplete binary response variables observed in a correlated data settings. Multiple imputation model is specified as a fully-parametric model based on a multivariate extension of mixed-effects models. Dichotomized imputed datasets are then analyzed using joint GEE models where covariates are associated with the marginal mean of responses with response-specific regression coefficients and a Kronecker product is accommodated for cluster-specific correlation structure for a given response variable and correlation structure between multiple response variables. The validity of the proposed MI-based JGEE (MI-JGEE) approach is assessed through a Monte Carlo simulation study under different scenarios. The simulation results, which are evaluated in terms of bias, mean-squared error, and coverage rate, show that MI-JGEE has promising inferential properties even when the underlying multiple imputation is misspecified. Finally, Adolescent Alcohol Prevention Trial data are used for illustration.     

Long live Fenerbahçe: The production boosting effects of football

The connection between Turkish industrial production performance and the success of a popular Turkish football team, namely Fenerbahçe, is the central theme of this article. The success of Fenerbahçe is interpreted as a proxy for the workers’ mood or morale. Performing a transfer function analysis on our monthly data set, we reveal positive feedback from Fenerbahçe’s success, which proxies workers’ mood/morale, to economic performance such that the monthly industrial growth rate increases by 0.26% with the number of games won by Fenerbahçe in European cups regardless of where the game is played. Evidence of the effects of games against domestic rivals on industrial performance is not statistically significant.     

Liu-Type Logistic Estimator

It is known that multicollinearity inflates the variance of the maximum likelihood estimator in logistic regression. Especially, if the primary interest is in the coefficients, the impact of collinearity can be very serious. To deal with collinearity, a ridge estimator was proposed by Schaefer et al. The primary interest of this article is to introduce a Liu-type estimator that had a smaller total mean squared error (MSE) than the Schaefer's ridge estimator under certain conditions. Simulation studies were conducted that evaluated the performance of this estimator. Furthermore, the proposed estimator was applied to a real-life dataset.     

Model selection using information criteria under a new estimation method: least squares ratio

In this study, we evaluate several forms of both Akaike-type and Information Complexity (ICOMP)-type information criteria, in the context of selecting an optimal subset least squares ratio (LSR) regression model. Our simulation studies are designed to mimic many characteristics present in real data – heavy tails, multicollinearity, redundant variables, and completely unnecessary variables. Our findings are that LSR in conjunction with one of the ICOMP criteria is very good at selecting the true model. Finally, we apply these methods to the familiar body fat data set.     

Supplier Selection for Framework Agreements in Humanitarian Relief

In this study, we consider the supplier selection problem of a relief organization that wants to establish framework agreements (FAs) with a number of suppliers to ensure quick and cost‐effective procurement of relief supplies in responding to sudden‐onset disasters. Motivated by the FAs in relief practice, we focus on a quantity flexibility contract in which the relief organization commits to purchase a minimum total quantity from each framework supplier over a fixed agreement horizon, and, in return, the suppliers reserve capacity for the organization and promise to deliver items according to pre‐specified agreement terms. Due to the uncertainties in demand locations and amounts, it may be challenging for relief organizations to assess candidate suppliers and the offered agreement terms. We use a scenario‐based approach to represent demand uncertainty and develop a stochastic programming model that selects framework suppliers to minimize expected procurement and agreement costs while meeting service requirements. We perform numerical experiments to understand the implications of agreement terms in different settings. The results show that supplier selection decisions and costs are generally more sensitive to the changes in agreement terms in settings with high‐impact disasters. Finally, we illustrate the applicability of our model on a case study.