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.     

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.     

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.     

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.     

Genetic Algorithm in the Wavelet Domain for Large p Small n Regression

Many areas of statistical modeling are plagued by the “curse of dimensionality,” in which there are more variables than observations. This is especially true when developing functional regression models where the independent dataset is some type of spectral decomposition, such as data from near-infrared spectroscopy. While we could develop a very complex model by simply taking enough samples (such that n > p), this could prove impossible or prohibitively expensive. In addition, a regression model developed like this could turn out to be highly inefficient, as spectral data usually exhibit high multicollinearity. In this article, we propose a two-part algorithm for selecting an effective and efficient functional regression model. Our algorithm begins by evaluating a subset of discrete wavelet transformations, allowing for variation in both wavelet and filter number. Next, we perform an intermediate processing step to remove variables with low correlation to the response data. Finally, we use the genetic algorithm to perform a stochastic search through the subset regression model space, driven by an information-theoretic objective function. We allow our algorithm to develop the regression model for each response variable independently, so as to optimally model each variable. We demonstrate our method on the familiar biscuit dough dataset, which has been used in a similar context by several researchers. Our results demonstrate both the flexibility and the power of our algorithm. For each response variable, a different subset model is selected, and different wavelet transformations are used. The models developed by our algorithm show an improvement, as measured by lower mean error, over results in the published literature.