The Social Inclusion and Inequality Nexus: EU versus non‐EU migrants

This study examines the relationship between social inclusions of migrant and income inequality. Both positive and negative impacts of social inclusion on income inequality have been observed in the previous literature. This study specifically considers two types of migration flow: migration flow from EU and migration flow from non‐EU. The aim of this article is twofold: 1) is there a strong association between social inclusion of migrants and income inequality, 2) is there any different impact of social inclusion of two types of migrants (EU vs non‐EU)? Using data from 33 mainly European countries over the period 2003‐2015 and controlling for savings rate, arable land rate and age‐dependency ratio, our results indicate that there is a significant negative relationship between social inclusion and income inequality. In particular, we find that social inclusion from non‐EU migrants significantly reduces income inequality compare with EU migrants.     

L Ranked Set Sampling: A Generalization Procedure for Robust Visual Sampling

In this article, a robust ranked set sampling (LRSS) scheme for estimating population mean is introduced. The proposed method is a generalization for many types of ranked set sampling that introduced in the literature for estimating the population mean. It is shown that the LRSS method gives unbiased estimator for the population mean with minimum variance providing that the underlying distribution is symmetric. However, for skewed distributions a weighted mean is given, where the optimal weights is computed by using Shannon's entropy. The performance of the population mean estimator is discussed along with its properties. Monte Carlo comparisons for detecting outliers are made with the traditional simple random sample and the ranked set sampling for some distributions. The results indicate that the LRSS estimator is superior alternative to the existing methods.     

Quality of Life and Well-Being Ranking of Selected Districts of Khyber Pakhtunkhwa,Pakistan

Quality of life (QoL) is a multidimensional concept encompassing many aspects of an individual’s existence. It is not just a measure of material resources but of intangible environments which affect quality of life. The study concentrates on the province of Khyber Pakhtunkhwa, Pakistan, and looks at the QoL and well-being in thirteen districts representing the province’s major population. It followed an integrative approach to measuring quality of life. Principal component analysis was used to tackle with multidimensionality. Weighted factor scores were used as an index for ranking QoL and well-being on the basis of social indicators chosen in different life domains. The results show that districts having high urban disposition top the ranks in QoL in Khyber Pakhtunkhwa, like Peshawar, Mardan, Swat and Charsadda, while Bannu, Kohat and Lower Dir fall amongst the bottom districts in terms of QoL. The rural/urban ranking also follows similar patterns with a few exceptions.     

Do Immigrants Catch‐up with the Natives in Terms of Earnings? Evidence from Individual Level Data of Canada

This article analyses differences in dynamic transitions into and out of any of the five hourly wage quintiles and quintile zero (unemployed and non‐employed people) between immigrants and natives for the period 1993‐2004. Using Longitudinal Level data from Survey of Labour and Income Dynamics (SLID) for men aged 25 to 55, we investigate how unobserved heterogeneity factors and initial conditions may affect individuals’ propensity to stay in or leave any of the wage quintiles. We also consider a dynamic multinomial logit model with the random effects approach. Empirical results show that state dependence exists in all hourly wage quintiles. Moreover, education, experience, marital status, immigrant minority status, and age at immigration are significant factors determining hourly wage differentials between immigrants and natives.     

Human Capital Outflow and Economic Misery: Fresh Evidence for Pakistan

This paper visits the impact of economic misery on human capital outflow using time series data over the period of 1975–2012. We have applied the combined cointegration tests and innovation accounting approach to examine long run and causal relationship between the variables. Our results affirm the presence of cointegration between the variables. We find that economic misery increases human capital outflow. Foreign remittances add in human capital outflow from Pakistan. The migration from Pakistan to rest of world is boosted by depreciation in local currency. Income inequality is also a major contributor to human capital outflow. The present study is comprehensive effort and may provide new insights to policy makers for handling the issue of human capital outflow by controlling economic misery in Pakistan.

     

Can Women Empowerment Explain Cross-Country Differences in Inequality? A Global Perspective

Social Indicators Research - This paper investigates the relationship between women empowerment and income inequality. In particular, we examine whether empowering women in terms of raising their...     

Role of AKRSP on Gender Development: A Case Study in Pakistan

ABSTRACT

In Pakistan, nongovernmental agencies (NGOs) play an important role in program implementation. This study focuses on the contribution of one of these NGO social service programs that serves women in a rural area of Pakistan. To assess program satisfaction and effectiveness, 200 females were surveyed to measure their opinions and overall satisfaction with this particular NGO. The key findings of the study showed that the Aga Khan Rural Support Program (AKRSP) had a significant positive impact on the lives of these women through increasing social and decision-making skills while improving their income and living standards by providing employment opportunities and training. Future recommendations are made to continue to sponsor such programs that focus on a group-support for women while also providing supportive services that empower decision-making and economic standing.     

Modelling the quality of work in the Italian social co-operatives combining NPCA-RSM and SEM-GME approaches

The objective of this paper is to describe and analyse with appropriate statistical models the links between work quality latent factors. Due to the complexity of the task, the analysis is carried out through a two-step approach:

• In the first step, we construct some multidimensional measures of the subjective quality of work, using nonlinear principal component analysis (NPCA) and Rasch analysis with the Rating Scale Model (NPCA-RSM);

• In the second step, we adopt a Structural Equation Model based on generalized maximum entropy (SEM-GME) to integrate the measures achieved with the previous step and to evaluate the relationships between the subjective work quality latent factors.

Therefore, the novel aspects of this paper are the following: (i) The integration between the NPCA-RSM and SEM-GME, which allows reduction of the variables analysed and evaluation of the measurement errors; (ii) The formalization of a Job Satisfaction Model for the study of the relationships between the subjective work quality latent factors in the Italian social services sector.