Foreign Labour Migration and the Economic Crisis in the EU: Ongoing and Remaining Issues of the Migrant Workforce in Germany

This paper provides an evaluation of the status of migrant workers in Germany amidst the global financial crisis. Findings of the study are drawn from the latest available data on the labour market performance of native-German and non-German migrant workers as well as other socioeconomic integration measures of the receiving state. Compared to the experience of migrants in most of the major receiving states of the EU, the status of the predominantly low-skilled sector-employed migrant workers in Germany, where primarily the skilled-workforce concentrated industries of high-value products is affected, has remained unchanged during the crisis. On the other hand, marginalisation of the ethnic and national minority population appears to be a persistent phenomenon marked by long-standing labour market exclusion. This is manifested in over two decades of double-digit unemployment rates of the foreign migrant population in the former ‘guest-worker’ importing country. This implies for the economy the need to settle long-term problems and implement strategies towards a better labour market integration of the minority migrant population beyond the recent recession.global financial crisis, low-skilled sector, migrant workers, guest-workers, labour market integration, minority migrant population

Post-Socialist International Migration: The Case of China-to-South Korea Ethnic Labour Migration

This paper examines an atypical south-north labour migration that emerged in the post-socialist international migration system: China-to-South Korea ethnic labour migration. Over the past decade, South Korea has experienced an unprecedented increase in the arrival of foreign labour. The majority of workers come from the People's Republic of China. Based on a contextual multivariate analysis of primary survey data on 525 predominantly undocumented Korean Chinese labour migrants in Seoul, this study reveals the underexplored economic dimension of ethnic migration in Northeast Asia. Empirical findings on this source of migrant labour in South Korea demonstrate that the China-to-South Korea ethnic population movement is an important yet an unknown dimension of the post-socialist global migration regime that is marked by the New Economics of international labour migration. The study suggests that ethnic migration from a socialist transition economy to a capital-rich economy linked through ancestral connections must be reconsidered in the context of the changing global migration and demographic landscapes, rather than the ethno-nationally romanticised view of the return of diaspora.ethnic labour migration, post-socialist global migration regime, new economics of international labour migration

Pitfalls of Immigrant Inclusion into the European Welfare State

This paper's main purpose is to gauge immigrants' demand for social assistance and services and identify the key barriers to social and labor market inclusion of immigrants in the European Union. The data from an online primary survey of experts from organizations working on immigrant integration in the EU is analyzed using simple comparative statistical methods; the robustness of the results is tested by means of Logit and ordered Logit statistical models. We find that the general public in Europe has rather negative attitudes towards immigrants. Although the business community views immigrants somewhat less negatively, barriers to immigrant labor market inclusion identified include language and human capital gaps, a lack of recognition of foreign qualifications, discrimination, intransparent labor markets and institutional barriers such as legal restrictions for foreign citizens. Exclusion from higher education, housing and the services of the financial sector aggravate these barriers. Changes in the areas of salaried employment, education, social insurance, mobility and attitudes are seen as most desired by members of ethnic minorities. The current economic downturn is believed to have increased the importance of active inclusion policies, especially in the areas of employment and education. These results appear to be robust with respect to a number of characteristics of respondents and their organizations.ethnic minorities, migration, labor market integration, economic crisis, enlarged European Union, welfare state

Cross-temporal and cross-national comparisons of party left-right positions

We investigate the cross-time and cross-nation comparability of party left-right position measurements by expert surveys and the Comparative Manifesto Project (CMP). While expert surveys show party left-right positions to be mostly static, we find the CMP records systematic party movements for one-third of the parties analyzed. On the issue of cross-national comparability, we find cross-national variation in expert surveys is muted. They contain little more than the variation associated with reputations based on party-family affiliation. The CMP measurements, on the other hand, contain variation attributable to national party-system differences. We conclude with thoughts about why all of this is so and about how one might navigate the expert survey limitations depending on the question one wants to answer about democratic politics and policy making

Kernel-convoluted Deep Neural Networks with Data Augmentation

The Mixup method (Zhang et al. 2018), which uses linearly interpolated data, has emerged as an effective data augmentation tool to improve generalization performance and the robustness to adversarial examples. The motivation is to curtail undesirable oscillations by its implicit model constraint to behave linearly at in-between observed data points and promote smoothness. In this work, we formally investigate this premise, propose a way to explicitly impose smoothness constraints, and extend it to incorporate with implicit model constraints. First, we derive a new function class composed of kernel-convoluted models (KCM) where the smoothness constraint is directly imposed by locally averaging the original functions with a kernel function. Second, we propose to incorporate the Mixup method into KCM to expand the domains of smoothness. In both cases of KCM and the KCM adapted with the Mixup, we provide risk analysis, respectively, under some conditions for kernels. We show that the upper bound of the excess risk is not slower than that of the original function class. The upper bound of the KCM with the Mixup remains dominated by that of the KCM if the perturbation of the Mixup vanishes faster than $O(n^{-1/2})$ where $n$ is a sample size. Using CIFAR-10 and CIFAR-100 datasets, our experiments demonstrate that the KCM with the Mixup outperforms the Mixup method in terms of generalization and robustness to adversarial examples

Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits

We propose a linear contextual bandit algorithm with $O(\sqrt{dT\log T})$ regret bound, where $d$ is the dimension of contexts and $T$ isthe time horizon. Our proposed algorithm is equipped with a novel estimator in which exploration is embedded through explicit randomization. Depending on the randomization, our proposed estimator takes contributions either from contexts of all arms or from selected contexts. We establish a self-normalized bound for our estimator, which allows a novel decomposition of the cumulative regret into \textit{additive} dimension-dependent terms instead of multiplicative terms. We also prove a novel lower bound of $\Omega(\sqrt{dT})$ under our problem setting. Hence, the regret of our proposed algorithm matches the lower bound up to logarithmic factors. The numerical experiments support the theoretical guarantees and show that our proposed method outperforms the existing linear bandit algorithms.Comment: Accepted in Artificial Intelligence and Statistics 202