Ethnic concentration and language fluency of immigrants : quasi-experimental evidence from the guest-worker placement in Germany (Ethnische Konzentration und Sprachkompetenz von Einwanderern : quasi-experimentelle Befunde aus der Gastarbeiteranwerbung in Deutschland)

"The paper analyses the impact of regional own-ethnic concentration on the language proficiency of immigrants. It solves the endogeneity of immigrants' location choices by exploiting the fact that guest-workers in Germany after WWII were initially placed by firms and labor agencies. We find a robust negative effect of ethnic concentration on immigrants' language ability. Simulation results of a simultaneous location and learning choice model confirm the presence of the effect and show how immigrants with high learning cost select into ethnic enclaves. Under the counterfactual scenario of a regionally equal distribution of immigrants the share of German-speakers increases only modestly." (Author's abstract, IAB-Doku) ((en))Einwanderer, Beschäftigungspolitik, Arbeitskräftemangel, Wohnort, Einwanderungsland, Sprachkenntnisse, deutsche Sprache, ethnische Gruppe, Ballungsraum

Ethnic Concentration and Language Fluency of Immigrants in Germany

Studies that investigate the effect of the regional ethnic composition on immigrant outcomes have been complicated by the self-selection of ethnic minorities into specific neighbourhoods. We analyse the impact of own-ethnic concentration on the language proficiency of immigrants by exploiting the fact that the initial placement of guest-workers after WWII was determined by labour demanding firms and the federal labour administration and hence exogenous to immigrant workers. Combining several data sets, we find a small but robust and significant negative effect of ethnic concentration on immigrants' language ability. Simulation results of a choice model in which location and learning decisions are taken simultaneously confirm the presence of the effect. Immigrants with high learning costs are inclined to move to ethnic enclaves, so that the share of German-speakers would increase only modestly even under the counterfactual scenario of a regionally equal distribution of immigrants across Germany.enclave, ethnic concentration, language proficiency, immigrants, Instrumental variable, random utility model

Ethnic concentration and language fluency of immigrants: quasi-experimental evidence from the guest-worker placement in Germany

"Dieses Paper analysiert die Wirkung von regionalen ethnischen Konzentrationen auf die Sprachkompetenz von Einwanderern. Wir lösen die Endogenität der Wohnortentscheidungen der Einwanderer in dem wir den Umstand nutzen, dass Einwanderer im Rahmen der Gastarbeiteranwerbung ihren ersten Arbeitsort nicht auswählen konnten. Wir finden belastbare negative Effekte der ethnischen Konzentration auf die Beherrschung der deutschen Sprache. Simulationsergebnisse eines Modells mit gleichzeitigen Lern- und Wohnortentscheidungen unterstützen diesen Befund und zeigen, dass Einwanderer mit hohen Lernkosten gezielt in ethnische Enklaven ziehen. Unter dem Szenario einer Gleichverteilung der Einwanderer über Deutschland würde sich der Anteil der deutsch sprechenden Einwanderer nur geringfügig erhöhen." (Autorenreferat)"The paper analyses the impact of regional own-ethnic concentration on the language proficiency of immigrants. It solves the endogeneity of immigrants' location choices by exploiting the fact that guest-workers in Germany after WWII were initially placed by firms and labor agencies. We find a robust negative effect of ethnic concentration on immigrants’ language ability. Simulation results of a simultaneous location and learning choice model confirm the presence of the effect and show how immigrants with high learning cost select into ethnic enclaves. Under the counterfactual scenario of a regionally equal distribution of immigrants the share of German-speakers increases only modestly." (author's abstract

SOEPpapers on Multidisciplinary Panel Data Research Do Ethnic Enclaves Impede Immigrants' Integration? Evidence from a Quasi- Experimental Social-Interaction Approach SOEPpapers on Multidisciplinary Panel Data Research at DIW Berlin Do Ethnic Enclaves Imp

This series presents research findings based either directly on data from the German SocioEconomic Panel Study (SOEP) or using SOEP data as part of an internationally comparable data set (e.g. CNEF, ECHP, LIS, LWS, CHER/PACO). SOEP is a truly multidisciplinary household panel study covering a wide range of social and behavioral sciences: economics, sociology, psychology, survey methodology, econometrics and applied statistics, educational science, political science, public health, behavioral genetics, demography, geography, and sport science. The decision to publish a submission in SOEPpapers is made by a board of editors chosen by the DIW Berlin to represent the wide range of disciplines covered by SOEP. There is no external referee process and papers are either accepted or rejected without revision. Papers appear in this series as works in progress and may also appear elsewhere. They often represent preliminary studies and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be requested from the author directly. Any opinions expressed in this series are those of the author(s) and not those of DIW Berlin. Research disseminated by DIW Berlin may include views on public policy issues, but the institute itself takes no institutional policy positions

Essays in labor economics with applications to Germany

textThis dissertation consists of three essays in Labor Economics. The first chapter estimates the costs for establishments of hires and separations for a panel of German establishments. The panel records the start and the termination of the employment of all employees in the surveyed establishments, allowing estimation of adjustment costs under different assumptions of how frequently establishments revise their labor demand. Under the assumption that establishments revise their labor demand every month, estimates suggest hiring costs per employee of approximately 5,000 Euros, and costs of separations of 1,000 Euros. Hiring costs vary considerably between skilled (8,000 to 28,000 Euros per hire) and unskilled (4,000 to 8,000 Euros) labor. Spatial aggregation (large establishments) is associated with lower cost estimates, and only monthly adjustment frequencies yield estimates consistent with theoretical predictions. The second chapter analyzes the role of regional ethnic capital - defined as the average years of schooling of ethnic groups - in the educational attainment of young second generation immigrants in Germany using information on naturalization and country of birth in a nationally representative survey. I find evidence for externalities of ethnic capital for ethnic groups. A higher average education of ethnics makes attendance of higher-quality secondary schools more likely. Moreover, the effect is mainly mediated through the ethnic concentration in the region. However, if higher than regional aggregates are used for the measurement of ethnic capital, no externalities are detected. The third chapter analyzes the impact of regional own-ethnic concentration on the language proficiency of immigrants in Germany. It solves the endogeneity of immigrants' location choices by exploiting the fact that guest-workers in Germany after WWII were initially placed by firms and labor agencies. We find a robust negative effect of ethnic concentration on immigrants' language ability. Simulation results of a simultaneous location and learning choice model confirm the presence of the effect and show how immigrants with high learning cost select into ethnic enclaves. Under the counterfactual scenario of a regionally equal distribution of immigrants the share of German-speakers increases only modestly.Economic

The effect of an unconventional fare decrease on the demand for bus journeys: A regression discontinuity approach

We analyse the effect of a change in the fare structure for bus journeys in London on different demand measures using a regression discontinuity design. We utilise data obtained from Transport for London following the implementation of a new bus fare policy in September 2016, in which a follow-up journey made within the hour of an initial journey became free. Drawing on millions of individual paid and unpaid journeys between June and December 2016 – thus covering the period just before and after the introduction of the new fare – we estimate the effect of this price policy on the number of paid bus journeys, follow-up journeys and bus passenger numbers. We find that the policy significantly increased the number of bus trips by 5% and follow-up journeys by 8%. Passenger numbers increased by 4%. We also find that the increase in demand was not only driven by new customers, but also by more intensive demand by existing customers

Machine Learning Models for Classification of Cushing's Syndrome Using Retrospective Data

Accurate classification of Cushing's Syndrome (CS) plays a critical role in providing the early and correct diagnosis of CS that may facilitate treatment and improve patient outcomes. Diagnosis of CS is a complex process, which requires careful and concurrent interpretation of signs and symptoms, multiple biochemical test results, and findings of medical imaging by physicians with a high degree of specialty and knowledge to make correct judgments. In this article, we explore the state of the art machine learning algorithms to demonstrate their potential as a clinical decision support system to analyze and classify CS to facilitate the diagnosis, prognosis, and treatment of CS. Prominent algorithms are compared using nested cross-validation and various class comparison strategies including multiclass, one vs. all, and one vs. one binary classification. Our findings show that Random Forest (RF) algorithm is most suitable for the classification of CS. We demonstrate that the proposed approach can classify CS with an average accuracy of 92% and an average F1 score of 91.5%, depending on the class comparison strategy and selected features. RF-based one vs. all binary classification model achieves sensitivity of 97.6%, precision of 91.1%, and specificity of 87.1% to discriminate CS from non-CS on the test dataset. RF-based multiclass classification model achieves average per class sensitivity of 91.8%, average per class specificity of 97.1%, and average per class precision of 92.1% to classify different subtypes of CS on the test dataset. Clinical performance evaluation suggests that the developed models can help improve physicians' judgment in diagnosing CS