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Urban research assigns immigrant enclaves an ambiguous role. While such areas are seen as rich in beneficial ethno-religious infrastructures and networks, they also tend to be located in deprived and stigmatised inner-city neighbourhoods. Research on neighbourhood attainment provides evidence for both, a desire to attain mainstream middle-class neighbourhoods, which grows the more immigrants and their descendants establish themselves in society, but also a continuing attraction of residing close to co-ethnics. To tease apart this ambiguity, we study how the life satisfaction of immigrants and their descendants depends on the characteristics of the neighbourhood they live in, and pay special attention to heterogeneity along generation, country of origin orientation and income. We use classic measures of neighbourhood quality vis-à-vis newly collected data on the spatial density of ethno-religious minority associations, places of worship and grocers. We link these data to the geocoded German Socio-Economic Panel to predict life satisfaction among immigrants and their descendants. To strengthen a causal interpretation of our results, we employ specifications that address self-selection into neighbourhoods and unobserved confounding. Contra the assumptions of standard assimilation models, we document that ethno-religious infrastructures contribute to increased life satisfaction primarily among the second generation, and there especially among sending-country oriented individuals. This suggests a continuing importance of origin-culture infrastructures for some groups. Furthermore, we find little evidence that overall neighbourhood quality, or the mere share of co-ethnics in a neighbourhood, increases life satisfaction either among immigrants or their descendants.

Context effects, where a characteristic of an upper-level unit or cluster (e.g., a country) affects outcomes and relationships at a lower level (e.g., that of the individual), are a primary object of sociological inquiry. In recent years, sociologists have increasingly analyzed such effects using quantitative multilevel modeling. Our review of multilevel studies in leading sociology journals shows that most assume the effects of lower-level control variables to be invariant across clusters, an assumption that is often implausible. Comparing mixed-effects (random-intercept and slope) models, cluster-robust pooled OLS, and two-step approaches, we find that erroneously assuming invariant coefficients reduces the precision of estimated context effects. Semi-formal reasoning and Monte Carlo simulations indicate that loss of precision is largest when there is pronounced cross-cluster heterogeneity in the magnitude of coefficients, when there are marked compositional differences among clusters, and when the number of clusters is small. Although these findings suggest that practitioners should fit more flexible models, illustrative analyses of European Social Survey data indicate that maximally flexible mixed-effects models do not perform well in real-life settings. We discuss the need to balance parsimony and flexibility, and we demonstrate the encouraging performance of one prominent approach for reducing model complexity.

Over the last two decades there has been a growing debate on the supposedly negative relation between ethnic diversity, public goods production and social cohesion. Despite the amount of evidence, existing in-depth qualitative reviews conclude that the literature is inconclusive. Advancing upon their work, I conduct a quantitative review of over 480 empirical findings from 172 studies. Rather than seeing the huge literature as consisting of an incomparable mass of studies, I argue that the diversity of the literature allows us to analyse the robustness of the general association (does it hold for the comparison of Nepalese villages and European countries alike?) and the conditions under which it is more likely to appear. Accordingly, the review fine-tunes the conclusions we can draw from the existing evidence by noting that the debate has generally produced slightly more confirmatory than confuting evidence. But more importantly, this tendency for validating findings increases considerably under certain conditions: (1) inquiries from regions of the world with rather salient ethnic boundaries, (2) analysis of small-scale neighbourhood contexts and (3) a focus on trust related sentiments or public goods production as outcomes. A rather problematic result of the review is that discipline matters: In comparison to findings published in political science or sociology journals, a considerably larger percentage of findings that are published in economics journals are confirmatory. I conclude by suggesting that interdisciplinary work is necessary and should focus on the conditions under which ethnic diversity is a significant predictor of public goods production and social cohesion. (author's abstract)

Does ethnic diversity erode social trust? Continued immigration and corresponding growing ethnic diversity have prompted this essential question for modern societies, but few clear answers have been reached in the sprawling literature. This article reviews the literature on the relationship between ethnic diversity and social trust through a narrative review and a meta-analysis of 1,001 estimates from 87 studies. The review clarifies the core concepts, highlights pertinent debates, and tests core claims from the literature on the relationship between ethnic diversity and social trust. Several results stand out from the meta-analysis. We find a statistically significant negative relationship between ethnic diversity and social trust across all studies. The relationship is stronger for trust in neighbors and when ethnic diversity is measured more locally. Covariate conditioning generally changes the relationship only slightly. The review concludes by discussing avenues for future research.

Zum Zusammenhang zwischen ethnischer Heterogenität, Sozialkapital (Vertrauen, freiwilliges Engagement, Mitgliedschaft in Organisationen und Vereinen etc.) und der Bereitstellungöffentlicher Güter lassen sich aus bislang vorliegenden Studien keine eindeutigen Befunde ableiten. Dies gilt insbesondere für den europäischen Kontext. Das Projekt Ethnische Vielfalt, soziales Vertrauen und Zivilengagement, gefördert vom Bundesministerium für Familie, Senioren, Frauen und Jugend, hatte zum Ziel, dieses Thema durch den Vergleich von drei Ländern, von unterschiedlichen Regionen und Städten in nationalem Kontext sowie durch Untersuchungen auf lokaler Ebene in Schulen besser zu beleuchten. Zur Datengewinnung wurden drei unterschiedliche methodische Ansätze miteinander verbunden: Im Rahmen der Umfragestudie Ethnic Diversity and Collective Action Survey (EDCAS) wurden insgesamt 10 200 Einwohner in 74 ausgewählten Regionen Deutschlands, Frankreichs und der Niederlande befragt. Die Daten ermöglichen den Vergleich von Städten, die sich nach dem Ausmaß ethnischer Diversität sowie in ihren integrationspolitischen Ansätzen voneinander unterscheiden. Um Einblick in die Prozesse zu gewinnen, die hinter den Effekten ethnischer Diversität stehen, wurden Fallstudien zur Elternpartizipation in Berliner und Lyoner Schulen organisiert. Darüber hinaus wurden Online- und Feldexperimente mit Anwohnern durchgeführt, um die kausalen Mechanismen dieser Effekte zu ergründen. Die hier dargelegten Ergebnisse beziehen sich auf ausschließlich auf die deutschen Daten. Bezogen auf die kognitive Dimension von Sozialkapital haben die Projektergebnisse den erwarteten negativen Zusammenhang zwischen objektiver, d.h. statistisch ausgewiesener ethnischer Vielfalt einerseits sowie Kooperationsvermögen und Vertrauen andererseits bestätigt. Ferner stellte sich heraus, dass auch die subjektive Wahrnehmung ethnischer Diversität einen eigenen Einfluss jenseits objektiver Diversitätsmaße ausübt. Mittels besonderer Betonung von Diversität in Experimenten konnte gezeigt werden, dass als hoch wahrgenommene ethnische Vielfalt die kausale Ursache für den Rückgang von Vertrauen ist. Im Hinblick auf die strukturelle Dimension von politischer Beteiligung und Mitgliedschaften in Organisationen sind die Ergebnisse jedoch weniger eindeutig: Zwar konnten wir mit einem Briefwurf-Experiment belegen, dass ethnische Diversität negative Auswirkungen auf tatsächliches pro-soziales Verhalten haben kann. In Bezug auf Engagement in Organisationen oder Beteiligung an Demonstrationen ließen sich jedoch keine signifikanten Effekte objektiver oder wahrgenommener Diversität nachweisen. Entgegen Putnams Deutung, dass Vielfalt zu einem Rückzug ins Private führe, gelangen wir zu der Schlussfolgerung, dass Diversität zwar Vertrauen und pro-soziales Verhalten beeinträchtigt, Menschen aber auch dazu aktivieren kann, der mangelnden Bereitstellung öffentlicher Güter in diversen Gemeinschaften organisiert entgegenzuwirken. ; Existing evidence on the relationship between ethnic heterogeneity, social capital (trust, voluntarism, associational membership etc.), and levels of public goods provision is inconclusive, especially for the European context. The project Ethnic Diversity, Social Trust, and Civic Engagement, financed by the German Federal Ministry of Family Affairs, Senior Citizens, Women and Youth, was intended to advance our knowledge of this relationship by conducting a comparative analysis across three countries, across regions and cities, as well as in the local organisational context of school communities, and using three complementary methodological approaches. First, the Ethnic Diversity and Collective Action Survey (EDCAS) across local populations in 74 selected regions in Germany, France, and the Netherlands with a total sample size of 10 200 allowed comparisons of cities with different levels of ethnic diversity and different policy approaches to deal with immigration. Second, case studies on parental participation in schools in Berlin and Lyon provided qualitative insight into the processes behind ethnic diversity effects. Finally, survey, online and field experiments with local residents were conducted to investigate the causal mechanisms behind these effects. Results presented in the paper refer only to the German data.

Quantitative comparative social scientists have long worried about the performance of multilevel models when the number of upper-level units is small. Adding to these concerns, an influential Monte Carlo study by Stegmueller (2013) suggests that standard maximum-likelihood (ML) methods yield biased point estimates and severely anti-conservative inference with few upper-level units. In this article, the authors seek to rectify this negative assessment. First, they show that ML estimators of coefficients are unbiased in linear multilevel models. The apparent bias in coefficient estimates found by Stegmueller can be attributed to Monte Carlo Error and a flaw in the design of his simulation study. Secondly, they demonstrate how inferential problems can be overcome by using restricted ML estimators for variance parameters and a t-distribution with appropriate degrees of freedom for statistical inference. Thus, accurate multilevel analysis is possible within the framework that most practitioners are familiar with, even if there are only a few upper-level units. ; Accepted for publication: Feb. 2019

Quantitative comparative social scientists have long worried about the performance of multilevel models when the number of upper-level units is small. Adding to these concerns, an influential Monte Carlo study by Stegmueller (2013) suggests that standard maximum-likelihood (ML) methods yield biased point estimates and severely anti-conservative inference with few upper-level units. In this article, the authors seek to rectify this negative assessment. First, they show that ML estimators of coefficients are unbiased in linear multilevel models. The apparent bias in coefficient estimates found by Stegmueller can be attributed to Monte Carlo Error and a flaw in the design of his simulation study. Secondly, they demonstrate how inferential problems can be overcome by using restricted ML estimators for variance parameters and a t-distribution with appropriate degrees of freedom for statistical inference. Thus, accurate multilevel analysis is possible within the framework that most practitioners are familiar with, even if there are only a few upper-level units.

AbstractQuantitative comparative social scientists have long worried about the performance of multilevel models when the number of upper-level units is small. Adding to these concerns, an influential Monte Carlo study by Stegmueller (2013) suggests that standard maximum-likelihood (ML) methods yield biased point estimates and severely anti-conservative inference with few upper-level units. In this article, the authors seek to rectify this negative assessment. First, they show that ML estimators of coefficients are unbiased in linear multilevel models. The apparent bias in coefficient estimates found by Stegmueller can be attributed to Monte Carlo Error and a flaw in the design of his simulation study. Secondly, they demonstrate how inferential problems can be overcome by using restricted ML estimators for variance parameters and a t-distribution with appropriate degrees of freedom for statistical inference. Thus, accurate multilevel analysis is possible within the framework that most practitioners are familiar with, even if there are only a few upper-level units.

Quantitative comparative social scientists have long worried about the performance of multilevel models when the number of upper-level units is small. Adding to these concerns, an influential Monte Carlo study by Stegmueller (2013) suggests that standard maximum-likelihood (ML) methods yield biased point estimates and severely anti-conservative inference with few upper-level units. In this article, the authors seek to rectify this negative assessment. First, they show that ML estimators of coefficients are unbiased in linear multilevel models. The apparent bias in coefficient estimates found by Stegmueller can be attributed to Monte Carlo Error and a flaw in the design of his simulation study. Secondly, they demonstrate how inferential problems can be overcome by using restricted ML estimators for variance parameters and a t-distribution with appropriate degrees of freedom for statistical inference. Thus, accurate multilevel analysis is possible within the framework that most practitioners are familiar with, even if there are only a few upper-level units.

The question whether ethnic diversity is associated with declining social cohesion has produced much controversy. We maintain that more attention must be paid to cognitive mechanisms to move the debate ahead. Using survey data from 938 localities in Germany, France, and the Netherlands, we explore a crucial individual-level mechanism: perceptions of diversity. We not only consider perceptions of the amount, but also of the qualitative nature of diversity. By asking about various qualitative aspects of diversity, we test the cognitive salience of three explanations that have been proposed in the literature for negative diversity effects: out-group biases, asymmetric preferences and coordination problems. We show that all three mechanisms matter. Perceptions both mediate statistical diversity effects, and have important explanatory power of their own. Moreover, we are able to address the question to what extend the relationship of perceived diversity and neighborhood social cohesion varies across policy contexts. Based on assumptions in the literature about positive impacts of inclusive and culturally pluralist immigrant integration policy approaches, we hypothesize that ethno-cultural diversity is less negatively related to neighborhood social cohesion in more inclusive policy contexts. Our results provide partial support for this hypothesis as perceived diversity has a significantly stronger negative impact on neighborhood cohesion in Germany. (author's abstract)