Aktuelle Publikationen

The digitization of music has changed how we consume, produce, and distribute music. In this paper, we explore the effects of digitization and streaming on the globalization of popular music. While some argue that digitization has led to more diverse cultural markets, others consider that the increasing accessibility to international music would result in a globalized market where a few artists garner all the attention. We tackle this debate by looking at how cross-country diversity in music charts has evolved over 4 years in 39 countries. We analyze two large-scale datasets from Spotify, the most popular streaming platform at the moment, and iTunes, one of the pioneers in digital music distribution. Our analysis reveals an upward trend in music consumption diversity that started in 2017 and spans across platforms. There are now significantly more songs, artists, and record labels populating the top charts than just a few years ago, making national charts more diverse from a global perspective. Furthermore, this process started at the peaks of countries’ charts, where diversity increased at a faster pace than at their bases. We characterize these changes as a process of Cultural Divergence, in which countries are increasingly distinct in terms of the music populating their music charts.

Qu’est-ce qui explique la représentation géographique dans le personnel professionnel des organisations intergouvernementales (OI) ? Nous abordons cette question d’un point de vue organisationnel en considérant les processus de recrutement des OI. Dans le système des Nations unies (ONU), les processus de recrutement sont conçus pour garantir le mérite bureaucratique, l’expérience et la formation étant les critères de mérite pertinents. Nous développons et testons une théorie de l’offre, en postulant que les différences dans l’offre de candidats qualifiés et très expérimentés dans les pays peuvent expliquer la représentation géographique. En nous appuyant sur les données relatives au personnel de 34 OI et sur les données relatives à l’offre de 174 États membres, et en tenant compte de l’endogénéité et des autres explications, nous n’avons observé aucune relation de ce type pour la formation. Cependant, les pays qui disposent d’un grand nombre de candidats ayant une expérience professionnelle et régionale pertinente présentent des valeurs significativement plus élevées en termes de représentation. Ces résultats offrent une explication complémentaire sur les raisons pour lesquelles certains pays sont plus fortement représentés dans le personnel professionnel international que d’autres. Ils révèlent également la nature du mérite bureaucratique au sein de l’ONU, qui semble privilégier les connaissances locales et l’expérience professionnelle par rapport à l’éducation formelle (occidentale).

National identities are often conceived of as factors that lend structure and stability to citizens’ political opinions on issues such as immigration. While citizens who define national membership in ethno-cultural terms are less likely to support immigration, those with a civic conception are more likely to do so. The authors propose that defining national identity along both ethno-cultural and civic lines may give rise to conflicting considerations, leading people to experience ambivalence, implying that national identities may serve less as a stabilizing force than suggested by previous research. Findings from heterogeneous choice models and a unique survey experiment show that German citizens with mixed conceptions of national identity had more variable and more malleable opinions than individuals with ideal-type conceptions during the 2015/2016 European refugee crisis. The findings point to an identity-based source of ambivalence and extend current understandings of how people form attitudes towards immigration.

Background
The coronavirus (COVID-19) pandemic presents an unprecedented crisis with potential negative mental health impacts.

Methods
This study used data collected via Youper, a mental health app, from February through July 2020. Youper users (N = 157,213) in the United States self-reported positive and negative emotions and anxiety and depression symptoms during the pandemic. We examined emotions and symptoms before (pre), during (acute), and after (sustained) COVID-related stay-at-home orders.

Results
For changes in frequency of reported acute emotions, from the pre to acute periods, anxiety increased while tiredness, calmness, happiness, and optimism decreased. From the acute to sustained periods, sadness, depression, and gratitude increased. Anxiety, stress, and tiredness decreased. Between the pre and sustained periods, sadness and depression increased, as did happiness and calmness. Anxiety and stress decreased. Among symptom measures, anxiety increased initially, from the pre to the acute periods, but later returned to baseline.

Limitations
The study sample was primarily comprised of young people and women. The app does not collect racial or ethnicity data. These factors may limit generalizability. Sample size was also not consistent for all data collected.

Conclusions
The present study suggests that although there were initial negative impacts on emotions and mental health symptoms in the first few weeks, many Americans demonstrated resilience over the following months. The impact of the pandemic on mental health may not be as severe as predicted, although future work is necessary to understand longitudinal effects as the pandemic continues.

Previous work on remittances and incumbency support has focused on recipients’ (pocketbook and sociotropic) economic assessments. In Mexico, however, crime has become the second (if not the first) concern of voters and security evaluations have become closely linked to the national executive’s performance. In this paper we posit that in Mexico remittances can also increase incumbent support through their effect on recipients’ security assessments. We attribute this finding to remittances allowing recipients to take crime-preventive measures and alleviate some of the pressure associated with crime and violence, which in turn improves recipients’ assessments of the security situation of the country and evaluations of the incumbent. Using individual data from Mexico for the period 2006–2017, we find that remittance recipients have higher levels of perceived personal safety and better national security evaluations than non-recipients, and that security assessments are a significant predictor of presidential approval rates. The evidence thus suggests that in Mexico, where crime is rising, and presidents are increasingly rewarded or blamed for containing violence, the effect of remittances on presidential approval also operates through improved personal safety perceptions and public security assessments.

Concurrent elections are widely used to increase turnout. We theorize and show empirically how concurrency affects electoral outcomes. First, concurrency increases turnout and thereby the participation of peripheral voters. Second, in combined elections, one electoral arena affects the other. In our case of majoritarian executive elections concurrent to proportional representation (PR) legislative elections, the centripetal tendency of majoritarian elections colors off to the concurrent PR race. Third, concurrency also entails spillovers of the incumbency advantage of executive officeholders to the concurrent legislative race. Drawing on quasi-random variation in local election timing in Germany, we show that concurrency increases turnout as well as council votes for the incumbent mayor's party and centrist parties more generally, with slightly more pronounced gains for the political left. As a consequence, concurrent elections consolidate party systems and political power by leading to less fragmented municipal councils and more unified local governments.

What do you do when your field research is called off? The original plan for political scientist Fabian Bergmann was to travel to Norway and Sweden to do research for his doctoral thesis on the indigenous Sámi people. Then the coronavirus hit. Now the project faces the challenge of making contacts at a distance.

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.

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dc.contributor.author: Eick, Gianna Maria

Esta sección analiza las respuestas gubernamentales a la pandemia de COVID-19 en cinco países de América Latina: Argentina, Brasil, Perú, México y Uruguay. Los artículos abordan la evolución de la pandemia y la construcción de políticas de emergencia en estos países, con énfasis en las dinámicas de cooperación y conflicto entre presidentes, congresos, burocracias y gobiernos subnacionales. En conjunto, los artículos apuntan a factores que contribuyeron a diversos grados de gobernanza de la crisis, desde el liderazgo presidencial hasta los límites estructurales de la acción gubernamental. En particular, los estudios permiten una evaluación más exhaustiva de las respuestas de los presidentes, que varían entre la inacción presidencial, la retórica populista y la toma de decisiones políticamente costosa.

This Themed Issue features theoretical, methodological, and empirical advancements of the state-of-the-art in behavioural public policy and administration. In this introduction, we develop a behaviourally-informed, integrated conceptual model of the policy process that embeds individual attitudes and behaviour into context at the meso and macro level. We argue that behavioural approaches can be situated within a broader tradition of methodological individualism. Despite focusing on the micro level of policy processes, the contributions in this issue demonstrate that the behavioural study of public policy and administration can go beyond the individual level and give important insights into policy and societal outcomes. Our model enables us to draw more substantial lessons from behavioural research by moving beyond the verification of individual behaviour change. If based on a broad conceptual design and methodological pluralism, behavioural policies bear the potential to better understand, investigate and shape social outcomes.

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dc.contributor.author: Gerdiken, Elena; Reinwald, Max; Kunze, Florian

Colexification is a linguistic phenomenon that occurs when multiple concepts are expressed in a language with the same word. Colexification patterns are frequently used to estimate the meaning similarity between words, but the hypothesis that these are related is still missing direct empirical validation at scale. Here, we show for the first time that words linked by colexification patterns capture similar affective meanings. Using pre-existing translation data, we extend colexification databases to cover much longer word lists. We achieve this with an unsupervised method of affective lexicon extension that uses colexification network data to interpolate the affective ratings of words that are not included in the original lexicon. We find positive correlations between network-based estimates and empirical affective ratings, which suggest that colexification networks contain information related to affective meanings. Finally, we compare our network method with state-of-the-art machine learning, trained on a large corpus, and show that our simple linguistics-informed unsupervised algorithm yields comparable performance with high explainability. These results show that it is possible to automatically expand affective norms lexica to cover exhaustive word lists when additional data are available, such as in colexification networks.