Between-Class Earnings Inequality in 30 European Countries

This article studies earnings inequality between social classes across 30 European countries. Class inequality in earnings is found across the board although there are some exceptions. However, the degree of class inequality varies strongly across countries being larger in Western and Southern European countries and smaller in Eastern and Northern European countries. Furthermore, we find that differences in class composition in terms of observed characteristics associated with earnings account for a substantial proportion of these between-class differences. Differences between classes in the returns to education and other characteristics play less of a role. In all these respects there is a sizeable cross-national variation. This points to important differences between countries in how earnings are structured by social class.

HSDLM

Imbalanced data presents many difficulties, as the majority of learners will be prejudice against the majority class, and in severe cases, may fully disregard the minority class. Over the last few decades, class inequality has been extensively researched using traditional machine learning techniques. However, there is relatively little analytical research in the field of deep learning with class inequality. In this article, the authors classify the imbalanced data with the combination of both sampling method and deep learning method. They propose a novel sampling-based deep learning method (HSDLM) to address the class imbalance problem. They preprocess the data with label encoding and remove the noisy data with the under-sampling technique edited nearest neighbor (ENN) algorithm. They also balance the data using the over-sampling technique SMOTE and apply parallelly three types of long short-term memory networks, which is a deep learning classifier. The experimental findings indicate that HSDLM is a promising and fruitful solution to working with strongly imbalanced datasets.

Class Distinction-Making through Etiquette, a Computational Approach

Social scientists of class and inequality have documented the rise of omnivorousness, informality, ordinariness, and emphasis on meritocracy. This apparent decline in class closure contrasts sharply with rising inequality and declining economic mobility. How are these competing developments reflected in everyday class distinction-making? In this article, we answer this question by applying Goffman’s work on the symbols of class status to the analysis of unique data. We use word embeddings to isolate and quantify the salience of six dimensions of class (affluence, cultivation, education, employment, morality, and status) to class distinction-making within a corpus of etiquette books published between 1922 and 2017. We find that education and employment are increasingly salient dimensions while status, affluence, cultivation, and morality decline as salient dimensions of class distinction-making. These results signal a decline of class operating as a status group through cultural closure, the rise of education and employment as the carriers of class in everyday life, and the corresponding legitimation of class position and class inequality on the basis of supposedly meritocratic grounds. This research opens up new avenues for research on class and the application of computational methods for investigations of social change.

Roads and the politics of thought

The chapter presents the politics of thought as an analytical terrain through which to broach the themes at the heart of this volume: the inadvertent role of roads in reproducing and generating hierarchy, class inequality, and social disruption. In bringing together two major research projects led by the authors, we illustrate how roads have been engaged through critical social sciences as an epistemological as well as a material vector of change. By outlining methodological and conceptual approaches to large road and infrastructure projects in South Asia, we show how ideas build roads. The chapter draws attention to frequently overlooked aspects of road construction – such as how future environmental impacts are routinely ignored in the political processes and construction practices that constitute the making of roads.

Wealth wars: how productivity gaps explain democratic erosion in advanced economies

Why are democracies backsliding? I contend that a large productivity gap between economic groups motivates those with low productivity to capture the state for rent-seeking. They assess their relative position as weak and are willing to sacrifice certain democratic guarantees in exchange for favorable policies. Erosion takes two forms. (1) With high inter-class inequality and a large productivity gap among economic industries, losing economic elites capture the state through a political outsider who enacts favorable policy. Once in office, the outsider expands his personal executive control and attacks key democratic veto players. (2) When inter-class inequality is high but the inter-industry productivity gap is small, a united economic elite coordinate to stop a populist takeover. Traditional political elites respond to the populist threat by curtailing basic freedoms of speech and association. I use both quantitative and case study evidence from the US and Spain to support my main hypotheses.

Occupational Social Class and Earnings Inequality in Europe: A Comparative Assessment

While there is renewed interest in earnings differentials between social classes, the contribution of social class to overall earnings inequality across countries and net of compositional effects remains largely uncharted territory. This paper uses data from the European Union Statistics on Income and Living Conditions to assess earnings differentials between social classes (as measured by ESeC) and the role of between-class inequality in overall earnings inequality across 30 European countries. We find that there is substantial variation in earnings differences between social classes across countries. Countries with higher levels of between-class inequality tend to display higher levels of overall earnings inequality, but this relationship is far from perfect. Even with highly aggregated class measures, between-class inequality accounts for a non-negligible share of total earnings inequality (between 15 and 25% in most countries). Controlling for observed between-class differences in composition shows that these account for much of the observed between-class earnings inequality, while in most countries between-class differences in returns to observed compositional variables do not play a major role. In all these respects we find considerable variation across countries, implying that both the size of between-class differences in earnings and the primary mechanisms that produce these class differences vary substantially between European countries.

Personal income inequality in USA from a two-class perspective: 2004-2018

US incomes follow a two class pattern -- an insight originally shown by physicists in the econophysics literature. The upper class fits a power-law, or Pareto distribution, while the lower class follows an exponential distribution. I show that these patterns hold over 2004-2018 and that the upper class has expanded, from 1-3 percent until 2001, to almost the top 6 percent by 2018. I find that growing income inequality is explained by growing \emph{between-class} inequality. As the fraction in the upper class increases, higher average incomes are allocated to more members of the population, while the lower class is constrained tightly around mean incomes that are an order of magnitude smaller.

Was (ver-)messen eigentlich Klassen? Zum Zusammenhang zwischen unterschiedlicher Operationalisierung sozialer Klassen und ihrer Erklärungskraft mit Blick auf Stratifikation und Ungleichheit in Deutschland

Deutsch: Darüber, wie die soziale Position in einer Gesellschaft am besten gemessen werden sollte, besteht keine Einigkeit. Da die Operationalisierung jedoch weitreichende Folgen für Forschungsdesign und Interpretation der Ergebnisse hat, werden hier sieben Konzeptionen sozialer Positionen auf ihre Erklärungskraft für ganz unterschiedliche Phänomene hin verglichen. Die Analyse sucht damit die vor allem methodische Frage zu beantworten, ob und wie sich die einzelnen Klassenmessungen in ihrer Erklärungskraft bezüglich Stratifikation und Klassenungleichheit bei 35 Eigenschaften von rund 25.000 Allbus-Befragten in Deutschland unterscheiden. Die Ergebnisse weisen darauf hin, dass Klassenzugehörigkeit Menschen nur in wenigen der hier untersuchten Eigenschaften wirklich stratifiziert. Gleichzeitig lassen sich aber bedeutende Klassenungleichheiten bei objektiven Statusindikatoren und intergenerationalen Mobilitätsmessungen finden. Während insgesamt Mikroklassen die höchste Erklärungskraft aufweisen, sind die Unterschiede in Bereichen, in denen aggregierte Klassifikationen eine besondere Erklärungsleistung für sich beanspruchen, marginal. Die Ergebnisse empfehlen neben den quasi paradigmatischen ESEC-Klassen auch andere der hier vorgestellten Klassifizierungen ergänzend in der Ungleichheitsanalyse einzusetzen. English: There is little agreement how to best measure social class position in contemporary societies. The chosen measurement, however, has substantial implications for a study‘s design and the interpretation of its findings. Therefore, I empirically compare the explanatory power of nine alternative social class concepts regarding their ability to map stratification and identify class inequality. The analysis is repeated for 35 characteristics measured in the Allbus data 1980 to 2018 for almost 25,000 individuals. Results indicate that class membership stratifies only few of the studied attributes. At the same time class concepts are able to detect meaningful class inequality especially in terms of but not limited to objective SES measures and social mobility indicators. While microclasses outperform more aggregated class measures in general, the differences are rather small in subject areas for which the latter theoretically claim particular explanatory power. In the spirit of parsimony, the results hence would seem to suggest the use of the more aggregated classifications at least with regard to some subject matters. I suggest to complement the almost exclusive usage of ESEC in contemporary stratification research with alternative class measures.

The moral life of econometric equations: Factoring class inequality into school quality valuations in Chile

Recent sociological scholarship on market design is ill-equipped to understand the normative and political aspects of experts’ practices in connection to political conflicts over the commodification of social rights. I develop an original approach to the politicized use of market devices to address collective concerns in a noneconomic policy field: education. When designing a high-stakes school accountability system, policymakers in Chile confronted a moral conundrum: should schools be valued according to their students’ absolute proficiency, or according to the school’s relative effectiveness? Progressive and conservative experts in charge of settling this dilemma pushed for using the statistical model (OLS vs. HLM) that yielded rankings that fit their moral preferences. Through qualitative analyses of experts’ real-world application of quantitative methods, as well as experts’ interpretations of these methods’ performative consequences, I mobilize the much-debated concept of “moral background” to unravel the conditions for subsuming ideological dissent into consensual forms of decision-making.