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Even though social class is at least as predictive of educational achievement as ethnicity in virtually all developed countries, experimental research on discrimination in education has overwhelmingly focused on the latter. We investigate both ethnic discrimination and social class discrimination by elementary school teachers in Germany. We conceptualize discrimination as causal effects of signals and use directed acyclic graphs (DAGs) to disentangle ethnic from social class discrimination. In our experiment, we asked randomly sampled elementary school teachers who teach immigrants to evaluate an essay written by a fourth-grader. Employing a 2x2x3 factorial design, we varied essay quality, child's gender, and ethnic and socioeconomic background using names as stimuli. We do not find evidence for discrimination in grading. However, our findings for teachers’ expectations of children's future performance suggest a discriminatory bias along the lines of both ethnicity and social class. The effect is conditional on essay quality - it only holds true for the better essay. We interpret our findings as evidence for models that highlight situational moderators such as the richness of information and ambiguity - e.g., statistical discrimination - but as evidence against simpler models of ingroup-favoritism or outgroup derogation, e.g., social identity theory or taste discrimination.

Education is a main cause of health inequality because it influences health behavior as well as structural conditions that impact health, such as living and working conditions. We examine how different educational groups reacted to the beginning of the COVID-19 pandemic in Germany by looking at health-related behavior - social distancing, increased hygiene, and mask wearing - as well as changes in working conditions - work from home, reduced working hours, and not working - as a structural indicator that can mitigate the risk of infection. More than three quarters of respondents in all educational groups complied with recommended social distancing and hand hygiene behaviors, and differences by education did not exceed ten percentage points. Regarding working conditions, highly educated respondents had a likelihood of over 45 percent to work from home during the pandemic. This number decreased to 17 and 11 percent for those with intermediate and low levels of education, respectively. It seems that education-based inequalities in the risk of infection with COVID-19 do not primarily stem from differences in health behavior but rather from structural causes, that is, inability to practice social distancing at work.

Petscher and Logan's (2014) description of quantile regression (QR) might mislead readers to believe it would estimate the relation between an outcome, y, and one or more predictors, x, at different quantiles of the unconditional distribution of y. However, QR models the conditional quantile function of y given x just as linear regression models the conditional mean function. This article's contribution is twofold: First, it discusses potential consequences of methodological misconceptions and formulations of Petscher and Logan's (2014) presentation by contrasting features of QR and linear regression. Second, it reinforces the importance of correct understanding of QR in empirical research by illustrating similarities and differences in various QR estimators and linear regression using simulated data.

In 'Discrimination in Education' the author makes methodological, theoretical, and empirical contributions to the study of discriminatory behavior by teachers towards students of different ethnicity, social class background, and gender. He reviews different motivations to study discrimination in education and beyond, provides an indepth discussion of numerous definitions of discrimination, and shows the potentials and limitations of theories from different disciplines to explain discrimination on the individual level and inequality between groups in the education system. In the empirical part of the book, the author examines the two major determinants of discrimination - prejudice and stereotypes - using data from the German General Social Survey (GGSS/ALLBUS) and the National Educational Panel Study (NEPS). The major empirical contribution in Discrimination in Education is an experimental study with a random sample of over 200 elementary school teachers. In this study, the author addresses several shortcomings of prior research. After discussing the complex findings of his experiment, he concludes by suggesting several routes future research should take to gather more evidence on discrimination in education, its determinants and consequences., In 'Discrimination in Education' leistet der Autor methodische, theoretische und empirische Beiträge zur Untersuchung diskriminierenden Verhaltens von Lehrkräften gegenüber Schülerinnen und Schülern unterschiedlicher Ethnizität, sozialer Herkunft und Geschlecht. Er beschreibt verschiedene Motivationen zur Untersuchung von Diskriminierung innerhalb und außerhalb des Bildungswesens, diskutiert ausführlich zahlreiche Definitionen von Diskriminierung und zeigt die Möglichkeiten und Grenzen von Theorien aus verschiedenen Disziplinen zur Erklärung von Diskriminierung auf der individuellen Ebene und der Ungleichheit zwischen Gruppen im Bildungswesen auf. Im empirischen Teil des Buches untersucht der Autor die beiden wichtigsten Determinanten von Diskriminierung - Vorurteile und Stereotype - anhand von Daten der Allgemeinen Bevölkerungsumfrage der Sozialwissenschaften (ALLBUS) und dem Nationalen Bildungspanel (NEPS). Der wichtigste empirische Beitrag in 'Discrimination in Education' ist eine experimentelle Studie mit einer Zufallsstichprobe von über 200 Grundschullehrkräften. Mit seinem Studiendesign adressiert der Autor verschiedene Limitationen der bisherigen Forschung. Nachdem er die vielschichtigen Ergebnisse seines Experiments diskutiert hat, macht er abschließend verschiedene Vorschläge, wie zukünftige Studien weitere Evidenz zum Problemkomplex Diskriminierung im Bildungswesen sammeln können.

Inequalities in language competences may be more or less pronounced depending not only on the social or immigration background indicator under consideration, but can also differ in size when looking at different positions of the competence distribution. Based on a nationwide sample of about 5-year-old preschool children this paper examines three research questions in this context: (1) To what extent are differences by social class, educational background, income, and immigrant background visible in children’s vocabulary? (2) Do certain patterns of inequality exist along the distribution of competences? (3) To what extent can these patterns be explained by cumulative effects? These questions were investigated using quantile regression on data from the National Educational Panel Study (NEPS). The results show that differences are most pronounced with regard to parents’ levels of formal education and their immigration background and concentrate in the lower areas of the competence distribution. In contrast, children of different socioeconomic and immigrant background differ only little in higher quantiles of language competences. These patterns remain largely intact when controlling for covariates., Ungleichheiten im sprachlichen Kompetenzstand können nicht nur in Abhängigkeit des jeweils betrachteten Indikators der sozioökonomischen und migrationsspezifischen Herkunft unterschiedlich groß ausfallen, sondern auch an unterschiedlichen Stellen der Kompetenzverteilung mehr oder weniger stark ausgeprägt sein. Dieser Beitrag untersucht dazu drei Forschungsfragen anhand einer bundesweiten Stichprobe von etwa fünfjährigen Kindergartenkindern: (1) In welchem Ausmaß zeigen sich Ungleichheiten im Wortschatz von Kindergartenkindern ent lang sozialer Klasse, Bildungshintergrund, Haushaltseinkommen und Migrations hintergrund? (2) Lassen sich bestimmte Muster von Ungleichheiten entlang der Kompetenzverteilung erkennen? (3) Inwiefern lassen sich etwaige Muster durch kumulative Effekte erklären? Diese Fragen werden mittels Quantilsregressionen anhand der Daten des Nationalen Bildungspanels (NEPS) analysiert. Die Ergebnisse zeigen, dass Ungleichheiten nach Bildungs- und Migrationshintergrund am größten ausfallen und sich vor allem auf den unteren Bereich der Leistungsverteilung konzentrieren. Dagegen unterscheidet sich der Wortschatz insbesondere der kompetentesten Kinder nur gering in Abhängigkeit der sozialen und migrationsspezifischen Herkunft. Diese Muster bleiben auch unter Kontrolle von Kovariaten weitestgehend erhalten.