Explore the vast range of titles available.

This paper documents the pervasiveness of job polarization in 16 Western European countries over the period 1993-2010. It then develops and estimates a framework to explain job polarization using routine-biased technological change and offshoring. This model can explain much of both total job polarization and the split into within-industry and between-industry components.

Many technological innovations replace workers with machines. But this capital–labor substitution need not reduce aggregate labor demand, because it simultaneously induces four countervailing responses: own-industry output effects; cross-industry input–output effects; between-industry shifts; and final demand effects. We quantify these channels using four decades of harmonized cross-country and industry data, whereby we measure automation as industry-level movements in total factor productivity that are common across countries. We find that automation displaces employment and reduces labor’s share of value added in the industries where it originates (a direct effect). In the case of employment, these own-industry losses are reversed by indirect gains in customer industries and induced increases in aggregate demand. By contrast, own-industry labor share losses are not recouped elsewhere. Our framework can account for a substantial fraction of the reallocation of employment across industries and the aggregate fall in the labor share over the last three decades. It does not, however, explain why the labor share fell more rapidly during the 2000s.

Student evaluations of teaching (SETs) are widely used to measure teaching quality in higher education and compare it across different courses, teachers, departments and institutions. Indeed, SETs are of increasing importance for teacher promotion decisions, student course selection, as well as for auditing practices demonstrating institutional performance. However, survey response is typically low, rendering these uses unwarranted if students who respond to the evaluation are not randomly selected along observed and unobserved dimensions. This paper is the first to fully quantify this problem by analyzing the direction and size of selection bias resulting from both observed and unobserved characteristics for over 3000 courses taught in a large European university. We find that course evaluations are upward biased, and that correcting for selection bias has non-negligible effects on the average evaluation score and on the evaluation-based ranking of courses. Moreover, this bias mostly derives from selection on unobserved characteristics, implying that correcting evaluation scores for observed factors such as student grades does not solve the problem. However, we find that adjusting for selection only has small impacts on the measured effects of observables on SETs, validating a large related literature which considers the observable determinants of evaluation scores without correcting for selection bias.

Since the early 1990s Europe, like the United States and the United Kingdom, has experienced job polarization, that is, a disproportionate increase in high-paid and low-paid employment. Pervasive job polarization is in line with the evidence that in advanced countries, technologies are becoming more intense in the use of nonroutine tasks concentrated in high-paid and low-paid service jobs, at the expense of routine tasks concentrated in manufacturing and clerical work. The evidence for alternative explanations--offshoring and inequality--is much weaker.

This paper uses historical labour market data for Belgium for the period 1846–2011 to illustrate how the employment impacts of the ongoing Digital Revolution after 1980 compare to those of the Second Industrial Revolution before 1980. Our analyses show that the period 1846–1947 was characterized by economy-wide skill-upgrading due to an increase in the demand for skilled relative to unskilled workers because of skill-biased technological change (SBTC). The period 1947–81 is characterized by particularly high labour market turbulence, in part due to a gradual switch from economywide skill-upgrading to job polarization. Consequently, the impact of the ongoing Digital Revolution on labour markets after 1980 is not uniquely characterized by exceptionally high labour market turbulence but by the nature of changes in the composition of jobs, namely a process of job polarization. To explain job polarization, the paper discusses the hypothesis of Routine-Biased Technological Change (RBTC) that has recently emerged in the academic literature.

Studying firm-level adjustments is important for understanding the economic effects of workplace automation. So far, emerging firm-level evidence is focused on robotics and the manufacturing sector. In this paper, we document that the adoption of automation technologies extends beyond manufacturing firms. We identify firm-level automation events and show that automating firms experience faster employment and revenue growth than do nonautomating firms. However, around automation events themselves, employment growth slows markedly. Notably, we find that these effects are similar for manufacturing and nonmanufacturing firms, suggesting that an increasing diffusion of automation technology has important consequences for firms and their workers.

Der technologische Fortschritt verändert die Fähigkeiten, die Beschäftigte in ihrem Beruf benötigen. Einige Fähigkeiten werden obsolet, andere kommen neu hinzu. Aber wie erlangen die Beschäftigten diese Fähigkeiten? Ein anhaltender Trend geht in Richtung längerer formaler Bildung und höherer Studienquoten. Es ist jedoch unwahrscheinlich, dass allein eine steigende Zahl von Hochschulabsolvierenden die benötigten, berufsspezifischen Fähigkeiten bereitstellt. Stattdessen müssen sich konkrete Inhalte innerhalb der Berufsausbildung ändern. In Salomons et al. (2025) untersuchen wir, ob sich die Ausbildungsinhalte an die Anforderungen des technologischen Wandels anpassen. Außerdem analysieren wir die Effekte von Modernisierungen der Ausbildungsverordnungen auf die Löhne von jüngeren Fachkräften, welche die benötigten Fähigkeiten während der Ausbildung erlernt haben, und von älteren Fachkräften, deren Ausbildung bereits abgeschlossen war.

Working Paper No. 24871 Many technological innovations replace workers with machines, but this capital-labor substitution need not reduce aggregate labor demand because it simultaneously induces four countervailing responses: own-industry output effects; cross-industry input-output effects; between-industry shifts; and final demand effects. We quantify these channels using four decades of harmonized cross-country and industry data, where we measure automation as industry-level movements in total factor productivity (TFP) that are common across countries. We find that automation displaces employment and reduces labor's share of value-added in the industries in which it originates (a direct effect). In the case of employment, these own-industry losses are reversed by indirect gains in customer industries and induced increases in aggregate demand. By contrast, own-industry labor share losses are not recouped elsewhere. Our framework can account for a substantial fraction of the reallocation of employment across industries and the aggregate fall in the labor share over the last three decades. It does not, however, explain why the labor share fell more rapidly during the 2000s

Many technological innovations replace workers with machines, but this capital-labor substitution need not reduce aggregate labor demand because it simultaneously induces four countervailing responses: own-industry output effects; cross-industry input-output effects; between-industry shifts; and final demand effects. We quantify these channels using four decades of harmonized cross-country and industry data, where we measure automation as industry-level movements in total factor productivity (TFP) that are common across countries. We find that automation displaces employment and reduces labor's share of value-added in the industries in which it originates (a direct effect). In the case of employment, these own-industry losses are reversed by indirect gains in customer industries and induced increases in aggregate demand. By contrast, own-industry labor share losses are not recouped elsewhere. Our framework can account for a substantial fraction of the reallocation of employment across industries and the aggregate fall in the labor share over the last three decades. It does not, however, explain why the labor share fell more rapidly during the 2000s