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. (JEL J21, J23, J24, M55, O33)

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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 crosscountry 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

This paper develops a simple and empirically tractable model of labor demand to explain recent changes in the occupational structure of employment as a result of technology, offshoring and institutions. This framework takes account not just of direct effects but indirect effects through induced shifts in demand for different products. Using data from 16 European countries, we find that the routinization hypothesis of Autor, Levy and Murnane (2003) is the most important factor behind the observed shifts in employment but that offshoring does play a role. We also find that shifts in product demand are acting to attenuate the impacts of recent technological progress and offshoring and that changes in wage-setting institutions play little role in explaining job polarization in Europe.

We provide the first estimate of the impacts of automation on individual workers by combining Dutch micro-data with a direct measure of automation expenditures covering firms in all private non-financial industries over 2000-2016. Using an event study differences-indifferences design, we find that automation at the firm increases the probability of workers separating from their employers and decreases days worked, leading to a 5-year cumulative wage income loss of about 8% of one year's earnings for incumbent workers. We find little change in wage rates. Further, lost wage earnings are only partially offset by various benefits systems and are disproportionately borne by older workers and workers with longer firm tenure. Compared to findings from a literature on mass layoffs, the effects of automation are more gradual and automation displaces far fewer workers, both at the individual firms and in the workforce overall.A broader empirical literature indeed makes clear that large-scale automation need not bring about labor displacement in aggregate, but rather, leads to labor reallocation. For one, even within the affected industry, automation can increase employment if industry demand is sufficiently elastic (Acemoglu and Restrepo (2018a,d); Bessen (2018)). Moreover, there is evidence that productivity gains generate employment increases in other industries through input-output linkages as well as final demand effects, offsetting any employment losses in automating industries (Autor and Salomons (2018); Gregory et al. (2018)). It should be noted that our analyses do not consider these countervailing forces: this implies our findings do not inform on the macroeconomic impacts of automation. However, to understand how automation affects work, it is critical to also study its effects on individual workers. After all, the absence of displacement in aggregate need not imply the absence of losses for individual workers directly affected by automation. These micro-level impacts are also of first-order importance for policymakers aiming to assuage adverse impacts out of distributional concerns. This paper is structured as follows. We first introduce our data source, Dutch matched employer-employee data which we link to a firm survey containing a direct measure of automation expenditures. Section 3 contains our empirical approach, outlining a definition of automation events and the resulting estimation framework using a combination of event study and differences-in-differences. Our results are divided into total impacts on workers' wage income (section 4), which we decompose into firm separation and employment impacts (section 5), and daily wage impacts conditional on employment (section 6). We next consider to what extent wage income losses are compensated by various benefit schemes (section 7), and how these losses differ across worker types (section 8). Lastly, in section 9 we consider the worker costs of automation conditional on displacement and compare these to income losses arising from mass lay-offs and firm closur...

A fast-growing literature shows that technological change is replacing labor in routine tasks, raising concerns that labor is racing against the machine. This paper is the first to estimate the labor demand effects of routine-replacing technological change (RRTC) for Europe as a whole and at the level of 238 European regions. We develop and estimate a task framework of regional labor demand in tradable and non-tradable industries, building on Autor & Dorn (2013a) and Goos, Manning and Salomons (2014), and distinguish the main channels through which technological change affects labor demand. These channels include the direct substitution of capital for labor in task production, but also the compensating effects operating through product demand and local demand spillovers. Our results show that RRTC has on net led to positive labor demand effects across 27 European countries over 1999-2010, indicating that labor is racing with the machine. This is not due to limited scope for human-machine substitution, but rather because sizable substitution effects have been overcompensated by product demand and its associated spillovers. However, the size of the product demand spillover --and therefore also RRTC's total labor demand effect--depends critically on where the gains from the increased productivity of technological capital accrue.

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 nonnegligible 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.

Is automation a labor-displacing force? This possibility is both an age-old concern and at the heart of a new theoretical literature considering how labor immiseration may result from a wave of 'brilliant machines,' which is in part motivated by declining labor shares in many developed countries. Comprehensive evidence on this labor-displacing channel is at present limited. Using the recent model of Acemoglu and Restrepo (2018b) as an analytical frame, we first outline the various channels through which automation impacts labor´s share of output. We then turn to empirically estimating the employment and labor share impacts of productivity growth-an omnibus measure of technological change-using data on 28 industries for 18 OECD countries since 1970. Our main findings are that although automation-whether measured by Total Factor Productivity growth or instrumented by foreign patent flows or robot adoption-has not been employment-displacing, it has reduced labor's share in value-added. We disentangle the channels through which these impacts occur, including: own-industry effects, cross-industry input-output linkages, and final demand effects accruing through the contribution of each industry's productivity growth to aggregate incomes. Our estimates indicate that the labor share-displacing effects of productivity growth, which were essentially absent in the 1970s, have become more pronounced over time, and are most substantial in the 2000s. This finding is consistent with automation having become in recent decades less labor-augmenting and more labor-displacing.