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AbstractThis article investigates the links between earnings, human capital and job tasks, using internationally comparable information from the OECD Survey of Adult Skills. The authors use the theoretical framework developed by Autor and Handel (2013) and extend their empirical results to 21 OECD countries. The data allow for a richer characterization of workers' human capital by including both educational attainment and a measure of cognitive skills. Their findings confirm the predictive power of job tasks in explaining wage differences, both between and within occupations, and provide empirical support for the model's prediction in the vast majority of countries.

RésuméLes auteurs examinent la relation entre les gains, le capital humain et les tâches. Ils reprennent le modèle théorique d'Autor et Handel (2013), tout en élargissant l'analyse empirique à 21 pays, grâce aux données de l'enquête de l'OCDE sur les compétences des adultes (PIAAC). Celles‐ci permettent en outre de caractériser plus précisément le capital humain des travailleurs, en fournissant une mesure directe des compétences cognitives. L'analyse empirique confirme l'influence du contenu en tâches de l'emploi sur les écarts de salaire, à la fois entre les catégories professionnelles et en leur sein. Les prédictions du modèle se vérifient dans la grande majorité des pays.

ResumenEn este artículo se investigan los vínculos entre ingresos, capital humano y tareas laborales, utilizando información internacionalmente comparable del PIAAC (OCDE) sobre competencias de adultos. Tomando el marco teórico de Autor y Handel (2013), se amplía el análisis empírico a 21 países de la OCDE. Los datos permiten tener en cuenta tanto el nivel de instrucción como una medida de las competencias cognitivas. Los resultados confirman que las tareas que implica cada ocupación tienen influencia en las diferencias salariales tanto entre ocupaciones como dentro de cada una y proporcionan pruebas empíricas del poder de predicción del modelo para un gran número de países.

The value of cross-country comparisons is at the heart of large-scale international surveys. Yet the validity of such comparisons is often challenged, particularly in the case of latent traits whose estimates are based on self-reported answers to a small number of questionnaire items. Many believe self-reports to be unreliable and not comparable, and indeed, formal statistical procedures very often reject the assumption that the questions are understood and answered in the same way in different countries (measurement invariance). A methodological conference on the comparability of questionnaire scales was hosted by the OECD on 8 and 9 November 2018. This meeting report summarises the discussions held at the conference about measurement invariance testing and instrument design. The report first provides a brief introduction to the measurement models and the accompanying invariance analyses typically used in the industry of large-scale international surveys and points to the main limitations of these current standard approaches. It then presents classical and novel ways to deal with imperfect comparability of measurements when scaling and reporting on continuous traits and on categorical latent variables. It finally discusses the extent to which item design can improve the cross-country comparability of the measured constructs (e.g. by adopting innovative item formats such as anchoring vignettes and situational judgement test items). It concludes with some general considerations for survey design and reporting on invariance analyses and survey results.

Abstract
This paper proposes a new classification of occupations based on the extent to which they put workers at risk of being infected by aerial-transmitted viruses. We expand on previous work that mainly focused on the identification of jobs that can be done from home by providing a more nuanced view of infection risks: in particular, we identify jobs that, although impossible to be done from home, expose workers to a low risk of infection. Jobs that cannot be done from home and that present a high risk of infection are labelled 'unsafe jobs'. We then combine our classification of infection risk with a list of 'essential occupations' that have been carried out even during the most severe lockdown measures: this provides a taxonomy ranking jobs along two dimensions, one related to workers' health and the other related to economic conditions. Using both survey and administrative data, we show that this taxonomy successfully predicts outcomes along these two dimensions, such as sick leaves, COVID-19-related work injuries, recourse to short-time work (STW) schemes and work from home. We also find that unsafe jobs are very unequally distributed across different types of workers, firms and sectors. Workers who are more vulnerable economically (women, youngsters, low educated, immigrants and workers on fixed-term contracts) are more likely to hold unsafe jobs and therefore more at risk of suffering from the economic consequences of a prolonged pandemic. We finally discuss possible paths to reform social protection systems, so that they can better support workers during the labour market adjustments that are likely to be spurred by the COVID-19 pandemic.