Higher education, employers’ monopsony power and the labour share in OECD countries

This paper examines the impact of higher education on the labour share. It is based on the following idea: as education offers adaptability skills, it should reduce employers’ monopsony power and, therefore, increase the labour share. This idea is developed in a two-sector model with search unemployment and wage competition between employers to attract/keep workers. Using panel data for eleven OECD countries, we show that the proportion of higher educated in the population has a significant positive effect on the labour share: typically, an increase of one standard deviation in higher education induces a three point increase in the labour share. The other determinants of the labour share are compatible with the theoretical model. They include the capital-output ratio (-), minimum to median wage ratio (+), union density (+). We also find that the unemployment rate has a negative and significant impact on the labour share, which, together with the positive impact of higher education, is incompatible with a three-factor model where factors are paid their marginal products.Search frictions; Adaptability; Labour share; Macroeconomic panel data

Higher education, employers’ monopsony power and the labour share in OECD countries

This paper examines the impact of higher education on the labour share. It is based on the following idea: as education offers adaptability skills, it should reduce employers’ monopsony power and, therefore, increase the labour share. This idea is developed in a two-sector model with search unemployment and wage competition between employers to attract/keep workers. Using panel data for eleven OECD countries, we show that the proportion of higher educated in the population has a significant positive effect on the labour share: typically, an increase of one standard deviation in higher education induces a three point increase in the labour share. The other determinants of the labour share are compatible with the theoretical model. They include the capital-output ratio (-), minimum to median wage ratio (+), union density (+). We also find that the unemployment rate has a negative and significant impact on the labour share, which, together with the positive impact of higher education, is incompatible with a three-factor model where factors are paid their marginal products

Impact of income redistribution on middle class households: A cross-country comparison based on the LIS data

In the context of economic and financial difficulties, the debate over the effect of income taxation and redistribution has come back in most of the European countries. In this paper, we use the LIS data to examine the impact of income redistribution on middle class households from a cross-country perspective. To this aim, we calculate the balance between, on the one hand, the taxes and social contributions those households have to pay and, on the other, what they receive as social transfers. The research question here is whether middle class households benefit more or less from income redistribution than lower and upper class households. According to this study, income redistribution schemes appear to be "redistributive" in most of the European countries, which means that households having high income contribute to the income of those having lower resources. However, the intensity of the redistribution varies from one country to another: basically, northern European countries, in line with the Beveridge "universal" approach are more redistributive than southern countries, where social protection is mainly financed by employers and employee social insurance contributions

Higher education, employers' monopsony power and the labour share in OECD countries

This paper examines the impact of higher education on the labour share. It is based on the following idea: as education offers adaptability skills, it should reduce employers' monopsony power and, therefore, increase the labour share. This idea is developed in a two-sector model with search unemployment and wage competition between employers to attract/keep workers. Using panel data for eleven OECD countries, we show that the proportion of higher educated in the population hasa significant positive effect on the labour share: typically, an increase of one standard deviation inhigher education induces a three point increase in the labour share. The other determinants of the labour share are compatible with the theoretical model. They include the capital-output ratio (-), minimum to median wage ratio (+), union density (+). We also find that the unemployment rate has a negative and significant impact on the labour share, which, together with the positive impact of higher education, is incompatible with a three-factor model where factors are paid their marginal products.Search frictions; Adaptability; Labour share; Macroeconomic panel data

The personal and the factor distributions of income in a cross-section of countries

The shares of capital and labour in national income vary substantially both over time and across countries. This paper shows that the factor distribution of income is an essential determinant of the personal distribution of income. We use cross-country and panel data for a group of developed and developing countries to show that a larger labour share is associated with a lower Gini coefficient of personal incomes. This effect is not only statistically significant but also economically important. An increase in the labour share in Mexico to that observed in the US would reduce the Gini coefficient of the former by between two and five points.

Une approche de l’effet du passage sur Internet d’une enquête en population générale

Pour faire suite à un précédent travail de recherche et afin de gérer au mieux le passage en ligne de son enquête barométrique sur les Conditions de vie et les Aspirations, le CRÉDOC a organisé la 37ème vague de son dispositif d'enquête de façon simultanée et identique, sur deux échantillons distincts, l'un interrogé en face-à-face, l'autre interrogé en ligne. Sur de nombreux indicateurs, établis aussi bien à partir de questions factuelles que de questions d'opinion, les deux modes de collecte établissent des résultats parfaitement comparables. Ces similitudes concernent des sujets aussi variés que le rapport à l'emploi, le logement, le cadre de vie et le sentiment de sécurité, la santé, le moral économique, les opinions sur l'union ou l'adoption par des couples de même sexe, les jugements portés sur le fonctionnement de la société ou les effets de la mondialisation, les opinions sur la pauvreté ou le chômage, le sentiment de restriction budgétaire ou la perception de l'évolution de son niveau de vie. Pour nombre d'indicateurs sur ces différents thèmes, il n'y a quasiment aucune différence entre les deux enquêtes. Le mode de collecte induit cependant des effets propres quant à la réalisation du terrain (durée de l'interrogation plus courte en ligne, possibilité pour un paneliste de répondre en plusieurs fois). Il n'est pas non plus sans effet sur les caractéristiques des répondants, chaque mode de collecte recelant ses avantages et ses inconvénients. Ainsi l'analyse du niveau de diplôme montre une surreprésentation des plus diplômés dans le panel online. Sur le critère de la taille du ménage ou encore sur le taux de propriétaires, les données issues de l'enquête en ligne sont, en revanche, plus proches de la réalité que les données issues de l'enquête en face-à-face. Enfin, les informations relatives aux revenus sont de moins bonne qualité dans l'enquête en ligne. Il est très difficile d'y exploiter la structure des revenus (par type de revenus ou par personne du foyer qui les perçoit) et, très souvent, on doit se contenter de la position donnée dans une échelle pour les revenus globaux de l'ensemble du foyer. Dans le même temps, le niveau de vie médian est plus élevé dans l'enquête en ligne (1 800 euros, + 20% par rapport à l'enquête en face-à-face). Au final, sur les questions généralistes du questionnaire (hors questions insérées par des clients et donc confidentielles), on mesure un écart moyen de l'ordre de 4 points par modalité de réponse entre les deux échantillons. Cet écart moyen, décliné par grands blocs thématiques (comprenant chacun de 9 à 17 questions), varie (en valeur absolue) de 1,6 points pour la situation d'emploi à 7,7 points pour les TIC