Routinization and Covid‑19: A Comparison Between the United States and Portugal

The purpose of this study is to identify the role of automatization in increasing wage inequality, by comparing the United States to Portugal. Using the PSID and Quadros de Pessoal (Personnel Records), we find that labor income dynamics are strongly determined by the variance of the individual fixed component. This effect is drastically reduced by adding information on workers’ occupational tasks, confirming that a decreasing price of capital and the consequent replacement of routine manual workers have deepened wage inequality. During the current crisis, we find that the ability to keep working is strongly related with the kind of occupation. As such, we foster the impact of a permanent demand shock using an overlapping generations model with incomplete markets and heterogeneous agents to quantitatively predict the impact of Covid‑ 19 and lockdown measures on wage premium and earnings inequality. We find that wage premia and earnings dispersion increase, suggesting that earnings inequality will increase at the expense of manual workers.

Global Earnings Inequality, 1970–2018

We estimate trends in global earnings dispersion across occupational groups by constructing a new database that covers 68 developed and developing countries between 1970 and 2018. Our main finding is that global earnings inequality has fallen, primarily during the 2000s and 2010s, when the global Gini coefficient dropped by 15 points and the earnings share of the world's poorest half doubled. Decomposition analyses show earnings convergence between countries and within occupations, while within-country earnings inequality has increased. Moreover, the falling global inequality trend was driven mainly by real wage growth, rather than changes in hours worked, taxes or occupational employment.

Human Capital Inequality: Empirical Evidence

This article focuses on the distribution of human capital and its implications for the accrual of economic resources to individuals and households. Human capital inequality can be thought of as measuring disparity in the ownership of labor factors of production, which are usually compensated in the form of wage income. Earnings inequality is tightly related to human capital inequality. However, it only measures disparity in payments to labor rather than dispersion in the market value of the underlying stocks of human capital. Hence, measures of earnings dispersion provide a partial and incomplete view of the underlying distribution of productive skills and of the income generated by way of them. Despite its shortcomings, a fairly common way to gauge the distributional implications of human capital inequality is to examine the distribution of labor income. While it is not always obvious what accounts for returns to human capital, an established approach in the empirical literature is to decompose measured earnings into permanent and transitory components. A second approach focuses on the lifetime present value of earnings. Lifetime earnings are, by definition, an ex post measure only observable at the end of an individual’s working lifetime. One limitation of this approach is that it assigns a value based on one of the many possible realizations of human capital returns. Arguably, this ignores the option value associated with alternative, but unobserved, potential earning paths that may be valuable ex ante. Hence, ex post lifetime earnings reflect both the genuine value of human capital and the impact of the particular realization of unpredictable shocks (luck). A different but related measure focuses on the ex ante value of expected lifetime earnings, which differs from ex post (realized) lifetime earnings insofar as they account for the value of yet-to-be-realized payoffs along different potential earning paths. Ex ante expectations reflect how much an individual reasonably anticipates earning over the rest of their life based on their current stock of human capital, averaging over possible realizations of luck and other income shifters that may arise. The discounted value of different potential paths of future earnings can be computed using risk-less or state-dependent discount factors.

Inclusive development. How is Poland doing in comparison to other OECD countries?

Inclusive development is a multifaceted conception, which makes it difficult to measure. Recent years, however, have brought some proposals for measuring this phenomenon, which opens up new opportunities to deepen the knowledge of how countries are doing in making their economic and social progress more inclusive. The aim of the paper is to examine the level of advancement of inclusive development in Poland in comparison to other OECD countries. The main hypothesis states that the development in Poland is less inclusive than the OECD average. The research covers data from 30 countries (OECD members, excluding the countries where such data were unavailable), and is based on the OECD’s proposal of measurement. The study was conducted with the use of data normalisation into unified indices, taxonomic methods (cluster analysis based on the Ward hierarchic method), and comparative analysis. The results indicate areas of improvement for Poland. These are issues connected with the functioning of the labour market (the level of labour productivity, employment ratio, earnings dispersion), access to loans for starting or expanding businesses, but also life expectancy, wealth distribution, early childhood education and care, and, most of all, characteristics related to the area of governance, such as trust in the government, and voter turnout.

A Distributional Framework for Matched Employer Employee Data

We propose a framework to identify and estimate earnings distributions and worker composition on matched panel data, allowing for two‐sided worker‐firm unobserved heterogeneity and complementarities in earnings. We introduce two models: a static model that allows for nonlinear interactions between workers and firms, and a dynamic model that allows, in addition, for Markovian earnings dynamics and endogenous mobility. We show that this framework nests a number of structural models of wages and worker mobility. We establish identification in short panels, and develop tractable two‐step estimators where firms are classified in a first step. Applying our method to Swedish administrative data, we find that log‐earnings are approximately additive in worker and firm heterogeneity. Our estimates imply the presence of strong sorting patterns between workers and firms, and a small contribution of firms—net of worker composition—to earnings dispersion. In addition, we document that wages have a direct effect on mobility, and that, beyond their dependence on the current firm, earnings after a job move also depend on the previous employer.