Taking the sector seriously

In a new study published in Social Indicators Research, I explore with two colleagues developments and the drivers of earnings inequality at the sectoral level using an own database that is publicly available. The study examines three key explanations for increasing earnings inequality exploiting sectoral variation within countries over time—namely, globalisation, technological change and waning labour union power. Interestingly, the results provide only limited support for the argument that international trade leads to higher levels of earnings inequality. When we focus the analysis on trade with less developed countries we find a positive association between trade and earnings inequality. With regard to technological change, our findings provide mixed evidence for the hypothesis that skill-biased technological change increases earnings inequality. Our results bring back the waning country-wide labour union power as an important driver of earnings inequality. This corresponds with the fact that our sectoral data reveal a more general trend towards rising inequality across sectors over time.

What economic factors can explain health outcomes across countries?

Major donors heavily rely on GNI per capita to allocate development assistance for health. In our paper, we question this paradigm by analyzing the determinants of health outcomes using cross-sectional data from 99 countries in 2012. We use disability-adjusted life years (Group I) per capita as our main indicator for health outcomes. We consider four primary variables: GNI per capita, institutional capacity, individual poverty and the epidemiological surroundings. We construct a health poverty line of 10·89 international-$ per day, which measures the minimum level of income an individual needs to have access to basic healthcare. We take the contagious nature of communicable diseases into account, by estimating the extent to which the population health in neighboring countries (the epidemiological surroundings) affects health outcomes. We apply a spatial two-stage least-squares model to mitigate the risks of reverse causality, and use additional IV estimations as sensitivity tests. Overall we find that GNI is not a significant predictor of health outcomes once other factors are controlled for.

Why doesn’t GDP growth lift household incomes?

With inequality rising and household incomes across developed countries stagnating, accurate monitoring of living standards cannot be achieved by relying on GDP per capita alone. In this VoxEU column I analyse with colleagues Brian Nolan and Max Roser the path of divergence between household income and GDP per capita for 27 OECD countries. It finds several reasons why GDP per capita has outpaced median incomes. We recommend to assign median income a central place in official monitoring and assessment of living standards over time.

Explaining the divergence between GDP per capita and median household income

Divergence between the evolution of GDP per capita and the income of a ‘typical’ household as measured in household surveys is giving rise to a range of serious concerns, especially in the USA. In a new working paper with Brian Nolan and Max Roser, we investigate the extent of that divergence and the factors that contribute to it across 27 OECD countries, using data from OECD National Accounts and the Luxembourg Income Study. While GDP per capita has risen faster than median household income in most of these countries over the period these data cover, the size of that divergence varied very substantially, with the USA a clear outlier. Our paper distinguishes a number of factors contributing to such a divergence, and finds wide variation across countries in the impact of the various factors. Further, both the extent of that divergence and the role of the various contributory factors vary widely over time for most of the countries studied. These findings have serious implications for the monitoring and assessment of changes in household incomes and living standards over time.

Automation and the welfare state

Current advances in information technology have led to a significant substitution of routine work by capital. In a new working paper together with David Rueda we develop a simple theoretical framework in which individuals in routine task-intensive occupations prefer public insurance against the increased risk of future income loss resulting from automation. Moreover, we contend that this relation will be stronger for richer individuals who have more to lose from automation. We focus on the role of occupational elements of risk exposure and challenge some general interpretations of the determinants of redistribution preferences. We test the implications of our theoretical framework with survey data for 17 European countries between 2002 and 2012. We find vulnerability to automation to be more significant than other occupational risks emphasized in the literature.

Welfare regimes and economic performance

Which model is best in delivering prosperity for its citizens? If we group countries with similar institutional settings are grouped together, can we see differences in median household income GDP per capita, or inequality? In a new working paper, we find remarkably wide variation across OECD countries in recent decades in economic performance. This variation is also seen within the liberal and coordinated market economy models distinguished in the varieties of capitalism literature, as well as within the welfare regimes commonly employed in welfare state analysis, with little difference between them in average growth rates.