Large multilateral organisations like WHO and the UN rely heavily on average income data in determining eligibility for, and the allocation of, development assistance for health. This column tests this paradigm by analysing the determinants of health outcomes for 99 countries. A country’s epidemiological surroundings, poverty gap, and institutional capacity appear to be much better predictors of health outcomes than gross national income. These findings suggest alternative metrics that could be leveraged in allocating development assistance for health.
From today onwards, I am working as a research fellow at the Overseas Development Institute (ODI) in London! I am part of their Social Protection and Social Policy team. I am very excited to contribute to their work on social protection and development; including studying and advising on the rise of the gig economy in low and middle income countries, taxes & transfers, and health insurance coverage. You can find my ODI profile here – and please reach out if you have overlapping interests.
Technological change presents an occupational risk for individuals in routine work, as these occupations are more prone to being automated. In a paper forthcoming with Comparative Political Studies with David Rueda, we show with survey data for 17 European countries between 2002-2012 that individuals in routine occupations prefer public insurance against the increased risk of future income loss resulting from automation. We conclude that vulnerability to automation is an important determinant of the demand for redistribution.
China’s rapid rise on the global economic stage might have substantial and unequal employment effects in advanced industrialized democracies given China’s large volume of low-wage labor. In a new paper written together with Olaf van Vliet, we analyse the effects of Chinese trade competition across 17 sectors in 18 countries. We devote attention to a new channel, increased competition from China in foreign export markets. Our empirical findings reveal overall employment declines in sectors more exposed to Chinese imports. Furthermore, our results suggest that employment effects are not equally shared across skill levels, as the share of hours worked worsens for low-skilled workers.
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.
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.