Research

JEL: F14, F16, J31

Abstract: This paper studies the implications for wage inequality of two distinct forms of globalisation, namely trade and foreign direct investment. I use German linked employer-employee data to (1) jointly estimate the exporter and the multinational wage premium and (2) to further distinguish between wage premia of multinational firms that are foreign owned (inward FDI) and domestically owned (outward FDI). My findings exhibit a clear hierarchy of firms’ international activities with regard to wage premia. I interpret these patterns using a theoretical framework, which incorporates ex-ante homogeneous workers, heterogeneous firms and search and matching frictions into a three-region model of trade and FDI with monopolistic competition. The model allows me to account for the observed empirical patterns, and delivers novel insights about the interplay between trade, FDI and labour market institutions. 

JEL: J23, J24, J44, N34, O33

Abstract: This paper documents novel facts on within-occupation task and skill changes over the past two decades in Germany. In a second step, it reveals a distinct relationship between occupational work content and exposure to artificial intelligence (AI) and automation (robots). Workers in occupations with high AI exposure, perform different activities and face different skill requirements, compared to workers in occupations exposed to robots. In a third step, the study uses individual labour market biographies to investigate the impact on wages between 2010 and 2017. Results indicate a wage growth premium in occupations more exposed to AI, contrasting with a wage growth discount in occupations exposed to robots. Finally, the study further explores the dynamic influence of AI exposure on individual wages over time, uncovering positive associations with wages, with nuanced variations across occupational groups.

Abstract. In 2011, the government of Rwanda implemented a health insurance premium policy change that led to a 200 percent increase in premiums to richer households and provided a premium waiver to poorer households identified through a community-based targeting process. We use three rounds of nationally representative cross-sectional surveys and apply a difference-in-differences with kernel propensity score matching to estimate the effect of this policy change on various labour market indicators in the short and medium terms. We find that premium increases had retrogressive effects non-agricultural time allocation and desire for additional work. Premiums waivers on the other hand led to reduction in hours allocated to wage activities and the probabilities of wage employment and desire for additional work though these effects did not sustain in the medium term. Gender and age desegregated heterogeneous assessments reveal that men and richer individuals responded to these changes more than the rest. The policy implications of this paper suggest that a revisiting of the community-based targeting is worthwhile especially in light of stalling poverty reduction in Rwanda in the last decade.

Abstract: This paper analyzes the distributional effects of international trade in the presence of mobility frictions and heterogeneous migration modes. We introduce a novel empirical measure of regional openness to migrants under China’s Hukou system, reflecting actual post-migration differential living costs faced by migrants. Utilizing household level and trade data, we establish a link between trade, the measured mobility frictions, heterogeneous migration modes, and remittances, and incorporate these factors into a spatial general equilibrium model. Our findings indicate that mobility frictions significantly influence the distributional effects of trade, with regions friendlier to migrants experiencing greater trade openness and equitable public service costs. The study provides new insights into the interplay between trade liberalization and migration policies, with implications for regional inequality and aggregate welfare.

JEL: J23, J24, J44, N34, O33

Abstract: We unbox developments in artificial intelligence (AI) to estimate how exposure to these developments affect firm-level labour demand, using detailed register data from Denmark, Portugal and Sweden over two decades. Based on data on AI capabilities and occupational work content, we develop and validate a time-variant measure for occupational exposure to AI across subdomains of AI, such as language modelling. According to the model, white-collar occupations are most exposed to AI, and especially white collar work that entails relatively little social interaction. We illustrate its usefulness by applying it to near-universal data on firms and individuals from Sweden, Denmark, and Portugal, and estimating firm labour demand regressions. We find a positive (negative) association between AI exposure and labour demand for high-skilled white (blue) collar work. Overall, there is an up-skilling effect, with the share of white-collar to blue collar workers increasing with AI exposure. Exposure to AI within the subdomains of image and language are positively (negatively) linked to demand for high-skilled white collar (blue collar) work, whereas other AI-areas are heterogeneously linked to groups of workers.

Abstract: This paper provides new evidence on how automation transforms firms' demand for skills and alters labor market outcomes. Leveraging firm-level data and a comprehensive dataset of job advertisements from Denmark, we extract detailed, multidimensional skill requirements using natural language processing techniques. A task-based conceptual framework guides our analysis, highlighting how automation reallocates tasks and reshapes skill demand both within and across occupations. Empirically, we decompose changes in skill demand into within- and between-occupation components and find that the within-occupation margin dominates. This suggests that automation primarily alters the content of existing jobs rather than shifting employment across occupations. We further show that automation is associated with increased demand for soft skills—particularly among managers and professionals—and a decline in demand for formally acquired hard skills among production workers. Finally, automation is associated with rising wages and employment for high-skill occupations, alongside greater training intensity among production workers.

JEL: E24, I24, J21, J14, J31

Abstract: This paper uses the structure of a two-sector two-factor model to attribute changes in the skill premium across countries to three potential sources: (i)  changes in the relative abundance of skilled workers, (ii) technological change and (iii) market size effects due to external economies of scale. I  employ the development and growth accounting methodology as analytic tool to assess the relative importance of each one of these channels in explaining changes in the skill premium across countries and time. My findings  add to the growing evidence that there is hardly any association between changes in the relative supply of skills and the observed evolution of the skill-premium. Furthermore, I show that the measure of the importance of market size effects governs the strength of the relationship between technological change and the skill-premium. Moreover, for strong enough economies of scale,  an increase in the relative supply of skills increases the  skill premium. Importantly, this finding points out that the scale of the economy may be an important factor in shaping developments of the skill premium, independent of the specific features of technological change.