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: 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.

Abstract: This paper provides new evidence on how automation reshapes firms' demand for skills, not only by changing the occupational composition, but also by reshaping what existing jobs require. Using matched data on firm-level automation investments and detailed job vacancy postings from Denmark, we extract multidimensional skill profiles through natural language processing and decompose changes in skill demand into within- and between-occupation components. Within-occupation adjustment is a quantitatively important margin, accounting for 14-39% of total skill demand change depending on skill type and occupational group. Drawing on a task-based framework that links automation to shifts in multiple skill types within occupations, we estimate the causal effect of automation using a staggered difference-in-differences design. The effects are heterogeneous across the occupational hierarchy: among managers and professionals, automation increases the demand for soft skills, shifting the within-occupation skill mix toward interpersonal and cognitive competencies; among production workers, adjustment operates primarily through reduced hiring rather than changes in skill requirements, while retraining intensity rises by 5 percentage points. Our findings highlight that automation operates through multiple adjustment margins, with implications for training policy and labour market resilience.

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.

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: Using two waves of nationally representative Danish firm surveys linked to employer-employee administrative registers, we study how adoption varies across artificial intelligence (AI) and related advanced technologies. We show that AI adoption is highly technology-specific. While firm size and digital infrastructure predict adoption broadly, workforce composition operates through distinct channels: STEM-educated workforces predict core AI adoption, whereas non-STEM university-educated workforces are associated with generative AI adoption, indicating different human capital complementarities. The factors associated with adoption differ from those predicting deployment breadth: firm size and digital maturity matter for both, whereas workforce composition primarily predicts adoption alone. Machine learning and natural language processing are deployed across multiple business functions, whereas other advanced technologies remain concentrated in specific operational domains. Individual-level evidence provides a foundation for these patterns, with awareness of workplace AI usage concentrated among managers and high-skilled workers. Self-reported AI knowledge is higher among younger and more educated individuals. Finally,

commonly used occupational AI exposure measures vary substantially in their ability to predict observed adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it.

JEL: E24, I24, J21, J14, J31

Abstract: Conventional skill-premium accounting attributes wage changes not explained by relative skill supply to shifts in relative demand or productivity. This paper shows that the resulting residual depends on the production structure used to map skill supply into wages. In a two-sector model, expansion of the skill-intensive sector supports additional intermediate-input varieties, generating external scale economies that make the skill-supply response less negative and increase the loading on relative productivity. A long-difference application to 40 countries finds only a weak and imprecisely estimated relationship between skill-supply growth and skill-premium growth. Relative to a constant-scale benchmark, the benchmark scale calibration reduces the wage-unit residual variance ratio by about 10 percent. In productivity-equivalent units, residual variance falls by about 29 percent because the productivty loading also rises. Substantial residual variation remains under both mappings. The results show that production assumptions affect how observed wage changes are divided between skill supply and the residual subsequently interpreted as demand or productivity.