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Sarah Schroeder

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sschroeder@econ.au.dk
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Sarah Schroeder är lektor i nationalekonomi vid Aarhus Universitet och Fellow vid Research Centre for Firms and Industry Dynamics (FIND). Hennes forskning undersöker hur globalisering och teknologisk förändring omformar arbetsmarknader, med fokus på handel, FDI och effekterna av avancerade teknologier på företag och arbetstagare. Hon kombinerar empirisk analys med teoretisk modellering för att studera hur dessa krafter påverkar löner, sysselsättning och företags beslut om internationalisering.

Relaterade publikationer

    Working paper

    Health insurance premium changes and labor supply: Evidence from a low-income country

    Schroeder, S.
    Ladda ner

    Publiceringsår

    2025

    Publicerat i

    European Economic Review

    Sammanfattning

    We study the effect of a health insurance premium policy change on labor supply. Using a matching with difference-in-differences strategy on pooled nationwide cross-sectional and panel data we find that both premium waivers and premium increases led to a reduction in labor supply by almost similar margins. We also show that the policy change reduced the probability of wage employment and increased domestic labor supply, pointing to potential income effect for waivers and potential manipulation effects for premium increases. Our results are robust to various specifications and raise concerns for the unintended effects of popular but likely inefficient community-based welfare targeting methods.

    Artikel (med peer review)

    Exporters, multinationals and residual wage inequality: Evidence and theory

    Schroeder, S.
    Ladda ner

    Publiceringsår

    2025

    Publicerat i

    European Economic Review

    Sammanfattning

    A growing empirical literature underscores the pivotal role of ”global firms” in shaping labour market outcomes, including inequality. These are firms that participate in the international economy across multiple dimensions, including both trade and foreign direct investment (FDI). This prompts an important question: Is wage inequality among workers with similar characteristics primarily influenced by firms engaged solely in exporting, those involved solely in FDI, or by multinational enterprises (MNEs) that do both? Using linked employer–employee panel data for Germany, this paper unveils nuanced patterns in wage premia among various internationalising establishments, where I identify sorting between workers and establishments as a key driver. I interpret these patterns using a theoretical model that incorporates trade and FDI with monopolistic competition, wherein heterogeneous firms operate within frictional labour markets as they search for workers. My model gives rise to a novel channel for the MNE wage premium, stemming from their ability to transfer their human resource practices to their plant abroad.

    Artikel (med peer review)

    Artificial intelligence, tasks, skills and wages: Worker-level evidence from Germany

    Engberg, E., Koch, M., Lodefalk, M., & Schroeder, S.
    Ladda ner

    Publiceringsår

    2025

    Publicerat i

    Research Policy

    Sammanfattning

    As a first step, the study documents novel evidence on changes in tasks and skills within occupations in Germany over the past two decades. It further identifies a distinct relationship between ex ante occupational work content and ex post exposure to artificial intelligence (AI) and automation through robots. Workers in occupations with high AI exposure perform different activities and face different skill requirements than workers in occupations primarily exposed to robots, suggesting that AI and robots substitute for different types of tasks and skills. The study also shows that changes in the task and skill content of occupations are related to their initial exposure to these technologies. Finally, using individual labour market biographies, the analysis investigates the relationship between AI exposure and wages. By examining the dynamic effects of AI exposure over time, the study finds positive associations with wages, with nuanced differences across occupational groups, thereby providing further insight into the substitutability and augmentability of AI.

    Artikel (med peer review)

    Artificial intelligence, hiring and employment: job postings evidence from Sweden

    Engberg, E., Hellsten, M., Javed, F., Lodefalk, M., Sabolová, R., Schroeder, S., & Tang, A

    Publiceringsår

    2025

    Publicerat i

     Applied Economics Letters

    Sammanfattning

    This paper investigates the impact of artificial intelligence (AI) on hiring and employment, using the universe of job postings published by the Swedish Public Employment Service from 2014 to 2022 and full-population administrative data for Sweden. We exploit a detailed measure of AI exposure according to occupational content and find that establishments exposed to AI are more likely to hire AI workers. Survey data further indicate that AI exposure aligns with greater use of AI services. Importantly, rather than displacing non-AI workers, AI exposure is positively associated with increased hiring for both AI and non-AI roles. In the absence of substantial productivity gains that might account for this increase, we interpret the positive link between AI exposure and non-AI hiring as evidence that establishments are using AI to augment existing roles and expand task capabilities, rather than to replace non-AI workers.

    Working paper

    Working Paper No. 380: Artificial Intelligence, Hiring and Employment: Job Postings Evidence from Sweden

    Lodefalk, M.
    Ladda ner

    Publiceringsår

    2024

    Publicerat i

    Ratio Working Paper Series.

    Sammanfattning

    This paper investigates the impact of artificial intelligence (AI) on hiring and employment, using the universe of job postings published by the Swedish Public Employment Service from 2014-2022 and universal register data for Sweden. We construct a detailed measure of AI exposure according to occupational content and find that establishments exposed to AI are more likely to hire AI workers. Survey data further indicate that AI exposure aligns with greater use of AI services. Importantly, rather than displacing non-AI workers, AI exposure is positively associated with increased hiring for both AI and non-AI roles. In the absence of substantial productivity gains that might account for this increase, we interpret the positive link between AI exposure and non-AI hiring as evidence that establishments are using AI to augment existing roles and expand task capabilities, rather than to replace non-AI workers.

    Working paper

    Working Paper No. 370: AI Unboxed and Jobs: A Novel Measure and Firm-Level Evidence from Three Countries

    Engberg, E., Görg, H., Lodefalk, M., Javed, F., Längkvist, M., Monteiro, N., Kyvik Nordås, H., Pulito, G., Schroeder, S., & Tang, A.
    Ladda ner

    Publiceringsår

    2023

    Publicerat i

    Ratio Working Paper Series.

    Sammanfattning

    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, including language modelling. According to our 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.