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Working paper No. 280: Recruiting for Small Business Growth: Micro-level Evidence

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Abstract

We examine the link between new employees in leading positions and subsequent productivity in small- and medium-sized (SME) enterprises. Managers and professionals are likely to possess important tacit knowledge. They are also in a position to influence the employing firm. Exploiting rich and comprehensive panel data for Sweden in the 2001-2010 period and employing semi-parametric and quasi-experimental estimation techniques, we find that newly recruited leading personnel have a positive and statistically significant impact on the productivity of the hiring SME. Interestingly, our results suggest that professionals with experience from international firms and enterprise groups contribute the most to total factor productivity. Overall, the findings suggest the importance of mobility of leading personnel for productivity-enhancing knowledge spillovers to SMEs.
Related content: Recruiting for Small Business Growth: Micro Level Evidence

Gidehag, A. & Lodefalk, M. (2016). Recruiting for Small Business Growth: Micro-level Evidence. Ratio Working Paper No. 280. Stockholm: Ratio.

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Author
Gidehag, A. & Lodefalk, M.
Publication year
2016
Published in

Ratio Working Paper

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  • Associate Professor

    Magnus Lodefalk

    magnus.lodefalk@oru.se

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This paper investigates the economic and societal impacts of Artificial Intelligence (AI) in the public sector, focusing on its potential to enhance productivity and mitigate labour shortages. Employing detailed administrative data and novel occupational exposure measures, we simulate future scenarios over a 20-year horizon, using Sweden as an illustrative case. Our findings indicate that advances in AI development and uptake could significantly alleviate projected labour shortages and enhance productivity. However, outcomes vary substantially across sectors and organisational types, driven by differing workforce compositions. Complementing the economic analysis, we identify key challenges that hinder AI’s effective deployment, including technical limitations, organisational barriers, regulatory ambiguity, and ethical risks such as algorithmic bias and lack of transparency. Drawing from an interdisciplinary conceptual framework, we argue that AI’s integration in the public sector must address these socio-technical and institutional factors comprehensively. To unlock AI’s full potential, substantial investments in technological infrastructure, human capital development, regulatory clarity, and robust governance mechanisms are essential. Our study thus contributes both novel economic evidence and an integrated societal perspective, informing strategies for sustainable and equitable public-sector digitalisation.

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We use individual survey data providing detailed information on stress, technology adoption, and work, worker, and employer characteristics, in combination with recent measures of AI and robot exposure, to investigate how new technologies affect worker stress. We find a persistent negative relationship, suggesting that AI and robots could reduce the stress level of workers in Germany. We furthermore provide evidence on potential mechanisms to explain our findings. Overall, the paper contributes to the economic literature by providing suggestive evidence of modern technologies changing the way we perform our work in a way that reduces stress and work pressure.

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

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

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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.

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