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Cited by 523

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

The Dynamics of Employment Growth: New Evidence from 18 Countries

Open access
·2014·OECD Science, Technology and Industry Policy Papers

Abstract

Motivated by the ongoing interest of policy makers in the sources of job creation, this paper presents results from a new OECD project on the dynamics of employment (DynEmp) based on an innovative methodology using firm-level data (i.e. national business registers or similar sources). It demonstrates that among small and medium sized enterprises (SMEs), young firms play a central role in creating jobs, whereas old SMEs tend to destroy jobs. This pattern holds robustly across 17 OECD countries and Brazil, extending recent evidence found in the United States. The paper also shows that young firms are always net job creators throughout the business cycle, even during the financial crisis. During the crisis, entry and post-entry growth by young firms were affected most heavily, although downsizing by old firms was responsible for most job losses. The results also highlight large cross-country differences in the growth potential of young firms, pointing to the role played by national policies in enabling successful firms to create jobs.

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Metadata

Venue
OECD Science, Technology and Industry Policy Papers
Published
2014-05-21
ISSN
2307-4957 (ISSN-L)
OpenAlex
W2162515645
Cited by
523
References
4
Type
report
Language
en
Open access
bronze

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CoChat. (2026). Citation check: The Dynamics of Employment Growth: New Evidence from 18 Countries. https://app.cochat.ai/papers/10.1787/5jz417hj6hg6-en
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The Dynamics of Employment Growth: New Evidence from 18 Countries. (2014). OECD Science, Technology and Industry Policy Papers. https://doi.org/10.1787/5jz417hj6hg6-en

Data sources

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.