Self-employment career patterns in the Netherlands: exploring individual and regional differences

Author:

Bay FranziskaORCID,Koster SierdjanORCID

Abstract

AbstractAlthough the self-employed represent 16.7% of the Dutch labor force (OECD 2020), their internal heterogeneity in profiles regarding motivations, characteristics and career trajectories remains unclear. Yet, understanding self-employment profiles and their spatial distribution may help understand differences in career progression of the self-employed. This study identifies and describes patterns in long-term career trajectories of the Dutch self-employed, and it explores spatial differences along the urban hierarchy. The study uses a life-course approach and register data of the whole population to find common patterns of careers among a sample of Dutch self-employed (N = 42,028) and their spatial distribution. We investigated careers through sequence and cluster analysis of individuals’ socio-economic statuses between 2003–2018. The analysis identifies 7 career clusters that collapse into three main career profiles: Mixed self-employment careers that combine self-employment with wage-employment, stable self-employment, and precarious self-employment. The clusters differ importantly in terms of the individual characteristics of the self-employed including age, gender, educational level and income. In terms of spatial distribution, the study shows that self-employment career profiles follow the urban hierarchy. Urban regions give way to all types of self-employment, while rural regions mainly exhibit stable self-employment. Precarious self-employment presents differently in urban and rural areas; in urban labor markets, we find self-employed individuals vulnerable to economic shocks, losing their jobs as a consequence of the financial crisis in 2007/08. In rural regions, formerly inactive workers become self-employed following the crisis.

Publisher

Springer Science and Business Media LLC

Subject

General Social Sciences,General Environmental Science

Reference41 articles.

1. Abbott A, Forrest J (1986) Optimal matching methods for historical sequences. J Interdiscip Hist 16:471–494

2. Abel JR, Deitz R (2015) Agglomeration and job matching among college graduates. Reg Sci Urban Econ 51:14–24

3. Acs Z (2006) How is entrepreneurship good for economic growth? Innov Technol Gov Global 1(1):97–107

4. Agresti A (2003) Categorical data analysis, vol 482. Wiley, New York

5. Andersson M, Koster S, Lavesson N (2016) Are start-ups the same everywhere. Geographies of Entrepreneurship 122

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3