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通过劳动力流动网络实现就业增长。

Employment growth through labor flow networks.

机构信息

Department of Computational Social Science and Center for Social Complexity, George Mason University, Fairfax, Virginia, United States of America.

出版信息

PLoS One. 2013 May 2;8(5):e60808. doi: 10.1371/journal.pone.0060808. Print 2013.

DOI:10.1371/journal.pone.0060808
PMID:23658682
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3642106/
Abstract

It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment micro-data, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm-worker interactions is an object we call the labor flow network (LFN). This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically-salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market.

摘要

在劳动经济学中,人们通常将所有寻找新工作的劳动者视为一个劳动力池,将所有有空缺职位的企业视为一个雇主池,然后将劳动者与工作岗位进行匹配。在这里,我们提出了一种新的方法来研究劳动力和企业动态。通过将新兴的网络科学与新获得的就业微观数据相结合,并在整个国家范围内进行全面综合,我们能够广泛描述劳动者在企业之间流动的过程。具体来说,对于一个经济体中的每个企业,我们将在劳动者在两个企业之间流动时(可能中间有一段失业期)在它们之间绘制边。经济体中企业-劳动者交互作用的整体图是我们称之为劳动力流动网络(LFN)的对象。这是首次对整个经济体的 LFN 进行特征化描述的研究。我们探索了这个网络的性质,包括它的拓扑结构、社区结构以及与经济变量的关系。结果表明,LFN 可用于识别具有高增长潜力的企业。我们将 LFN 与其他高绩效企业的概念联系起来。具体来说,结果表明,不到 10%的企业贡献了近 90%的全部就业增长。最后,我们提出了一个模型,其中实证上显著的 LFN 是从分散劳动力市场中异质适应性主体的相互作用中涌现出来的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b56/3642106/e038b2bbc6b6/pone.0060808.g006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b56/3642106/e038b2bbc6b6/pone.0060808.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b56/3642106/d20987250b88/pone.0060808.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b56/3642106/a1b0f00e357e/pone.0060808.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b56/3642106/35da5b288bd7/pone.0060808.g003.jpg
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本文引用的文献

1
Network structure of production.生产的网络结构。
Proc Natl Acad Sci U S A. 2011 Mar 29;108(13):5199-202. doi: 10.1073/pnas.1015564108. Epub 2011 Mar 14.
2
Economic networks: the new challenges.经济网络:新挑战
Science. 2009 Jul 24;325(5939):422-5. doi: 10.1126/science.1173644.
3
The building blocks of economic complexity.经济复杂性的构成要素。
Entropy (Basel). 2023 May 11;25(5):784. doi: 10.3390/e25050784.
4
Universal resilience patterns in labor markets.劳动力市场的普遍弹性模式。
Nat Commun. 2021 Mar 30;12(1):1972. doi: 10.1038/s41467-021-22086-3.
5
Occupational mobility and automation: a data-driven network model.职业流动性与自动化:一种数据驱动的网络模型。
J R Soc Interface. 2021 Jan;18(174):20200898. doi: 10.1098/rsif.2020.0898. Epub 2021 Jan 20.
6
Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters.全球劳动力流动网络揭示了地缘产业集群的层级组织和动态。
Nat Commun. 2019 Aug 1;10(1):3449. doi: 10.1038/s41467-019-11380-w.
7
Toward understanding the impact of artificial intelligence on labor.迈向理解人工智能对劳动力的影响。
Proc Natl Acad Sci U S A. 2019 Apr 2;116(14):6531-6539. doi: 10.1073/pnas.1900949116. Epub 2019 Mar 25.
8
The evolving network of labor flows in the Stockholm Region: Sector dynamics, connectivity and stability.
Appl Netw Sci. 2017;2(1):34. doi: 10.1007/s41109-017-0056-x. Epub 2017 Oct 10.
Proc Natl Acad Sci U S A. 2009 Jun 30;106(26):10570-5. doi: 10.1073/pnas.0900943106. Epub 2009 Jun 22.
4
Finding community structure in networks using the eigenvectors of matrices.利用矩阵特征向量在网络中寻找社区结构。
Phys Rev E Stat Nonlin Soft Matter Phys. 2006 Sep;74(3 Pt 2):036104. doi: 10.1103/PhysRevE.74.036104. Epub 2006 Sep 11.
5
Tuning clustering in random networks with arbitrary degree distributions.在具有任意度分布的随机网络中调整聚类
Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Sep;72(3 Pt 2):036133. doi: 10.1103/PhysRevE.72.036133. Epub 2005 Sep 30.
6
Containing pandemic influenza at the source.从源头控制大流行性流感。
Science. 2005 Aug 12;309(5737):1083-7. doi: 10.1126/science.1115717. Epub 2005 Aug 3.
7
Assortative mixing in networks.网络中的选择性混合。
Phys Rev Lett. 2002 Nov 11;89(20):208701. doi: 10.1103/PhysRevLett.89.208701. Epub 2002 Oct 28.
8
Containing bioterrorist smallpox.含有生物恐怖主义天花病毒。
Science. 2002 Nov 15;298(5597):1428-32. doi: 10.1126/science.1074674.
9
Hierarchical organization of modularity in metabolic networks.代谢网络中模块化的层次组织。
Science. 2002 Aug 30;297(5586):1551-5. doi: 10.1126/science.1073374.
10
Specificity and stability in topology of protein networks.蛋白质网络拓扑结构的特异性与稳定性。
Science. 2002 May 3;296(5569):910-3. doi: 10.1126/science.1065103.