Concentration in US Labor Markets: Evidence from Online Vacancy Data

Azar, José; Marinescu, Ioana; Steinbaum, Marshall; Taska, Bledi

doi:10.2139/ssrn.3133344

Cited by 52 publications

(74 citation statements)

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“…Bunting (1962, Appendix 16) finds a positive relationship between wages and concentration. A presumably incomplete list of recent papers includes: Azar, Marinescu, and Steinbaum (2017), Azar et al (2018), Benmelech, Bergman, and Kim (2018), Hershbein, Macaluso, and Yeh (2018), Lipsius (2018), Qui and Sojourner (2019), Rinz (2018), and Schubert, Stansbury, and Taska (2019).…”

Section: Wage-concentration Regressions In the Modelmentioning

confidence: 99%

Granular Search, Market Structure, and Wages

Jarosch¹,

Nimczik²,

Sorkin³

2019

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Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Foundation, IZA runs the world's largest network of economists, whose research aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.

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Section: Wage-concentration Regressions In the Modelmentioning

confidence: 99%

Granular Search, Market Structure, and Wages

Jarosch¹,

Nimczik²,

Sorkin³

2019

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show abstract

“…Deming and Kahn (2017) use job vacancy descriptions that have been processed into keywords to define general skills that have explanatory power for both pay and firm performance beyond the usual labour market classifications. Azar et al (2018) leverage online job vacancies, with job title text cleaned and standardised, to estimate the labour market concentration according to the Herfindahl-Hirchsman index.…”

Section: Literaturementioning

confidence: 99%

Transforming Naturally Occurring Text Data Into Economic Statistics: The Case of Online Job Vacancy Postings

Turrell¹,

Speigner²,

Djumalieva³

et al. 2019

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Using a dataset of 15 million UK job adverts from a recruitment website, we construct new economic statistics measuring labour market demand. These data are 'naturally occurring', having originally been posted online by firms. They offer information on two dimensions of vacancies-region and occupationthat firm-based surveys do not usually, and cannot easily, collect. These data do not come with official classification labels so we develop an algorithm which maps the free form text of job descriptions into standard occupational classification codes. The created vacancy statistics give a plausible, granular picture of UK labour demand and permit the analysis of Beveridge curves and mismatch unemployment at the occupational level.

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“…However, wages are also dependent on the labor market concentration and buyer-supplier relationships. In this regard, recent work on labor market monopsony and large-scale buyers showed that labor market concentration can reduce wages within certain type of occupations and industries (Azar et al, 2017(Azar et al, , 2018Wilmers, 2018;Rinz, 2018). This implies also that wages are partially determined by the industry and related labor market within that industry.…”

Section: Literature On Occupations Industries and Social Stratificationmentioning

confidence: 99%

Mapping Stratification: The Industry-Occupation Space Reveals the Network Structure of Inequality

et al. 2019

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Social stratification is determined not only by income, education, race, and gender, but also by an individual's job characteristics and their position in the industrial structure. Utilizing a dataset of 76.6 million Brazilian workers and methods from network science, we map the Brazilian Industry-Occupation Space (BIOS). The BIOS measures the extent to which 600 occupations co-appear in 585 industries, resulting in a complex network that shows how industrial-occupational communities provide important information on the network segmentation of society. Gender, race, education, and income are concentrated unevenly across the core-periphery structure of the BIOS. Moreover, we identify 28 industrial occupational communities from the BIOS network structure and report their contribution to total income inequality in Brazil. Finally, we quantify the relative poverty within these communities. In sum, the BIOS reveals how the coupling of industries and occupations contributes to mapping social stratification.

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Concentration in US Labor Markets: Evidence from Online Vacancy Data

Cited by 52 publications

References 0 publications

Granular Search, Market Structure, and Wages

Granular Search, Market Structure, and Wages

Transforming Naturally Occurring Text Data Into Economic Statistics: The Case of Online Job Vacancy Postings

Mapping Stratification: The Industry-Occupation Space Reveals the Network Structure of Inequality

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