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

Arthur Turrell, Bradley Speigner (), Jyldyz Djumalieva, David Copple and James Thurgood

No 25837, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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 occupation—that 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.

JEL-codes: C55 E24 J63 (search for similar items in EconPapers)
Date: 2019-05
New Economics Papers: this item is included in nep-big, nep-cmp, nep-eur and nep-lab
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Published as Transforming Naturally Occurring Text Data into Economic Statistics: The Case of Online Job Vacancy Postings , Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, James Thurgood. in Big Data for Twenty-First-Century Economic Statistics , Abraham, Jarmin, Moyer, and Shapiro. 2022

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