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

Arthur Turrell, Bradley J. Speigner, Jyldyz Djumalieva, David Copple, James Thurgood

NBER Working Paper No. 25837
Issued in May 2019
NBER Program(s):Labor Studies

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.

Supplementary materials for this paper:

• Code for assigning occupational labels to job ad text

Acknowledgments

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Document Object Identifier (DOI): 10.3386/w25837

Forthcoming: Transforming Naturally Occurring Text Data into Economic Statistics: The Case of Online Job Vacancy Postings, Arthur Turrell, Bradley J. Speigner, Jyldyz Djumalieva, David Copple, James Thurgood. in Big Data for Twenty-First Century Economic Statistics, Abraham, Jarmin, Moyer, and Shapiro. 2020