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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


This chapter is a preliminary draft unless otherwise noted. It may not have been subjected to the formal review process of the NBER. This page will be updated as the chapter is revised.

Chapter in forthcoming NBER book Big Data for 21st Century Economic Statistics, Katharine G. Abraham, Ron S. Jarmin, Brian Moyer, and Matthew D. Shapiro
Conference held March 15-16, 2019
Forthcoming from University of Chicago Press
in NBER Book Series Studies in Income and Wealth

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.

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This chapter first appeared as NBER working paper w25837, 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
 
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