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

In: Big Data for Twenty-First-Century Economic Statistics

Author

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  • Arthur Turrell
  • Bradley Speigner
  • Jyldyz Djumalieva
  • David Copple
  • James Thurgood

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.
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Suggested Citation

  • Arthur Turrell & Bradley Speigner & Jyldyz Djumalieva & David Copple & James Thurgood, 2019. "Transforming Naturally Occurring Text Data into Economic Statistics: The Case of Online Job Vacancy Postings," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 173-207, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:14277
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    Cited by:

    1. Bellatin, Alejandra & Galassi, Gabriela, 2022. "What COVID-19 May Leave Behind: Technology-Related Job Postings in Canada," IZA Discussion Papers 15209, Institute of Labor Economics (IZA).
    2. Oleksandr Faryna & Tho Pham & Oleksandr Talavera & Andriy Tsapin, 2020. "Wage Setting and Unemployment: Evidence from Online Job Vacancy Data," Economics Discussion Papers em-dp2020-02, Department of Economics, University of Reading.
    3. Maciej Berk{e}sewicz & Herman Cherniaiev & Robert Pater, 2021. "Estimating the number of entities with vacancies using administrative and online data," Papers 2106.03263, arXiv.org.
    4. Caglayan, Mustafa & Talavera, Oleksandr & Xiong, Lin, 2022. "Female small business owners in China: Discouraged, not discriminated," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    5. Vydra Simon & Kantorowicz Jaroslaw, 2021. "Tracing Policy-relevant Information in Social Media: The Case of Twitter before and during the COVID-19 Crisis," Statistics, Politics and Policy, De Gruyter, vol. 12(1), pages 87-127, June.
    6. Faryna, Oleksandr & Pham, Tho & Talavera, Oleksandr & Tsapin, Andriy, 2022. "Wage and unemployment: Evidence from online job vacancy data," Journal of Comparative Economics, Elsevier, vol. 50(1), pages 52-70.
    7. Vacha, Stepan, 2021. "Labour demand in the UK during the COVID-19 pandemic : evidence from online job postings," Warwick-Monash Economics Student Papers 13, Warwick Monash Economics Student Papers.
    8. Pietro Giorgio Lovaglio, 2022. "Do job vacancies variations anticipate employment variations by sector? Some preliminary evidence from Italy," LABOUR, CEIS, vol. 36(1), pages 71-93, March.
    9. Stef Garasto & Jyldyz Djumalieva & Karlis Kanders & Rachel Wilcock & Cath Sleeman, 2021. "Developing experimental estimates of regional skill demand," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2021-02, Economic Statistics Centre of Excellence (ESCoE).
    10. David Evans & Claire Mason & Haohui Chen & Andrew Reeson, 2024. "Accelerated demand for interpersonal skills in the Australian post-pandemic labour market," Nature Human Behaviour, Nature, vol. 8(1), pages 32-42, January.
    11. Kakuho Furukawa & Yoshihiko Hogen & Yosuke Kido, "undated". "Labor Market of Regular Workers in Japan: A Perspective from Job Advertisement Data," Bank of Japan Working Paper Series 23-E-7, Bank of Japan.
    12. Mary A. Burke & Alicia Sasser Modestino & Shahriar Sadighi & Rachel B. Sederberg & Bledi Taska, 2019. "No Longer Qualified? Changes in the Supply and Demand for Skills within Occupations," Working Papers 20-3, Federal Reserve Bank of Boston.
    13. Turrell, Arthur & Speigner, Bradley & Copple, David & Djumalieva, Jyldyz & Thurgood, James, 2021. "Is the UK’s productivity puzzle mostly driven by occupational mismatch? An analysis using big data on job vacancies," Labour Economics, Elsevier, vol. 71(C).
    14. He, Chuan & Mau, Karsten & Xu, Mingzhi, 2021. "Trade Shocks and Firms Hiring Decisions: Evidence from Vacancy Postings of Chinese Firms in the Trade War," Labour Economics, Elsevier, vol. 71(C).
    15. Joel Alcedo & Alberto Cavallo & Bricklin Dwyer & Prachi Mishra & Antonio Spilimbergo, 2022. "Back to Trend: COVID Effects on E-commerce in 47 Countries," NBER Working Papers 29729, National Bureau of Economic Research, Inc.
    16. Alejandra Bellatin & Gabriela Galassi, 2022. "What COVID-19 May Leave Behind: Technology-Related Job Postings in Canada," Staff Working Papers 22-17, Bank of Canada.
    17. He, Chuan & Mau, Karsten & Xu, Mingzhi, 2021. "Trade Shocks and Firms Hiring Decisions:," Research Memorandum 001, Maastricht University, Graduate School of Business and Economics (GSBE).

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    More about this item

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs

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