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Evaluating a new earnings indicator. Can we improve the timeliness of existing statistics on earnings by using salary information from online job adverts?

Author

Listed:
  • Jyldyz Djumalieva
  • Stef Garasto
  • Cath Sleeman

Abstract

This paper examines how the salary information from online job adverts might be used to improve the timeliness of official statistics on earnings. The unique dataset underpinning the analysis contains over 51 million adverts for UK positions, collected between January 2012 and September 2018. The data was sourced from Burning Glass Technologies, a leading labour market intelligence company. We trial a mixture of forecasting approaches, including traditional econometric models and the relatively newer recurrent neural networks. For 2 out of 13 industries and for 5 out of 6 occupation groups, salaries from online job adverts are shown to improve the accuracy of earnings forecasts over and above official data on its own. More broadly, this paper provides a detailed methodology for evaluating a novel data source, such as salaries from job adverts, to inform an official statistical series, such as earnings.

Suggested Citation

  • Jyldyz Djumalieva & Stef Garasto & Cath Sleeman, 2020. "Evaluating a new earnings indicator. Can we improve the timeliness of existing statistics on earnings by using salary information from online job adverts?," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-19, Economic Statistics Centre of Excellence (ESCoE).
    • Handle: RePEc:nsr:escoed:escoe-dp-2020-19
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    References listed on IDEAS

    as
    1. Turrell, Arthur & Speigner, Bradley & Djumalieva, Jyldyz & Copple, David & Thurgood, James, 2018. "Using job vacancies to understand the effects of labour market mismatch on UK output and productivity," Bank of England working papers 737, Bank of England.
    2. Jyldyz Djumalieva1 & Cath Sleeman, 2018. "An Open and Data-driven Taxonomy of Skills Extracted from Online Job Adverts," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-13, Economic Statistics Centre of Excellence (ESCoE).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    arima models; earnings; forecasting; neural networks; online job adverts;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • J30 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - General

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