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Survey vs Scraped Data: Comparing Time Series Properties of Web and Survey Vacancy Data

Author

Listed:
  • Pedraza Pablo de

    (University of Amsterdam and European Commission, Joint Research Centre (JRC), Unit I.1, Modelling, Indicators & Impact Evaluation, Via E. Fermi 2749, TP 361, Ispra (VA), I-21027, Italy)

  • Visintin Stefano

    (University of Amsterdam/AIAS and Universidad Camilo José Cela, Facultad de Tecnología y Ciencia, Urb. Villafranca del Castillo, Calle Castillo de Alarcón, 49, 28692, Villanueva de la Cañada, Madrid, Spain)

  • Tijdens Kea

    (University of Amsterdam/AIAS, Postbus 94025, 1090 GAAmsterdam, The Netherlands)

  • Kismihók Gábor

    (Leibniz Information Centre for Science and Technology, Welfengarten 1 B, 30167Hannover, Germany)

Abstract

This paper studies the relationship between a vacancy population obtained from web crawling and vacancies in the economy inferred by a National Statistics Office (NSO) using a traditional method. We compare the time series properties of samples obtained between 2007 and 2014 by Statistics Netherlands and by a web scraping company. We find that the web and NSO vacancy data present similar time series properties, suggesting that both time series are generated by the same underlying phenomenon: the real number of new vacancies in the economy. We conclude that, in our case study, web-sourced data are able to capture aggregate economic activity in the labor market.

Suggested Citation

  • Pedraza Pablo de & Visintin Stefano & Tijdens Kea & Kismihók Gábor, 2019. "Survey vs Scraped Data: Comparing Time Series Properties of Web and Survey Vacancy Data," IZA Journal of Labor Economics, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 8(1), pages 103-116, June.
    • Handle: RePEc:vrs:izajle:v:8:y:2019:i:1:p:103-116:n:4
    DOI: 10.2478/izajole-2019-0004
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    Citations

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    Cited by:

    1. de Pedraza, Pablo & Vollbracht, Ian, 2020. "The Semicircular Flow of the Data Economy and the Data Sharing Laffer curve," GLO Discussion Paper Series 515, Global Labor Organization (GLO).
    2. Pablo de Pedraza & Martin Guzi & Kea Tijdens, 2020. "Life satisfaction of employees, labour market tightness and matching efficiency," International Journal of Manpower, Emerald Group Publishing Limited, vol. 42(3), pages 341-355, July.
    3. Pablo Pedraza & Ian Vollbracht, 2023. "General theory of data, artificial intelligence and governance," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.

    More about this item

    Keywords

    web crawling; statistical inference; time series; vacancies; Labor demand; data collection;
    All these keywords.

    JEL classification:

    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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