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The Power of Big Data : Historical Time Series on German Education

Author

Listed:
  • Claude DIEBOLT

    (Université de Strasbourg)

  • Gabriele FRANZMANN

    (GESIS Leibniz Institute for the Social Sciences)

  • Ralph HIPPE

    (European Commission, Joint Research Center (JRC))

  • Jürgen SENSCH

    (GESIS Leibniz Institute for the Social Sciences)

Abstract

Numerous primary investigators collected and processed long-termed time series on German educational statistics in the context of their studies. As a result, there are a multitude of quantitative empirical studies. On the one hand, there is the project group on German Educational Statistics. 1 Its projects were targeted at describing and analyzing the long-term structural changes of the German educational system on a broad empirical and statistical basis. On the other hand, there are comprehensive data compilations of individual research projects, focusing on a wide variety of special educational research topics. The online database “histat” provides central digital access to these datasets on German educational history. Currently, it offers more than 120,000 long-term time series on the German educational system for a period of 200 years. The striking size of the database shows its key importance for researchers in the field of education. Thus, this paper aims to provide useful insights into the background of the database, the special characteristics of the data compilations and their analytical potential. Additionally, examples are given of how the data have already been used by researchers.

Suggested Citation

  • Claude DIEBOLT & Gabriele FRANZMANN & Ralph HIPPE & Jürgen SENSCH, 2017. "The Power of Big Data : Historical Time Series on German Education," JODE - Journal of Demographic Economics, Cambridge University Press, vol. 83(3), pages 329-376, September.
  • Handle: RePEc:ctl:louvde:v:83:y:2017:i:3:p:329-376
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    File URL: https://doi.org/10.1017/dem.2017.11
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    References listed on IDEAS

    as
    1. Claude Diebolt & Bachir El Murr, 2004. "A cobweb model of higher education and labour market dynamics," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 47(3-4), pages 409-430.
    2. Claude Diebolt & Michael Haupert, 2018. "Cliometrics," Working Papers of BETA 2018-01, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    3. Thierry Aimar & Francis Bismans & Claude Diebolt, 2012. "Economic Cycles: A Synthesis," Working Papers 12-11, Association Française de Cliométrie (AFC).
    4. repec:kap:jecgro:v:23:y:2018:i:1:d:10.1007_s10887-017-9151-1 is not listed on IDEAS
    5. Claude Diebolt & Bachir El Murr, 2004. "Educational Development and Labour Markets," Quality & Quantity: International Journal of Methodology, Springer, vol. 38(2), pages 127-145, April.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Big data; Cliometrics; Demography; Education; Germany;

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • I2 - Health, Education, and Welfare - - Education
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • N33 - Economic History - - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy - - - Europe: Pre-1913
    • N34 - Economic History - - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy - - - Europe: 1913-

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