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Big Data and Unemployment Analysis

Author

Listed:
  • Simionescu, Mihaela
  • Zimmermann, Klaus F.

Abstract

Internet or "big" data are increasingly measuring the relevant activities of individuals, households, firms and public agents in a timely way. The information set involves large numbers of observations and embraces flexible conceptual forms and experimental settings. Therefore, internet data are extremely useful to study a wide variety of human resource issues including forecasting, nowcasting, detecting health issues and well-being, capturing the matching process in various parts of individual life, and measuring complex processes where traditional data have known deficits. We focus here on the analysis of unemployment by means of internet activity data, a literature starting with the seminal article of Askitas and Zimmermann (2009a). The article provides insights and a brief overview of the current state of research.

Suggested Citation

  • Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
  • Handle: RePEc:zbw:glodps:81
    as

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    File URL: https://www.econstor.eu/bitstream/10419/162198/1/GLO_DP_0081.pdf
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    References listed on IDEAS

    as
    1. Amelie Constant & Klaus F. Zimmermann, 2008. "Im Angesicht der Krise: US-Präsidentschaftswahlen in transnationaler Sicht," DIW Wochenbericht, DIW Berlin, German Institute for Economic Research, vol. 75(44), pages 688-701.
    2. Jaroslav Pavlicek & Ladislav Kristoufek, 2015. "Nowcasting Unemployment Rates with Google Searches: Evidence from the Visegrad Group Countries," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-11, May.
    3. Dean Fantazzini, 2014. "Nowcasting and Forecasting the Monthly Food Stamps Data in the US Using Online Search Data," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-27, November.
    4. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    5. Andriana Bellou, 2015. "The impact of Internet diffusion on marriage rates: evidence from the broadband market," Journal of Population Economics, Springer;European Society for Population Economics, vol. 28(2), pages 265-297, April.
    6. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    7. Zhi Su, 2014. "Chinese Online Unemployment-Related Searches and Macroeconomic Indicators," Frontiers of Economics in China, Higher Education Press, vol. 9(4), pages 573-605, December.
    8. Alessia Naccarato & Andrea Pierini & Stefano Falorsi, 2015. "Using Google Trend Data To Predict The Italian Unemployment Rate," Departmental Working Papers of Economics - University 'Roma Tre' 0203, Department of Economics - University Roma Tre.
    9. Nikolaos Askitas, 2015. "Google search activity data and breaking trends," IZA World of Labor, Institute of Labor Economics (IZA), pages 206-206, November.
    10. Askitas, Nikos & Zimmermann, Klaus F., 2011. "Detecting Mortgage Delinquencies," IZA Discussion Papers 5895, Institute of Labor Economics (IZA).
    11. Janna Besamusca & Kea Tijdens, 2015. "Comparing collective bargaining agreements for developing countries," International Journal of Manpower, Emerald Group Publishing, vol. 36(1), pages 86-102, April.
    12. Concha Artola & Fernando Pinto & Pablo de Pedraza García, 2015. "Can internet searches forecast tourism inflows?," International Journal of Manpower, Emerald Group Publishing, vol. 36(1), pages 103-116, April.
    13. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    14. Gunter J. Hitsch & Ali Hortaçsu & Dan Ariely, 2010. "Matching and Sorting in Online Dating," American Economic Review, American Economic Association, vol. 100(1), pages 130-163, March.
    15. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    16. Emilio Zagheni & Ingmar Weber, 2015. "Demographic research with non-representative internet data," International Journal of Manpower, Emerald Group Publishing, vol. 36(1), pages 13-25, April.
    17. Peter Kuhn & Hani Mansour, 2014. "Is Internet Job Search Still Ineffective?," Economic Journal, Royal Economic Society, vol. 124(581), pages 1213-1233, December.
    18. Tao Chen & Erin Pik Ki So & Liang Wu & Isabel Kit Ming Yan, 2015. "The 2007–2008 U.S. Recession: What Did The Real-Time Google Trends Data Tell The United States?," Contemporary Economic Policy, Western Economic Association International, vol. 33(2), pages 395-403, April.
    19. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "Health and well-being in the great recession," International Journal of Manpower, Emerald Group Publishing, vol. 36(1), pages 26-47, April.
    20. Nuno Barreira & Pedro Godinho & Paulo Melo, 2013. "Nowcasting unemployment rate and new car sales in south-western Europe with Google Trends," Netnomics, Springer, vol. 14(3), pages 129-165, November.
    21. Benjamin Edelman, 2012. "Using Internet Data for Economic Research," Journal of Economic Perspectives, American Economic Association, vol. 26(2), pages 189-206, Spring.
    22. Meltem Gulenay Chadwick & Gonul Sengul, 2015. "Nowcasting the Unemployment Rate in Turkey : Let's ask Google," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 15(3), pages 15-40.
    23. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
    24. Peter Kuhn, 2014. "The internet as a labor market matchmaker," IZA World of Labor, Institute of Labor Economics (IZA), pages 1-18, May.
    25. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    26. repec:iza:izawol:journl:y:2014:p:18 is not listed on IDEAS
    27. Nikolaos Askitas & Klaus F. Zimmermann, 2013. "Nowcasting Business Cycles Using Toll Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 299-306, July.
    28. Francesco, D'Amuri, 2009. "Predicting unemployment in short samples with internet job search query data," MPRA Paper 18403, University Library of Munich, Germany.
    29. Tefft, Nathan, 2011. "Insights on unemployment, unemployment insurance, and mental health," Journal of Health Economics, Elsevier, vol. 30(2), pages 258-264, March.
    Full references (including those not matched with items on IDEAS)

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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Big Data and Unemployment Analysis
      by maximorossi in NEP-LTV blog on 2017-08-11 17:44:18

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    More about this item

    Keywords

    big data; unemployment; internet; Google; internet penetration rate;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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