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The internet as a data source for advancement in social sciences

Author

Listed:
  • Nikolaos Askitas
  • Klaus F. Zimmermann

Abstract

Purpose - – The purpose of this paper is to recommend the use of internet data for social sciences with a special focus on human resources issues. It discusses the potentials and challenges of internet data for social sciences. The authors present a selection of the relevant literature to establish the wide spectrum of topics, which can be reached with this type of data, and link them to the papers in this Design/methodology/approach - – Internet data are increasingly representing a large part of everyday life, which cannot be measured otherwise. The information is timely, perhaps even daily following the factual process. It typically involves large numbers of observations and allows for flexible conceptual forms and experimental settings. Findings - – Internet data can successfully be applied to a very wide range of human resource issues including forecasting (e.g. of unemployment, consumption goods, tourism, festival winners and the like), nowcasting (obtaining relevant information much earlier than through traditional data collection techniques), detecting health issues and well-being (e.g. flu, malaise and ill-being during economic crises), documenting the matching process in various parts of individual life (e.g. jobs, partnership, shopping), and measuring complex processes where traditional data have known deficits (e.g. international migration, collective bargaining agreements in developing countries). Major problems in data analysis are still unsolved and more research on data reliability is needed. Research limitations/implications - – The data in the reviewed literature are unexplored and underused and the methods available are confronted with known and new challenges. Current research is highly original but also exploratory and premature. Originality/value - – The paper reviews the current attempts in the literature to incorporate internet data into the mainstream of scholarly empirical research and guides the reader through this Special Issue. The authors provide some insights and a brief overview of the current state of research.

Suggested Citation

  • Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing, vol. 36(1), pages 2-12, April.
  • Handle: RePEc:eme:ijmpps:v:36:y:2015:i:1:p:2-12
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    References listed on IDEAS

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    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. Fantazziini, Dean, 2014. "Nowcasting and Forecasting the Monthly Food Stamps Data in the US using Online Search Data," MPRA Paper 59696, University Library of Munich, Germany.
    3. Konstantin A. Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption?: A Real-Time Evidence for the US," Discussion Papers of DIW Berlin 997, DIW Berlin, German Institute for Economic Research.
    4. Albert Saiz & Uri Simonsohn, 2013. "Proxying For Unobservable Variables With Internet Document-Frequency," Journal of the European Economic Association, European Economic Association, vol. 11(1), pages 137-165, February.
    5. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    6. 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.
    7. 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.
    8. Stephens-Davidowitz, Seth, 2014. "The cost of racial animus on a black candidate: Evidence using Google search data," Journal of Public Economics, Elsevier, vol. 118(C), pages 26-40.
    9. 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.
    10. Yan Carrière‐Swallow & Felipe Labbé, 2013. "Nowcasting with Google Trends in an Emerging Market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 289-298, July.
    11. Askitas, Nikos & Zimmermann, Klaus F., 2011. "Detecting Mortgage Delinquencies," IZA Discussion Papers 5895, Institute for the Study of Labor (IZA).
    12. 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.
    13. 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.
    14. Peter Kuhn & Kailing Shen, 2013. "Gender Discrimination in Job Ads: Evidence from China," The Quarterly Journal of Economics, Oxford University Press, vol. 128(1), pages 287-336.
    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. 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.
    17. Kureková, Lucia Mýtna & Beblavy, Miroslav & Thum, Anna-Elisabeth, 2014. "Using Internet Data to Analyse the Labour Market: A Methodological Enquiry," IZA Discussion Papers 8555, Institute for the Study of Labor (IZA).
    18. 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.
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    21. Margaret Maurer-Fazio & Lei Lei, 2015. "“As rare as a panda”: How facial attractiveness, gender, and occupation affect interview callbacks at Chinese firms," International Journal of Manpower, Emerald Group Publishing, vol. 36(1), pages 68-85, April.
    22. Guzi, Martin & de Pedraza, Pablo, 2013. "A Web Survey Analysis of the Subjective Well-being of Spanish Workers," IZA Discussion Papers 7618, Institute for the Study of Labor (IZA).
    23. 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.
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    25. 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.
    26. Askitas, Nikos & Zimmermann, Klaus F., 2011. "Health and Well-Being in the Crisis," IZA Discussion Papers 5601, Institute for the Study of Labor (IZA).
    27. 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)

    Citations

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    Cited by:

    1. Karolien Lenaerts & Miroslav Beblavý & Brian Fabo, 2016. "Prospects for utilisation of non-vacancy Internet data in labour market analysis—an overview," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 5(1), pages 1-18, December.
    2. Stefano Visintin & Kea Tijdens & Maarten van Klaveren, 2015. "Skill mismatch among migrant workers: evidence from a large multi-country dataset," IZA Journal of Migration and Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 4(1), pages 1-34, December.
    3. Lucia Kureková & Miroslav Beblavý & Anna Thum-Thysen, 2015. "Using online vacancies and web surveys to analyse the labour market: a methodological inquiry," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 4(1), pages 1-20, December.
    4. Nikolaos Askitas, 2016. "Big Data is a big deal but how much data do we need?
      [Big Data gut und schön. Aber wie viel Data brauchen wir?]
      ," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(2), pages 113-125, October.
    5. repec:kap:empiri:v:44:y:2017:i:3:d:10.1007_s10663-017-9374-6 is not listed on IDEAS
    6. 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.
    7. Nikolaos Askitas, 2015. "Google search activity data and breaking trends," IZA World of Labor, Institute for the Study of Labor (IZA), pages 206-206, November.
    8. Asgari, Mahdi & Nemati, Mehdi & Zheng, Yuqing, 2018. "Nowcasting Food Stock Movement using Food Safety Related Web Search Queries," 2018 Annual Meeting, February 2-6, 2018, Jacksonville, Florida 266323, Southern Agricultural Economics Association.
    9. Fabo, B., 2017. "Towards an understanding of job matching using web data," Other publications TiSEM b8b877f2-ae6a-495f-b6cc-9, Tilburg University, School of Economics and Management.
    10. repec:rsr:supplm:v:65:y:2017:i:4:p:107-114 is not listed on IDEAS
    11. repec:nsr:escoed:escoe-dp-2018-04 is not listed on IDEAS
    12. Javier Sebastian, 2016. "Blockchain in financial services: Regulatory landscape and future challenges," Working Papers 16/21, BBVA Bank, Economic Research Department.
    13. repec:eee:intfor:v:33:y:2017:i:4:p:801-816 is not listed on IDEAS
    14. Stefano Visintin & Kea Tijdens & Stephanie Steinmetz & Pablo de Pedraza, 2015. "Task implementation heterogeneity and wage dispersion," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 4(1), pages 1-24, December.
    15. Kureková, Lucia Mýtna & Žilin?íková, Zuzana, 2015. "Low-Skilled Jobs and Student Jobs: Employers' Preferences in Slovakia and the Czech Republic," IZA Discussion Papers 9145, Institute for the Study of Labor (IZA).
    16. Lucia Mýtna Kureková & Zuzana Žilinčíková, 2016. "Are student jobs flexible jobs? Using online data to study employers’ preferences in Slovakia," IZA Journal of European Labor Studies, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 5(1), pages 1-14, December.
    17. Beblavý, Miroslav & Fabo, Brian & Lenaerts, Karolien, 2016. "Skills Requirements for the 30 Most-Frequently Advertised Occupations in the United States: An analysis based on online vacancy data," CEPS Papers 11406, Centre for European Policy Studies.
    18. repec:eee:tefoso:v:130:y:2018:i:c:p:99-113 is not listed on IDEAS
    19. repec:eee:tefoso:v:130:y:2018:i:c:p:114-122 is not listed on IDEAS
    20. Kureková, Lucia Mýtna & Žilin?íková, Zuzana, 2016. "What is the Value of Foreign Work Experience? Analysing Online CV Data in Slovakia," IZA Discussion Papers 9921, Institute for the Study of Labor (IZA).

    More about this item

    Keywords

    Forecasting; World wide web; Human resources and the internet; Internet data; Web data;

    JEL classification:

    • J00 - Labor and Demographic Economics - - General - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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