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Using Internet Data for Economic Research

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  • Benjamin Edelman

Abstract

The data used by economists can be broadly divided into two categories. First, structured datasets arise when a government agency, trade association, or company can justify the expense of assembling records. The Internet has transformed how economists interact with these datasets by lowering the cost of storing, updating, distributing, finding, and retrieving this information. Second, some economic researchers affirmatively collect data of interest. For researcher-collected data, the Internet opens exceptional possibilities both by increasing the amount of information available for researchers to gather and by lowering researchers' costs of collecting information. In this paper, I explore the Internet's new datasets, present methods for harnessing their wealth, and survey a sampling of the research questions these data help to answer. The first section of this paper discusses "scraping" the Internet for data—that is, collecting data on prices, quantities, and key characteristics that are already available on websites but not yet organized in a form useful for economic research. A second part of the paper considers online experiments, including experiments that the economic researcher observes but does not control (for example, when Amazon or eBay alters site design or bidding rules); and experiments in which a researcher participates in design, including those conducted in partnership with a company or website, and online versions of laboratory experiments. Finally, I discuss certain limits to this type of data collection, including both "terms of use" restrictions on websites and concerns about privacy and confidentiality.

Suggested Citation

  • Benjamin Edelman, 2012. "Using Internet Data for Economic Research," Journal of Economic Perspectives, American Economic Association, vol. 26(2), pages 189-206, Spring.
  • Handle: RePEc:aea:jecper:v:26:y:2012:i:2:p:189-206
    Note: DOI: 10.1257/jep.26.2.189
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    File URL: http://www.aeaweb.org/articles.php?doi=10.1257/jep.26.2.189
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Francesco D'Amuri & Juri Marcucci, 2012. "The predictive power of Google searches in forecasting unemployment," Temi di discussione (Economic working papers) 891, Bank of Italy, Economic Research and International Relations Area.
    2. repec:eee:intfor:v:33:y:2017:i:4:p:801-816 is not listed on IDEAS
    3. repec:eee:ecmode:v:69:y:2018:i:c:p:127-133 is not listed on IDEAS
    4. 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.
    5. 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).
    6. Zhang, Yongjie & Feng, Lina & Jin, Xi & Shen, Dehua & Xiong, Xiong & Zhang, Wei, 2014. "Internet information arrival and volatility of SME PRICE INDEX," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 70-74.
    7. Norma Burow & Miriam Beblo & Denis Beninger & Melanie Schröder, 2017. "Why Do Women Favor Same-Gender Competition? Evidence from a Choice Experiment," Discussion Papers of DIW Berlin 1662, DIW Berlin, German Institute for Economic Research.
    8. 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.
    9. 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.
    10. Arne Feddersen & Brad Humphreys & Brian Soebbing, 2013. "Sentiment Bias in National Basketball Association Betting," Working Papers 13-03, Department of Economics, West Virginia University.
    11. Carlianne Patrick & Amanda Ross & Heather Stephens, 2016. "Designing Policies to Spur Economic Growth: How Regional Scientists Can Contribute to Future Policy Development and Evaluation," Working Papers 16-04, Department of Economics, West Virginia University.
    12. Zhang, Yongjie & Zhang, Yuzhao & Shen, Dehua & Zhang, Wei, 2017. "Investor sentiment and stock returns: Evidence from provincial TV audience rating in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 288-294.
    13. 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).
    14. repec:eee:tefoso:v:130:y:2018:i:c:p:99-113 is not listed on IDEAS
    15. Shen, Dehua & Zhang, Wei & Xiong, Xiong & Li, Xiao & Zhang, Yongjie, 2016. "Trading and non-trading period Internet information flow and intraday return volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 519-524.
    16. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
    17. repec:eee:phsmap:v:490:y:2018:i:c:p:928-934 is not listed on IDEAS
    18. repec:eee:ecmode:v:64:y:2017:i:c:p:496-501 is not listed on IDEAS
    19. 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).
    20. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).

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    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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