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The Data Revolution and Economic Analysis

Author

Listed:
  • Liran Einav

    () (Stanford University)

  • Johnathan Levin

    () (NBER)

Abstract

Many believe that “big data” will transform business, government and other aspects of the economy. In this article we discuss how new data may impact economic policy and economic research. Large-scale administrative datasets and proprietary private sector data can greatly improve the way we measure, track and describe economic activity. They also can enable novel research designs that allow researchers to trace the consequences of different events or policies. We outline some of the challenges in accessing and making use of these data. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in economics.

Suggested Citation

  • Liran Einav & Johnathan Levin, 2013. "The Data Revolution and Economic Analysis," Discussion Papers 12-017, Stanford Institute for Economic Policy Research.
  • Handle: RePEc:sip:dpaper:12-017
    as

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    File URL: http://www-siepr.stanford.edu/repec/sip/12-017.pdf
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    References listed on IDEAS

    as
    1. Alberto Cavallo, 2018. "Scraped Data and Sticky Prices," The Review of Economics and Statistics, MIT Press, vol. 100(1), pages 105-119, March.
    2. Liran Einav & Theresa Kuchler & Jonathan Levin & Neel Sundaresan, 2011. "Learning from Seller Experiements in Online Markets," Discussion Papers 10-033, Stanford Institute for Economic Policy Research.
    3. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    4. Peter J. Klenow & Oleksiy Kryvtsov, 2008. "State-Dependent or Time-Dependent Pricing: Does it Matter for Recent U.S. Inflation?," The Quarterly Journal of Economics, Oxford University Press, vol. 123(3), pages 863-904.
    5. Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2011. "The Long-Term Impacts of Teachers: Teacher Value-Added and Student Outcomes in Adulthood," NBER Working Papers 17699, National Bureau of Economic Research, Inc.
    6. Steven L. Scott & Hal R. Varian, 2015. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Chapters,in: Economic Analysis of the Digital Economy, pages 119-135 National Bureau of Economic Research, Inc.
    7. Thomas Piketty & Emmanuel Saez, 2003. "Income Inequality in the United States, 1913–1998," The Quarterly Journal of Economics, Oxford University Press, vol. 118(1), pages 1-41.
    8. 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.
    9. Thomas Barrios & Rebecca Diamond & Guido W. Imbens & Michal Kolesár, 2012. "Clustering, Spatial Correlations, and Randomization Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 578-591, June.
    10. Liran Einav & Chiara Farronato & Jonathan D. Levin & Neel Sundaresan, 2013. "Sales Mechanisms in Online Markets: What Happened to Internet Auctions?," NBER Working Papers 19021, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. The fuss about big data
      by Economic Logician in Economic Logic on 2013-09-25 19:01:00

    Citations

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    Cited by:

    1. Christian Baker & Jeremy Bejarano & Richard W. Evans & Kenneth L. Judd & Kerk L. Phillips, 2014. "A Big Data Approach to Optimal Sales Taxation," BYU Macroeconomics and Computational Laboratory Working Paper Series 2014-03, Brigham Young University, Department of Economics, BYU Macroeconomics and Computational Laboratory.
    2. Whitaker, Stephan D., 2018. "Big Data versus a survey," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 285-296.
    3. Nathan, Max & Rosso, Anna, 2015. "Mapping digital businesses with big data: Some early findings from the UK," Research Policy, Elsevier, vol. 44(9), pages 1714-1733.
    4. Figlio, D. & Karbownik, K. & Salvanes, K.G., 2016. "Education Research and Administrative Data," Handbook of the Economics of Education, Elsevier.
    5. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    6. Lane, Julia I. & Owen-Smith, Jason & Rosen, Rebecca F. & Weinberg, Bruce A., 2015. "New linked data on research investments: Scientific workforce, productivity, and public value," Research Policy, Elsevier, vol. 44(9), pages 1659-1671.
    7. repec:eee:tefoso:v:130:y:2018:i:c:p:99-113 is not listed on IDEAS
    8. Wagner Piazza Gaglianone & João Victor Issler, 2014. "Microfounded Forecasting," Working Papers Series 372, Central Bank of Brazil, Research Department.
    9. Prüfer, Jens & Schottmuller, C., 2017. "Competing with Big Data," Discussion Paper 2017-006, Tilburg University, Tilburg Law and Economic Center.
    10. Marieke Bos & Emily Breza & Andres Liberman, 2016. "The Labor Market Effects of Credit Market Information," NBER Working Papers 22436, National Bureau of Economic Research, Inc.
    11. Avi Goldfarb & Shane M. Greenstein & Catherine E. Tucker, 2015. "Introduction to "Economic Analysis of the Digital Economy"," NBER Chapters,in: Economic Analysis of the Digital Economy, pages 1-17 National Bureau of Economic Research, Inc.
    12. Jin-Hyuk Kim & Tin Cheuk Leung, 2013. "Quantifying the Impacts of Digital Rights Management and E-Book Pricing on the E-Book Reader Market," Working Papers 13-03, NET Institute.
    13. repec:eee:iepoli:v:42:y:2018:i:c:p:56-65 is not listed on IDEAS
    14. David Figlio & Krzysztof Karbownik & Kjell Salvanes, 2017. "The Promise of Administrative Data in Education Research," Education Finance and Policy, MIT Press, vol. 12(2), pages 129-136, Spring.
    15. Tuhkuri, Joonas, 2016. "ETLAnow: A Model for Forecasting with Big Data – Forecasting Unemployment with Google Searches in Europe," ETLA Reports 54, The Research Institute of the Finnish Economy.
    16. repec:ksa:szemle:1733 is not listed on IDEAS

    More about this item

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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