IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/19035.html
   My bibliography  Save this paper

The Data Revolution and Economic Analysis

Author

Listed:
  • Liran Einav
  • Jonathan D. Levin

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 & Jonathan D. Levin, 2013. "The Data Revolution and Economic Analysis," NBER Working Papers 19035, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:19035
    Note: AG ED EEE EFG EH IO LS PE PR
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w19035.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    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. 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.
    3. 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, President and Fellows of Harvard College, vol. 123(3), pages 863-904.
    4. 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.
    5. Liran Einav & Theresa Kuchler & Jonathan D. Levin & Neel Sundaresan, 2011. "Learning from Seller Experiments in Online Markets," NBER Working Papers 17385, National Bureau of Economic Research, Inc.
    6. 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.
    7. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls," Papers 1201.0224, arXiv.org, revised May 2012.
    8. Avi Goldfarb & Catherine Tucker, 2012. "Privacy and Innovation," Innovation Policy and the Economy, University of Chicago Press, vol. 12(1), pages 65-90.
    9. 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.
    10. Thomas Piketty & Emmanuel Saez, 2003. "Income Inequality in the United States, 1913–1998," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(1), pages 1-41.
    11. 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.
    12. 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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuriy Gorodnichenko & Viacheslav Sheremirov & Oleksandr Talavera, 2018. "Price Setting in Online Markets: Does IT Click?," Journal of the European Economic Association, European Economic Association, vol. 16(6), pages 1764-1811.
    2. Chen, Daniel L. & Levonyan, Vardges & Yeh, Susan, 2016. "Policies Affect Preferences: Evidence from Random Variation in Abortion Jurisprudence," IAST Working Papers 16-58, Institute for Advanced Study in Toulouse (IAST).
    3. Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
    4. Chen, Daniel L. & Yeh, Susan, 2022. "How do rights revolutions occur? Free speech and the first amendment," TSE Working Papers 22-1396, Toulouse School of Economics (TSE).
    5. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    6. Fernando Borraz & Alberto Cavallo & Roberto Rigobon & Leandro Zipitria, 2016. "Distance and Political Boundaries: Estimating Border Effects under Inequality Constraints," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 21(1), pages 3-35, January.
    7. Strittmatter, Anthony & Wunsch, Conny, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," Working papers 2021/05, Faculty of Business and Economics - University of Basel.
    8. Rishabh Tyagi & Peter Eibich & Vegard Skirbekk, 2024. "Gender norms and partnership dissolution following involuntary job loss in Germany," MPIDR Working Papers WP-2024-027, Max Planck Institute for Demographic Research, Rostock, Germany.
    9. Chen, Daniel L. & Yeh, Susan, 2016. "How Do Rights Revolutions Occur? Free Speech and the First Amendment," TSE Working Papers 16-705, Toulouse School of Economics (TSE).
    10. Hulya Bakirtas & Vildan Gulpinar Demirci, 2022. "Can Google Trends data provide information on consumer’s perception regarding hotel brands?," Information Technology & Tourism, Springer, vol. 24(1), pages 57-83, March.
    11. Zhou, Siwen, 2018. "Exploring the Driving Forces of the Bitcoin Exchange Rate Dynamics: An EGARCH Approach," MPRA Paper 89445, University Library of Munich, Germany.
    12. Chen, Daniel L., 2018. "Judicial Analytics and the Great Transformation of American Law," TSE Working Papers 18-974, Toulouse School of Economics (TSE).
    13. Hal Varian, 2021. "Economics at Google," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 56(4), pages 195-199, October.
    14. Chen, Daniel L. & Sethi, Jasmin, 2016. "Insiders, Outsiders, and Involuntary Unemployment: Sexual Harrassment Exacerbates Gender Inequality," IAST Working Papers 16-44, Institute for Advanced Study in Toulouse (IAST).
    15. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
    16. Alvarez, Fernando & Lippi, Francesco & ,, 2013. "Small and large price changes and the propagation of monetary shocks," CEPR Discussion Papers 9770, C.E.P.R. Discussion Papers.
    17. Brent, Neiman, 2011. "A state-dependent model of intermediate goods pricing," Journal of International Economics, Elsevier, vol. 85(1), pages 1-13, September.
    18. Borraz, Fernando & Zipitría, Leandro, 2010. "Price Setting in Retailing: the Case of Uruguay," MPRA Paper 27712, University Library of Munich, Germany.
    19. Siwen Zhou, 2021. "Exploring the driving forces of the Bitcoin currency exchange rate dynamics: an EGARCH approach," Empirical Economics, Springer, vol. 60(2), pages 557-606, February.
    20. Linton, O. & Seo, M. & Whang, Y-J., 2020. "Testing Stochastic Dominance with Many Conditioning Variables," Cambridge Working Papers in Economics 2004, Faculty of Economics, University of Cambridge.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:19035. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.