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The Data Revolution and Economic Analysis

In: Innovation Policy and the Economy, Volume 14

Author

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  • Liran Einav
  • Jonathan 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.
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Suggested Citation

  • Liran Einav & Jonathan Levin, 2013. "The Data Revolution and Economic Analysis," NBER Chapters, in: Innovation Policy and the Economy, Volume 14, pages 1-24, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:12942
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    References listed on IDEAS

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    1. Avi Goldfarb & Catherine Tucker, 2012. "Privacy and Innovation," NBER Chapters, in: Innovation Policy and the Economy, Volume 12, pages 65-89, National Bureau of Economic Research, Inc.
    2. Alberto Cavallo, 2018. "Scraped Data and Sticky Prices," The Review of Economics and Statistics, MIT Press, vol. 100(1), pages 105-119, March.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    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.
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    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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