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Technology and Big Data Are Changing Economics: Mining Text to Track Methods

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  • Janet Currie
  • Henrik Kleven
  • Esmée Zwiers

Abstract

The last 40 years have seen huge innovations in computing technology and data availability. Data derived from millions of administrative records or by using (as we do) new methods of data generation such as text mining are now common. New data often requires new methods, which in turn can inspire new data collection. If history is any guide, some methods will stick and others will prove to be a flash in the pan. However, the larger trends towards demanding greater credibility and transparency from researchers in applied economics and a “collage” approach to assembling evidence will likely continue.

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  • Janet Currie & Henrik Kleven & Esmée Zwiers, 2020. "Technology and Big Data Are Changing Economics: Mining Text to Track Methods," NBER Working Papers 26715, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:26715
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    1. Angus Deaton, 2010. "Instruments, Randomization, and Learning about Development," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 424-455, June.
    2. Raj Chetty & John N. Friedman & Nathaniel Hilger & Emmanuel Saez & Diane Whitmore Schanzenbach & Danny Yagan, 2011. "How Does Your Kindergarten Classroom Affect Your Earnings? Evidence from Project Star," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(4), pages 1593-1660.
    3. Ashenfelter, Orley C, 1978. "Estimating the Effect of Training Programs on Earnings," The Review of Economics and Statistics, MIT Press, vol. 60(1), pages 47-57, February.
    4. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.
    5. Moffitt, Robert A., 1999. "New developments in econometric methods for labor market analysis," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 24, pages 1367-1397, Elsevier.
    6. Emmanuel Saez, 2010. "Do Taxpayers Bunch at Kink Points?," American Economic Journal: Economic Policy, American Economic Association, vol. 2(3), pages 180-212, August.
    7. Card, David & Sullivan, Daniel G, 1988. "Measuring the Effect of Subsidized Training Programs on Movements in and out of Employment," Econometrica, Econometric Society, vol. 56(3), pages 497-530, May.
    8. Guido W. Imbens, 2010. "Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 399-423, June.
    9. David Card & Stefano DellaVigna, 2013. "Nine Facts about Top Journals in Economics," Journal of Economic Literature, American Economic Association, vol. 51(1), pages 144-161, March.
    10. Ashenfelter, Orley & Card, David, 1985. "Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs," The Review of Economics and Statistics, MIT Press, vol. 67(4), pages 648-660, November.
    11. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-209, January.
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    17. Benjamin F. Jones, 2021. "The Rise of Research Teams: Benefits and Costs in Economics," Journal of Economic Perspectives, American Economic Association, vol. 35(2), pages 191-216, Spring.
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    19. Yukun Ma & Pedro H. C. Sant'Anna & Yuya Sasaki & Takuya Ura, 2023. "Doubly Robust Estimators with Weak Overlap," Papers 2304.08974, arXiv.org, revised Apr 2023.
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    21. Albers, Thilo N.H. & Kappner, Kalle, 2023. "Perks and pitfalls of city directories as a micro-geographic data source," Explorations in Economic History, Elsevier, vol. 87(C).
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    24. Albers, Thilo N. H. & Kappner, Kalle, 2022. "Perks and Pitfalls of City Directories as a Micro-Geographic Data Source," Rationality and Competition Discussion Paper Series 315, CRC TRR 190 Rationality and Competition.
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    JEL classification:

    • A0 - General Economics and Teaching - - General
    • B0 - Schools of Economic Thought and Methodology - - General
    • C0 - Mathematical and Quantitative Methods - - General
    • H0 - Public Economics - - General
    • I0 - Health, Education, and Welfare - - General
    • J0 - Labor and Demographic Economics - - General
    • L0 - Industrial Organization - - General

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