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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.

Suggested Citation

  • 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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    13. Clemens, Jeffrey & Strain, Michael R., 2021. "The Heterogeneous Effects of Large and Small Minimum Wage Changes: Evidence over the Short and Medium Run Using a Pre-analysis Plan," IZA Discussion Papers 14747, Institute of Labor Economics (IZA).
    14. Peter Hull & Michal Kolesár & Christopher Walters, 2022. "Labor by design: contributions of David Card, Joshua Angrist, and Guido Imbens," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(3), pages 603-645, July.
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    16. Laura Argys & Thomas Mroz & M. Melinda Pitts, 2023. "Modeling Event Studies with Heterogeneous Treatment Effects," FRB Atlanta Working Paper 2023-11, Federal Reserve Bank of Atlanta.
    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.
    18. Ballestar, María Teresa & García-Lazaro, Aida & Sainz, Jorge & Sanz, Ismael, 2022. "Why is your company not robotic? The technology and human capital needed by firms to become robotic," Journal of Business Research, Elsevier, vol. 142(C), pages 328-343.
    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.
    20. Dmitry Arkhangelsky & Aleksei Samkov, 2024. "Sequential Synthetic Difference in Differences," Papers 2404.00164, arXiv.org.
    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).
    22. Jeffrey Clemens & Drew McNichols & Joseph J. Sabia, 2020. "The Long-Run Effects of the Affordable Care Act: A Pre-Committed Research Design Over the COVID-19 Recession and Recovery," NBER Working Papers 27999, National Bureau of Economic Research, Inc.
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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.
    25. Jiafeng Chen & Daniel L. Chen & Greg Lewis, 2020. "Mostly Harmless Machine Learning: Learning Optimal Instruments in Linear IV Models," Papers 2011.06158, arXiv.org, revised Jun 2021.

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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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