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Big Data versus a survey

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  • Whitaker, Stephan D.

Abstract

Economists are shifting resources from work on survey data to work involving “Big Data.” This analysis is an empirical exploration of the trade-offs this substitution requires. Parallel models are estimated using Equifax credit bureau data and Survey of Consumer Finances data. After adjustments to account for different variable definitions and sampled populations, it is possible to arrive at similar models of total household debt. However, the estimates are sensitive to the adjustments. In this example, some external education and income measures are successfully integrated with the big data, but other external aggregates fail to adequately substitute for survey responses.

Suggested Citation

  • Whitaker, Stephan D., 2018. "Big Data versus a survey," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 285-296.
    • Handle: RePEc:eee:quaeco:v:67:y:2018:i:c:p:285-296
    DOI: 10.1016/j.qref.2017.07.011
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    as
    1. Céline CARRERE & Jaime MELO DE, 2009. "Non-Tariff Measures: What do we Know, What Should be Done?," Working Papers 200933, CERDI.
    2. Liran Einav & Jonathan Levin, 2014. "The Data Revolution and Economic Analysis," Innovation Policy and the Economy, University of Chicago Press, vol. 14(1), pages 1-24.
    3. Sonka, Steve, 2014. "Big Data and the Ag Sector: More than Lots of Numbers," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 17(1), pages 1-20, February.
    4. Wickens, Michael R, 1972. "A Note on the Use of Proxy Variables," Econometrica, Econometric Society, vol. 40(4), pages 759-761, July.
    5. John L. Czajka, "undated". "Can Administrative Records Be Used to Reduce Nonresponse Bias?," Mathematica Policy Research Reports 5a88b9fed835433f943c08646, Mathematica Policy Research.
    6. Peter Lynn & Annette Jäckle & Stephen P. Jenkins & Emanuela Sala, 2012. "The impact of questioning method on measurement error in panel survey measures of benefit receipt: evidence from a validation study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(1), pages 289-308, January.
    7. Kevin Pugh & Gigi Foster, 2014. "Australia's National School Data and the ‘Big Data’ Revolution in Education Economics," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 47(2), pages 258-268, June.
    8. Chungui Qiao, 2005. "Combining Administrative and Survey Data to Derive Small‐area Estimates Using Loglinear Modelling," LABOUR, CEIS, vol. 19(4), pages 767-800, December.
    9. Merxe Tudela & Garry Young, 2005. "The determinants of household debt and balance sheets in the United Kingdom," Bank of England working papers 266, Bank of England.
    10. Milton Friedman, 1957. "A Theory of the Consumption Function," NBER Books, National Bureau of Economic Research, Inc, number frie57-1, March.
    11. Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25(3), pages 513-551.
    12. Meta Brown & Andrew F. Haughwout & Donghoon Lee & Wilbert Van der Klaauw, 2011. "Do we know what we owe? A comparison of borrower- and lender-reported consumer debt," Staff Reports 523, Federal Reserve Bank of New York.
    13. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    14. David W. Nickerson & Todd Rogers, 2014. "Political Campaigns and Big Data," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 51-74, Spring.
    15. Diane K Schooley & Debra Drecnik Worden, 2010. "Fueling the Credit Crisis: Who Uses Consumer Credit and What Drives Debt Burden?," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 45(4), pages 266-276, October.
    16. Rebecca N. Warburton & William P. Warburton, 2004. "Canada Needs Better Data for Evidence-Based Policy: Inconsistencies Between Administrative and Survey Data on Welfare Dependence and Education," Canadian Public Policy, University of Toronto Press, vol. 30(3), pages 241-256, September.
    17. Ansolabehere, Stephen & Hersh, Eitan, 2012. "Validation: What Big Data Reveal About Survey Misreporting and the Real Electorate," Political Analysis, Cambridge University Press, vol. 20(4), pages 437-459.
    18. John M. Abowd & Martha H. Stinson, 2013. "Estimating Measurement Error in Annual Job Earnings: A Comparison of Survey and Administrative Data," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1451-1467, December.
    19. Milton Friedman, 1957. "Introduction to "A Theory of the Consumption Function"," NBER Chapters, in: A Theory of the Consumption Function, pages 1-6, National Bureau of Economic Research, Inc.
    20. Philippe Liégeois & Frédéric Berger & Nizamul Islam & Raymond Wagener, 2011. "Cross-validating administrative and survey datasets through microsimulation," International Journal of Microsimulation, International Microsimulation Association, vol. 4(1), pages 54-71.
    21. Sarah Brown & Gaia Garino & Karl Taylor, 2013. "Household Debt And Attitudes Toward Risk," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 59(2), pages 283-304, June.
    22. Thomas D. Cook, 2014. "“Big Data” In Research On Social Policy," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 33(2), pages 544-547, March.
    23. McCallum, B T, 1972. "Relative Asymptotic Bias from Errors of Omission and Measurement," Econometrica, Econometric Society, vol. 40(4), pages 757-758, July.
    24. repec:mpr:mprres:7586 is not listed on IDEAS
    25. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    26. Donghoon Lee & Wilbert Van der Klaauw, 2010. "An introduction to the FRBNY Consumer Credit Panel," Staff Reports 479, Federal Reserve Bank of New York.
    27. Molly Dahl & Thomas DeLeire & Jonathan A. Schwabish, 2011. "Estimates of Year-to-Year Volatility in Earnings and in Household Incomes from Administrative, Survey, and Matched Data," Journal of Human Resources, University of Wisconsin Press, vol. 46(4), pages 750-774.
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    Cited by:

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    2. Jack DeWaard & Janna Johnson & Stephan Whitaker, 2019. "Internal migration in the United States: A comprehensive comparative assessment of the Consumer Credit Panel," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(33), pages 953-1006.
    3. Madeleine I. G. Daepp, 2022. "Small-area moving ratios and the spatial connectivity of neighborhoods: Insights from consumer credit data," Environment and Planning B, , vol. 49(3), pages 1129-1146, March.

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    More about this item

    Keywords

    Big Data; Survey data; Household debt;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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