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Iatrogenic Specification Error: A Cautionary Tale of Cleaning Data

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  • Bollinger, Christopher R.

    ()
    (University of Kentucky)

  • Chandra, Amitabh

    ()
    (Harvard Kennedy School)

Abstract

In empirical research it is common practice to use sensible rules of thumb for cleaning data. Measurement error is often the justification for removing (trimming) or recoding (winsorizing) observations whose values lie outside a specified range. We consider a general measurement error process that nests many plausible models. Analytic results demonstrate that winsorizing and trimming are only solutions for a narrow class of measurement error processes. Indeed, for the measurement error processes found in most social-science data, such procedures can induce or exacerbate bias, and even inflate the variance estimates. We term this source of bias "Iatrogenic" (or econometrician induced) error. Monte Carlo simulations and empirical results from the Census PUMS data and 2001 CPS data demonstrate the fragility of trimming and winsorizing as solutions to measurement error in the dependent variable. Even on asymptotic variance and RMSE criteria, we are unable to find generalizable justifications for commonly used cleaning procedures.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 1093.

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Length: 24 pages
Date of creation: Mar 2004
Date of revision:
Publication status: published in: Journal of Labor Economics, 2005, 23 (2), 235-257
Handle: RePEc:iza:izadps:dp1093

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Keywords: winsorizing; measurement error models; trimming;

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Cited by:
  1. Lozano, Fernando A., 2012. "What Happened to God's Time? The Evolution of Secularism and Hours of Work in America, Evidence from Religious Holidays," IZA Discussion Papers 6552, Institute for the Study of Labor (IZA).
  2. Djavad Salehi-Isfahani & Insan Tunali & Ragui Assaad, 2009. "A Comparative Study Of Returns To Education Of Urban Men In Egypt, Iran, And Turkey," Middle East Development Journal (MEDJ), World Scientific Publishing Co. Pte. Ltd., World Scientific Publishing Co. Pte. Ltd., vol. 1(02), pages 145-187.
  3. Kocherlakota, Narayana & Pistaferri, Luigi, 2005. "Asset Pricing Implications of Pareto Optimality with Private Information," CEPR Discussion Papers, C.E.P.R. Discussion Papers 4930, C.E.P.R. Discussion Papers.
  4. Puja Vasudeva Dutta, 2006. "Returns to Education: New Evidence for India, 1983-1999," Education Economics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 14(4), pages 431-451.
  5. William H. J. Hubbard, 2011. "The Phantom Gender Difference in the College Wage Premium," Journal of Human Resources, University of Wisconsin Press, vol. 46(3), pages 568-586.
  6. Elizabeth Asiedu & James Freeman, 2008. "The Effect of Corruption on Investment Growth: Evidence from Firms in Latin America, Sub-Saharan Africa and Transition Countries," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 200802, University of Kansas, Department of Economics.
  7. Michiel Bijlsma & Ferry Haaijen & Casper van Ewijk, 2014. "Economic growth and funded pension systems," CPB Discussion Paper 279, CPB Netherlands Bureau for Economic Policy Analysis.
  8. Lee, Nayoung & Moon, Hyungsik Roger & Weidner, Martin, 2012. "Analysis of interactive fixed effects dynamic linear panel regression with measurement error," Economics Letters, Elsevier, Elsevier, vol. 117(1), pages 239-242.
  9. Mika Maliranta & Pekka Ilmakunnas, 2005. "Decomposing productivity and wage effects of intraestablishment labor restructuring," Labor and Demography, EconWPA 0511003, EconWPA.
  10. Barry Reilly & Puja Vasudeva Dutta, 2005. "The Gender Pay Gap and Trade Liberalisation: Evidence for India," PRUS Working Papers, Poverty Research Unit at Sussex, University of Sussex 32, Poverty Research Unit at Sussex, University of Sussex.
  11. Harley Frazis & Mark A Loewenstein, 2006. "Wage Compression and the Division of Returns to Productivity Growth: Evidence from EOPP," Working Papers, U.S. Bureau of Labor Statistics 398, U.S. Bureau of Labor Statistics.

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