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Iatrogenic Specification Error: A Cautionary Tale of Cleaning Data

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  • Christopher R. Bollinger
  • Amitabh Chandra

Abstract

It is common in empirical research to use what appear to be 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. This paper considers identification in a linear model when the dependent variable is mismeasured. The results examine the common practice of trimming and winsorizing to address the identification failure. In contrast to the physical and laboratory sciences, measurement error in social science data is likely to be more complex than simply additive white noise. We consider a general measurement error process which nests many processes including the additive white noise process and a contaminated sampling process. Analytic results are only tractable under strong distributional assumptions, but demonstrate that winsorizing and trimming are only solutions for a particular class of measurement error processes. Indeed, trimming and winsorizing may induce or exacerbate bias. We term this source of bias Iatrogenic' (or econometrician induced) error. The identification results for the general error process highlight other approaches which are more robust to distributional assumptions. Monte Carlo simulations demonstrate the fragility of trimming and winsorizing as solutions to measurement error in the dependent variable.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0289.

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Date of creation: Mar 2003
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Publication status: published as Bollinger, Christopher R. and Amitabh Chandra. "Iatrogenic Specification Error: A Cautionary Tale Of Cleaning Data," Journal of Labor Economics, 2005, v23(2,Apr), 235-257.
Handle: RePEc:nbr:nberte:0289

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Cited by:
  1. Djavad Salehi-Isfahani & Insan Tunali & Ragui Assaad, 2009. "A comparative study of returns to education of urban men in Egypt, Iran, and Turkey," Working Papers e07-17, Virginia Polytechnic Institute and State University, Department of Economics.
  2. Mika Maliranta & Pekka Ilmakunnas, 2005. "Decomposing productivity and wage effects of intraestablishment labor restructuring," Labor and Demography 0511003, EconWPA.
  3. Narayana R. Kocherlakota & Luigi Pistaferri, 2007. "Asset Pricing Implications of Pareto Optimality with Private Information," Levine's Bibliography 321307000000000701, UCLA Department of Economics.
  4. Nayoung Lee & Hyungsik Roger Moon & Martin Weidner, 2011. "Analysis of interactive fixed effects dynamic linear panel regression with measurement error," CeMMAP working papers CWP37/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  5. Harley Frazis & Mark A Loewenstein, 2006. "Wage Compression and the Division of Returns to Productivity Growth: Evidence from EOPP," Working Papers 398, U.S. Bureau of Labor Statistics.
  6. 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.
  7. 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.
  8. Puja Vasudeva Dutta, 2006. "Returns to Education: New Evidence for India, 1983-1999," Education Economics, Taylor & Francis Journals, vol. 14(4), pages 431-451.
  9. 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).
  10. Barry Reilly & Puja Vasudeva Dutta, 2005. "The Gender Pay Gap and Trade Liberalisation: Evidence for India," PRUS Working Papers 32, Poverty Research Unit at Sussex, University of Sussex.

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