Regression Coefficient Identification Decay in The Presence of Infrequent Classification Errors
Recent evidence from Bound, Brown, and Mathiowetz (2001) and Black, Sanders, and Taylor (2003) suggests that reporting errors in survey data routinely violate all of the classical measurement error assumptions. The econometrics literature has not considered the consequences of fully arbitrary measurement error for identification of regression coefficients. This paper highlights the severity of the identification problem given the presence of even infrequent arbitrary errors in a binary regressor. In the empirical component, health insurance misclassification rates of less than 1.3% generate double-digit percentage point ranges of uncertainty about the variable's true marginal effect on the use of health services. (c) 2010 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 92 (2010)
Issue (Month): 4 (November)
|Contact details of provider:|| Web page: http://mitpress.mit.edu/journals/|
|Order Information:||Web: http://mitpress.mit.edu/journal-home.tcl?issn=00346535|
When requesting a correction, please mention this item's handle: RePEc:tpr:restat:v:92:y:2010:i:4:p:1017-1023. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Kristin Waites)
If references are entirely missing, you can add them using this form.