Economists have devoted increasing attention to the magnitude and consequences of measurement error in their data. Most discussions of measurement error are based on the "classical[equal, rising dots] assumption that errors in measuring a particular variable are uncorrelated with the true value of that variable, the true values of other variables in the model, and any errors in measuring those variables. In this survey, we focus on both the importance of measurement error in standard survey-based economic variables and on the validity of the classical assumption.We begin by summarizing the literature on biases due to measurement error, contrasting the classical assumption and the more general case. We then argue that, while standard methods will not eliminate the bias when measurement errors are not classical, one can often use them to obtain bounds on this bias. Validation studies allow us to assess the magnitude of measurement errors in survey data, and the validity of the classical assumption. In principle, they provide an alternative strategy for reducing or eliminating the bias due to measurement error.We then turn to the work of social psychologists and survey methodologists which identifies the conditions under which measurement error is likely to be important. While there are some important general findings on errors in measuring recall of discrete events, there is less direct guidance on continuous variables such as hourly wages or annual earnings.Finally, we attempt to summarize the validation literature on specific variables: annual earnings, hourly wages, transfer income, assets, hours worked, unemployment, job characteristics like industry, occupation, and union status, health status, health expenditures, and education. In addition to the magnitude of the errors, we also focus on the validity of the classical assumption. Quite often, we find evidence that errors are negatively correlated with true values.The usefulness of validation data in telling us about errors in survey measures can be enhanced if validation data is collected for a random portion of major surveys (rather than, as is usually the case, for a separate convenience sample for which validation data could be obtained relatively easily); if users are more actively involved in the design of validation studies; and if micro data from validation studies can be shared with researchers not involved in the original data collection.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. 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.
Publisher Info
Download reference. The following formats are available: HTML
(with abstract),
plain text
(with abstract),
BibTeX,
RIS (EndNote, RefMan, ProCite),
ReDIF This chapter was published in: J.J. Heckman & E.E. Leamer (ed.) Handbook of Econometrics, , chapter 59, pages 3705-3843, 2001.
For technical questions regarding this item, or to correct its listing, contact: (Heidi Boesdal).
Related research
This chapter was published in the following book, which is listed on IDEAS: J.J. Heckman & E.E. Leamer (ed.), 2001.
"Handbook of Econometrics,"
Handbook of Econometrics,
Elsevier,
edition 1, volume 5, number 5, September.
[Downloadable!] (restricted) Keywords:
Other versions of this item:
Find related papers by JEL classification: C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.) This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page.