The Phantom Menace: Omitted Variable Bias in Econometric Research
Kevin A. Clarke
Additional contact information
Kevin A. Clarke: Department of Political Science University of Rochester Rochester, New York, USA, kevin.clarke@rochester.edu
Conflict Management and Peace Science, 2005, vol. 22, issue 4, 341-352
Abstract:
Quantitative political science is awash in control variables. The justification for these bloated specifications is usually the fear of omitted variable bias. A key underlying assumption is that the danger posed by omitted variable bias can be ameliorated by the inclusion of relevant control variables. Unfortunately, as this article demonstrates, there is nothing in the mathematics of regression analysis that supports this conclusion. The inclusion of additional control variables may increase or decrease the bias, and we cannot know for sure which is the case in any particular situation. A brief discussion of alternative strategies for achieving experimental control follows the main result.
Keywords: omitted variable bias; specification; control variables; research design (search for similar items in EconPapers)
Date: 2005
References: Add references at CitEc
Citations: View citations in EconPapers (24)
Downloads: (external link)
https://journals.sagepub.com/doi/10.1080/07388940500339183 (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:sae:compsc:v:22:y:2005:i:4:p:341-352
DOI: 10.1080/07388940500339183
Access Statistics for this article
More articles in Conflict Management and Peace Science from Peace Science Society (International)
Bibliographic data for series maintained by SAGE Publications (sagediscovery@sagepub.com).