Bayesian Model Averaging and Model Selection: Two Sides of the Same Coin in the Identification of Determinants of Trade Union Density?

Brandl, Bernd

Abstract

One of the main problems in empirical sciences is the uncertainty about the relevance of variables. In the debate on the variables that provide a systematic and robust explanation of the share of employees that are members of trade unions, i.e. of trade union density, the problem of variable uncertainty is striking. In regression analyses there is the problem of having to select variables. One problem in the union density discussion is that depending on the chosen combination of regressors different results in the identification of relevant variables are achieved. To systematically analyze which variables are relevant the literature suggests model averaging and selection strategies. While the two strategies have advantages and disadvantages, the aim of this paper is to apply both. Based on a characteristic cross-country panel data set we find differences and similarities based on our evaluation and ask whether a methodological triangulation is possible.

Citation

Brandl, B. (2009). Bayesian Model Averaging and Model Selection: Two Sides of the Same Coin in the Identification of Determinants of Trade Union Density?. Central European Journal of Operations Research, 17(1), 13-29. https://doi.org/10.1007/s10100-008-0072-0

• Journal Article Type: Article
• Online Publication Date: Nov 15, 2008
• Publication Date: 2009-03
• Deposit Date: Jul 15, 2015
• Journal: Central European Journal of Operations Research
• Print ISSN: 1435-246X
• Electronic ISSN: 1613-9178
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 1
• Pages: 13-29
• DOI: https://doi.org/10.1007/s10100-008-0072-0
• Public URL: https://durham-repository.worktribe.com/output/1434709