Published online by Cambridge University Press: 08 November 2011
This article argues that long periods out of office make parties impatient and more willing to make concessions over portfolio allocation in exchange for participation in a coalition cabinet. Two hypotheses are analysed: on the one hand, being in opposition for a long time should put parties at a disadvantage when bargaining over office payoffs. On the other, this effect should not apply to the formateur party, since formation offers are based on the receivers’ impatience. The empirical results largely support these expectations. Additional evidence of the causality of the main effect is obtained through the use of matching techniques based on the propensity score.
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8 Hereafter, I will use female gender for the proposer and male for the receivers.
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16 The countries are Austria, Belgium, Denmark, Finland, France (Fifth Rep.), Iceland, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, Sweden and (West) Germany.
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19 In fact the calculations were based on months rather than years, although the final variables here are presented in years but without losing measurement detail (e.g.: 15 months = 1.25 years).
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21 Recall that for those parties with non-democratic periods the starting year is a later one, and that for France the dataset only takes into account the Fifth Republic (that is, from 1959 onwards).
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23 See Indridason, ‘Live for Today, Hope for Tomorrow?’ for a thorough discussion of the issue.
24 Warwick and Druckman, ‘The Portfolio Allocation Paradox’, p. 647. For more information on the lumpiness concept, see also Warwick, Paul V. and Druckman, James N., ‘Portfolio Salience and the Proportionality of Payoffs in Coalition Governments’, British Journal of Political Science, 31 (2001), 627–649CrossRefGoogle Scholar.
25 For the sake of presentational simplicity, in these tables Time Out of Office is measured simply in absolute terms. Results with the relative measure are highly similar.
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29 There have been recent efforts to develop an extension of the propensity score methodology that allows estimating average causal effects with continuous treatments (see Hirano, Keisuke and Imbens, Guido W., ‘The Propensity Score with Continuous Treatments’, in Andrew Gelman and Xiao-Li Meng, Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, eds (Chichester, W. Sussex: Wiley, 2004)Google Scholar). However, it is a fairly new method that has rarely been applied. After considering this technique, in the end I decided that it was more reasonable to proceed in the standard way and ‘binarize’ the treatment.
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31 See Falcó-Gimeno, ‘Portfolio Allocation and Time Out of Office in Coalition Governments’.
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