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Parties Getting Impatient: Time Out of Office and Portfolio Allocation in Coalition Governments

Published online by Cambridge University Press:  08 November 2011

Albert Falcó-Gimeno
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Abstract

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.

Type
Research Article
Information
British Journal of Political Science , Volume 42 , Issue 2 , 29 February 2012 , pp. 393 - 411
Copyright
Copyright © Cambridge University Press 2011

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References

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8 Hereafter, I will use female gender for the proposer and male for the receivers.

9 In another work related to this one I offer formal proof that this is the case. See Falcó-Gimeno, Albert, ‘Portfolio Allocation and Time Out of Office in Coalition Governments’, Juan March Institute, CEACS Estudio/Working Paper 2011/254 (2011), 1–36Google Scholar.

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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.

17 This concept first appears in Warwick, Paul V. and Druckman, James N.. ‘The Portfolio Allocation Paradox: An Investigation into the Nature of a Very Strong but Puzzling Relationship’, European Journal of Political Research, 45 (2006), p. 657CrossRefGoogle Scholar. Other authors had previously shown the usefulness of taking Gamson's mispredictions as a dependent variable (see Schofield, Norman and Laver, Michael, ‘Bargaining Theory and Portfolio Payoffs in European Coalition Governments 1945–83’, British Journal of Political Science, 15 (1985), 143–164CrossRefGoogle Scholar).

18 The weighted measure chosen is what Warwick and Druckman call ‘Weighted Portfolio Share II’ in Warwick and Druckman, ‘The Portfolio Allocation Paradox’. See also Druckman and Warwick, ‘The Missing Piece’ for a thorough discussion of the measurement of portfolio salience.

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).

22 Warwick and Druckman, ‘The Portfolio Allocation Paradox’, p. 657.

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.

26 Arguments in this vein are outlined, for instance, in Müller and Strom, Policy, Office, or Votes. For an application to the Spanish case, see Reniu, Josep M. and Bergman, Torbjörn, ‘Estrategias, Objetivos y Toma de Decisiones de los Partidos Políticos Españoles en la Formación de Gobiernos Estatales’, Política y Sociedad, 40 (2003), 63–76Google Scholar.

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28 See Becker, Sascha O. and Ichino, Andrea, ‘Estimation of Average Treatment Effects Based on Propensity Scores’, Stata Journal, 2 (2002), 358–377Google Scholar.

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.

30 Laver, Michael and Ben Hunt, William. Policy and Party Competition (New York: Routledge, 1992)Google Scholar.

31 See Falcó-Gimeno, ‘Portfolio Allocation and Time Out of Office in Coalition Governments’.

32 Caliendo, Marco and Kopeinig, Sabine, ‘Some Practical Guidance for the Implementation of Propensity Score Matching’, Journal of Economic Surveys, 22 (2008), 31–72CrossRefGoogle Scholar, pp. 38–9.

33 Rubin, Donald B. and Thomas, Neal, ‘Matching Using Estimated Propensity Scores: Relating Theory to Practice’, Biometrics, 52 (1996), 249–264CrossRefGoogle Scholar.

34 The Stata module used to perform such analyses has been psmatch2 (see Leuven, Edwin and Sianesi, Barbara, ‘Psmatch2: Stata Module to Perform Full Mahalanobis and Propensity Score Matching, Common Support Graphing, and Covariate Imbalance Testing’, version 3.1.5 2may2009, computer software (Boston College Department of Economics, 2003)Google Scholar, Statistical Software Components). As usual when estimating average treatment effects for the treated with matching techniques, I have computed bootstrap standard errors with fifty iterations (as default in psmatch2).