Institutional change in parliament through cross-border partisan emulation

Abstract

Institutional responses of parliaments to international developments are widely regarded as efficient changes because they tend to be unaffected by partisan preferences and benefit all members of parliament equally. This article challenges that common notion by providing evidence that the institutional responses of national parliaments to European integration are in large part the result of international partisan emulation. Spatial regression analyses robustly show that parliamentary EU oversight institutions diffuse across member states whose majority parties have similar constitutional preferences. A parliament is more likely to emulate the EU oversight institution of another parliament if their majority parties have similar ideas about the territorial distribution of power and institutional framework for policy making. This result has important implications for our understanding of institutional change in parliament. Responses of parliaments to external developments may appear non-partisan at first sight but unfold partisan characteristics if one looks beyond the domestic level.

Keywords:

• European Union
• institutional change
• national parliaments
• parliamentary oversight

Notes

Acknowledgements

Previous versions of this paper were presented at the 74th Annual MPSA Conference in Chicago, the 6th EPSA Conference in Brussels, and University College Dublin (UCD). I would like to thank Thomas Winzen for sharing data with me. I also want to thank Daniel Bischof, Jens Blom-Hansen, James Cross, Christoffer Green-Pedersen, Pieter de Wilde and Thomas Winzen for helpful comments.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes on contributor

Roman Senninger is an Assistant Professor in the Department of Political Science at Aarhus University. His research focuses primarily on European Union politics and party competition. He has published in journals such as European Journal of Political Research and European Union Politics. [[email protected]]

Notes

1 Another important advantage of the described measure is that it provides information about EU oversight institutions over time.

2 These data cover EU member states from 1958, with additional data becoming available with the accession of new member states. Data for Romania are not available. EAC strength is a standardised measurement bounded at 0 and 2. A more detailed description of the variable is given in the data section.

3 Among the EU-15 countries, only Finland, Sweden, and the UK never reformed their EAC in the period from 1990 to 2010. All other EU-15 EACs became stronger. Ireland and Portugal are the only two countries that experienced negative reforms, resulting in changes to lower levels of EAC strength than in the year before. However, the outcomes of these negative reforms were transient, and the EACs of the two countries quickly became stronger again. For details, please see Figure A1 in the online appendix.

4 COSAC is a French acronym and stands for Conference of Community and European Affairs Committees of Parliaments of the European Union.

5 Likewise, decisions by national parliaments about EU policies as part of the ‘early warning system’ are not made independent of each other (Malang et al. 2017).

6 Cultural similarity is operationalised on the basis of central tendencies in the religious orientation of the majority population (majority Catholic, majority Protestant, majority Orthodox).

7 Scholars also rely on parties’ EU support to explain the relationship between political parties and EU oversight institutions. While there is certainly an overlap between parties’ constitutional preferences and EU support, the latter is solely directed at processes at the EU level (EU institutions and policies). As such, parties’ preferences for domestic institutional responses to authority transfers to the EU level remain unreported. Constitutional preferences, on the contrary, capture broader questions about parties’ desired institutional framework for policy making as well as their preferred levels of centralisation, which can be applied to cases of domestic institutional responses to European integration.

8 As mentioned before, Romania is missing because no data on the dependent variables are available. In addition, Cyprus, Bulgaria, and Malta are missing because data on independent variables (constitutional preferences and strength of other parliamentary committees) are not available. My data cover EU member states from 1992, with additional data becoming available with the accession of new member states.

9 One can differentiate between several common approaches: (1) spatial error models that include dependence in a second error term in addition to the independent error component; (2) spatial $X$-models that identify dependence in the weighted values of one or more independent variables; and (3) spatial lag models that regress the dependent variable on the weighted values of the dependent variable of all other units (Franzese and Hayes 2008; Plümper and Neumeyer 2010).

10 However, it could also be argued that government parties need time to react to institutional choices of other member states. Even though parliaments have much more contact and exchange more information than ever before, it is difficult to say whether government parties always know what is happening in other parliaments and anticipate their reform steps. In addition, the implementation and reform of EU oversight institutions might be a tedious process. Therefore, I also consider models that allow for a one-year time lag of the spatially lagged variable. From a methodological point of view, this erases the simultaneity problem of S-OLS models because the spatial lag is time lagged and therefore does not carry the same information as the dependent variable on the left-hand side of the equation (Beck et al. 2006; Franzese and Hayes 2008).

11 As described in the next subsection, the spatial weights matrix carrying absolute differences in parties’ constitutional preferences connect all units $i$ with the same number of units $k.$ In this case, row-standardisation is convenient because it gives the same metric or units to the dependent variable and the spatial lag (Neumayer and Plümper 2016). However, more importantly, the decision to row-standardise the weights matrix is theoretically justified. First, all EU member states have the same number of connections to other member states and observe the same pool of parliaments during international cooperation. Second, there is no argument suggesting that countries that have overall smaller absolute distances in constitutional preferences should receive a weaker spatial stimulus than countries with overall larger absolute differences in constitutional preferences. For a comprehensive discussion of row-standardisation of weights matrices, see and Neumayer and Plümper (2012 2016).

12 Member states belong to one of the regions: North, South, Central, West. The time period from 1992 to 2010 is divided into the following sub-periods: < =1994, 1995–2003, 2004–2006, > =2007. Opting for regional and period fixed effects implies that, similar to Bormann and Winzen (2016), I do not include unit-fixed effects, as I believe that not only changes but also different levels in EU oversight institutions of other member states matter. However, in the online appendix, regression results from models that replace regional fixed effects with country fixed effects are presented (Table A2). The findings and interpretation regarding the spatial lagged variables are robust to this alternative model specification.

13 Time-lagged spatial effect models can be expressed in the following way:

$$y_{\mathit{it}}=\rho \sum _k[\frac{W_{\mathit{ikt}}}{\sum _k{W}_{\mathit{ikt}}}{y}_{k{t}_{-1}}]+\varphi y_{i{t}_{-1}}+\beta X_{\mathit{it}}+{ϵ}_{\mathit{it}}$$