Why voluntary municipal merger projects fail: evidence from popular votes in Switzerland

ABSTRACT

What determines the failure of local government amalgamation referenda? Existing research suggests that functional pressures act as a push factor towards local territorial reform, whereas considerations of political self-determination exert a pull effect. However, we know little about the respective importance of these countervailing forces. In this paper, I analyze popular vote decisions on mergers of 541 municipalities involved in 166 different merger projects in three Swiss cantons since the new millennium. The results show that both functional pressures and concerns for self-determination are linked to popular vote outcomes: small municipalities are less likely to reject a merger. Concerns for self-determination matter, but only when the pressures of smallness are not overwhelming: a higher vote share of right-wing parties and a preponderance of other municipalities in the merger coalition increase the probability that voters reject a merger project. This has implications for policy-makers’ strategies when drafting and promoting voluntary local amalgamation reforms.

KEYWORDS:

• Amalgamation
• local government reform
• referendum
• local autonomy
• polity size
• local democracy

Acknowledgments

Previous versions of this paper have been presented at a colloquium at the University of Zurich and at the 2018 Annual Conference of the Swiss Political Science Association in Geneva. I would like to thank Chris Ewert, Lucas Leeman, the participants of these workshops, as well as the two anonymous reviewers for their helpful comments and suggestions. All remaining errors are my own.

Disclosure statement

No potential conflict of interest was reported by the author.

Supplementary material

Supplemental data for this article can be accessed here.

Notes

1. Cantons are the important intermediate government tiers between the local and the national level in Switzerland.

2. Periurban municipalities are located at the outskirts of urban areas and exhibit both urban and rural elements (Federal Statistical Office 2012).

3. For the distinction between merger initiation, normally by local representatives, and merger decision by the affected constituencies, see Strebel (2018).

4. The details of the three incentive systems are presented in Appendix A.

5. Exceptions are the studies by Calciolari, Cristofoli, and Macciò (2013) and Strebel (2018) for Switzerland and Miyazaki (2014) for Japan. All of them include failed merger projects in their analysis of merger determinants.

6. Moreover, scale economies can be achieved not only through municipal mergers, but also through inter-municipal cooperation schemes in different policy domains (Steiner 2003).

7. TAN stands for traditionalist/authoritarian/nationalist ideology. It constitutes one extreme of the so-called ‘new politics’ cleavage. The other extreme is the GAL (green/alternative/liberal) ideology (Hooghe, Marks, and Wilson 2002).

8. The literature on common-pool problems in the course of merger processes also deserves brief attention here: Municipalities involved in mergers often engage in last-minute spending before the merger is implemented. The rationale for doing so is that the accumulated debt will be taken over by the new municipality and hence free-riding on the common pool of the future municipality is possible (Hinnerich 2009). In the present analysis, I don’t take this common-pool problem into account, since the last-minute spending normally only starts after the decision to implement the merger has been taken (Saarimaa and Tukiainen 2015). In the case at hand, municipalities are one step prior to this stage. Starting to overspend their budget, assuming that the merger will pass at the ballots when it might not, would thus be a very risky strategy.

9. The formula for calculating the financial incentive for merger projects in the three cantons is presented in Appendix A.

10. Weese (2015) provides an extensive analysis of the impact of the national government’s financial incentives on voluntary municipal merger activity in Japan. He shows that the intensity of the financial incentive is positively linked to merger probability among Japanese municipalities.

11. Descriptive statistics for these indicators can be found in Table B.1 (Online Appendix).

12. A comparison of mean differences at the level of the individual municipality and at the level of the merger coalition can be found in Tables B.2 and B.3 in Online Appendix B.

13. Alternative explanations are not very relevant for understanding merger rejection, except for the tax rate difference and for having been involved in a failed merger project prior to the current one. The probability of rejecting a merger decreases by 26 percentage points when moving from the minimum to the maximum value of the tax rate difference. And for municipalities with a failed merger attempt the probability to reject a merger lies 10 percentage points lower compared to those without prior merger attempts. Merging with more than two municipalities and having a parliament are significantly associated with merger rejection, but only when population size is not included in the model (models (4) and (5)). The canton- and time-period fixed effects are all insignificant (see Table B.4 in Online Appendix B). To test the robustness of the findings under alternative specifications, I have estimated linear probability models, logistic regression models with standard errors clustered by merger coalition, and logistic regression models with robust standard errors. The substantive results remain the same across all these specifications (see Table C.1, C.2, and C.3 in Online Appendix C).

14. See model (5) in Table B.4 (Online Appendix).

15. I report a linear probability model here as a robustness check, since the significance and the size of the marginal effects of interaction terms in logistic regression models depend on the values of the covariates in the model (Ai and Norton 2003). The log-odds of the interaction effect (reported for the multilevel logistic regression models in Table 3) are not affected by this problem, however. Graphs illustrating the interaction effects from the multilevel logistic regression models and from the linear probability models can be found in Figure C.1 and C.2 in Online Appendix C.

Additional information

Notes on contributors

Michael Andrea Strebel

Michael Andrea Strebel is a post-doctoral researcher at the University of Zurich. His main research interests include citizens’ attitudes towards multi-level governance, citizens’ perceptions of democratic legitimacy, local government mergers, and metropolitan governance from a comparative perspective.