# Do Municipal Mergers Undermine Democratic Participation ?

Pauline Mille

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# Do Municipal Mergers Undermine Democratic Participation?

Pauline MILLE

Université de Rennes - CREM

Janvier 2026
# Do Municipal Mergers Undermine Democratic Participation?\*†

Pauline Mille‡

January 5, 2026

## Abstract

This paper contributes to the existing literature on municipal mergers by highlighting heterogeneous political consequences associated with territorial reforms. The study evidences that municipal mergers reduce by 8 percentage points voter turnout, aligning with broader empirical trends. Employing a Difference-in-Differences approach and data from French municipalities from 2008, 2014 and 2020, the analysis reveals that mergers have a stronger negative effect on voter turnout in rural, less populated areas and in large-scale consolidations. The decline is also more pronounced in municipalities with limited electoral competition. The effect is mitigated by an increase in the number of polling stations and changes in the political color of the majority of the chief town municipality after the elections.

JEL-Classification: H70, C23, D72, D73.

Keywords: Electoral turnout, municipal mergers, municipal amalgamations, Difference-in-Differences, Propensity Score Matching.

<footnote>\*This research was funded in whole or in part by the French National Research Agency (ANR) as part of the CITIZENS project ANR-22-CE26-0019.</footnote>

<footnote>†I am very grateful to Benoît Le Maux, Sonia Paty, Vincent Merlin, Zineb Abidi, Augusto Cerqua, Marco Letta, Mara E. Vidali and Roberto Brunetti for their suggestions. I thank the participants of the LAGV 2024 Conference, the EPCS 2025 Conference, the 2025 TEPP Conference, the CREM seminar in Caen, the workshop EDGE (Angers) and the PhD seminars at CREM (Rennes) and GATE (Lyon) for their valuable discussions.</footnote>

<footnote>‡Univ Rennes, CNRS, CREM - UMR6211, F-35000 Rennes France, Condorcet Center. Corresponding author: pauline.mille@univ-rennes.fr.</footnote>
# 1. Introduction

Municipalities play a central role in local democracy, providing opportunities for civic engagement and participatory governance (McDonnell, 2020). Municipal mergers<fnref n="1" /> are often motivated by economic concerns, such as improving efficiency, achieving economies of scale, and reducing costs (Arcelus et al., 2015). They have become a prominent item on the political agenda of many countries, (Steiner et al., 2016; Koch and Rochat, 2017; Bhatti and Hansen, 2019), contributing to a significant reduction in the number of European municipalities in recent years (Allers and Geertsema, 2016). While the economic consequences of mergers have been widely studied (see Tavares, 2018, for a review), their effects on political participation and local governance are less studied (Baldersheim and Rose, 2010; Askim et al., 2016; Koch and Rochat, 2017).

A large literature examines the relationship between municipal size and voter turnout (Verba et al., 1978; Oliver, 2001; Tavares and Carr, 2013). Advocates of smaller municipalities argue that close contact between residents and elected officials, stronger social networks, and lower information costs enhance democratic accountability and political efficacy (Verba and Nie, 1987; Denters et al., 2014; Koch and Rochat, 2017; Allers et al., 2021). Mergers may thus reduce reduce turnout, weaken community attachment, and diminish individual political influence (Downs, 1957; Dagger, 1981; Denters et al., 2014; Roesel, 2017; Heinisch et al., 2018; Allers et al., 2021). Residents also often voice concerns about changes to services, such as schools or post offices, which may influence electoral behavior. These challenges are most pronounced when smaller municipalities are absorbed into larger ones, often leading to dissatisfaction with local services and more negative evaluations of elected officials (Kjaer et al., 2010; Yamada and Arai, 2020; Allers et al., 2021). Furthermore, as the representative-to-citizen ratio declines, the quality of representation may deteriorate, further discouraging political participation (Chorianopoulos, 2012; Horiuchi et al., 2015; Zeedan, 2017). Conversely, larger municipalities may benefit from administrative efficiency, higher budgets, more competitive elections, and potentially greater political engagement through proportional representation and social diversity (Blais and Carty, 1990; Karp and Banducci, 2008; Gallego et al., 2012; McDonnell, 2020; Cerqua et al., 2025). This could motivate citizens’ participation to election (Hay, 2007; Ryšavý and Bernard, 2013; Andersen et al., 2014) and further stimulate engagement by highlighting political and social cleavages (McDonnell, 2020; Allers et al., 2021).

Overall, the findings suggest that municipal mergers tend to reduce voter turnout. Using

<footnote n="1">¹The terms "municipal amalgamations" and "mergers" are used interchangeably in the literature.</footnote>
Difference-in-differences estimation, Lapointe et al. (2018) report a long-term decline in turnout in Finland's small municipalities post-merger, while Rodrigues and Tavares (2020) observe a similar decrease in Portugal's sub-municipal units, though with recovery by 2017. In Switzerland, Koch and Rochat (2017) use a fixed-effects estimation and find a significant decline in turnout in merging municipalities, particularly after the first election, while Allers et al. (2021) report a persistent decrease in turnout in the Netherlands for at least five years following mergers. Similarly, Heinisch et al. (2018) observe a negative effect in Austria's Styria region post-reform. Negative effects are also found in Israel (Zeedan, 2017), Japan (Horiuchi et al., 2015) and Germany (Fritz and Feld, 2015; Roesel, 2017). Yet, these findings are not unanimous. Steiner et al. (2016) find no discernible impact on local democracy in Switzerland based on perception data, while Mabuchi (2001) reports comparable results for certain Japanese prefectures. Conversely, Bhatti and Hansen (2019) identify modest positive effects on turnout, although these effects diminish and turn negative when accounting for the scale of the amalgamations. The tension between economic efficiency and democratic quality suggests that mergers can simultaneously improve service provision while disrupting local engagement and representation (Denters et al., 2014; Roesel, 2017; Heinisch et al., 2018; Fathimath, 2017).

This paper examines the effect of municipal mergers on local turnout rates. It exploits a unique context of a recent large wave of mergers alongside a substantial control group, allowing for the analysis of heterogeneous effects. The voluntary nature of these mergers provides an opportunity to compare municipalities that chose to merge with those that remained independent, helping to isolate the causal impact of mergers on electoral participation. By combining rigorous estimation techniques with detailed institutional knowledge, the study offers new insights into how municipal mergers influence local political engagement. The analysis identifies the types of municipalities most affected and underscores the broader implications of territorial reforms for democratic participation.

France's highly fragmented territorial landscape, with around 35,000 municipalities, offers a particularly rich context for studying local governance and electoral behavior, providing substantial variation in political, social and institutional conditions. In 2015, a new disposition combined with financial incentives led to the merger of approximately 2,500 municipalities, creating about 800 new ones. France's ongoing wave of voluntary municipal mergers provides a natural setting to assess the impact of structural reforms on political engagement, making it an ideal case for understanding the broader implications of territorial reorganization on democratic participation. The large number of municipalities facilitates the construction of a substantial and well-matched control group of non-merging municipalities. This diversity
enhances the external validity of the findings and allows for the examination of heterogeneous effects across different contexts. In the first instance, a Propensity Score Matching (PSM) allows for the construction of a balanced dataset between merging and non-merging municipalities' covariates. Then, a Difference-in-Differences (DiD) estimation evidences a negative effect of mergers on turnout of approximately 8 percentage points. This result is consistent with pre-existing literature.

The heterogeneity analysis reveals that the negative effect of mergers on turnout is more pronounced in rural and sparsely populated areas. This effect, which persists over time, is also stronger in cases of large-scale mergers. Political variables indicate that the electoral system does not significantly influence the direction of this effect. However, a reduction in political competition, as measured by the number of candidate lists, exacerbates the decline in turnout. Conversely, the negative impact of mergers on voter participation is mitigated in municipalities that experience an increase in the number of polling stations or a change in the political affiliation of the mayor following the elections.

The remainder of the paper is organized as follows: Section 2 introduces the French institutional background, highlighting the role of municipalities and the legislative framework. Section 3 provides a description of the data, the summary statistics and Section 4 describes the identification strategy. Section 5 reports the estimation results for both the baseline and heterogeneity analyses, while Section 6 summarizes the key findings and draws the study's conclusions.

## 2. Institutional setting

### 2.1. *French municipalities and municipal elections*

Municipal governments are the lowest tier of public administration, responsible for providing essential local services and representing the interests of residents. This range of duties includes civil registry, housing, education, road infrastructure, social services, security, and local finances among others. They can be shared with other local authorities.

The municipal council, elected every six years by the registered voters of the municipality, is composed of elected officials and a mayor. Its size depends on the municipality's population (Code général des collectivités territoriales, 2014). There is no term limit for French mayors; they may be elected to office multiple times. Voters must be at least 18 years old and French citizens, or EU citizens registered on the municipal voter list. Since 1997, voter registration is automatic for new voters, though individuals who move to a new municipality must manually
register in their new locality.

French municipal elections use a two-round system that varies by municipality size. In municipalities with less than 1,000 inhabitants, a plurality-at-large system applies, while in larger units, a list-based proportional system with a majority bonus is used, requiring gender parity and often leading to runoffs if no list wins an absolute majority in the first round.

<figure><img src="image_2.png" /><figcaption>**Figure 1:** Evolution of turnout in French municipal elections (1958-2020).</figcaption></figure>

Note: Author, based on france.politique.fr. The figure depicts the evolution of turnout rates for first and second round in France for municipal elections from 1958 to 2020. Values are expressed as percentages.

Despite the central role of municipal elections in local democracy, French municipal turnout rates have steadily declined over recent decades, as illustrated by Figure 1. In 1958, participation in both rounds exceeded 70%. After a brief rise, turnout began to decline from the 1980s onward, dropping by 10 percentage points by 2014. It continued in 2020, reaching an even lower level due to the COVID-19 crisis, as health concerns and lockdown measures further discouraged participation (see Appendix D.2 for more details).

## 2.2. Municipal mergers: dynamics and legislative background

France has a significantly higher number of municipalities than its European neighbors (see Figure A.3). They account for approximately 40% of all municipalities in Europe but only 15% of the population, reinforcing critiques that they are too small and overly dense in territorial coverage (Bideau, 2019). This unusually high number has deep historical roots,
dating back to the more than 40,000 municipalities established during the French Revolu-
tion (Petitet, 1998; Offner, 2006; Bideau, 2019) (see Appendix D for a detailed discussion).
This territorial fragmentation challenges local governance, limiting municipalities’ capacity
to deliver services effectively. In the early 2010s, half of all local units had fewer than
500 inhabitants and 80% fewer than 2,000, constraining administrative efficiency and service
delivery (Martins, 1995). Furthermore, it has become increasingly difficult to find candidates
for office in smaller municipalities, often resulting in smaller council sizes (as permitted by
law) (Code général des collectivités territoriales, 2014) and, consequently, lower levels of
representation in the municipalities concerned.

<figure><img src="image_3.png" /><figcaption>**Figure 2:** Evolution of the number of municipalities in France over time (1960-2023).</figcaption></figure>

Note: Author, data provided by the INSEE. The figure depicts the evolution of the number of French municipalities over time, starting from 1960 to 2023.

Municipal mergers in France have been encouraged for decades to address the challenges of excessive territorial fragmentation. Back in 1971, the *Loi Marcellin* ("Law Marcellin") sought to promote voluntary municipal mergers but achieved limited success due to local resistance. As shown in Figure 2, the number of municipalities initially declined before rising again as a result of subsequent de-mergers. More recent reforms have promoted consolidation through the creation of *communes nouvelles* ("new municipalities"), designed to streamline local governance while preserving local identities. Established in 2010, the framework initially saw limited adoption, with only 13 municipalities created by 2014. The 2015 revision (Code général des collectivités territoriales, 2015b) renewed momentum by granting greater influence to local councilors and allowing merged municipalities to retain delegated status (*communes déléguées*), with a delegated mayor, advisory council and local office. Between
2015 and 2020, 91% of municipalities opted for this arrangement, typically consisting of a chief town and one or more delegated municipalities.

A financial act enacted in the 2014 Finance Law also guarantees a state endowment (*Dotation Globale de Fonctionnement - DGF*) for the first three years after the amalgamation. New municipalities established before January 1, 2016 with populations between 1,000 and 10,000 received a 5% increase in their state endowment. Subsequent finance laws in 2018 and 2019 extended these provisions. However, this financial incentive is limited and not the sole determinant of a successful municipal merger, as its impact diminishes after three years. Nevertheless, it remains noteworthy, particularly in the context of frequent freezes and reductions in state endowments (Denètre, 2016).

For a merger to be approved, it must receive the consent of all participating municipal councils. Although the process is usually initiated by the municipal councils, it can also be proposed by an intermunicipal authority or by the prefect. Public consultation is not mandatory unless unanimous approval is not achieved. In such cases, a local referendum must be held (see Appendix D for details). The law currently does not facilitate de-mergers, although some municipalities have recently found ways to circumvent this restriction.

## 3. Data and descriptive statistics

The analysis focuses on first-round municipal elections of 2008, 2014, and 2020.<fnref n="2" /> The final sample includes 36,151 municipalities, of which 2,444 (about 7%) underwent a merger between 2014 and 2019, resulting in the creation of 768 new municipalities. To avoid potential bias, municipalities that split or first merged before later splitting are excluded. Since municipal boundaries must be finalized a year before elections, the dataset captures mergers occurring between 2014 (after the elections) and 2019.

Appendix A provides a detailed description of the data, including the variables, definitions and sources. The analysis relies on four main data sources: *Data.gouv*, *INSEE*, *Observatoire des territoires* and *data.economie.gouv*, which provide data on electoral outcomes, municipal mergers, socio-demographic characteristics (i.e. population size, share of younger people, share of elderly, share of women and unemployment at the department level), territorial characteristics (i.e. rural, area, density, overall operating grant, operating expenditures, capital expenditures, fiscal potential, public equipment), and political characteristics (i.e. political color, second round, COVID-19) at the municipal level. Some variables specific to the changes

<footnote n="2">²The municipal elections of 2001 are excluded from the analysis as the data available only accounts for municipalities with more than 9,000 inhabitants, *de facto* excluding most of merging municipalities.</footnote>
induced by a merging process are integrated (i.e. change in name, change in electoral rule, change in the number of voting places, increase in the number of running candidates...). Table A.2 provides an example of how merging municipalities are tracked based on their geographic code.

<table><caption>**Table 1:** Summary statistics: comparison of merging and non-merging municipalities.</caption><thead><tr><th rowspan="2">Variables</th><th colspan="5">Merging (N=2,444)</th><th colspan="5">Non-merging (N=33,707)</th><th>SDM</th></tr><tr><th>Mean</th><th>Median</th><th>SD</th><th>Min</th><th>Max</th><th>Mean</th><th>Median</th><th>SD</th><th>Min</th><th>Max</th><th>Mean</th></tr></thead><tbody><tr><td colspan="12" style="text-align: center; font-weight: bold;">Dependent Variable</td></tr><tr><td>Turnout 2008</td><td>0.777</td><td>0.777</td><td>0.083</td><td>0.36</td><td>1</td><td>0.785</td><td>0.79</td><td>0.092</td><td>0.368</td><td>1</td><td>-0.096</td></tr><tr><td>Turnout 2014</td><td>0.734</td><td>0.732</td><td>0.092</td><td>0.351</td><td>1</td><td>0.745</td><td>0.748</td><td>0.099</td><td>0.325</td><td>1</td><td>-0.116</td></tr><tr><td>Turnout 2020</td><td>0.505</td><td>0.497</td><td>0.132</td><td>0.199</td><td>0.936</td><td>0.592</td><td>0.591</td><td>0.146</td><td>0.162</td><td>1</td><td>-0.625</td></tr><tr><td colspan="12" style="text-align: center; font-weight: bold;">Socio-demographic Characteristics</td></tr><tr><td>Multiple</td><td>0.047</td><td>0</td><td>0.213</td><td>0</td><td>1</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>Population 2008</td><td>978.311</td><td>407.5</td><td>2,462.012</td><td>7</td><td>52,488</td><td>1,658.423</td><td>423</td><td>7,499.656</td><td>1</td><td>439,475</td><td>-0.122</td></tr><tr><td>Population 2013</td><td>1,011.032</td><td>422.5</td><td>2,481.766</td><td>8</td><td>53,236</td><td>1,808.697</td><td>439</td><td>15,314.367</td><td>1</td><td>2,229.628</td><td>-0.073</td></tr><tr><td>Population 2020</td><td>4,462.711</td><td>2,327</td><td>8,761.128</td><td>79</td><td>131,767</td><td>1,854.386</td><td>442</td><td>15,152.72</td><td>1</td><td>2,145,905</td><td>0.211</td></tr><tr><td>Share of young 2008</td><td>0.057</td><td>0.056</td><td>0.023</td><td>0</td><td>0.303</td><td>0.057</td><td>0.056</td><td>0.025</td><td>0</td><td>0.451</td><td>0.007</td></tr><tr><td>Share of young 2013</td><td>0.052</td><td>0.052</td><td>0.021</td><td>0</td><td>0.25</td><td>0.054</td><td>0.053</td><td>0.023</td><td>0</td><td>0.446</td><td>-0.093</td></tr><tr><td>Share of young 2020</td><td>0.052</td><td>0.052</td><td>0.012</td><td>0.01</td><td>0.119</td><td>0.052</td><td>0.05</td><td>0.022</td><td>0</td><td>0.408</td><td>0.027</td></tr><tr><td>Share of elderly 2008</td><td>0.183</td><td>0.171</td><td>0.068</td><td>0</td><td>0.491</td><td>0.183</td><td>0.171</td><td>0.07</td><td>0</td><td>0.741</td><td>0.001</td></tr><tr><td>Share of elderly 2013</td><td>0.195</td><td>0.183</td><td>0.071</td><td>0</td><td>0.507</td><td>0.196</td><td>0.184</td><td>0.073</td><td>0</td><td>0.833</td><td>-0.009</td></tr><tr><td>Share of elderly 2020</td><td>0.238</td><td>0.23</td><td>0.065</td><td>0.075</td><td>0.473</td><td>0.229</td><td>0.217</td><td>0.077</td><td>0</td><td>1</td><td>0.119</td></tr><tr><td>Share of women 2008</td><td>0.496</td><td>0.497</td><td>0.026</td><td>0.341</td><td>0.624</td><td>0.497</td><td>0.5</td><td>0.028</td><td>0</td><td>0.775</td><td>-0.042</td></tr><tr><td>Share women 2013</td><td>0.497</td><td>0.499</td><td>0.026</td><td>0.325</td><td>0.619</td><td>0.499</td><td>0.501</td><td>0.027</td><td>0</td><td>1</td><td>-0.073</td></tr><tr><td>Share women 2020</td><td>0.504</td><td>0.504</td><td>0.016</td><td>0.368</td><td>0.582</td><td>0.499</td><td>0.501</td><td>0.027</td><td>0.2</td><td>1</td><td>0.209</td></tr><tr><td>Unemployment 2008</td><td>7.303</td><td>7.1</td><td>1.162</td><td>4.5</td><td>11.6</td><td>7.908</td><td>7.6</td><td>1.592</td><td>4.5</td><td>11.6</td><td>-0.434</td></tr><tr><td>Unemployment 2014</td><td>9.39</td><td>9.5</td><td>1.382</td><td>6.2</td><td>14.5</td><td>10.223</td><td>10.1</td><td>1.793</td><td>6.2</td><td>15.4</td><td>-0.52</td></tr><tr><td>Unemployment 2020</td><td>7.19</td><td>7.4</td><td>1.077</td><td>4.7</td><td>11.2</td><td>7.836</td><td>7.6</td><td>1.399</td><td>4.7</td><td>12.5</td><td>-0.517</td></tr><tr><td colspan="12" style="text-align: center; font-weight: bold;">Territorial Characteristics</td></tr><tr><td>Rural</td><td>0.923</td><td>1</td><td>0.267</td><td>0</td><td>1</td><td>0.88</td><td>1</td><td>0.325</td><td>0</td><td>1</td><td>0.144</td></tr><tr><td>Area 2008</td><td>15.132</td><td>11.315</td><td>13.528</td><td>0.08</td><td>161.79</td><td>15.043</td><td>10.8</td><td>15.094</td><td>0.04</td><td>758.93</td><td>0.006</td></tr><tr><td>Area 2014</td><td>15.132</td><td>11.315</td><td>13.528</td><td>0.08</td><td>161.79</td><td>15.043</td><td>10.8</td><td>15.094</td><td>0.04</td><td>758.93</td><td>0.006</td></tr><tr><td>Area 2020</td><td>69.864</td><td>46.11</td><td>62.547</td><td>3.79</td><td>408.05</td><td>14.906</td><td>10.72</td><td>15.064</td><td>0.04</td><td>758.93</td><td>1.208</td></tr><tr><td>Density 2008</td><td>88.24</td><td>37.344</td><td>284.384</td><td>1.046</td><td>6,911.321</td><td>156.775</td><td>38.293</td><td>708.828</td><td>0.067</td><td>26,134.44</td><td>-0.127</td></tr><tr><td>Density 2014</td><td>90.864</td><td>39.053</td><td>283.386</td><td>0.828</td><td>6,754.717</td><td>162.204</td><td>39.96</td><td>737.71</td><td>0.067</td><td>27,080.498</td><td>-0.128</td></tr><tr><td>Density 2020</td><td>86.943</td><td>48.163</td><td>239.053</td><td>1.069</td><td>5,205.118</td><td>167.436</td><td>40.524</td><td>755.542</td><td>0.078</td><td>27,908.299</td><td>-0.144</td></tr><tr><td>Avg. endowment change</td><td>34.133</td><td>31.147</td><td>91.329</td><td>-1381.863</td><td>1365.133</td><td>-33.416</td><td>-30.018</td><td>61.236</td><td>-3715.333</td><td>774.287</td><td>0.869</td></tr><tr><td>Size of the merger</td><td>4.944</td><td>3</td><td>4.146</td><td>1</td><td>22</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td colspan="12" style="text-align: center; font-weight: bold;">Political Characteristics</td></tr><tr><td>Number of lists (2020)</td><td>1.769</td><td>2</td><td>0.881</td><td>1</td><td>8</td><td>2.115</td><td>2</td><td>1.424</td><td>1</td><td>19</td><td>-0.293</td></tr><tr><td>Two round (2020)</td><td>0.102</td><td>0</td><td>0.303</td><td>0</td><td>1</td><td>0.127</td><td>0</td><td>0.333</td><td>0</td><td>1</td><td>-0.078</td></tr><tr><td>Change in the number of polling offices</td><td>0.908</td><td>1</td><td>0.289</td><td>0</td><td>1</td><td>0.034</td><td>0</td><td>0.182</td><td>0</td><td>1</td><td>3.619</td></tr><tr><td>Change in the electoral rule</td><td>0.543</td><td>1</td><td>0.498</td><td>0</td><td>1</td><td>0.013</td><td>0</td><td>0.114</td><td>0</td><td>1</td><td>1.466</td></tr><tr><td>Change in the municipality's name</td><td>0.948</td><td>1</td><td>0.221</td><td>0</td><td>1</td><td>0.003</td><td>0</td><td>0.053</td><td>0</td><td>1</td><td>5.879</td></tr><tr><td>Change in the political color of the majority</td><td>0.385</td><td>0</td><td>0.487</td><td>0</td><td>1</td><td>0.231</td><td>0</td><td>0.422</td><td>0</td><td>1</td><td>0.338</td></tr><tr><td>Increase in the number of candidates</td><td>0.13</td><td>0</td><td>0.336</td><td>0</td><td>1</td><td>0.315</td><td>0</td><td>0.465</td><td>0</td><td>1</td><td>-0.456</td></tr><tr><td>Distance from cluster (in km²)</td><td>131.212</td><td>94.262</td><td>106.976</td><td>1.678</td><td>586.979</td><td>190.784</td><td>144.394</td><td>142.691</td><td>0</td><td>581.601</td><td>-0.472</td></tr></tbody></table>

Note: There are 36,151 observations, reduced to 34,475 in 2020 after the mergers. In total, 2,444 municipalities merge. For continuous variables, the Standardized Difference in Means (SDM) between conditions i = A, B is defined as $(\bar{X}_A - \bar{X}_B)/(\sqrt{\frac{\sigma_A + \sigma_B}{2}})$, with $\bar{X}_i$ the variable's average value and $\sigma_i$ the variable's standard deviation. For dummy variables, the SMD is given by $(P_A - P_B)/(\sqrt{\frac{P_A(1-P_A) + P_B(1-P_B)}{2}})$. It is considered that differences of 0.25 or less are considered as good balance (Imbens and Rubin, 2015).

Table 1 describes the summary statistics for the main variables, distinguishing between merging and non-merging municipalities. Most of French municipalities are rural and small, with populations concentrated in urban areas while the majority of municipalities are located
in the countryside (Figure A.1). Merging municipalities are similar in size to the non-merging average before merging but became significantly larger afterward. Demographic characteristics, such as gender distribution and age structure, are broadly consistent across municipalities.

Figure A.4 shows that, before merging, municipalities had similar debt levels, though merging municipalities had lower operating expenses and fiscal potential.<fnref n="3" /> After 2014, merging municipalities saw an increase in per capita state endowments and debt, partly driven by higher investment spending, while non-merging municipalities experienced a decrease in endowments.

A principal component analysis is conducted to understand how variables interact (Figure A.2). Democratic participation is positively correlated with rural areas and the share of older individuals, and negatively correlated with population density, size and the share of younger people. The share of women does not align with existing literature, though the small arrow size should lead to a cautious interpretation. The same applies to unemployment rates. Mergers are positively correlated with changes in name, electoral rules, and the number of polling stations, as expected.

As shown in Figure 1, turnout rates have steadily declined over the past decades. Table 1 indicates that turnout fell between 2014 and 2020 for both merging and non-merging municipalities, with the decline more pronounced in merging municipalities. Figure 3 further emphasizes this trend, showing little difference between the groups before the merger period, followed by a clear divergence that results in a substantial gap by 2020.

# 4. Identification strategy

## 4.1. Difference-in-Differences

To estimate the causal effect of mergers on voter turnout, a Difference-in-Differences (DiD) estimation is implemented. The institutional setting creates a natural treatment and control structure, where treated municipalities are those that merge and untreated are those that do not. This allows for a comparison of turnout between merging and non-merging municipalities. Under the parallel trends assumption (i.e. in the absence of mergers, both groups would have followed similar turnout trajectories), the difference in trends can be interpreted as the causal effect of mergers on turnout. Figure 3 helps assess the validity of the design by displaying a sharp change in average turnout between 2014 and 2020, the period when the mergers were

<footnote n="3">³Budgetary variables are not directly interpretable at a single point in time and will therefore not be included in the main analysis; they are presented here solely to provide contextual information on differences between merging and non-merging municipalities.</footnote>
<figure><img src="image_4.png" /><figcaption>**Figure 3:** Comparison of the evolution of turnout rates between non-merging and merging municipalities.</figcaption></figure>

Note: Author, based on INSEE. The figure depicts the evolution of turnout rates for the first-round of French municipal elections for 2008, 2014 and 2020 comparing merging (1) and non-merging (0) municipalities. Vertical dashed lines mark the onset and end of the municipal merger period studied in this paper.

implemented. By comparing changes in turnout before and after merger between treated an non-treated, DiD removes the influence of time-invariant unobservables (e.g., local civic culture, campaign and candidate quality, unmeasured local events...), isolating the causal effects of mergers on electoral turnout. The following equation is estimated:

$$ \text{Turnout}_{it} = \beta_0 + \beta_1 \text{Merger}_i + \beta_2 \text{Time}_t + \beta_3 (\text{Merger}_i \times \text{Time}_t) + \beta_4 \text{Control}'_{it} + \varepsilon_{it} \quad (1) $$

where *Turnout*<sub>it</sub> stands for municipal turnout rates in municipality *i* in year *t*, *Merger* is a dummy that equals 1 when the municipality merges and 0 otherwise and *Time* is the post variable that represents the change in turnout observed in non-merging municipalities. *i* stands for the municipality and *t* for time. The treatment effect, i.e. the impact of municipal mergers on turnout, is depicted by the interaction between those two variables. The analysis is completed with a set of control variables. *ε*<sub>i,t</sub> is the error term. To perform the analysis, panel data is required. Since some municipalities disappear from the dataset in 2020 due to mergers, the issue is addressed by treating them as having virtually merged in 2014, allowing for a consistent single observation over time. Appendix A.2 details the procedure used. The final panel dataset includes 768 merging municipalities and 33,707 non-merging ones. Finally,
in addition to estimating the average treatment effect, an heterogeneity analysis is conducted to examine whether the effect of mergers on turnout differs across municipalities with varying demographic and political profiles.

## 4.2. Challenges to identification

While merging and non-merging municipalities appear similar in the raw cross-sectional data (see Table 1 for the descriptive statistics), after the merger, the treated municipalities no longer exist as separate units and must be aggregated into a single observation to construct a consistent panel. This aggregation creates substantial imbalance relative to non-merging municipalities. To address this issue and improve comparability in the panel data, Propensity Score Matching (PSM) is applied, to address differences in observable characteristics, such as socio-demographic composition or political variables. By balancing the distribution of observed characteristics between the two groups, PSM approximates the conditions of a randomized experiment and thus reduces the risk of confounding. To account for unmeasured factors, the DiD approach is applied to the matched sample. This combined strategy leverages the strengths of both methods: PSM improves comparability across observable characteristics, while DiD controls for fixed unobserved heterogeneity.

One might also question the validity of the estimation due to the onset of the pandemic. Given that the 2020 election is the only post-merger election available, it is unavoidable to include it in the analysis. However, it is important to note that all French municipalities, whether merged or not, were simultaneously affected by the same shock. The analysis also focuses on first-round election results, which took place before the general population lockdown (see Appendix D.2 for more details). To control for potential bias, the regressions include a measure of COVID-19 exposure, defined as the distance to the nearest cluster on election day (Noury et al., 2021).

## 4.3. Propensity Score Matching

PSM matches treated units with untreated units that exhibit similar probabilities of treatment, as estimated through a propensity score model of relevant pre-treatment covariates. The matching focuses on the Average Treatment Effect (ATE) to estimate a general policy evaluation rather than the policy impact on treated (ATT).

The literature evidences a wide range of factors that influence voter turnout, including demographic, socio-economic and institutional characteristics. To account for these potential confounders, propensity scores are estimated using a rich set of pre-treatment municipal
characteristics, namely: the rural status (Blais, 2006), population density (Blais and Dobrzynska, 1998), total population size (Cancela and Geys, 2016), age structure (shares of younger and older residents) (Jankowski, 2007; Castanho Silva, 2024), gender composition (share of women) (Stockemer and Sundstrom, 2023), and departmental-level unemployment rate (Aytaç et al., 2020). Balancing treated and control municipalities along these dimensions ensures that the subsequent DiD analysis more credibly isolates the causal effect of mergers on turnout.

To ensure robustness and improve the quality of the matched sample, multiple matching algorithms are applied. As a first step, Nearest Neighbor Matching (NNM) is implemented, whereby each treated municipality is matched with a control unit that has the closest estimated propensity score. However, NNM alone may yield suboptimal matches when the closest available control unit still differs substantially in its propensity score, thus weakening comparability. To address this limitation, the analysis is complemented with caliper matching. This method restricts matches to control units whose propensity scores fall within a predefined range, or "caliper", of the treated unit's score, based on standardized deviations. Drawing on the observed distribution of propensity scores (Figure B.5), a series of increasingly strict calipers is applied: 0.025, 0.005 and 0.0005. The corresponding effective caliper thresholds are reported in Table B.6. While tighter calipers substantially reduce the number of matched pairs, this loss in sample size is mitigated by the large pool of available control municipalities, increasing the likelihood of identifying suitable control units with closely matching propensity scores and allowing for high-quality matches without compromising statistical power. This abundance of potential matches reduces the reliance on simulation-based approaches—such as those employed in Lapointe et al. (2018)—for constructing synthetic controls or approximating counterfactual outcomes. As a result, the matching procedure yields high-quality pairs and allows credible estimation of the causal effect of municipal mergers.

Table 2 provides evidence of improved covariate balance following the matching procedure, demonstrating a substantial reduction in pre-treatment differences between treated and control municipalities. These results confirm the effectiveness of the matching algorithm in aligning the distributions of key characteristics across groups, thereby enhancing the validity of subsequent causal estimates.

### 4.4. Pre-trend

A prerequisite for the validity of DiD estimation strategy is the parallel trends assumption, which stipulates that, in the absence of treatment, the average outcomes for the treatment and control groups would have evolved similarly over time, i.e. pre-merger turnout patterns
<table><caption>**Table 2:** Matching quality indicators – difference in control variables: two sample *t*-test.</caption><thead><tr><th rowspan="3">Variable</th><th colspan="2">Before matching</th><th colspan="8">Matching algorithm</th></tr><tr><th colspan="2">Nearest neighbor</th><th colspan="2">Caliper = 0.025</th><th colspan="2">Caliper = 0.005</th><th colspan="2">Caliper = 0.0005</th></tr><tr><th>Merging</th><th>Non-merging</th><th>Merging</th><th>Non-merging</th><th>Merging</th><th>Non-merging</th><th>Merging</th><th>Non-merging</th></tr></thead><tbody><tr><td>Rural</td><td>0.9141</td><td>0.8802***</td><td>0.882</td><td>0.881</td><td>0.8853</td><td>0.8908**</td><td>0.8932</td><td>0.9***</td><td>0.9093</td><td>0.9031</td></tr><tr><td>Density</td><td>96.367</td><td>162.204***</td><td>154.248</td><td>160.544**</td><td>128.132</td><td>142.024***</td><td>101.076</td><td>97.683**</td><td>91.648</td><td>89.152</td></tr><tr><td>Share old</td><td>0.2028</td><td>0.1959***</td><td>0.2</td><td>0.196***</td><td>0.1983</td><td>0.1958***</td><td>0.1972</td><td>0.1959**</td><td>0.1971</td><td>0.1964</td></tr><tr><td>Share young</td><td>0.0549</td><td>0.0543</td><td>0.0545</td><td>0.0543</td><td>0.0547</td><td>0.0541***</td><td>0.0546</td><td>0.0537***</td><td>0.0544</td><td>0.0539</td></tr><tr><td>Share women</td><td>0.5029</td><td>0.4992***</td><td>0.5003</td><td>0.4993***</td><td>0.5004</td><td>0.4993***</td><td>0.5013</td><td>0.4992***</td><td>0.5013</td><td>0.4996***</td></tr><tr><td>Population</td><td>3334.01</td><td>1808.7***</td><td>3443.18</td><td>1796.14***</td><td>3324.52</td><td>1525.88***</td><td>3083.75</td><td>1272.15***</td><td>2865.09</td><td>1269.4***</td></tr><tr><td>Unemployment</td><td>9.3872</td><td>10.2232***</td><td>10.2685</td><td>10.2032***</td><td>10.2406</td><td>10.1479***</td><td>10.1605</td><td>10.035***</td><td>9.9356</td><td>9.831***</td></tr></tbody></table>

Note: *p<0.1; **p<0.05; ***p<0.01. Balance of covariates before and after matching for turnout rates. The use of tighter calipers during matching leads to enhanced balance on covariates.

between merging and on-merging municipalities should be similar. Given the limited number of pre-treatment periods (2008 and 2014), an event study that offers a suggestive examination of the treatment dynamics, rather than a formal test of parallel trends, is conducted. This preliminary comparison, though constrained, serves as a useful diagnostic tool in the absence of longer historical series. Figure 4 provides no evidence of a significant effect, and similar results are observed in the event-study conducted on a matched sample with stricter calipers (see Figure B.6).

<figure><img src="image_5.png" /><figcaption>**Figure 4:** Visual inspection of pre-trend with NNM data.</figcaption></figure>

Note: Event study on NNM data. While the data allow for a comparison of turnout levels in the two years preceding the mergers, the limited number of pre-merger periods constrains formal assessment of parallel trends.
# 5. The impact of municipal mergers on voter turnout

## 5.1. *Baseline results*

Table 3 reports the results of the DiD estimation based on the data derived from the NNM approach.<fnref n="4" /> Results from the *Baseline* regression reveal a negative effect of municipal mergers on voter turnout of nearly 8 percentage points. This effect remains robust after controlling for key covariates (*Controls*) and the impact of COVID-19 (*Controls* + *COVID*). The complete set of regression results, including controls, is displayed in Table B.8.

<table><caption>**Table 3:** DiD based on NNM: effect of mergers on voter turnout - Reduced regression.</caption><thead><tr><th></th><th colspan="3">Dependent variable: Turnout</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr><tr><th></th><th>Baseline</th><th>Controls</th><th>Controls + COVID</th></tr></thead><tbody><tr><th>Merger</th><td>-0.005<br>(0.003)</td><td>0.012***<br>(0.005)</td><td>0.010**<br>(0.005)</td></tr><tr><th>Time</th><td>-0.154***<br>(0.001)</td><td>-0.167***<br>(0.003)</td><td>-0.208***<br>(0.004)</td></tr><tr><th>Merger x Time</th><td>-0.079***<br>(0.004)</td><td>-0.084***<br>(0.004)</td><td>-0.074***<br>(0.004)</td></tr><tr><th>Constant</th><td>0.744***<br>(0.001)</td><td>1.061***<br>(0.038)</td><td>1.075***<br>(0.036)</td></tr><tr><th>Observations</th><td>583,185</td><td>583,185</td><td>583,185</td></tr><tr><th>R<sup>2</sup></th><td>0.451</td><td>0.567</td><td>0.582</td></tr><tr><th>Controls</th><td>No</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Note: This table reports the results of DiD estimation of merging municipalities on turnout based on data issued from the NNM approach. The list of control variables includes: Multiple, Density, Area, Rural, Population, Share of young, Share of elderly, Share of women, Unemployment, Avg. Endowment and Distance from cluster. Table B.8 gathers the respective coefficients associated to controls. Standard errors clustered at the municipality level are in parentheses. P-values: *p<0.1; **p<0.05; ***p<0.01.

Robustness checks using caliper values of 0.025, 0.005, and 0.0005 yield consistent results (Appendix B.7). Map D.10 suggests a regional concentration of municipal mergers across the territory. To account for potential regional heterogeneity and the localized nature of some mergers, alternative regressions are estimated with standard errors clustered at the regional

<footnote n="4">⁴Table B.7 reports the singles differences observed between the two groups.</footnote>
level, using both pre- and post-2015 regional boundaries. The results are robust (see Tables B.8 and B.9).

The estimated effect is larger in magnitude than those reported by Lapointe et al. (2018), who find a long-term reduction in turnout of approximately 4 percentage points in Finland. Similarly, the effect exceeds the -2 percentage points estimate reported by Allers et al. (2021). This stronger impact may reflect differences in institutional context, the voluntary nature of French mergers, the timing of the reforms, or their short-term nature. Further exploration of the underlying mechanisms is provided in the heterogeneity analysis section.

## 5.2. Heterogeneity analysis

Research on municipal mergers points to various factors influencing voter turnout. Investigating whether and how the effects vary across different contexts—such as population size, political competition, or socio-demographic composition—can provide insight into the drivers of both the decision to merge and its consequences. The analysis not only helps identify which types of municipalities are most affected, but also sheds light on the broader implications of territorial reforms for democratic participation. This section is structured around two main dimensions: the effects of changes in territorial and population size (Section 5.2.1) and the potential political implications for local democracy (Section 5.2.2).

### 5.2.1. Size effects: territory and population

The effect of mergers on turnout is more pronounced within rural municipalities (Table 4). Given the high concentration of French municipalities in sparsely populated rural areas, where administrative fragmentation is most acute, mergers have predominantly occurred in these contexts. As rural residents tend to exhibit higher baseline levels of electoral participation (Blais, 2006), the observed decline in turnout may partly reflect disruptions to established local ties and governance structures. To better isolate the impact of mergers, it is essential to account for population size, density, and related sociodemographic factors that may mediate the relationship between territorial restructuring and political engagement.

Older individuals typically vote at higher rates than younger cohorts (Teixeira, 1987; Jankowski, 2007). However, the 2020 municipal elections occurred during the early stages of the COVID-19 pandemic, which may have disproportionately discouraged turnout among older voters due to their heightened vulnerability to the virus. To isolate the causal effect of mergers on turnout, variation by the share of older residents is investigated. Although the interaction term is positive, it is not statistically significant (Table 5). This suggests that
<table><caption>**Table 4:** Triple differences estimation: Urban vs. Rural areas.</caption><thead><tr><th></th><th>Dependent variable: Turnout<br>(1)<br>Controls + COVID</th></tr></thead><tbody><tr><td>Merger</td><td>0.030<sup>***</sup><br>(0.007)</td></tr><tr><td>Time</td><td>-0.245<sup>***</sup><br>(0.004)</td></tr><tr><td>Merger x Time</td><td>-0.042<sup>***</sup><br>(0.011)</td></tr><tr><td>Merger x Rural</td><td>-0.022<sup>***</sup><br>(0.008)</td></tr><tr><td>Time x Rural</td><td>0.042<sup>***</sup><br>(0.002)</td></tr><tr><td>Merger x Time x Rural</td><td>-0.035<sup>***</sup><br>(0.012)</td></tr><tr><td>Constant</td><td>1.068<sup>***</sup><br>(0.036)</td></tr><tr><td>Observations</td><td>583,185</td></tr><tr><td>R<sup>2</sup></td><td>0.585</td></tr><tr><td>Controls</td><td>Yes</td></tr></tbody></table>

Note: This table illustrates the results of a triple DiD to compare rural (1) and urban (0) areas, based on data issued from the NNM approach. The list of control variables includes: Multiple, Density, Area, Rural, Population, Share of young, Share of elderly, Share of women, Unemployment, Avg. Endowment and Distance from cluster. Standard errors are clustered at the municipal level in parentheses. P-values: *p<0.1; **p<0.05; ***p<0.01.

<table><caption>**Table 5:** Triple differences estimation: Share of elderly.</caption><thead><tr><th></th><th>Dependent variable: Turnout<br>(1)<br>Controls + COVID</th></tr></thead><tbody><tr><td>Merger</td><td>-0.004<br>(0.009)</td></tr><tr><td>Time</td><td>-0.216<sup>***</sup><br>(0.004)</td></tr><tr><td>Merger x Time</td><td>-0.086<sup>***</sup><br>(0.015)</td></tr><tr><td>Merger x Share of elderly</td><td>0.072<sup>*</sup><br>(0.042)</td></tr><tr><td>Time x Share of elderly</td><td>0.052<sup>***</sup><br>(0.018)</td></tr><tr><td>Merger x Time x Share of elderly</td><td>0.039<br>(0.062)</td></tr><tr><td>Constant</td><td>1.092<sup>***</sup><br>(0.036)</td></tr><tr><td>Observations</td><td>583,185</td></tr><tr><td>R<sup>2</sup></td><td>0.583</td></tr><tr><td>Controls</td><td>Yes</td></tr></tbody></table>

Note: This table presents the results of a triple DiD estimation examining the impact of the elderly population potentially affected by the COVID outbreak, based on data issued from the NNM approach. The list of control variables includes: Multiple, Density, Area, Rural, Population, Share of young, Share of elderly, Share of women, Unemployment, Avg. Endowment and Distance from cluster. Standard errors are clustered at the municipal level in parentheses. P-values: *p<0.1; **p<0.05; ***p<0.01.
the negative effect of municipal mergers on turnout is not concentrated in areas with higher proportions of older individuals. In other words, the decline in participation cannot be solely attributed to COVID-related vulnerabilities, reinforcing the interpretation that the observed effect primarily reflects the democratic consequences of the mergers themselves.

<table><caption>**Table 6:** Triple differences estimation: Low densely vs. high densely populated municipalities.</caption><thead><tr><th></th><th>Dependent variable: Turnout<br>(1)<br>Controls + COVID</th></tr></thead><tbody><tr><td>Merger</td><td>0.017***<br>(0.004)</td></tr><tr><td>Time</td><td>-0.220***<br>(0.004)</td></tr><tr><td>Merger x Time</td><td>-0.064***<br>(0.005)</td></tr><tr><td>Merger x Low density</td><td>-0.009*<br>(0.005)</td></tr><tr><td>Time x Low density</td><td>0.059***<br>(0.002)</td></tr><tr><td>Merger x Time x Low density</td><td>-0.014*<br>(0.008)</td></tr><tr><td>Constant</td><td>0.983***<br>(0.034)</td></tr><tr><td>Observations</td><td>583,185</td></tr><tr><td>R<sup>2</sup></td><td>0.618</td></tr><tr><td>Controls</td><td>Yes</td></tr></tbody></table>

Note: This table illustrates the results of a triple DiD estimation to compare municipalities based on the population density, based on data issued from the NNM approach. The density dummy cuts the sample in half at the median (=40.22 inhabitants per km²) of the population density in 2014 (1 = below median, 0 = above median). The list of control variables includes: Multiple, Density, Area, Rural, Population, Share of young, Share of elderly, Share of women, Unemployment, Avg. Endowment and Distance from cluster. Standard errors are clustered at the municipal level in parentheses. P-values: *p<0.1; **p<0.05; ***p<0.01.

Beyond age composition, population size also plays a central role in shaping voter turnout, with smaller municipalities often exhibiting higher participation rates, as limited population size facilitates closer interactions between citizens and elected officials (Allers et al., 2021), strengthens community ties (DellaVigna et al., 2016), and enhances the perceived influence of each individual vote (Tavares and Carr, 2013). However, some studies suggest that larger populations may also encourage turnout by increasing social heterogeneity and offering more representative electoral systems, such as proportional representation, which can motivate greater engagement (Gallego et al., 2012; Ryšavė and Bernard, 2013; Rodrigues and Tavares, 2020). Municipal mergers appear to have a broadly similar effect on turnout across municipalities of different population sizes, as the triple interaction term is positive but not statistically significant (Table C.10). However, the effect is more pronounced in low-density areas—where social ties may be weaker—with results significant at the 10% level (Table 6). It suggests that
the dilution of local identity and reduced perceived influence over municipal decisions may contribute to a sharper decline in electoral participation.

<table><caption>**Table 7: Estimation: Effect of merger scale on turnout.**</caption><thead><tr><th></th><th>Dependent variable: Turnout<br>(1)<br>Controls + COVID</th></tr></thead><tbody><tr><td>Above median</td><td>0.003<br>(0.008)</td></tr><tr><td>Time</td><td>-0.208<sup>***</sup><br>(0.004)</td></tr><tr><td>Below median</td><td>0.007<br>(0.005)</td></tr><tr><td>Merger above median x Time</td><td>-0.103<sup>***</sup><br>(0.008)</td></tr><tr><td>Merger below median x Time</td><td>-0.067<sup>***</sup><br>(0.005)</td></tr><tr><td>Constant</td><td>1.218<sup>***</sup><br>(0.036)</td></tr><tr><td>Observations</td><td>583,185</td></tr><tr><td>R<sup>2</sup></td><td>0.570</td></tr><tr><td>Controls</td><td>Yes</td></tr></tbody></table>

Note: This table presents the results of an estimation assessing how merger size affects voter turnout, based on data issued from the NNM approach. The sample is split at the median (=3). The coefficients for Merger above × Time and Merger below × Time are statistically significantly different from each other, with a p-value < 2.2e-16. The list of control variables includes: Multiple, Density, Area, Rural, Population, Share of young, Share of elderly, Share of women, Unemployment, Avg. Endowment and Distance from cluster. Standard errors are clustered at the municipal level in parentheses. P-values: *p<0.1; **p<0.05; ***p<0.01.

While demographic and territorial factors shed light on general patterns in turnout, they may not account for factors that can shape citizens' perceptions of continuity, identity, and administrative complexity, all of which may influence electoral participation. The merging process implies the collaboration of at least two municipalities that join their forces to create a single new entity. Depending on the scale of the merger, i.e., the number of municipalities involved, the perception might be different, as merging with a large number of neighbors may cause a sense of identity loss and drowning. Table 7 confirms a stronger effect in municipalities involved in larger merger processes, with coefficients are tested statistically different. Regarding identity loss more specifically, a change in the municipalities' name is used as a proxy.<fnref n="5" /> The results show no statistically significant differences in turnout between municipalities that underwent a name change and those that retained their original name (Table C.11).

The impact of the merger year on turnout is also examined, hypothesizing that any initial

<footnote n="5">⁵As the analysis is conducted on a panel data set, exhibiting the data for the chief town in 2020, the change in the municipality's name is the one of the chief town. The chief town can keep the same name or change it to encompass the identity of delegated municipalities.</footnote>
voter resentment may diminish over time, leading to a stronger negative effect in earlier merger years compared to more recent ones. Table C.12 does not provide support for this argument, as the negative effect appears to persist across the different years of the merger.

In summary, the impact of municipal mergers on turnout appears to be more pronounced in rural and low-density areas, where community ties and proximity to local government are often stronger. As the effect persist over time, in these contexts, mergers may disrupt established social and political dynamics, contributing to greater electoral disengagement. Additionally, larger-scale mergers tend to amplify the negative effect on turnout, suggesting that the magnitude of structural change plays a role in shaping citizen participation.

### 5.2.2. Consequences for local democracy

Beyond administrative and demographic considerations, mergers can influence political competition, voter engagement, and representation. By examining factors such as changes in the electoral rule, electoral competitiveness and party dominance, the analysis aims to shed light on how political dynamics interact with institutional reforms at the municipal level.

Electoral systems are widely recognized as important determinants of voter turnout. Proportional representation, in particular, is often viewed as more equitable, as it tends to more accurately reflect voter preferences and foster greater electoral competition (Blais and Carty, 1990; Eggers, 2015). French municipal elections use a two-round system that varies by municipality size. In municipalities with less than 1,000 inhabitants, a plurality-at-large system applies, while in larger ones, a list-based proportional system with a majority bonus is used. Table C.13 presents the results from various specifications accounting for differences in the electoral system. The coefficient associated with the proportional system is unexpectedly negative. However, in the French context, the plurality-at-large system may also foster voter expressiveness, as it allows individuals to selectively choose their preferred candidates from different lists (i.e., through *panachage*), potentially enhancing engagement. No specific effect of the electoral system is found on the relationship between mergers and turnout. Neither a change in the electoral rule nor a shift to proportional representation appears to significantly influence this relationship, as indicated by the non-significant coefficients on the triple interaction terms.

As mergers result in changes to municipal size and structure, adjustments to polling stations are expected, involving the redistribution of voters and the integration of polling arrangements across the formerly separate municipalities. Regression (1) in Table 8 shows that increasing the number of polling stations mitigates the negative effect of mergers on turnout,
<table><caption>**Table 8:** Triple differences estimation: Political indicators.</caption><thead><tr><th rowspan="3"></th><th colspan="4">Dependent variable: Turnout</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th>Increase in polling offices</th><th>One list</th><th>Increase in candidates</th><th>Change in political bloc</th></tr></thead><tbody><tr><td colspan="5">Controls + COVID</td></tr><tr><td>Merger</td><td>0.028***<br>(0.008)</td><td>0.022***<br>(0.005)</td><td>0.002<br>(0.005)</td><td>0.003<br>(0.005)</td></tr><tr><td>Time</td><td>-0.206***<br>(0.004)</td><td>-0.196***<br>(0.005)</td><td>-0.242***<br>(0.004)</td><td>-0.190***<br>(0.004)</td></tr><tr><td>Increase in polling offices</td><td>-0.034***<br>(0.005)</td><td></td><td></td><td></td></tr><tr><td>One list</td><td></td><td>-0.022***<br>(0.003)</td><td></td><td></td></tr><tr><td>Increase in candidates</td><td></td><td></td><td>-0.039***<br>(0.001)</td><td></td></tr><tr><td>Change of political bloc</td><td></td><td></td><td></td><td>-0.068***<br>(0.002)</td></tr><tr><td>Merger x Time</td><td>-0.056***<br>(0.010)</td><td>-0.026***<br>(0.005)</td><td>-0.063***<br>(0.004)</td><td>-0.079***<br>(0.006)</td></tr><tr><td>Merger x Increase in polling offices</td><td>0.008<br>(0.009)</td><td></td><td></td><td></td></tr><tr><td>Time x Increase in polling offices</td><td>-0.048***<br>(0.005)</td><td></td><td></td><td></td></tr><tr><td>Merger x Time x Increase in polling offices</td><td>0.025**<br>(0.012)</td><td></td><td></td><td></td></tr><tr><td>Merger x One list</td><td></td><td>0.018***<br>(0.006)</td><td></td><td></td></tr><tr><td>Time x One list</td><td></td><td>-0.139***<br>(0.002)</td><td></td><td></td></tr><tr><td>Merger x Time x One list</td><td></td><td>-0.022***<br>(0.006)</td><td></td><td></td></tr><tr><td>Merger x Increase in candidates</td><td></td><td></td><td>0.012*<br>(0.006)</td><td></td></tr><tr><td>Time x Increase in candidates</td><td></td><td></td><td>0.101***<br>(0.002)</td><td></td></tr><tr><td>Merger x Time x Increase in candidates</td><td></td><td></td><td>0.006<br>(0.009)</td><td></td></tr><tr><td>Merger x Change of political bloc</td><td></td><td></td><td></td><td>0.035***<br>(0.005)</td></tr><tr><td>Time x Change of political bloc</td><td></td><td></td><td></td><td>-0.058***<br>(0.002)</td></tr><tr><td>Merger x Time x Change of political bloc</td><td></td><td></td><td></td><td>0.031***<br>(0.009)</td></tr><tr><td>Constant</td><td>1.188***<br>(0.034)</td><td>1.391***<br>(0.081)</td><td>1.237***<br>(0.036)</td><td>1.050***<br>(0.037)</td></tr><tr><td>Observations</td><td>583,185</td><td>298,312</td><td>581,229</td><td>564,115</td></tr><tr><td>R<sup>2</sup></td><td>0.574</td><td>0.800</td><td>0.594</td><td>0.604</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

<footnote>Note: This table illustrates the results of a triple DiD estimations, based on data issued from the NNM approach. The first column compares municipalities with a single list running for office in 2020 (one list = 1 or 0). The second column highlights the effect of an increase in the number of candidates between 2014 and 2020. The third column highlights the same effect but for municipalities experiencing an increase in the number of polling offices. The last column compares municipalities that observed a change in the political bloc of the majority between 2014 and 2020. The list of control variables includes: Multiple, Density, Area, Rural, Population, Share of young, Share of elderly, Share of women, Unemployment, Avg. Endowment and Distance from cluster. Standard errors are clustered at the municipal level in parentheses. P-values: *p<0.1; **p<0.05; ***p<0.01.</footnote>
as indicated by the positive coefficient. This increasing can preserve accessibility and voter familiarity.

Changes in electoral competitiveness are measured using three variables. Regression (2) from Table 8 reveals that the negative effect of municipal mergers on turnout is significantly more pronounced in municipalities where only one list was running in the 2020 elections. It suggests that reduced electoral competition may exacerbate the demobilizing impact of mergers, potentially due to perceived lack of choice or inevitability of outcomes, which can discourage voter participation. In contrast, regression (3) indicates that an increase in the number of candidates does not have a significant moderating effect on the merger-turnout relationship, implying that simply having more names on the ballot does not necessarily translate into higher engagement in the context of structural changes like mergers. Finally, regression (4) shows a positive and significant association between a change in the political color of the majority bloc in the chief town and turnout, suggesting that expected political turnover may reinvigorate voter interest and participation, even in the aftermath of mergers.

Hence, the primary political mechanism behind the turnout decline appears to be reduced electoral competition, as reflected in the number of lists running for office. Two potential mitigating factors emerge: first, increasing the number of polling stations may help preserve accessibility and voter engagement in newly merged municipalities; second, a shift in the majority political bloc of the chief town may signal renewed competition or political change, which can stimulate turnout.

# 6. Conclusion

This paper investigates the impact of the 2015-2019 French municipal mergers' wave on first-round turnout rates. Information on mergers is collected as well as electoral, sociodemographic, territorial and political data. Using this comprehensive dataset, different propensity score matching (PSM) techniques are applied—including nearest neighbor matching (NNM) and caliper matching—to construct a well-balanced comparison sample of merging and non-merging municipalities, thereby improving the alignment of covariate distributions across groups. A Difference-in-Differences estimation is then conducted and evidences a negative effect of municipal mergers on turnout of approximately 8pp. This effect remains robust after controlling for relevant covariates and accounting for the impact of the COVID-19 outbreak during the electoral period. It also holds across various estimations conducted on matched samples using different caliper widths and when clustering standard errors at the regional level to capture potential territorial specificities. While the current framework does not permit
a long-term assessment of the effects of mergers on voter turnout, the short-term findings are consistent in direction with those reported in previous studies (Lapointe et al., 2018; Allers et al., 2021).

The heterogeneity analysis shows that the effect is stronger in rural and less densely populated areas, as well as in larger-scale mergers. The impact of mergers on turnout is also more pronounced when electoral competition is limited, and it persists over time without fading in subsequent years. However, the effect is mitigated when the municipality experiences an increase in the number of polling stations, or when the political majority in the chief town shifts to a different bloc after the election. The analysis reveals both which municipalities are most affected and the wider effects of territorial reforms on democratic participation.

Overall, the findings point to a potential democratic cost associated with municipal consolidation, particularly in contexts where proximity and identity play a central role in civic engagement. While mergers are primarily driven by economic considerations, the short-term political consequences examined in this paper should also be taken into account. Those territorial changes and consequences must be carefully anticipated, especially in a context of strong attachment of French people to their municipality (see CEVIPOF et AMF, 2016).

The analysis is not without its limitations and presents several shortcomings. One of the key challenges is the inability to study long-term effects of municipal mergers on turnout in this context due to the availability of data, which restricts the scope of the investigation. Additionally, the outcomes of municipal mergers depend not only on institutional changes but also on human decisions and behaviors, which are complex and often difficult to observe or measure systematically. Additionally, the analysis is limited to two years before and one year after the merger waves, coinciding with the COVID-19 outbreak in 2020. While the regressions control for this shock without altering the results, this limitation prevents assessment of longer-term effects. Studying the long-term impact of municipal mergers would be an interesting avenue for future research.

The French municipal reform process has often been described as "silent" and has not been widely perceived as a success (Bideau, 2019). One of its limitations lies in the minimal opportunities for citizen participation, as public consultations are only mandated under narrowly defined conditions. Enhancing democratic engagement—such as allowing residents to vote on municipal mergers, as is practiced in Switzerland (Strebel, 2018)—could reinforce civic responsibility and potentially mitigate declines in voter turnout.

Recent policy developments, however, point to a shift toward a more supportive framework. Following sustained advocacy by the Association of French Mayors and the adoption of the Declaration of New Municipalities in June, the 2024 Finance Law introduced a more
favorable and stable financial framework for merged municipalities. This reform includes two key provisions aimed at strengthening fiscal support. Moreover, early feedback from local elected officials suggests a generally positive reception of these measures, indicating a more optimistic outlook for future reforms.
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# Appendix

## A. Data description and descriptive statistics

### A.1. Data description

For sociodemographic characteristics, data on population size is collected for the years 2008, 2013⁶, and 2020. To gain insight on population characteristics, several variables are computed: the share of young people (18-24 years old), the share of elderly (> 65 years old) and the share of women. To add an economic dimension to the analysis, data on the unemployment rates per department in 2008, 2014 and 2020 is collected.

For territorial characteristics, municipalities are classified as rural or urban. Data on municipal area (km²) for 2008, 2014, and 2020 are then added. Because available datasets are based on the 2023 French territorial geography, municipalities that later merged are excluded, as they no longer appear in these sources. To overcome this limitation, a web-scraping procedure was used to collect area data from the official Wikipedia pages of each municipality. Combined with population figures, these data are used to calculate population density. To capture budgetary aspects, I use data on the overall operating grant (Dotation Globale de Fonctionnement – DGF), which is temporarily increased by the central government for three years following a merger. Financial variables include debt per capita, operating expenditures, and capital expenditures. Operating expenditures reflect recurrent service costs, while capital expenditures capture changes in municipal assets. Fiscal potential is measured as per capita revenue from local direct taxes (housing and property taxes on built and undeveloped land).

For political variables, in addition of turnout, the dataset accounts for the political color of yayors in municipalities with a population size greater than 3,500 inhabitants, completed with the number of lists candidates in jurisdictions of more than 1,000 individuals. A dummy variable indicates whether a second electoral round occurred. In addition, to account for the impact of COVID-19 during this specific electoral period, the distance from each municipality to the nearest of the seven identified COVID-19 clusters as of March 15 is computed (Noury et al., 2021) (see Appendix D.2).

Municipal mergers may induce institutional changes that affect electoral turnout. To capture these effects, a dummy variable indicates whether the merged municipality changed its name. Since the municipal electoral system changes at the 1,000-inhabitant threshold, an additional variable identifies any shift in electoral rules that could influence turnout.<fnref n="7" /> Finally, mergers may alter the number of polling stations or the municipality's administrative status (e.g., becoming a chief town or a delegated municipality). Two additional dummy variables capture these changes.

<footnote n="6">⁶Data on population size and composition for 2014 are not available using the French territorial boundaries as defined in 2014.</footnote>

<footnote n="7">⁷In the turnout literature, proportional representation is generally associated with higher participation (Eggers, 2015). In France, municipalities with more than 1,000 inhabitants are subject to a more proportional electoral system.</footnote>
<table><caption>Table A.1: Data definition, description, and expected signs.</caption><thead><tr><th>Variable</th><th>Source</th><th>Type</th><th>Definition</th><th>Expected sign</th></tr></thead><tbody><tr><td colspan="5" style="text-align: center; font-style: italic;">Socio-demographic characteristics</td></tr><tr><td>Population size<br>(2008, 2014, 2020)</td><td>INSEE</td><td>Quantitative,<br>continuous</td><td>Total number of inhabitants per municipality.</td><td>Negative</td></tr><tr><td>Share of elderly<br>(2008, 2014, 2020)</td><td>INSEE, own<br>computation</td><td>Quantitative,<br>continuous</td><td>Total number of people aged 65+ divided by the total population in that municipality.</td><td>Positive</td></tr><tr><td>Share of young<br>(2008, 2014, 2020)</td><td>INSEE, own<br>computation</td><td>Quantitative,<br>continuous</td><td>Total number of people aged between 18 and 24 years old divided by the total population in that municipality.</td><td>Negative</td></tr><tr><td>Share of women<br>(2008, 2014, 2020)</td><td>INSEE, own<br>computation</td><td>Quantitative,<br>continuous</td><td>Total number of women divided by the total population in that municipality.</td><td>Positive</td></tr><tr><td>Unemployment<br>(2008, 2014, 2020)</td><td>INSEE</td><td>Quantitative,<br>continuous</td><td>Unemployment rate in the department of each municipality in 2014 and 2020.</td><td>Negative</td></tr><tr><td colspan="5" style="text-align: center; font-style: italic;">Territorial/financial characteristics</td></tr><tr><td>Rural</td><td>INSEE</td><td>Categorical</td><td>Takes the values 1 when the municipality is from a rural area and 0 if urban.</td><td>Positive</td></tr><tr><td>Area (2008, 2014,<br>2020)</td><td>Observatoire des territoires and webscraping</td><td>Quantitative,<br>continuous</td><td>For 2020, data is from l'Observatoire des territoires. For 2014, as municipalities merge, they disappear from files that are downloadable with the corresponding geography of 2024. Data for merging municipalities in 2014 is webscraped from their former Wikipedia pages using the software R.</td><td>Negative</td></tr><tr><td>Density (2008,<br>2014, 2020)</td><td>Own computation</td><td>Quantitative,<br>continuous</td><td>Total population in the municipality divided by its area.</td><td>Positive/Negative</td></tr><tr><td>Fiscal potential</td><td>data.economie.gouv</td><td>Quantitative,<br>continuous</td><td>The sum of the following local direct taxation products per capita: Residence tax (including THLV), Property tax on built properties and Property tax on undeveloped properties (2011-2022).</td><td>Potential effect on merger</td></tr><tr><td>Debt</td><td>data.economie.gouv</td><td>Quantitative,<br>continuous</td><td>Amount of debt per capita in the municipality (2011-2022).</td><td>Potential effect on merger</td></tr><tr><td>Functioning expenses</td><td>data.economie.gouv</td><td>Quantitative,<br>continuous</td><td>Amount of functioning expenses per capita in the municipality (2011-2022).</td><td>Potential effect on merger</td></tr><tr><td>Investment expenses</td><td>data.economie.gouv</td><td>Quantitative,<br>continuous</td><td>Amount of investment expenses per capita in the municipality (2011-2022).</td><td>Potential effect on merger</td></tr><tr><td colspan="5" style="text-align: center; font-style: italic;">Political characteristics</td></tr><tr><td>Turnout (2008,<br>2014, 2020)</td><td>Data.gouv</td><td>Quantitative,<br>continuous</td><td>It measures the share of casted votes out of the number of registered people in the municipality. 2008 is used for pre-trend.</td><td>Independent variable</td></tr><tr><td>Distance from COVID-19 cluster<br>(2020)</td><td>Own computation based on Noury et al. (2021)</td><td>Quantitative,<br>continuous</td><td>Nearest distance of each municipality from a COVID-19 cluster in 2020.</td><td>Negative</td></tr></tbody></table>
<table><tr><td>Change of political bloc</td><td>data.gouv</td><td>Categorical</td><td>Takes value 1 when there is a change in the political bloc of the Mayor from 2014 to 2020. From electoral files, one can withdraw the political color of the elected mayor. Using a classification available at the following link (https://www.data.gouv.fr/fr/datasets/the matique-elections-municipales-2020/), political parties are grouped into different blocks: far left, left, center, right, far right, others and no political affiliation. Affiliation is mandatory above 3,500 inhabitants. A comparison is made between 2014 and 2020.</td><td>Potential effect on merger and treatment</td></tr><tr><td>One list (2020)</td><td>data.gouv</td><td>Categorical</td><td>Takes value one when a single list is running for office in 2020. Only available for municipalities with more than 1,000 inhabitants.</td><td>Potential effect on treatment</td></tr><tr><td>Second round</td><td>data.gouv</td><td>Quantitative, continuous</td><td>Whether the municipality underwent a second round of municipal elections in 2020 and the electoral results. Subset of about 4,500 municipalities.</td><td>Potential effect on treatment</td></tr><tr><td>Increase in the number of candidates</td><td>data.gouv</td><td>Categorical</td><td>Takes value 1 when there is an increase in the number of candidates running for election between 2014 and 2020, and 0 otherwise.</td><td>Positive (intuition)</td></tr><tr><td colspan="5">Municipal mergers induced characteristics</td></tr><tr><td>Merger</td><td>INSEE</td><td>Categorical</td><td>Takes value 1 when the municipality mergers between 2015 and 2019, and takes the value 0 otherwise.</td><td>Negative</td></tr><tr><td>Change of electoral rule</td><td>Service.Public.fr</td><td>Categorical</td><td>Takes the value 1 if the municipality has observed a change in municipal electoral rule. For municipalities with less than 1,000 inhabitants, plurality-at-large is used. For municipalities with more than 1,000 inhabitants, plurinomial proportional system is used.</td><td>Positive for PR</td></tr><tr><td>Change in the number of voting places</td><td>Data.gouv and own computation</td><td>Categorical</td><td>Takes values 1 of the municipality experience a change in its number of voting places between 2014 and 2020.</td><td>Negative (intuition)</td></tr><tr><td>Change in the municipality's name</td><td>Own computation</td><td>Categorical</td><td>Takes value 1 if the municipality observes a change in its name and 0 otherwise.</td><td>Negative (intuition)</td></tr><tr><td>Relative change of the amount of state endowment three years pre/post merger</td><td>data.economie.gouv</td><td>Quantitative, continuous</td><td>The relative amount of the dotation globale de fonctionnement per capita change in each merging municipality. An average of the amount of three years before merging and after are computed to observe a positive/negative change.</td><td>Potential effect on merger</td></tr><tr><td>Size of the merger</td><td>data.gouv and own computation</td><td>Quantitative, discrete</td><td>Counts the number of municipalities involved in the creation of a new municipality</td><td>Negative</td></tr></table>

Note: Years of study are 2008, 2014 and 2020. This table summarizes the main data sources.
<table><caption>**Table A.2:** Example of *INSEE* code tracking for a merging municipality in 2016.</caption><thead><tr><td>Dep code 2014</td><td>Municipal code 2014</td><td>INSEE code 2014</td><td>Name 2014</td><td>INSEE code 2020</td><td>Name 2020</td></tr></thead><tbody><tr><td>88</td><td>204</td><td>88204</td><td>Girmont</td><td>88465</td><td>Capavenir Vosges</td></tr><tr><td>88</td><td>337</td><td>88337</td><td>Oncourt</td><td>88465</td><td>Capavenir Vosges</td></tr><tr><td>88</td><td>465</td><td>88465</td><td>Thaon-les-Vosges</td><td>88465</td><td>Capavenir Vosges</td></tr></tbody></table>

Note: This table illustrates how the geographic codes of merging municipalities are tracked.

<figure><img src="image_6.png" /><figcaption>**Figure A.1:** Distribution of population and municipalities.</figcaption></figure>

Note: Author, based on INSEE. Figure (a) describes the number of municipalities and the population size (2013) by area (rural or urban). Figure (b) describes the distribution of municipalities by population intervals. They highlight the territorial fragmentation with a large concentration of the population in small and rural municipalities.
<figure><img src="image_7.png" /><figcaption>**Figure A.2:** Principal Component Analysis.</figcaption></figure>

Note: Author. This figure depicts the results of a principal component analysis, aiming at understanding how variables interact together. Small arrows suggest cautious interpretation, while orthogonal variables are uncorrelated. Arrows with similar length and position indicate positive correlation, while opposite arrows signify negative correlation. Participation is for 2020, municipal characteristics are for 2013 and 2014 and the variables including the word "change" are a comparison between 2014 and 2020.

<table><caption>**Table A.3:** Number of merging municipalities per year.</caption><thead><tr><td>Year</td><td>Number of merging municipalities</td></tr></thead><tbody><tr><td>2014</td><td>13</td></tr><tr><td>2015</td><td>1,083</td></tr><tr><td>2016</td><td>680</td></tr><tr><td>2017</td><td>110</td></tr><tr><td>2018</td><td>673</td></tr><tr><td>2019</td><td>6</td></tr></tbody></table>

Note: This table counts the number of merging municipalities per year, for a total of 2,444 merging municipalities.
**Figure A.3:** Relation between the population (in log) and the number of municipalities in Europe.

<figure><img src="image_8.png" /></figure>

Note: Author, based on OECD and European Committee of the Regions. The figure depicts the relationship between the number of municipalities per European country and their associated population size in logarithm. France (FR_2014 and FR_2020) stands out from its neighbors with similar population sizes, such as Italy (ITA) and Germany (DEU).
<figure><img src="image_9.png" /><figcaption>(e) Evolution of fiscal potential per capita</figcaption></figure>

Note: Data provided by data.economie.gouv.fr.
## A.2. Sample of analysis

The DiD analysis requires the data to be structured in a panel format. As discussed in Section 4, a key challenge is that some municipalities disappear over time due to mergers. Table A.4 offers a visual representation of data transformation. It takes the example of three municipalities, namely A, B and C that merge during the period into a single entity: A. While each pre-merger municipality is observed in 2014 ($X_{1,2014}$), the 2020 data contain only the surviving municipality (the chief town), $X_{1,2020}$. This transformation constructs a 2014 variable ($X_{1}^{\text{trans 2014}}$) that represents the merged municipality as if the merger had already occurred. The same process is repeated for the year 2008.

<table><caption>**Table A.4:** Accounting for merging municipalities in 2014.</caption><thead><tr><th>Pre-merger muni.</th><th>Post-merger muni.</th><th>X<sub>1</sub>2014</th><th>X<sub>1</sub><sup>trans 2014</sup></th><th>X<sub>1</sub>2020</th></tr></thead><tbody><tr><td>A</td><td>A</td><td>X<sub>1</sub>A</td><td>(X<sub>1</sub>A + X<sub>1</sub>B + X<sub>1</sub>C)/3</td><td>X<sub>1</sub>A</td></tr><tr><td>B</td><td>A</td><td>X<sub>1</sub>B</td><td>...</td><td>...</td></tr><tr><td>C</td><td>A</td><td>X<sub>1</sub>C</td><td>...</td><td>...</td></tr></tbody></table>

After the transformation, the data are structured in panel form. See Table A.5 for an illustration. Each municipality is observed in 2008 (time = -1), 2014 (time = 0), and 2020 (time = 1); B and C are merged into A and thus excluded.

<table><caption>**Table A.5:** Panel data set construction.</caption><thead><tr><th>Municipality</th><th>Merger</th><th>Time</th><th>X<sub>1</sub></th><th>...</th><th>X<sub>n</sub></th></tr></thead><tbody><tr><td>A</td><td>1</td><td>-1</td><td>X<sub>1A,-1</sub></td><td>...</td><td>X<sub>nA,-1</sub></td></tr><tr><td>A</td><td>1</td><td>0</td><td>X<sub>1A,0</sub></td><td>...</td><td>X<sub>nA,0</sub></td></tr><tr><td>A</td><td>1</td><td>1</td><td>X<sub>1A,1</sub></td><td>...</td><td>X<sub>nA,1</sub></td></tr><tr><td>D</td><td>0</td><td>-1</td><td>X<sub>1D,-1</sub></td><td>...</td><td>X<sub>nD,-1</sub></td></tr><tr><td>D</td><td>0</td><td>0</td><td>X<sub>1D,0</sub></td><td>...</td><td>X<sub>nD,0</sub></td></tr><tr><td>D</td><td>0</td><td>1</td><td>X<sub>1D,1</sub></td><td>...</td><td>X<sub>nD,1</sub></td></tr></tbody></table>
# B. Electoral consequences of municipal amalgamations

## B.1. Propensity Score Matching

<table><caption>**Table B.6:** PSM - caliper and associated e-caliper values.</caption><thead><tr><td>Caliper Values</td><td>Value</td></tr></thead><tbody><tr><td>No</td><td>Null</td></tr><tr><td>0.2</td><td>0.0028</td></tr><tr><td>0.1</td><td>0.0014</td></tr><tr><td>0.05</td><td>7e-04</td></tr><tr><td>0.025</td><td>4e-04</td></tr><tr><td>0.005</td><td>1e-04</td></tr><tr><td>0.0005</td><td>1e-05</td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>**Figure B.5:** Comparison of the distribution of propensity scores before and after matching using nearest neighbor matching (NNM).</figcaption></figure>

Note: Author. Distribution of propensity scores before and after nearest neighbor matching. The visible common support confirms the appropriateness of the matching strategy.
## B.2. Difference-in-differences

<table><caption>**Table B.7:** Single differences by comparing the average level of turnout in different groups.</caption><thead><tr><th rowspan="2">Group</th><th colspan="3">Intervention</th></tr><tr><th>Before (T=0)</th><th>After (T=1)</th><th>Δ after/before</th></tr></thead><tbody><tr><td>Non-treated (merger=0)</td><td>Ȳ<sup>00</sup> = 0.745</td><td>Ȳ<sup>01</sup> = 0.592</td><td>Ȳ<sup>01</sup> - Ȳ<sup>00</sup> = -0.153</td></tr><tr><td>Treated (merger=1)</td><td>Ȳ<sup>10</sup> = 0.734</td><td>Ȳ<sup>11</sup> = 0.505</td><td>Ȳ<sup>11</sup> - Ȳ<sup>10</sup> = -0.229</td></tr><tr><td>Δ After/Before</td><td>Ȳ<sup>10</sup> - Ȳ<sup>00</sup> = -0.011</td><td>Ȳ<sup>11</sup> - Ȳ<sup>01</sup> = -0.087</td><td>Ȳ<sup>11</sup> + Ȳ<sup>00</sup> - Ȳ<sup>01</sup> - Ȳ<sup>10</sup> = -0.076</td></tr></tbody></table>
<figure><img src="image_11.png" /><figcaption>**Figure B.6:** Visual inspection of turnout patterns in the pre-merger period.</figcaption></figure>

Note: While the data allow for a comparison of turnout levels in the two years preceding the mergers, the limited number of pre-merger periods constrains formal assessment of parallel trends.
<table><caption>**Table B.8:** DiD based on NNM: effect of mergers on voter turnout - Complete regression.</caption><thead><tr><th></th><th colspan="3">Dependent variable: Turnout</th></tr><tr><th></th><th>(1)<br>Baseline</th><th>(2)<br>Controls</th><th>(3)<br>Controls + COVID</th></tr></thead><tbody><tr><td>Merger</td><td>-0.005<br>(0.003)</td><td>0.012***<br>(0.005)</td><td>0.010**<br>(0.005)</td></tr><tr><td>Time</td><td>-0.154***<br>(0.001)</td><td>-0.167***<br>(0.003)</td><td>-0.208***<br>(0.004)</td></tr><tr><td>Multiple</td><td></td><td>0.001<br>(0.014)</td><td>0.001<br>(0.013)</td></tr><tr><td>Density</td><td></td><td>-0.00002***<br>(0.00000)</td><td>-0.00002***<br>(0.00000)</td></tr><tr><td>Area</td><td></td><td>-0.0002*<br>(0.0001)</td><td>-0.0002**<br>(0.0001)</td></tr><tr><td>Rural</td><td></td><td>0.068***<br>(0.006)</td><td>0.066***<br>(0.006)</td></tr><tr><td>Population</td><td></td><td>-0.00000*<br>(0.00000)</td><td>-0.00000*<br>(0.00000)</td></tr><tr><td>Share of young</td><td></td><td>-0.693***<br>(0.094)</td><td>-0.599***<br>(0.082)</td></tr><tr><td>Share of elderly</td><td></td><td>0.373***<br>(0.025)</td><td>0.309***<br>(0.024)</td></tr><tr><td>Share of women</td><td></td><td>-0.843***<br>(0.075)</td><td>-0.832***<br>(0.070)</td></tr><tr><td>Unemployment</td><td></td><td>0.001<br>(0.001)</td><td>0.00002<br>(0.001)</td></tr><tr><td>Avg. endowment</td><td></td><td>-0.0001*<br>(0.00005)</td><td>-0.0001<br>(0.00005)</td></tr><tr><td>Distance from cluster</td><td></td><td></td><td>0.0002***<br>(0.00002)</td></tr><tr><td>Merger x Time</td><td>-0.079***<br>(0.004)</td><td>-0.084***<br>(0.004)</td><td>-0.074***<br>(0.004)</td></tr><tr><td>Constant</td><td>0.744***<br>(0.001)</td><td>1.061***<br>(0.038)</td><td>1.075***<br>(0.036)</td></tr><tr><td>Observations</td><td>583,185</td><td>583,185</td><td>583,185</td></tr><tr><td>R<sup>2</sup></td><td>0.451</td><td>0.567</td><td>0.582</td></tr></tbody></table>

Note: This table reports the results of DiD estimation of merging municipalities on turnout based on data issued from the Nearest Neighbor Matching approach. Standard relationships from the voting literature are evidenced: positive effect for rural area (Blais, 2006), negative effect of population size (Cancela and Geys, 2016), etc. Standard errors clustered at the municipality level are in parentheses. P-values: \*p<0.1; \*\*p<0.05; \*\*\*p<0.01.
<table><caption>**Table B.9:** Expected value of coefficients for controls.</caption><thead><tr><td>Variable</td><td>Coefficient sign</td><td>Expected from literature</td></tr></thead><tbody><tr><td>Density</td><td>Negative</td><td>Yes (Blais and Dobrzynska, 1998)</td></tr><tr><td>Area</td><td>Negative</td><td>Yes (Bhatti and Hansen, 2019)</td></tr><tr><td>Rural</td><td>Positive</td><td>Yes (Blais, 2006)</td></tr><tr><td>Population</td><td>Negative</td><td>Yes (Cancela and Geys, 2016)</td></tr><tr><td>Share of young</td><td>Negative</td><td>Yes (Jankowski, 2007)</td></tr><tr><td>Share of elderly</td><td>Positive</td><td>Yes (Castanho Silva, 2024)</td></tr><tr><td>Share of women</td><td>Negative</td><td>No (Stockemer and Sundstrom, 2023)</td></tr><tr><td>Unemployment</td><td>Not significant</td><td>Yes (Aytaç et al., 2020)</td></tr><tr><td>Distance from cluster</td><td>Positive</td><td>Yes (Noury et al., 2021)</td></tr></tbody></table>

Note: The table reports the sign of the coefficient from the complete regression and addresses whether this relationship with turnout is expected from the literature.
<table><caption>Figure B.7: Regression results with Caliper - Reduced version.</caption><thead><tr><th></th><th colspan="3">Dependent variable: Turnout</th><th></th><th colspan="3">Dependent variable: Turnout</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr><tr><th></th><th>Baseline</th><th>Controls</th><th>Controls + COVID</th><th></th><th>Baseline</th><th>Controls</th><th>Controls + COVID</th></tr></thead><tbody><tr><th>Merger</th><td>-0.006**<br>(0.003)</td><td>0.010**<br>(0.004)</td><td>0.008**<br>(0.004)</td><th>Merger</th><td>-0.007**<br>(0.003)</td><td>0.009**<br>(0.004)</td><td>0.007*<br>(0.004)</td></tr><tr><th>Time</th><td>-0.154***<br>(0.001)</td><td>-0.167***<br>(0.003)</td><td>-0.208***<br>(0.004)</td><th>Time</th><td>-0.155***<br>(0.001)</td><td>-0.171***<br>(0.003)</td><td>-0.208***<br>(0.004)</td></tr><tr><th>Merger x Time</th><td>-0.080***<br>(0.004)</td><td>-0.083***<br>(0.004)</td><td>-0.073***<br>(0.004)</td><th>Merger x Time</th><td>-0.081***<br>(0.004)</td><td>-0.083***<br>(0.004)</td><td>-0.074***<br>(0.004)</td></tr><tr><th>Constant</th><td>0.744***<br>(0.001)</td><td>1.058***<br>(0.038)</td><td>1.073***<br>(0.036)</td><th>Constant</th><td>0.744***<br>(0.001)</td><td>1.063***<br>(0.036)</td><td>1.077***<br>(0.034)</td></tr><tr><th>Observations</th><td>576,840</td><td>576,840</td><td>576,840</td><th>Observations</th><td>541,980</td><td>541,980</td><td>541,980</td></tr><tr><th>R<sup>2</sup></th><td>0.455</td><td>0.567</td><td>0.582</td><th>R<sup>2</sup></th><td>0.463</td><td>0.573</td><td>0.585</td></tr><tr><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

(a) Caliper 0.025

(b) Caliper 0.005

<table><thead><tr><th></th><th colspan="3">Dependent variable: Turnout</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr><tr><th></th><th>Baseline</th><th>Controls</th><th>Controls + COVID</th></tr></thead><tbody><tr><th>Merger</th><td>-0.005*<br>(0.003)</td><td>0.009*<br>(0.005)</td><td>0.007<br>(0.005)</td></tr><tr><th>Time</th><td>-0.155***<br>(0.001)</td><td>-0.173***<br>(0.003)</td><td>-0.209***<br>(0.004)</td></tr><tr><th>Merger x Time</th><td>-0.080***<br>(0.004)</td><td>-0.082***<br>(0.004)</td><td>-0.073***<br>(0.005)</td></tr><tr><th>Constant</th><td>0.744***<br>(0.001)</td><td>1.073***<br>(0.038)</td><td>1.090***<br>(0.037)</td></tr><tr><th>Observations</th><td>79,866</td><td>79,866</td><td>79,866</td></tr><tr><th>R<sup>2</sup></th><td>0.463</td><td>0.579</td><td>0.590</td></tr><tr><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

(c) Caliper 0.0005

Note: These tables report the results of DiD estimation of merging municipalities on turnout based on data issued from the NNM approach with calipers (0.025, 0.005 and 0.0005). The list of control variables includes: Multiple, Density, Area, Rural, Population, Share of young, Share of elderly, Share of women, Unemployment, Avg. Endowment and Distance from cluster. Standard errors clustered at the municipality level are in parentheses. P-values: *p<0.1; **p<0.05; ***p<0.01.
<table><caption>**Figure B.8:** Regression results with cluster at the former regional level - Reduced version.</caption><thead><tr><th></th><th colspan="3">Dependent variable: Turnout</th><th colspan="3">Dependent variable: Turnout</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(1)</th><th>(2)</th><th>(3)</th></tr><tr><th></th><th>Baseline</th><th>Controls</th><th>Controls + COVID</th><th>Baseline</th><th>Controls</th><th>Controls + COVID</th></tr></thead><tbody><tr><th>Merger</th><td>-0.005<br>(0.006)</td><td>0.012**<br>(0.006)</td><td>0.010*<br>(0.005)</td><td>-0.006<br>(0.006)</td><td>0.010**<br>(0.005)</td><td>0.008<br>(0.005)</td></tr><tr><th>Time</th><td>-0.154***<br>(0.005)</td><td>-0.167***<br>(0.008)</td><td>-0.208***<br>(0.014)</td><td>-0.154***<br>(0.005)</td><td>-0.167***<br>(0.008)</td><td>-0.208***<br>(0.014)</td></tr><tr><th>Merger x Time</th><td>-0.079***<br>(0.007)</td><td>-0.084***<br>(0.005)</td><td>-0.074***<br>(0.006)</td><td>-0.080***<br>(0.006)</td><td>-0.083***<br>(0.005)</td><td>-0.073***<br>(0.006)</td></tr><tr><th>Constant</th><td>0.744***<br>(0.008)</td><td>1.061***<br>(0.053)</td><td>1.075***<br>(0.050)</td><td>0.744***<br>(0.008)</td><td>1.058***<br>(0.052)</td><td>1.073***<br>(0.050)</td></tr><tr><th>Observations</th><td>583,185</td><td>583,185</td><td>583,185</td><td>576,840</td><td>576,840</td><td>576,840</td></tr><tr><th>R<sup>2</sup></th><td>0.451</td><td>0.567</td><td>0.582</td><td>0.455</td><td>0.567</td><td>0.582</td></tr><tr><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

(a) NNM

(b) Caliper 0.025

<table><thead><tr><th></th><th colspan="3">Dependent variable: Turnout</th><th colspan="3">Dependent variable: Turnout</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(1)</th><th>(2)</th><th>(3)</th></tr><tr><th></th><th>Baseline</th><th>Controls</th><th>Controls + COVID</th><th>Baseline</th><th>Controls</th><th>Controls + COVID</th></tr></thead><tbody><tr><th>Merger</th><td>-0.007<br>(0.006)</td><td>0.009*<br>(0.005)</td><td>0.007<br>(0.005)</td><td>-0.005<br>(0.006)</td><td>0.009*<br>(0.005)</td><td>0.007<br>(0.005)</td></tr><tr><th>Time</th><td>-0.155***<br>(0.005)</td><td>-0.171***<br>(0.008)</td><td>-0.208***<br>(0.013)</td><td>-0.155***<br>(0.005)</td><td>-0.173***<br>(0.009)</td><td>-0.209***<br>(0.014)</td></tr><tr><th>Merger x Time</th><td>-0.081***<br>(0.006)</td><td>-0.083***<br>(0.004)</td><td>-0.074***<br>(0.006)</td><td>-0.080***<br>(0.006)</td><td>-0.082***<br>(0.004)</td><td>-0.073***<br>(0.005)</td></tr><tr><th>Constant</th><td>0.744***<br>(0.008)</td><td>1.063***<br>(0.049)</td><td>1.077***<br>(0.048)</td><td>0.744***<br>(0.008)</td><td>1.073***<br>(0.053)</td><td>1.090***<br>(0.051)</td></tr><tr><th>Observations</th><td>541,980</td><td>541,980</td><td>541,980</td><td>79,866</td><td>79,866</td><td>79,866</td></tr><tr><th>R<sup>2</sup></th><td>0.463</td><td>0.573</td><td>0.585</td><td>0.463</td><td>0.579</td><td>0.590</td></tr><tr><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

(c) Caliper 0.005

(c) Caliper 0.0005

Note: These tables report the results of DiD estimation of merging municipalities on turnout based on data issued from the Nearest Neighbor Matching approach with calipers (0.025, 0.005 and 0.0005). The list of control variables includes: Multiple, Density, Area, Rural, Population, Share of young, Share of elderly, Share of women, Unemployment, Avg. Endowment and Distance from cluster. Standard errors clustered at the former regional level are in parentheses. P-values: \*p<0.1; \*\*p<0.05; \*\*\*p<0.01.
<table><caption>Figure B.9: Regression results with cluster at the actual regional level - Reduced version.</caption><thead><tr><th></th><th colspan="3">Dependent variable: Turnout</th><th colspan="3">Dependent variable: Turnout</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(1)</th><th>(2)</th><th>(3)</th></tr><tr><th></th><th>Baseline</th><th>Controls</th><th>Controls + COVID</th><th>Baseline</th><th>Controls</th><th>Controls + COVID</th></tr></thead><tbody><tr><th>Merger</th><td>-0.005<br>(0.007)</td><td>0.012**<br>(0.006)</td><td>0.010*<br>(0.006)</td><td>-0.006<br>(0.007)</td><td>0.010**<br>(0.005)</td><td>0.008<br>(0.006)</td></tr><tr><th>Time</th><td>-0.154***<br>(0.005)</td><td>-0.167***<br>(0.010)</td><td>-0.208***<br>(0.015)</td><td>-0.154***<br>(0.005)</td><td>-0.167***<br>(0.010)</td><td>-0.208***<br>(0.015)</td></tr><tr><th>Merger x Time</th><td>-0.079***<br>(0.008)</td><td>-0.084***<br>(0.006)</td><td>-0.074***<br>(0.007)</td><td>-0.080***<br>(0.008)</td><td>-0.083***<br>(0.006)</td><td>-0.073***<br>(0.007)</td></tr><tr><th>Constant</th><td>0.744***<br>(0.007)</td><td>1.061***<br>(0.046)</td><td>1.075***<br>(0.047)</td><td>0.744***<br>(0.007)</td><td>1.058***<br>(0.049)</td><td>1.073***<br>(0.050)</td></tr><tr><th>Observations</th><td>583,185</td><td>583,185</td><td>583,185</td><td>576,840</td><td>576,840</td><td>576,840</td></tr><tr><th>R<sup>2</sup></th><td>0.451</td><td>0.567</td><td>0.582</td><td>0.455</td><td>0.567</td><td>0.582</td></tr><tr><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

(a) NNM

(b) Caliper 0.025

<table><thead><tr><th></th><th colspan="3">Dependent variable: Turnout</th><th colspan="3">Dependent variable: Turnout</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(1)</th><th>(2)</th><th>(3)</th></tr><tr><th></th><th>Baseline</th><th>Controls</th><th>Controls + COVID</th><th>Baseline</th><th>Controls</th><th>Controls + COVID</th></tr></thead><tbody><tr><th>Merger</th><td>-0.007<br>(0.007)</td><td>0.009*<br>(0.005)</td><td>0.007<br>(0.006)</td><td>-0.005<br>(0.007)</td><td>0.009<br>(0.006)</td><td>0.007<br>(0.006)</td></tr><tr><th>Time</th><td>-0.155***<br>(0.005)</td><td>-0.171***<br>(0.010)</td><td>-0.208***<br>(0.015)</td><td>-0.155***<br>(0.006)</td><td>-0.173***<br>(0.010)</td><td>-0.209***<br>(0.015)</td></tr><tr><th>Merger x Time</th><td>-0.081***<br>(0.007)</td><td>-0.083***<br>(0.005)</td><td>-0.074***<br>(0.006)</td><td>-0.080***<br>(0.007)</td><td>-0.082***<br>(0.005)</td><td>-0.073***<br>(0.006)</td></tr><tr><th>Constant</th><td>0.744***<br>(0.007)</td><td>1.063***<br>(0.045)</td><td>1.077***<br>(0.047)</td><td>0.744***<br>(0.007)</td><td>1.073***<br>(0.040)</td><td>1.090***<br>(0.041)</td></tr><tr><th>Observations</th><td>541,980</td><td>541,980</td><td>541,980</td><td>79,866</td><td>79,866</td><td>79,866</td></tr><tr><th>R<sup>2</sup></th><td>0.463</td><td>0.573</td><td>0.585</td><td>0.463</td><td>0.579</td><td>0.590</td></tr><tr><th>Controls</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

(c) Caliper 0.005

(c) Caliper 0.0005

Note: These tables report the results of DiD estimation of merging municipalities on turnout based on data issued from the Nearest Neighbor Matching approach with calipers (0.025, 0.005 and 0.0005). The list of control variables includes: Multiple, Density, Area, Rural, Population, Share of young, Share of elderly, Share of women, Unemployment, Avg. Endowment and Distance from cluster. Standard errors clustered at the new regional level are in parentheses. P-values: *p<0.1; **p<0.05; ***p<0.01.
<table><caption>**Table C.10:** Triple differences estimation: Small vs. Large municipalities.</caption><thead><tr><th></th><th>Dependent variable: Turnout<br>(1)<br>Controls + COVID</th></tr></thead><tbody><tr><td>Merger</td><td>0.036***<br>(0.004)</td></tr><tr><td>Time</td><td>-0.244***<br>(0.004)</td></tr><tr><td>Merger x Time</td><td>-0.047***<br>(0.005)</td></tr><tr><td>Merger x Population &lt; 1,000</td><td>-0.022***<br>(0.006)</td></tr><tr><td>Time x Population &lt; 1,000</td><td>0.062***<br>(0.002)</td></tr><tr><td>Merger x Time x Population &lt; 1,000</td><td>-0.004<br>(0.008)</td></tr><tr><td>Constant</td><td>0.913***<br>(0.033)</td></tr><tr><td>Observations</td><td>583,185</td></tr><tr><td>R<sup>2</sup></td><td>0.633</td></tr><tr><td>Controls</td><td>Yes</td></tr></tbody></table>

Note: This table illustrates the results of a triple DiD estimation to compare municipalities based on the population size, based on data issued from the NNM approach. The population dummy cuts the sample in half at 1,000 inhabitants (1 = below threshold, 0 = above threshold). The list of control variables includes: Multiple, Density, Area, Rural, Population, Share of young, Share of elderly, Share of women, Unemployment, Avg. Endowment and Distance from cluster. Standard errors are clustered at the municipal level in parentheses. P-values: \*p<0.1; \*\*p<0.05; \*\*\*p<0.01.

# C. Heterogeneity analysis

## C.1. Size effects: territory and population
<table><caption>**Table C.11:** Triple differences estimation: Effect of a change in municipality's name on turnout.</caption><thead><tr><th></th><th>Dependent variable: Turnout<br>(1)<br>Controls + COVID</th></tr></thead><tbody><tr><td>Merger</td><td>-0.006<br>(0.006)</td></tr><tr><td>Time</td><td>-0.209***<br>(0.004)</td></tr><tr><td>Merger x Time</td><td>-0.055***<br>(0.009)</td></tr><tr><td>Merger x Change in name</td><td>-0.003<br>(0.010)</td></tr><tr><td>Time x Change in name</td><td>-0.021<br>(0.014)</td></tr><tr><td>Merger x Time x Change in name</td><td>-0.002<br>(0.017)</td></tr><tr><td>Constant</td><td>1.209***<br>(0.036)</td></tr><tr><td>Observations</td><td>583,185</td></tr><tr><td>R<sup>2</sup></td><td>0.569</td></tr><tr><td>Controls</td><td>Yes</td></tr></tbody></table>

Note: This table presents the results of triple DiD estimation assessing how a change in municipality's name affects voter turnout, based on data issued from the NNM approach. The list of control variables includes: Multiple, Density, Area, Rural, Population, Share of young, Share of elderly, Share of women, Unemployment, Avg. Endowment and Distance from cluster. Standard errors are clustered at the municipal level in parentheses. P-values: \*p<0.1; \*\*p<0.05; \*\*\*p<0.01.
<table><caption>**Table C.12:** Triple differences estimation: Effect on turnout according to merging year.</caption><thead><tr><th></th><th>Dependent variable: Turnout<br>(1)<br>Controls + COVID</th></tr></thead><tbody><tr><td>Merger2014</td><td>-0.038***<br>(0.013)</td></tr><tr><td>Time</td><td>-0.212***<br>(0.005)</td></tr><tr><td>Merger2015</td><td>0.0005<br>(0.005)</td></tr><tr><td>Merger2016</td><td>0.006<br>(0.006)</td></tr><tr><td>Merger2017</td><td>0.017<br>(0.011)</td></tr><tr><td>Merger2018</td><td>0.012*<br>(0.007)</td></tr><tr><td>Merger2019</td><td>-0.068***<br>(0.017)</td></tr><tr><td>Merger2014 x Time</td><td>-0.033<br>(0.024)</td></tr><tr><td>Merger2015 x Time</td><td>-0.071***<br>(0.007)</td></tr><tr><td>Merger2016 x Time</td><td>-0.065***<br>(0.008)</td></tr><tr><td>Merger2017 x Time</td><td>-0.080***<br>(0.021)</td></tr><tr><td>Merger2018 x Time</td><td>-0.061***<br>(0.008)</td></tr><tr><td>Merger2019 x Time</td><td>0.002<br>(0.016)</td></tr><tr><td>Constant</td><td>1.222***<br>(0.035)</td></tr><tr><td>Observations</td><td>583,185</td></tr><tr><td>R<sup>2</sup></td><td>0.570</td></tr><tr><td>Controls</td><td>Yes</td></tr></tbody></table>

Note: This table presents the results of an estimation assessing how the merger year affects voter turnout, based on data issued from the NNM approach. Although statistical tests indicate significant differences between the treatment effects for the years 2015–2019, 2016–2019, 2017–2019, and 2018–2019, the coefficient for 2019 itself is not statistically significant. As a result, these differences do not reflect meaningful variation in treatment effects across years. The list of control variables includes: Multiple, Density, Area, Rural, Population, Share of young, Share of elderly, Share of women, Unemployment, Avg. Endowment and Distance from cluster. Standard errors are clustered at the municipal level in parentheses. P-values: *p<0.1; **p<0.05; ***p<0.01.
## C.2. Political characteristics

<table><caption>**Table C.13:** Triple differences estimation: Electoral rule.</caption><thead><tr><th></th><th colspan="3" style="text-align: center;">Dependent variable: Turnout</th></tr><tr><th></th><th style="text-align: center;">(1)</th><th style="text-align: center;">(2)</th><th style="text-align: center;">(3)</th></tr><tr><th></th><th style="text-align: center;">Electoral rule</th><th style="text-align: center;">Change in rule</th><th style="text-align: center;">Change to proportional</th></tr><tr><th></th><th colspan="3" style="text-align: center;">Controls + COVID</th></tr></thead><tbody><tr><td>Merger</td><td style="text-align: center;">0.005<br>(0.005)</td><td style="text-align: center;">0.007<br>(0.005)</td><td style="text-align: center;">0.007<br>(0.005)</td></tr><tr><td>Time</td><td style="text-align: center;">-0.183***<br>(0.004)</td><td style="text-align: center;">-0.208***<br>(0.004)</td><td style="text-align: center;">-0.208***<br>(0.004)</td></tr><tr><td>Proportional system</td><td style="text-align: center;">-0.077***<br>(0.002)</td><td></td><td></td></tr><tr><td>Change in electoral rule</td><td></td><td style="text-align: center;">-0.022***<br>(0.004)</td><td></td></tr><tr><td>Change to proportional rule</td><td></td><td></td><td style="text-align: center;">-0.023***<br>(0.005)</td></tr><tr><td>Merger x Time</td><td style="text-align: center;">-0.040***<br>(0.007)</td><td style="text-align: center;">-0.068***<br>(0.005)</td><td style="text-align: center;">-0.068***<br>(0.005)</td></tr><tr><td>Merger x Proportional system</td><td style="text-align: center;">0.023***<br>(0.005)</td><td></td><td></td></tr><tr><td>Time x Proportional system</td><td style="text-align: center;">-0.062***<br>(0.002)</td><td></td><td></td></tr><tr><td>Merger x Time x Proportional system</td><td style="text-align: center;">0.004<br>(0.009)</td><td></td><td></td></tr><tr><td>Merger x Change in electoral rule</td><td></td><td style="text-align: center;">0.031***<br>(0.007)</td><td></td></tr><tr><td>Time x Change in electoral rule</td><td></td><td style="text-align: center;">-0.031***<br>(0.007)</td><td></td></tr><tr><td>Merger x Time x Change in electoral rule</td><td></td><td style="text-align: center;">-0.001<br>(0.013)</td><td></td></tr><tr><td>Merger x Change to proportional rule</td><td></td><td></td><td style="text-align: center;">0.031***<br>(0.008)</td></tr><tr><td>Time x Change to proportional rule</td><td></td><td></td><td style="text-align: center;">-0.037***<br>(0.008)</td></tr><tr><td>Merger x Time x Change to proportional rule</td><td></td><td></td><td style="text-align: center;">0.005<br>(0.013)</td></tr><tr><td>Constant</td><td style="text-align: center;">0.975***<br>(0.033)</td><td style="text-align: center;">1.219***<br>(0.036)</td><td style="text-align: center;">1.219***<br>(0.036)</td></tr><tr><td>Observations</td><td style="text-align: center;">583,185</td><td style="text-align: center;">583,185</td><td style="text-align: center;">583,185</td></tr><tr><td>R<sup>2</sup></td><td style="text-align: center;">0.635</td><td style="text-align: center;">0.569</td><td style="text-align: center;">0.569</td></tr><tr><td>Controls</td><td style="text-align: center;">Yes</td><td style="text-align: center;">Yes</td><td style="text-align: center;">Yes</td></tr></tbody></table>

<footnote>Note: This table illustrates the results of a triple DiD estimations, based on data issued from the NNM approach. The first column compares municipalities with a plurality at large vs. plurinomial proportional voting systems (dummy = 1 for proportional). The second column compares municipalities that observed a change in electoral rule between 2014 and 2020. The last column compares municipalities that observed a change in electoral rule on the same period to the plurinomial proportional system. The list of control variables includes: Multiple, Density, Area, Rural, Population, Share of young, Share of elderly, Share of women, Unemployment, Avg. Endowment and Distance from cluster. Standard errors are clustered at the municipal level in parentheses. P-values: \*p<0.1; \*\*p<0.05; \*\*\*p<0.01.</footnote>
D. Complementary information

D.1. *French municipalities, a bit of history*

The large number of French municipalities has its origins in the Revolution of 1789. Back then, the determination of new municipal boundaries involved a choice between creating 'local action groups' of 3,000 to 4,000 inhabitants or retaining the existing parishes. Opting for the latter resulted in the establishment of more than 40,000 municipalities (Petitet, 1998; Offner, 2006; Bideau, 2019).

Over the years, successive reforms have sought to consolidate municipalities and reduce their overall number. Between 1790 and 2010, the number of local units decreased from about 40,000 to approximately 36,700 (Ozouf-Marignier and Verdier, 2013). Various measures were introduced to achieve this objective. In 1971, the Marcellin Law (Loi Marcellin) introduced a "merger-association" (fusion-association) model, which allowed municipalities to retain a certain level of autonomy while encouraging cooperation and merging with neighboring municipalities. While around 2,000 municipalities merged, the law provided for post-merger withdrawal which led to the restoration of approximately 200 municipalities, resulting in a mixed success (Tellier, 2017; Bideau, 2019). Figure 2 evidences this reduction, quickly followed by a small increase in the number of municipalities.

In 2010, a new piece of legislation was enacted, supplanting the *Loi Marcellin* and introducing the statute of the *commune nouvelle* ("new municipality"). To address municipal fragmentation, the measure facilitated simpler mergers (*Code général des collectivités territoriales*, 2010). Despite this initiative, municipalities showed persistent hesitancy, with 13 new municipalities established between 2010 and 2014. In 2015, the implementation of a financial incentive led to the consolidation of 2,500 municipalities (*Code général des collectivités territoriales, 2015b*). In 2019, the law was updated (*Code général des collectivités territoriales, 2019*) to continue and complete the reform efforts initiated in 2015. However, this momentum was disrupted by the onset of COVID-19. More recently, in 2024—outside the scope of this analysis—the Finance Bill revised financial provisions to strengthen incentives for municipal mergers (*Association des Maires de France, 2024*).

D.2. *COVID-19 in France and its impact on municipal elections*

The latest French municipal elections took place in 2020, coinciding with the worldwide break of COVID-19. Although the virus appeared as early as late 2019 in China, it was not until mid-February that clusters started to emerge in France. A month later, on March 12, the French President Emmanuel Macron announced the closure of schools in France while affirming his decision to maintain the first round of municipal elections scheduled for March 15, just three days later. The day before the elections, the Prime Minister announced the closure of all non-essential businesses, such as restaurants, cafes, and cinemas. Just two days after the first round of elections, the general population lockdown was put in place, which also politically implied the postponement of the second round of elections that were supposed to take place the following Sunday (March 22). This second round eventually took place at the end of June for approximately 5,000 municipalities. In this particular sanitary context and the concern
generated by it, democratic manifestations have been impacted.

The literature has recently focused on the impact of the pandemic on turnout, agreeing on negative consequences and evidencing differences based on socioeconomic or territorial characteristics. In Italy, Picchio and Santolini (2022) highlight that an increase in the elderly mortality rate decreases turnout by 0.5 percentage points. This effect is more pronounced in densely populated municipalities, where the proximity increases the risk of getting infected. Similarly, Constantino et al. (2021) find that higher COVID-19 death counts in the month preceding the election, as well as elevated death rates in neighboring municipalities, reduce turnout. Both the perceived and objective risks associated with elevated COVID-19 incidence increase as the spatial and temporal proximity to the event grows closer: in earlier stages of the pandemic COVID-19 cases and death rates correlated with higher turnout. In the French context, voter turnout declined in municipalities closer to COVID-19 clusters. The impact varied based on the proportion of older residents, who faced higher health risks, as well as factors like population density and electoral competitiveness—especially in municipalities with only one candidate list (Noury et al., 2021). Despite the unprecedented circumstances, when weighing civic duty against health concerns, voters generally prioritized participation (Santana et al., 2020).

Following Noury et al. (2021), the analysis controls for the outbreak of COVID-19 by accounting for the distance of each municipality to its nearest cluster evidenced on the election day (first-round). Figure D.10 shows merging municipalities in blue and COVID-19 clusters as of March 15 in black. This variable is included as a control. Detailed data on cases and deaths were limited, as the election occurred at the very start of the outbreak.

<figure><img src="image_12.png" /><figcaption>**Figure D.10:** Merging municipalities and COVID-19 clusters in March 2020.</figcaption></figure>

Note: Author, data from Géoservices. This map represent the merging municipalities in dark blue and the black points denote the clusters known as of March 15, 2020.
### D.3. Legislative background: different ways through which a merger can occur

The legislative framework governing municipal mergers in France imposes several conditions. Municipalities must share territorial contiguity, even if only at a single point. Cross-departmental mergers require the consent of both departmental and regional assemblies, while the prefect has discretionary power to approve or deny merger proposals.

Municipalities planning a merger must anticipate territorial and administrative changes and may receive support from councilors and partial departmental subsidies. The new entity must reorganize governance, services, archives, and personnel, harmonizing working conditions and social housing, while carefully planning delegated municipalities and inter-municipal arrangements.

Until the first general renewal (i.e. municipal elections), the municipal council includes all members from the former municipalities' councils, following concordant deliberations. After the renewal, newly merged municipalities temporarily have a larger council to ensure fair representation, maintaining this composition until the second post-merger election. For mergers involving many municipalities, specific rules prevent a drastic reduction in councilors (Code général des collectivités territoriales, 2015a).

The decision to merge can be initiated in four different ways<fnref n="8" />, by:

1. All municipal councils of the concerned municipalities.

2. At least two-thirds of municipal councils, representing over two-thirds of the total population.

3. A community council, if the project covers its entire perimeter.

4. The prefect.

Two scenarios arise for popular consultation: (1) the project covers part of one or more inter-municipal cooperations, or (2) it covers the entire territory of an inter-municipal cooperation.

<table><caption>**Table D.14:** Initiatives, required conditions, and consultation for municipal mergers.</caption><thead><tr><th>Scope of project</th><th>Initiative</th><th>Required conditions for majority</th><th>Mandatory consultation</th></tr></thead><tbody><tr><td rowspan="2">Partial inter-municipal area</td><td>All municipalities desiring to merge</td><td>Favorable opinion from each municipal council</td><td>No</td></tr><tr><td>Prefect</td><td>Favorable opinion from 2/3 of municipal councils representing ≥2/3 of the population</td><td>Yes, if majority achieved without approval from all councils</td></tr><tr><td rowspan="3">Full inter-municipal area</td><td>2/3 of municipal councils of an EPCI with own taxation</td><td>2/3 of councils representing ≥2/3 of the population</td><td>Yes, if majority achieved without approval from all councils</td></tr><tr><td>Council of intermunicipal cooperation</td><td>2/3 of councils representing ≥2/3 of the population</td><td>Yes, if majority achieved without approval from all councils</td></tr><tr><td>Prefect</td><td>2/3 of councils representing ≥2/3 of the population</td><td>Yes, if majority achieved without approval from all councils</td></tr></tbody></table>

<footnote n="8">⁸Refer to the practical guide made available by the French Mayors' Association (AMF) at the following link: https://www.amf.asso.fr/documents-2020-2026-creer-une-commune-nouvelle--guide-pratique-retours-dexperience/40917 and (Code général des collectivités territoriales, 2015b).</footnote>