Beyond the Single-Tier Perspective

From the introduction of the term ‘multi-level governance’ in 1993 (Marks Reference Marks, Cafruny and Rosenthal1993), all canonical studies on multi-level governance have stressed one key attribute of multi-level systems – interdependence across tiers (Hooghe Reference Hooghe1996; Hooghe and Marks Reference Hooghe and Marks2001; Marks, Hooghe, and Blank Reference Marks, Hooghe and Blank1996; Papadopoulos Reference Papadopoulos, Lachapelle and Oñate2007; Rhodes Reference Rhodes1997; Rokkan and Flora Reference Rokkan and Flora2000; Scharpf Reference Scharpf2001). In multi-level systems, because no level holds absolute power to achieve (or prevent) political solutions, this necessarily involves interaction, competition, and collaboration across levels. Interdependencies generate the need for interaction across levels, and studying such interactions requires data that reach beyond a single tier. Self-evidently, interdependencies equally exist across units within one level – let us say, across regions. These are horizontal dependencies, while requirements for coordination and interdependencies between tiers are vertical (Schakel and Romanova Reference Schakel and Romanova2021).

Empirically, vertical interdependencies can take many forms but are typically informed by the description of specific competencies backed up by authority (Hooghe, Marks, and Schakel Reference Hooghe, Marks and Schakel2016). In highly decentralized systems, the country level depends on the coordination and cooperation of lower levels – as studies on education, health, and immigration have repeatedly shown (see, for example, Bélanger and Lavenex Reference Bélanger and Lavenex2023; Garritzmann, Röth, and Kleider Reference Garritzmann, Röth and Kleider2021; Zapata-Barrero, Caponio, and Scholten Reference Zapata-Barrero, Caponio and Scholten2017). Not only does the distribution of policy competencies matter but also whether those competencies are backed up by fiscal capacities. This has been termed the difference between fiscally balanced and imbalanced multi-level arrangements (Lin and Zhou Reference Lin and Zhou2021). Fiscal dependencies can exist in many ways; for example, in asymmetry between policy responsibilities and funding opportunities or in terms of public deficits and bail-out obligations (Hernández Rodríguez Reference Hernández Rodríguez2008) – the so-called bailout problem (Von Hagen and Eichengreen Reference Von Hagen and Eichengreen1996). The distribution of resources on one level might in part depend on the political willingness and ideological or organizational alignment to actors on other tiers, as the literature on pork-barrel politics and alignment has shown (Hanretty Reference Hanretty2021; Kleider, Röth, and Garritzmann Reference Kleider, Röth, Garritzmann, Däubler, Müller and Stecker2020; Solé-Ollé and Sorribas-Navarro Reference Solé-Ollé and Sorribas-Navarro2008). Thus, interdependencies exist even in the constellation of a formally clear separation of authority across levels (self-rule). In many multi-level systems, interdependencies are formally in-built by defining authority over policy domains as a joint competence across levels (shared rule). Self and shared rules provide different institutional incentives for competition and collaboration in multi-level systems (Mueller Reference Mueller and Kincaid2019). In short, multi-level systems provide a variety of interdependencies that are inscribed into their political systems.

Because interdependencies are in-built into the institutions of multi-level systems, political behaviour is shaped across levels, too. Studies on second-order elections have demonstrated how dynamics on one level can affect behaviour on another (Baethge, Dallendörfer, and Kaiser Reference Baethge, Dallendörfer and Kaiser2019; Marsh Reference Marsh1998; Reif, Schmitt, and Norris Reference Reif, Schmitt and Norris1997; Schakel and Jeffery Reference Schakel and Jeffery2013; Swenden and Maddens Reference Swenden, Maddens, Swenden and Maddens2009). Similar conclusions can be drawn from the debate about blame attribution in multi-level systems (Baute and Pellegata Reference Baute and Pellegata2023; Heinkelmann-Wild and Zangl Reference Heinkelmann-Wild and Zangl2020; León, Jurado, and Madariaga Reference León, Jurado, Madariaga, Däubler, Müller and Stecker2020).

At the partisan level, many studies have addressed how the fortunes of a programmatic orientation of specific parties or party families on one level can be influenced by parties on another level (Deschouwer Reference Deschouwer2003; Detterbeck and Hepburn Reference Detterbeck, Hepburn, Erk and Swenden2010; Fabre Reference Fabre2008; Guinjoan Reference Guinjoan2016; Meguid Reference Meguid2015; Swenden and Maddens Reference Swenden, Maddens, Swenden and Maddens2009; Thorlakson Reference Thorlakson, Lachapelle and Oñate2018). The causes of voters’ and politicians’ behaviour on one level might often be informed by considerations on another level or multiple levels simultaneously. The effects parties have on each other across levels do not, of course, remain on the micro level but can influence entire party systems (Hepburn Reference Hepburn, Detterbeck and Hepburn2018, 168). In other words, micro effects translate into macro phenomena such as polarization or fragmentation that, again, create follow-up dynamics. Very few studies have looked at such micro-macro, macro-micro, or macro-macro interactions (see for a similar argument Golder et al. Reference Golder, Lago and Blais2017).

The study of all these cross-level interactions and dynamics necessarily requires integrated data across levels that are typically gathered for every individual study in isolation. Accordingly, the accumulation of evidence on how multi-level systems operate could benefit from ready-made datasets that allow researchers to leapfrog resource-intensive data generation and processing steps. For a long time, those data have not existed and are still relatively sparse. In the following section, we describe existing cross-level data sources and point to the additions that our datasets provide.

Three New Datasets

If researchers are working with a cross-level question and using existing datasets, the chance is very high that Arjan Schakel is involved. Arjan Schakel has been involved in mapping political authority in multi-level systems (Hooghe, Marks, and Schakel Reference Hooghe, Marks and Schakel2016; Shair-Rosenfield, Schakel, and Niedwiecki Reference Shair-Rosenfield, Schakel and Niedwiecki2021), and has mapped fine-grained policy competencies across levels (Schakel Reference Schakel2009; Schakel Reference Schakel2010). He gathered extensive data on regional, regionally disaggregated national, and European elections (Schakel and Romanova Reference Schakel and Romanova2023). With Emanuele Massetti, he engaged in mapping the ideology of regionalist parties and collected data on regional government compositions (Massetti and Schakel Reference Massetti and Schakel2016; Schakel Reference Schakel, Detterbeck and Hepburn2018).Footnote ² The gathering of our data has benefited from Arjan Schakel’s pioneering work in many ways.

Despite some overlap, our three datasets provide important complements to the existing datasets that cover multiple levels. First, our datasets are easy to merge and thus provide a single source that combines electoral, institutional and partisan information on the partisan and macro levels. Second, they provide electoral data on regional elections and regionally disaggregated country-level elections in countries that have not so far been covered. Third, it is by far the most encompassing dataset on the regional level that includes partisan ideology measures, as well as regional government composition and ideology measures. Finally, combining partisan ideology and government ideology data on the regional and country levels with institutional variables provides a new and unique opportunity to study multi-level politics.

Notwithstanding our core claim that multi-level data are required for multi-level questions, we provide the data in a structure of three distinct datasets. These distinct datasets allow for various individualized combinations of merged multi-level data while, at the same time facilitating the parsimonious use of single datasets for other research questions. For example, disaggregated country-level election data might not be of interest to researchers working on regional elections. Others might benefit only from the dataset providing regional government positions. Thus, we encourage multi-level research but do not want thereby to hamper single-tier research.

In the following, we describe the complementary contribution of the new datasets in more detail. The first two datasets cover different aspects of elections in multi-level systems at the party level. The third moves to the regional party system level, including information such as regional government positions, regional electoral systems, and socio-demographic data on the regional level. Most importantly, the three datasets are easy to combine and allow the study of cross-level interactions. Before we introduce the three new datasets, it is helpful to set out three concepts that are crucial for the composition of all three: the definition of a region, the definition of a party, and the temporal specification:

The Regionally Disaggregated Country Elections Dataset (RD|CED)

The Regionally Disaggregated Country Elections dataset (RD|CED) entails country-level election results at the regional level. This dataset is unique in terms of temporal and geographical coverage and was initially gathered to analyse the electoral importance of regions for country governments. Accordingly, it includes many variables based on partisan electoral results that capture the relative electoral importance of regions from a country’s partisan perspective (see Alonso Reference Alonso2012; O’Neill Reference O’Neill2003; Röth and Kaiser Reference Röth and Kaiser2019; Röth et al. Reference Röth, Kaiser and Varol2016 for related arguments).

In terms of coverage, Schakel’s data cover substantially more countries in Eastern Europe whereas the RD|CED adds some countries in Latin America. Furthermore, the RD|CED – while typically starting in the mid-1940s – is more up-to-date (terminating in 2019 instead of 2009). However, both datasets can and should be combined as necessary. The most substantial difference is that the RD|CED is already combined into a single data frame and includes IDs that allow easy merging with other datasets, such as those on party positions (Manifesto Project IDs, Regional Manifestos Project IDs, Chapel Hill Expert Survey IDs) or the Regional Authority Index (harmonized regional IDs). Furthermore, the RD|CED can be combined with our two further datasets (compare Table 1 for the key contribution of the RD|CED). Please consult the RD|CED codebook for a detailed overview of concepts, decisions, sources, coverage and variables (Röth et al. Reference Röth, Saldivia Gonzatti and Kaftan2025c).

Table 1. Key contributions of the RD|CED

The Regional Elections Dataset (RED)

The Regional Elections dataset (RED) covers regional election results. Coverage is necessarily less comprehensive than the RD|CED because regional elections must exist in the first place in order to generate data. However, whenever regional elections took place in the sample of the RD|CED, we also captured the results at the level of single parties. The RED can thus be easily merged with the RD|CED dataset to compare electoral results across levels or regions.

We invested a good deal of effort in providing party positions on the regional level. Country-level party positions exist in various forms, typically based either on manifestos with good coverage across time and geography or on expert surveys, reaching beyond pledges from party platforms but with lower coverage, particularly across time. For the regional level, while there exists a manifesto-based equivalent (Alonso, Gómez, and Cabeza Reference Alonso, Gómez and Cabeza2013), it has significantly lower coverage (covering regions in the UK, Spain, and Italy), but no systematic expert survey data exists. For the provision of regional party positions with broader coverage, we proceed as follows:

1. (1) Party positions from the country-level Manifesto Project are used, based on the nearest temporal match and party ID.

2. (2) Parties not thereby covered are given decade averages from the same party family in the Manifesto Project. Party family affiliations are qualitatively located, using ideological markers such as social democracy, socialism or conservatism that we identified in descriptions of the parties.

The party positions are based on three distinct procedures and cover an overall left-right (RILE; Lehmann et al. Reference Lehmann, Franzmann and Al-Gaddooa2024), a state market, and a cultural dimension. We included the RILE as the most prominent measurement of an overall left-right dimension. We decided to add a cultural and economic dimension based on manifesto data but scaled by latent item response models (compare Garritzmann, Röth, and Kleider Reference Garritzmann, Röth and Kleider2021; Röth Reference Röth2017) because it has been shown to have higher convergence validity with expert surveys. Furthermore, we provide a cultural and economic dimension because scholars increasingly acknowledge the multi-dimensionality of party competition.

Using country-level party positions for parties that compete on the regional level is probably the most controversial decision in the setup of this dataset. We justify and validate this choice in the codebook of the RED (part 4), where we discuss the different options in terms of validity, comparability, and coverage. Validating our positions with regional manifestos is a methodological challenge in itself, which is described in closer detail in the RED codebook. The correlation between the overall left-right positions of the same party on the regional and country levels depends on the positional measure. For the standard measure, RILE, the correlation is 0.73, whereas item response-based positions reach a correlation of 0.84 (based on manifestos from the UK, Spain, and Italy). For such validation, we put regional and country-level manifestos in a single positional space by running latent item response models on a combined dataset (compare Table 2 for the results). The size of these correlations is comparable to the size of correlations between positions for European and country elections (Braun and Schmitt Reference Braun and Schmitt2020) or the comparison of regional and country-level manifesto-based positions in Germany (compare Kleider, Röth, and Garritzmann Reference Kleider, Röth, Garritzmann, Däubler, Müller and Stecker2020).

Table 2. Correlation table of country-level and regional party positions of the same parties (closest temporal match)

Note : Results are based on a generalized item response model using the logarithm of dimension-based issue salience as provided by the country and regional manifesto projects. We exclude observations where the distance between the regional and national manifesto was higher than 5 years.

Those who prefer alternative measurements are free to take advantage of the different harmonized manifesto IDs. The dataset includes IDs and alternative positional measurements from datasets such as CHES (Jolly, Bakker, and Hooghe Reference Jolly, Bakker and Hooghe2022), the Regional Manifestos Project (Alonso, Gómez, and Cabeza Reference Alonso, Gómez and Cabeza2013) and the country-level Manifesto Project (Lehmann et al. Reference Lehmann, Franzmann and Al-Gaddooa2024). The RED entails yearly dummies for parties’ inclusion in the regional cabinet, acknowledging that regional cabinets change during electoral periods (compare Table 3 for the key contribution of the RED). Please consult the RED codebook for a detailed overview of concepts, decisions, sources, coverage, and variables (Röth et al. Reference Röth, Saldivia Gonzatti and Kaftan2025a).

Table 3. Key contributions of the RED

The Regional Party-System Dataset (RPSD)

The Regional Party-System dataset (RPSD) aggregates and complements party-level data into a dataset with regional party-system information. It covers not only regional government positions but a series of party-system-level features such as turnout, party-system fragmentation, party-system polarization, and regional centres of gravity. Furthermore, we include country cabinet information to take cross-level relations between regions and countries into account. Country government ideology scores on comparable scales allow the comparison to regional governments and serve as a basis for ideological alignment/proximity scores across levels. We complement the RPSD with institutional information such as electoral systems, district magnitude, degree of self-rule or shared rule, and overall regional authority. Finally, we add regional socio-demographics to the RPSD. Thus, the data include regional unemployment rates, regional growth, GDP per capita, population, and population density measures (compare Table 4 for the key contribution of the RPSD). Please consult the RPSD codebook for a detailed overview of concepts, decisions, sources, coverage and variables (Röth et al. Reference Röth, Saldivia Gonzatti and Kaftan2025b).

Table 4. Key contributions of the RPSD

All three datasets can be easily combined to match party-level with party-system-level variables across two territorial tiers. This allows a whole new dimension of data to address questions related to the cross-level nature of multi-level systems. However, such a data structure quickly becomes complex, and temporalities across levels must be synchronized. If we think about ideological alignment or cross-level congruence in the vote, we face country-region dyads. Those dyads change with every change in a variable at either level. We make this manageable by creating yearly data that use the concept of the longest attribute in a year. If we think of ideological alignment and a regional cabinet governing throughout an entire year, and we observe a shift at the country level in October, we attribute the entire year to the alignment score visible from January to October.

Sources

The most important variables in the datasets rely on election results as well as partisan and institutional information. We primarily used national and regional electoral commission reports, national statistical yearbooks, archival data from electoral commissions, and secondary sources if no primary sources were available. Wikipedia was among these secondary sources; it reports reliable information on regional and country-level elections, as well as regional and national government compositions (Döring and Schwander Reference Döring and Schwander2015). Resource constraints prevented the extensive use of media reports. Election data provided by Schakel (Reference Schakel2013a; Reference Schakel2021) validated and occasionally complemented some of our own. In cases where regional results were missing or political-administrative reforms rearranged the region’s municipal composition, we aggregated municipal-level election results. Where data conflicted, we used the most comprehensive source available with a strong preference for primary sources.

Identifying and merging partisan information across sources and levels is a time-consuming and sometimes complicated task. Party names often differ, and alliances are sometimes but not always considered. The continuity of parties after splits or renaming is handled differently across sources. We treated this issue by using IDs that have a strong preference for continuity. For example, where a party changes its name but remains organizationally highly similar, we change the name but keep the same ID. This decision facilitates the study of parties over time but might be problematic for other purposes: in such cases, users might need to consider creating new IDs. For a more detailed description of the sources and decisions, please consult the country notes in the codebooks.

Data access

Version 1 of our datasets is accessible via Havard Dataverse. Through our GitHub repository, anyone can propose extensions and corrections to our data on a rolling basis. After reviewing and curating push requests, further versions will be periodically updated. The aggregated data can be inspected and displayed on the new data portal: http://multi-level-cross-level-politics.eu/.