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Studying Multi-Level Systems with Cross-Level Data: Introducing Three Integrated Datasets

[journal article]

Röth, Leonce
Saldivia Gonzatti, Daniel
Kaftan, Lea
Kaiser, André

Abstract

Most political systems consist of multiple layers. Yet datasets are predominantly situated at a single territorial tier, encouraging methodological nationalism, regionalism, and localism. We present three new integrated datasets that include electoral, institutional, ideological, and government comp... view more

Most political systems consist of multiple layers. Yet datasets are predominantly situated at a single territorial tier, encouraging methodological nationalism, regionalism, and localism. We present three new integrated datasets that include electoral, institutional, ideological, and government composition data on the country and regional level (RD|CED, RED and RPSD). With this data, we cover 337 country elections on the regional level, 2,226 regional elections, and 2,825 regional cabinets in 365 regions of 21 countries from 1941 to 2019, accounting for 800 political parties and their ideological positions. Combined, these data complement and extend existing datasets and facilitate the study of political interaction across levels. Data are available at http://multi-level-cross-level-politics.eu/ or can be accessed through the Havard Dataverse repository. We conclude with an agenda for future cross-level studies.... view less

Keywords
political system; multi-level system; methodology; data capture

Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Basic Research, General Concepts and History of Political Science

Free Keywords
ideology; regional elections; regional governments

Document language
English

Publication Year
2025

Page/Pages
p. 1-15

Journal
British Journal of Political Science, 55 (2025)

DOI
https://doi.org/10.1017/S0007123424000553

ISSN
1469-2112

Status
Published Version; peer reviewed

Licence
Creative Commons - Attribution-ShareAlike 4.0

FundingFunded by the German Research Foundation (DFG) - Grant numbers: KA 1741/10-1 and KA 1741/10-2


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© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.