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Climbing up, letting down, or putting the ladder away? Regional ministers’ movements in multilevel systems

Published online by Cambridge University Press:  02 December 2024

Matteo Boldrini*
Affiliation:
Department of Political Science, Luiss Guido Carli, Rome, Italy
Selena Grimaldi*
Affiliation:
Department of Political Science, Communication and International Relation, University of Macerata, Macerata, Italy
*
Corresponding author: Matteo Boldrini; Email: mboldrini@luiss.it; Selena Grimaldi; Email: selena.grimaldi@unimc.it
Corresponding author: Matteo Boldrini; Email: mboldrini@luiss.it; Selena Grimaldi; Email: selena.grimaldi@unimc.it

Abstract

Regional cabinet members (RCMs) are key political actors in subnational politics, especially in federal systems or in countries that have undergone a process of regionalization or devolution, even though they are still less studied than federal or national ministers. Italy and its regionalization process represent an interesting case study to understand how and under what conditions members of regional cabinets can exit from politics or move upwards, or downwards the different tiers of government. By using an original dataset of 721 RCMs in ordinary statute regions we tested through multinomial regression analysis whether political and institutional or personal factors influence their movements (both legislative or executive) upward, that is, toward national and/or European positions, backward toward local (both provincial or municipal) positions or their exit from politics. Our results show that both political–institutional and individual factors matter. In particular, displaying a previous national career favors upward movements while being a technician or independent favors the exit from politics.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Società Italiana di Scienza Politica
Figure 0

Table 1. Distribution of the dependent variables

Figure 1

Table 2. Distribution of the independent variables

Figure 2

Table 3. Descriptive statistics of previous local, regional, and national careers (in days)

Figure 3

Table 4. Results of the multinomial regression analysis

Figure 4

Figure 1. Predictive margins of statistically significant variables with 95% CIs.Note: The dependent variable is standardized from 0 to 1.

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Boldrini and Grimaldi Dataset

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