Political equality in unequal territories: the impact of territorial inequalities on voter turnout in Italy’s regional elections

ABSTRACT

This study investigates how disparities in the distribution of economic, social, and institutional capital across territories affect voter turnout. Analysis of Italian regional elections held from 2003 to 2021 reveals that electoral participation is higher in more economically developed regions than in less developed ones. However, the effect of economic conditions becomes more tangible in territories featuring high levels of social interconnectedness, whilst the institutional capital does not have a significant effect on electoral participation. By showing that voter turnout depends on the interaction between social and economic factors, this study indicates the need for a holistic approach to encouraging political participation, combining long and short-term strategies addressing territories’ societal and economic assets. Another important implication of this article is that ‘context matters’ also at the subnational level. This suggests that future work on turnout should rely more on comparisons across territories within countries.

KEYWORDS:

• Territorial inequalities
• voter turnout
• economic capital
• social capital
• institutional capital

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1 In Italy, these correspond the region defined at the NUTS (Nomenclature of Territorial Units for Statistics) 2 level.

2 L. Cost 3/2001 that reformed the Title V of the Italian Constitution.

3 The direct election of the president was introduced in ordinary regions with L. Cost. 1/1999, and in special statute regions with L. Cost. 2/2001.

4 Aosta Valley, Friuli Venezia-Giulia, Trentino-Alto Adige Sardinia, and Sicily.

5 https://www.arjanschakel.nl/images/RAI/europe_eu/ITA_2021.pdf (accessed: 20 December 2022).

6 https://www.istat.it/en/archivio/16777.

7 This indicator is based on the annual Multi-purpose survey on households ‘Aspects of daily life’ carried out by the ISTAT.

8 From the Multi-purpose survey on households ‘Aspects of daily life’.

9 Measured as a dummy variable, with value 1 if there were at least 5 regions holding elections on the same day, and 0 if otherwise.

10 Measured as the distance in days between the former general election and the regional election.

11 Measured as the percentage point difference between the votes for the elected president and those for the main competitor, measured as percentage of the popular vote.

12 Since 2001, due to early elections, the ordinary regions have disarranged their electoral calendars (Bolgherini, Grimaldi, and Valbruzzi 2021). Accordingly, elections can be clustered by electoral cycles (2003–2006; 2008–2011; 2012–2015; 2017–2021), where each region went to vote once, except for the cycle 2017–2021, when Calabria and the Aosta Valley had early elections after one and two years, respectively.

13 Another solution would be to use multi-level mixed linear regressions with elections as level-1 unit of analysis and regions as level-2 (Stockemer and Scruggs 2012). After running multi-level regressions, however, the likelihood ratio tests indicate that, when adding all predictors in the model, OLS models should be preferred. Nonetheless, multi-level models (not shown in the text) yield the same results as OLS.