Abstentionist Voting – Between Disengagement and Protestation in Neglected Areas: A Spatial Analysis of The Paris Metropolis

Abstract

This article analyzes electoral behaviors related to voting abstention in the Metropolis of Paris. We highlight the interest of a contextual approach to examine non-voting behaviors. Using socio-economic and demographic data at the level of municipalities, we construct a spatial model to explain the reasons for abstention. Our results support the idea that abstentionism expresses a disengagement behavior as well as a protestation behavior. People disengage from politics because they believe that politicians (no matter which party is in power) will not be able to change their situation. This hypothesis applies to non-voters peripheral to political life. We also show that these people tend to live in socio-economically marginalized areas. The protest attitude is found especially in “left-behind” areas that have experienced a significant decline in the supply of public services and local shops. Bridging the divide in these neglected areas is essential to avoid further marginalization and growing protest.

Geography of contestation: A study on the Yellow Vest movement and the rise of populism in France

“…The proportion of precarious inhabitants is higher in large agglomerations, especially at their margins, and strong disparities in income and living conditions are observed in these urban spaces. These inequalities can lead to protestation behavior, as Bourdin and Tai (2021) demonstrated. This is also highlighted by the significant negative effect of the distance to the metropolises, which applies to people living in rural areas but most of all to workers who are forced to commute from the remote suburbs to distant places of work located in the city centers or in other peri‐urban areas.…”

“…In particular, the logic of class voting has been profoundly transformed. The geographical fractures of socioeconomic poverty are widening, and the optimism–pessimism divide is emerging as a new reading grid (Bourdin & Tai, 2021; Evans, 2018). Clearly, the results of the last presidential elections and the legislative elections that followed further accentuated this trend.…”

Examining voter turnout using multiscale geographically weighted regression: The case of Slovakia

“…This is especially important as previous studies have predominantly focused on the relationship between 'left-behind places,' geography of discontent, and populist/protest/radical voting (recently, e.g. Suchánek & Hasman, 2023;Urso et al, 2023), but not specifically on abstaining from voting (for a rare exception see Bourdin & Tai, 2022). In our study, we have also identified that territories which can generally be characterised as socioeconomically disadvantaged, peripheral, and in overall decline, report even lower than expected voter turnout (see Fig.…”

Spatial spillover effects associated with the Sweden Democrats' growing support

“…One of the most notable advantages is the possibility of examining spillover effects, that is, whether space or geography matters (Elhorst, 2014). This manifests itself in electoral geography in the sense that a change in an explanatory variable in one region can affect the votes in a neighbouring region (Bourdin & Tai, 2021). Furthermore, it does not treat individual districts as ‘floating islands’ (Fujita & Mori, 2005, p. 395), but is able to analyse them in their real geographical context, in relation to their neighbouring areas.…”

“…Furthermore, it does not treat individual districts as ‘floating islands’ (Fujita & Mori, 2005, p. 395), but is able to analyse them in their real geographical context, in relation to their neighbouring areas. The strength of electoral geography studies using the tools of regional science is that they study contextual effects in greater depth, as they consider economic, demographic and geographical aspects simultaneously (Bourdin & Tai, 2021). According to Fiorino and colleagues (2021), the advantage of using a spatial econometric toolkit is that it allows the analysis to take into account potential unobservable geographical or spatial factors that, for example, multilevel models cannot.…”