Discontent in the ‘peripheries’: an investigation of the rise of populism in Italy

ABSTRACT

The concept of periphery, going beyond the mere interpretation in geographical terms, incorporates a ‘relational’ character that is implicitly characterized by connotations of power and inequality. Peripheries can be actively created as an outcome of shifts in economic and political decision-making at various scales. The recent populist wave has brought to the fore the issue of peripheries as ‘left behind’ places, striking back in the ballot boxes. In this paper, we investigate the role of different dimensions of peripherality and their changing geographies on populist voting patterns by analysing the growth of non-traditional parties between the general elections held in Italy in 2013 and 2018. First, we consider a spatial dimension of peripherality, that is, the geography of access to services of general interest. Second, we explore some factors that might be associated with a condition of peripherality, meant as marginality. We find that the growth of discontent in Italy has different explanatory factors in core and peripheral areas. Irrespective of geographical peripherality, where, however, populist votes are more concentrated, our findings reveal that a condition of marginality may feed a sense of revenge connected to the feeling of ‘not mattering’ also in urban areas.

KEYWORDS:

• populism
• voting patterns
• peripherality
• inner areas
• Italy

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1 The growth rate is calculated as follows: $(\mathrm{V}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{d}\mathrm{V}\mathrm{o}\mathrm{t}\mathrm{e}{\mathrm{s}}_{2018}-\mathrm{V}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{d}\mathrm{V}\mathrm{o}\mathrm{t}\mathrm{e}{\mathrm{s}}_{2013}/\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{V}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{d}\mathrm{V}\mathrm{o}\mathrm{t}\mathrm{e}{\mathrm{s}}_{2013})\ast 100$.

2 It is worth specifying that electoral outcomes provided by the Ministry of Interior do not include the ISTAT code for municipalities, but only their names as a string variable. We therefore carry out a fuzzy matching to align electoral data to our covariates. Considering that the official list of municipalities changed several times between 2013 and 2018 (some municipalities have been merged with larger cites, others changed their name), and because of the fuzziness of the matching procedure based on strings, of 8084 municipalities in 2013, we could automatically match only 7235 municipalities (about 89% of the initial 2013 sample) in order to keep the list of municipalities balanced between 2013 and 2018. We further lose an additional 476 municipalities because of missing values in our set of covariates collected from ISTAT. More specifically, we missed observations for foreign employment rate, youth employment rate and seismic risk. This procedure leaves 6759 observations in the final sample. However, municipalities excluded from the estimation sample represent minor municipalities that are not spatially sorted in specific areas. Excluded municipalities have a median population of 857 people (population density of 24 people/km²), compared with a median population of 4763 people for in-sample municipalities (population density of 127 people/km²). The use of population weights in our estimates accounts for size effects, so that the small population size of excluded municipalities should not significantly alter our results.

3 See http://ottomilacensus.istat.it/documentazione/.

4 In order to check for the statistical significance of the differences detected between the two spatial groups considered, we perform the Chow test for equality of log-income coefficients. We use income because its effect is strong, robust, and consistent across all models and spatial classes. Furthermore, it is widely acknowledged as a crucial predictor of populist voting in the academic literature on the topic.