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Far-right political parties have won considerable shares of the vote in elections across Europe throughout the 21st century, with parties such as the National Rally in France and Alternative für Deutschland in Germany achieving unprecedented electoral breakthroughs. While the causal mechanisms underlying the success of these parties are well established in the comparative politics literature, the emergence of the Irish Far-Right represents a recent and empirically understudied phenomenon. Ireland has historically been considered an outlier in the European far-right landscape, yet the 2024 local elections saw a notable increase in Far-Right electoral participation, suggesting that Ireland may be undergoing a broader political realignment consistent with trends observed elsewhere in Europe. This paper investigates the influence of ethnic diversity on the electoral success of Far-Right political parties in Ireland during the 2024 local elections. Specifically, it tests the applicability of ethnic contact theory and ethnic threat theory to the Irish context. Ethnic threat theory posits that increasing ethnic diversity generates perceived cultural and economic threats among the native population, fuelling support for far-right parties, while ethnic contact theory conversely suggests that greater exposure to ethnic minorities reduces prejudice and undermines Far-Right support. Understanding which of these mechanisms operates in Ireland is both theoretically and politically significant, as it speaks to whether the Irish case conforms to or departs from established European patterns. The remainder of this paper is structured as follows: Section 1 classifies this paper’s definition of the Irish Far-Right. Section 2 reviews existing literature on ethnic diversity and Far-Right support. Section 3 outlines the data and modelling approach employed. Section 4 presents the empirical results, and Section 5 concludes with a discussion of the findings and avenues for future research.

Classifying the Irish Far-Right

This paper adopts Mudde’s definition of the Far-Right as parties and movements which view social inequalities, particularly class, race, gender, and racial inequalities, as natural and positive, which should be either defended or left alone by the state, and parties that are hostile to liberal democracy (Mudde, 2019).Nativism is a key characteristic of the Far-Right movement and holds that “states should be inhabited exclusively by members of the native groups and that non-native elements are fundamentally threatening to the homogenous nation-state” (Mudde, 2019, p.27).Following this caricature, four political parties within Ireland were deemed by this paper to constitute the Irish Far-Right: The Nationalist Party, The Irish Freedom Party, The Irish People, and Ireland First.

Literature Review

The influence of the local share of ethnic minority residents on Far-Right electoral support among native populations can be understood through two competing mechanisms: ethnic contact theory and ethnic threat theory. Based on Allport’s contact theory and Blau’s opportunity structure theory, ethnic contact theory posits that a higher concentration of ethnic minorities in a residential environment will increase contact between majority and minority groups, resulting in positive contact experiences (van Wijk, Bolt, & Tolsma, 2020).Such contact can challenge existing fears and prejudices among native residents, thereby reducing negative attitudes toward minorities and lowering support for Far-Right candidates. In contrast, ethnic threat theory argues that the presence of immigrants leads to negative attitudes if natives perceive immigrants as a threat to their way of life (Fremerey, Hornig, & Schaffner, 2024).The sense of threat is expected to intensify as the size and visibility of minority groups increase, which can encourage support for Far-Right parties. This mechanism has been widely substantiated, with perceived threat and negative interethnic attitudes repeatedly shown to be strong predictors of Far-Right political support (van Wijk, Bolt, & Tolsma, 2020).These two mechanisms can be interpreted in several ways. First, one mechanism may dominate the other, leading to the following competing hypotheses:

H1: Ethnic diversity reduces Far-Right electoral success

H2: Ethnic diversity increases Far-Right electoral success

Second, the mechanisms may interact, producing a nonlinear relationship between ethnic diversity and Far-Right support. Prior research suggests the existence of threshold effects: a substantial concentration or “critical mass” of minority residents may be required before perceptions of ethnic threat emerge (van Wijk, Bolt, & Tolsma, 2020).Below this threshold, contact theory may dominate; above it, threat theory may become more influential. This implies a U-shaped relationship between ethnic diversity and Far-Right electoral success.

H3: Ethnic diversity reduces Far-Right electoral success at lower levels of minority presence, but beyond a critical threshold, further increases in diversity raise Far-Right support

Third, Rydgren proposes an ethnic competition thesis, which argues that voters support Far-Right parties because they want to reduce competition from immigrants over scarce resources, such as the labour market or welfare state benefits (Rydgren, 2007).From this, we can posit that Far-Right electoral success may be influenced by ethnic diversity, conditional on economic outcomes, namely income and employment.

H4: Ethnic diversity increases Far-Right electoral success conditional on economic outcomes.

The presence of ethnic threat theory could also influence the political machinations of Far-Right parties. Knowing that ethnically diverse areas will be sympathetic to their messaging, Far-Right parties may selectively compete in areas where demographic conditions favour threat-based mobilisation.

H5: Ethnic Diversity increases the likelihood of Far-Right candidate entry

Alternatively, Far-Right electoral success can be attributed to factors distinct from ethnic diversity. Alzheimer and Carter find that certain socio-demographic groups, namely male voters and those with lower levels of education, have shown themselves more likely to vote for the parties of the extreme right than others (Arzheimer & Carter, 2006).Furthermore, Inglehart and Norris suggest a cultural backlash thesis, which argues that Far-Right electoral success can be explained as a reaction against progressive cultural change (Inglehart & Norris, 2016), specifically sparking backlash amongst religious individuals.

Data and Methodology

Data Source

Analyses were conducted using the Irish census and voting data from the 2024 Irish local elections. Observations were aggregated to Ireland’s 166 Local Electoral Areas (LEAs). LEAs were selected as the unit of analysis because they are the smallest electoral units for which both demographic and first-preference voting data are consistently available. This level of aggregation provides sufficient variation in socio-demographic characteristics and electoral outcomes to examine the relationship between ethnic diversity and Far-Right vote share. The 2022 census was chosen as it provides the most up-to-date demographic information preceding the elections. The 2024 local elections were selected because they provide the most direct measure of how demographic characteristics shape political behaviour at the local level.

Dependent Variables

The continuous variable, Far-Right Vote Share, measured the proportion of first-preference votes cast for all Far-Right candidates in each LEA. The variable ranges from 0 to 1. Furthermore, a binary variable, Far-Right Candidate Entry, measured whether far-right candidates were present in an LEA (1) or absent (0).

Explanatory Variables

The continuous variable, Diversity, measured the proportion of non-white residents in each LEA. The variable ranges from 0 to 1. It is worth noting that this variable may understate ethnic diversity, as a non-negligible amount of residents did not state their ethnicity in the census. To capture potential non-linear effects, a squared term of the diversity variable, Diversity², was included. This variable indicates whether the relationship between ethnic diversity and Far-Right vote share changes at higher levels of diversity. The interaction term variables, Diversity x Income and Diversity x Unemployment, measured whether the effect of ethnic diversity is conditioned by local economic circumstances.

Control Variables

Measures of income, education, unemployment, gender, religion, and the number of Far-Right candidates for each LEA were included in all models to control for socioeconomic, cultural, and candidate supply factors. Income measured the mean net household income (€) for each LEA. This variable was subsequently divided by 1,000 so that coefficients represent the effect of a €1,000 increase in average income, improving interpretability. Education measured the proportion of residents with an ordinary bachelor's degree or higher. Unemployment measured the proportion of residents in short and long-term unemployment. Gender measured the proportion of male residents in each LEA. Religion measured the proportion of catholic residents in each LEA. Candidate measured the number of Far-Right candidates competing in each local election. Descriptive statistics for each variable are shown in Table 1.

Table 1: Descriptive Statistics

Modelling Approach

To examine the determinants of Far-Right vote share across LEAs, a multivariate quadratic regression was employed.

Far-Right Vote Sharei = β0 + β1Diversityi + β2Diversityi2 + β3(Diversityi×Incomei) + β4(Diversityi×Unemploymenti) + β5Xi + μi

The sample included all LEA’s where at least one Far-Right candidate was present. Robust standard errors are reported throughout to account for potential heteroskedasticity in the error terms. The first model estimated the linear effect of ethnic diversity and served as a baseline to which all subsequent models could be compared. Confirmation of H1 or H2 requires that the ethnic diversity coefficient is both statistically significant and positive or negative, respectively. The second model added a quadratic term to test for non-linear effects. Confirmation of H3 requires that the linear term coefficient is statistically significant and negative, and the quadratic term coefficient is statistically significant and positive. The third and fourth models added control variables. The fifth model included interaction terms to test whether the effect of ethnic diversity on Far‑Right vote share is conditioned by local economic contexts. Confirmation of H4 requires that either one or both of the interaction term coefficients are statistically significant and positive.

The bounded nature of this regression’s dependent variable raises functional form and inference issues. OLS assumptions posit that the dependent variable can take any value from −∞ to +∞, meaning the regression can generate predicted values outside of the vote share proportion range. Furthermore, this issue also violates OLS’s homoskedasticity assumption, making standard errors unreliable. Therefore, as a robustness test, a fractional logit model is also used to estimate the effect of ethnic diversity on Far-Right vote share. Coined by Papke and Wooldridge, this functional form addresses these issues and serves as a viable alternative to the OLS models (Papke & Wooldridge, 1996).

To examine the determinants of far-right candidate entry across LEAs (H5), a binary logistic regression model was employed.

Rani = β0 + β1Diversityi + β2Xi + μi

The sample included all 166 LEAs. A logistic regression is appropriate in this context as the dependent variable is dichotomous, and OLS regression would be unsuitable given that it can produce predicted probabilities outside the [0,1] range and assumes a linear relationship between predictors and the outcome, both of which are untenable for a binary dependent variable. Robust standard errors are reported throughout to account for potential heteroskedasticity in the error terms. Two model specifications are presented: a bivariate model including only diversity, and a fully specified model incorporating controls. Confirmation of H5 requires that the coefficient on ethnic diversity is both positive and statistically significant.

Aggregation at the LEA level in both regression models raises spatial heterogeneity concerns. Specifically, as Dublin is the most ethnically diverse and urbanised region in Ireland, there is a concern that this paper’s results may disproportionately reflect Dublin-specific dynamics rather than capturing the general nationwide relationship between ethnic diversity and Far-Right electoral outcomes. Therefore, as a robustness check, models are re-estimated excluding all Dubin LEAs. If the key coefficients remain stable in sign and significance, this provides greater confidence that the results generalise beyond Dublin and are not driven by the unique demographic and urban characteristics of the capital.

Finally, to address concerns regarding multicollinearity, a Variance Inflation Factor (VIF) diagnostic was conducted. Multicollinearity is a particular concern given the socioeconomic nature of the control variables, as measures such as education, unemployment, and income are likely to exhibit correlation.

Limitations

This paper’s analysis is subject to several limitations that should be considered when interpreting its results. Due to the recent emergence of the far-right in Ireland, it was not possible to control for endogeneity arising from political factors, namely, candidate charisma and underlying political sentiment. These omitted variables may correlate with both ethnic diversity and far-right electoral outcomes, potentially resulting in biased coefficient estimates across model specifications. Once additional electoral data for Irish far-right parties becomes available, panel data analysis would help address these concerns by allowing for the inclusion of LEA-level fixed effects, which would absorb time-invariant unobserved heterogeneity and isolate more credibly the effect of ethnic diversity on Far-Right electoral performance. Furthermore, as this analysis aggregates at the LEA level, it is subject to the ecological fallacy. Accordingly, this study interprets results as area-level associations rather than individual-level causal effects. Finally, this analysis does not account for potential spillover effects between neighbouring LEAs. If Far-Right support or ethnic diversity in one LEA influences outcomes in adjacent LEAs, the assumption of independent observations is violated, introducing spatial autocorrelation into the error terms. Future research could address this through the use of spatial regression models.

Results

Table 2: Ethnic Diversity and Far-Right Electoral Success

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Dependent Variable = Far-Right Vote Share

Table 2 shows the effect of ethnic diversity on Far-Right vote share and predicts no significant effects once accounting for control variables. Model 1 shows a significant positive linear effect of ethnic diversity on Far-Right vote share ( β = 0.146, p<0.01). However, this model explains little variation in Far-Right vote share (R2 = 0.105). Model 3, which includes control variables, explains considerably more variance in the dependent variable (R2 = 0.451) and estimates an insignificant, slight positive linear effect ( β = 0.012, p>0.1). This suggests that the baseline ethnic diversity effect observed in Model 1 is largely explained by socioeconomic confounders rather than reflecting an independent effect of diversity on Far-Right vote share. Models 2 and 4, respectively, find no significant non-linear effects. Model 5 estimates a marginally significant positive interaction term between diversity and income (β = 0.003, p<0.1). Several concerns limit the conclusions that can be drawn from this result. The coefficient's positive direction is counterintuitive, and its small magnitude and significance level raise questions about its substantive significance. The diversity and unemployment interaction term is negative and insignificant, providing no evidence that unemployment moderates the ethnic diversity effect (β = -1.219, p>0.1). Across the fully specified models, several control variables demonstrate consistent and significant effects. Education exerts a significant negative effect on Far-Right vote share across models 3,4, and 5 (β = -0.110, -0.101, -0.113, respectively, all p<0.05). Unemployment is positively and significantly associated with Far-Right vote share in models 3 and 4 (β =0.757 and 0.927, respectively, both p<0.05). The number of candidates is positive and highly significant across all fully specified models (β =0.014,0.015, 0.014, respectively, all p<0.01). Gender and religion do not reach statistical significance in any specification.

Overall, the results provide no support for H1, H2, H3, or H4 as ethnic diversity does not exert a significant independent effect on far-right vote share once socioeconomic controls are accounted for, no robust non-linear relationship is detected, and no reliable conclusions could be drawn from the interaction terms. The fractional logit models shown in columns 6 and 7 confirm the previous results. Model 6 estimates a significant positive effect of ethnic diversity on Far-Right Vote share (β = 3.960, p<0.01). This effect fails to retain significance once accounting for control variables in model 7. This robustness check reinforces the conclusion that diversity does not exert an independent effect on far-right vote share once socioeconomic controls are accounted for.

Table 3: Logit Regression

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Dependent Variable = Far-Right Candidate Entry

Table 3 shows the effect of ethnic diversity on the likelihood of Far-Right candidate entry and predicts a significant positive effect across both specifications. Model 1 estimates a baseline significant positive effect of ethnic diversity on candidate entry (β = 10.56, p<0.05). This effect retains significance and increases in magnitude in model 2 (β = 18.645, p<0.05), which includes the full set of control variables. In regard to control variables, income and gender display significant negative effects on the likelihood of candidate entry (β = -0.062, p<0.1) and (β = -56.762, p<0.05) respectively, indicating that LEAs with higher income levels and with higher proportions of male residents are associated with a lower probability of Far-Right candidate entry. The high magnitude of the gender coefficient reflects the fact that a one-unit change represents a shift from 0% to 100% male and should be interpreted accordingly. Education, unemployment, and religion do not reach statistical significance. Overall, the logit results provide support for the hypothesis that ethnic diversity increases the likelihood of far-right candidate entry (H5), with this effect proving robust to the inclusion of socioeconomic controls.

Notably, when Dublin LEAs are excluded, ethnic diversity loses significance in the fully specified logit model (Table A2), suggesting that the effect of diversity on Far-Right candidate entry may be concentrated in Dublin and may not generalise uniformly across Irish LEAs. This likely reflects Dublin's unique position as Ireland's primary urban centre and the region of greatest demographic change, and warrants caution when interpreting the broader generalisability of this finding.

Table 4: Variance Inflation Factors

Table 4 reports the Variance Inflation Factors (VIF) for each variable. All VIF values fall below the conventional threshold of 5, with a mean VIF of 3.28, indicating that multicollinearity does not pose a significant threat to the reliability of the estimated coefficients. The highest value is recorded for education (VIF = 4.86), likely reflecting its correlation with other socioeconomic variables, though this remains within acceptable bounds. These diagnostics provide confidence that the coefficient estimates reported across the model specifications are not materially distorted by multicollinearity.

Discussion

This paper investigates whether ethnic diversity influences the electoral success of Far-Right political parties in Ireland. Existing literature posits that ethnic contact and ethnic threat theories exert a strong influence on far-right vote share. This paper finds little evidence of these effects during the 2024 Irish local elections once controlling for socioeconomic variables, namely education and unemployment, suggesting that material conditions rather than ethnic diversity are the more proximate drivers of far-right electoral success in the Irish context. However, this paper does find evidence that ethnic diversity exerts a significant positive effect on Far-Right candidate entry, suggesting that diversity operates as a meaningful supply-side determinant of Far-Right electoral participation at the LEA level. The lack of evidence relating to electoral success may partly reflect the limited data available. The Irish far-right is a nascent movement. Candidates received very small vote shares across a relatively small number of LEAs, constraining statistical power and potentially obscuring effects that may become more detectable as the movement matures. Future research should revisit these questions as additional electoral data becomes available.

Appendix

Table A1: Ethnic Diversity and Far-Right Electoral Success – Excluding Dublin LEAs

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Dependent Variable = Far-Right Vote Share

Table A2: Logit Regression – Excluding Dublin LEAs

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Dependent Variable = Far-Right Candidate Entry

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