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| http://www.leftfutures.org/2014/05/why-is-the-extreme-right-on-the-rise-in-europe/ |
There are three main theoretical approaches that explain the propensity of individuals to cast a vote for extreme right-wing parties in the political science literature.
Personality Traits & Value Orientations
Psychological models were the predominant paradigm in explaining the mechanism that led to the rise of fascism in the World War II period. The Frankfurt School developed theories of psychological interests, outlining the importance of authoritarianism (Adorno, Frenkel Brunswik, Levinson & Nevitt, 1950.) Theoretical models in the shape of the Adorno Model were operationalized on a nine point scale. The model bore similar resemblance with the Freudian psychoanalysis model (Fromm 1932, Scheepers et. al., 1990). Empirical studies have found a positive relationship between authoritarianism and extreme right-wing support. (Billiet, Swyngedouw & Carton; Lubbers & Scheepers, 2000;) Contemporary studies have also analysed the effects of strong leadership, but with little success (Mudde, 1999; Lubbers, 2001) and psychological models are seldom explored in the literature.
Social Disintegration
The disintegration theory has its roots in the work of the integration thesis outlined by Durkheim. (Durkheim, 1897) Classical sociological approaches in the literature on the far-right have explored the effect of norms and how a breakdown in social norms can occur due to social change. (Parsons, 1942) These studies overlap psychological accounts, through exploring the role of affective emotions such as anxiety, anger, and arousal on decision-making. Psychological studies have argued that affective emotions may trigger respondents to long for strong leadership and authoritarian values. (Parsons, 1942) Social disintegration thesis may be also brought about by fear of anxiety towards marginalised groups in society, such as a perceived fear on issues such as immigration and unemployment.
Both issue topics are likely to be high on the agenda of respondents who equate immigration with unemployment and may change the habitual patterns of ordinary voters, triggering the surveillance system and eroding habitual patterns of behaviour away. (Marcus et al, 2000; Dalton, et al, 2007; P.83) Sociological studies by Arendt have outlined that disintegration is likely to occur during times of economic contraction, with working class voters being hit the worst, which could conceivably bring about a rise in support for far-right parties at the expense of left-wing parties. (Arendt, 1952; Bendix, 1952)
Broadly speaking, the consensus in the literature is that extreme right-wing voters hold anti-immigrant views, are staunchly Eurosceptic, and question the democratic legitimacy of the state. Multivariate regression models have highlighted the strong effect of political attitudes when controlling for the effect of socio-demographic variables. (Arzheimer & Carter 2006; Swyngedouw; 2007) Cross-sectional data conducted by YouGov and Ipsos Mori on the British National Party has shown that attitudes are the core drivers of support, with anti-immigrant sentiment and intolerant racial perceptions integral factors. (Goodwin et al, 2009) However, Lubbers (2001) notes that individual level studies have been limited to small cases in specific country case studies and that the explanatory power of models is enhanced with cross-comparative level studies at the individual level where a larger number of cases are examined. (Van der Brug et al., 2000)
Arzheimer (2008) also used a structural equation model to identify latent variables of extreme right-wing support. Arzheimer’s multilevel model strategy showed that socio-demographic predictors were weak indicators of extreme right-wing party support, with immigration and unemployment rates being strong contextual predictors of far-right support. (Arzheimer, 2009) However, Arzheimer’s country level evidence showed that unemployment rates had a negative effect on extreme right-wing support and suggests that voters shift back to mainstream parties during economic contraction and is clearly contradictory to what the literature states.(Werts, 2010; P.8) In addition, institutional level variables have been explored in the political science literature. Political opportunity structures have been examined in different ways by political scientists. Scholars such as Kitschelt have outlined that resources, the role of institutions, and political system variables are important in measuring the effect of political institutions on the far-right vote. (Kitschelt 1986: P.58) Arzheimer & Carter’s study on political opportunity structures examined the role of party system, institutional, and structural level factors and found that political institutions had significant effect on far-right support. (Arzheimer & Carter, 2006)
The data comes from the biannual Eurobarometer survey. The data is taken from 1980-2002. Contextual level and political opportunity structure data are gathered from OECD databases, alongside the CMRP, UNCHR 2002 statistical yearbook, and Lijphart (1999) study on institutional factors and have been filtered into the dataset. Though alternative data sources such as the European Social Survey data could have been used, the Eurobarometer data has a good coverage of extreme right-wing parties and attitudinal variables in the dataset. Countries with low levels of extreme right-wing support such as in the Republic of Ireland and the United Kingdom have been omitted from the analysis, as social desirability bias through a small number of cases may distort the results if adopted in the logistic regression model. Fifteen countries are included in the analysis. The dependent variable is coded where 1= the propensity of voting for an extreme right-wing party and 0= voting for another party and therefore takes on a binary logit function. The following extreme right-wing parties were chosen for the subsequent analysis: the Freedom Party in Austria, the Front National and Vlaams Belang in Belgium, the Danish People's Party in Denmark, the True Finns in Finland, the National Front in France, the German People's Union, the Republicans in Germany, the National Front and Political Spring in Greece, the National Alliance and the Northern League in Italy, the Center Parties and the Lijst Pim Fortuyn in the Netherlands, the Progress Party in Norway, the Christian Democrats in Portugal, the various Falange Parties in Spain and New Democracy in Sweden.
H2: Political Attitudes Model: Political attitudes are likely to be important predictors of extreme right support. Dissatisfaction with democracy, negative perceptions of the European Union are likely to drive extreme right support. Extreme right supporters are also highly likely to have low levels of life satisfaction and low levels of post-materialist values, in line with ethnic competition theory. (Arzheimer, 2009; Goodwin 2010)
H3: Political Opportunity Structures Model: Higher levels of electoral distortion where single-member-plurality systems operate are likely to have a detrimental effect on extreme right support. A higher degree of federalism is likely to lead to an increase in far-right support, as there are more elections where voters can protest against mainstream parties by voting for the extreme right.
H4: Contextual Level model: The higher the number of asylum seekers and the greater the unemployment rate is likely to lead to increased levels of extreme right-wing support, in accordance with ethnic competition and group conflict theory.
Cross-tabulation analysis was used to establish the preliminary relationship of socio-demographic variables on extreme right support. Effects were strong, particularly amongst male voters and the Chi-squared measure showed a high degree of statistical significance amongst the socio-demographic variables. The strongest predictor variable in the socio-demographic model is male voters, who are around 1.67 times more likely to vote for the extreme right than their female counterparts are, thus showing the predisposition of male voters to support extreme right parties. This is a common trend in the pan-European literature. (Norris, 2005; Mayer 1995;)When age is analysed, the strongest effect is amongst younger voters, with the 18-29 category being 1.32 times more likely to vote for the extreme right than the other categories and is statistically significant at the 99% level. The 30-45 categories also show high levels of support for extreme right voters. However, older voters are far less likely to vote for the extreme right. (Goodwin, 2009; Hainsworth 2008) The logistic regression model confirms that higher educated voters are far more likely to vote for the extreme right. When factoring in occupation, working class voters are highly significant and are strong predictors of voting for the extreme right, with retired voters less likely to vote for the extreme right. As hypothesized, unemployment has a strong effect, with trade union membership also being significant. (Swyngedouw, 2009) Inconclusive results are found on the effects of religiosity however. Though socio-demographic variables appear to be a strong measure of extreme right-wing support, the Pseudo R2 value is low at 2%. When the three models are controlled for in the overall logistic regression model in Table 1.4, the socio-demographic model has weak explanatory power in comparison to the other models.
Model II- Political Attitudes:
The logistic regression model on political attitudes has a much stronger Pesudo R2 of 18% and explains a greater degree of variance than the socio-demographic model. Model II shows that extreme right-wing respondents hold negative attitudes towards domestic and supranational institutions. This is in line with a number of cross national studies. Dissatisfaction with democracy is a core driver of support, with extreme right supporters who are dissatisfied with democracy being 2.06 times more likely to vote than their satisfied counterparts. Similarly, negative perceptions of the European Union are a strong predictor of voting for the extreme right and again this may be due to the strong nationalistic ideology that far-right parties espouse. (Goodwin et al, 2010; Lubbers, 2002) The respondent’s left-right score also has a strong effect. However, post-materialism is a fairly weak predictor of voting for the extreme right though as supporters of the far right tend to place an emphasis on maintaining traditional structures in society. (Hainsworth, 2008)
Model III- Political Opportunity Structures Model:
Scholars such as Arzheimer (2006; 2009) have previously found inconclusive results when political opportunity structures have been analysed. In a similar manner, the model in this paper is fairly limited with a pseudo R2 of 0.2%. Though higher levels of electoral disproportionality have a strong effect alongside the Lijphart Index of Federalism on the extreme right-wing vote, the results must be taken with a pinch of salt as the Pseudo R2 value is low. The government left variable is also weak and there is little intuition to this variable’s inclusion in Model III. Evidently, the political opportunity structure model is unlikely to have a substantial effect in the overall logistic regression model.
As before, working class voters have a strong propensity to vote for the extreme right, alongside the unemployed. However, respondents who are retired or who belong to a trade union have a non-significant effect on extreme right-wing support. Being non-religious appears to increase the likelihood of voting for the extreme right in this model. (Arzheimer, 2006) All political opportunity structure variables are statistically significant, with the Gallagher Index and federalism significant at the 99% level. So far, logistic regression models have been used to analyse the effects of socio-demographic, political attitudes, and political opportunity structure models, now we turn to analysing contextual level variables on the extreme right vote.
Table 1.1- Logistic regression model of Socio-demographics & extreme right-wing voting:
Both issue topics are likely to be high on the agenda of respondents who equate immigration with unemployment and may change the habitual patterns of ordinary voters, triggering the surveillance system and eroding habitual patterns of behaviour away. (Marcus et al, 2000; Dalton, et al, 2007; P.83) Sociological studies by Arendt have outlined that disintegration is likely to occur during times of economic contraction, with working class voters being hit the worst, which could conceivably bring about a rise in support for far-right parties at the expense of left-wing parties. (Arendt, 1952; Bendix, 1952)
Ethnic Competition
The literature on ethnic competition theory centres on social identity and racism amongst extreme right-wing voters. (Lipset & Bendix, 1951; Tajfel et al., 1971) Historically, social psychological accounts outlined how the Nazi Party used fear amongst ordinary voters to scapegoat ethnic minorities in the form of the Jewish people and how they blamed this group for the economic situation in Germany. (Dollard et al., 1939) Experimental research in social psychology has verified group conflict theory. (Sherif & Sherif, 1979) Contemporary empirical studies have examined group conflict theories where contextual level studies have analysed the effects of unemployment levels and ethnic composition on support for the extreme right in Western Europe. (Arzheimer, 2009) Similarly, the political science literature has examined the effects of asylum seekers on the extreme right-wing vote, finding a positive and statistically significant relationship. (Knigge, 1998; Arzheimer & Carter, 2006)Socio-Demographic Profile of Extreme Right Voters
The literature is replete with micro level cross-sectional accounts focusing on the characteristics of extreme right-wing voters. Cross-sectional studies in France, the United Kingdom, Belgium, and Germany have analysed the effects of socio-demographics on the extreme right-wing vote. (Mayer; 1981; 1985, Goodwin (2009); Swyngedouw et al, 2009) The empirical research has confirmed that the profile of the far-right in Western Europe tends to be composed of white, male, lower income, and unemployed voters. Scholars such as Norris have identified the strong gender divide within extreme right supporters and link this to the authoritarian ideology of far-right programmes. (Norris, 2005) Cross-sectional studies conducted on Vlaams Belang, Front National, and the British National Party indicated that respondents with lower levels of educational attainment are highly correlated with voting for the extreme right. (Swyngedouw et al; 2009; Hainsworth; 2008) The one exception is the Freedom Party of Austria which has consistently polled highly in first order elections amongst the professional managerial and university educated classes. (Luther, 2000)Attitudinal Drivers
Though the socio-demographic literature confirms that a core set of disaffected voters vote for far-right parties in Western Europe, the explanatory power of socio-demographic models are fairly weak as they merely measure prior dispositions of far-right support. (Arzheimer; 2006) Recently, the literature has shifted away from analysing socio-demographics towards analysing the attitudinal dispositions of extreme right-wing voters on a broad range of issues. These studies have operationalized Converse’s definition of attitude objects. (Converse, 1964; 1974) Attitudes of respondents towards immigration, dissatisfaction with the European Union, homosexuality, and a range of other issues have been explored. (Arzheimer 2006; Goodwin 2009)Broadly speaking, the consensus in the literature is that extreme right-wing voters hold anti-immigrant views, are staunchly Eurosceptic, and question the democratic legitimacy of the state. Multivariate regression models have highlighted the strong effect of political attitudes when controlling for the effect of socio-demographic variables. (Arzheimer & Carter 2006; Swyngedouw; 2007) Cross-sectional data conducted by YouGov and Ipsos Mori on the British National Party has shown that attitudes are the core drivers of support, with anti-immigrant sentiment and intolerant racial perceptions integral factors. (Goodwin et al, 2009) However, Lubbers (2001) notes that individual level studies have been limited to small cases in specific country case studies and that the explanatory power of models is enhanced with cross-comparative level studies at the individual level where a larger number of cases are examined. (Van der Brug et al., 2000)
Contextual Drivers
The academic focus on the extreme right has been primarily at the micro level and the literature on macro predictors has been thin. Recently, scholars have focussed on the macro and meso predictors of extreme right-wing support. (Arzheimer & Carter, 2006) Early macro level studies conducted by Jackman and Volpert analysed the effects of group conflict ‘between non-Western immigrants and the indigenous population over scarce resources’. (Arzheimer 2008, P.4; Jackman & Volpert 1996; Knigge 1998; Lubbers et al. 2002) However, Jackman & Volpert’s comparative level study has been criticised on the grounds that it failed to explore both micro and macro levels together. Other scholars such as Golder conducted time series analysis between 1970 and 2000, finding that contextual level variables such as the unemployment and immigration rate in a country were important in explaining extreme right-wing support. (Golder, 2003) Yet the methodological procedures of this study have been called into question, alongside the failure to create interaction variables. Nonetheless these macro level studies failed to explore the interaction between contextual level variables, socio-demographics and political attitudes.Methods: Multilevel Modelling
The rise of advanced statistical techniques in the social sciences has enabled scholars to apply a range of techniques in analysing micro and macro level relationships on the extreme right without falling foul of the ecological fallacy problem. Arzheimer utilised a multi-level logistic regression to analyse the effects of socio-demographics, institutional, and contextual level variables. This study also divided contextual level variables into short and long-term variables, whilst at the same time analysing party system variables. A key advantage of multi-level models is their ability to analyse macro level nested data, where statistical relationships can be inferred. (Gelman et al, 2007)Arzheimer (2008) also used a structural equation model to identify latent variables of extreme right-wing support. Arzheimer’s multilevel model strategy showed that socio-demographic predictors were weak indicators of extreme right-wing party support, with immigration and unemployment rates being strong contextual predictors of far-right support. (Arzheimer, 2009) However, Arzheimer’s country level evidence showed that unemployment rates had a negative effect on extreme right-wing support and suggests that voters shift back to mainstream parties during economic contraction and is clearly contradictory to what the literature states.(Werts, 2010; P.8) In addition, institutional level variables have been explored in the political science literature. Political opportunity structures have been examined in different ways by political scientists. Scholars such as Kitschelt have outlined that resources, the role of institutions, and political system variables are important in measuring the effect of political institutions on the far-right vote. (Kitschelt 1986: P.58) Arzheimer & Carter’s study on political opportunity structures examined the role of party system, institutional, and structural level factors and found that political institutions had significant effect on far-right support. (Arzheimer & Carter, 2006)
2. Methodology
No clear consensus on defining the extreme right has been reached in the literature. This paper will adopt Mudde’s terminology. In sum, extreme right-wing parties in Europe can be defined as having the following elements; i. political dissatisfaction with mainstream parties, ii. xenophobic ideology, iii. strong emphasis on law and order, iv. authoritarianism, v. an indirect racist undercurrent. Though van der Brug et al’s (2000) anti-immigrant definition is plausible; I believe that the confines are too narrow (Mudde, 1999) and thus adopt Mudde’s definition for the sake of parsimony.The data comes from the biannual Eurobarometer survey. The data is taken from 1980-2002. Contextual level and political opportunity structure data are gathered from OECD databases, alongside the CMRP, UNCHR 2002 statistical yearbook, and Lijphart (1999) study on institutional factors and have been filtered into the dataset. Though alternative data sources such as the European Social Survey data could have been used, the Eurobarometer data has a good coverage of extreme right-wing parties and attitudinal variables in the dataset. Countries with low levels of extreme right-wing support such as in the Republic of Ireland and the United Kingdom have been omitted from the analysis, as social desirability bias through a small number of cases may distort the results if adopted in the logistic regression model. Fifteen countries are included in the analysis. The dependent variable is coded where 1= the propensity of voting for an extreme right-wing party and 0= voting for another party and therefore takes on a binary logit function. The following extreme right-wing parties were chosen for the subsequent analysis: the Freedom Party in Austria, the Front National and Vlaams Belang in Belgium, the Danish People's Party in Denmark, the True Finns in Finland, the National Front in France, the German People's Union, the Republicans in Germany, the National Front and Political Spring in Greece, the National Alliance and the Northern League in Italy, the Center Parties and the Lijst Pim Fortuyn in the Netherlands, the Progress Party in Norway, the Christian Democrats in Portugal, the various Falange Parties in Spain and New Democracy in Sweden.
3. Hypotheses
H1: Socio-Demographic Model: Male voters, lower educated voters, manual workers, and the unemployed are more likely to vote for an extreme right party in accordance with the contemporary literature.H2: Political Attitudes Model: Political attitudes are likely to be important predictors of extreme right support. Dissatisfaction with democracy, negative perceptions of the European Union are likely to drive extreme right support. Extreme right supporters are also highly likely to have low levels of life satisfaction and low levels of post-materialist values, in line with ethnic competition theory. (Arzheimer, 2009; Goodwin 2010)
H3: Political Opportunity Structures Model: Higher levels of electoral distortion where single-member-plurality systems operate are likely to have a detrimental effect on extreme right support. A higher degree of federalism is likely to lead to an increase in far-right support, as there are more elections where voters can protest against mainstream parties by voting for the extreme right.
H4: Contextual Level model: The higher the number of asylum seekers and the greater the unemployment rate is likely to lead to increased levels of extreme right-wing support, in accordance with ethnic competition and group conflict theory.
4. Data Analysis & Discussion
Model I- Socio-demographic model:Cross-tabulation analysis was used to establish the preliminary relationship of socio-demographic variables on extreme right support. Effects were strong, particularly amongst male voters and the Chi-squared measure showed a high degree of statistical significance amongst the socio-demographic variables. The strongest predictor variable in the socio-demographic model is male voters, who are around 1.67 times more likely to vote for the extreme right than their female counterparts are, thus showing the predisposition of male voters to support extreme right parties. This is a common trend in the pan-European literature. (Norris, 2005; Mayer 1995;)When age is analysed, the strongest effect is amongst younger voters, with the 18-29 category being 1.32 times more likely to vote for the extreme right than the other categories and is statistically significant at the 99% level. The 30-45 categories also show high levels of support for extreme right voters. However, older voters are far less likely to vote for the extreme right. (Goodwin, 2009; Hainsworth 2008) The logistic regression model confirms that higher educated voters are far more likely to vote for the extreme right. When factoring in occupation, working class voters are highly significant and are strong predictors of voting for the extreme right, with retired voters less likely to vote for the extreme right. As hypothesized, unemployment has a strong effect, with trade union membership also being significant. (Swyngedouw, 2009) Inconclusive results are found on the effects of religiosity however. Though socio-demographic variables appear to be a strong measure of extreme right-wing support, the Pseudo R2 value is low at 2%. When the three models are controlled for in the overall logistic regression model in Table 1.4, the socio-demographic model has weak explanatory power in comparison to the other models.
Model II- Political Attitudes:
The logistic regression model on political attitudes has a much stronger Pesudo R2 of 18% and explains a greater degree of variance than the socio-demographic model. Model II shows that extreme right-wing respondents hold negative attitudes towards domestic and supranational institutions. This is in line with a number of cross national studies. Dissatisfaction with democracy is a core driver of support, with extreme right supporters who are dissatisfied with democracy being 2.06 times more likely to vote than their satisfied counterparts. Similarly, negative perceptions of the European Union are a strong predictor of voting for the extreme right and again this may be due to the strong nationalistic ideology that far-right parties espouse. (Goodwin et al, 2010; Lubbers, 2002) The respondent’s left-right score also has a strong effect. However, post-materialism is a fairly weak predictor of voting for the extreme right though as supporters of the far right tend to place an emphasis on maintaining traditional structures in society. (Hainsworth, 2008)
Model III- Political Opportunity Structures Model:
Scholars such as Arzheimer (2006; 2009) have previously found inconclusive results when political opportunity structures have been analysed. In a similar manner, the model in this paper is fairly limited with a pseudo R2 of 0.2%. Though higher levels of electoral disproportionality have a strong effect alongside the Lijphart Index of Federalism on the extreme right-wing vote, the results must be taken with a pinch of salt as the Pseudo R2 value is low. The government left variable is also weak and there is little intuition to this variable’s inclusion in Model III. Evidently, the political opportunity structure model is unlikely to have a substantial effect in the overall logistic regression model.
Binary Logistic Regression Model- Modelling the socio-demographic, political attitudes, and political opportunity structure models:
When the socio-demographic, political attitudes and political opportunity structure models are analysed together in logistic regression format, clearly political attitudes have the strongest effect and provides the best model for predicting the propensity to vote for the extreme right. Antipathy towards the European Union and dissatisfaction with democracy are the strongest drivers of support for the extreme right according to dimension reduction models.(Goodwin, 2009; Swyngedouw, 2009) Post-materialism and the left-right score of a respondent have fairly strong effects, yet satisfaction with life is non-significant as expected. Due to the nature of controlling in logistic regression analysis, the coefficients of the predictor variables have changed. In the individual socio-demographic model, the coefficient for farmers has decreased, alongside the effects of male and 18-24 voters. The odds ratios for both male and younger voters are high and both variables are still significant at the 99% level, with older voters being less inclined to vote for the extreme right.As before, working class voters have a strong propensity to vote for the extreme right, alongside the unemployed. However, respondents who are retired or who belong to a trade union have a non-significant effect on extreme right-wing support. Being non-religious appears to increase the likelihood of voting for the extreme right in this model. (Arzheimer, 2006) All political opportunity structure variables are statistically significant, with the Gallagher Index and federalism significant at the 99% level. So far, logistic regression models have been used to analyse the effects of socio-demographic, political attitudes, and political opportunity structure models, now we turn to analysing contextual level variables on the extreme right vote.
Table 1.1- Logistic regression model of Socio-demographics & extreme right-wing voting:
SOCIO-DEMOGRAPHICS MODEL
|
ODDS RATIO
|
STANDARD ERROR
|
GENDER
Ref Category=
Female
|
||
MALE
|
1.67**
|
0.06
|
AGE
Ref Category=
46-65
|
||
18-29
|
1.32**
|
0.07
|
30-45
|
0.96
|
0.05
|
66+
|
0.91
|
0.06
|
OCCUPATION
Ref Category=
Other
|
||
FARMER
|
1.49**
|
0.09
|
WORKER
|
1.43**
|
0.07
|
RETIRED
|
1.19 **
|
0.08
|
UNEMPLOYED
|
1.59 **
|
0.11
|
TRADE UNION MEMBERSHIP
|
0.88 **
|
0.03
|
EDUCATIONAL
ATTAINMENT
|
||
HIGH SCHOOL LEAVER
|
1.22 **
|
0.05
|
UNIVERSITY
|
0.87**
|
0.04
|
RELIGIOSITY
|
||
NOT CHRISTIAN
|
1.04
|
0.04
|
Pseudo R2= 2%
|
||
*Bold figures
denote significance at the 95% level
|
||
**Bold
figures denote significance at the 99% level
|
POLITICAL
ATTITUDES MODEL
|
ODDS
RATIO
|
STANDARD
ERROR
|
DISSATISFACTION WITH
LIFE
|
1.05*
|
0.02
|
DISSATISFACTION WITH
DEMOCRACY
|
2.06**
|
0.04
|
LEFT- RIGHT RESPONDENT
SCORE
|
1.65**
|
0.01
|
POST MATERIALISM
|
0.7**
|
0.04
|
DISSATISFACTION WITH
THE EUROPEAN UNION
|
2.23**
|
0.1
|
Pseudo R2= 20%
|
||
*Bold figures denote
significance at the 95% level
|
||
**Bold figures denote
significance at the 99% level
|
POLITICAL OPPORTUNITY STRUCTURES MODEL
|
ODDS RATIO
|
STANDARD ERROR
|
GALLAGHER INDEX OF
DISPROPORTIONALITY
|
1.03*
|
0.01
|
LEFT PARTIES IN % OF
TOTAL CABINET POSTS
|
1.03**
|
0.01
|
LIJPHART INDEX OF
FEDERALISM
|
1.00**
|
0.01
|
Pseudo R2=
0.2%
|
||
*Bold figures
denote significance at the 95% level
|
||
**Bold
figures denote significance at the 99% level
|
COMPOSITE MODEL
|
ODDS RATIOS
|
STANDARD ERROR
|
|||
SOCIO-DEMOGRAPHIC
VARIABLES
|
|||||
GENDER
Ref Category=
Female
|
|||||
MALE
|
**1.59
|
0.08
|
|||
AGE
Ref Category:
46-65
|
|||||
18-29
|
**1.52
|
0.1
|
|||
30-45
|
1.12
|
0.07
|
|||
66+
|
0.87
|
0.08
|
|||
OCCUPATION
Ref Category=
Other
|
|||||
FARMER
|
0.97
|
0.08
|
|||
WORKER
|
**1.47
|
0.09
|
|||
RETIRED
|
1.12
|
0.1
|
|||
UNEMPLOYED
|
**1.42
|
0.13
|
|||
TRADE UNION MEMBERSHIP
|
0.96
|
0.05
|
|||
EDUCATIONAL
ATTAINMENT
|
|||||
HIGH SCHOOL LEAVER
|
**1.22
|
0.07
|
|||
*0.87
|
0.06
|
||||
UNIVERSITY
|
|||||
RELIGIOSITY
|
|||||
NOT CHRISTIAN
|
**1.47
|
0.07
|
|||
POLITICAL
ATTITUDES VARIABLES
|
|||||
DISSATISFACTION WITH LIFE
|
1
|
0.03
|
|||
DISSATISFACTION WITH
DEMOCRACY
|
**2.09
|
0.06
|
|||
LEFT- RIGHT RESPONDENT
SCORE
|
**1.67
|
0.02
|
|||
POST MATERIALISM
|
**0.6
|
0.05
|
|||
DISSATISFACTION WITH THE
EUROPEAN UNION
|
**2.25
|
0.14
|
|||
POLITICAL
OPPORTUNITY STRUCTURES VARIABLES
|
|||||
GALLAGHER INDEX OF
DISPROPORTIONALITY
|
**0.97
|
0.01
|
|||
LEFT PARTIES IN % OF
TOTAL CABINET POSTS
|
**0.99
|
0
|
|||
LIJPHART INDEX OF
FEDERALISM
|
**0.9
|
0.02
|
|||
PSEUDO R2=
20%
|
|||||
*Bold figures
denote significance at the 95% level
|
|||||
**Bold
figures denote significance at the 99% level
|
|||||
Multilevel model- Modelling the effects of contextual level determinants on support for the Extreme Right:
Hierarchical models allow the researcher to systematically analyse both individual and aggregate level variables together. (Gelman et al, 2007) A mutlilevel logit model was produced and the effects of the four models are examined in ascertaining which model has the highest degree of explanatory power in explaining the probability of voting for extreme right-wing parties.
The multilevel logit model shows that socio-demographic models are overall weak predictors of support for the extreme right, with younger voters, male, unemployed and working class voters being significant at the 99% level. When controlling for contextual level characteristics, political attitudes models are highly significant, with dissatisfaction with the European Union and democracy scoring highly. Educational attainment for respondents with university degrees is negative. Political opportunity structure models are weak when controlling for contextual level variables, however the effect of decentralization is significant.
The multilevel model highlights the strong effect of contextual level variables on the extreme right support. The number of asylum seekers alongside the number of unemployed is statistically significant and the strongest predictor variable in the model is the number of asylum seekers. However, the interaction effect between levels of unemployment and immigration is both negative and non-significant. Similar effects have been found when analysing the impact of contextual variables. (Arzheimer, 2009)
The strength of the contextual level model highlights the importance of economic discontent, with a general perception being that mainstream parties are not delivering on core issues that matter to most people such as the economy. Furthermore, contemporary mainstream parties on the left are adopting polices such as the free movement of labour. This ideological shift away from the left is likely to have caused animosity amongst blue-collar and unskilled workers, thereby reinforcing ethnic competition theory. (Norris, 2005; Hainsworth, 2008) Consequently, it is no wonder that in the last few years the extreme right has seen a resurgence of support amongst the working classes and quite clearly the state of the economy is of paramount importance. (Mudde, 2007)
Table 1.5- Multilevel logit model testing the four models that drive extreme right-wing support- Inclusion of Contextual Level Variables:
MODEL GROUPS
|
COEFFICIENTS
|
STANDARD
ERROR
|
SOCIO-DEMOGRAPHIC
MODEL
|
||
GENDER
Ref Category=
Female
|
||
MALE
|
**0.47
|
0.03
|
AGE
Ref Category=
46-65
|
||
18-29
|
**0.42
|
0.04
|
30-45
|
**0.18
|
0.04
|
66+
|
**-0.17
|
0.05
|
OCCUPATION
Ref Category=
Other
|
||
FARMER OWNER
|
0.03
|
0.05
|
WORKER
|
**0.36
|
0.04
|
RETIRED
|
0.03
|
0.05
|
UNEMPLOYED
|
**0.46
|
0.06
|
EDUCATIONAL
ATTAINMENT
|
||
MIDDLE/HIGH
|
0.04
|
0.03
|
UNIVERSITY
|
**-0.34
|
0.04
|
POLITICAL ATTITUDES
MODEL
|
||
LEFT –RIGHT
|
**0.55
|
0.007
|
DISSATISFIED WITH THE
EUROPEAN UNION
|
**0.74
|
0.4
|
DISSATISFIED WITH
DEMOCRACY
|
**0.61
|
0.02
|
POLITICAL
OPPORTUNITY STRUCTURES MODEL
|
||
GALLAGHER INDEX OF
DISPROPORTIONALITY
|
-0.04
|
0.02
|
LIJPHART INDEX OF
FEDERALISM
|
**-0.74
|
0.03
|
CONTEXTUAL
LEVEL MODEL
|
||
ASYLUM SEEKERS
|
**0.90
|
0.13
|
UNEMPLOYMENT
|
**0.12
|
0.04
|
ASYLUM SEEKERS X
UNEMPLOYMENT
|
-0.012
|
0.03
|
UNEMPLOYMENT BENEFITS
|
**-0.04
|
0.008
|
UNEMPLOYMENT BENEFITS X
UNEMPLOYMENT
|
-0.002
|
0.004
|
UNEMPLOYMENT BENEFITS X
ASYLUM SEEKERS
|
**-0.03
|
0.01
|
TOUGHNESS
|
**-0.2
|
0.03
|
SALIENCE
|
**-0.2
|
0.03
|
VARIANCE
|
**0.10
|
0.01
|
VARIANCE X SALIENCE
|
**-0.002
|
0.0007
|
Number of Observations=
174, 452
|
||
Number of Groups: 267
|
||
**Bold
figures denote significance at the 99% level
|
||
Conclusion
This research paper has analyzed four rival models based to test the propensity of voting for the extreme right. The logistic regression models confirmed that the best predictor model was political attitudes, with the core drivers of support for the extreme right occurring amongst respondents who were dissatisfied with democracy and have negative attitudes towards the European Union. Socio-demographic variables such as being male, younger voters, respondents with lower levels of education, and being working class are all highly significant and replicated work undertaken by numerous studies at the cross sectional and cross national level.
The results for political opportunity structures showed the importance of decentralization and higher levels of electoral distortion appear to have a positive effect on extreme right-wing support. The number of asylum seekers and unemployed are the best predictors of the extreme right vote at the macro level. However, statistical interactions show that the percentage of unemployment alongside the percentage of asylum seekers are negatively associated and have little substantive effect when combined together. Future research should examine further this relationship and relationship between the financial crisis from 2008-2012 and extreme right-wing support from a comparative perspective.
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