The analysis builds on previous research exploring the impact of far-right support on citizenship policy. Using Bayesian analyses, this research evaluates the impact of far-right success on citizenship policy restrictiveness and citizenship policy outcomes per year across 29 European countries between 2003–2014. Results reveal that far-right success is a statistically and substantively significant factor in determining citizenship policy restrictiveness as well as rates of naturalization. However, differential levels of impact suggest that far-right influence is not uniform throughout the policy process.
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The question of how far right a party has to be in order to impact citizenship policy is outside the scope of this paper. However, these analyses provide counter-arguments to Akkerman (2012a, b) assertion that centre-right parties are primarily responsible for, and may even make more restrictive changes than the far-right in immigration and citizenship policy legislation (Akkerman 2012b).
This is contiguous with Meguid’s (2005) argument that mainstream parties might co-opt radical right policies in order to reduce their vote share.
The dataset includes at least two years of complete data for each country. However, due to the unavailability of census and survey data for some years, all 12 years are not represented for each country. The countries and years available in the dataset include Austria (2003–2009), Belgium (2003–2014), Bulgaria (2007–2012), Croatia (2009–2010), Cyprus (2007–2012), Czech Republic (2003–2014), Denmark (2003–2014), Estonia (2003–2014), Finland (2003–2014), France (2003–2014), Germany (2003–2014), Greece (2003–2010), Hungary (2003–2014), Ireland (2003–2014), Italy (2003–2004), Latvia (2003–2012), Lithuania (2009–2014), Luxembourg (2003–2004), the Netherlands (2003–2014), Norway (2003–2014), Poland (2003–2014), Portugal (2003–2014), Romania (2007–2008), Slovakia (2005–2014), Slovenia (2003–2014), Spain (2003–2014), Sweden (2003–2014), Switzerland (2003–2014), and the United Kingdom (2003–2014).
In the dataset, 27.76% of the observations indicate that countries grant citizenship automatically, 68.57% of the observations suggest that citizenship is granted based on some criteria, and 3.67% of observations do not grant citizenship based on birth (i.e., Switzerland).
The number of years of residency required for naturalization ranges from 3 (Poland) to 15 (Austria) years. The mean number of years was 7.07.
The number of years of residency required for naturalization of a citizen’s spouse ranges from 0 (Czech Republic, Portugal, Romania, Spain, and Sweden) to 6 (Austria) years. The mean number of years was 3.04.
The number of years a person must be married to a citizen in order to be considered for naturalization ranged from 0 (Czech Republic and United Kingdom) to 22 (Estonia) years. The mean number of years was 3.64. There was one empirical observation that deserves attention here. In Estonia, in order to be granted citizenship through marriage the couple must have been married prior to 1992. Therefore, the number of years a couple must be married in Estonia increases every year. This is the only situation where a specific numeric value was not given.
In the dataset, 53.11% of the observations have no restrictions on dual citizenship, 37.36% of the observations allow dual citizenship with some conditions, and 9.52% of the observations do not allow dual citizenship at all.
Similarly, Howard’s (2010) CPI index aggregates these three factors into one value for each observation.
The foreign-born measure was collected from the The Organisation for Economic Co-operation and Development (OECD) (2017). The smallest foreign-born proportion of the population was .0042 (Romania—2006), the mean was .0988, and the largest proportion was .343 (Luxembourg—2002). It is important to note that a calculated change variable was included in previous statistical modeling. The results were substantively the same. For ease of interpretation and model convergence concerns, the variable was left as a simple proportion in the final results discussion.
In the dataset, 86.81% of the countries are members of the European Union in a particular year (lagged).
The minimum GDP per capita change was a decrease of $16,730 (Norway—2009), the mean was an increase of $1,779, and the maximum GDP per capita change was an increase of $13,950 (Luxembourg—2003).
The worst GDP growth rate change was − 17.67 (Lithuania—2009), the mean rate of change was − 0.176, and the largest growth rate change was 16.66 (Lithuania—2010).
The lowest unemployment rate change was − 4.4 (Estonia—2011), the mean change in rate was .21, and the highest change in the unemployment rate was 8.3 (Estonia—2009).
It is important to note that these variables were recoded multiple ways. The most applicable alternative would be to leave the variables on a 0–10 scale and then average them. However, no substantive difference in the findings occurred when coding the variable in this manner. In addition, the average correlation between the variables was slightly higher with the final coding presented in this paper.
This process involved the use of factor analysis and simple correlation tests in order to test the relationship between these variables for each round of the ESS survey. The correlations between the three variables averaged a correlation of .661 over all waves of the study. Correlations were quite high. Further, previous statistical testing utilized a change variable of the anti-immigrant variable. For ease of interpretation and model convergence, the variable was left as a simple lagged proportion.
In some instances, the use of the ESS in creating this variable involved the use of individual country files when countries were left out of the cumulative datasets in particular years. In addition, since the survey is only implemented in even number years, the odd years were calculated by averaging the immigrant sentiment variable for the year before and the year after the particular odd number year.
In the relatively small number of cases where there are two far-right parties, the combined vote share for these parties was calculated (ex. Germany’s National Democratic (NPD) and Republikaner (REP) parties). Further, if there were two national elections (upper house and lower house) in a given year, the average vote share and seat share for the lower house were utilized.
The list of far-right parties by country is as follows: Austria—Freedom Party of Austria (FPÖ), Belgium—Vlaams Belang (VB); Bulgaria—Attack; Croatia—Croatian Pure Party of Rights (HCSˇP); Cyprus—National Popular Front (ELAM); Czech Republic—Worker’s Party of Social Justice (DS); Denmark—Danish People’s Party (DPP); Estonia—Estonian Independence Party (EIP); Finland—Finns Party (True Finns); France—National Front (NF); Germany—Germany’s National Democratic (NPD) and Republikaner (REP); Greece—Golden Dawn; Hungary—Jobbik; Ireland—none; Italy—Forza Nuova and Northern League (NL); Latvia—National Alliance (NA); Lithuania—none; Luxembourg—none; Netherlands—List Pim Fortuyn (LPF) and Party for Freedom and Democracy (PVV); Norway—Progress Party (FrP); Poland—none; Portugal—National Renovator Party (PNR); Romania—Greater Romania Party (PRM); Slovakia—Slovak National Party (SNS); Slovenia—Slovenian National Party (SNS); Spain—none; Sweden—Sweden Democrats (SD); Switzerland—Swiss People’s Party (SVP); United Kingdom—British National Party (BNP).
The mean vote share for far-right parties was 5.64%, and the highest was 28.9% (Switzerland). The mean seat share was 5.37%, and the highest was 36.07% (Austria).
Bayesian vector auto-regressive models, which should be utilized to estimate whether one variable does a better job at explaining the other variable in situations of endogeneity, could not be estimated here. The problem is that far-right vote share and seat share do not contain enough variation for accurate estimation in this dataset. The fact that some countries have a single vote share percentage representing multiple years creates a problem for model convergence. The collection of more data in the future may allow the utilization of Bayesian vector auto-regressive models in determining whether the lag of one variable is better at predicting another variable’s outcomes, rather than the reverse situation.
Therefore, readers interested in the results from frequentist models can simply look at the prior means and variances presented here.
It is important to discuss model convergence for all models in this article. First, each parameter, for both chains, passed Gelman and Rubin, Geweke, and Heidelberger and Welch tests. The Gelman and Rubin test statistics give a potential scale reduction factor of one for all parameters. These test statistics indicate that there is no need to run the chains longer in order to improve convergence of the stationarity distribution, since statistics of 1.2 or higher are the cutoff. The Geweke diagnostic test statistics indicate that the means of the parameters from two different locations in the chains have converged to a standard normal distribution. Finally, all parameters passed the stationarity and half-width tests of the Heidelberger and Welch test. In addition, trace plots of the Markov chains indicated that there was no trending present for the chains, or the individual parameters for each chain. Lastly, density plots conveyed that the distribution of the posterior parameters was normally distributed.
The model that estimates citizenship policy and includes far-right seat share instead of vote share differs in that the vote share variable is replaced with seat share in the model equation. Further, prior means and prior variance values are different [(μ = − .186, − .063, .214, − .053, .017, − .029, .277, .175, .531); (τ = .153, .049, .167, .054, .055, .051, .053, .052, .053)].
The model that estimates the proportion naturalized and includes far-right seat share instead of vote share differs in that the vote share variable is replaced with seat share in the model equation. Further, prior means and prior variance values are different [(μ = .119,.048,− .138,− .067,− .012,.002,.651,− .025,.234,− .137); (τ = .143,.048,.157,.052,.053,.048,.054,.051,.065,.061)].
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The authors will share all data, coding, and appendix material for replication purposes upon request. The authors thank Ivan Ascher, David A. Armstrong II, Kathleen A. Dolan, Shale Horowitz, Thomas M. Holbrook, Natasha Borges Sugiyama, the editors of Comparative European Politics, as well as the anonymous reviewers, for reading drafts and their valuable critiques and suggestions.
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Hansen, M.A., Clemens, J.L. What to expect when you’re electing: the relationship between far-right strength and citizenship policy in Europe. Comp Eur Polit 17, 673–695 (2019). https://doi.org/10.1057/s41295-018-0120-7
- Far right parties
- Radical right parties
- Citizenship policy
- Populist radical right parties