Abstract
National identity claims have taken a growing importance in the French political landscape these last decades. While representations of French national identity have long been scrutinised from an elite perspective, much less is known about “What is it to be French?” from a citizen’s perspective. This article aims at filling this gap. Using the European Values Study data, from 1981 to 2018, it first offers a general measure of change in French people national claims during that period. Building on a classification, it then proposes to disentangle five different types of French nationalism. Finally, the article tests the explanatory power of these types of nationalism on support for attitudes and a few policy preferences which involve representations of the in-group and the out-group such as immigration and solidarity policies.
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Notes
See Deloye (2013), for an overview on national identity and everyday life.
See, however, Throssel’s qualitative study on how French and British children learn national feeling of belonging (2015).
The first encompassing quantitative survey on national identity, the ISSP (International Social Survey Programme) 1995 module on identity and citizenship, was not run in France. We therefore only have French ISSP data set on identity and citizenship for 2003 and 2013.
See Mayer (1997) for an analysis of the 1978, 1988 and 1995 post-electoral surveys in the study of French nationalism.
Note that if the distinction was made to differentiate types of nations as the results of institutional and legal norms (Smith 1995) it then became an analytical tool when trying to differentiate different national feelings of belonging.
On this tension between the norm of Universalism and the French Republic’s practices, see as well the analyses proposed in the special issue on French identity politics in Mouvements (Simon and Zappi 2005).
Note that in this article, we use the term “nationalism” in order to designate all expressions through which people acknowledge or claim their belonging to a nation. There are discussions in the literature on the use of this term. Some consider it only designates its most exclusive form. Dekker et al. thus describe nationalism as characterized by “feeling a sense of belonging to a particular “nation” with a common origin, wanting to keep that “nation” as pure as possible, and desiring to establish and/or maintain a separate and independent state for that particular nation” (Dekker et al. 2003, p. 347). In doing so, authors acknowledge the reality of “good” and “bad” types of national identification. On the contrary, using the term nationalism helps to get rid of such normative distinction and to comprehend the phenomenon as a whole.
These questions, known as the “national identity module”, were previously asked in ISSP 1995, 2003 and 2013 as well as in the 2008 and 2018 EVS surveys. Depending on the year and the survey, the module gathers 5–7 items. In the EVS data set, there was a change in one of the items between 2008 and 2018 which does not allow building a comparable index or a comparable typology using all five items.
This gradation parallels the gradation observed in the 2008 result.
Some researchers consider that individual national attitudes form a cumulative hierarchy (Dekker et al. 2003, p. 347). We do not find such hierarchy within the EVS items.
This might reflect the fact that some items might be considered either as civic or ethnic depending on the context (Jones and Smith 2001). It could specifically be the case with culture, as it is rather a catchword which might bear different interpretations, and it may be peculiar in France, as claimed by Martigny (2008).
These results should be taken with caution due to the small size of French Muslims in EVS sample (99).
See Appendix 1 Section 3 in order to understand how this index is built.
See the recodification of all these variables in the Appendix 1 Section 3.
Obviously, this tension participates in the philosophy of French integration analysed by Favell: “to become French, it has to be the case that an individual’s identity is not definitively determined by their racial or cultural origins, or indeed any other national identity that might clash with their new adopted French identity” (Favell 1998: 69).
Note that another literature links immigration and attitude towards welfare (see Alesina et al. 1999; Putnam 2007; Eger 2010; Larsen 2011). This literature postulates that the more diverse ethnically a nation is, the less the support for welfare policy among its citizens (See, however, the discussion of such literature by Banting and Kymlicka 2003; Jayet 2013; Burgoon 2014).
See, however, Nielsen (1996–1997) and Brubaker (2004) on the difficulty in containing culture to an ethnic dimension.
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Acknowledgements
I would like to thank French Politics’ reviewers as well as Pierre Bréchon, Bruno Cautrès and Frédéric Gonthier for their very useful comments. Many thanks as well are due to Sandrine Astor, who has been of invaluable methodological help when analysing the data and writing this article. At each step, she worked by my side to face all the methodological difficulties I encountered. This article owes her a lot! All errors remain of course mine.
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Appendix 1
Appendix 1
All analyses are issued from the European Values Study 2018 French Dataset. We used post-stratification weights for the descriptive results. All results are based on an “only French citizens” sample.
In order to disentangle varieties of nationalism, our analyses are based on five items. French people were asked to position themselves on a four-point scale rating the importance for being truly French (very important, rather important, not important, not important at all): Be born in France (v189), Respect French law and political institutions (v190), Have French ancestry (v191), Be able to speak French (v192), Share French culture (v193). To distinguish the different types of nationalism, we use a Multiple Correspondence Analysis (MCA) and an Ascending Hierarchical Clustering (AHC) (See Appendix S1 online for details).
Section 1: Multiple correspondence analysis (MCA)
Our analysis refers to 2451 individuals. We excluded 140 individuals, who are not French citizens. As non-frequent categories (frequencies less than 5%) participate heavily in the determination of the axes, we gathered them with other categories. We transformed v190 and v192 into binary variables (“very important” versus the other categories). v193 is recoded into 3 categories (very important, rather important, not and not at all important). “Don’t know” and “no answer” are treated as passive categories (3–16 individuals are concerned depending on the variables). We thus run our specific MCA (Le Roux and Rouanet 2010b) on 5 active variables and 15 active categories.
In an MCA there are as many axes as the number of active variables’ categories minus one (14 for this analysis after recoding some of the included variables). Six eigenvalues are exceeding the average inertia. The modified rate (according to Benzecri) of axis 1 is equal to 68.3%; adding axis 2 brings the cumulated rate to 87.9% (see Appendix S1 online for further information on these 14 axes (eigenvalues, variance rates and modified rates)).
Section 2: Ascending hierarchical clustering (AHC)
AHC assigns each individual to one cluster. It finds the most similar pair of clusters and merges them into a single cluster. It then computes distances, i.e. similarities, between this new cluster and each of the old clusters. It ends up repeating these two steps until all individuals are clustered into a single cluster (Borgatti 1994).
Here, the AHC is applied to a cloud of points with the variance criterion. It is run on the first seven axes provided by the MCA. Together they explain 88% of the variance of the whole projected cloud (100% of the modified variance after Benzecri’s correction; see Appendix S1 online for further explanations on the AHC method).
AHC describes all clustering through a dendrogram and recommends focusing the analysis on either three, five or six clusters. We have decided to focus on five clusters (see Appendix S1 online for further information on how these clusters were built).
At first sight, some of the types we distinguished do not appear that different. This is both the case for the ardent and the ethnic nationalists and for the civic and cultural ones. One could argue a three-type classification would have been a better methodological choice, for clarity and parsimonious reasons. Looking at the dendrogram however, when limited to three types, the ardent nationalists compound a distinctive group, the ethnic belong to the same type as the cultural nationalists and the civic to the same type as the relaxed nationalists. This strengthens our choice to work with a five-type classification.
When interpreting a cluster, we need to know how large is the cluster and what categories are over- and underrepresented. The actives categories with the strongest over and underrepresentation for each of the five clusters are listed in Table 2. With only five variables and 14 dimensions, it is not surprising that several categories are overrepresented in more than one cluster, and that single categories strongly dominate in smaller clusters (Hjellbrekke 2019) (Table
6).
Section 3: Description of variables used in the regression analyses
For the purpose of the analyses and due to the large number of independent variables introduced in the regression models, we have transformed a certain number of variables in order to keep enough individuals in each category (Table
7).
As the parallel regression assumption was rejected for some of the predictors, we use a Generalized Ordered Logit model called partial proportional odds model where the parallel lines constraint is only relaxed for those variables where it is not justified (Long and Freese 2014). Results of these models are presented in Tables 4 and 5 of the article. If one or more categories of the independent variable do not respect statistical constraints, regression coefficients are different for each category of the dependent variable. In our analyses only a few categories of the independent variables do not abide by these conditions, which doesn’t affect the coefficient for most of our dependent variables categories. The predicted probabilities of each category of the dependents variables for the 5 types of nationalism can be found in the appendix S1 (see Fig. 3) (Table
8).
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Belot, C. Disentangling varieties of French nationalism, why does it matter?. Fr Polit 19, 218–249 (2021). https://doi.org/10.1057/s41253-020-00145-3
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DOI: https://doi.org/10.1057/s41253-020-00145-3