Why people vote for thin-centred ideology parties? A multi-level multi-country test of individual and aggregate level predictors.

The present research investigates the individual and aggregate level determinants of support for thin-centred ideology parties across 23 European countries. Employing a multilevel modelling approach, we analysed European Social Survey data round 7 2014 (N = 44000). Our findings show that stronger identification with one's country and confidence in one's ability to influence the politics positively but perceiving the system as satisfactory and responsive; trusting the institutions and people, and having positive attitudes toward minorities, i.e., immigrants and refugees, negatively predict support for populist and single issue parties. The level of human development and perceptions of corruption at the country level moderate these effects. Thus, we provide the first evidence that the populist surge is triggered by populist actors' capacity to simultaneously invoke vertical, "ordinary" people against "the elites", and horizontal, "us" against "threatening aliens", categories of people as well as the sovereignty of majority over minorities. These categories and underlying social psychological processes of confidence, trust, and threats are moderated by the general level of human development and corruption perceptions in a country. It is, therefore, likely that voting for populist parties will increase as the liberally democratic countries continue to prosper and offer better opportunities for human development. Stronger emphasis on safeguarding the integrity of the economic and democratic institutions, as our findings imply, and preserving their ethical and honest, i.e., un-corrupt, nature can keep this surge under check.

Introduction

In this study, we focus on the rise of populism in liberal democracies in Europe. We define populism as a two-dimensional framework of ideas [1], that includes but not limited to simultaneous invocation of vertical, “ordinary” people against “the elites”, and horizontal, “us” against “threatening aliens”, categories of people as well as the sovereignty of majority over minorities, and overprotective anti-institutionalism that purports to protect economic and cultural rights of “people” against threats from top, bottom, or from outside [1-3]. Our operationalisation of populism as a “two-dimensional” framework of inequality (vertical) and difference (horizontal) allows us to go beyond the radical right and left divide, and to include not only right or left wing populist parties but also “single issue” parties [4] that focus on an “all compassing issue” without an ideological basis across the political continuum [5]. Liberal democracies on the other hand are political systems that protect the civil, political, and economic rights of their citizens and provide access to educational, economical, and social opportunities on the basis of equality within an ethical and fair system [6]. In the current research, we explore how a host of individual and country level processes lead to more support for thin ideology parties. At the individual level we focus on subjective experiences resulting from human tendency to categorize their social environment into groups [7-9], ensuing threats and status concerns. We consider these processes as embedded in perceived vertical inequalities (dis-trust in institutions, satisfaction with the system). In addition, we also investigate (national identification), attitudes toward immigrants, and attitudes toward support for policies benefitting refugees embedded in perceived horizontal differences. We then focus on how these individual processes interact with country level factors such as human development opportunities (HDI) and corruption perceptions influence populist voting. such as attachment to one’s country (national identification).

Thus, we contribute to the ongoing debate on populism by showing that support for populist parties is driven by a host of seemingly innocuous social psychological mechanisms triggered by perceived inequalities, i.e., trust, system perceptions, and perceived differences, e.g., national identification attitudes toward new minorities [10]. We then show how aggregate level proxies of these differences, corruption perceptions and the level human development moderate both vertical and horizontal dimensions. By doing so, we connect political scientific research on populism with social psychological processes and provide a general account of the factors that motivate populist support across the political spectrum.

Why do people support thin-centred ideology parties?

One particular line of research shows that economic anxieties, perceptions of multiculturalism, and increasing numbers of immigrants and refugees motivate support for the populist parties [1, 2, 11]. The so called economic anxiety thesis [12] posits that certain segments, the low paid manual working classes, of the societies in the developed world perceive themselves as left behind in the new liberal economies of a globalized world. These segments also hold economic and political elites as responsible for their plight and turn to populist parties and leaders who exploit their resentment against the system.

This prediction is based on the assumption that the post-industrial societies have gone through drastic changes that transformed the labour politics and society in general. Increasing reliance on knowledge as a commodity, automation in the workplace, and the collapse of the manufacturing based economies coupled with the influx of foreign capital, goods, and cheaper labour in the form of migrants and refugees have made “the ordinary people” or “the silent majority”, low-paid manual labourers, more vulnerable to economic ups and downs. This sense of vulnerability in turn breeds resentment against the state and motivates support for populist actors who appear to defend the silent masses against the so-called elite and promise to bring back the glory of the old days [12]. In a similar vein, cultural backlash hypothesis [13, 14] predicts that post-materialist generations growing up in the economically and physically secure welfare democracies have had access to better and more educational opportunities and supported a range of improvements in human rights, environmental protection, and gender equality. These changes threaten the core traditional values of older and less educated generations and trigger an authoritarian reaction [14] making them more amenable to the exploits of the populist parties and leaders [2, 15, 16]. In fact, there is ample evidence on how individuals respond to such threats.

For instance, previous research on intergroup relations shows that individuals’ perceptions of their group as relatively deprived and disadvantaged compared to their group’s past or future conditions motivates discrimination [17, 18] against whom they attribute the responsibility of their present or anticipated disadvantage. Similarly, when individuals perceive their resources, lifestyles, and their belief systems as threatened they react by discriminating against the sources of threat [19, 20].

Taken together, the economic anxiety and cultural backlash hypotheses, i.e. the losers of globalization, predict that populist parties with thin-centred ideologies would garner the most support from older, less educated, less paid individuals living in the post industrialist countries with higher net migration rate with stuttering economies. Recent evidence however has been inconclusive and supported only one dimension of the losers of globalization thesis. Economically less well-off individuals tend to vote for radical left and right parties when their country’s’ economic performance is favourable and the level of immigration is modest [15].

An alternative line of research argues that it is not a general sense of relative deprivation but a sense of relative wealth that enhances populist support [21, 22]. According to the wealth paradox hypothesis as economy performs well and the society prospers the economically well-off display the most xenophobic and anti-immigrant attitudes. This is partly motivated by fear of future deprivation and loss of wealth, and partly by a sense of relative gratification resulting from in-group’s attractiveness [23-25]. This sense of gratification in turn intensifies the allure of the in-group, and triggers fear of possible loss of some privileges as well as an experience of guilt about them. Together, both fear and guilt motivate proactive discrimination against the outgroups, immigrants and refugees, as the economically well-off anticipate the immigrants and refugees to challenge the disparities [22]. The anticipation of challenge then drives the support for populist parties who thrive on these concerns. Although scarce, empirical support for “the wealth paradox” shows that those who experience higher levels of gratification display the strongest opposition to immigration [22].

In the present research, we argue that these seemingly contradictory arguments result from populist capacity to construct and reinforce identities on the basis of perceived vertical inequalities, “us” versus “elites”, and perceived horizontal differences, “us” versus “aliens”. Social psychological research on mobilization and political activism shows that individuals are motivated to engage in political action when they perceive themselves as deprived, relatively, compared to the other outgroups when they perceive their privileges and their way of life as threatened [26], or when they subjectively think that they have the capacity to change their actual or future disadvantage [27]. More specifically, because populist narrative delineates group boundaries and emphasizes both perceived disadvantages between “the ordinary people” and the “corrupt politicians and elites” (vertically) on one hand, and perceived threats from “aliens” (horizontally) on the other, it goes beyond the right versus left categories. Right and right leaning populist actors use this framework to project the elites (vertical) and the foreigners, e.g. refugees, immigrants (horizontal) as enemies of the ingroup, the ordinary people [7, 28, 29]. Left and left leaning populist actors on the other hand. Thus, by invoking both vertical inequalities and horizontal differences across a wide range of categories, populist narrative categorizes people into “us” versus “them” and projects the “others” as threats [7]. Although crucial to mobilization, this collective identity is not enough to get people to support a particular party or a leader. Individuals will only vote for those actors who appeal to their discomfort. The populist actors to employ this identity that they crafted to mobilize “us” against those who are responsible for the hardships and threats that “we” are experiencing [7, 30]. Two factors could facilitate these processes at the country level. First, and in line with the wealth paradox, one could argue that more and better access to educational, economic, and social opportunities in a country results in improved welfare of the citizens in that country and gives them an inflated sense of empowerment. At the same time, when this sense of empowerment is accompanied by sense of “us “delineated by vertical deprivation, current or future, sense of dissatisfaction with the system, and distrust in the institutions and horizontal threats from “aliens” turn to the populist parties.

Second, “The corruption” rhetoric that the populist actors employ portrays the establishment as corrupt and blames it for the hardships the ordinary people are going through. Once the perceptions of corruption are in place, the populist actors use these perceptions to push forward several conclusions. First and foremost, they emphasize that the establishment is not with the ordinary people which now cunningly include both the people and them as defenders of the ordinary people’s rights. Second, they also emphasize that the establishment work against the will of ordinary people and therefore it is not to be trusted.

This emphasis on ingroup and outgroup as “the establishment”, brings forward the issue of trust and system responsiveness. In general, trust is the belief that the other party will not exploit the vulnerabilities that the actors have [31, 32]. We define trust as the perception that state institutions will stay true to deliver what is expected from them [33, 34]. As such, trust based on iterative interactions with the state institutions and other parties. Institutionalized trust is also grounded in what we call the “non-rivalry” assumption that the state institutions and individuals are not competitors to each other [35]. This assumption resonates with the fundamental structure of the liberal economies that provide and protect civil, political, and economic rights of their citizens, and implies that higher levels of trust in the public institutions is an indicator of perceptions of public institutions as well-performing units [36]. Creating the in-group versus the outgroup distinction challenges the non-rivalry assumption and foments distrust between the in-group, the ordinary people, and the out-group, the establishment. In our analysis, we include both institutional trust and generalized trust.

The outline we provide above suggest that support for populist parties is a co-product of core social psychological processes, i.e., social identity, trust, satisfaction with the system, perceptions of efficacy, attitudes toward immigrants and refugees, and structural factors, level of affluence and the corresponding opportunities for human development as well corruption perceptions. Thus, we incorporate these country level dynamics to our conceptual model and deduce several testable hypotheses that might explain the rise of populist parties with a thin ideological base.

At the individual level, we draw from social psychological literature on social identity [37] and collective action [27]. We hypothesize that stronger identification with one’s country (CI) and confidence in one’s ability to take active part in politics (PC) grounded in this identification would be positively associated with support for populist parties (Hypothesis 1a and Hypothesis 1b, H1a and H1b respectively). Conversely, perceived system responsiveness (PSR), dimensions of trust, generalized (TR) and institutional (ITR); general sense of satisfaction (SAT), attitudes toward immigrants (AtI) and support for policies benefiting refugees (AtR) would be negatively associated with support for populist parties (Hypothesis 2a, Hypothesis 2b, Hypothesis 2c, Hypothesis 2d; Hypothesis 2e; and Hypothesis 2f; H2a; H2b, H2c, H2d, H2e, and H2f, respectively)

At the country level, our core prediction is that the effect of individual level social psychological variables on populist voting will be influenced by two aggregate level processes, the level of human development in a country, operationalized as Human Development Index [HDI, 38] and perceptions of corruption measured by Corruption Perception Index [CPI, 39]. Thus, we expect HDI to positively moderate the effects of country identification, political confidence, perceived system responsiveness, and trust in institutions and individuals, and attitudes toward immigrants and refugees on populist support. At higher levels of HDI both the positive effects of country identification and political confidence one hand (Hypothesis 3a; H3a) and the negative effect of perceived system responsiveness, dimensions of trust and attitudes toward immigrants and refugees on populist voting (Hypothesis 3b; H3b) on populist voting on other would be stronger. Conversely, we expect the negative effect general satisfaction with the system on populist voting to be reversed as HDI increases (Hypothesis 3c, H3a).

Our expectations for the moderating role of corruption perceptions at the country level differ slightly then that of human development opportunities. We expect the positive effect of country identification on populist voting and political confidence to be reversed (Hypothesis 4a and 4b; H4a, 4b, respectively). As the individuals perceive the establishment as more corrupt the country identification will have a negative effect on populist voting while political confidence will have a positive effect on populist voting (H4b). On the other hand, we expect the negative effect of perceived system responsiveness, dimensions of trust, and general satisfaction to disappear when they perceive the establishment as corrupt (Hypothesis 4c; H4c) if not reversed. More specifically, when individuals perceive the establishment as corrupt this might nullify the effect of these variables on populist voting as they are grounded in perceptions of institutions’ as being able to deliver. Last but not least, we expect the negative impact favourable attitudes toward immigrant and refugees to be enhanced to the extent that they perceive the establishment as corrupt (Hypothesis 4d, H4d).

To test these hypotheses, we followed a model building strategy and employed multilevel logistic regression which is a convenient analytical strategy when individual data is nested in some higher-level units, in our case in countries. Our decision is to depart from unidimensional indicators of wealth and inequality (namely GDP per capita, Gini coefficient, net migration rate, social welfare expenditure by country, respectively) employed by previous research [15] and focus instead on the subjective experience at individual and aggregate level and a multidimensional measures of human development (HDI) and corruption perceptions.

Method

Data and measures

We tested our hypotheses using ESS data (Round 7) across 23 European countries. Our individual data is nested in countries and our dependent variable is binary, i.e., voted for populist or single-issue parties in the last election (see online Appendix in S1 File for the list of the parties we included in the analyses). In addition to our social psychological variables at the individual-level and aggregate variables at the country-level, i.e., HDI and CPI, we also included demographic variables for control purposes.

Dependent variable

Our dependent variable is whether individuals voted for populist or single parties or not. ESS data contains information on voting during the last elections. We created a binary variable and coded individual responses 0 (voted for a non-populist party) and 1 (voted for a populist party, see the list of the parties we included in our analysis in the S1 File). We employ the most recent classification scheme used by Pew Research Center in their report European Public Opinion Three Decades After the Fall of Communism [40].

Demographic variables

All of demographic variables are categorical, that is, gender (G: 0 = male and 1 = female), paid work (P 0 = not in paid work and 1 = in paid work), and the highest level of education (E1 = less than lower secondary education, 2 = lower secondary education completed, 3 = upper secondary education completed, 4 = post secondary non-tertiary education completed, and 5 = tertiary education completed).

Individual level variables

Country identification (CI) is measured on a 11-point (0, not at all emotionally attached; 10, very emotionally attached). Political confidence is measured by two items (r = .79, p < .001; PC ‘able to take active role in politics’ and ‘confident in own ability to participate in politics’; 0, not at all; 5, a great deal). We assessed perceived (political) system responsiveness (PSR) by two items (r = .60, p < .001): ‘How much would you say the political system in [country] allows people like you to have a say in what the government does/to have an influence on politics?’ (1, not at all; 5, a great deal). We used three items (α = .76) to measure generalized trust (TR 0, you can’t be too careful/most people try to take advantage of me/people mostly look out for themselves; 10, most people can be trusted/most people try to be fair/people mostly try to be helpful). Similarly, we used five items (α = .88) to measure institutional trust (ITR), the level of trust individuals have in the institutions of their country, i.e., the parliament, the legal system, the police, the politicians, and the political parties (0, no trust at all; 10, complete trust).

We used six items (α = .79) to measure the general level of satisfaction with different aspects of the system on an 11-point scale (SAT 0, extremely dissatisfied/bad; 10, extremely satisfied/good). The participants indicated their level of satisfaction with life as a whole, with the economy in country, national government, the way democracy works, education, and health services in their country. We used three items to measure (α = .87) attitudes toward immigrants (AtI) on a 11-point scale (0, bad for the economy/culture/ social life; 10, good for the economy/culture/ social life). In a similar vein, three items were employed as a proxy to attitudes toward refugees (AtR α = .61): “The government should be generous in judging people’s applications for refugee status”, ‘Most refugee applicants not in real fear of persecution own countries’ and ‘Granted refugees should be entitled to bring close family members’ (1, agree strongly; 5, disagree strongly). We reverse coded the first and the third item so cumulatively higher values represent more support toward policies benefitting refugees. Although we have no specific hypothesis regarding political orientation, the ESS survey has an item (11-point scale ordinal variable 0, left; 10 right) so we included this item in our analyses as a control variable.

Country level variables

We measure the level of human development with human development index (HDI) prepared by the United Nations Development Agency [41]. HDI is based on Amartya Sen’s [42] conceptualization of development as a tool to improve human experience by expanding individuals capacity to be healthier, more knowledgeable, and to be able to be a fully active, civically and politically, member of the society. It is a composite index that represents the three dimensions of development: a) longevity, b) knowledge, and c) access to resources [43, 44]. These three dimensions then are incorporated to rank countries on a scale that ranges from 0 (lowest level of human capacity for development) to 1 (highest level of human capacity for human development).

We measured the perceived level of corruption with corruption perception index (CPI). As a composite index, CPI incorporates perceptions of various forms of corruption, e.g. bribery, diversion of public funds, or nepotism in the civil service, as well existence of mechanisms to combat corruption [39]. The index ranges from 0 (very high level of corruption) to 100 (no corruption). Taken together, both variables allow us to gauge the capacity of development that each country offers to its citizens as well as the perceptions of the corruption as determinants of support for populist parties as predicted by the ideational perspective. We report means, standard deviations, and correlations between our variables of interest in Table 1.

Table 1

Descriptive statistics and correlations of the variables included in the models.

	HDI	CPI	PO	CI	PC	TR	ITR	SAT	AtI	AtR	PSR
HDI
CPI	0.82
PO	-0.03	0.01
CI	-0.02	0.01	0.15
PC	0.22	0.18	0.04	0.02
TR	0.29	0.31	0.01	0.11	0.16
ITR	0.28	0.32	0.06	0.17	0.19	0.46
SAT	0.30	0.32	0.15	0.19	0.12	0.42	0.68
AtI	0.21	0.28	-0.15	0.01	0.24	0.33	0.33	0.30
AtR	0.12	0.16	-0.24	-0.04	0.16	0.21	0.19	0.11	0.56
PSR	0.27	0.29	-0.01	0.05	0.42	0.30	0.49	0.43	0.32	0.22
Mean	0.90	70.83	5.16	8.02	2.31	5.76	5.10	5.78	5.44	3.07	2.36
(SD)	(0.03)	(14.11)	(2.29)	(2.02)	(0.97)	(1.77)	(1.93)	(1.57)	(2.16)	(0.84)	(0.84)

Analysis and results

We fit a series of models to predict the log odds of populist voting to evaluate our hypotheses. We also include odds ratios and their confidence intervals to interpret the results for each hypothesis under consideration and to inspect whether the assumptions of the multilevel logistic regression models that are used to evaluate our hypotheses are reasonably satisfied by the ESS data.

An important condition for adapting a multilevel approach and employing aggregate level variables is to empirically demonstrate that there is between level variance across our variables. Thus, the intercept-only model can be used to determine whether the use of multilevel logistic regression modelling on predicting the outcome populist voting is superior to that of standard (single-level) logistic regression modelling. For this purpose, we calculated an intra-class correlation coefficient (ICC) ranging from 0 to 1 based on the proportion between the between-country variation and the total variation (i.e., the sum of the between-country variation and the within-country variation for populist voting). By utilizing a simulation based approach [45] which involves the estimated values of the overall intercept (i.e., ) and the random intercept variance (i.e., ) for the intercept-only model, we obtained the ICC as 0.23. This means that 23% of the effects on the probability of populist voting for a participant in the survey come from between-country dissimilarities, while 77% of these effects come from within-country dissimilarities. Thus, we conclude that the use of an intercept-varying multilevel logistic regression model in predicting the outcome populist voting is more reasonable than that of a standard (single-level) logistic regression (see Tables 2 and 3). Additional analysis showed that the model that contains both individual-level and country-level main effects, M2, fit the data better than the intercept-only model, M0, and the model containing only individual-level main effects, M1, as indicated by lower AIC and BIC values, and deviance values.

Table 2

Intercept-only model and models containing only main effects.

Main effects (fixed)	M 0		M 1		M 2
Individual-level	β	SE	β	SE	β	SE
(Intercept)	-1.53***	.29	-1.60***	.31	-1.56***	.29
gender			-.17***	.04	-.17***	.04
paidwork			-.07	.04	-.07	.04
education2			.04	.10	.04	.10
education3			.22*	.09	.22*	.09
education4			.19	.11	.19.	.11
education5			.03	.09	.03	.09
PO			.15***	.02	.15***	.02
PC			.05*	.02	.05*	.02
CI			.03.	.02	.03.	.02
PSR			-.09***	.02	-.09***	.02
TR			-.05*	.02	-.05*	.02
ITR			-.15***	.03	-.15***	.03
SAT			-.23***	.03	-.23***	.03
AtI			-.07**	.02	-.07**	.02
AtR			-.16***	.02	-.16***	.02
Country-level
HDI					1.06*	.46
CPI					-0.90*	.46
Random effects	στ_0j2	σ_τ_0j	στ_0j2	σ_τ_0j	στ_0j2	σ_τ_0j
(Intercept)	1.88	1.37	1.96	1.40	1.60	1.27
Model fit indices
AIC	18192.6		17489.7		17488.8
BIC	18208.5		17624.4		17639.3
Deviance	18188.6		17455.7		17450.8

Table 3

Models containing cross-level (two-way) interactions.

(Fixed effects)	Model M31			Model M32			Model M33
Individual-level main effects	β	SE		β	SE		β	SE
(Intercept)	-1.55***	.29		-1.58***	.30		-1.57***	.30
Gender	-.18***	.04		-.17***	.04		-.17***	.04
Paidwork	-.08	.04		-.07	.04		-.07	.04
education2	.05	.10		.05	.10		.06	.10
education3	.23*	.09		.21*	.09		.22*	.09
education4	.20	.11		.19	.11		.20.	.11
education5	.05	.09		.04	.09		.05	.09
PO	.16***	.02		.15***	.02		.15***	.02
CI	.02	.02		.03	.02		.01	.02
PC	.06**	.02		.05*	.02		.06**	.02
PSR	-.10***	.03		-.08***	.03		-.09***	.03
TR	-.05*	.02		-.06**	.02		-.05*	.02
ITR	-.21***	.03		-.12***	.03		-.17***	.03
SAT	-.22***.	.03		-.27***	.03		-.24***	.03
AtI	-.08**	.02		-.14***	.03		-.13***	.03
AtR	-.15***	.02		-.13***	.03		-.13***	.03
Country-level main effects
HDI	1.17*	.47		1.27**	.48		1.23*	.48
CPI	-1.02*	.47		-1.13*	.48		-1.10*	.48
Cross-level interactions
CI:HDI	.13***	.04	SAT:HDI	.41***	.04	CI:HDI	.11**	.04
PC:HDI	-.09*	.04	AtI:HDI	-.01	.04	PC:HDI	-.06	.04
PSR:HDI	.11**	.04	AtR:HDI	-.15***	.04	PSR:HDI	.08	.04
TR:HDI	-.15***	.04	SAT:CPI	-.35***	.04	TR:HDI	-.18***	.04
ITR:HDI	.32***	.04	AtI:CPI	-.15***	.04	ITR:HDI	.13**	.05
CI:CPI	-.11***	.03	AtR:CPI	.08	.04	SAT:HDI	.34***	.05
PC:CPI	.11**	.04				AtI:HDI	.01	.04
PSR:CPI	-.13**	.04				AtR:HDI	-.15***	.04
TR:CPI	.08*	.03				CI:CPI	-.11**	.03
ITR:CPI	-.34***	.04				PC:CPI	.11**	.04
						PSR:CPI	-.09*	.04
						TR:CPI	.13***	.04
						ITR:CPI	-.18***	.05
						SAT:CPI	-.23***	.05
						AtI:CPI	-.16***	.04
						AtR:CPI	.08	.04
(Random effects)	στ_0j2	σ_τ_0j		στ_0j2	σ_τ_0j		στ_0j2	σ_τ_0j
(Intercept)	1.69	1.30		1.78	1.33		1.77	1.33
Model fit indices
AIC	17336.3			17258.0			17216.7
BIC	17566.2			17456.1			17494.0
Deviance	17278.3			17208.0			17146.7

Predicting the log odds of populist voting

We utilize the method of maximum likelihood to estimate model parameters and their standard errors when evaluating our hypotheses. We report the findings with respect to the effects of demographic variables, individual level social psychological variables, aggregate level variables, and the interactions of social psychological variables and aggregate variables on populist voting, incrementally, in Table 3 via M31, M32, and M33. For economics of space and clarity however we focus on M33, as it enables us to test all four hypotheses simultaneously (See Table 3 and S1 File for additional information on the analyses).

From a demographic point of view, males (β = -.17, SE = .04, p < .001), those who completed upper secondary (β = .22, SE = .09, p < .05, and who are right leaning (β = .15, SE = .02, p < .001) tend to vote more for the populist parties.

When all variables of interest and their interactions were entered into the model (M33) political confidence (PC; β = .07, SE = .02, p < .001) but not country identification had a positive effect on populist voting. As expected, however, dimensions of trust, generalized (TR; β = -.05, SE = .02, p < .05) and institutional (ITR; β = -.17, SE = .03, p < .001), perceived system responsiveness (PSR; β = -.09, SE = .03, p < .001), and overall satisfaction with the system (SAT; β = -.24, SE = .03, p < .001). Last but not least, positive attitudes toward immigrants (AtI β = -.13, SE = .03, p < .001) and toward refugees (AtI β = -.13, SE = .02, p < .001) were negatively associated with populist voting.

At the aggregate level, results show that there is a strong positive effect of HDI (β = 1.23, SE = .48, p < .001) on populist voting. As the level of human development in a country increases so does the populist voting. The effect of CPI was also significant and in the expected direction. Perceiving the system as ethical and honest, i.e. higher values on the CPI is associated with decreased support for populism (CPI; β = -1.10, SE = .48, p < .05).

Looking at the moderating effects of HDI, the results show that when HDI increased, political confidence (PC:HDI) did not longer predict populist voting but country identification did (CI:HDI, β = .11, SE = .04, p < .01; Fig 1A). In countries that rank higher on HDI the more people identify with their countries the more they support populist parties. The negative effect of generalized trust did not change as HDI increased (TR:HDI, β = -.18, SE = .04, p < .001; Fig 1B). Surprisingly however HDI reversed the negative effect of institutional trust (ITR:HDI, β = .13, SE = .05, p < .05; Fig 1C) on populist voting. The negative effect of institutional trust at the individual level changes direction. In countries that rank higher on HDI, the more people trust in the institutions of their country the more they support populist parties.

Fig 1

A-E. Plots showing the moderating effect of HDI on CI, TR, ITR, SAT, and AtR, respectively. Pink and blue lines show the probabilities of populist voting with increasing values of HDI for low and high values of attitudinal variables, respectively. Pink and blue areas display the corresponding confidence intervals.

In a similar vein, providing the strongest support for the wealth paradox, HDI reversed the negative effect of system satisfaction on populist voting. As HDI increased effect of system satisfaction become positive (β = .34, SE = .05, p < .001; Fig 1D). In countries that rank higher on HDI, overall satisfaction with the system is associated with more support for populist parties. The negative impact of attitudes toward refugees (AtR) on populist voting at the individual did not change (AtR:HDI; β = -.15, SE = .04, p < .001; Fig 1E) as HDI increased but the negative impact of attitudes toward immigrants on populist voting was reversed but did not reach the level of significance.

With regards to the moderating effect of CPI on social psychological variables-populist voting paths (note that higher values on CPI shows that the system is perceived less corrupt) our findings show that CPI moderated the effect of CI on populist voting (CI:CPI, β = -.11, SE = .03, p < .001, Fig 2A). In countries where the system is perceived ethical and honest the more people identify with their country the less they support populist parties. In similar vein, we observed similar effects of CPI on the effects of other social psychological variables on populist voting. Surprisingly, the positive effect of PC on populist voting at the individual level did not change (PC:CPI, β = -.11, SE = .04, p < .05; Fig 2B) as CPI increased. Individuals’ confidence in their ability to take active part in politics positively predict populist voting only when the system is perceived less corrupt. In similar vein, the negative effects of perceived system responsiveness (PSR) remained as significantly negative (Fig 2C).

Fig 2

Plots showing the moderating effect of CPI on CI, PC, PSR, TR, ITR, SAT, AtI, and AtR, respectively.

Pink and blue lines show the probabilities of populist voting with increasing values of CPI for low and high values of social psychological variables, respectively. Pink and blue areas display the corresponding confidence intervals.

Surprisingly, CPI also reversed the negative effects of trust dimensions on populist voting. Previously negative, the effect of generalized trust became positive (TR:CPI, β = .13, SE = .04, p < .001; Fig 2D), Interestingly, previously negative effect of institutional trust did not change (β = -.18, SE = .04, p < .001; Fig 2E). In countries where the system is perceived as ethical and honest, the more people trust others the more they supported populist parties. However, the more they trust institutions the less they support populist parties. We outline the implications of this difference in the general discussion.

Last but not least, as CPI increased, the negative effects of overall satisfaction and attitudes toward immigrants on populist voting did not change (Fig 2F and 2G) while the effect attitudes toward refugees (AtR:CPI) changed to negative but did not reach to statistical significance.

To increase confidence in our results and for ease of interpretation we conducted additional analyses of the odd ratios with Wald confidence intervals (Table 4). The odds ratios can be directly obtained by exponentiating the estimated values of the model parameters when predicting the log odds of the outcome populist voting. To illustrate, consider the effect of the predictor gender on the log odds of the outcome populist voting when evaluating hypothesis H1 using model M31, where . Then, the odds ratio representing the association between the outcome populist voting and the predictor gender can be calculated as OR = exp() = exp(-.18) ≈ .84. The interpretation of this value and the other odds ratios should be made with reference to the value of 1. For example, since the confidence interval between the lower and upper bounds of this odds ratio [.78, .91] does not contain the value of 1, we conclude that there is a significant negative association between gender and populist voting. This means that the probability of populist voting (instead of mainstream voting) is approximately 1/.84 ≈1.19 times smaller for the females when compared to the reference category, i.e., males. Similarly, the odds ratios for the association between HDI human and populist voting is OR = exp() = exp(1.17) ≈ 3.22 and the confidence interval between the lower and upper bounds for this odds ratio [1.27, 8.09] does not contain 1. This means that the probability of populist voting (instead of mainstream voting) increases approximately 3.22 times by one unit increase in human development index. In contrast, the odds ratio between the outcome populist voting and the predictor CI does not indicate a significant association between these variables. That is, OR = exp() = exp(.02) ≈ 1.02 is almost equal to 1 and the confidence interval between the lower and upper bounds for this odds ratio (.98, 1.076 includes 1.).

Table 4

The odds ratios and Wald confidence intervals between brackets for the models containing cross-level (two-way) interactions.

Individual-level main effects	Model M31	Model M32	Model M33
	Odds ratios (Wald CI)	Odds ratios (Wald CI)	Odds ratios (Wald CI)
(Intercept)	.21 (.12, .38)	.21 (.11, .37)	.21 (.11, .37)
gender	.84 (.78, .91)	.85 (.78, .91)	.84 (.78, .91)
paidwork	.93 (.86, 1.00)	.93 (.86, 1.01)	.93 (.86. 1.01)
education2	1.05 (0.87, 1.28)	1.05 (.87, 1.28)	1.07 (.87. 1.28)
education3	1.25 (1.05, 1.50)	1.23 (1.03, 1.47)	1.24 (1.03. 1.47)
education4	1.22 (0.99, 1.52)	1.21 (.97, 1.50)	1.22 (.97. 1.50)
education5	1.05 (.87, 1.26)	1.04 (.87, 1.25)	1.05 (.87, 1.25)
PC	1.07 (1.02, 1.11)	1.05 (1.01, 1.10)	1.07 (1.01, 1.10)
PO	1.17 (1.12, 1.22)	1.16 (1.12, 1.21)	1.16 (1.12, 1.21)
CI	1.02 (.98, 1.06)	1.03 (.99, 1.07)	1.01 (.99, 1.07)
PSR	.91 (.86, .96)	.92 (.88, .97)	.91 (.88, .97)
TR	.95 (.90, .99)	.94 (.90, .98)	.95 (.90, .98)
ITR	.81 (.76, .86)	.88 (.84, .93)	.84 (.83, .93)
SAT	.80 (.76, .85)	.76 (.72, .81)	.79 (.72, .81)
AtI	.93 (.88,.97)	.87 (.83,.92)	.87 (.83, .92)
AtR	.86 (.82, .90)	.87 (.83, 0.92)	.88 (.83, 0.92)
Country-level main effects
HDI	3.22 (1.27, 8.09)	3.55 (1.38, 9.15)	3.41 (1.38, 9.15)
CPI	.36 (.14,.91)	.32 (.13, .84)	.33 (.13, .84)

Additional tests

To further increase our confidence in the findings, we conducted additional tests to investigate whether the results are biased due tomulticollinearity, strong correlation (s) between two or more predictors in a regression model. Although multicollinearity does not distort the reliability or power of the model as a whole [46], it may cause spuriously high standard errors of individual parameter estimates, and thus, undependable test statistics [47]. For example, it is a quite common scenario in the results of regression analysis where the F-test for a model indicates a significant overall effect of predictors on an outcome, while none of the t-tests in the same study demonstrate a considerable individual impact. Severe multicollinearity in the data may prevent researchers from diffrentiating individual impacts of predictors on the model outcome. This, in turn, mightcause interpretation problems.

Multicollinearity requires even more careful attention in logistic hierarchical modeling than the standard logistic regression, since it can occur not only for subject level variables, but also for aggregate level variables. As can be seen on the bottom panel of Table 1, only the correlation between aggregate level variables HDI and CPI (i.e., |0.82| > 0.7) is quite large indicating a possible multicollinearity problem in the data in line with the suggestion made in [48]. However, the high correlations among the predictors do not necessarily mean the data at hand suffer from the multicollinearity problem. Another diagnostic test for detecting multicollinearity is the variance inflation factor (VIF). This factor measures the inflation in the variance of the estimate for each regression coefficient in the model by taking into account the correlations among the predictors in the data. The VIFs that are larger than 3, 5, or 10 or the small values of its inverse, the tolerance (TOL) are considered to be a sign of multicollinearity problem in the data [49]. In addition to these values, we elaborate on two additional diagnostic measures which are Leamer’s measure [50], and corrected variance inflation factor (CVIF) [51] for detecting the extent of multicollinearity in the data in line with previous research [48]. Similar to the TOLs, the Leamer’s measure takes the values of between 0 and 1 for which the values close to zero indicate the existence of multicollinearity in the data. The CVIFs that are larger than 10 indicate a possible multicollinearity problem [49].

Table 5 displays these four measures obtained for the ESS (Round 7) data. Based on the results presented in this table, the VIFs for all the subject level variables are smaller than 3. The VIFs for aggregate variables HDI (i.e., 3.202 > 3) and CPI (i.e., 3.264 > 3) indicate a possible multicollinearity in the data. However, since other variables in the data have small VIFs and the VIFs for HDI and CPI are smaller than 5 (or 10) and not very large than 3, we conclude that the ESS data do not suffer from a severe multicollinearity problem. The results for other measures in Table 5 are in accordance with that for the VIF. As expected, the TOL s and Leamer’s measures of the variables HDI and CPI are smaller than that of other variables in the data. However, these values are not too close to zero and the CVIFs for all variables in the model are quite smaller than the decision point 10 which indicates that there is no multicollinearity problem in the data.

Table 5

Multicollinearity diagnostics results for the ESS data (Round 7).

	VIF	TOL	Leamer	CVIF
HDI	3.202	0.312	0.559	3.363
CPI	3.264	0.306	0.555	3.428
PO	1.123	0.890	0.944	1.179
CI	1.071	0.934	0.966	1.125
PC	1.267	0.789	0.889	1.330
TR	1.397	0.716	0.846	1.467
ITR	2.207	0.453	0.673	2.318
SAT	2.105	0.475	0.689	2.211
AtI	1.714	0.583	0.764	1.800
AtR	1.519	0.658	0.811	1.595
PSR	1.622	0.617	0.785	1.704

Last but not least. It is important to mention that the continuous variables in the ESS data (Round 7) are standardized [7] to improve the interpretation of main effects in the presence of interactions. There are two main types of standardization techniques in the context of multilevel modeling: Standardization with grand mean centering and standardization with group mean centering. We standardized the continuous variables in the data using the most commonly used technique which is the standardization with grand mean centering in which the overall means of the variables are subtracted from their values and consequently divided by their standard deviations. Note that, we do not report the conditional effects of the individual level variables on the outcome at different values of the aggregate level variables in this study. The rationale behind this choice is that obtaining conditional effects is more valuable by applying standardization with group mean centering (stated otherwise within cluster centering) rather than grand mean centering in the presence of cross-level interactions [52, 53].

General discussion

From demographics point of view, our results replicate previous research that shows males [54, 55], those less educated, and right-wing or right leaning individuals support populist parties more [56]. In terms of individual and country level processes, our findings also suggest that support for populist and thin ideology parties is driven by a fundamental intergroup mechanism, i.e., social identity and status concerns, which in turn are influenced by country level processes, i.e., opportunities available for human development (HDI) and corruption perceptions (CPI). Below, we first discuss individual level processes. We then elaborate on our findings with regards to the direct effects of our country level variables HDI and CPI. Last but not least, we unpack our findings on cross–level interactions.

First and foremost, at the individual level. we did not find support for H1a, the positive effect of country identification on populist voting. Results showed that such an effect, if any is contingent on the levels of human development and corruption perceptions at the country level. While, previous research on identity and political behaviour argues that stronger identification with one’s group is associated with political activism [57, 58], our results show that when the contents of that identity, in our case national identity, is perceived and experienced differently, this association is not straightforward. Our results, however, fully supported (H1b). Past research shows that dimensions of efficacy, i.e., participative versus group versus movement efficacy are associated with activism [59, 60]. These results replicate and extend findings from previous research in the sense that political confidence as a proxy of efficacy predicts populist support at the individual level.

We predicted that perceiving the political system as responsive, generalized and institutional trust, satisfaction with the system, and positive attitudes toward immigrants and refugees would be negatively associated with populist voting (H2a; H2b, H2c, H2d, H2e, and H2f). Previous research shows that trust in institutions [61] and satisfaction with the system [62] reduce support for populism. Our results replicate this finding. Most research to date investigated whether populist attitudes predict negative attitudes toward immigrants and refugees [63, 64], Our results confirm this finding in the opposite direction. We found that more positive attitudes toward refugees are associated with less support for populist parties. Cumulatively these findings confirm H2a-H2f.

Moderating effects of country level variables on individual level variables

At the aggregate level, support for populist parties increases as the level of human development increases. As expected, populist voting also changes as a function of perceived corruption. The less corrupt the system is seen the less people support populist parties.

First, As HDI increases one attachment to her country, satisfaction with the system, and more importantly trust in the institutions positively predict support for populist parties. As the level of human development opportunities increases the null effect of country identification and the negative effect of satisfaction with the system as well as the negative effect of institutional trust on populist voting becomes positive. Taken together, we argue that this supports the “wealth paradox [22] but not the losers of globalization thesis [2, 16]. These findings contradict earlier findings at the individual level that populist support will be highest when individuals are not satisfied and when they do not trust the system [61, 62]. Future multilevel research using more specific measures of these variables, especially of country identification is welcome. In a similar vein, more complex models looking at the indirect effects of country identification via dimensions of trust, political confidence, or system satisfaction on populist voting might be able to reveal more about the impact of country identification on populist voting via alternative dimensions.

Second, the level of human development also moderates the effect of attitudes toward immigrants and support for policies benefitting the refugees. Initially negative, the effect of positive attitudes toward immigrants on populist voting disappears when we factor in the level of human development. In addition, whereas initially not significant the effect of attitudes toward refugees becomes significant. In more developed countries positive attitudes toward refugee benefitting policies decrease support for populist parties. As our results show, there is a distinction between how people perceive immigrants and refugees. Perhaps, immigrants are perceived as more competent and thus threatening [65] than the refugees but incorporating aggregate level variables trumps this effect.

Third, our full model implies that corruption perceptions too moderate the effect of individual level variables on populist voting. Attachment to one’s country for instance is associated with less support for populist parties when the system is seen as ethical and honest. In similar vein trust in other individuals negatively predicts populist support at the individual level. When, however, the system is perceived as ethical more generalized trust is associated with more support for populist parties. The trust in institutions, as expected, negatively predicts populist voting when the system is seen as ethical and fair. This perhaps provides a glimmer of hope towards combatting the populist surge. Earlier research shows that level of human development and corruption perceptions go hand in hand. Higher levels of human development are associated with lower level of corruption perceptions [66]. Measures toward emphasizing the ethical and honest nature of the system could outweigh the positively moderating effect of human development on populist voting.

Limitations and implications

Comprehensiveness and novelty of our results notwithstanding, we acknowledge that we need to be cautious while interpreting the findings and drawing wider conclusions. First and foremost, we collapse left, centre, and right-wing parties and analyse them together. Recent research shows that support for populist parties across the political spectrum is motivated by alternative processes [67]. However, our focus on subjective experiences that are pervasive across the political spectrum, and operationalizing populism as a thin-centred ideology allows us to include all parties across the political spectrum. Accordingly, this helps us to identify a general set of psychological processes that are associated with populism irrespective of the ideological position. Second, we only use ESS Round 7 and provide a snapshot of the processes at a single point in time. Thus although comprehensive our results might not show the full scale of how populist attitudes changed over time. Third, our focus is limited in the sense that we do not provide a comparative analysis of our aggregate variables vis-à-vis traditional predictors (i.e., GDP per capita, Gini coefficient, net migration rate, or social welfare expenditure) established by previous research. Last but not least, we do not elaborate on country level differences. Taken together these limitations raise important questions that can be answered by future research. Longitudinal studies employing data from several rounds of ESS would be particularly welcome to compare traditional aggregate level predictors with relatively novel variables we include in our models. In a similar vein, case studies comparing countries that rank higher on HDI (e.g., Norway, Switzerland, or Iceland) with those ranking relatively lower (e.g., Hungary and Poland).

Conclusion

In sum, these results provide important insights into how support for populism is a product of individual level social psychological processes, one’s attachment to one’s country, dimensions of trust, perceptions of the system, and one’s satisfaction with the system. Perhaps, our most noteworthy finding is how two defining features of liberal economies, higher level of opportunities for human development within a system that is seen as ethical and honest, i.e., uncorrupted, influence and even reverse, rather paradoxically, the influence of some fundamental social psychological processes. Tellingly, the findings suggest that the support for populism, at least in our data, is likely to continue as the liberal democratic systems prosper but governments and other institutional actors can mitigate the enhancing effects of this prosperity on populist voting.

(DOCX)

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19 May 2021

PONE-D-21-05815

Why People Vote for Thin-Centred Ideology Parties? A Multi-Level Multi-Country Test of Individual and Aggregate Level Predictors

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Reviewer #1: The paper presents a multi-level analysis of country-wide and individual predictors of vote for populist parties in 23 European countries.

I appreciated the Authors' approach to the phenomenon and their attempt to bring together an individual-based focus, typical of social psychological research, and a focus on aggragate-level cross-national differences, typical of comparative political science research. I also found some of the results quite interesting in this light, as they suggest that different macro-social context can change and even reverse the effects of individual variables.

I also have some reservations on the paper, based on two main points, namely the theoretical framing of the introduction, which is not always consistent, and the design and description of the analyses, which should be checked to make sure that it complies with sufficient statistical standards. You can find below my observations on these two main points.

As for the first issue, the Authors' argument starts from two well established theories on the roots of support for populist movements, the economic anxiety and cultural backlash hypotheses, and then moves on to the wealth paradox hypothesis, which combines some elements of the cultural backlash hypothesis (the prevalence of racism and anti-immigrant attitudes) with an economic explanation, attributing it to prospective fear of a diminished status, rather than actual impoverishment. The following paragraph (Ideational approach to populism), however, deviates from this framework, introducing different factors (education at the societal level, and entitlement and empowerment at the individual level), and discussing the role of the crucial element of populist leaders and their rhetoric. The effect of this factor is not discussed directly, but rather connected with the idea of the "corrupted élite" argument enabling citizens' group categorisation (people-ingroup vs. élite-outgroup) and their distrust in politicians and the political system. This connection appears less linear than the preceding ones, and should be revised and more explicitly supported theoretically, for instance by referring to past research on social identity in political and collective action, an area the Authors seem to be familiar with, given the work they reference. Several other constructs are introduced in the latter part of this paragraph (p. 9), some of which probably deserved a more thorough discussion, such as the concept of country identification, which has been already investigated in relation to populism (e.g., see Marchlewska et al., 2018).

When they came to formulate specific hypotheses for their study, the Author chose to mix together some of the elements of the previously established models/hypotheses, making two new hypotheses apparently based on the valence of the predicted relationship with populist vote (positive for H1, negative for H2), and a separate hypothesis (H3) regarding the aggregate-level expected predictors of populist vote. They then combine these hypotheses into a moderation hypothesis (H4), in which they only state that the effects predicted in H1-3 would be "enhanced". From the subsequent elaboration (p. 11, l. 244-252) it seems that they expect to find stronger effects of the predictors used in H1&2 in the conditions which, according to H3, would lead to greater support for populist parties, but it is not completely clear if they expect the effects to become weaker or reverse in the opposite conditions (e.g., with low HDI and high CPI). This hypothesis should be more thoroughly discussed and explained. I am not fully convinced of the former three hypotheses, either, as I think it would be more appropriate to base them on existing theories and hypotheses from the literature (e.g., the economic anxiety and cultural backlash hypotheses), rather than regrouping variables. But perhaps the Authors can provide a convincing rationale for their choice.

My second reservation concerns the multi-level analyses reported in the results. I concur with the Authors that this is the appropriate approach to investigate the interplay between individual-level and aggregate-level variables in promoting vote for populist parties, and I appreciated their work in establishing the appropriateness of the models they tested (see the preliminary analyses reported on p. 15-16 and in the supplementary materials). What I am concerned about, however, is the reliability of the results, given the number of variables included in the model and the unclear links among them. I suspect that the models might have a multicollinearity problem, as some variables are probably strongly correlated with each other. The Authors should report multicollinarity diagnostic indexes (some are proabably available in the statistical packages used to run the analyses) to exclude or at least quantify the problem in their results, and help readers in their interpretation. Perhaps some basic zero-order correlations among the predictors could be reported in Table 1, as well. Furthermore, the description of the moderation effects (pp. 17-20) is not always clear, for instance when they say that when the moderator is "taken into account" the effects of the other variables change or do not change. A more typical approach would have been to report conditional effects of the focal predictors (the individual-level ones) at different levels of the moderators (the aggregate-level variables), but again the Authors may provide an explanation and justification for their choice.

All things considered, I found the paper interesting and I appreciated its goal of bringing together two different approaches to the object of analysis, so I hope the Authors will be able to make the necessary improvements to provide theoretically sound and statistically convincing support for their endeavour.

Reviewer #2: Overall, I enjoyed reading the manuscript, I though it drew on interesting individual and aggregate level data and there are some really interesting findings in there about populist support in general (as a phenomenon) regardless of ideological leaning. That being said, there is also some concern about this approach – as Mudde argues with the use of thin-centred ideology for defining populism, populism only becomes contextualized and given meaning when it becomes anchored within a thick ideology (which will differ in left- and right-wing contexts). I would like to have seen more discussion in the introduction of the ideational approach as to how mapping out these trends in the data, without contextualizing populism as either right or left, is justified. I think the logic is there to some degree, the emphasis on understanding the characteristics and links to social psych and structural-level phenomena, but this could be clarified a bit more. Similarly, by the time the reader gets to the discussion, this has been somewhat forgotten, and a reminder would be useful.

The paper outlines a series of social psychological processes that are said to fuel populist support (including identification, trust, system satisfaction, attitudes towards immigrants, efficacy and structural factors) yet these are discussed more in a narrative form and less in a way that links them theoretically. While this might not be necessary, an overarching ‘model’ or framework that positions these phenomena within an identity/hierarchy and system framework could be useful. Something similar to this has been recently outlined in the following paper ( https://doi.org/10.1016/j.copsyc.2020.06.009 ) which draws on many of the same concepts, papers and ideas developed here, but tries to position them within a theoretical framework of sorts. In particular, the discussion on hierarchy and status concerns seems relevant here, as it is not only that identities are mobilized as ingroup and outgroup, but also that these are positioned in ways that are threatening and perceived as unjust (relative deprivation findings), which subsequently align both low-income and high-income earners within the same ‘ingroup’ because it is not only about economic anxieites, as you point out, but also about the status concerns they express. Furthermore, the paper makes reference to Brubaker’s distinction between horizontal and vertical opposition which might be useful in accounting for how the ingroup is both defined against a horizontal other (corrupt elites) and vertical other (migrants/ refugees) in contexts of right-wing populism. Having a look at these references might help develop a sharper framing of the various items used, rather than stating that these are taken from different literatures and brought together. Mainly, I make this point to strengthen the overall rationale and framing for how these individual level and structural level processes interplay, and for the hypotheses generated which are sound.

Lastly, I think the discussion needs a bit of work. As mentioned above, there are some contradictory trends in the data when the aggregate level analysis is included, and this needs to be spelled out a bit more in detail. The discussion feels like it mainly repeats the findings but doesn’t move beyond that to consider more in-depth the implications (theoretically and practically) of these novel findings. This is mainly a push for the authors to really consider why these findings emerged and what they say about understandings of populism. This is nicely done in relation to trust, but could be expanded on for other processes discussed. Perhaps tightening the framework that links the different processes in the introduction helps to revise this part.

Minor points:

A re-read of the abstract for typos, missing punctuation and missing words such as ‘a’ ,’of’ etc. is needed. I have listed a few of these below, but not all.

Abstract, typo:

Thus, we provide the first evidence that the populist surge is a product of a complex set (of) social psychological mechanisms that are moderated by the general level of development and corruption perceptions in a country

P2, line 31 – extra “ after ideology: of populism as a “thin-centred” ideology”

P2, line 34: ). Liberal democracies on the other hand (stating ‘on the other hand’ assumes there’s a ‘on the one hand’ prior in the text, which doesn’t appear, so I’d suggest revising this)

P2 , line 48: the level (of) human development and corruption

P3, line 69 – In (a) similar vein, (the) cultural backlash…

P 7, line 145 - Although crucial to mobilization (for mobilizing? / to the mobilization of?) this collective identity,

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Reviewer #1: Yes: Mauro Bertolotti

Reviewer #2: No

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1 Dec 2021

Response to the reviewers

Dear Editor,

Thank you very much for the detailed feedback and for the supportive editorial process. Upfront please accept our sincere apologies for not completing this revision earlier. The loss of a close family member and health related problems prevented the lead author from engaging with and completing the review.

We think we did our best to integrate the feedback into the manuscript. Below, you can find our detailed responses. Changes are highlighted as track changes. We numbered each comment and provided our responses accordingly.

Once again, allow us to express our gratitude for the supportive editorial process and the insightful comments which made the manuscript stronger. Please do not hesitate to contact us if you have further comments.

Sincerely,

Huseyin Cakal

P.S. We were not aware of the journal’s requirement of completing the review within 6 months of receiving editorial feedback. This is of course not an excuse, but we want to reiterate our commitment to revise the manuscript again should the reviewers consider it necessary.

R1-1

R1 comments that “the Authors' argument starts from two well established theories on the roots of support for populist movements, the economic anxiety and cultural backlash hypotheses, and then moves on to the wealth paradox hypothesis, which combines some elements of the cultural backlash hypothesis (the prevalence of racism and anti-immigrant attitudes) with an economic explanation, attributing it to prospective fear of a diminished status, rather than actual impoverishment. The following paragraph (Ideational approach to populism), however, deviates from this framework, introducing different factors (education at the societal level, and entitlement and empowerment at the individual level), and discussing the role of the crucial element of populist leaders and their rhetoric. The effect of this factor is not discussed directly, but rather connected with the idea of the "corrupted élite" argument enabling citizens' group categorisation (people-ingroup vs. élite-outgroup) and their distrust in politicians and the political system. This connection appears less linear than the preceding ones, and should be revised and more explicitly supported theoretically, for instance by referring to past research on social identity in political and collective action, an area the Authors seem to be familiar with, given the work they reference. Several other constructs are introduced in the latter part of this paragraph (p. 9), some of which probably deserved a more thorough discussion, such as the concept of country identification, which has been already investigated in relation to populism (e.g., see Marchlewska et al., 2018).”

We thank R1 for these insightful and useful comments. We have now incorporated these ideas and R2 comments on the same issue to the manuscript. More specifically, we have changed our framing our populism in line with Brubaker’s vertical vs horizontal perspective and incorporated Obradovic et al’s argument to our theorizing. In addition, we dropped our emphasis on ideational approach to populism.

R2-2 R1 also comments that “When they came to formulate specific hypotheses for their study, the Author chose to mix together some of the elements of the previously established models/hypotheses, making two new hypotheses apparently based on the valence of the predicted relationship with populist vote (positive for H1, negative for H2), and a separate hypothesis (H3) regarding the aggregate-level expected predictors of populist vote. They then combine these hypotheses into a moderation hypothesis (H4), in which they only state that the effects predicted in H1-3 would be "enhanced". From the subsequent elaboration (p. 11, l. 244-252) it seems that they expect to find stronger effects of the predictors used in H1&2 in the conditions which, according to H3, would lead to greater support for populist parties, but it is not completely clear if they expect the effects to become weaker or reverse in the opposite conditions (e.g., with low HDI and high CPI). This hypothesis should be more thoroughly discussed and explained. I am not fully convinced of the former three hypotheses, either, as I think it would be more appropriate to base them on existing theories and hypotheses from the literature (e.g., the economic anxiety and cultural backlash hypotheses), rather than regrouping variables. But perhaps the Authors can provide a convincing rationale for their choice.”

We have now completely revised our hypotheses and re-arranged them in line with our introduction (pp8-10). We think this rearranged format provides clarity but we are happy to revise again if the RRs consider it necessary.

R1-3. R1 comments that “My second reservation concerns the multi-level analyses reported in the results. I concur with the Authors that this is the appropriate approach to investigate the interplay between individual-level and aggregate-level variables in promoting vote for populist parties, and I appreciated their work in establishing the appropriateness of the models they tested (see the preliminary analyses reported on p. 15-16 and in the supplementary materials). What I am concerned about, however, is the reliability of the results, given the number of variables included in the model and the unclear links among them. I suspect that the models might have a multicollinearity problem, as some variables are probably strongly correlated with each other. The Authors should report multicollinarity diagnostic indexes (some are proabably available in the statistical packages used to run the analyses) to exclude or at least quantify the problem in their results, and help readers in their interpretation. Perhaps some basic zero-order correlations among the predictors could be reported in Table 1, as well. Furthermore, the description of the moderation effects (pp. 17-20) is not always clear, for instance when they say that when the moderator is "taken into account" the effects of the other variables change or do not change.”

We have now incorporate additional statistics on multicollinearity and centering (pp18-20)- and descriptive statistics p 13) to help ease interpretation of the results.

R1-4

And adds that “A more typical approach would have been to report conditional effects of the focal predictors (the individual-level ones) at different levels of the moderators (the aggregate-level variables), but again the Authors may provide an explanation and justification for their choice.”

We are not aware of such a technical capability in R, our choice of software or theoretical work that discusses obtaining the conditional effects of focal predictors at different level of moderators in multilevel models. However, we are more than happy to re-consider this option if the R1 could provide some details.

All things considered, I found the paper interesting and I appreciated its goal of bringing together two different approaches to the object of analysis, so I hope the Authors will be able to make the necessary improvements to provide theoretically sound and statistically convincing support for their endeavour.

We are most grateful for R1’s positive approach and insightful comments.

R2-1

R2 contends that “I would like to have seen more discussion in the introduction of the ideational approach as to how mapping out these trends in the data, without contextualizing populism as either right or left, is justified. I think the logic is there to some degree, the emphasis on understanding the characteristics and links to social psych and structural-level phenomena, but this could be clarified a bit more. Similarly, by the time the reader gets to the discussion, this has been somewhat forgotten, and a reminder would be useful”

This is indeed one of key issues that we struggled with most. However, after careful consideration and in line with both R1’s comments above and R2’s further comments below we decide the employ Brubaker’s 2 dimensional framework on populism. We believe this approach fits nicely with our aim to analyse populist voting across both ends of the political spectrum. We elaborate on this more below. .

R2-2

R2 also comments that “The paper outlines a series of social psychological processes that are said to fuel populist support (including identification, trust, system satisfaction, attitudes towards immigrants, efficacy and structural factors) yet these are discussed more in a narrative form and less in a way that links them theoretically. While this might not be necessary, an overarching ‘model’ or framework that positions these phenomena within an identity/hierarchy and system framework could be useful. Something similar to this has been recently outlined in the following paper (https://doi.org/10.1016/j.copsyc.2020.06.009) which draws on many of the same concepts, papers and ideas developed here, but tries to position them within a theoretical framework of sorts”

We are most grateful to R2 for pointing this work and Brubaker’s work to us. In our readings, we have completely missed this line of work. We now believe we have incorporated this comment by emphasizing this in our introduction (pp1-2 & pp4 -5).

R2-3

R3 also adds that “ In particular, the discussion on hierarchy and status concerns seems relevant here, as it is not only that identities are mobilized as ingroup and outgroup, but also that these are positioned in ways that are threatening and perceived as unjust (relative deprivation findings), which subsequently align both low-income and high-income earners within the same ‘ingroup’ because it is not only about economic anxieites, as you point out, but also about the status concerns they express. Furthermore, the paper makes reference to Brubaker’s distinction between horizontal and vertical opposition which might be useful in accounting for how the ingroup is both defined against a horizontal other (corrupt elites) and vertical other (migrants/ refugees) in contexts of right-wing populism”

As per our response to R2-2 comments we have erroneously ignored this line of work. We now refer to Brubaker’s 2 dimensional model as well as to the arguments put forward by Obradovic et al.

Also

“ Having a look at these references might help develop a sharper framing of the various items used, rather than stating that these are taken from different literatures and brought together. Mainly, I make this point to strengthen the overall rationale and framing for how these individual level and structural level processes interplay, and for the hypotheses generated which are sound”.

We are most grateful for these positive comments. Although, we did our best to incorporate them we might have still fallen short of R3 expectations. We are therefore happy to consider additional guidance on this.

R2-4

“R2 comments that “Lastly, I think the discussion needs a bit of work. As mentioned above, there are some contradictory trends in the data when the aggregate level analysis is included, and this needs to be spelled out a bit more in detail. The discussion feels like it mainly repeats the findings but doesn’t move beyond that to consider more in-depth the implications (theoretically and practically) of these novel findings. This is mainly a push for the authors to really consider why these findings emerged and what they say about understandings of populism. This is nicely done in relation to trust, but could be expanded on for other processes discussed. Perhaps tightening the framework that links the different processes in the introduction helps to revise this part.”

Once again, these are very helpful. We did our best to rework our discussion and reinforced with additional discussion on how our findings contradict or align with previous research.

Minor points:

A re-read of the abstract for typos, missing punctuation and missing words such as ‘a’ ,’of’ etc. is needed. I have listed a few of these below, but not all.

Abstract, typo:

Thus, we provide the first evidence that the populist surge is a product of a complex set (of) social psychological mechanisms that are moderated by the general level of development and corruption perceptions in a country

P2, line 31 – extra “ after ideology: of populism as a “thin-centred” ideology”

P2, line 34: ). Liberal democracies on the other hand (stating ‘on the other hand’ assumes there’s a ‘on the one hand’ prior in the text, which doesn’t appear, so I’d suggest revising this)

P2 , line 48: the level (of) human development and corruption

P3, line 69 – In (a) similar vein, (the) cultural backlash…

P 7, line 145 - Although crucial to mobilization (for mobilizing? / to the mobilization of?) this collective identity,

We have now corrected these typos.

Submitted filename: Response to the reviewersFinal 2.docx

Click here for additional data file.

26 Jan 2022

28 Jan 2022

R1

R2 comments that I am happy with how the revised manuscript addresses my comments; the revisions to the framing of the paper allow for more coherence and clarity as to what it is doing, and it helps also in allowing for the discussion to be explicit about its contributions”

We are most grateful for these encouraging words.

R2 further comments that “I would only suggest that the authors proofread the changes made. In reading the tracked changes version of the document I came across a number of instances where proof-reading was required. i include only two examples below from the Discussion, but i recommend the authors still read eveyrthing over (or have someone else do so - sometimes we become quite blind to these things when we've been working on a manuscript for a long time!)

"by a fundamental intergroup mechanisms, i.e. identity and status concerns which in turn influenced by country level processes" - 'which in turn ARE influenced by'

"Results showed that such an effect, if any is contingent to the levels of human development and corruption perceptions at the country level." ''Results showed that such an effect, if any, is contingent to... (usually it's contingent on?')”

We have proofread the ms and further improved the overall style & edited in these suggestions.

Submitted filename: Response to the reviewers R2.docx

Click here for additional data file.

11 Feb 2022

Why People Vote for Thin-Centred Ideology Parties? A Multi-Level Multi-Country Test of Individual and Aggregate Level Predictors

PONE-D-21-05815R2

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21 Feb 2022

PONE-D-21-05815R2

Why People Vote for Thin-Centred Ideology Parties? A Multi-Level Multi-Country Test of Individual and Aggregate Level Predictors

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