Understanding Religious Tolerance: Socioeconomic Influences Across Diverse Nations

Abstract

This study examines individuals’ religious tolerance (RT) using fuzzy hybrid TOPSIS and quantile regression applied to the WZB dataset. First, RT is measured using a thermometer scale, which is transformed into a semantic scale and synthesised into RT indicators for comparison. A quantile regression model is then used to assess the impact of socioeconomic factors on RT. The analysis covers eight nations – Germany, Cyprus, the USA, Lebanon, Palestine, Israel, Turkey and Kenya – based on four items assessing opinions about Jews, Christians, Muslims and atheists. The results show that Germans have the highest RT, while Lebanese have the lowest. Our results show that socioeconomic factors play an important role in shaping RT. Interesting insights are also obtained regarding the existing difference observed across countries.

Keywords

atheists Christian Fuzzy-Hybrid TOPSIS Jewish Muslims religions tolerance

Introduction

In the academic literature, the study of religious tolerance (RT) has made significant progress (Allport et al., 1954; Coward, 1986; Dion, 2002; Oviedo, 2016; Persson and Savulescu, 2013; Pettigrew and Tropp, 2013). The topic has been studied extensively, with particular emphasis on the possible relationships it may have with some socioeconomic characteristics (Ferrara, 2012; Kubicek et al., 2009; Oliveira and Menezes, 2018; Phillips and Kitchens, 2021; Rees, 2009). Kanol (2021) examines RT using four items from the Religious Fundamentalism and Radicalisation Survey module of the WZB–Berlin Social Science Center dataset. RT is measured by a feeling thermometer scale representing opinions about four groups: (a) Jews, (b) Christians, (c) Muslims and (d) atheists.

The study employs a fuzzy hybrid TOPSIS method to calculate and rank composite indices of RT, thereby addressing the inherent vagueness in attitudinal data. Unlike principal component analysis (PCA) or Confirmatory Factor Analysis (CFA) – which validate latent constructs under strong assumptions – fuzzy hybrid TOPSIS is flexible, assumption-light, and produces interpretable rankings, making it suitable for comparing RT across groups and countries. We apply both thermometer and semantic scales, using two aggregation methods for the former ( ‘down’ and ‘up’) and transforming the latter into 10- and 11-point versions. Quantile regression then assesses the socioeconomic determinants of RT. This approach complements prior work and showcases advanced quantitative methods in RT research.

The article proceeds by providing a Theoretical Background in the ‘Brief theoretical background on RT’ section. The ‘Data’ section presents the data extracted for this study, and the ‘Methodology’ section illustrates the methodologies implemented. The ‘Empirical results’ section will provide the results, while the ‘Discussion’ section will discuss them. Finally, the ‘Conclusions’ section will illustrate the conclusions of the study.

Brief theoretical background on RT

RT is a complex and multifaceted concept that encompasses not only coexistence but also mutual respect, understanding and the active recognition of pluralism (Dudin et al., 2018). Historically, the concept of tolerance has evolved considerably. In early civilisations such as Mesopotamia and Egypt, tolerance often had pragmatic roots, allowing for the coexistence of different religious practices (Shaw, 2008). However, it was not until the Middle Ages that religious exclusion became prominent, with the institutional dominance of Christianity and Islam (Soyer, 2007). The early modern period saw a renewed interest in the importance of tolerance, epitomised by the Treaty of Westphalia in 1648, which institutionalised religious coexistence in Europe (Armstrong, 2014).

Modern theories have extended this notion of RT as that which can develop social resilience at the individual level in multicultural settings. Puri and Kumar (2021) call for a move beyond liberal tolerance to an engaged pluralism that promotes mutual transformation and dialogue. This is also in line with the philosophical approach of Razavi and Ambuel (1997), who see continuous engagement and self-reflection as part of the definition of tolerance. These views, therefore, support RT not as a passive acceptance of the fact of diversity but as an active engagement with it as a means of ensuring social cohesion.

As in Preston (2010), religion is often seen as the source of morality, with absolute rules of right and wrong handed down by God. In addition, one of the foundational lessons of nearly every major world religion teaches us to treat others as we would like to be treated. For example, Jesus’ parable of the Good Samaritan (Luke 10: 25–37) (Preston et al., 2010). However, as in Allport et al. (1954), the role of religion is a paradox., having a contradictory role. On the one hand, it encourages mutual help, love for peace, and tolerance. Some researchers analysing religiosity found a positive correlation with prosocial attitudes and behaviours (Oviedo, 2016). Thus, believers tend to be more cooperative than atheists when assigned to a group (Yilmaz and Isler, 2019). On the other hand, religion has been the ideological banner of hostility, violence, and wars (Coward, 1986). Religious believers showed more hostility towards people outside their group than non-believers (Batson et al., 1993), and Christians have shown greater hostility towards non-believers than non-believers towards Christian believers (Kanol, 2021).

Sociological accounts emphasise the importance of RT in shaping behaviour and social organisation. Functionalists assume that tolerance serves to prevent conflict and promote stability (Nor et al., 2018), whereas conflict theorists view differences between religious groups, exacerbated by disparities, as a cause of division (Durkheim, 2021). Similarly, Crockett and Voas (2003) and Voas (2014) suggest that shifting attitudes reflect contradictions between old norms and new values shaped by societal structures and experiences.

Higher education and economic well-being correlate with tolerant attitudes, so education and income have a dominant influence on RT (Arneback and Jämte, 2022; Ferrara, 2012; Katnik, 2002). Education stimulates critical thinking, sympathy and cultural sensitivity, which promotes tolerance in multicultural societies (Sierra-Huedo et al., 2024). According to Bourdieu’s theory, religion is a form of cultural capital within socioeconomic hierarchies (McKinnon, 2017). However, challenges to RT persist, spurred by personal ideologies and structural inequalities. Ethnocentrism and fundamentalism tend to resist pluralism, perceiving variability as a threat (Astor and Mayrl, 2020; Verkuyten, 2018). However, these are affected by broader systemic factors, as noted by Joas (2015), and need to be counterbalanced by institutional and moral frameworks. Wineapple (2024) contextualises debates surrounding gender, sexuality, and identity within broader conflicts of power and morality.

Data

Data extracted are provided by the WZB–Social Science Centre Berlin through its survey module on Religious Fundamentalism and Radicalisation. The cross-sectional survey (between November 2016 and June 2017) was designed by Kanol et al. (2021), focusing on religious radicalisation among groups such as Christians, Jews, Muslims, and atheists in Cyprus, Germany, Israel, Kenya, Lebanon, the Palestinian Territories, Turkey, and the United States. These countries were chosen for the study because of their different religious, political and historical contexts. Germany is an example of a secular democracy facing challenges related to immigration and religious diversity (Modood & Sealy, 2021). In contrast, Israel, Palestine and Lebanon reflect regions where religious identity is deeply intertwined with conflict and national identity (Uwaydah, 2024). Further perspectives are provided by Kenya, which has a mix of indigenous, Christian, and Muslim practices (Wangila, 2023), and Turkey, which has a mix of secularism and Islamic traditions (Yavuz, 2020). Such contexts highlight how historical, cultural and political factors shape RT in different types of social structures.

The sample comprises 10,046 interviews. As shown in Table 1, the USA and Cyprus have the highest representation in the sample, while Palestine has the lowest. Younger people (under 25: 23.64%; 26–35: 25.99%) are over-represented in the sample, while those over 75 are the least represented group (2.19%). Men and women are equally represented, accounting for almost 50% of the sample each. Most respondents rarely or never experienced religious discrimination (38.90% and 31.68%, respectively). More than 17% or 33% of respondents disagreed or completely disagreed that those who cause mischief and do evil in the eyes of God or Allah should be killed. Interviewers were more likely to be Muslim (57.19%) or Christian (31.81%), with only 11% identifying as Jewish. The majority of respondents earn less than €3000 per month, with those earning between €500 and €1000 per month being the most numerous. Most respondents have a bachelor’s degree (20.33%) or have completed upper secondary education. Finally, while the majority of respondents were housewives/househusbands (14.42%), a considerable proportion of the sample were pensioners (8.42%). For more details, see Table 1.

Table 1.

Descriptive statistics.

Label	N	%	Label	N	%
Country			Religion
Cyprus	1357	13.51	Christian	3196	31.81
Germany	1281	12.75	Muslim	5745	57.19
Israel	1212	12.06	Jewish	1105	11.00
Kenya	1197	11.92	Monthly Income
Lebanon	1190	11.85	Below 500 €	1229	12.23
Palestine	843	8.39	500–1000 €	2194	21.84
USA	1546	15.39	1000–2000 €	1936	19.27
Turkey	1420	14.13	2000–3000 €	1417	14.11
Age			3000–4000 €	634	6.31
<=25	2375	23.64	4000–5000 €	521	5.19
A26-35	2611	25.99	More than 5000 €	276	2.75
A36-45	1755	17.47	More than $350,000	22	0.22
A46-55	1482	14.75	Education
A56-65	1001	9.96	No education	192	1.91
A66-75	602	5.99	Primary education	1417	14.11
>75	220	2.19	Lower secondary education	1475	14.68
Gender			Upper secondary education	2571	25.59
Male	4972	49.49	Post-secondary	1085	10.8
Female	5011	49.88	tertiary education	373	3.71
Religion discrimination			Bachelor’s degree	2042	20.33
Never	3908	38.9	Master’s degree	829	8.25
Rarely	3183	31.68	Main Status
Often	1811	18.03	Housewife/husband	1449	14.42
All the time	791	7.87	Pensioner	846	8.42
Evil killed			Disability	172	1.71
Completely agree	869	8.65	Unemployed	712	7.09
Agree	1015	10.1	Student	998	9.93
Neither agree nor disagree	1089	10.84	Parental leave	43	0.43
Disagree	1782	17.74	Other	282	2.81
Completely disagree	3358	33.43	In paid job	5117	50.94

Some categories do not add up to 100% because the missing values were considered a category for each variable to avoid distorting the sample representation. However, these categories of missing values are not shown in this table as they were not considered interesting for this analysis.

The WZB dataset contains four items on RT. As in Kanol (2021), the items chosen to measure RT are the answers to the following question: ‘What is your opinion about the following groups [. . .]?’Respondents have to give an opinion from 0 to 100, so they give 0 if they have a very negative opinion and 100 if they have a very positive opinion. Respondents have to give their opinion on four groups: (a) Jews, (b) Christians, (c) Muslims and (d) Atheists. This scale allows for a much greater degree of specificity than the more common binary or categorical responses that can obscure nuances of tolerance. The inclusion of Jews, Christians, Muslims and atheists reflects a wide global diversity, with different religious and non-religious identities of varying historical, cultural and social significance. This selection also fits with the survey’s focus on intergroup relations and religious radicalisation, as these groups represent distinct perspectives that often feature prominently in discussions of RT and pluralism.

Methodology

Social scientists commonly measure attitudes in order to analyse their causes (Fishbein and Ajzen, 1977; Tesser, 1978; Zajonc, 1968), their changes (Hovland et al., 1953; Matz and Wood, 2005; Petty and Cacioppo, 1986) and their effects on behaviour (Lord et al., 1979). Scientists have a wide range of measurement techniques to measure citizens’ attitudes. The measurements may differ due to different optimality philosophies and the availability of resources that limit the assessment procedure (Albarracín et al., 2014).

Attitude measurement was first formalised on the assumption that attitudes could only be accurately assessed through a carefully selected large number of questions (Cohen et al., 2002; Likert, 1932). Today, however, attitude assessments mostly use single questions that are relatively simple in wording and structure, and there is considerable variability in methodologies, suggesting that there is not necessarily an optimal way to achieve the goal of accurate measurement (Albarracín et al., 2014).

Alwin (1997) compared different measurement techniques in a study of quality of life. He compares the results of two different survey methods, the first by extrapolating information through a questionnaire using semantic scales, and the second by implementing a thermometer scale. He concludes that scales that provide a wider range of responses are more reliable. As mentioned, the study analyses three scales: the 10-point, the 11-point semantic and the 0–100 thermometer scale. Data analysis was carried out using R packages, specifically for the application of Fuzzy Hybrid TOPSIS and Quantile Regression methods.

Fuzzy set logic

Following Gholamizadeh et al. (2022), the application of fuzzy set analysis in this study is motivated by its ability to model complex phenomena characterised by ambiguity and uncertainty, often present in survey responses. With fuzzy sets, membership need not be binary or ‘crisp’, but can be of varying degrees, capturing much finer shades of individual behaviour or attitudes (Ragin, 2000). This is particularly important for expressing subtle attitude variations that could easily be hidden within dichotomous or linear scales (Gholamizadeh et al., 2022).

Let $X$ be a set of real numbers $(ℝ)$ , that is, $X = {x_{1}, x_{2}, \dots, x_{n}} \in ℝ$ , a fuzzy set $\tilde{A} = {(x, μ_{A} (x)) | x \in X}$ in $X$ is a set of ordered pairs, where $μ_{A} (x)$ is a membership function, and $μ_{A} (x) : X \to [0, 1]$ . $μ_{A} (x)$ is the membership function used as a proxy for the relative truth for each $x \in A$ (Mamdani and Assilian, 1999; Zadeh, 2010). Here, $x \in A$ denotes that the element $X$ belongs to set A. Thus, a set $X$ is defined as the discourse universe of fuzzy set theory and is a generalisation of classical set theory (Indelicato and Martín, 2023).

Thus, since the answers given by questionnaires tend to be vague and uncertain, the information is transformed into Triangular Fuzzy Numbers (TFN), using a 3-tuple $(a_{1}, a_{2}, a_{3})$ of real numbers that are assigned to each point of the scale, as follows:

μ_{A} (x) = {\begin{matrix} \frac{x - a_{1}}{a_{2} - a_{1}} a_{1} \leq x \leq a_{2} \\ \frac{a_{3} - x}{a_{3} - a_{2}} a_{2} \leq x \leq a_{3} \\ 0 o t h e r w i s e \end{matrix}

(1)

The information provided by the survey will be converted to TFN in a universe of discourse within the range [0, 100]. Table 2 shows how the 0–100 scale provided by the questionnaires is transformed into two semantic ten- and eleven-point scales, respectively.

Table 2.

Thermometer to semantic scales transformation.

10-points scale	Thermometer scale	11-points scale	Thermometer scale
1	0-9	1	0-9
2	10-19	2	10-19
3	20-29	3	20-29
4	30-39	4	30-39
5	40-49	5	40-49
6	50-59	6	50-59
7	60-69	7	60-69
8	70-79	8	70-79
9	80-89	9	80-89
10	90-100	10	90-99
		11	100

For each group under analysis, for example, country, the information was aggregated through the Fuzzy Set Logic Algebra and the average fuzzy number is given by

\tilde{A} = (a_{1}, a_{2}, a_{3}) = (\frac{1}{n}) \otimes ({\tilde{A}}_{1} \oplus {\tilde{A}}_{2} \oplus \dots \oplus {\tilde{A}}_{3}) = (\frac{\sum_{i = 1}^{n} a_{1}^{(i)}}{n}, \frac{\sum_{i = 1}^{n} a_{2}^{(i)}}{n}, \frac{\sum_{i = 1}^{n} a_{3}^{(i)}}{n})

(2)

where $n$ indicates the sample size, $\otimes$ indicates the multiplication of a scalar and a TFN and $\oplus$ the internal addition of TFN (Buckley, 1985). A TFN matrix is obtained, wherein the elements are again triangular fuzzy numbers (Kumar, 2017). The sharp values are then obtained through the weighted average of the respective TFN as follows, giving double weight to the most likely value of the TFN than to the extreme values:

v_{\tilde{A}} = \frac{(a_{1} + 2 a_{2} + a_{3})}{4}

(3)

Fuzzy-TOPSIS

TOPSIS enriches the analysis by constructing a composite indicator of RT and ranking individuals or groups according to their relative distance to the so-called ideal solutions. TOPSIS synthesises information into a synthetic indicator that provides a comprehensive and sensitive assessment. Fuzzy Logic and TOPSIS combination provides a strong analytical framework, with each method playing its relative strength in dealing with ambiguity and complexity in assessing RT. The method consists of 3 consecutive stages. First, the positive and negative ideal solutions will be calculated (Hwang and Yoon, 1981) as follows:

A_{j}^{+} = {(\max V_{i j}), j = 1, 2, \dots, J} i = 1, 2, \dots, m

(4)

A_{j}^{-} = {(\min V_{i j}), j = 1, 2, \dots, J} i = 1, 2, \dots, m

(5)

where $i = 1 to m (groups)$ refers to the m demographic groups analysed in the study (e.g. by country), and j = 1 to J refers to the J items of RT, and $V_{i j}$ are crisp values. Thus, the positive ideal solution refers to the maximum value of the sample observations, and the negative ideal solution is the minimum value. In the study, all the items are considered beneficial, as higher values represent more positive RT (Behzadian et al., 2012).

Then, the distance of each group with the ideal solutions is calculated using the Euclidean distance between each observation group and the ideal solutions as follows:

S_{i}^{+} = \sqrt{\sum_{j = 1}^{J} {(A_{j}^{+} - V_{i j})}^{2}}

(6)

S_{i}^{-} = \sqrt{\sum_{j = 1}^{J} {(A_{j}^{-} - V_{i j})}^{2}}

(7)

The RT indicator, which measures citizens’ RT, is given by the relative ratio of the distance of each group with respect to the ideal solution and the sum of the distances between each group and both ideal solutions. Mathematically, the ratio is given by

R T_{i} = \frac{S_{i}^{-}}{S_{i}^{+} + S_{i}^{-}} \to [0, 1]

(8)

The interpretation of RT is straightforward, as the higher the value, the higher the tolerance of the group. Therefore, group tolerance could be analysed by ranking RT in descending order (Indelicato, 2023).

Quantile regression

This section aims to determine how socioeconomic factors affect individuals’ RT. Quantile regression extends other regression models, making it possible to observe which factors influence RT, as this methodology allows researchers to examine how the effects of explanatory variables vary at different points in the distribution of RT. Unlike ordinary least squares regression, which focuses only on the mean effect, quantile regression provides a more robust framework for detecting heterogeneity in the effects of socioeconomic factors on RT, allowing for a deeper understanding of the effects by examining different quantiles of the dependent variable as a function of the explanatory variables. Thus, quantile regression provides valuable insights into the data’s central and extreme tendencies (Östh et al., 2018).

Let $y = x^{'} β + ε$ be a linear regression model. The quantile regression model is given by the following assumption:

Q [y | x, q] = x^{'} β q + ε

Such that

\Pr o b [y \leq x^{'} β q | x] = q

0 < q < 1

(9)

where $q$ is the $q_{t h}$ quantile. If $t h$ is equal to 0.5, it is the median quantile. $ε$ is the error term.

As this is a semiparametric model, the model provides richer results because the coefficients specified are indexed by $q$ . Since $q$ can vary between 0 and 1, there are infinite possible parameter vectors (Greene, 2018). The quantile regression model is a richer specification than the linear model and typically provides a richer interpretation of the variables’ influence.

Let $b_{q}$ the estimator of $β_{q}$ in the $q_{t h}$ quantile. The estimator is calculated by minimising the following function:

F_{n} (β_{q} | y, x) = \sum_{i = y \leq x_{i}^{'} β_{q}}^{n} q | y_{i} - x_{i}^{'} β_{q} | + \sum_{i = = y \leq x_{i}^{'} β_{q}}^{n} (1 - q) | y_{i} - x_{i}^{'} β_{q} | = \sum_{i = 1}^{n} g (y_{i} - x_{i}^{'} β_{q} | q)

(10)

where $g (e_{i, q} | q) = {\begin{matrix} q e_{i q} i f e_{i q} \geq 0 \\ (1 - q) q e_{i q} i f e_{i q} < 0 \end{matrix}$ , $e_{i, q} = y_{i} - x_{i}^{'} β_{q}$ .

Here, the dependent variable is the RT. The explained variables are country, age, religion, to be raised in a religion, gender, primary status, education, income, evil killed, group treatment, and religious discrimination. Three quantiles have been chosen: the 0.25 quantile, the median quantile, and the 0.75 quantile. Covariates are dummy variables where 1 represents the presence of the condition. For example, if the gender dummy variable is equal to 1, is female, or is male otherwise.

Empirical results

Religious tolerance

In this article, the TOPSIS approach has been applied to data provided by the WZB (Kanol et al., 2021) to measure RT (RT). A thermometer scale (from 0: not at all favourable to 100: very favourable) was used. TOPSIS was applied in two different ways: (a) a TOPSIS indicator was calculated by groups (thermometer down), and (b) TOPSIS was applied at the individual analysis level and then aggregated by groups (thermometer up). In addition, after transforming the thermometer scale into two semantic scales (10 and 11 points, named atr10 and atr11, respectively), the fuzzy TOPSIS approach was applied to obtain two new RT indices.

Table 3 shows all the results for each scenario studied, and in general, the RT values are similar. Germans, Israelis and Turks show more positive RT, while citizens of Cyprus, Lebanon and Palestine have lower RT scores. Older people show higher levels of RT, while those under 35 are less tolerant. The results also show that belonging to one religion rather than another can influence RT. Ranking the three Abrahamic religions, it appears that Jews have higher values of RT than Christians and Muslims. Those who profess Islam are the least tolerant. It is also observed that being raised by parents of the same religion does not affect RT. Regarding the primary status of the respondents, the results show that pensioners, the disabled and those on parental leave are more tolerant than housewives, the unemployed or students. Regarding education and income, citizens with higher education and higher income are more tolerant than those with lower education and lower income.

Table 3.

Religious tolerance indicators.

Group	t_d	t_u	Atr10	atr11	Group	t_d	t_u	Atr10	atr11
Total	0.453	0.509	0.457	0.454	Below 500 € or $19.999	0.337	0.462	0.334	0.345
Cyprus	0.294	0.427	0.286	0.283	500–1000 € or $20.000-$39.999	0.349	0.462	0.351	0.354
Germany	0.803	0.664	0.82	0.815	1000–2000 € or $40.000-$59.999	0.434	0.497	0.437	0.433
Israel	0.722	0.63	0.73	0.716	2000–3000 € or $60.000-$79.999	0.489	0.516	0.497	0.49
Kenya	0.322	0.43	0.33	0.332	3000–4000 € or $80.000-$119.999	0.623	0.576	0.631	0.613
Lebanon	0.278	0.395	0.288	0.291	4000–5000 € or $120.000-$199.999	0.497	0.521	0.509	0.497
Palestine	0.311	0.417	0.318	0.327	⩾ 5000 € or $200.000-$349.999	0.591	0.566	0.605	0.589
USA	0.334	0.463	0.337	0.354	More than $350.000	0.583	0.561	0.583	0.542
Turkey	0.695	0.61	0.697	0.659	Evil killed Completely agree	0.365	0.475	0.361	0.375
<= 25	0.411	0.491	0.417	0.418	Evil killed Agree	0.324	0.438	0.327	0.332
26-35	0.443	0.501	0.448	0.445	Evil killed Neither agree nor disagree	0.484	0.517	0.488	0.475
36-45	0.437	0.506	0.442	0.44	Evil killed Disagree	0.489	0.517	0.496	0.486
46-55	0.504	0.53	0.508	0.503	Evil killed Completely disagree	0.574	0.555	0.58	0.568
56-65	0.485	0.52	0.485	0.481	Never	0.388	0.485	0.391	0.395
66-75	0.512	0.532	0.511	0.504	Rarely	0.493	0.519	0.501	0.492
> 75	0.565	0.557	0.559	0.549	Often	0.537	0.542	0.539	0.532
Christian	0.524	0.533	0.528	0.515	All the time	0.419	0.497	0.419	0.42
Muslim	0.368	0.473	0.371	0.381
Jewish	0.693	0.624	0.699	0.676
Conv (Y)	0.452	0.508	0.455	0.453
Conv (N)	0.485	0.515	0.489	0.481
Male	0.452	0.507	0.456	0.452
Female	0.457	0.511	0.459	0.458
No education	0.264	0.412	0.261	0.284
Primary education	0.274	0.424	0.272	0.287
Lower secondary education	0.361	0.475	0.363	0.376
Upper secondary education	0.439	0.502	0.441	0.44
Post-secondary	0.614	0.571	0.619	0.607
Short cycle	0.614	0.569	0.614	0.592
Bachelor’s degree or equivalent	0.48	0.514	0.49	0.477
Master’s degree or equivalent	0.749	0.639	0.765	0.738
Housewife/husband	0.323	0.454	0.324	0.336
Pensioner	0.518	0.533	0.516	0.51
Disability	0.596	0.555	0.596	0.573
Unemployed	0.378	0.469	0.381	0.381
Student	0.415	0.492	0.417	0.42
Parental leave	0.53	0.546	0.539	0.537
Other	0.518	0.524	0.517	0.51
In paid job	0.487	0.521	0.494	0.486
Occupation (NA)	0.591	0.575	0.589	0.602

Own elaboration. t_d: TOPSIS based on thermometer down, t_u: TOPSIS based on thermometer up; atr10: Fuzzy-TOPSIS based on 10-point semantic scale; atr11: Fuzzy-TOPSIS based on 11-point semantic scale.

The questionnaire also asks respondents whether those who cause mischief and do evil in the eyes of God should be killed. Those who agree or strongly agree (citizens are more fundamentalist) show a less tolerant attitude than those who disagree. Concerning the experience of discrimination based on religion, we find contradictory results, as those who often or rarely experience discrimination are more tolerant than those who never or always experience religious discrimination.

The four indicators give similar results, but closer analysis reveals differences that can be explored using Spearman’s correlation coefficient. Spearman’s rho quantifies the strength of the association between two variables and ranges from –1 to +1, with values closer to 1 indicating stronger correlations between rankings (Myers and Sirois, 2004; Spearman, 1904). Figure 1 shows the Spearman correlation results (rho) for the four indicators assessing citizens’ RT. Notably, the fuzzy TOPSIS approach, based on a 10-point semantic scale, shows the most significant divergence between the indicators. This suggests that transforming the original 0–100 thermometer scale into a 10-point semantic scale may not be appropriate, and a word of caution is given here to researchers. In contrast, Figure 1 shows a remarkable consistency between the rankings derived from the thermometer and 11-point semantic scales. This consistency underlines the robustness and reliability of these two approaches to assessing RT.

Figure 1.

Rho Spearman correlation.

Socioeconomic influences on RT

Looking at the Rho Spearman correlation, the variable atr11 is chosen as a measure of RT to analyse the effects of socioeconomic factors. A quantile regression model is estimated to capture covariate effects at three different levels of RT: low, represented by the 25th quantile; median, represented by the 50th quantile; and high, represented by the 75th quantile. The regressors are dummy variables that take the value 1 if the condition is true, for example, ⩽25 equals 1 if the subject is under 25. Reference categories were defined for each categorical covariate: country, Cyprus; for age, under 25; for other covariates, the baselines are: being male, having no formal education, identifying as a Christian, being a housewife/husband, being in the lowest income bracket, being in the control group, never having experienced religious discrimination and agreeing with the statements that people who cause evil in the eyes of God or Allah should be killed.

Table 4 shows the estimated coefficients across the three quantiles. While neither age, religion, gender, nor employment status significantly affected RT, the respondent’s country of origin did. All countries except Lebanon had a positive significant effect on lower RT scores relative to Cyprus. In contrast, Germany, Israel, and Turkey had a much stronger positive effect for higher RT scores. Country-fixed effects in the quantile regression model accounted for contextual differences in the countries considered. Table 4 shows the magnitude of the influence of each country on RT for different quantiles, taking into account variations due to cultural, economic or political differences.

Table 4.

Quantile regressions.

Religious Tolerance	Coef.		Coef.		Coef.
	0,25 quantile		0,5 quantile		0,75 quantile
Country (Cyprus = 0)
Germany	0.114	***	0.178	***	0.379	***
Israel	0.132	***	0.190	***	0.246	***
Kenya	0.016	**	0.029	**	0.020
Lebanon	−0.031	***	0.002		−0.009
Palestine	0.018	**	0.033	**	0.016
USA	0.021	**	0.038	**	0.029
Turkey	0.101	***	0.134	***	0.200	***
Age (⩽ 25 = 0)
26-35	−0.002		−0.002		−0.002
36-45	0.002		0.004		0.012
46-55	0.004		0.005		0.018
56-65	0.005		0.011		0.016
66-75	0.009		0.011		0.018
> 75	0.004		0.011		0.029
Religion (Christian = 0)
Muslim	−0.005		−0.014		−0.013
Jewish	0.007		−0.053		−0.115
Conversion (Yes = 0)
Conv No	−0.002		0.014		0.015
Gender (Male = 0)
Female	0.002		0.003		−0.001
Gender NA	0.004		0.011		−0.007
Education (No education = 0)
Primary education	0.000		0.006		−0.001
Lower secondary education	0.007		0.029	*	0.023
Upper secondary education	0.006		0.032	*	0.016
Post-secondary	0.002		0.027		0.018
Short-cycle tertiary education	0.000		0.027		0.016
Bachelor’s degree or equivalent	0.016		0.042	**	0.034
Master’s degree or equivalent	0.032	***	0.085	***	0.070	**
Main Status (Housewife/man = 0)
Pensioner	0.004		0.010		0.007
Disability	−0.002		0.014		0.019
Unemployed	0.006		0.005		0.011
Student	0.008		0.010		0.015
Parental leave	−0.003		0.024		0.034
Other	−0.009		0.001		−0.001
In paid job	0.003		0.005		−0.001
Income (Below 500€ = 0)
500–1000 € or $20,000-$39,999	0.002		0.006		0.004
1000–2000 € or $40,000-$59,999	0.007		0.017	*	0.014
2000–3000 € or $60,000-$79,999	0.008		0.020	*	0.014
3000–4000 € or $80,000-$119,999	0.013	*	0.024	*	0.007
4000–5000 € or $120,000-$199,999	0.021	***	0.023	*	0.014
⩾ 5000 € or $200,000-$349,999	0.021	**	0.036	**	0.013
More than $350,000	−0.023		−0.010		0.033
Evil killed (Completely agree = 0)
Evil killed Agree	−0.003		−0.006		−0.026	*
Evil killed Neither agree nor di	0.000		−0.002		−0.024
Evil killed Disagree	0.007		0.010		−0.011
Evil killed Completely disagree	0.002		0.011		−0.005
Religious discrimination (Never = 0)
Rarely	0.000		−0.005		−0.008
Often	−0.003		−0.009		−0.015
All the time	−0.010	*	−0.022	**	−0.023	*

Own Elaboration. *p value ⩽ 0.05; **p value ⩽ 0.01; ***p value ⩽ 0.001. Model fit was assessed using pseudo R², which yielded values of 0.27, 0.30, and 0.29 for the 0.25, 0.50, and 0.75 quantiles, respectively.

Education also played an important role. Individuals with a master’s degree had a higher RT than those with no formal education, especially in the lower quantiles. For the median RT, all levels of education from lower secondary education onwards – except a bachelor’s degree – had a positive effect. A bachelor’s degree is associated with higher RT at the upper quantiles, which may indicate a nuanced relationship between education and tolerance levels. Income level also affected RT, with medium and high-income categories positively affecting lower RT scores. These findings suggest that economic stability promotes more inclusive attitudes among those scoring lower in the tolerance spectrum. In addition, respondents’ attitudes towards killing those who cause evil and do wrong in the eyes of God or Allah also negatively influenced higher RT scores compared to full agreement with this measure. Similarly, individuals who reported experiencing religious discrimination consistently reported lower RT than those who had never experienced religious discrimination.

Discussion

At the national level, the results show two separate blocks of RT. First, Germany’s leading position in RT confirms Peter’s (2009) findings. This could be the result of the German government’s active policy of integrating religious minorities; for example, the ‘German Islam Conference’ was set up after the 9/11 attacks to promote the integration of Muslims in the country. Schweitzer (2007) attributes this to the German education system, which aims to promote tolerance in a multicultural society by avoiding hatred and prejudice. In contrast, Cyprus and Lebanon have lower levels of RT. Both countries face the challenge of coexisting with two dominant religions, Islam and Christianity (Constantinou et al., 2012; Faour, 2007). In Cyprus, religion is a highly political issue. Although Christianity is the majority religion in the country, it remains ethnically divided in its approach to religious education, and debates about teaching religion in schools reflect these tensions (Zembylas and Loukaidis, 2018). In Lebanon, the situation is even more complex. There are no official statistics on the proportion of Muslims and Christians in the country, as the last census attempted to address the ambivalence surrounding the balance between these two post-Jewish Abrahamic religions (Dralonge, 2008). Historical divisions between religious groups have contributed to significant conflicts, most notably the Lebanese Civil War from 1970 to 1990 (Ibrahim, 1998; Joseph, 2011).

Age is also an important determinant of RT, as the general trend seems to be that the older a person gets, the more tolerant they are compared to members of younger generations. This could be because older people seek comfort in religion (Oliveira and Menezes, 2018), while younger generations see religion and religious institutions as obstacles to civil rights and social advancement (Kubicek et al., 2009). Similarly, the dominant monotheistic religions, Judaism, Christianity and Islam, present unique challenges to the promotion of RT. The theological underpinnings of monotheism, in which the universe is said to be governed by a single divine being, can create barriers to tolerance both within these religions and towards others (Persson and Savulescu, 2013). As Morales (2008) notes, this exclusivist perspective can limit monotheistic traditions’ ability to develop a strong basis for RT. Consequently, the theological resources available within these traditions to promote tolerance may be inherently limited (Neusner and Chilton, 2008).

Our analysis, employing RT indicators derived from the fuzzy hybrid TOPSIS approach, yielded similar ranking order results. However, a note of caution is warranted. While the ranking order is established, the quantile regression analysis did not corroborate significant effects for age and monotheist religions, suggesting that the observed ranking may not fully reflect a true causal relationship. According to Karpov and Lisovskaya (2008), religious intolerance is more a product of reactionary ideologies and regional socio-political conditions than of religious beliefs and practices themselves. Such a perspective suggests that the relationship between religion and intolerance is complex and externally modulated.

Our results also show that the main status of citizens does not significantly influence RT. This observation contrasts with the findings of Katnik’s 2002 study, which suggested that workers who were born in the respondent’s country of residence showed higher levels of hostility towards foreign groups. In addition to status, income is an important determinant of RT. Our research highlights a trend whereby lower income levels correlate with greater religious intolerance. This phenomenon may be partly explained by the understanding that income inequality often fosters personal insecurity, which Rees (2009) argues is an important driver of intolerant attitudes towards out-groups. Also, education plays a fundamental role in shaping individuals’ attitudes towards religion. Governments in various countries have actively supported the promotion of RT within their education systems (Ferrara, 2012; Schweitzer, 2007). Interestingly, our research findings reveal a significant divergence in the appreciation of RT between individuals with master’s degrees and those with lower levels of education. This divergence underlines the transformative influence of education in shaping one’s perspective on religious issues.

The survey includes a question asking respondents whether they agree or disagree with the idea of killing those who harm God or Allah. The results are inconclusive as to the extent to which those who disagree with killing in the name of God and Allah have more RT than those who strongly agree. This does not confirm one of the main tenets of fundamentalism, namely that killing those who do evil against God or Allah can be advocated (Phillips and Kitchens, 2021), which is always highly uncorrelated with RT. However, fundamentalism refers to a strict adherence to an inflexible and exclusivist reading of religious doctrine in a universally valid and non-negotiable form (Verkuyten, 2018). Such a perspective is often seen as inherently incompatible with RT, which promotes pluralism, mutual respect and coexistence (Indelicato and Martín, 2024a). However, the lack of a strong correlation between this extreme belief and RT in the survey suggests a more complex relationship, suggesting that fundamentalist attitudes do not always translate into outright intolerance.

Finally, results also show that respondents who have experienced religious discrimination are less tolerant than those who have never experienced discrimination. According to Phillips and Kitchens (2021), people who are discriminated against develop intolerance towards out-groups as a response to mistreatment. Therefore, even if one can fall into the apparent discriminatory-tolerant dualism, citizens who have suffered religious discrimination often have an aversion to other groups that are not part of their social sphere (Phillips and Kitchens, 2021). Thus, the study also highlights the importance of addressing religious discrimination and income inequality, as these factors are associated with higher levels of religious intolerance. Addressing these issues can help create a more inclusive and tolerant society. Moreover, the study suggests that religion may be a barrier to RT, especially for monotheistic religions such as Christianity and Islam. Governments and religious institutions may need to work together to promote a more nuanced understanding of religious teachings and values that can support RT.

Conclusions

This article examines citizens’ RT using data from the Religious Fundamentalism and Radicalisation module of the WZB–Social Science Research Center Berlin. Synthetic indicators of RT were constructed at both aggregate and individual levels, using methods based on fuzzy logic and TOPSIS. The quantile regression model was used to examine the impact of socioeconomic variables on RT. The results show RT scores are high in Germany and relatively low in Lebanon. Socioeconomic factors such as age, income, and education positively impact RT.

The study builds on previous research (Hook et al., 2017; Jeong, 2017; Kanol, 2021) and introduces innovative methods to evaluate RT. The results demonstrate the robustness of the 11-point and thermometer scales, while the 10-point semantic scale diverges significantly, so the transformation in the 10-point semantic scale is not recommended. By using these methods, the study uncovers nuanced relationships between socioeconomic variables, providing new insights into RT dynamics.

We employed a fuzzy hybrid TOPSIS method to generate and rank composite indices of RT, offering a comprehensible and flexible alternative to conventional psychometric techniques. Unlike structural equation modelling (SEM), principal component analysis (PCA) or item response theory (IRT), which test latent structures but assume linearity and normality, fuzzy hybrid TOPSIS formally handles ambiguity in attitudes and produces clear group rankings without invoking strong distributional assumptions. Although it does not measure dimensionality or internal consistency, its ability to handle vagueness and facilitate comparison makes it highly suitable for our research goal. Further research comparing fuzzy MCDM with other psychometric models like PCA and CFA, or item response theory (IRT) or structural equation modelling (SEM) could reinforce the validity of applying fuzzy hybrid TOPSIS to the study of attitudinal latent variables.

While contributing to the broader literature, the study has limitations. It focuses on data from eight countries and uses a cross-sectional database, which limits the analysis of temporal dynamics. Future research should include data from the International Social Survey Programme and examine longitudinal changes in RT. In addition, the study recognises the diversity within Christianity, Islam and Judaism and emphasises the need to consider variations in beliefs and practices between and within these religions. This approach aims to improve understanding and interpretation of the results. Finally, the quantile regression model only considers three quantiles, which limits the disaggregation of RT bands. Further research is needed to identify which RT levels are most influenced by socioeconomic factors. Despite these limitations, the study provides a valuable methodological framework for analysing social science issues and encourages its application in future research.

Footnotes

Acknowledgements

This research was developed also thanks to the opportunity of the Short-Term Scientific Mission (STSM) of the COST Action COREnet (CA20107). The STSM was a valuable opportunity to learn from and collaborate with other scholars, and it helped to shape the direction of this research.

Author contributions

Conceptualisation A.I., and J.C.M; methodology, J.C.M.; software, A.I., and J.C.M; validation, A.I., and J.C.M.; investigation, A.I., and J.C.M.; data curation, A.I., and J.C.M.; writing – original draft preparation, A.I., and J.C.M.; writing – review and editing, A.I., and J.C.M. All authors have read and agreed to the published version of the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Dr Alessandro Indelicato research is funded by the research fellowship ‘Catalina Ruiz’ provided by the Consejo de Economía, Conocimiento y Empleo of the Gobierno de Canarias, the Agencia Canaria De Investigación Innovación Y Sociedad De La Información (ACIISI), and Fondo Social Europeo of the EU, through the Universidad de Las Palmas de Gran Canaria (Spain).

ORCID iD

Alessandro Indelicato

Availability of data and materials

(accessed on 15 June 2022).

Author biographies

Alessandro Indelicato is a postdoctoral researcher currently affiliated with the Universidad de Las Palmas de Gran Canaria in Spain, within the Department of Applied Economics. His expertise spans fuzzy logic, statistics, econometrics, and the study of migration processes and attitudes towards immigrants. Alessandro’s academic studies began at the Università di Bologna (Italy), where he earned a degree in Statistical Sciences in 2016, followed by a Master’s in Statistics, Economics, and Management in 2018. His keen interest in statistics and econometrics led him to pursue a PhD in Tourism, Economics, and Business at the Universidad de Las Palmas de Gran Canaria (Spain), which he successfully completed in 2022, graduating cum laude and with international distinction, with a thesis titled ‘Quantitative Methods to Measure Attitudes Towards Immigrants and National Identity’. Before his current role, Alessandro worked as a postdoctoral researcher at the Università IUAV di Venezia in 2023. Alessandro’s research is rooted in the intersection of economics, sociology, and statistical methods. He employs fuzzy logic, an advanced decision-making framework, to explore and model socioeconomic phenomena, particularly within the contexts of immigration, national identity, and religion. His work often examines the intricate relationships between political, social, and economic factors and how they influence public attitudes towards immigrants and national identity. One of Alessandro’s notable contributions is his innovative application of the Fuzzy Clustering ECO-Extended Apostle Model. He has co-authored numerous high-impact scientific papers, many of which have been published in renowned international journals such as Travel Behaviour and Society, Transportation Planning and Technology, Journal of International Migration and Integration, Cross-Cultural Research, Quality and Quantity, and Migration Letters. In addition to his research, Alessandro leads Working Group 5 of the ‘Connecting Theory and Practical Issues of Migration and Religious Diversity’ (COREnet), an interdisciplinary network aimed at producing, exchanging, and building knowledge and collaborations across Europe on the topics of migration and religious diversity. COREnet particularly focuses on integrating theoretical and practical perspectives on these issues, with a strong emphasis on bottom-up research.

Juan Carlos Martín Hernández is a full professor at the Universidad de Las Palmas de Gran Canaria, specialising in Applied Economics. He holds a Bachelor in Mathematics and a PhD in Economic and Business Sciences. Juan Carlos has held several leadership positions within the university. Juan Carlos’s academic training includes advanced studies in Industrial Economics, Econometrics, and the Economics of Regulation and Antitrust at the University of California, Berkeley, where he was a Visiting Scholar. He was the Director of the PhD Programme in Integral Development and Innovation of Tourist Destinations (2011–2014) and Vice Dean for Mobility Programmes, Academic Exchanges, and International Relations at the Faculty of Economics, Business, and Tourism (2009–2013). His extensive involvement in mobility programmes extends beyond Europe; he has also engaged in numerous academic exchanges in South America showing his adaptability and global perspective. Juan Carlos is also actively involved in professional networks and research groups. He was Vice President of NECTAR (Network on European Communications and Transport Activities Research) from 2008 to 2024 and is a founding member of the Ibero-American Research Network on Air Transport, established in 2006. The Spanish National Agency for Evaluation and Foresight (ANEP) frequently seeks his expertise. Juan Carlos’s research focuses on tourism and transport economics, efficiency, service quality, and consumer behaviour analysis. His significant contributions in these fields have garnered widespread recognition. His work includes co-authored publications in leading international journals. Now, his research is his innovative use of fuzzy segmentation analysis and hybrid-fuzzy TOPSIS methods, which provide deeper insights into consumer satisfaction and behaviour in various sectors, including gastronomy, air transport, and attitudes towards immigrants and religion. He is also the Leader of Working Group 5 in the ‘Connecting Theory and Practical Issues of Migration and Religious Diversity’ (COREnet), an interdisciplinary network to build and share knowledge on migration and religious diversity across Europe. According to Web of Science (WoS), he is the author of 173 publications (129 in the WOS Core Collection) with 2737 citations and 31 h-index. According to Scopus, he is the author of 133 publications with 3101 citations and 31 h-index. WoS and Scopus data were updated on 17 September 2024. The article ‘Using fuzzy numbers for measuring quality of service in the hotel industry’, published in Tourism Management 28(2), was the most cited paper in the journal from 2007 to 2011. It currently has 261 citations, according to Scopus.

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