Inequality and conspiracy beliefs

Inequality and conspiracy beliefs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Inequality and conspiracy beliefs Leonardo Becchetti, Davide Bellucci, Fabio Pisani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3823608/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Around one third of Europeans are conspiracy believers. Using European Social Survey data, we find that income inequality is an important driver of political, scientific and COVID-19 conspiracy beliefs, with regional inequality being positively and significantly correlated with conspiracy beliefs at individual level. Believers argue significantly more that the local government should address income inequality problems, while it is not doing enough for them. Furthermore, average sample moods about government commitment on inequality at regional level are significantly and positively correlated with conspiracy beliefs, even after controlling for individual opinions. Instrumental variable approaches suggest that the observed correlation hides a causality link. Our findings identify a novel underinvestigated effect of income inequality and suggest another positive effect of policies aimed at reducing it. JEL numbers: A13, A14. income inequality conspiracy COVID-19. Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Conspiracy theories, where a small target group is considered responsible for adversities hitting a given population, have been generally used throughout history as interpretative shortcuts providing relief against human suffering and hardships. Their success has often depended on the fact that human beings are in search of purpose and meaning and the difficulty in giving meaning and justification to negative states of affairs can be reduced by such theories. Conspiracy beliefs have been familiar in many periods of human history. In a tradition originating from ancient times witchcraft has been the simplistic explanation and scapegoat for adversities hitting past and contemporary primitive populations (Evans-Pritchard, 1937). Later, plague spreaders and Jews accused of economic conspiracy have been two typical targets in difficult periods to justify in turn epidemics and economic difficulties. As history tells us conspiracy beliefs are regrettably not free from adverse social consequences. First, they undermine trust in institutions and therefore the same pillars of democracy (Mari et al. 2022 ). Second, they trigger hatred and prosecution from extreme believers against the scapegoat groups deemed responsible for the bad state of affairs (Marone, 2021 ; Sallam, 2021 , Srol et al. 2022 ). In more recent times conspiracy has taken on a more political structure that overlaps in most parts with populism and brings with it the idea that good is with “the people” and evil with some elites that are judged responsible for the more severe problems experienced by the people (inequality, epidemic diseases). The COVID-19 pandemics gave new strength to conspiracy believers who developed with strong determination theories in which the creation of epidemics was a deliberate policy strategy of the political or economic elites to limit individual freedom and make profits by experimenting and selling new vaccines (for an extensive survey of contributions on antecedents of them see van Mulukom et al. 2022 ). Conspiracy in general, and populism in particular, therefore share many common characteristics such as the simplified dichotomy between good and evil groups, the mistrust on institutions and the simplified interpretation of the reality where causality is identified without any rigorous investigation in events that are actually likely to be the result of a complex interplay of different factors. It is surprising that, given the increasing relevance and strong revival of this phenomenon today and the many economic factors that can presumably be drivers of such beliefs, the literature investigating its drivers is scarce and concentrated only in psychology among social sciences, although growing considerably in the last years. Our empirical contribution aims to contribute to filling this gap. Since economic difficulties have always been considered a possible cause of conspiracy beliefs an investigation on socioeconomic drivers of them is missing and of foremost importance. Among the few contributions focusing on it, Hornsey et al. (2023) find that conspiracy believers are more likely to have negative opinions on current and future domestic economic prosperity and show that GDP per capita is negatively correlated with conspiracy beliefs. Several recent contributions highlight how lower income is positively and significantly correlated with COVID-19 conspiracy theories (Constantinou et al., 2021 ; Romer and Jamieson, 2020 ; Sallam et al., 2020; van Mulukom, 2022). Casara et al. ( 2023 ) find lower levels of tax compliance and higher support for progressive taxation among conspiracy believers on a sample of around 2,000 online participants. Our paper extends the analysis in this specific field of research focusing on inequality. Our research hypothesis is that regional inequality and the perceived need for government action against inequality in a given region are significantly and positively correlated with conspiracy beliefs. We argue that this is the case because subjective well-being is strongly affected by relative income (Ferrer-I-Carbonell, 2005 ; D’Ambrosio and Frick, 2007 ; Brown et al., 2015 ) and inequality is likely to be perceived as an adverse event for which conspiracy beliefs and the identification of a clear target of culprits contribute to give meaning and relief. These mechanisms are reinforced by the fact that high inequality increases distance between top income earners and the rest of society fuelling the perception that the elites are distant and powerful and can use their power by manoeuvring against the rest of the people, thereby contributing to increase income inequality. We test our research hypothesis using data from the European Social Survey on three conspiracy belief questions and calculating income inequality levels in 32 regions. Our research hypothesis is not rejected since regional income inequality is positively and significantly correlated with conspiracy beliefs. Our findings are confirmed when we use two individual opinions related to insufficient policies against inequality ( government should do more against inequality , government is not doing enough against inequality ). We also find that the average mood of the regional sample on these two perceptions is significantly correlated with conspiracy beliefs, net of impact of the individual opinion. This last finding shows that the local culture of insufficient action against inequality is also a powerful factor affecting conspiracy beliefs beyond individual opinions. We finally test the causality nexus between inequality and conspiracy beliefs by using instrumental variable approach and showing with a falsification test that our instrument is valid. Provided that our causality analysis holds, our findings have straightforward policy implications since policies aimed at reducing inequality can in turn reduce conspiracy beliefs and their likely negative consequences on trust on institutions and hatred toward the conspiring elites. 2. Data and descriptive findings Our data source is the European Social Survey (ESS), an established cross-country survey that has gained special attention in the economic literature related to the empirical research investigating the role of social values. The ESS has run 10 survey rounds until 2022[1] with newly selected cross-sectional samples. It is only with the tenth wave (2020) however that conspiracy questions have been introduced. Our conspiracy variables are built on the following three ESS questions: i. A small secret group of people is responsible for making all major decisions in world politics (political conspiracy) ii. Groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public (scientific conspiracy) iii. COVID-19 is the result of deliberate and concealed efforts of some government or organization (COVID-19 conspiracy) In all of the three cases the respondent can give five possible answers ( strongly agree, agree, neither agree nor disagree, disagree, strongly disagree ). The first question relates to political conspiracy and has the typical characteristics of conspiracy theories (existence of a small group of conspirators taking decisions that shape the destiny of the rest of the world and therefore are responsible for what happens, secrecy of their actions, and attribution of what happens to the action of this group). The second question relates to scientific conspiracy and has the same characteristics of the first plus a more explicit negative assessment of the role of conspirators who, according to the statement, “ manipulate, fabricate, or suppress evidence in order to deceive the public ”. This additional characteristic makes it clearer than under political conspiracy that the action of conspirators is not “enlightened”, or for the good of the rest of the people. The third question relates to the conspiracy of COVID-19 and includes a negative judgement as well in terms of the deliberate and concealed effort to create or spread the pandemics of some government or organization. When looking at descriptive statistics on our conspiracy dependent variables we find that the share of conspiracy beliefs on the first question (those who strongly agree or agree on political conspiracy) is around 35 percent, falling to 30 percent for believers to scientific and COVID-19 conspiracy. The share of those strongly disagreeing is around 15 percent for political and scientific conspiracy and around 8 percent for COVID-19 conspiracy) (see Table 1 for variable legend and Table 2 for descriptive statistics). Table 1 : Variable legend Political conspiracy A small secret group of people is responsible for making all major decisions in world politics (1=strongly disagree; 2= disagree; 3= neither agree nor disagree; 4= agree; 5= strongly agree) Scientific conspiracy Groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public. (1=strongly disagree; 2= disagree; 3= neither agree nor disagree; 4= agree; 5= strongly agree) Covid-19 conspiracy COVID-19 is the result of deliberate and concealed efforts of some government or organisation (1=strongly disagree; 2= disagree; 3= neither agree nor disagree; 4= agree; 5= strongly agree) Government should do against inequality G overnment should reduce differences in income level (1=strongly disagree; 2= disagree; 3= neither agree nor disagree; 4= agree; 5= strongly agree) Government does against inequality In country the government takes measures to reduce differences in income levels (0-10 values, 0 = does not apply at all and 10 = applies completely) ISCED education dummies ES-ISCED I, less than lower secondary, ES-ISCED II, lower secondary, ES-ISCED IIIb, lower tier upper, ES-ISCED IIIa, upper tier upper secondary; ES-ISCED IV, advanced vocational, ES-ISCED V1, lower tertiary education, ES-ISCED V2, higher tertiary education. Male (0/1) dummy taking value one if the respondent is male. Age Respondent age Father’s education Father’s highest level of education (less than lower secondary, lower secondary, upper secondary vocational, upper secondary general, advanced vocational, lower tertiary education, higher tertiary education). Income class Placement of respondent household total net income in one of the income deciles of the country (1=lowest, 10=highest) Marital status dummies (0/1) dummies picking up the following marital status conditions: married/civil union, separated/divorced, widowed, never married Employment status dummies (0/1) dummies picking up the following employment status conditions: unemployed, paid worker, retired. Table 2 : Descriptive statistics Variables Count Mean SD Min Max Political conspiracy: A small secret group of people is responsible for making all major decisions in world politics Strongly agree 17,294 0.100 0.299 0 1 Agree 17,294 0.255 0.436 0 1 Neither agree nor disagree 17,294 0.251 0.434 0 1 Disagree 17,294 0.241 0.428 0 1 Strongly disagree 17,294 0.153 0.360 0 1 Scientific conspiracy: Groups of scientists manipulate, fabricate, or suppress evidence inorder to deceive the public Strongly agree 17,463 0.073 0.260 0 1 Agree 17,463 0.227 0.419 0 1 Neither agree nor disagree 17,463 0.265 0.441 0 1 Disagree 17,463 0.282 0.450 0 1 Strongly disagree 17,463 0.153 0.360 0 1 COVID-19 conspiracy: COVID-19 is the result of deliberate and concealed efforts of some government or organisation Strongly agree 17,307 0.098 0.297 0 1 Agree 17,307 0.207 0.405 0 1 Neither agree nor disagree 17,307 0.277 0.448 0 1 Disagree 17,307 0.240 0.427 0 1 Strongly disagree 17,307 0.178 0.383 0 1 Regional inequality (GINI) 18,013 0.274 0.052 0.196 0.415 Regional inequality (MLD) 18,013 0.138 0.057 0.066 0.301 Government should do against inequality 17,856 3.953 0.984 1 5 Government does against inequality 17,819 8.102 2.139 0 10 Average regional 'government should do' 18,013 2.066 0.334 1.545 3.043 Average regional 'government does' 18,013 4.051 1.035 2.257 6.467 Father (ISCED) education level 16,992 3.025 1.801 1 7 Age 18,013 51.79 17.920 15 90 Male 18,013 0.453 0.498 0 1 Income decile Decile 1 18,013 0.073 0.260 0 1 Decile 2 18,013 0.101 0.302 0 1 Decile 3 18,013 0.111 0.314 0 1 Decile 4 18,013 0.117 0.321 0 1 Decile 5 18,013 0.124 0.329 0 1 Decile 6 18,013 0.111 0.314 0 1 Decile 7 18,013 0.111 0.314 0 1 Decile 8 18,013 0.106 0.308 0 1 Decile 9 18,013 0.080 0.272 0 1 Decile 10 18,013 0.067 0.249 0 1 Marital status In union with a partner 18,013 0.489 0.500 0 1 Separated/Divorced 18,013 0.123 0.328 0 1 Widowed 18,013 0.104 0.305 0 1 Single/Never married 18,013 0.285 0.451 0 1 Education Less than lower secondary 18,013 0.058 0.234 0 1 Lower secondary 18,013 0.137 0.343 0 1 Lower tier upper secondary 18,013 0.121 0.326 0 1 Upper tier upper secondary 18,013 0.322 0.467 0 1 Advanced vocational 18,013 0.094 0.292 0 1 Lower tertiary education 18,013 0.130 0.336 0 1 Higher tertiary education 18,013 0.138 0.345 0 1 Employment status Paid work 18,013 0.533 0.499 0 1 Looking for job 18,013 0.049 0.216 0 1 Retired 18,013 0.280 0.449 0 1 GDP per capita 18,013 21.30 11.26 6.200 45.900 Our focus of research concerning drivers of conspiracy beliefs is on inequality measures at regional level (see Appendix A for the region list). More specifically we use two objective inequality measures drawn from the EU-SILC database namely the Gini coefficient and the Mean Log Deviation ( MLD ) , with the latter satisfying desirable decomposition properties but, on the other hand, is more sensitive than the Gini index to the extremes of the distribution. We also use in empirical estimates two subjective inequality measures taken from the ESS. In a first question about subjective perception of inequality policies ESS respondents must give their level of consent to the following statement: government should reduce differences in income levels . The possible answers are agree strongly, agree, neither agree nor disagree, disagree, disagree strongly. The vast majority of respondents agree strongly (32.3 percent) or agree (42.6 percent), while only around 10 percent of survey participants disagree or strongly disagree (Figure 1). A second similar statement in the ESS survey is In country the government takes measures to reduce differences in income levels . Answers here are possible on a 0-10 basis where 0 stands for does not apply at all and 10 for applies completely . The 0-answer indicating complete disagreement on the relevance of government actions to reduce differences in income is chosen by 13.6 percent of respondents, with around 68 percent of them not going above 5. Only 2.1 percent declare that the statement applies completely to their government (Figure 2). An important difference between the two statements is that, in the first case, the judgement is on what the government should do, while in the second case on what the government is doing. In both cases the answer can be mediated by unobserved factors. In the first case, by the respondent’s political preference on what should be the appropriate government action against income inequality (and by the perception that the government is not doing enough with respect to one’s own desired income redistribution), while in the second case, by the respondent’s perception of the actual government effort. In the first case we find in general a positive correspondence between respondents desired intensity of government action and actual country inequality levels, with respondents in Nordic countries at the bottom and Eastern European country citizens at the top (Figure 3). In the second case we find strict correspondence between average sample country level evaluation of what the government is doing and effective inequality levels, with countries with lower inequality levels recording the highest scores (Finland, Norway, Denmark and Sweden) and countries with higher inequality levels recording the lowest scores (Bulgaria, Croatia Portugal and Latvia) (Figure 4). [1] The ESS is accessible through the following link https://www.europeansocialsurvey.org/. 3. Econometric findings In order to test our research hypothesis, we estimate the following ordered probit specification: where the dependent variable ( Consp_Belief ) is a qualitative discrete variable taking value five if the respondent strongly agrees on the conspiracy belief, 4 if she/he agrees, and up to 1 if she/he strongly disagrees. The variable measures in turn political, scientific, and COVID-19 conspiracy beliefs in three different estimated specifications. The main regressor of interest is the regional inequality calculated with the Gini or, alternatively, the MLD index on the EU-SILC regional sample. Controls include dummies for the highest ISCED education level attained by the respondent, a male gender dummy, age and age squared, dummies for the income decile, the employment status, and the marital status of the respondent. Regional per capita GDP is added among controls and regional dummies capture fixed regional effects. All specifications are estimated with sample post-stratification weights and robust standard errors. Descriptive findings on our controls show that almost half of the sample leaves with partner, while around 28 percent is single. Graduates are around 27 percent, while 19 percent of respondents have less than secondary education. 5 percent in the sample are unemployed, while around 28 percent retired (Table 2). Econometric findings from the estimated specification show that regional inequality indicators (either Gini or MLD) are significantly and positively correlated with conspiracy beliefs (main findings in Table 3, with full estimate details in Appendix B). As expected, the coefficients of the given (Gini or MLD) inequality indicators are higher in the political conspiracy estimate than in the scientific or COVID-19 estimate since it is reasonable that economic drivers impact more upon political than scientific conspiracy beliefs. Among controls we find an inverse U-shaped effect of age and a negative and significant effect of income deciles (from the seventh on) vis-à-vis the first income decile omitted benchmark. As expected, education is negatively and significantly correlated with conspiracy beliefs since it is reasonable to assume that higher education amplifies interpretative capacity thereby reducing the probability of believing to simplified interpretations of the reality. The unemployment status is also positively and significantly correlated with our dependent variable, consistent with the hypothesis that personal economic problems increase the likelihood of formulating such beliefs in order to provide relief and reduce the responsibility for personal failure (Table 3). Table 3 : The effect of regional inequality on different conspiracy opinions (1) (2) (3) (4) (5) (6) Regional inequality (GINI) 6.927*** 5.237*** 4.574*** (0.807) (0.748) (0.807) Regional inequality (MLD) 8.007*** 6.054*** 5.287*** (0.933) (0.865) (0.933) SES controls Yes Yes Yes Yes Yes Yes Regional dummies Yes Yes Yes Yes Yes Yes Observations 12,949 12,842 12,511 12,949 12,842 12,511 Region cluster SE in parentheses; *** p<0.01, ** p<0.05, * p<0.1 Legend: We estimate specification (1) of section 3. The three conspiracy questions are : i) a small secret group of people is responsible for making all major decisions in world politics; (political conspiracy) ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public; (scientific conspiracy) iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation (COVID-19 conspiracy) . Answers are numbered as (stro ngly agree=5, agree=4, neither agree nor disagree=3, disagree=2, strongly disagree=1 ). Dep. var. in columns 1 and 4 political conspiracy beliefs; dep. var. in columns 2 and 5 scientific conspiracy beliefs; dep. var. in columns 3 and 6 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market. To further test our research hypothesis, we slightly change our specification by creating a 0/1 dependent variable taking value one when the respondent strongly agrees or agrees to the given conspiracy belief statement and zero when she/he disagrees or strongly disagrees. In this case we drop from the sample observations where individuals say they neither agree nor disagree, interpreting these answers in the sense of a not clear position or reflexion on the issue. Estimated findings show that signs and significance of the main coefficients of interest do not change with respect to the previous specification (Table 4). In this case, the economic magnitude of the new estimate can be more easily interpreted. We find that, based on our coefficient, a one standard deviation increase in the Gini inequality indicator raises the probability of formulating political conspiracy beliefs by around 19 percent, scientific conspiracy beliefs by around 13 percent and COVID-19 beliefs by around 20 percent. Table 4 : The effect of regional inequality on the probability of becoming conspiracy believer (1) (2) (3) (4) (5) (6) Regional inequality (GINI) 9.035*** 7.433*** 6.819*** (1.152) (1.128) (1.130) Regional inequality (MLD) 10.44*** 8.592*** 7.882*** (1.332) (1.304) (1.306) SES controls Yes Yes Yes Yes Yes Yes Regional dummies Yes Yes Yes Yes Yes Yes Observations 12,949 12,842 12,511 12,949 12,842 12,511 Region cluster SE in parentheses; *** p<0.01, ** p<0.05, * p<0.1 Legend. The three conspiracy questions are : i) a small secret group of people is responsible for making all major decisions in world politics; (political conspiracy) ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public; (scientific conspiracy) iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation (COVID-19 conspiracy) . The dependent variable takes value one if the individual fully agrees or agrees on the conspiracy statement and zero if she/he disagrees or fully disagrees. Dep. var. in columns 1 and 4 political conspiracy beliefs; dep. var. in columns 2 and 5 scientific conspiracy beliefs; dep. var. in columns 3 and 6 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market. In our third specification we want to test whether the two variables measuring subjective perceptions related to what the government does or should do against inequality have as well a positive and significant effect on conspiracy beliefs. The advantage of these variables is that their variability is at individual and not regional level and therefore we have a larger number of different and independent observations to test our hypothesis. The disadvantage is obviously that endogeneity concerns are stronger in this case since it is possible that third omitted drivers affect both judgement about government action against inequality and conspiracy beliefs, thereby creating a spurious correlation among the last two variables. We will tackle this problem with instrumental variable estimates in the next section. The base estimate with subjective perception related to inequality replaces in the above-described specification (1) the regional inequality variable with a variable taking value one if respondents strongly disagree with the statement that g overnment should reduce differences in income levels , two if they disagree up to five if they strongly agree. The variable is not a direct individual assessment of local inequality since it relates to the importance of policies against it. The opinion about the importance of these policies should depend on two factors. First, inequality is serious in the country/region. At least from a descriptive point of view we observe that this factor should matter as average agreement with this statement is much higher in countries with high inequality (see Figure 3). Second, the individual has a political opinion that inequality must be addressed under the assumption that it hits her/his subjective wellbeing. On this second point political opinions are obviously mixed since the literature on inequality and subjective wellbeing presents mixed findings (Brockett, 1992; Binswanger et al., 1993; Collier et al., 2004). The recent subjective wellbeing literature tries to interpret them by showing that inequality of opportunity (inequality depending on factors that cannot be affected by the individual such as birth, gender, etc.) has a strong negative effect, while inequality of effort (depending on factors that are under individual control such as education) is less negative (Becchetti et al. 2023). In essence, the negative impact of inequality on subjective wellbeing is mediated by the perception of vertical mobility. If such perception is high, then inequality hits less. Our empirical findings show that individual judgement about the importance of government intervention against inequality is positive and significant, as expected in all the three conspiracy estimates (5 and Table 6). By using dummies for each unit of the “government should do” measure we calculate that strong agreement is associated to 8 percent higher probability of formulating political conspiracy beliefs, 6.4 percent higher probability of formulating scientific beliefs and 8.5 higher probability of formulating COVID-19 beliefs. Table 5 : The effect of respondent opinion on the need of government action against inequality on different conspiracy opinions (1) (2) (3) G overnment should reduce differences in income level 0.0346*** 0.0551*** 0.0589*** (0.0104) (0.0102) (0.0105) SES controls Yes Yes Yes Regional dummies Yes Yes Yes Observations 17,156 17,320 17,169 Region cluster SE in parentheses; *** p<0.01, ** p<0.05, * p<0.1 Specifications are as in Table 3 but regional inequality indicators are replaced by respondent consent to the statement Government should reduce differences in income level ( 5=strongly agree; 4=agree; 3=neither agree nor disagree; 2= disagree; 1=strongly disagree). The three conspiracy questions are : i) a small secret group of people is responsible for making all major decisions in world politics; (political conspiracy) ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public; (scientific conspiracy) iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation (COVID-19 conspiracy) . Answers are numbered as (stro ngly agree=5, agree=4, neither agree nor disagree=3, disagree=2, strongly disagree=1 ). Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market. Table 6 : The effect of respondent opinion on the need of government action against inequality on the probability of becoming conspiracy believer (1) (2) (3) G overnment should reduce differences in income level 0.0668*** 0.0944*** 0.0928*** (0.0143) (0.0146) (0.0150) SES controls Yes Yes Yes Regional dummies Yes Yes Yes Observations 12,847 12,739 12,409 Region cluster SE in parentheses; *** p<0.01, ** p<0.05, * p<0.1 Specifications are as in Table 4 but regional inequality indicators are replaced by respondent consent to the statement Government should reduce differences in income level (5=strongly agree; 4=agree; 3=neither agree nor disagree; 2= disagree; 1=strongly disagree). The three conspiracy questions are : i) a small secret group of people is responsible for making all major decisions in world politics; (political conspiracy) ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public; (scientific conspiracy) iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation (COVID-19 conspiracy) . The dependent variable takes value one if the individual fully agrees or agrees on the conspiracy statement and zero if she/he disagrees or fully disagrees. Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market. In a slightly different specification, we use the statement in country the government takes measures to reduce differences in income levels. The question here relates to the perception of what the government is doing against inequality. The results are substantially similar, with a negative and significant correlation between this variable and conspiracy beliefs, net of the impact of all the considered controls (Table 7 and Table 8). This implies that a more severe judgement of government action against inequality is positively and significantly correlated with conspiracy beliefs. In terms of economic significance a unit change from the mean of the variable leads to a reduction of 2.5, 3.2 and 3 percent of the probability of formulating political, scientific and COVID-19 beliefs respectively. Table 7 : The effect of respondent opinion on government action against inequality on different conspiracy opinions (1) (2) (3) Government takes measures* -0.0626*** -0.0833*** -0.0782*** (0.00540) (0.00543) (0.00562) SES controls Yes Yes Yes Regional dummies Yes Yes Yes Observations 12,949 12,842 12,511 Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 Specifications are as in Table 3 but regional inequality indicators are replaced by respondent consent to the statement in country the government takes measures to reduce differences in income levels (0= the respondent does not agree at all, 10= agrees at all) . The three conspiracy questions are : i) a small secret group of people is responsible for making all major decisions in world politics; (political conspiracy) ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public; (scientific conspiracy) iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation (COVID-19 conspiracy) . Answers are numbered as (stro ngly agree=5, agree=4, neither agree nor disagree=3, disagree=2, strongly disagree=1 ). Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market. Table 8 : The effect of respondent opinion on government action against inequality on the probability of becoming conspiracy believer (1) (2) (3) Government takes measures* -0.0525*** -0.0682*** -0.0692*** (0.00399) (0.00386) (0.00387) SES controls Yes Yes Yes Regional dummies Yes Yes Yes Observations 17,294 17,463 17,307 Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 Specifications are as in Table 4 but regional inequality indicators are replaced by respondent consent to the question : in country the government takes measures to reduce differences in income levels (0= the respondent does not agree at all, 10= agrees at all) . The three conspiracy questions are : i) a small secret group of people is responsible for making all major decisions in world politics; (political conspiracy) ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public; (scientific conspiracy) iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation (COVID-19 conspiracy) . The dependent variable takes value one if the individual fully agrees or agrees on the conspiracy statement and zero if she/he disagrees or fully disagrees. Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market. 4. Discussion and robustness checks Our results on the impact of regional inequality level presented in Tables 3 – 4 (differently from what happens when the key variable becomes individual opinions about domestic inequality as in Tables 5 – 8 ) depend on 32 regional observations (Table A1 in Appendix). In a first robustness check we see whether our results are robust when omitting one of the regions at a time and find that it is the case (results are presented in Appendix A , Tables A.2 and A.3). In a second robustness check we want to see whether local culture about the need of government intervention (or perception that the government is not doing enough) affects conspiracy beliefs, net of the impact of the individual opinion. Therefore, we calculate the average value of the relevant subjective opinion variable at the regional level and introduce it as an additional regressor in our estimate. What we find is that both the individual and the average regional opinion are significant, showing that the regional culture on the need of government intervention matters (Tables 9 and 10 ). Table 9 The effect of average regional opinion on the need of government action on inequality on the probability of becoming a conspiracy believer (1) (2) (3) (4) (5) (6) Average regional response on G overnment should reduce differences in income level 2.532*** 2.397*** 2.083*** 1.937*** 1.911*** 1.827*** (0.323) (0.326) (0.316) (0.317) (0.317) (0.318) G overnment should reduce differences in income level 0.0668*** 0.0944*** 0.0928*** (0.0143) (0.0146) (0.0150) SES controls Yes Yes Yes Yes Yes Yes Regional dummies Yes Yes Yes Yes Yes Yes Observations 12,949 12,847 12,842 12,739 12,511 12,409 Robust standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1 Specifications are as in Table 3 (first three columns) and Table 4 (last three columns) but regional inequality indicators are replaced by respondent consent to the statement Government should reduce differences in income level ( 5 = strongly agree; 4 = agree; 3 = neither agree nor disagree; 2 = disagree; 1 = strongly disagree) and by the average sample answer of respondents of the same country. The three conspiracy questions are: i) a small secret group of people is responsible for making all major decisions in world politics; (political conspiracy) ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public; (scientific conspiracy) iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation (COVID-19 conspiracy). Answers are numbered as (stro ngly agree = 5, agree = 4, neither agree nor disagree = 3, disagree = 2, strongly disagree = 1 ) in columns 1–3, while in columns 4–6 the dependent variable takes value one if the individual fully agrees or agrees on the conspiracy statement and zero if she/he disagrees or fully disagrees. Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market. Table 10 The effect of average regional opinion on government action against inequality on the probability of becoming a conspiracy believer (1) (2) (3) (4) (5) (6) Average regional response on Government takes measures* -2.360*** -2.287*** -1.942*** -1.826*** -1.781*** -1.762*** (0.301) (0.305) (0.295) (0.303) (0.295) (0.301) Government takes measures* -0.0626*** -0.0833*** -0.0782*** (0.00540) (0.005943) (0.00562) SES controls Yes Yes Yes Yes Yes Yes Regional dummies Yes Yes Yes Yes Yes Yes Observations 12,949 12,949 12,842 12,842 12,511 12,511 Robust standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1 Specifications are as in Table 3 (first three columns) and Table 4 (last three columns) but regional inequality indicators are replaced by respondent consent to the statement in country the government takes measures to reduce differences in income levels (0 = the respondent does not agree at all, 10 = agrees at all) and by the average sample answer of respondents of the same country. The three conspiracy questions are: i) a small secret group of people is responsible for making all major decisions in world politics; (political conspiracy) ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public; (scientific conspiracy) iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation (COVID-19 conspiracy). Answers are numbered as (stro ngly agree = 5, agree = 4, neither agree nor disagree = 3, disagree = 2, strongly disagree = 1 ) in columns 1–3, while in columns 4–6 the dependent variable takes value one if the individual fully agrees or agrees on the conspiracy statement and zero if she/he disagrees or fully disagrees. Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market. We also create a new variable that combines information from the two opinions about policies against inequality. More specifically we extract the opinion on the gap between what the government should do and what it is doing against inequality. To do so, we need to rescale the opinion about government action against inequality in the 1–5 range of the question about what government should do and then calculate the gap as a difference between the variable ‘government should do’ and the rescaled variable ‘government does’. We then perform an estimate adding the new variable to the specifications estimated in Table 6 including the government “should-do/does against inequality gap” variable. Our results show that the gap is strongly positive and significant, net of the impact of the government-should-do variable (Table 11 ). Endogeneity is an issue in our estimates that must be addressed to test whether correlation implies a causality link. Our approach to address endogeneity concerns consists of finding a relevant and valid instrument for individual perception. For this purpose we use a Bartik style Shift-Share instrument (Bartik, 1991 ). Table 11 Effect of the gap between what the government should do and what it does to fight income inequality on opinions about and the probability of being conspiracy believers (1) (2) (3) (4) (5) (6) “Should do/does” gap* 0.177*** 0.214*** 0.209*** 0.131*** 0.175*** 0.167*** (0.0113) (0.0116) (0.0118) (0.0121) (0.0121) (0.0125) G overnment should reduce differences in income level -0.270*** -0.287*** -0.291*** -0.178*** -0.241*** -0.235*** (0.0163) (0.0166) (0.0171) (0.0176) (0.0178) (0.0186) SES controls YES YES YES YES YES YES Regional dummies YES YES YES YES YES YES Observations 12,837 12,729 12,405 12,837 12,729 12,405 Robust standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1 Specifications are as in Table 3 (first three columns) and Table 4 (last three columns) but regional inequality indicators are replaced by the “Should do/does” gap* calculated as the difference between agreement on statement G overnment should reduce differences in income level and in country the government takes measures to reduce differences in income levels (scaled in the 1–5 interval) and respondent consent to the statement in country the government takes measures to reduce differences in income levels (0 = the respondent does not agree at all, 10 = agrees at all, rescaled in the 1–5 interval) . Respondent answer to the question the government takes measures to reduce differences in income levels is also added in the specification. The three conspiracy questions are: i) a small secret group of people is responsible for making all major decisions in world politics; (political conspiracy) ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public; (scientific conspiracy) iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation (COVID-19 conspiracy). Answers are numbered as (stro ngly agree = 5, agree = 4, neither agree nor disagree = 3, disagree = 2, strongly disagree = 1 ) in columns 1–3, while in columns 4–6 the dependent variable takes value one if the individual fully agrees or agrees on the conspiracy statement and zero if she/he disagrees or fully disagrees. Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market. Our instrument is the 20–59 dependency ratio in year 2011 at NUTS1 level. The past value of the dependency ratio can directly affect the perception of inequality of individuals, but it is likely that it is not directly correlated with the likelihood to believe in conspiracy theories nor that it depends on inequality perceptions. Our instrument builds on the Bartik’s methodology as we construct the index delocalizing the instrument at geographical and time level (Ferri, 2022 ). In fact, our instrument uses the shares of individuals aged between 0 and 19 years and above 60 years over the population aged between 20 and 59 years, at larger geographical scope that the level used to compute regional inequality (NUTS2), and 9 years before the observation of our variable of interest. The Bartik’s methodology decomposes the instrument into a Shift and Share component, where the Shift is usually replaced with the interest variable observed at a larger categorical scope (i.e. geographical or per characteristics), and the Share with a time lagged value. The Shift delocalization plays the most relevant role in addressing endogeneity issues. NUTS1 values of dependency ratio cannot depend on single shocks at lower regional level. Therefore, it is assumed that NUTS2 level shocks of dependency ratio only partially affect NUTS1 values, and that perceived inequality at NUTS2 level is affected by dependency ratios at greater geographical scope, but that there cannot be reverse causality link. The rationale behind the use of lagged values is similar to the Shift delocalization. The lag transformation corresponds to delocalization over time rather than over the geographical dimension. The IV we adopt in our strategy, therefore, is defined as follows: $${Dependency ratio 20-59}_{i,t-1}=\frac{{Population 0-19}_{i,t-1}+{Population 60+}_{i,t-1}}{{Population 20-59}_{i,t-1}}$$ Table 12 reports the results of the IV regression on the effect of individual opinion on the need of government action against inequality on different conspiracy opinions. Results confirm the evidence obtained in Table 5 . Table 12 Instrumental variable estimates on the effect of individual opinion on the need of government action against inequality on different conspiracy opinions. (1) (2) (3) Second stage Second stage Second stage Dep var.: political consp. beliefs Dep var.: scientific consp. beliefs Dep var.: COVID-19 consp. beliefs G overnment should reduce differences in income level 0.097*** 0.089** 0.090*** (0.015) (0.038) (0.034) SES controls Yes Yes Yes Regional dummies Yes Yes Yes Observations 17,156 17,320 17,169 Robust standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1 Specifications are as in Table 5 . The regional inequality indicator is proxied by respondent consent to the statement Government should reduce differences in income level ( 5 = strongly agree; 4 = agree; 3 = neither agree nor disagree; 2 = disagree; 1 = strongly disagree). The three conspiracy questions are: i) a small secret group of people is responsible for making all major decisions in world politics; (political conspiracy) ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public; (scientific conspiracy) iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation (COVID-19 conspiracy). Answers are numbered as (stro ngly agree = 5, agree = 4, neither agree nor disagree = 3, disagree = 2, strongly disagree = 1 ). Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market. Instrumental variable: dependency ratio 20–59 in year 2011. Table A 12 Reports the full specification. Finally, we check whether the instrument is valid with a falsification test where we estimate our model using the instrument as regressor in the subsample of individuals who strongly disagree or disagree that government should address the inequality problem. When we do so we find that the dependency ratio is not significant that is, our instrument does not affect the dependent variable when the instrumented variable is not in action (Table 13 ). Therefore, we conclude that the instrument affects our dependent variable only through the instrumented variable. Table 13 Falsification test (validity of the instrument selected for IV estimates) (1) (2) (3) Dependency ratio 20/59 2011 -0.003 0.001 -0.019 (0.020) (0.013) (0.015) SES controls Yes Yes Yes Regional dummies Yes Yes Yes Observations 1,841 1,848 1,840 Country level clustered standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1 Specifications are as in Table 3 but regional inequality indicators are replaced by respondent father ISCED education level and the estimate is limited to those fully disagreeing or disagreeing to the statement that Government should reduce differences in income level . The three conspiracy questions are: i) a small secret group of people is responsible for making all major decisions in world politics; (political conspiracy) ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public; (scientific conspiracy) iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation (COVID-19 conspiracy). Answers are numbered as (stro ngly agree = 5, agree = 4, neither agree nor disagree = 3, disagree = 2, strongly disagree = 1 ). Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. The dependent variable takes value one if the individual fully agrees or agrees on the conspiracy statement and zero if she/he disagrees or fully disagrees. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market. Instrumental variable: dependency ratio 20–59 in year 2011. The model is estimated in the subsample of individuals who strongly disagree or disagree that government should address the inequality problem 5. Conclusions Political, scientific, and medical conspiracy beliefs are nowadays widespread and spread on the web by social media where the hierarchical subordination of the opinion of ordinary people without expertise in the specific field with respect to scientists seems to disappear. Conspiracy beliefs undermine the same foundations of democracy and social cohesion, since they weaken trust in institutions and trigger hatred toward the supposed conspiring elites. Given the relevance of the topic and the interconnection between social and economic factors that presumably explain it, the gap of research in the research literature is surprising and needs to be bridged. Our research hypothesis aims to fill this gap by arguing that conspiracy has economic roots in income inequality. Our econometric findings do not reject it by showing that inequality is a significant driver of conspiracy beliefs. More specifically, regional inequality is significantly and positively correlated with them and is also correlated with subjective declarations that the local government should do something to reduce inequality and does not do enough to tackle the problem. These subjective declarations are in turn positively and significantly correlated with conspiracy beliefs. In our robustness check we find that their significance persists when augmenting our specification with the average sample regional opinion on the need to address inequality that is in turn significant. This shows that the local mood about inequality significantly and independently contributes to conspiracy beliefs. We then test causality of the observed nexus using as instrument the dependency ratio with shift-share approach. The instrument is relevant, and the second stage instrumented coefficient is positive and significant. With a falsification test we evaluate instrument validity by showing that the instrument affects the dependent variable only through the instrumented regressors. Our results identify another unexplored negative effect of inequality. We in fact show that the latter does not just negatively affect subjective wellbeing and weaken social cohesion but also fuels conspiracy beliefs that, in turn, as shown in the literature, can undermine trust on institutions and trigger hatred against the “conspiring” elites. The straightforward policy implication is that ex post (tax progressive) and ex ante (investment in public health and education) policies aiming to reduce inequality can reduce the above-mentioned negative effects. A limit of our research is the lack of time repeated data on conspiracy beliefs, introduced only in the last ESS wave. Maintaining this question in the survey would contribute to create a time dimension in this research, thereby giving the opportunity to test within effects (effects of changes in inequality on changes in conspiracy beliefs) improving our knowledge on the phenomenon and its causes and effects. Declarations Author Contribution Leonardo Becchetti developed the research hypothesis and organised its empirical testing. All the three authors contributed to the econometric part of the paper. All the three authors revised all the paper and created conclusions References Bartik, Timothy. 1991. Who Benefits from State and Local Economic Development Policies? Kalamazoo, MI: W.E. Upjohn Institute for Employment Research. Becchetti L., Colcerasa F., Pisani F., Peragine V., 2023, Inequality of Opportunity and Life Satisfaction, mimeo Binswanger, H., Deininger, K., Feder, G. Agricultural Land Relations in the Developing World, American Journal of Agricultural Economics, vol. 75(5), pp. 1242-1248. (1993). Brockett, C. Measuring Political Violence and Land Inequality in Central America, American Political Science Review, vol. 86(1), pp. 169-176. (1992). Brown, S., Gray, D., Roberts, J. The Relative Income Hypothesis: a comparison of methods, Economics Letters, vol. 130, pp. 47-50. (2015). Casara S., B.G., Filippi, S., Suitner, C., Dollani, E. and Maass, A., 2023. Tax the élites! The role of economic inequality and conspiracy beliefs on attitudes towards taxes and redistribution intentions. British Journal of Social Psychology , 62 (1), pp.104-118. Collier, P., Hoeffler, A., Söderbom, M. On the Duration of Civil War, Journal of Peace Research, vol. 41(3), pp. 253-273. (2004). Constantinou, M., Kagialis, A., Karekla, M., 2021. COVID-19 Scientific Facts vs. Conspiracy Theories: Is Science Failing to Pass Its Message? Int. J. Environ. Res. Publ. Health 18 (12), 6343. D’Ambrosio, C., & Frick, J. R. Income Satisfaction and Relative Deprivation: an empirical link, Social Indicators Research, vol. 81 (3), pp. 497–519. (2007). Ferri, B. Novel Shift-Share Instruments and Their Applications. Boston College. (2022). Evans-Pritchard, E.E., 1937. Magic among the Azande. London: Oxford . Ferrer-i-Carbonell, A., 2005. Income and well-being: an empirical analysis of the comparison income effect. Journal of public economics , 89 (5-6), pp.997-1019. Jolley, D., Paterson, J.L., 2020. Pylons ablaze: examining the role of 5G COVID-19 conspiracy beliefs and support for violence. Br. J. Soc. Psychol. 59, 628–640. Mari, S., Gil de Zuniga, H., Suerdem, A., Hanke, K., Brown, G., Vilar, R., Boer, D. and Bilewicz, M., 2022. Conspiracy theories and institutional trust: examining the role of uncertainty avoidance and active social media use. Political Psychology , 43 (2), pp.277-296. Marone, F., 2021. Hate in the time of coronavirus: exploring the impact of the COVID-19 pandemic on violent extremism and terrorism in the West. Secur. J. 35, 205–225. Romer, D., Jamieson, K.H., 2020. Conspiracy theories as barriers to controlling the spread of COVID-19 in the US. Soc. Sci. Med. 263, 113356. Sallam, M., 2021. COVID-19 vaccine hesitancy worldwide: a concise systematic review of vaccine acceptance rates. Vaccines 9, 160. Srol, J., ˇCavojov´a, V., Ballov´a Mikuˇskov´a, E., 2022. Finding someone to blame: the link between COVID-19 conspiracy beliefs, prejudice, support for violence, and other negative social outcomes. Front. Psychol. 12, 726076. van Mulukom, V., Pummerer, L.J., Alper, S., Bai, H., Čavojová, V., Farias, J., Kay, C.S., Lazarevic, L.B., Lobato, E.J., Marinthe, G. and Banai, I.P., 2022. Antecedents and consequences of COVID-19 conspiracy beliefs: A systematic review. Social Science & Medicine , 301 , p.114912. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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In country the government takes measures to reduce differences in income levels. Answers on a 0-10 basis where 0 stands for does not apply at all and 10 for applies completely.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3823608/v1/aba776d17ac9ab8d58728faf.png"},{"id":49143477,"identity":"981998a4-be62-46c7-93f8-8c0f356759bf","added_by":"auto","created_at":"2024-01-03 18:52:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":14270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCountry level ESS sample average consensus on importance of government action against inequality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLegend: average sample country level consent to the statement: government should reduce differences in income levels (1 if strongly agree or agree, zero if strongly disagree or disagree)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3823608/v1/f3065a85c16fa4e4e8ba915f.png"},{"id":49143476,"identity":"f1c6b385-1aae-4d5e-af9c-51831a368ca7","added_by":"auto","created_at":"2024-01-03 18:52:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":13565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCountry level ESS sample average consensus on government doing enough against inequality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLegend: average sample country level consent to the statement: In country the government takes measures to reduce differences in income levels. Answers on a 0-10 basis where 0 stands for does not apply at all and 10 for applies completely.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3823608/v1/ef628cbaa0c2b166d380be0e.png"},{"id":55550440,"identity":"ed1b276f-64db-4b1c-9b07-4ea099d5a268","added_by":"auto","created_at":"2024-04-29 21:15:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1233061,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3823608/v1/d6ae4fba-bc85-4194-b16b-8d6e132b0385.pdf"},{"id":49144143,"identity":"cc1a39ff-715d-4629-996e-ae3decab81a0","added_by":"auto","created_at":"2024-01-03 19:00:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":134933,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-3823608/v1/b229e53bf74df000f4d7d69c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inequality and conspiracy beliefs","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eConspiracy theories, where a small target group is considered responsible for adversities hitting a given population, have been generally used throughout history as interpretative shortcuts providing relief against human suffering and hardships. Their success has often depended on the fact that human beings are in search of purpose and meaning and the difficulty in giving meaning and justification to negative states of affairs can be reduced by such theories.\u003c/p\u003e \u003cp\u003eConspiracy beliefs have been familiar in many periods of human history. In a tradition originating from ancient times witchcraft has been the simplistic explanation and scapegoat for adversities hitting past and contemporary primitive populations (Evans-Pritchard, 1937). Later, plague spreaders and Jews accused of economic conspiracy have been two typical targets in difficult periods to justify in turn epidemics and economic difficulties. As history tells us conspiracy beliefs are regrettably not free from adverse social consequences. First, they undermine trust in institutions and therefore the same pillars of democracy (Mari et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Second, they trigger hatred and prosecution from extreme believers against the scapegoat groups deemed responsible for the bad state of affairs (Marone, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sallam, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Srol et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn more recent times conspiracy has taken on a more political structure that overlaps in most parts with populism and brings with it the idea that good is with \u0026ldquo;the people\u0026rdquo; and evil with some elites that are judged responsible for the more severe problems experienced by the people (inequality, epidemic diseases). The COVID-19 pandemics gave new strength to conspiracy believers who developed with strong determination theories in which the creation of epidemics was a deliberate policy strategy of the political or economic elites to limit individual freedom and make profits by experimenting and selling new vaccines (for an extensive survey of contributions on antecedents of them see van Mulukom et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Conspiracy in general, and populism in particular, therefore share many common characteristics such as the simplified dichotomy between good and evil groups, the mistrust on institutions and the simplified interpretation of the reality where causality is identified without any rigorous investigation in events that are actually likely to be the result of a complex interplay of different factors.\u003c/p\u003e \u003cp\u003eIt is surprising that, given the increasing relevance and strong revival of this phenomenon today and the many economic factors that can presumably be drivers of such beliefs, the literature investigating its drivers is scarce and concentrated only in psychology among social sciences, although growing considerably in the last years. Our empirical contribution aims to contribute to filling this gap.\u003c/p\u003e \u003cp\u003eSince economic difficulties have always been considered a possible cause of conspiracy beliefs an investigation on socioeconomic drivers of them is missing and of foremost importance. Among the few contributions focusing on it, Hornsey et al. (2023) find that conspiracy believers are more likely to have negative opinions on current and future domestic economic prosperity and show that GDP per capita is negatively correlated with conspiracy beliefs. Several recent contributions highlight how lower income is positively and significantly correlated with COVID-19 conspiracy theories (Constantinou et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Romer and Jamieson, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sallam et al., 2020; van Mulukom, 2022). Casara et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) find lower levels of tax compliance and higher support for progressive taxation among conspiracy believers on a sample of around 2,000 online participants.\u003c/p\u003e \u003cp\u003eOur paper extends the analysis in this specific field of research focusing on inequality. Our research hypothesis is that regional inequality and the perceived need for government action against inequality in a given region are significantly and positively correlated with conspiracy beliefs. We argue that this is the case because subjective well-being is strongly affected by relative income (Ferrer-I-Carbonell, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; D\u0026rsquo;Ambrosio and Frick, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Brown et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and inequality is likely to be perceived as an adverse event for which conspiracy beliefs and the identification of a clear target of culprits contribute to give meaning and relief. These mechanisms are reinforced by the fact that high inequality increases distance between top income earners and the rest of society fuelling the perception that the elites are distant and powerful and can use their power by manoeuvring against the rest of the people, thereby contributing to increase income inequality.\u003c/p\u003e \u003cp\u003eWe test our research hypothesis using data from the European Social Survey on three conspiracy belief questions and calculating income inequality levels in 32 regions. Our research hypothesis is not rejected since regional income inequality is positively and significantly correlated with conspiracy beliefs. Our findings are confirmed when we use two individual opinions related to insufficient policies against inequality (\u003cem\u003egovernment should do more against inequality\u003c/em\u003e, \u003cem\u003egovernment is not doing enough against inequality\u003c/em\u003e). We also find that the average mood of the regional sample on these two perceptions is significantly correlated with conspiracy beliefs, net of impact of the individual opinion. This last finding shows that the local culture of insufficient action against inequality is also a powerful factor affecting conspiracy beliefs beyond individual opinions. We finally test the causality nexus between inequality and conspiracy beliefs by using instrumental variable approach and showing with a falsification test that our instrument is valid. Provided that our causality analysis holds, our findings have straightforward policy implications since policies aimed at reducing inequality can in turn reduce conspiracy beliefs and their likely negative consequences on trust on institutions and hatred toward the conspiring elites.\u003c/p\u003e"},{"header":"2. Data and descriptive findings","content":"\u003cp\u003eOur data source is the European Social Survey (ESS), an established cross-country survey that has gained special attention in the economic literature related to the empirical research investigating the role of social values.\u003c/p\u003e\n\u003cp\u003eThe ESS has run 10 survey rounds until 2022[1] with newly selected cross-sectional samples. It is only with the tenth wave (2020) however that conspiracy questions have been introduced. Our conspiracy variables are built on the following three ESS questions:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ei. A small secret group of people is responsible for making all major decisions in world politics\u0026nbsp;\u003c/em\u003e(political conspiracy)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eii. Groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public\u0026nbsp;\u003c/em\u003e(scientific conspiracy)\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eiii. COVID-19 is the result of deliberate and concealed efforts of some government or organization\u0026nbsp;\u003c/em\u003e(COVID-19 conspiracy)\u003c/p\u003e\n\u003cp\u003eIn all of the three cases the respondent can give five possible answers (\u003cem\u003estrongly agree, agree, neither agree nor disagree, disagree, strongly disagree\u003c/em\u003e). The first question relates to political conspiracy and has the typical characteristics of conspiracy theories (existence of a small group of conspirators taking decisions that shape the destiny of the rest of the world and therefore are responsible for what happens, secrecy of their actions, and attribution of what happens to the action of this group). The second question relates to scientific conspiracy and has the same characteristics of the first plus a more explicit negative assessment of the role of conspirators who, according to the statement, \u0026ldquo;\u003cem\u003emanipulate, fabricate, or suppress evidence in order to deceive the public\u003c/em\u003e\u0026rdquo;. This additional characteristic makes it clearer than under political conspiracy that the action of conspirators is not \u0026ldquo;enlightened\u0026rdquo;, or for the good of the rest of the people. The third question relates to the conspiracy of COVID-19 and includes a negative judgement as well in terms of the \u003cem\u003edeliberate and concealed effort\u003c/em\u003e to create or spread the pandemics of some government or organization.\u003c/p\u003e\n\u003cp\u003eWhen looking at descriptive statistics on our conspiracy dependent variables we find that the share of conspiracy beliefs on the first question (those who strongly agree or agree on political conspiracy) is around 35 percent, falling to 30 percent for believers to scientific and COVID-19 conspiracy. The share of those strongly disagreeing is around 15 percent for political and scientific conspiracy and around 8 percent for COVID-19 conspiracy) (see Table 1 for variable legend and Table 2 for descriptive statistics).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e: Variable legend\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003ePolitical conspiracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eA small secret group of people is responsible for making all major decisions in world politics\u0026nbsp;\u003c/em\u003e(1=strongly disagree; 2= disagree; 3= neither agree nor disagree; 4= agree; 5= strongly agree)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eScientific conspiracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGroups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(1=strongly disagree; 2= disagree; 3= neither agree nor disagree; 4= agree; 5= strongly agree)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eCovid-19 conspiracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCOVID-19 is the result of deliberate and concealed efforts of some government or organisation \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(1=strongly disagree; 2= disagree; 3= neither agree nor disagree; 4= agree; 5= strongly agree)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eGovernment should do against inequality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eG\u003cem\u003eovernment should reduce differences in income level\u0026nbsp;\u003c/em\u003e(1=strongly disagree; 2= disagree; 3= neither agree nor disagree; 4= agree; 5= strongly agree)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eGovernment does against inequality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIn country the government takes measures to reduce differences in income levels\u003c/em\u003e (0-10 values, 0 =\u003cem\u003edoes not apply at all\u003c/em\u003e and 10 = \u003cem\u003eapplies completely)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eISCED education dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eES-ISCED I, less than lower secondary, ES-ISCED II, lower secondary, ES-ISCED IIIb, lower tier upper, ES-ISCED IIIa, upper tier upper secondary; ES-ISCED IV, advanced vocational, ES-ISCED V1, lower tertiary education, ES-ISCED V2, higher tertiary education.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e(0/1) dummy taking value one if the respondent is male.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eRespondent age\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eFather\u0026rsquo;s education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eFather\u0026rsquo;s highest level of education (less than lower secondary, lower secondary, upper secondary vocational, upper secondary general, advanced vocational, lower tertiary education, higher tertiary education).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eIncome class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003ePlacement of respondent household total net income in one of the income deciles of the country (1=lowest, 10=highest)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eMarital status dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e(0/1) dummies picking up the following marital status conditions: married/civil union, separated/divorced, widowed, never married\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eEmployment status dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e(0/1) dummies picking up the following employment status conditions: unemployed, paid worker, retired.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e: Descriptive statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCount\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003e\u003cem\u003ePolitical conspiracy: A small secret group of people is responsible for making all major decisions in world politics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eStrongly agree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eNeither agree nor disagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eStrongly disagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003e\u003cem\u003eScientific conspiracy: Groups of scientists manipulate, fabricate, or suppress evidence inorder to deceive the public\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.570552147239264%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eStrongly agree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eNeither agree nor disagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eStrongly disagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003e\u003cem\u003eCOVID-19 conspiracy: COVID-19 is the result of deliberate and concealed efforts of some government or organisation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eStrongly agree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eNeither agree nor disagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eStrongly disagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eRegional inequality (GINI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\" valign=\"top\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eRegional inequality (MLD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\" valign=\"top\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eGovernment should do against inequality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e3.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eGovernment does against inequality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e17,819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e8.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e2.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eAverage regional \u0026apos;government should do\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e2.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e3.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eAverage regional \u0026apos;government does\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e4.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e1.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e2.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e6.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eFather (ISCED) education level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e16,992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e3.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e1.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e51.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e17.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003e\u003cem\u003eIncome decile\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eDecile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eDecile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eDecile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eDecile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eDecile 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eDecile 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eDecile 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eDecile 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eDecile 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eDecile 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003e\u003cem\u003eMarital status\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eIn union with a partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eSeparated/Divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eSingle/Never married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003e\u003cem\u003eEducation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eLess than lower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eLower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eLower tier upper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eUpper tier upper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eAdvanced vocational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eLower tertiary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eHigher tertiary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003e\u003cem\u003eEmployment status\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003ePaid work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eLooking for job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.282208588957054%\"\u003e\n \u003cp\u003eGDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e18,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e21.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\"\u003e\n \u003cp\u003e11.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e6.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.343558282208589%\" colspan=\"2\"\u003e\n \u003cp\u003e45.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3067484662576687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eOur focus of research concerning drivers of conspiracy beliefs is on inequality measures at regional level (see Appendix A for the region list). More specifically we use two objective inequality measures drawn from the EU-SILC database namely the \u003cem\u003eGini coefficient\u0026nbsp;\u003c/em\u003eand the \u003cem\u003eMean Log Deviation\u0026nbsp;\u003c/em\u003e(\u003cem\u003eMLD\u003c/em\u003e)\u003cem\u003e,\u003c/em\u003e with the latter satisfying desirable decomposition properties but, on the other hand, is more sensitive than the Gini index to the extremes of the distribution.\u003c/p\u003e\n\u003cp\u003eWe also use in empirical estimates two subjective inequality measures taken from the ESS. In a first question about subjective perception of inequality policies ESS respondents must give their level of consent to the following statement: \u003cem\u003egovernment should reduce differences in income levels\u003c/em\u003e. The possible answers are \u003cem\u003eagree strongly, agree, neither agree nor disagree, disagree, disagree strongly.\u0026nbsp;\u003c/em\u003eThe vast majority of respondents agree strongly (32.3 percent) or agree (42.6 percent), while only around 10 percent of survey participants disagree or strongly disagree (Figure 1).\u003c/p\u003e\n\u003cp\u003eA second similar statement in the ESS survey is \u003cem\u003eIn country the government takes measures to reduce differences in income levels\u003c/em\u003e. Answers here are possible on a 0-10 basis where 0 stands for \u003cem\u003edoes not apply at all\u003c/em\u003e and 10 for \u003cem\u003eapplies completely\u003c/em\u003e. The 0-answer indicating complete disagreement on the relevance of government actions to reduce differences in income is chosen by 13.6 percent of respondents, with around 68 percent of them not going above 5. Only 2.1 percent declare that the statement applies completely to their government (Figure 2).\u003c/p\u003e\n\u003cp\u003eAn important difference between the two statements is that, in the first case, the judgement is on what the government should do, while in the second case on what the government is doing. In both cases the answer can be mediated by unobserved factors. In the first case, by the respondent\u0026rsquo;s political preference on what should be the appropriate government action against income inequality (and by the perception that the government is not doing enough with respect to one\u0026rsquo;s own desired income redistribution), while in the second case, by the respondent\u0026rsquo;s perception of the actual government effort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the first case we find in general a positive correspondence between respondents desired intensity of government action and actual country inequality levels, with respondents in Nordic countries at the bottom and Eastern European country citizens at the top (Figure 3).\u003c/p\u003e\n\u003cp\u003eIn the second case we find strict correspondence between average sample country level evaluation of what the government is doing and effective inequality levels, with countries with lower inequality levels recording the highest scores (Finland, Norway, Denmark and Sweden) and countries with higher inequality levels recording the lowest scores (Bulgaria, Croatia Portugal and Latvia) (Figure 4).\u003c/p\u003e\n\u003cdiv id=\"ftn1\"\u003e\n \u003cp\u003e[1] The ESS is accessible through the following link https://www.europeansocialsurvey.org/.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Econometric findings","content":"\u003cp\u003eIn order to test our research hypothesis, we estimate the following ordered probit specification:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003ewhere the dependent variable (\u003cem\u003eConsp_Belief\u003c/em\u003e) is a qualitative discrete variable taking value five if the respondent strongly agrees on the conspiracy belief, 4 if she/he agrees, and up to 1 if she/he strongly disagrees. The variable measures in turn political, scientific, and COVID-19 conspiracy beliefs in three different estimated specifications. The main regressor of interest is the regional inequality calculated with the Gini or, alternatively, the MLD index on the EU-SILC regional sample. Controls include dummies for the highest ISCED education level attained by the respondent, a male gender dummy, age and age squared, dummies for the income decile, the employment status, and the marital status of the respondent. Regional per capita GDP is added among controls and regional dummies capture fixed regional effects. \u0026nbsp;All specifications are estimated with sample post-stratification weights and robust standard errors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDescriptive findings on our controls show that almost half of the sample leaves with partner, while around 28 percent is single. Graduates are around 27 percent, while 19 percent of respondents have less than secondary education. 5 percent in the sample are unemployed, while around 28 percent retired (Table 2). \u0026nbsp;Econometric findings from the estimated specification show that regional inequality indicators (either Gini or MLD) are significantly and positively correlated with conspiracy beliefs (main findings in Table 3, with full estimate details in Appendix B). As expected, the coefficients of the given (Gini or MLD) inequality indicators are higher in the political conspiracy estimate than in the scientific or COVID-19 estimate since it is reasonable that economic drivers impact more upon political than scientific conspiracy beliefs. Among controls we find an inverse U-shaped effect of age and a negative and significant effect of income deciles (from the seventh on) vis-\u0026agrave;-vis the first income decile omitted benchmark. As expected, education is negatively and significantly correlated with conspiracy beliefs since it is reasonable to assume that higher education amplifies interpretative capacity thereby reducing the probability of believing to simplified interpretations of the reality. The unemployment status is also positively and significantly correlated with our dependent variable, consistent with the hypothesis that personal economic problems increase the likelihood of formulating such beliefs in order to provide relief and reduce the responsibility for personal failure (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe effect of regional inequality on different conspiracy opinions\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegional inequality (GINI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.927***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.237***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.574***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.807)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.748)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.807)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegional inequality (MLD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.007***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.054***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.287***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.933)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.865)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.933)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSES controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegional dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12,949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12,842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12,511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12,949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12,842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12,511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003eRegion cluster SE in parentheses; *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eLegend: We estimate specification (1) of section 3. The three conspiracy questions are\u003cem\u003e: i) a small secret group of people is responsible for making all major decisions in world politics;\u0026nbsp;\u003c/em\u003e(political conspiracy)\u003cem\u003e\u0026nbsp;ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public;\u003c/em\u003e (scientific conspiracy)\u003cem\u003e\u0026nbsp;iii) COVID-19 \u0026nbsp;is the result of deliberate and concealed efforts of some government or organisation\u0026nbsp;\u003c/em\u003e(COVID-19 conspiracy)\u003cem\u003e. Answers are numbered as\u0026nbsp;\u003c/em\u003e(stro\u003cem\u003engly agree=5, agree=4, neither agree nor disagree=3, disagree=2, strongly disagree=1\u003c/em\u003e). Dep. var. in columns 1 and 4 political conspiracy beliefs; dep. var. in columns 2 and 5 scientific conspiracy beliefs; dep. var. in columns 3 and 6 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market.\u003c/p\u003e\n\u003cp\u003eTo further test our research hypothesis, we slightly change our specification by creating a 0/1 dependent variable taking value one when the respondent strongly agrees or agrees to the given conspiracy belief statement and zero when she/he disagrees or strongly disagrees. In this case we drop from the sample observations where individuals say they neither agree nor disagree, interpreting these answers in the sense of a not clear position or reflexion on the issue. Estimated findings show that signs and significance of the main coefficients of interest do not change with respect to the previous specification (Table 4). In this case, the economic magnitude of the new estimate can be more easily interpreted. We find that, based on our coefficient, a one standard deviation increase in the Gini inequality indicator raises the probability of formulating political conspiracy beliefs by around 19 percent, scientific conspiracy beliefs by around 13 percent and COVID-19 beliefs by around 20 percent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe effect of regional inequality on the probability of becoming conspiracy believer\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRegional inequality (GINI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.035***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7.433***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.819***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRegional inequality (MLD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10.44***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8.592***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7.882***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.332)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.304)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.306)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSES controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRegional dummies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12,949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12,842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12,511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12,949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12,842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12,511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003eRegion cluster SE in parentheses; *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eLegend. The three conspiracy questions are\u003cem\u003e: i) a small secret group of people is responsible for making all major decisions in world politics;\u0026nbsp;\u003c/em\u003e(political conspiracy)\u003cem\u003e\u0026nbsp;ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public;\u003c/em\u003e (scientific conspiracy)\u003cem\u003e\u0026nbsp;iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation\u0026nbsp;\u003c/em\u003e(COVID-19 conspiracy)\u003cem\u003e.\u003c/em\u003e The dependent variable takes value one if the individual fully agrees or agrees on the conspiracy statement and zero if she/he disagrees or fully disagrees. Dep. var. in columns 1 and 4 political conspiracy beliefs; dep. var. in columns 2 and 5 scientific conspiracy beliefs; dep. var. in columns 3 and 6 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market.\u003c/p\u003e\n\u003cp\u003eIn our third specification we want to test whether the two variables measuring subjective perceptions related to what the government does or should do against inequality have as well a positive and significant effect on conspiracy beliefs. The advantage of these variables is that their variability is at individual and not regional level and therefore we have a larger number of different and independent observations to test our hypothesis. The disadvantage is obviously that endogeneity concerns are stronger in this case since it is possible that third omitted drivers affect both judgement about government action against inequality and conspiracy beliefs, thereby creating a spurious correlation among the last two variables. We will tackle this problem with instrumental variable estimates in the next section. The base estimate with subjective perception related to inequality replaces in the above-described specification (1) the regional inequality variable with a variable taking value one if respondents strongly disagree with the statement that g\u003cem\u003eovernment should reduce differences in income levels\u003c/em\u003e, two if they disagree up to five if they strongly agree. The variable is not a direct individual assessment of local inequality since it relates to the importance of policies against it. The opinion about the importance of these policies should depend on two factors. First, inequality is serious in the country/region. At least from a descriptive point of view we observe that this factor should matter as average agreement with this statement is much higher in countries with high inequality (see\u0026nbsp;Figure 3). Second, the individual has a political opinion that inequality must be addressed under the assumption that it hits her/his subjective wellbeing. On this second point political opinions are obviously mixed since the literature on inequality and subjective wellbeing presents mixed findings (Brockett, 1992;\u0026nbsp;Binswanger et al., 1993; Collier et al., 2004). The recent subjective wellbeing literature tries to interpret them by showing that inequality of opportunity (inequality depending on factors that cannot be affected by the individual such as birth, gender, etc.) has a strong negative effect, while inequality of effort (depending on factors that are under individual control such as education) is less negative (Becchetti et al. 2023). In essence, the negative impact of inequality on subjective wellbeing is mediated by the perception of vertical mobility. If such perception is high, then inequality hits less.\u003c/p\u003e\n\u003cp\u003eOur empirical findings show that individual judgement about the importance of government intervention against inequality is positive and significant, as expected in all the three conspiracy estimates (5 and Table 6). By using dummies for each unit of the \u0026ldquo;government should do\u0026rdquo; measure we calculate that strong agreement is associated to 8 percent higher probability of formulating political conspiracy beliefs, 6.4 percent higher probability of formulating scientific beliefs and 8.5 higher probability of formulating COVID-19 beliefs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe effect of respondent opinion on the need of government action against inequality on different conspiracy opinions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.70103092783505%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.70103092783505%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.70103092783505%\"\u003e\n \u003cp\u003eG\u003cem\u003eovernment should reduce differences in income level\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.0346***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.0551***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.0589***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.70103092783505%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e(0.0104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e(0.0102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e(0.0105)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.70103092783505%\"\u003e\n \u003cp\u003eSES controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.70103092783505%\"\u003e\n \u003cp\u003eRegional dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.70103092783505%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e17,156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e17,320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e17,169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"85.85858585858585%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eRegion cluster SE in parentheses; *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.141414141414142%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSpecifications are as in Table 3 but regional inequality indicators are replaced by respondent consent to the statement \u003cem\u003eGovernment should reduce differences in income level (\u003c/em\u003e5=strongly agree; 4=agree; 3=neither agree nor disagree; 2= disagree; 1=strongly disagree).\u0026nbsp;The three conspiracy questions are\u003cem\u003e: i) a small secret group of people is responsible for making all major decisions in world politics;\u0026nbsp;\u003c/em\u003e(political conspiracy)\u003cem\u003e\u0026nbsp;ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public;\u003c/em\u003e (scientific conspiracy)\u003cem\u003e\u0026nbsp;iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation\u0026nbsp;\u003c/em\u003e(COVID-19 conspiracy)\u003cem\u003e. Answers are numbered as\u0026nbsp;\u003c/em\u003e(stro\u003cem\u003engly agree=5, agree=4, neither agree nor disagree=3, disagree=2, strongly disagree=1\u003c/em\u003e). Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe effect of respondent opinion on the need of government action against inequality on the probability of becoming conspiracy believer\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"60.60606060606061%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60.60606060606061%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60.60606060606061%\" valign=\"bottom\"\u003e\n \u003cp\u003eG\u003cem\u003eovernment should reduce differences in income level\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0668***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0944***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0928***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60.60606060606061%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.0143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.0146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.0150)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60.60606060606061%\" valign=\"bottom\"\u003e\n \u003cp\u003eSES controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60.60606060606061%\" valign=\"bottom\"\u003e\n \u003cp\u003eRegional dummies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60.60606060606061%\" valign=\"bottom\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e12,847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e12,739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\n \u003cp\u003e12,409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eRegion cluster SE in parentheses; *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSpecifications are as in Table 4 but regional inequality indicators are replaced by respondent consent to the statement \u003cem\u003eGovernment should reduce differences in income level (5=strongly agree; 4=agree; 3=neither agree nor disagree; 2= disagree; 1=strongly disagree).\u003c/em\u003e The three conspiracy questions are\u003cem\u003e: i) a small secret group of people is responsible for making all major decisions in world politics;\u0026nbsp;\u003c/em\u003e(political conspiracy)\u003cem\u003e\u0026nbsp;ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public;\u003c/em\u003e (scientific conspiracy)\u003cem\u003e\u0026nbsp;iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation\u0026nbsp;\u003c/em\u003e(COVID-19 conspiracy)\u003cem\u003e.\u0026nbsp;\u003c/em\u003eThe dependent variable takes value one if the individual fully agrees or agrees on the conspiracy statement and zero if she/he disagrees or fully disagrees. Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market.\u003c/p\u003e\n\u003cp\u003eIn a slightly different specification, we use the statement \u003cem\u003ein\u003c/em\u003e\u003cem\u003e\u0026nbsp;country the government takes measures to reduce differences in income levels.\u0026nbsp;\u003c/em\u003eThe question here relates to the perception of what the government is doing against inequality. The results are substantially similar, with a negative and significant correlation between this variable and conspiracy beliefs, net of the impact of all the considered controls (Table 7 and Table 8). This implies that a more severe judgement of government action against inequality is positively and significantly correlated with conspiracy beliefs. In terms of economic significance a unit change from the mean of the variable leads to a reduction of 2.5, 3.2 and 3 percent of the probability of formulating political, scientific and COVID-19 beliefs respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003cstrong\u003e: The effect of respondent opinion on government action against inequality on different conspiracy opinions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eGovernment takes measures*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-0.0626***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e-0.0833***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e-0.0782***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e(0.00540)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e(0.00543)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e(0.00562)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\"\u003e\n \u003cp\u003eSES controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\"\u003e\n \u003cp\u003eRegional dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e12,949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e12,842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e12,511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eRobust standard errors in parentheses; *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSpecifications are as in Table 3 but regional inequality indicators are replaced by respondent consent to the statement \u003cem\u003ein country the government takes measures to reduce differences in income levels (0= the respondent does not agree at all, 10= agrees at all)\u003c/em\u003e. The three conspiracy questions are\u003cem\u003e: i) a small secret group of people is responsible for making all major decisions in world politics;\u0026nbsp;\u003c/em\u003e(political conspiracy)\u003cem\u003e\u0026nbsp;ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public;\u003c/em\u003e (scientific conspiracy)\u003cem\u003e\u0026nbsp;iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation\u0026nbsp;\u003c/em\u003e(COVID-19 conspiracy)\u003cem\u003e. Answers are numbered as\u0026nbsp;\u003c/em\u003e(stro\u003cem\u003engly agree=5, agree=4, neither agree nor disagree=3, disagree=2, strongly disagree=1\u003c/em\u003e). Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003cstrong\u003e: The effect of respondent opinion on government action against inequality on the probability of becoming conspiracy believer\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eGovernment takes measures*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-0.0525***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e-0.0682***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e-0.0692***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e(0.00399)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e(0.00386)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e(0.00387)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\"\u003e\n \u003cp\u003eSES controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\"\u003e\n \u003cp\u003eRegional dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89795918367347%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e17,294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e17,463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e17,307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eRobust standard errors in parentheses; *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSpecifications are as in Table 4 but regional inequality indicators are replaced by respondent consent to the question\u003cem\u003e: in country the government takes measures to reduce differences in income levels (0= the respondent does not agree at all, 10= agrees at all)\u003c/em\u003e. The three conspiracy questions are\u003cem\u003e: i) a small secret group of people is responsible for making all major decisions in world politics;\u0026nbsp;\u003c/em\u003e(political conspiracy)\u003cem\u003e\u0026nbsp;ii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public;\u003c/em\u003e (scientific conspiracy)\u003cem\u003e\u0026nbsp;iii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation\u0026nbsp;\u003c/em\u003e(COVID-19 conspiracy)\u003cem\u003e.\u0026nbsp;\u003c/em\u003eThe dependent variable takes value one if the individual fully agrees or agrees on the conspiracy statement and zero if she/he disagrees or fully disagrees. Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market.\u003c/p\u003e"},{"header":"4. Discussion and robustness checks","content":"\u003cp\u003eOur results on the impact of regional inequality level presented in Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e (differently from what happens when the key variable becomes individual opinions about domestic inequality as in Tables \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e) depend on 32 regional observations (Table \u003cspan class=\"InternalRef\"\u003eA1\u003c/span\u003e in Appendix). In a first robustness check we see whether our results are robust when omitting one of the regions at a time and find that it is the case (results are presented in \u003cspan class=\"InternalRef\"\u003eAppendix A\u003c/span\u003e, Tables A.2 and A.3).\u003c/p\u003e\n\u003cp\u003eIn a second robustness check we want to see whether local culture about the need of government intervention (or perception that the government is not doing enough) affects conspiracy beliefs, net of the impact of the individual opinion. Therefore, we calculate the average value of the relevant subjective opinion variable at the regional level and introduce it as an additional regressor in our estimate. What we find is that both the individual and the average regional opinion are significant, showing that the regional culture on the need of government intervention matters (Tables \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe effect of average regional opinion on the need of government action on inequality on the probability of becoming a conspiracy believer\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage regional response on\u003c/p\u003e\n \u003cp\u003eG\u003cem\u003eovernment should reduce differences in income level\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.532***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.397***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.083***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.937***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.911***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.827***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.326)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.316)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.317)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.317)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.318)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003cem\u003eovernment should reduce differences in income level\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0668***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0944***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0928***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0150)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegional dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eRobust standard errors in parentheses; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSpecifications are as in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (first three columns) and Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e (last three columns) but regional inequality indicators are replaced by respondent consent to the statement \u003cem\u003eGovernment should reduce differences in income level (\u003c/em\u003e5\u0026thinsp;=\u0026thinsp;strongly agree; 4\u0026thinsp;=\u0026thinsp;agree; 3\u0026thinsp;=\u0026thinsp;neither agree nor disagree; 2\u0026thinsp;=\u0026thinsp;disagree; 1\u0026thinsp;=\u0026thinsp;strongly disagree) and by the average sample answer of respondents of the same country. The three conspiracy questions are: \u003cem\u003ei) a small secret group of people is responsible for making all major decisions in world politics;\u003c/em\u003e (political conspiracy) \u003cem\u003eii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public;\u003c/em\u003e (scientific conspiracy) \u003cem\u003eiii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation\u003c/em\u003e (COVID-19 conspiracy). \u003cem\u003eAnswers are numbered as\u003c/em\u003e (stro\u003cem\u003engly agree\u0026thinsp;=\u0026thinsp;5, agree\u0026thinsp;=\u0026thinsp;4, neither agree nor disagree\u0026thinsp;=\u0026thinsp;3, disagree\u0026thinsp;=\u0026thinsp;2, strongly disagree\u0026thinsp;=\u0026thinsp;1\u003c/em\u003e) in columns 1\u0026ndash;3, while in columns 4\u0026ndash;6 the dependent variable takes value one if the individual fully agrees or agrees on the conspiracy statement and zero if she/he disagrees or fully disagrees. Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe effect of average regional opinion on government action against inequality on the probability of becoming a conspiracy believer\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage regional response on \u003cem\u003eGovernment takes measures*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.360***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.287***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.942***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.826***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.781***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.762***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.301)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.305)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.295)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.295)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(0.301)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGovernment takes measures*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0626***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0833***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.0782***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00540)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005943)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(0.00562)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegional dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e12,511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eRobust standard errors in parentheses; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSpecifications are as in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (first three columns) and Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e (last three columns) but regional inequality indicators are replaced by respondent consent to the statement \u003cem\u003ein country the government takes measures to reduce differences in income levels (0\u0026thinsp;=\u0026thinsp;the respondent does not agree at all, 10\u0026thinsp;=\u0026thinsp;agrees at all)\u003c/em\u003e and by the average sample answer of respondents of the same country. The three conspiracy questions are: \u003cem\u003ei) a small secret group of people is responsible for making all major decisions in world politics;\u003c/em\u003e (political conspiracy) \u003cem\u003eii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public;\u003c/em\u003e (scientific conspiracy) \u003cem\u003eiii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation\u003c/em\u003e (COVID-19 conspiracy). \u003cem\u003eAnswers are numbered as\u003c/em\u003e (stro\u003cem\u003engly agree\u0026thinsp;=\u0026thinsp;5, agree\u0026thinsp;=\u0026thinsp;4, neither agree nor disagree\u0026thinsp;=\u0026thinsp;3, disagree\u0026thinsp;=\u0026thinsp;2, strongly disagree\u0026thinsp;=\u0026thinsp;1\u003c/em\u003e) in columns 1\u0026ndash;3, while in columns 4\u0026ndash;6 the dependent variable takes value one if the individual fully agrees or agrees on the conspiracy statement and zero if she/he disagrees or fully disagrees. Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market.\u003c/p\u003e\n\u003cp\u003eWe also create a new variable that combines information from the two opinions about policies against inequality. More specifically we extract the opinion on the gap between what the government should do and what it is doing against inequality. To do so, we need to rescale the opinion about government action against inequality in the 1\u0026ndash;5 range of the question about what government should do and then calculate the gap as a difference between the variable \u0026lsquo;government should do\u0026rsquo; and the rescaled variable \u0026lsquo;government does\u0026rsquo;. We then perform an estimate adding the new variable to the specifications estimated in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e including the government \u0026ldquo;should-do/does against inequality gap\u0026rdquo; variable. Our results show that the gap is strongly positive and significant, net of the impact of the government-should-do variable (Table \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e). Endogeneity is an issue in our estimates that must be addressed to test whether correlation implies a causality link. Our approach to address endogeneity concerns consists of finding a relevant and valid instrument for individual perception. For this purpose we use a Bartik style Shift-Share instrument (Bartik, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab11\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffect of the gap between what the government should do and what it does to fight income inequality on opinions about and the probability of being conspiracy believers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;Should do/does\u0026rdquo; gap*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.177***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.214***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.209***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.131***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.175***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0125)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003cem\u003eovernment should reduce differences in income level\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.270***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.287***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.291***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.178***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.241***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.235***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0186)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegional dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eRobust standard errors in parentheses; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSpecifications are as in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (first three columns) and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e (last three columns) but regional inequality indicators are replaced by the \u0026ldquo;Should do/does\u0026rdquo; gap* calculated as the difference between agreement on statement G\u003cem\u003eovernment should reduce differences in income level\u003c/em\u003e and \u003cem\u003ein country the government takes measures to reduce differences in income levels (scaled in the 1\u0026ndash;5 interval)\u003c/em\u003e and respondent consent to the statement \u003cem\u003ein country the government takes measures to reduce differences in income levels (0\u0026thinsp;=\u0026thinsp;the respondent does not agree at all, 10\u0026thinsp;=\u0026thinsp;agrees at all, rescaled in the 1\u0026ndash;5 interval)\u003c/em\u003e. Respondent answer to the question \u003cem\u003ethe government takes measures to reduce differences in income levels\u003c/em\u003e is also added in the specification. The three conspiracy questions are: \u003cem\u003ei) a small secret group of people is responsible for making all major decisions in world politics;\u003c/em\u003e (political conspiracy) \u003cem\u003eii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public;\u003c/em\u003e (scientific conspiracy) \u003cem\u003eiii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation\u003c/em\u003e (COVID-19 conspiracy). \u003cem\u003eAnswers are numbered as\u003c/em\u003e (stro\u003cem\u003engly agree\u0026thinsp;=\u0026thinsp;5, agree\u0026thinsp;=\u0026thinsp;4, neither agree nor disagree\u0026thinsp;=\u0026thinsp;3, disagree\u0026thinsp;=\u0026thinsp;2, strongly disagree\u0026thinsp;=\u0026thinsp;1\u003c/em\u003e) in columns 1\u0026ndash;3, while in columns 4\u0026ndash;6 the dependent variable takes value one if the individual fully agrees or agrees on the conspiracy statement and zero if she/he disagrees or fully disagrees. Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market.\u003c/p\u003e\n\u003cp\u003eOur instrument is the 20\u0026ndash;59 dependency ratio in year 2011 at NUTS1 level. The past value of the dependency ratio can directly affect the perception of inequality of individuals, but it is likely that it is not directly correlated with the likelihood to believe in conspiracy theories nor that it depends on inequality perceptions.\u003c/p\u003e\n\u003cp\u003eOur instrument builds on the Bartik\u0026rsquo;s methodology as we construct the index \u003cem\u003edelocalizing\u003c/em\u003e the instrument at geographical and time level (Ferri, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). In fact, our instrument uses the shares of individuals aged between 0 and 19 years and above 60 years over the population aged between 20 and 59 years, at larger geographical scope that the level used to compute regional inequality (NUTS2), and 9 years before the observation of our variable of interest.\u003c/p\u003e\n\u003cp\u003eThe Bartik\u0026rsquo;s methodology decomposes the instrument into a Shift and Share component, where the Shift is usually replaced with the interest variable observed at a larger categorical scope (i.e. geographical or per characteristics), and the Share with a time lagged value.\u003c/p\u003e\n\u003cp\u003eThe Shift delocalization plays the most relevant role in addressing endogeneity issues. NUTS1 values of dependency ratio cannot depend on single shocks at lower regional level. Therefore, it is assumed that NUTS2 level shocks of dependency ratio only partially affect NUTS1 values, and that perceived inequality at NUTS2 level is affected by dependency ratios at greater geographical scope, but that there cannot be reverse causality link. The rationale behind the use of lagged values is similar to the Shift delocalization. The lag transformation corresponds to delocalization over time rather than over the geographical dimension.\u003c/p\u003e\n\u003cp\u003eThe IV we adopt in our strategy, therefore, is defined as follows:\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$${Dependency ratio 20-59}_{i,t-1}=\\frac{{Population 0-19}_{i,t-1}+{Population 60+}_{i,t-1}}{{Population 20-59}_{i,t-1}}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e reports the results of the IV regression on the effect of individual opinion on the need of government action against inequality on different conspiracy opinions. Results confirm the evidence obtained in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab12\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInstrumental variable estimates on the effect of individual opinion on the need of government action against inequality on different conspiracy opinions.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecond stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecond stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecond stage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDep var.: political consp. beliefs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDep var.: scientific consp. beliefs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDep var.: COVID-19 consp.\u003c/p\u003e\n \u003cp\u003ebeliefs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003cem\u003eovernment should reduce differences in income level\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegional dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17,156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17,320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17,169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eRobust standard errors in parentheses; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSpecifications are as in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The regional inequality indicator is proxied by respondent consent to the statement \u003cem\u003eGovernment should reduce differences in income level (\u003c/em\u003e5\u0026thinsp;=\u0026thinsp;strongly agree; 4\u0026thinsp;=\u0026thinsp;agree; 3\u0026thinsp;=\u0026thinsp;neither agree nor disagree; 2\u0026thinsp;=\u0026thinsp;disagree; 1\u0026thinsp;=\u0026thinsp;strongly disagree). The three conspiracy questions are: \u003cem\u003ei) a small secret group of people is responsible for making all major decisions in world politics;\u003c/em\u003e (political conspiracy) \u003cem\u003eii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public;\u003c/em\u003e (scientific conspiracy) \u003cem\u003eiii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation\u003c/em\u003e (COVID-19 conspiracy). \u003cem\u003eAnswers are numbered as\u003c/em\u003e (stro\u003cem\u003engly agree\u0026thinsp;=\u0026thinsp;5, agree\u0026thinsp;=\u0026thinsp;4, neither agree nor disagree\u0026thinsp;=\u0026thinsp;3, disagree\u0026thinsp;=\u0026thinsp;2, strongly disagree\u0026thinsp;=\u0026thinsp;1\u003c/em\u003e). Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market. Instrumental variable: dependency ratio 20\u0026ndash;59 in year 2011. Table A 12 Reports the full specification.\u003c/p\u003e\n\u003cp\u003eFinally, we check whether the instrument is valid with a falsification test where we estimate our model using the instrument as regressor in the subsample of individuals who strongly disagree or disagree that government should address the inequality problem. When we do so we find that the dependency ratio is not significant that is, our instrument does not affect the dependent variable when the instrumented variable is not in action (Table \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e). Therefore, we conclude that the instrument affects our dependent variable only through the instrumented variable.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab14\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFalsification test (validity of the instrument selected for IV estimates)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDependency ratio 20/59 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegional dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eCountry level clustered standard errors in parentheses; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSpecifications are as in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e but regional inequality indicators are replaced by respondent father ISCED education level and the estimate is limited to those fully disagreeing or disagreeing to the statement that \u003cem\u003eGovernment should reduce differences in income level\u003c/em\u003e. The three conspiracy questions are: \u003cem\u003ei) a small secret group of people is responsible for making all major decisions in world politics;\u003c/em\u003e (political conspiracy) \u003cem\u003eii) groups of scientists manipulate, fabricate, or suppress evidence in order to deceive the public;\u003c/em\u003e (scientific conspiracy) \u003cem\u003eiii) COVID-19 is the result of deliberate and concealed efforts of some government or organisation\u003c/em\u003e (COVID-19 conspiracy). \u003cem\u003eAnswers are numbered as\u003c/em\u003e (stro\u003cem\u003engly agree\u0026thinsp;=\u0026thinsp;5, agree\u0026thinsp;=\u0026thinsp;4, neither agree nor disagree\u0026thinsp;=\u0026thinsp;3, disagree\u0026thinsp;=\u0026thinsp;2, strongly disagree\u0026thinsp;=\u0026thinsp;1\u003c/em\u003e). Dep. var. in column 1 political conspiracy beliefs, dep. var. in column 2 scientific conspiracy beliefs; dep. var. in column 3 COVID-19 conspiracy beliefs. The dependent variable takes value one if the individual fully agrees or agrees on the conspiracy statement and zero if she/he disagrees or fully disagrees. SES control include marital status, education, income, occupation and GDP per capita. Omitted benchmark: ISCED higher tertiary education level, lowest income decile, married, female, neither retired nor in the job market.\u003c/p\u003e\n\u003cp\u003eInstrumental variable: dependency ratio 20\u0026ndash;59 in year 2011. The model is estimated in the subsample of individuals who strongly disagree or disagree that government should address the inequality problem\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003ePolitical, scientific, and medical conspiracy beliefs are nowadays widespread and spread on the web by social media where the hierarchical subordination of the opinion of ordinary people without expertise in the specific field with respect to scientists seems to disappear. Conspiracy beliefs undermine the same foundations of democracy and social cohesion, since they weaken trust in institutions and trigger hatred toward the supposed conspiring elites. Given the relevance of the topic and the interconnection between social and economic factors that presumably explain it, the gap of research in the research literature is surprising and needs to be bridged.\u003c/p\u003e \u003cp\u003eOur research hypothesis aims to fill this gap by arguing that conspiracy has economic roots in income inequality. Our econometric findings do not reject it by showing that inequality is a significant driver of conspiracy beliefs. More specifically, regional inequality is significantly and positively correlated with them and is also correlated with subjective declarations that the local government should do something to reduce inequality and does not do enough to tackle the problem. These subjective declarations are in turn positively and significantly correlated with conspiracy beliefs. In our robustness check we find that their significance persists when augmenting our specification with the average sample regional opinion on the need to address inequality that is in turn significant. This shows that the local mood about inequality significantly and independently contributes to conspiracy beliefs.\u003c/p\u003e \u003cp\u003eWe then test causality of the observed nexus using as instrument the dependency ratio with shift-share approach. The instrument is relevant, and the second stage instrumented coefficient is positive and significant. With a falsification test we evaluate instrument validity by showing that the instrument affects the dependent variable only through the instrumented regressors.\u003c/p\u003e \u003cp\u003eOur results identify another unexplored negative effect of inequality. We in fact show that the latter does not just negatively affect subjective wellbeing and weaken social cohesion but also fuels conspiracy beliefs that, in turn, as shown in the literature, can undermine trust on institutions and trigger hatred against the \u0026ldquo;conspiring\u0026rdquo; elites. The straightforward policy implication is that ex post (tax progressive) and ex ante (investment in public health and education) policies aiming to reduce inequality can reduce the above-mentioned negative effects.\u003c/p\u003e \u003cp\u003eA limit of our research is the lack of time repeated data on conspiracy beliefs, introduced only in the last ESS wave. Maintaining this question in the survey would contribute to create a time dimension in this research, thereby giving the opportunity to test within effects (effects of changes in inequality on changes in conspiracy beliefs) improving our knowledge on the phenomenon and its causes and effects.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLeonardo Becchetti developed the research hypothesis and organised its empirical testing. All the three authors contributed to the econometric part of the paper. All the three authors revised all the paper and created conclusions\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBartik, Timothy. 1991. Who Benefits from State and Local Economic Development Policies? Kalamazoo, MI: W.E. Upjohn Institute for Employment Research.\u003c/li\u003e\n\u003cli\u003eBecchetti L., Colcerasa F., Pisani F., Peragine V., 2023, Inequality of Opportunity and Life Satisfaction, mimeo\u003c/li\u003e\n\u003cli\u003eBinswanger, H., Deininger, K., Feder, G. Agricultural Land Relations in the Developing World, American Journal of Agricultural Economics, vol. 75(5), pp. 1242-1248. (1993).\u003c/li\u003e\n\u003cli\u003eBrockett, C. Measuring Political Violence and Land Inequality in Central America, American Political Science Review, vol. 86(1), pp. 169-176. (1992).\u003c/li\u003e\n\u003cli\u003eBrown, S., Gray, D., Roberts, J. The Relative Income Hypothesis: a comparison of methods, Economics Letters, vol. 130, pp. 47-50. (2015).\u003c/li\u003e\n\u003cli\u003eCasara S., B.G., Filippi, S., Suitner, C., Dollani, E. and Maass, A., 2023. Tax the \u0026eacute;lites! The role of economic inequality and conspiracy beliefs on attitudes towards taxes and redistribution intentions. \u003cem\u003eBritish Journal of Social Psychology\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e(1), pp.104-118.\u003c/li\u003e\n\u003cli\u003eCollier, P., Hoeffler, A., S\u0026ouml;derbom, M. On the Duration of Civil War, Journal of Peace Research, vol. 41(3), pp. 253-273. (2004).\u003c/li\u003e\n\u003cli\u003eConstantinou, M., Kagialis, A., Karekla, M., 2021. COVID-19 Scientific Facts vs. Conspiracy Theories: Is Science Failing to Pass Its Message? Int. J. Environ. Res. Publ. Health 18 (12), 6343. \u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Ambrosio, C., \u0026amp; Frick, J. R. Income Satisfaction and Relative Deprivation: an empirical link, \u003cem\u003eSocial Indicators Research, \u003c/em\u003evol. \u003cem\u003e81\u003c/em\u003e(3), pp. 497\u0026ndash;519. (2007).\u003c/li\u003e\n\u003cli\u003eFerri, B. Novel Shift-Share Instruments and Their Applications. Boston College. (2022).\u003c/li\u003e\n\u003cli\u003eEvans-Pritchard, E.E., 1937. Magic among the Azande. \u003cem\u003eLondon: Oxford\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eFerrer-i-Carbonell, A., 2005. Income and well-being: an empirical analysis of the comparison income effect. \u003cem\u003eJournal of public economics\u003c/em\u003e, \u003cem\u003e89\u003c/em\u003e(5-6), pp.997-1019.\u003c/li\u003e\n\u003cli\u003eJolley, D., Paterson, J.L., 2020. Pylons ablaze: examining the role of 5G COVID-19 conspiracy beliefs and support for violence. Br. J. Soc. Psychol. 59, 628\u0026ndash;640. \u003c/li\u003e\n\u003cli\u003eMari, S., Gil de Zuniga, H., Suerdem, A., Hanke, K., Brown, G., Vilar, R., Boer, D. and Bilewicz, M., 2022. Conspiracy theories and institutional trust: examining the role of uncertainty avoidance and active social media use. \u003cem\u003ePolitical Psychology\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(2), pp.277-296.\u003c/li\u003e\n\u003cli\u003eMarone, F., 2021. Hate in the time of coronavirus: exploring the impact of the COVID-19 pandemic on violent extremism and terrorism in the West. Secur. J. 35, 205\u0026ndash;225. \u003c/li\u003e\n\u003cli\u003eRomer, D., Jamieson, K.H., 2020. Conspiracy theories as barriers to controlling the spread of COVID-19 in the US. Soc. Sci. Med. 263, 113356. \u003c/li\u003e\n\u003cli\u003eSallam, M., 2021. COVID-19 vaccine hesitancy worldwide: a concise systematic review of vaccine acceptance rates. Vaccines 9, 160.\u003c/li\u003e\n\u003cli\u003eSrol, J., ˇCavojov\u0026acute;a, V., Ballov\u0026acute;a Mikuˇskov\u0026acute;a, E., 2022. Finding someone to blame: the link between COVID-19 conspiracy beliefs, prejudice, support for violence, and other negative social outcomes. Front. Psychol. 12, 726076.\u003c/li\u003e\n\u003cli\u003evan Mulukom, V., Pummerer, L.J., Alper, S., Bai, H., Čavojov\u0026aacute;, V., Farias, J., Kay, C.S., Lazarevic, L.B., Lobato, E.J., Marinthe, G. and Banai, I.P., 2022. Antecedents and consequences of COVID-19 conspiracy beliefs: A systematic review. \u003cem\u003eSocial Science \u0026amp; Medicine\u003c/em\u003e, \u003cem\u003e301\u003c/em\u003e, p.114912.\u003cstrong\u003e\u003cbr\u003e \u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"income inequality, conspiracy, COVID-19.","lastPublishedDoi":"10.21203/rs.3.rs-3823608/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3823608/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAround one third of Europeans are conspiracy believers. Using European Social Survey data, we find that income inequality is an important driver of political, scientific and COVID-19 conspiracy beliefs, with regional inequality being positively and significantly correlated with conspiracy beliefs at individual level. Believers argue significantly more that the local government should address income inequality problems, while it is not doing enough for them. Furthermore, average sample moods about government commitment on inequality at regional level are significantly and positively correlated with conspiracy beliefs, even after controlling for individual opinions. Instrumental variable approaches suggest that the observed correlation hides a causality link. Our findings identify a novel underinvestigated effect of income inequality and suggest another positive effect of policies aimed at reducing it.\u003c/p\u003e\n\u003cp\u003eJEL numbers: A13, A14.\u003c/p\u003e","manuscriptTitle":"Inequality and conspiracy beliefs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-03 18:52:23","doi":"10.21203/rs.3.rs-3823608/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d144253-0031-4d6b-b77e-29a8092fcd7b","owner":[],"postedDate":"January 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-29T21:07:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-03 18:52:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3823608","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3823608","identity":"rs-3823608","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}