In a critique of Hegel, Marx wrote the following paragraph, whose third sentence has become a classic:
Religious distress is at the same time the expression of real distress and the protest against real distress. Religion is the sigh of the oppressed creature, the heart of a heartless world, just as it is the spirit of a spiritless situation. It is the opium of the people. The abolition of religion as the illusory happiness of the people is required for their real happiness. The demand to give up the illusion about its condition is the demand to give up a condition which needs illusions.
The extract “[Religion] is the opium of the people” is often seen as a put-down of the masses who use faith as a drug. But from the context you can see that Marx was not so callous, for he was writing about religion as a sad and inevitable response to the distress of life.
And this is what sociologists are beginning to tell us. I’ve previously written about work by Tomas Rees and Gregory Paul showing that high levels of faith among countries are correlated with high levels of “life uncertainties,” quantified in various ways such as income inequality, absence of national health care, high rates of crime, and so on. Unless religious belief itself leads to socially unhealthy societies rife with uncertainty, the studies suggest that, as Marx noted above, social distress promotes religiosity.
A recent paper in Cross-Cultural Research by Nigel Barber buttresses this suggestion by showing that, among 137 countries, there’s a strong negative correlation between measures of “material security” and religiosity: those countries that are most religious are also those in which individuals are less secure, with security measured in several ways. These correlations were predicted in advance by Barber. (He is a biopsychologist who appears to be an independent scholar: his given address is a private one in Birmingham, Alabama).
Barber’s hypothesis, called the “uncertainty hypothesis,” is that “supernatural belief may be one way of controlling the uncertainty of our lives.” His prediction is “if religion helps people to cope with uncertainty, then more secure modern environments having greater existential security would engender less religious belief.” His more explicit predictions were these:
I predicted that atheism would increase with economic development as people acquired a better capacity to withstand the hostile forces of nature through improved scientific knowledge, technological development, greater affluence, food security, and increased rule of law including the stronger centralized government characteristic of developed countries. Development was assessed in terms of the proportion of the labor force employed in agriculture (a negative index given that developed nations have fewer agricultural workers). It was also predicted that nonbelief would increase with the proportion of the population enrolled in third-level education both because this is an index of economic development and because it is a vector for natural science ideas that may challenge religious claims. As people acquired greater economic security, I predicted that disbelief in God would increase (Norris & Inglehart, 2004). Economic insecurity is exacerbated by unequal distribution of income (Gini coefficient) because more of the resources are concentrated in the hands of an economic elite creating poverty and deprivation at the bottom of the social hierarchy. Conversely, societies having a welfare state aim to help the poor by redistribution of resources. The welfare state requires heavier personal taxation and was measured indirectly in terms of taxation as a proportion of GDP.
Disbelief in God was also predicted to increase as health security rose. Health security was assessed in terms of the load of infectious diseases and pathogens.
Barber collected data on these issues from 137 countries. The index of religiosity was taken from Zuckerman (2007), as “the proportion of people reporting that they did not believe in God.” There is a possibility of error here since the question was not asked in the same way in every country.
The variable tested for their correlation with religiosity were these:
- Whether or not the nation was or is Communist (i.e., whether or not there were official strictures against religion)
- Whether or not the country was Islamic (apostasy and sometimes atheism are criminalized under Sharia law)
- Degree of economic development, quantified as proportion of labor force engaged in agriculture (the lower the proportion, the more developed the society)
- The proportion of young people enrolled in third-level education (i.e., university education)
- Economic security quantified as the Gini coefficient, a measure of income equality that varies between 0 (complete equality) and 1 (maximal inequality)
- Level of personal taxation, which is taken as the degree to which a nation is a “welfare state,” i.e., creates more security for its citizens
- Health security, measured as the prevalence of 22 pathogens
As Barber predicted, each of these variables showed a statistically significant relationship with religiosity in the expected ways: religiosity was higher in Muslim countries and lower in Communist ones, negatively associated with the proportion of agriculturalists, health security, and income inequality, and positively associated with third-level education and taxation. Below is the table of correlations of the varables with religious disbelief. The table also shows the correlations among the various indices of “security” (asterisks show significant correlations). A regression analysis of each variable on disbelief (not shown here, but in the paper) also revealed a significant association in the same direction for every variable.
Barber concludes that his hypothesis was supported: “Taken together, the results show that the incidence of religious disbelief in a country (Zuckerman, 2007) is very strongly predicted by economic development, by favorable health conditions and by a more equal distribution of income as well as a well-developed welfare state (insofar as this is measured by high levels of personal taxation relative to GDP).”
But there is a big problem with these results. As the table shows, the different indices of “security” are also correlated with each other. For example, there’s a strong negative correlation (-0.69) between the degree of agricultural labor and the percentage of people getting third-level educations. Likewise, pathogen load is negatively correlated with level of education and positively correlated with degree of agricultural labor. Income inequality is negatively correlated with taxation (a measure of “welfare stateness”). Pathogen load is negatively correlated with whether a country is/was Communist, but positively correlated with whether a state is Islamic.
These cross-correlations among the different indices of “security” mean that we cannot use each of them as an independent variable affecting religiosity. We don’t know, for example, whether the negative correlation between disbelief in God and income inequality reflects a direct influence of the latter on the former (countries with higher inequality have higher belief in God), or only that income inquality affects religiosity because that inequality is itself a sign of poor health (pathogen load has a 0.5 correlation with the Gini coefficient). What this means is that you cannot say that each of the seven variables is itself significantly associated with religiosity. They are not independent.
The author could have addressed this problem in two ways. First, he could have done what Greg Paul did, and simply combine the variables into a single index of societal well-being. Alternatively, and better, Barber could have used multiple regression, a method that gets rid of the cross-correlation between variables to look at the real effect of each one uncontaminated by its association with the others. Unless I’m missing something in the author’s analysis, he didn’t do either of these. Any decent journal would have mandates some statistical analysis to gauge the effect of each variable, by itself, on religiosity.
So what can we conclude? The results generally support the “uncertainty” hypothesis because each variable is correlated in the expected direction with religiosity. But what we cannot say is that each of the seven variables itself has an independent effect on belief and disbelief. That awaits a multiple-regression analysis—or other statistical tests that get rid of the problem of cross-correlation. The data for this are in fact already available to Barber.
And, of course, we all know that correlation is not causation. Even if each of these variables was independently and significantly associated with religiosity, we still don’t know in which direction (or neither) the causality runs. It’s formally possible, for instance, that more religious societies promote income inequality and poor health, but for most variables that suggestion seems less parsimonious.
Barber, N. A cross-national test of the uncertainty hypothesis of religious belief. Cross-Cultural Research. Published online before print May 11, 2011, doi: 10.1177/1069397111402465
Zuckerman, P. 2007. Atheism: Contemporary numbers and patterns. (Available free on Zuckerman’s website.) pp. 47-68 in The Cambridge Companion to Atheism (M. Martin, ed.), Cambridge University Press, Cambridge, UK.