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The power of religion

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Abstract

This paper studies to what extent religion has been used to legitimize political power throughout the world and how this matters for current institutions. Historically, some rulers have used religion to legitimize their power, while others relied on more democratic means. This tendency, termed divine legitimization, incentivized rulers to embed religion into institutions. We illustrate within a simple framework that the use of religion to legitimize power and the consequent institutionalization of religion may help explain why religion and religious institutions have persisted despite modernization. To test empirically, we combine data on pre-modern religious beliefs across 1265 ethnographic societies, various geographic data, and current data on the prevalence of religious laws in 176 countries. We provide evidence in support of divine legitimization and the resulting institutionalization of religion. For identification, we exploit exogenous variation in the incentives to employ religion for power purposes. We further document that countries that relied on divine legitimization are more autocratic today and their populace more religious. These results contribute to our understanding of the persistence of religious as well as autocratic institutions.

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Notes

  1. Morris et al. (2015), Platteau (2008), Harari (2014), Cronk (1994), Irons (2001).

  2. Rubin et al. (2017), Kuran (2012), Platteau (2017), Iyer (2016), Becker et al. (2016), Kuran (2018), Putnam and Campbell (2012), Finke and Rodney (2005), Djupe and Calfano (2013), Hertzke et al. (2018), Jelen (2006), Bénabou et al. (2020).

  3. Marx (1844), Weber (1905); Durkheim (1912), Freud (1927). We are not the first to show results contradicting the secularization hypothesis. See Stark and Finke (2000), Glaeser and Sacerdote (2008), and Iannaccone (1998) for discussions.

  4. Morris et al. (2015), Cronk (1994), Rubin et al. (2017), Aldashev and Platteau (2014), Belloc et al. (2016), Chaney (2013), Bénabou et al. (2020).

  5. Stratification can be any type of hierarchy between people based on their wealth, social class, hereditary aristocracy, or resources.

  6. Others have pointed out the link between high gods and the historical trajectories and complexity of societies (Norenzayan, 2013; Beheim et al., 2019; Johnson, 2016. While Norenzayan (2013) argues that supernatural punishment may be the result of cultural group evolution, Johnson (2016) argues that it is the result of individual-level genetic selection pressures.

  7. A range of scholars have argued and provided evidence for the hypothesis that high gods solve free-rider problems in early societies before the invention of institutions (e.g. Purzycki et al. 2016). The literature is summarized by Norenzayan et al. (2016) who additionally argue that causality likely runs from the evolution of Big Gods to the emergence of large-scale societies. Other evidence suggested that complex societies might have preceded moralizing gods Whitehouse et al. (2019). However, Beheim et al. (2019) documented that this alternative direction of causality vanishes if one treats missing values correctly. The article by Whitehouse et al. (2019) has been retracted by Nature as a result. However, Whitehouse et al. (2021) respond with a corrected analysis arguing that beliefs in moralizing supernatural punishment only appear after the largest increases in social complexity.

  8. The relationship is stronger among Christian and Muslim societies, and to a lesser extent Buddhist societies, and this tendency is absent among the few Hindu countries.

  9. Others have argued for the use of religion to legitimize power (Kuran, 2012; Rubin et al., 2017; Platteau, 2017). Our results attest that this tendency generalizes across the major religions.

  10. See e.g. Stark and Finke (2000), Glaeser and Sacerdote (2008), and Iannaccone (1998) for discussions.

  11. See also Purzycki et al. (2016) and Enke (2019) for a discussion of the relationship between religion, kinship structure and prosociality.

  12. See also Becker et al. (2021); Bentzen (2021); Iannaccone (1998) and Iyer (2016) for reviews of the literature.

  13. Other scholars argued for such a relationship Rubin et al. (2017); Kuran (2012); Bénabou et al. (2020).

  14. Foster (2002), Kirch (1989), Trigger (1993).

  15. Shermer and McFarland (2004), Wright (2010).

  16. For instance, it is worth to note that the church had blessed the arrangement between the elite and the laymen as ordained by god in many parts of Medieval Europe, as well as other parts of the world such as India with the Caste system. Serfs and agricultural labourers worked for the nobles with no rights and no way of ever changing their lives, as God made them high or lowly and ordered their estate Holloway (2016).

  17. One could also argue that divine legitimacy is central in traditional authority, where tradition is what drives legitimacy. This tradition could be rooted in religion, which would thus indirectly explain legitimacy.

  18. Morris et al. (2015); Cronk (1994); Rubin et al. (2017).

  19. Aldashev and Platteau (2014), Rubin et al. (2017), Belloc et al. (2016), Chaney (2013).

  20. Literature emphasizes institutions in general as a rather persistent component of societies Acemoglu et al. (2001); Rubin (2011).

  21. Marlowe and Hadza (2010), Marshall (1962), Norenzayan (2013), Watts et al. (1804), Swanson (1960).

  22. Animism is the oldest known belief system adhered by hunter-gatherer societies Peoples et al. (2016), suggesting that objects, places, and creatures possess a distinct spiritual essence Stringer (1999).

  23. Morris et al. (2015); Platteau (November 2008); Harari (2014); Cronk (1994); Irons (2001).

  24. See Sect. 2 for examples of how a ruler can implement divine legitimization –e.g., via co-opting the religious clergy. See also Bisin et al. (2019).

  25. We confirm empirically that punishing and intervening gods are necessary if the ruler wishes to exploit them to legitimize their power, while indifferent gods are just as useless for power purposes as having no high gods at all, see columns 7 and 8 of Table 3.

  26. Variable v34 in the Atlas.

  27. Michalopoulos and Xue (2021) exploit information from folklores to consistency check several measures from the Ethnographic Atlas, including the measure of high gods. They document that ethnographic societies with a tradition of high gods are significantly more likely to display punishment in their oral traditions, and episodes featuring rewards and supernatural entities are also more common. This increases our confidence in the high gods measure.

  28. Variable v66 in the Atlas.

  29. The coding of this variable was done by Fox (2011).

  30. Different societies were measured at different points in time. The time fixed effects correspond mostly to decades, and are meant to account for variation due to the differential timing. To construct the time fixed effects, we form time-intervals of at least 30 societies. When less than 30 societies are measured in a particular decade, we increase the time-period until 30 societies or more fall within the time-frame. There are 67 language groups in the main sample of column (3) of Table 1.

  31. We aggregate to the country level by averaging over the \(High \ Gods\) variable across ethnographic societies within country c: \(High \ Gods_{c}\) = \(\frac{1}{N}\sum _{s=1}^{N}High \ Gods_{sc}\). Results are robust to other aggregation techniques (Table A.6).

  32. Settlement complexity is measured using the variable from the Ethnographic Atlas quantifying settlement patterns (v30). The variable has nine categories running from the lowest degree of complexity being nomadic or fully migratory to the highest degree being complex settlements. We define agricultural societies based on variable v28, which measures the intensity of agriculture. We define a society as agricultural if variable v28 is non-missing and includes anything but “no agriculture”. What we define as agricultural societies thus includes societies based on casual agriculture, extensive or shifting agriculture, horticulture, intensive agriculture, and intensive irrigated agriculture. The rest of the geographic confounders are calculated using ArcGIS technology on agriculture suitability (Ramankutty et al., 2002), temperature, and soil constraints (Bentzen et al., 2017). For more details about the variables, see the Data Appendix.

  33. It is important to note that our results are not an artefact of spatial correlation. We perform two exercises to address this. First, for our baseline specifications, Panel A of Table A.1 presents spatial correlation-adjusted Conley standard errors at various correlation ranges, assuming a linearly declining spatial weighting kernel Conley (1999). Standard errors are allowed to be correlated within the neighborhoods of 300, 500, 1000, or 2000 km. In comparison, for example, Nunn and Wantchekon (2011) set a cutoff of about 300 km in Africa, while Ashraf and Galor (2013) set a threshold of 500 km in their global analysis. We see in Panel A of Table A.1 that our results are robust to spatial correlation-adjusted Conley standard errors. If anything, our baseline language group-clustered standard errors are more conservative than Conley standard errors. Second, we generate spatially correlated noise at various correlation ranges to evaluate how well spatial noise can explain our dependent variable, High Gods, compared to our variable of interest, Stratified society. In our simulations, spatial noise of a given society i is correlated with the noise of all other neighboring societies within a given correlation range, where the weights of societies are inversely related to their distance from society i. Spatial correlation ranges are 300 km, 500 km, 1000 km, and 2000 km. Figure A.2 presents the distribution of the standardized effect of spatially correlated noise on High Gods from 1000 simulations in a specification akin to our baseline specification of column 3 of Table 1 (a regression of High Gods on spatially correlated noise, and language and decade fixed effects). The distribution of the spatial noise effect is centered around zero. Note also that, in the baseline specification, standardized coefficient on Stratified Society corresponds to 0.14 (with 0.11 unstandardized coefficient), whereas spatial noise has a maximum effect of about 0.03. In sum, there is no indication that our results suffer from spatial correlation.

  34. They are also not driven by specific religious denominations: class stratification raises the likelihood of beliefs in high gods among societies located in countries that are currently dominated by Christian, Muslim, or Buddhist majorities (positive composite effects in Table A.3). There is no impact in societies located in current Hindu majority countries (zero composite effect), which is likely due to their low numbers; 27 societies (4% of the sample) are located in countries that are currently dominated by Hindus (India and Nepal).

  35. Variables v880, v881, v882, v883, and v884 in the SCCS.

  36. Mediums are people that can communicate with spirits. Shamans have a connection to the otherworld, have the power to heal the sick, communicate with spirits, and escort souls of the dead to the afterlife. Healing is the practice of prayer and gestures that are believed to elicit divine intervention in spiritual and physical healing.

  37. This finding is not driven by spatial correlation (see Panel B of Table A.1).

  38. Slavery is defined based on variable v70 in the Ethnographic Atlas, measuring the type of slavery. We define a dummy equal to zero if slavery is indicated as being “absent or nearly absent”, and one if any of the other categories were reported. Thus, slavery is defined as being present if one of the following types of slavery existed: incipient or nonhereditary, slavery was reported but type not identified, hereditary and socially significant.

  39. For instance, the Ethnographic Atlas holds information on high gods for two societies in Thailand, Lawa and Siamese. The high god measure for Lawa is zero, meaning that high gods were absent and that for Siamese is 0.33 meaning that a high god was present but not concerned with human affairs. The country-level measure of high gods for Thailand equals 0.167 accordingly. Results are robust to various aggregation methods from the ethnographic societies to countries (Table A.6).

  40. These results are not driven by spatial correlation (see Table A.7 and Figure A.3).

  41. Results are robust to controlling for other denominations and various geographic measures (Table A.8). The result holds for Christian, Muslim, and, to a lesser extent, for Buddhist majority countries (Table A.9). Again, the impact is absent in Hindu-majority countries, which may be due to there being only 2 countries with a Hindu majority (India and Nepal).

  42. These results are robust to weighting by population size. For example, the baseline coefficient on High Gods, in column 2 of Table 6, increases to 0.68 (s.e.: 0.11).

  43. Bentzen et al. (2019) also show that there is nothing particularly ‘European’ about the historical link from early sub-national institutions to modern national institutions.

  44. Irrigation potential is marginally good for democracy in 10 countries without a history of high gods (China, Fiji, South Korea, Laso, Lesotho, Nepal, New Zealand, the Solomon Islands, Sri Lanka, and Vietnam). This positive effect is mainly due to increased prosperity in irrigation societies: the estimate on irrigation potential turns insignificant when accounting for the complexity of the historic societies or current GDP per capita.

  45. One concern is that high gods is a function of irrigation potential, and thus, the interaction simply signals some non-linear effect of irrigation potential. This does not seem to be driving the results. Adding a squared term or the logarithm of irrigation potential does not alter the results.

  46. There are various questions on religiosity, but Inglehart et al. (2003) single out six questions that capture the global variation in religiosity. All six are shown in Table A.12, which documents that the conclusions hold for most measures.

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Authors and Affiliations

  1. Department of Economics, University of Copenhagen, Copenhagen, Denmark

    Jeanet Sinding Bentzen

  2. Department of Economics, Lund University, Lund, Sweden

    Gunes Gokmen

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  1. Jeanet Sinding Bentzen
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  2. Gunes Gokmen
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Correspondence to Jeanet Sinding Bentzen.

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We thank the Editor, Oded Galor, and three anonymous referees for useful comments and suggestions. Furthermore, we thank Sascha Becker, David de la Croix, Carl-Johan Dalgaard, Steven Durlauf, Ruben Enikolopov, Boris Gershman, Timur Kuran, Nathan Nunn, Jean-Philippe Platteau, Jared Rubin, Mara Squicciarini, Asger Mose Wingender, and seminar participants at the University of Warwick, University of Munich, University of Copenhagen, King’s College London, New Economic School, Higher School of Economics, the AALIMS Princeton Conference on the Political Economy of the Muslim World, the Association of Religion, Economics, and Culture (ASREC) meetings, the World Economic History Congress, and the Econometric Society World Congress for valuable comments.

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Bentzen, J.S., Gokmen, G. The power of religion. J Econ Growth 28, 45–78 (2023). https://doi.org/10.1007/s10887-022-09214-4

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