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Religion, rulers, and conflict

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Abstract

We offer new data and a new analytical approach to examine the roots of today’s civil conflicts that lie deeply in religious and political history. Religion’s effect on today’s conflicts come not from contemporary fractionalization or polarization, but from the deep-rooted effects of historical fragmentation coupled with rulers who could manipulate divisions by favoring co-religionists. To test the resulting hypotheses, we use a new dataset that includes annual information regarding the religious and political histories of today’s societies since the year 1000. We run regression analysis at both country and ethnic group levels. The results show that the likelihood of contemporary new conflicts is higher in societies that historically experienced religious fragmentation with rulers who shared religion with one of the groups and could thus favor coreligionists over others. Economic inequality and political grievances served as channels of transmission.

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Notes

  1. Arbatli et al. (2020: 732–4), Basadeau et al. (2016: 228–9), McBride and Richardson (2012: 118), Montalvo and Reynal-Querol (2019: 257), Svensson (2020).

  2. Our analysis is also related to a body of literature that explains conflict through religion-based hatred and irreconcilable hostility between groups (Huntington, 1996). Rather than consider the hatred and hostility as being a matter of current religious beliefs and preferences, however, we examine their historical roots, and use an empirical strategy to estimate their effect on conflict.

  3. For the association between ethnic polarization and civil conflicts, see Montalvo and Reynal-Querol (2005) and Esteban et al. (2012). See also Arbatli et al. (2020: 732–4), Basadeau et al. (2016: 228–9), Collier and Hoeffler (2004), Fearon and Laitin (2003), Huber and Mayoral (2019), McBride and Richardson (2012: 118), Montalvo and Reynal-Querol (2019: 257), Svensson (2020) for observations regarding the success of standard measures of religious diversity.

  4. See, for example, Cosgel and Miceli (2009) and Cosgel et al. (2012).

  5. The second-order condition for a maximum is satisfied given the concavity of the B functions.

  6. Specifically, (2) implies that ∂θ/∂δ1 < 0, given B” < 0. Thus, if δ1 = δ2, θ/(1 − θ) will be proportional to α/(1 − α), but as δ1 falls, θ will rise, all else equal.

  7. See Cosgel and Miceli (2009) for a formal model of this.

  8. For a detailed description of the construction of this dataset, see Coşgel (2016).

  9. Any categorization of religions is inherently problematic due to the difficulties of comparison and standardization across different traditions. Rather than introduce bias by implementing our own criteria, we simply used the broad categories commonly used in recent quantitative studies.

  10. See Coşgel (2016) for a detailed discussion of the development of “Historical Polities Data” and its limitations and areas of further development.

  11. Although we report here only the correlation with the intermediate level of aggregation of indices introduced by Desmet et al. (2012), the correlations are significant for other levels of aggregation as well.

  12. Version 18. See Gleditsch et al. (2002), Pettersson and Eck (2018).

  13. For the operationalization of the separate elements of this definition of conflict, see http://www.pcr.uu.se/research/ucdp/definitions/.

  14. In general, the literature has focused on three dimensions of civil conflict: onset, duration and incidence. See Sambanis (2004) for a discussion.

  15. The data for ruggedness and percent desert variables are from Nunn and Puga (2012), forest area (% of land area) is from World Bank’s World Development Indicators, and other geographic and climate variables are from Arbatli et al. (2020).

  16. We obtained genetic diversity and historical regional frontiers variables from Arbatli et al. (2020), distance to major trade routes from Bentzen et al. (2017), state antiquity index from Bockstette et al. (2002) and Putterman and Bockstette (2012), Neolithic transition from Putterman (2008), and duration of settlement from Ahlerup and Olsson (2012). We imputed missing values of state antiquity, Neolithic transition and settlement duration with the average value of neighboring countries to get a consistent sample size in regression analysis. We extracted historical population data from HYDE version 3.2 (Klein Goldewijk et al., 2017) by using country shape files from (https://gadm.org/data.html).

  17. The specific centers used for our calculations are Lumbini, Nepal (Buddhism); Wittenberg, Germany (Protestanism); Istanbul, Turkey (Orthodox Christianity); Karbala, Iraq (Shia Islam); Mecca, Saudi Arabia (Sunni Islam); and Vatican City (Roman Catholicism). These are the centers of universal religions or their sub-branches that have historically expanded out from their birthplaces, eventually becoming main religions in other territories. Scholars of religion may disagree with our choices of centers. While we acknowledge controversies regarding centers of religions, we have made informed but pragmatic choices of locations that best serve the purpose of estimation and robust to alternative specifications.

  18. The advantages of our approach in using walking time/distance rather than the aerial distance, which has been typically used in the recent literature (Ashraf & Galor, 2013; Coşgel et al., 2018), is that walking mode of travel incorporates variations in topography and obstacles on the way. Moreover, by using walking time rather than walking distance we are able to incorporate differences in elevation between two points and other factors that depend on the direction of travel.

    In calculating travel time across continents, we require routes to go through the following waypoints: Cairo, Egypt (Africa-Asia), Istanbul, Turkey (Asia-Europe), Phnom Penh, Cambodia (Asia-Oceania), Palos de la Frontera, Spain (Europe-Western Hemisphere), Santa María la Antigua del Darién, Columbia (Europe-South America) and Tenochtitlan, Mexico (Europe-Central and North America). The first three of these waypoints are based on Ramachandran et al. (2005), and the latter three are based on historical information regarding the starting location of European overseas exploration and the first sites of European conquest in the southern and central/northern sections of the Western Hemisphere (i.e., two sites because of the Darién Gap).

    For information regarding the travel time and distance from these centers to each country, we used Python script to retrieve the data from Google server. Since Google currently does not provide data for routes through China, we used Bing to calculate the walking distance from China and in routes from Mongolia, Japan, Taiwan, and South and North Korea going through China. Whenever the route from a country to a religious center inevitably involved travel through a body of water, we used the average walking equivalent (5 km per hour) to incorporate this segment in our calculations. This questionable approximation is roughly consistent with the amount of time (about two months) that Columbus took to cross the Atlantic in his first voyage (about 6500 km).

  19. We follow the usual estimation procedure of adding one to the count before log-transforming to retain observations for countries with no recorded new conflicts. See, for example, Arbatlı et al. (2020).

  20. For examples of similar decomposition analyses in the related literature see Ashraf et al., (2021: Table 22.2) and Henderson et al., (2018: Table II). We used the Stata command called “rego” for the decomposition.

  21. Recent research has shown various forms of endogeneity between nation states and political economy concerns, such as population diversity, trade regimes, political system, civil conflicts, and public good provision. See, for example, Alesina et al., (2000, 2017, 2019), Alesina and Spolaore (2003), Desmet et al. (2011).

  22. See Arbatlı et al., (2020: 729–30) for a discussion of the benefits of dual analysis at the national and ethnic-homeland levels in studying the effect of genetic diversity on conflict. In addition to mitigating potential endogeneity concerns with national borders, this approach makes it possible to explore the effects of genetic diversity at different scales, disentangle the impacts within and across ethnic groups, and reduce potential concerns regarding the relationship between conflicts and population movements.

  23. Using GeoEPR from Wucherpfennig et al. (2011) to identify the homelands of ethnic groups, we extracted terrain ruggedness data from Nunn and Puga (2012), mean and range of elevation data from National Oceanic and Atmospheric Administration (NOAA, 1995) and U.S. National Geophysical Data Center. Terrain Base, diurnal temperature range, volatility of temperature, and volatility of precipitation are from Fick and Hijmans (2017), mean and range of soil suitability data are from Ramankutty et al. (2002), and mines data are from U.S. Geological Survey (2005), Mineral Resources Data System: U.S. Geological Survey, Reston, Virginia.

  24. The “Group grievance indicator” in part of the “Fragile States Index”. For methodology and other details, see fragilestatesindex.org.

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Acknowledgements

We are grateful to Oded Galor, an anonymous Associate Editor, and three anonymous referees for extremely useful comments and suggestions. We have also benefited from discussions with Dan Bogart, John Brown, Resul Cesur, David Cuberes, Klaus Desmet, Tim Guinnane, James Fenske, Phil Hoffman, Noel Johnson, Mark Koyama, Laura Mayoral, Michael McBride, Stelios Michalopoulos, Nathan Nunn, Marta Reynal-Querol, Jared Rubin, James Robinson, Stergios Skaperdas, Melanie Xue, and participants at the Association for the Study of Religion, Economics, and Culture annual meeting in Boston (February, 2017); Brown University Growth Lab (November, 2019); Clark University (October, 2019); Conflict and Development Conference at UC Irvine (February 2016); University of Connecticut (October 2016); and World Interdisciplinary Network for Institutional Research Conference in Boston (September, 2016).

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  1. Department of Economics, University of Connecticut, Unit 1063, 365 Fairfield Way, Storrs, CT, 06269, USA

    Metin M. Coşgel & Thomas J. Miceli

  2. Department of Economics, Marmara University, Istanbul, Turkey

    Sadullah Yıldırım

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Coşgel, M.M., Miceli, T.J. & Yıldırım, S. Religion, rulers, and conflict. J Econ Growth 28, 439–480 (2023). https://doi.org/10.1007/s10887-023-09228-6

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