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Religiosity and income: A panel cointegration and causality analysis

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  • Herzer, Dierk
  • Strulik, Holger

Abstract

In this paper we examine the long-run relationship between religiosity and income using retrospective data on church attendance rates for a panel of countries from 1925 to 1990. We employ panel cointegration and causality techniques to control for omitted variable and endogeneity bias and test for the direction of causality. We show that there exists a negative long-run relationship between the level of religiosity, measured by church attendance, and the level of income, measured by the log of GDP per capita. The result is robust to alternative estimation methods, potential outliers, sample selection, different measures of church attendance, and alternative specifications of the income variable. Long-run causality runs in both directions, higher income leads to declining religiosity and declining religiosity leads to higher income.

Suggested Citation

  • Herzer, Dierk & Strulik, Holger, 2013. "Religiosity and income: A panel cointegration and causality analysis," University of Göttingen Working Papers in Economics 168, University of Goettingen, Department of Economics.
  • Handle: RePEc:zbw:cegedp:168
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    3. Krenz, Astrid, 2013. "Political institutions and trade-evidence for the long-run relationship and causality," University of Göttingen Working Papers in Economics 182, University of Goettingen, Department of Economics.
    4. David de la Croix & Fabio Mariani & Marion Mercier, 2023. "Driven By Institutions, Shaped By Culture: Human Capital And The Secularization Of Marriage In Italy," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(4), pages 1777-1818, November.
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    6. Tiago Neves Sequeira & Ricardo Viegas & Alexandra Ferreira-Lopes, 2017. "Income and religion: a heterogeneous panel data analysis," Review of Social Economy, Taylor & Francis Journals, vol. 75(2), pages 139-158, April.
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    9. Ferrara, Andreas & Testa, Patrick A., 2020. "Resource Blessing? Oil, Risk, and Religious Communities as Social Insurance in the U.S. South," CAGE Online Working Paper Series 513, Competitive Advantage in the Global Economy (CAGE).
    10. Salman Syed Ali & Hamid Hasan, 2019. "Does Religiosity Affect Multidimensional Poverty? Evidence from World Values Survey (2010-2014) هل يؤثر التدين على الفقر متعدد الأبعاد؟ أدلة من المسح العالمي للقيم (2014-2010م)," Journal of King Abdulaziz University: Islamic Economics, King Abdulaziz University, Islamic Economics Institute., vol. 32(2), pages 3-24, January.
    11. Klaus Prettner & Holger Strulik, 2017. "It's a Sin—Contraceptive Use, Religious Beliefs, and Long-run Economic Development," Review of Development Economics, Wiley Blackwell, vol. 21(3), pages 543-566, August.
    12. Selamah Abdullah Yusof & Mohammad Arif Budiman & Ruzita Mohammad Amin, 2018. "Relationship between Religiosity and Individual Economic Achievement: Evidence from South Kalimantan, Indonesia العلاقة بين التدين والإنجاز الاقتصادي للأفراد: أدلة من جنوب كاليمانتان، إندونيسيا," Journal of King Abdulaziz University: Islamic Economics, King Abdulaziz University, Islamic Economics Institute., vol. 31(2), pages 3-16, July.
    13. Kurek, Przemysław J. & Fałkowski, Jan, 2022. "Religiosity and political participation - Panel data evidence from post-communist Poland," European Journal of Political Economy, Elsevier, vol. 75(C).
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    More about this item

    Keywords

    religiosity; church attendance; income; panel cointegration; causality;
    All these keywords.

    JEL classification:

    • N30 - Economic History - - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy - - - General, International, or Comparative
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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