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Democracy influences climate change concern

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  • Levi, Sebastian
  • Goldberg, Matthew H.

Abstract

Climate change concern varies widely across countries. In 2019, 80% of Greeks were at least somewhat worried about climate change, compared to 20% of Egyptians. We argue that variation in climate change concern is partially caused by differences in democracy. Civil liberties protect climate communicators from state repression, censorship, and violence. We offer empirical evidence for the causal effect of democracy on climate change concern using data from 611,909 individuals from 118 countries collected between 2007 and 2019. Exploiting variation in civil liberties across countries and time, we find one unit change in the 7-point civil liberty index to influence climate change concern by 2.3 [95% CI: ±1] percentage points. The effect is much stronger in wealthy countries and less educated cohorts. We also present evidence for our causal pathway using qualitative interviews and by modeling the association between democracy, climate protest, media coverage, and climate concern with simultaneous equations.

Suggested Citation

  • Levi, Sebastian & Goldberg, Matthew H., 2021. "Democracy influences climate change concern," SocArXiv 6vk9d, Center for Open Science.
  • Handle: RePEc:osf:socarx:6vk9d
    DOI: 10.31219/osf.io/6vk9d
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    1. N/A, 2004. "The World Economy," National Institute Economic Review, National Institute of Economic and Social Research, vol. 190(1), pages 8-32, October.
    2. Kaminska Olena & Lynn Peter, 2017. "Survey-Based Cross-Country Comparisons Where Countries Vary in Sample Design: Issues and Solutions," Journal of Official Statistics, Sciendo, vol. 33(1), pages 123-136, March.
    3. Dang,Hai-Anh H. & Lanjouw,Peter F., 2013. "Measuring poverty dynamics with synthetic panels based on cross-sections," Policy Research Working Paper Series 6504, The World Bank.
    4. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    5. Kelejian, Harry H. & Prucha, Ingmar R., 2004. "Estimation of simultaneous systems of spatially interrelated cross sectional equations," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 27-50.
    6. Dupuy, Kendra & Ron, James & Prakash, Aseem, 2016. "Hands Off My Regime! Governments’ Restrictions on Foreign Aid to Non-Governmental Organizations in Poor and Middle-Income Countries," World Development, Elsevier, vol. 84(C), pages 299-311.
    7. Institute for Economics and Peace, 2017. "Global Peace Index 2017," Working Papers id:11991, eSocialSciences.
    8. Sebastian Levi & Christian Flachsland & Michael Jakob, 2020. "Political Economy Determinants of Carbon Pricing," Global Environmental Politics, MIT Press, vol. 20(2), pages 128-156, May.
    9. Carlos Cinelli & Chad Hazlett, 2020. "Making sense of sensitivity: extending omitted variable bias," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(1), pages 39-67, February.
    10. Uri Simonsohn & Joseph P. Simmons & Leif D. Nelson, 2020. "Specification curve analysis," Nature Human Behaviour, Nature, vol. 4(11), pages 1208-1214, November.
    11. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
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