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How Much Should We Trust the Dictator’s GDP Growth Estimates?

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  • Luis R. Martínez

Abstract

I study the overstatement of economic growth in autocracies by comparing self-reported GDP figures to night-time light recorded by satellites from outer space. I show that the night-time-light elasticity of GDP is larger in authoritarian regimes, even accounting for differences in multiple country characteristics. This autocracy gradient in the elasticity is greater when the incentive to exaggerate economic growth is stronger or when the constraints on exaggeration are weaker. The results suggest that autocracies overstate yearly GDP growth by approximately 35%. Adjusting the data for manipulation leads to a more nuanced view on the recent economic success of autocracies.

Suggested Citation

  • Luis R. Martínez, 2022. "How Much Should We Trust the Dictator’s GDP Growth Estimates?," Journal of Political Economy, University of Chicago Press, vol. 130(10), pages 2731-2769.
    • Handle: RePEc:ucp:jpolec:doi:10.1086/720458
    DOI: 10.1086/720458
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    Cited by:

    1. Cipullo, Davide & Reslow, André, 2022. "Electoral cycles in macroeconomic forecasts," Journal of Economic Behavior & Organization, Elsevier, vol. 202(C), pages 307-340.
    2. Thiemo Fetzer & Ivan Yotzov, 2023. "(How) Do Electoral Surprises Drive Business Cycles? Evidence from a New Dataset," CESifo Working Paper Series 10584, CESifo.
    3. Dang,Hai-Anh H. & Pullinger,John James & Serajuddin,Umar & Stacy,Brian William, 2024. "Reviewing Assessment Tools for Measuring Country Statistical Capacity," Policy Research Working Paper Series 10717, The World Bank.
    4. Shapiro, Daniel & Oh, Chang Hoon & Zhang, Peng, 2023. "Nighttime lights data and their implications for IB research," Journal of International Management, Elsevier, vol. 29(5).
    5. Grossman, Gene M. & Helpman, Elhanan, 2023. "Electoral competition with fake news," European Journal of Political Economy, Elsevier, vol. 77(C).
    6. Liu, Honglin & Liu, Qiao & Liu, Yufei, 2023. "The world price of macro opacity: Through the lens of nighttime satellites," Economics Letters, Elsevier, vol. 228(C).
    7. Diep Hoang Phan, 2023. "Lights and GDP relationship: What does the computer tell us?," Empirical Economics, Springer, vol. 65(3), pages 1215-1252, September.
    8. Tanner Regan & Giorgio Chiovelli & Stelios Michalopoulos & Elias Papaioannou, 2023. "Illuminating Africa?," Working Papers 2023-11, The George Washington University, Institute for International Economic Policy.
    9. Yit Wey Liew & Muhammad Habibur Rahman & Audrey Kim Lan Siah, 2023. "Rail Stations To Development: Evidence From Colonial Malaya," Working Papers 2023_01, Durham University Business School.
    10. Bonggeun Kim & John Gibson & Geua Boe‐Gibson, 2024. "Measurement errors in popular night lights data may bias estimated impacts of economic sanctions: Evidence from closing the Kaesong Industrial Zone," Economic Inquiry, Western Economic Association International, vol. 62(1), pages 375-389, January.
    11. Daniel Freund & Samuel B. Hopkins, 2023. "Towards Practical Robustness Auditing for Linear Regression," Papers 2307.16315, arXiv.org.
    12. Sundar Ponnusamy & Mohammad Abbas Hakeem, 2024. "Ethnic inequality and public health," Health Economics, John Wiley & Sons, Ltd., vol. 33(1), pages 41-58, January.
    13. Mengjiao Wang & Xiaofang Xu & Liyuan Zheng & Xiaolu Xu & Yukuo Zhang, 2023. "Analysis of the Relationship between Economic Development and Water Resources–Ecological Management Capacity in China Based on Nighttime Lighting Data," IJERPH, MDPI, vol. 20(3), pages 1-19, January.

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