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Electoral Integrity and the Wealth of Nations

Authors: Jong-Eun Lee;

Electoral Integrity and the Wealth of Nations

Abstract

Why do some societies, from authoritarian rule to liberal democracy, generate prosperity while others stagnate? This paper identifies electoral integrity as the binding institutional constraint: the threshold condition determining whether political and civil freedoms translate into economic productivity. Free regimes already exhibit four times the GDP per capita of Not-Free counterparts ($23,579 vs. $6,221), with lower inequality (Gini 35.2 vs. 37.9)-directly challenging the conventional trade-off between efficiency and equity. Yet this gap is driven by a precise institutional mechanism. Using a global panel of 85 countries (2006-2019), we employ a three-stage empirical strategy-panel fixed-effects analysis, Explainable AI, and a heterogeneous-agent structural model-to identify a critical non-linear threshold: below an Electoral Integrity score of 0.6-0.7, the economic returns to freedom effectively vanish. The erosion of electoral integrity is not merely a political failure but a quantifiable economic catastrophe.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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