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Article

Стабильность и выживание демократий: от гипотезы Липсета к производительности экономики

Ахременко А. С., Петров А. П., Филиппов И. Б.

This article is focused upon the survival of democratic regimes and successfulness of democratization. The dynamical mathematical model is presented in the paper. The model’s departing point is the hypothesis of S.M. Lipset  and A. Przeworski that the growth of the welfare leads to a mitigation of the interest groups` conflict over the redistribution of resources. This mitigation is met as a result of interrelated processes of broadening of a «compromise space», which is a range of mutually accepted policies, and of a convergence of different groups` preferences over the redistribution in an area of moderate policies.

The presented model illustrates how social capital (more precisely – its component responsible for trust between strangers) and institutional quality favor the stabilization of democratic regimes through the increase of economic productivity and welfare. According to the predictions of the model, total factor productivity (TFP) – understood as the opportunity of individuals and/or firms to cooperate efficiently – increases the overall wealth of the society  given the same stock of human capital. It fosters the consolidation of democracy due to reduction of social tensions and improvement of functioning of democratic mechanism of economic policy elaboration. Following these results the hypothesis of positive impact of TFP on the survival of democracy is formulated.

The hypothesis was tested by means of survival analysis on the quantitative database on episodes of democratization («Regimes in the World»). The survival analysis showed that TFP is a significant and important predictor that lowers the risk of unsuccessful ending of the democratization episode (of the return back to more autocratic regime). The increase in TFP on 10 percentage points is ceteris paribus associated to the decrease of risk of leaving the track of democratization in 1.2-1.4 times. The obtained results are robust to the changes in the model specification or in a list of control variables.