Explaining the Policy Constraints of Anti-democratic Regimes by Means of Sequential OLS-Regressions

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2019

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The Ohio State University Libraries in partnership with the Institute of Philosophy and Sociology, Polish Academy of Sciences

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Abstract

One of the key problems of many sociological regression models is their modest explanatory power. This has not only to do with the insufficient development of the underlying theories but also with the free will of the concerned social actors, which manifests itself in irrational, spontaneous, and sometimes even arbitrary decisions. The foreign and economic policy of the US government under Donald Trump is an excellent example of this source of indeterminacy. An alternative and more promising approach is an explanation of the constraints of social behaviour by the unequal distribution of power resources and the competing interests of the actors concerned. This approach requires, on the one hand, enough observational data which include cases that reached the analysed constraints. On the other hand, there is a need for statistical procedures which estimate and explain these constraints. Assuming that sufficient amounts of data are available, this paper proposes the use of sequential OLS regressions, which eliminate step by step non-critical observations in order to identify the cases that reached the mentioned constraints. For illustrative purposes, the author analyses the policy space of anti-democratic regimes with regard to their possibilities of curbing democracy. On the basis of the democracy scores of Freedom House, the author explores the governmental constraints set by (i) national civil societies and (ii) international NGOs for the promotion of political/civil rights. The related sequential regressions allow for an assessment of how effective the different constraints are and how far democracy may deteriorate in the worst case under given structural conditions.

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Policy constraints, OLS regression, International comparisons, Democratisation, Anti-democratic regimes

Citation

Ask: Research and Methods. Volume 28, Issue 1 (2019), pp. 43-59