Legacies of Stalin or Putin? Public Opinion and Historical Memory in Ukraine

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Abstract

Our research considers the relationship between historical memory and political evaluations of the past and present. We first examine how historical reflection on the Soviet Union under Stalin is influenced by memories of familial suffering during World War II and victimization under the widespread Soviet gulag prison system. Based on a 2019 representative survey of Ukraine, we show that respondents who recall family members being injured or killed fighting during World War II and those who recount families being imprisoned in Soviet gulags have increased positive and negative appraisals of the Soviet Union under Stalin respectively. However, we also find that favorable opinions of Stalin are strongly predicted by approval of Vladimir Putin, who has actively promoted rehabilitation of Stalin’s legacy to bolster personalist rule at home and justify revisionist agendas abroad, including in Ukraine. Our results underscore interactions between the present and past in shaping historical memory such that what appears as enduring legacies of the past could also be a function of present political circumstances.

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A general approach to causal mediation analysis.

Kosuke Imai, Luke Keele, Dustin Tingley (2010)

Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the inability to specify the key identification assumption, and the difficulty of extending the framework to nonlinear models. In this article, we propose an alternative approach that overcomes these limitations. Our approach is general because it offers the definition, identification, estimation, and sensitivity analysis of causal mediation effects without reference to any specific statistical model. Further, our approach explicitly links these 4 elements closely together within a single framework. As a result, the proposed framework can accommodate linear and nonlinear relationships, parametric and nonparametric models, continuous and discrete mediators, and various types of outcome variables. The general definition and identification result also allow us to develop sensitivity analysis in the context of commonly used models, which enables applied researchers to formally assess the robustness of their empirical conclusions to violations of the key assumption. We illustrate our approach by applying it to the Job Search Intervention Study. We also offer easy-to-use software that implements all our proposed methods. PsycINFO Database Record (c) 2010 APA, all rights reserved.