Replication Data for: The Politics of (De)Liberalization: Studying Partisan Effects Using Mixed-Effects Models

Liberalization is a perennial topic in politics and political science. We first review a broad scholarly debate, showing that the mainstream theories make rival and contradictory claims regarding the role of political parties in (de)liberalization reforms. We then develop a framework of conditional partisan influence, arguing that and under what conditions par-ties matter. We test our (and rival) propositions with a new dataset on (de)liberalization reforms in 23 democracies since 1973 covering several policy areas. Methodologically, we argue that existing quantitative studies are problematic: They rely on time-series cross-section models using country-year observations; but governments do not change annually, so that the number of observations is artificially inflated, resulting in incorrect estimates. We propose mixed-effects models instead, with country-year observations nested in cabi-nets, which are nested in countries and years. The results show under what conditions par-ties matter for (de)liberalization. More generally, the paper argues that mixed-effects mod-els should become the new standard for studying partisan influences.

自由化是政治学与政治科学领域经久不衰的研究议题。本文首先梳理学界广泛的学术论战，结果表明主流理论就政党在（去）自由化（(de)liberalization）改革中所扮演的角色，提出了相互对立且自相矛盾的主张。随后本文构建了有条件的政党影响力分析框架，论证了政党发挥影响的时机与具体条件。我们依托1973年以来覆盖23个民主国家的全新数据集，对本文及对立理论的核心命题进行检验，该数据集涵盖多个政策领域的（去）自由化改革相关信息。从方法论视角来看，本文指出现有定量研究存在明显缺陷：此类研究采用基于国家-年度观测值的时间序列截面模型（time-series cross-section models），但政府并非每年更迭，因此观测值数量被人为虚增，进而导致估计结果出现偏差。为此本文转而提出混合效应模型（mixed-effects models），将国家-年度观测值嵌套于内阁层面，而内阁又嵌套于国家与年度维度之下。研究结果揭示了政党在（去）自由化改革中发挥作用的具体条件。更具普遍意义而言，本文主张混合效应模型应成为研究政党影响力的新标准范式。