Does government party support decline in a monotonic fashion throughout the legislative cycle or do we observe a u-shaped “electoral cycle effect”? Moving beyond the study of midterm election results, this is the first comparative study to assess the cyclical pulse of government party support in parliamentary democracies based on voting intention polls from 171 cycles in 22 countries. On average, government parties lose support during the first half of the electoral cycle, but at most partially recover from their initial losses. Under single-party government and when prime ministers control cabinet dissolution, support tends to follow the previously assumed u-shaped pattern more strongly. Finally, we find that government parties hardly recover from early losses since the 2000s.

As non-democratic regimes have adapted to the proliferation of social media, they have began actively engaging with Twitter to enhance regime resilience. Using data taken from the Twitter accounts of Venezuelan legislators during the 2014 anti-Maduro protests in Venezuela, we fit a topic model on the text of the tweets and analyze patterns in hashtag use by the two coalitions. We argue that the regime’s best strategy in the face of an existential threat like the narrative developed by La Salida and promoted on Twitter was to advance many competing narratives that addressed issues unrelated to the opposition’s criticism. Our results show that the two coalitions pursued different rhetorical strategies in keeping with our predictions about managing the conflict advanced by the protesters. This article extends the literature on social media use during protests by focusing on active engagement with social media on the part of the regime. This approach corroborates and expands on recent research on inferring regime strategies from propaganda and censorship.

This paper presents a new approach for studying temporal sequences across ordinal variables. It involves three complementary approaches (frequency tables, transitional graphs, and dependency tables), as well as an established adaptation based on Bayesian dynamical systems, inferring a general system of change. The frequency tables count pairs of values in two variables and transitional graphs depict changes, showing which variable tends to attain high values first. The dependency tables investigate which values of one variable are prerequisites for values in another, as a more direct test of causal hypotheses. We illustrate the proposed approaches by analyzing the V-Dem dataset, and show that changes in electoral democracy are preceded by changes in freedom of expression and access to alternative information.

China’s rapid rise on the global economic stage has substantial and unequal employment effects in advanced industrialized democracies given China’s large volume of low-wage labor. Thus far, these effects have not been analyzed in the comparative political economy literature. Building on pooled time-series data, we analyze the effects of Chinese trade competition across 17 sectors in 18 countries. We devote attention to a new channel, increased competition from China in foreign export markets. Our empirical findings reveal overall employment declines in sectors more exposed to Chinese imports. Furthermore, our results suggest that employment effects are not equally shared across skill levels, as the share of hours worked worsens for low-skilled workers.

Political scientists interested in estimating how public opinion varies by constituency have developed several strategies for supplementing limited constituency survey data with additional sources of information. We present two evaluation studies in the previously unexamined context of British constituency-level opinion: an external validation study of party vote share in the 2010 general election and a cross-validation of opinion toward the European Union. We find that most of the gains over direct estimation come from the inclusion of constituency-level predictors, which are also the easiest source of additional information to incorporate. Individual-level predictors combined with post-stratification particularly improve estimates from unrepresentative samples, and geographic local smoothing can compensate for weak constituency-level predictors. We argue that these findings are likely to be representative of applications of these methods where the number of constituencies is large.

This article challenges Fixed Effects (FE) modeling as the ‘default’ for time-series-cross-sectional and panel data. Understanding different within and between effects is crucial when choosing modeling strategies. The downside of Random Effects (RE) modeling—correlated lower-level covariates and higher-level residuals—is omitted-variable bias, solvable with Mundlak's (1978a) formulation. Consequently, RE can provide everything that FE promises and more, as confirmed by Monte-Carlo simulations, which additionally show problems with Plümper and Troeger's FE Vector Decomposition method when data are unbalanced. As well as incorporating time-invariant variables, RE models are readily extendable, with random coefficients, cross-level interactions and complex variance functions. We argue not simply for technical solutions to endogeneity, but for the substantive importance of context/heterogeneity, modeled using RE. The implications extend beyond political science to all multilevel datasets. However, omitted variables could still bias estimated higher-level variable effects; as with any model, care is required in interpretation.