Whether democratic and non-democratic regimes perform differently in social provision policy is an important issue to social scientists and policy makers. As political regimes are rarely changing, their long-term or dynamic effects on the outcome are of concern to researchers when they evaluate how political regimes affect social policy. However, estimating the dynamic effects of rarely changing variables in the analysis of time-series cross-sectional data by conventional estimators may be problematic when the unit effects are included in the model specification. This article proposes a model to account for and estimate the correlation between the unit effects and explanatory variables. Applying the proposed model to 18 Latin American countries, this article finds evidence that democracy has a positive effect on social spending both in the short and long term.

The study of international relations (IR), and political science more broadly, has derived great benefits from the recent growth of conceptualizing and modeling political phenomena within their broader network contexts. More than just a novel approach to evaluating old puzzles, network analysis provides a whole new way of theoretical thinking. Challenging the traditional dyad-driven approach to the study of IR, networks highlight actor interdependence that goes beyond dyads and emphasizes that many traditional IR variables, such as conflict, trade, alliances, or international organization memberships must be treated and studied as networks. Properties of these networks (e.g., polarization, density), and of actor positions within them (e.g., similarity, centrality), will then reveal important insights about international events. Network analysis, however, is not yet fully adapted to account for important methodological issues common to IR research, specifically the issue of endogeneity or possible nonindependence between actors’ position within international networks and the outcomes of interest: for example, alliance network may be nonindependent from the conflict or trade network. We adopt an instrumental variable approach to explore and address the issue of endogeneity in network context. We illustrate the issue and the advantages of our approach with Monte Carlo analysis, as well as with several empirical examples from IR literature.

Previous research in political methodology argues that researchers do not need to include a product term in a logistic regression model to test for interaction if they suspect interaction due to compression alone. I disagree with this claim and offer analytical arguments and simulation evidence that when researchers incorrectly theorize interaction due to compression, models without a product term bias the researcher, sometimes heavily, toward finding interaction. However, simulation studies also show that models with a product term fit a broad range of non-interactive relationships surprisingly well, enabling analysts to remove most of the bias toward finding interaction by simply including a product term.

Literature on the compensation hypothesis overwhelmingly concentrates on either the macro or micro level of the relationship between globalization and welfare spending. This paper explicitly addresses this shortcoming by using individual citizens and country-specific characteristics in a hierarchical model framework. We start by examining individual’s context-conditional reactions to actual economic globalization and welfare generosity; after which, we make the effect of actual economic globalization (welfare generosity) conditional on whether the individual is a globalization winner or loser. In contrast to theoretical expectations, our results indicate that actual economic globalization does not affect people’s perception in the manner expected by the compensation hypothesis. However, individuals display more positive attitudes toward globalization if welfare state generosity is proxied using government spending on active labor market programs.

We present a new method to analyze party manifestos to benefit the placement of political parties per se and to advance the study of elections. Our method improves on existing manual coding approaches by (1) generating semantically complete units based on syntax, (2) standardizing units into a subject–predicate–object structure, and (3) employing a fine-grained and flexible hierarchical coding scheme. We evaluate our approach by comparing estimates for the 2002, 2006, and 2008 Austrian national elections with those yielded by previous studies that employ the entire range of available measurement strategies. We also demonstrate how we link our new manifesto data with other kind of data produced in theAustrian National Election Study, especially mass and elite (party candidate) surveys.

I present a theoretical account of the politics of privatization that predicts left-wing support for the policy is conditional on the proportionality of the electoral system. In contrast to accounts that see privatization as an inherently right-wing policy, I argue that, like trade policy, it has the feature of creating distributed benefits and concentrated costs. Less proportional electoral systems create incentives for the Left to be responsive to those who face the concentrated costs, and thus for them to oppose privatization more strongly. More proportional systems reduce these incentives and increase the extent to which distributed benefits are internalized by elected representatives. Hypotheses are derived from this theory at both the individual and macro-policy level, and then tested separately. Quantitative evidence on public opinion from the 1990s and privatization revenues from Western European countries over the period 1980–2005 supports the argument.

Spatial/spatiotemporal interdependence—that is, that outcomes, actions or choices of some unit-times depend on those of other unit-times—is substantively important and empirically ubiquitous in binary outcomes of interest across the social sciences. Estimating and interpreting binary-outcome models that incorporate such spatial/spatiotemporal dynamics directly is difficult and rarely attempted, however. This article explains the inferential challenges posed by spatiotemporal interdependence in binary-outcome models and recent advances in their estimation. Monte Carlo simulations compare the performance of one of these consistent and asymptotically efficient methods (maximum simulated likelihood, using recursive importance sampling) to estimation strategies naïve about (inter-) dependence. Finally, it shows how to calculate, in terms of probabilities of outcomes, the estimated spatial/spatiotemporal effects of (and response paths to) hypotheticals of substantive interest. It illustrates with an application to civil war in Sub-Saharan Africa.

In spatial econometrics, W refers to the matrix that weights the value of the spatially lagged variable of other units. As unimportant as it may appear, W specifies, or at least ought to specify, why and how other units of analysis affect the unit under observation. This article shows that theory must inform five crucial specification choices taken by researchers. Specifically, the connectivity variable employed in W must capture the causal mechanism of spatial dependence. The specification of W further determines the relative relevance of source units from which spatial dependence emanates, and whether receiving units are assumed to be identically or differentially exposed to spatial stimulus. Multiple dimensions of spatial dependence can be modeled as independent, substitutive or conditional links. Finally, spatial effects need not go exclusively in one direction, but can be bi-directional; recipients can simultaneously experience positive spatial dependence from some sources and negative dependence from others. The importance of W stands in stark contrast to applied researchers’ typical use of crude proxy variables (such as geographical proximity) to measure true connectivity, and the practice of adopting standard modeling conventions rather than substantive theory to specify W. This study demonstrates which assumptions these conventions impose on specification choices, and argues that theories of spatial dependence will often conflict with them.

Evans and Chzhen (2016a) challenge Clarke et al.’s (Clarke et al. 2004; Clarke et al. 2009) “valence” model of voting behavior in British elections. Specifically, they take issue with results based on individual-level analyses relating subjective performance assessments to the (self-reported) vote measured concurrently. The argument is a basic one: there is reason to think that evaluations of performance are caused by—and not causes of—the vote. That this may be true is supported by a large and growing body of research in political science, much of which focuses on economic voting (e.g., Campbell et al. 1960; Kramer 1983; Wlezien et al. 1997; Bartels 2002; Anderson, Mendes and Tverdova 2004; Evans and Andersen 2006; Ladner and Wlezien 2007; Evans and Pickup 2010). Estimates of valence effects in Clarke et al.’s individual-level analyses thus may be correspondingly biased. It is an important issue for the study of valence and also other electoral research relying on perceptual and attitudinal predictors.

The influential valence model of voting developed over the last decade by the British Election Study (BES) team assumes that party and leadership performance evaluations have a causal impact on party choice. An alternative perspective argues that such performance evaluations are instead the consequences of party choice. This article examines the analytical and empirical underpinnings of the BES valence model and compares it to the party-driven approach. To do so, it estimates cross-lagged structural equation models of the association between Labour Party preference and evaluations of the Labour government's performance during the 2005–10 British electoral cycle. It shows that party preference has a stronger effect on performance evaluations than vice versa; performance evaluations have no significant effect on party preference toward the end of the electoral cycle. The study also finds that, contrary to claims made concerning their merits as simplifying heuristics, performance assessments have no impact on short-term movements in party choice for less politically attentive voters. To a substantial degree, evaluations of party performance express—rather than explain—party choice, and would appear to have limited merit as simplifying heuristics.

This paper responds to Evans and Kat’s critique of the valence politics model of electoral choice. Their critique is deficient in several respects. First, the authors do not test the valence politics model, which is motivated by a theory of voting rather than a claim about the relationship between generalized measures of “party preference” and “party performance.” Second, Evans and Kat do not provide theoretical grounding for partisanship, which they claim is strongly exogenous to other variables of interest. Third, there are several specification and testing problems with their structural equation model. We study the properties of the valence model using a vector error correction model of aggregate monthly survey data gathered throughout the New Labour Era. Consistent with theoretical expectations, key valence politics variables constitute a powerful cointegrated system in which the dynamics of partisanship are endogenous to other variables in the system.

Whiteley et al. criticize our re-analysis of the valence model of party choice. In reply, we argue that they are mistaken with regard to their understanding of some of the claims made in our paper and, in one instance, the variables included in the analysis. We also point to flaws in their interpretation of the distinction between weak and contemporaneous endogeneity and the assumptions of their vector error correction model. Their argument privileging aggregate analysis introduces predictable inferential problems and also, it might be suggested, potentially casts doubt on their own research, which uses individual-level analysis to make the case for the valence model. For these reasons, we see no reason to moderate the negative implications of our analysis for the validity of the model.