The question of whether and how federalism influences a country's welfare state has been a longstanding concern of political scientists. However, no agreement exists on exactly how, and under what conditions, federal structures impact the welfare state. This article examines this controversy. It concludes theoretically that the specific constellation of federal structures and distribution of powers need to be considered when theorising the effects of federalism on the welfare state. Using the case of Belgium and applying the synthetic control method, it is shown in the article that without the federalism reform of 1993, the country would have had further decreases in social spending rather than a consolidation of this spending in the years after 1993. In the case of Belgium, the combination of increased subnational spending autonomy in a still national financing system provided ideal conditions for a positive federalism effect on social spending to occur.

Although having been practised in the Social Sciences for decades, it was only in recent years that process tracing has gained prominence in methodological debates in political science. In spite of its popularity, however, there has been little success in formalising its methodology, defining its standards, and identifying its range of applicability. This symposium aims at furthering our understanding of the methodology by discussing four essential aspects: the underlying notion of causality, the role of theory, the problem of measurement in qualitative research, and the methodology's relationship with other forms of qualitative inquiry. It brings together methodological and substantive articles by young European scholars and summarises a round-table discussion with Peter A. Hall held at a workshop at the University of Oldenburg, Germany, in November 2010.

Age is often found to be associated with a plenitude of socioeconomic, politico-administrative, biological and thanatological variables. Much less attention has been paid by scholars, however, to explaining ‘age’. In this paper we address this unfortunate scientific lacuna by developing a model of ‘age’ as a function of several factors suggested by (post)rational choice and social constructionist theories. Using state-of-the-art multilevel statistical techniques, our analysis allows the determinants of age to vary with the institutional characteristics of European countries. Our findings convincingly show that generalized trust in strangers, support for incumbent extremist political parties in provincial elections held in the month of January, and the percentage of overqualified women in the cafeterias of national parliaments are all statistically significant explanations of ‘age’. Our findings have obvious implications for conspiracy theorists, organizational advisors, spin doctors and ordinary charlatans.

Building on a Lakatosian approach that sees Social Science as an endeavour that confronts rival theories with systematic empirical observations, this article responds to probing questions that have been raised about the appropriate ways in which to conduct systematic process analysis and comparative enquiry. It explores varieties of process tracing, the role of interpretation in case studies, and the relationship between process tracing and comparative historical analysis.

A fit between theory and method is essential in theory – guided empirical research. Achieving such a fit in process tracing is less straightforward than it may seem at first glance. There are two different types of processes that one can theorise and, consequently, two varieties of process tracing. The two varieties are introduced by empirical examples and distinguished with respect to four characteristics. Failure to determine the form of process tracing at hand may lead to invalid causal inferences.

Despite the recent methodological advancements in causal panel data analysis, concerns remain about unobserved unit-specific time-varying confounders that cannot be addressed by unit or time fixed effects or their interactions. We develop a Bayesian sensitivity analysis (BSA) method to address the concern. Our proposed method is built upon a general framework combining Rubin’s Bayesian framework for model-based causal inference (Rubin [1978], The Annals of Statistics 6(1), 34–58) with parametric BSA (McCandless, Gustafson, and Levy [2007], Statistics in Medicine 26(11), 2331–2347). We assess the sensitivity of the causal effect estimate from a linear factor model to the possible existence of unobserved unit-specific time-varying confounding, using the coefficients of the treatment variable and observed confounders in the model for the unobserved confounding as sensitivity parameters. We utilize priors on these coefficients to constrain the hypothetical severity of unobserved confounding. Our proposed approach allows researchers to benchmark the assumed strength of confounding on observed confounders more systematically than conventional frequentist sensitivity analysis techniques. Moreover, to cope with convergence issues typically encountered in nonidentified Bayesian models, we develop an efficient Markov chain Monte Carlo algorithm exploiting transparent parameterization (Gustafson [2005], Statistical Science 20(2), 111–140). We illustrate our proposed method in a Monte Carlo simulation study as well as an empirical example on the effect of war on inheritance tax rates.

Bessel van der Kolk’s book The Body Keeps the Score has maintained exceptional cultural and clinical influence since its publication in 2014, remaining a best-seller and shaping public discourse on trauma. Its central claims – that trauma causes lasting neurobiological damage and that body-based treatments are uniquely effective – have been widely embraced but seldom subjected to systematic critical evaluation in peer-reviewed literature. This commentary synthesises the evidentiary basis for these claims as a counterweight to an influential narrative. It situates these findings within broader discussions of neuroscience framing, cultural appeal and evidence-based communication, underscoring the need for rigorous, balanced engagement with widely disseminated mental health narratives.

In recent years, many efforts have been made to bring quantitative and qualitative methods into dialogue. This article also moves in that direction. However, in contrast to most works, the present attempt does not concern the large-N/small-N issue but focuses instead on the sole single case study framework. Within this framework, two counterfactual methods, the historical counterfactual method, the qualitative one, and the synthetic control method, the quantitative one, have gained great importance without however meeting. This paper aims to advance mixed-methods research by bridging the gap between these two approaches. More precisely, it has assessed whether these two methods can be used together to understand what would have happened in a single case Z in the absence of an event X. The case study of the impact of Thatcher’s election on the UK pension system is then presented as an example of the joint use of the two methods.

A large empirical literature examines how judges’ traits affect how cases get resolved. This literature has led many to conclude that judges matter for case outcomes. But how much do they matter? Existing empirical findings understate the true extent of judicial influence over case outcomes since standard estimation techniques hide some disagreement among judges. We devise a machine learning method to reveal additional sources of disagreement. Applying this method to the Ninth Circuit, we estimate that at least 38% of cases could be decided differently based solely on the panel they were assigned to.

Existing approaches to conducting inference about the Local Average Treatment Effect or LATE require assumptions that are considered tenuous in many applied settings. In particular, Instrumental Variable techniques require monotonicity and the exclusion restriction while principal score methods rest on some form of the principal ignorability assumption. This paper provides new results showing that an estimator within the class of principal score methods allows conservative inference about the LATE without invoking such assumptions. I term this estimator the Compliance Probability Weighting estimator and show that, under very mild assumptions, it provides an asymptotically conservative estimator for the LATE. I apply this estimator to a recent survey experiment and provide evidence of a stronger effect for the subset of compliers than the original authors had uncovered.

Many philosophers think that doing philosophy cultivates valuable intellectual abilities and dispositions. Indeed this is a premise in a venerable argument for philosophy’s value. Unfortunately, empirical support for this premise has heretofore been lacking. We provide evidence that philosophical study has such effects. Using a large dataset (including records from over half a million undergraduates at hundreds of institutions across the United States), we investigate philosophy students’ performance on verbal and logical reasoning tests, as well as measures of valuable intellectual dispositions. Results indicate that students with stronger verbal abilities, and who are more curious, open-minded, and intellectually rigorous, are more likely to study philosophy. Nonetheless, after accounting for such baseline differences, philosophy majors outperform all other majors on tests of verbal and logical reasoning and on a measure of valuable habits of mind. This offers the strongest evidence to date that studying philosophy does indeed make people better thinkers.

Political scientists regularly rely on a selection-on-observables assumption to identify causal effects of interest. Once a causal effect has been identified in this way, a wide variety of estimators can, in principle, be used to consistently estimate the effect of interest. While these estimators are all justified by appeals to the same causal identification assumptions, they often differ greatly in how they make use of the data at hand. For instance, methods based on regression rely on an explicit model of the outcome variable but do not explicitly model the treatment assignment process, whereas methods based on propensity scores explicitly model the treatment assignment process but do not explicitly model the outcome variable. Understanding the tradeoffs between estimation methods is complicated by these seemingly fundamental differences. In this paper we seek to rectify this problem. We do so by clarifying how most estimators of causal effects that are justified by an appeal to a selection-on-observables assumption are all special cases of a general weighting estimator. We then explain how this commonality provides for diagnostics that allow for meaningful comparisons across estimation methods—even when the methods are seemingly very different. We illustrate these ideas with two applied examples.

We present a method for narrowing nonparametric bounds on treatment effects by adjusting for potentially large numbers of covariates, using generalized random forests. In many experimental or quasi-experimental studies, outcomes of interest are only observed for subjects who select (or are selected) to engage in the activity generating the outcome. Outcome data are thus endogenously missing for units who do not engage, and random or conditionally random treatment assignment before such choices is insufficient to identify treatment effects. Nonparametric partial identification bounds address endogenous missingness without having to make disputable parametric assumptions. Basic bounding approaches often yield bounds that are wide and minimally informative. Our approach can tighten such bounds while permitting agnosticism about the data-generating process and honest inference. A simulation study and replication exercise demonstrate the benefits.