Quantile models are widely used across the natural and social sciences to analyze heterogeneous phenomena that conventional mean-based approaches often obscure. Yet, despite their growing importance in many disciplines, their adoption in political science has remained comparatively limited, in part because the field still lacks an accessible introduction tailored to its substantive questions and empirical practices. This Element addresses that gap by showing how quantile models can expand the methodological repertoire of political science and deepen our understanding of political phenomena. Combining methodological innovation with practical guidance, this Element introduces quantile models for both continuous and discrete response variables and illustrates their use with real-world political examples. All empirical applications are accompanied by publicly available data, code, and software, making the Element a useful resource for both teaching and research. This title is also available as Open Access on Cambridge Core.

This article integrates two strands of game-theoretical literature, one on delegated bargaining and the other on compliance, to explain the failure of the two key military dispute-settlement agreements between Russia and Ukraine. While both strands of literature analyze principal–agent problems, they reach contrasting conclusions about the role of transparency and the public’s role in securing agreements and ensuring compliance. We reconcile these perspectives by distinguishing analytically between agreement and compliance efficiency. Although closed-door delegated bargaining can improve the efficiency of reaching an agreement, we demonstrate that the efficiency of compliance hinges crucially on the level of public trust in the delegate. This implies that public mistrust of Ukraine’s delegate in the Minsk negotiations undermined compliance with the two Minsk agreements. Nonetheless, our simulation results also show that effective compliance is still attainable. Even an agreement with only moderate efficiency can be upheld provided that public trust in the delegate is not too low.

Estimates of sensitive questions from list experiments are often much less precise than desired. We address this well-known inefficiency problem by presenting an informative Bayesian approach that combines indirect measures with prior information. Specifying informed priors amounts to a principled combination of information that increases the efficiency of model estimates. This framework generalizes a range of different modeling approaches for list experiments, such as the inclusion of direct items, auxiliary information, the double list experiment, and the combination of list experiments with other indirect questioning techniques. As we demonstrate in real-world examples from political science, the informative Bayesian approach not only improves the utility but also changes the substantive implications drawn from list experiments.

Why do some crisis-response policies trigger more protests than others? What does this reveal about how governments should introduce restrictive–but necessary–measures during times of crisis? This paper revisits prevailing explanations for protests against Covid-19 policies by proposing a conditional grievance theory of protest. We argue that the protest-inducing effect of containment measures depends not only on their restrictiveness, but also on the timing, scale and persistence of accompanying economic support. When economic support is both substantial and synchronised with the imposition of restrictions, material grievances are dampened and mobilisation is less likely. We test this argument using the first full time-series cross-sectional dataset of the Covid-19 pandemic for 22 European countries measured at daily intervals. Our findings show that while more restrictive policies tend to increase protest activity, this effect is significantly mitigated in contexts where economic support is high and well-timed. These results highlight the importance of designing crisis governance strategies that align policy restrictiveness with timely and persistent economic relief.

Leveraging recent advances in natural language processing, this chapter introduces techniques for analyzing legislative texts. It covers regular expressions, dictionary-based methods, supervised and unsupervised text classification methods, part-of-speech (POS) tagging and dependency parsing. To illustrate the usefulness of these techniques, this chapter applies POS tagging and dependency parsing to a corpus of European Union (EU) legislation from the legislative period between 2014 and 2019, which includes 106 directives, 274 regulations and 41 decisions. The chapter analyzes how often EU legislation employs passive voice and negations, which drafting experts and guidelines advise against. Additionally, we explore variations across different types of legislation and procedures and over time. The chapter finds that, on average, 41.0% sentences in a legislative act include passive voice and 9.7% use negations. While both language features do not vary over time, the chapter finds that directives use, on average, more passive voice and negations than regulations and decisions. The analysis also reveals variation across areas. For example, tax-related legislation uses more passive voice than texts on communication networks, whereas legislation on the internal market uses more negations than employment-related legislation.

Learning to Govern Together in Representative Democracy introduces a dynamic theory of coalition governance, focusing on the temporal constraints that coalition parties face when governing together in representative democracies. The book examines how coalition partners, over the course of a legislative term, adapt their strategies for joint policy-making as they encounter varying levels of cooperation and competition in partnership. Two distinct models of learning are developed, namely the portfolio-learning model, where ministers learn through interactions within their portfolios, and the partisan-learning model, which accounts for learning from co-partisan experiences across portfolios. Empirical evidence from 11 parliamentary democracies demonstrates that ministerial office-holders infer the type of partnership from their experienced parliamentary scrutiny, with consequences for their legislative behavior, such as initiating government bill proposals earlier in the term if they perceive a cooperative partnership, or later in the term if they perceive a competitive partnership. The book makes a significant contribution to our understanding of coalition effectiveness, stability, and satisfaction with governance.

Coalition policymaking concerns not only who decides what in which jurisdiction but also when, how speedy and in what rhythm. Due to the limited time budget and shadow of future elections, parties in charge of respective ministerial portfolios have to strategically organize their policy agendas to trade off between policy and electoral incentives in the face of coalition partners who monitor and control ministerial autonomy. However, despite the burgeoning literature on coalition governance, the temporal dimension of ministerial agenda control is less well understood. I advance this research by proposing a model to directly account for the influence of time budgets on timing decisions of ministers in policy initiation. In this model, I distinguish between different timing strategies of policy initiation a ministerial party may possibly adopt and identify in equilibrium a conditional postponing strategy by which ministers facing high scrutiny of coalition partners will postpone bill initiation till the end of the term. The empirical examination lends support to my argument and further demonstrates that the timing strategy of ministers can also be influenced by coalition conflict and policy saliency of bills.

Although democratic governance imposes temporal constraints, the timing of government policymaking activities such as bill initiation is still poorly understood. This holds especially under coalition governments, in which government bills need to find approval by a partner party in parliament. We propose a dynamic temporal perspective, in which at the beginning of a term ministers do not know whether they face a cooperative or competitive partner but they learn this over time and use their agenda control to time further bill initiation in response. A circular regression analysis using data on more than 25,000 government bills from 11 parliamentary democracies over 30 years supports this temporal perspective, showing that ministers initiate bills later in the term when their previous bills have experienced greater scrutiny. Ministers further delay bill initiation when coalition parties' incentives to deviate from compromise increase and when they have less power to constrain their bills' scrutiny.

We explain the referendums on British membership of the European Communities and European Union from a principal–agent perspective between the Prime Minister and the rank-and-file. We show that announcing a referendum on the Prime Minister’s membership proposal helps the incumbent party to win the general election when the rank-and-file is divided on the terms of membership. When the Prime Minister overcomes the rank-and-file’s mistrust of her effectiveness in negotiating new membership terms with other member states, the voters are more likely to follow her proposal. However, when intra-party controversies reveal principal–agent problems, the initially uninformed voters can learn about the dysfunctionality of the terms and are more likely to reject the Prime Minister’s proposal.

In political science, data with heterogeneous units are used in many studies, such as those involving legislative proposals in different policy areas, electoral choices by different types of voters, and government formation in varying party systems. To disentangle decision-making mechanisms by units, traditional discrete choice models focus exclusively on the conditional mean and ignore the heterogeneous effects within a population. This paper proposes a conditional binary quantile model that goes beyond this limitation to analyze discrete response data with varying alternative-specific features. This model offers an in-depth understanding of the relationship between the explanatory and response variables. Compared to conditional mean-based models, the conditional binary quantile model relies on weak distributional assumptions and is more robust to distributional misspecification. The model also relaxes the assumption of the independence of irrelevant alternatives, which is often violated in practice. The method is applied to a range of political studies to show the heterogeneous effects of explanatory variables across the conditional distribution. Substantive interpretations from counterfactual scenarios are used to illustrate how the conditional binary quantile model captures unobserved heterogeneity, which extant models fail to do. The results point to the risk of averaging out the heterogeneous effects across units by conditional mean-based models.