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.