Informal bargaining in bicameral systems: Explaining delegation by the Council of the European Union and the European Parliament

This project is about the effects of institutional design on decision-making in the European Union. Specifically: delegation to informal inter-institutional legislative bargaining (the ‘informal arena’). I develop a spatial complete information model to explain the decision to delegate to the ‘informal arena’ and test its empirical implications. The meta-theoretical umbrella for this project is New Institutionalism (more specifically, Rational Choice Institutionalism) and I view the decision to delegate through a principal-agent lens, i.e., delegation may result in policy outcomes that differ from counterfactual non-delegated acts (agency-drift). I contribute to the theoretical and empirical literatures on informal law-making in the European Union and legislative organisation more generally. In the EU, the ‘formal arena’ co-exists with the ‘informal arena.’ In the formal arena, bills shuttle back and forth between two chambers in a maximum of three reading stages. In the informal arena, inter-institutional negotiations are delegated. The delegations meet behind closed doors and the resulting compromise is rubber-stamped by the parent chambers. The extant literature suggests that law-making in the informal arena leads to agency-drift. The questions that I address in this project are: when does delegation to the informal arena take place and, equally, when does delegation not take place? Furthermore, does delegation lead to agency-drift? My findings suggest that delegation is less likely, the greater the risk of agency-drift and more likely the greater the legislative workload cost of not delegating. I show that the bicameral system alters the incentive structure of legislative actors such that agency-drift is rare or moderate if it occurs

Data Innovation for International Development: An overview of natural language processing for qualitative data analysis

Availability, collection and access to quantitative data, as well as its limitations, often make qualitative data the resource upon which development programs heavily rely. Both traditional interview data and social media analysis can provide rich contextual information and are essential for research, appraisal, monitoring and evaluation. These data may be difficult to process and analyze both systematically and at scale. This, in turn, limits the ability of timely data driven decision-making which is essential in fast evolving complex social systems. In this paper, we discuss the potential of using natural language processing to systematize analysis of qualitative data, and to inform quick decision-making in the development context. We illustrate this with interview data generated in a format of micro-narratives for the UNDP Fragments of Impact project

Contesting Europe: Eurosceptic Dissent and Integration Polarization in the European Parliament

This article provides a comprehensive analysis of Eurosceptic contestation within the legislative arena of the European Parliament (EP) from 2009 to 2019. Under what conditions do Eurosceptics vote differently from their Europhile peers? The literatures on European integration, party competition and policy types lead us to expect variation in Eurosceptic contestation across policy areas. Drawing on roll-call votes in the EP, we introduce two new measures of such contestation: Eurosceptic dissent, that is, the extent to which Eurosceptics diverge from the Europhile plurality, and integration polarization, that is, the extent to which Eurosceptics and Europhiles oppose each other as cohesive camps. Our two indicators show that Eurosceptic contestation is particularly pronounced when the EP votes on cultural, distributive and constituent issues. When voting on redistributive policies, in contrast, dissent and polarization are curbed by national and ideological diversity

Improved multilevel regression with post-stratification through machine learning

Multilevel regression with post-stratification (MrP) has quickly become the gold standard for small area estimation. While the first MrP models did not include context- level information, current applications almost always make use of such data. When using MrP, researchers are faced with three problems: how to select features, how to specify the functional form, and how to regularize the model parameters. These problems are especially important with regard to features included at the context level. We propose a systematic approach to estimating MrP models that addresses these issues by employing a number of machine learning techniques. We illustrate our approach based on 89 items from public opinion surveys in the US and demonstrate that our approach outperforms a standard MrP model, in which the choice of context-level variables has been informed by a rich tradition of public opinion research

Improved multilevel regression with post-stratification through machine Learning (autoMrP)

Multilevel regression with post-stratification (MrP) has quickly become the gold standard for small area estimation. While the first MrP models did not include contextlevel information, current applications almost always make use of such data. When using MrP, researchers are faced with three problems: how to select features, how to specify the functional form, and how to regularize the model parameters. These problems are especially important with regard to features included at the context level. We propose a systematic approach to estimating MrP models that addresses these issues by employing a number of machine learning techniques. We illustrate our approach based on 89 items from public opinion surveys in the US and demonstrate that our approach outperforms a standard MrP model, in which the choice of contextlevel variables has been informed by a rich tradition of public opinion research

Improved Multilevel Regression with Post-Stratification Through Machine Learning (autoMrP)

Multilevel regression with post-stratification (MrP) has quickly become the gold standard for small area estimation. While the first MrP models did not include context-level information, current applications almost always make use of such data. When using MrP, researchers are faced with three problems: how to select features, how to specify the functional form, and how to regularize the model parameters. These problems are especially important with regard to features included at the context level. We propose a systematic approach to estimating MrP models that addresses these issues by employing a number of machine learning techniques. We illustrate our approach based on 89 items from public opinion surveys in the US and demonstrate that our approach outperforms a standard MrP model, in which the choice of context-level variables has been informed by a rich tradition of public opinion research