In this new blog post, my co-authors and I introduce a novel machine-learning approach to identifying important laws. We apply the new method to classify over 9,000 UK statutory instruments, and discuss the pros and cons of their approach.

Thousands of laws are published every year. In Britain, more than 300 public acts and almost 25,000 statutory instruments reached the statute book between 2010 and 2020. But which of these laws are really significant, and which ones are relatively minor? This is an important question for businesses and individuals. It is also one that many social scientists grapple with when studying law-making.

Read the full post at the LSE British Politics blog.

Radoslaw Zubek (2020) `Committee Strength in Parliamentary Democracies: A New Index’. European Journal of Political Research.

Much recent research on coalitions and policy-making in parliamentary democracies requires high quality data on the strength of legislative institutions. In this note, I introduce a new index of committee policing strength which improves on existing measures in important ways. I specify key index parameters using a binary rooted tree model and engage human coders to score formal rules. I obtain a novel time-series of committee policing strength in 17 western and eastern European democracies since 1945. I validate the new estimates through convergent validation and discuss ways in which the new index contributes to future work.

Radoslaw Zubek, Abhishek Dasgupta, David Doyle (2020) ‘Measuring the Significance of Policy Outputs with Positive Unlabeled Learning’. American Political Science Review. First View, 19 October 2020.

Identifying important policy outputs has long been of interest to political scientists. In this work, we propose a novel approach to the classification of policies. Instead of obtaining and aggregating expert evaluations of significance for a finite set of policy outputs, we use experts to identify a small set of significant outputs and then employ positive unlabeled (PU) learning to search for other similar examples in a large unlabeled set. We further propose to automate the first step by harvesting ‘seed’ sets of significant outputs from web data. We offer an application of the new approach by classifying over 9,000 government regulations in the United Kingdom. The obtained estimates are successfully validated against human experts, by forecasting web citations, and with a construct validity test.

Niels D. Goet, Thomas G. Fleming, Radoslaw Zubek (2019) ‘Procedural Change in the UK House of Commons, 1811-2015‘ Legislative Studies Quarterly. Online First.

Recent research has shown an increasing interest in the historical evolution of legislative institutions. The development of the United Kingdom Parliament has received particularly extensive attention. In this paper, we contribute to this liter- ature in three important ways. First, we introduce a complete, machine-readable dataset of all the Standing Orders of the UK House of Commons between 1811 and 2015. Second, we demonstrate how this dataset can be used to construct innovative measures of procedural change. Third, we illustrate a potential empir- ical application of the dataset, offering an exploratory test of several expectations drawn from recent theories of formal rule change in parliamentary democracies. We conclude that the new dataset has the potential to substantially advance our understanding of legislative reforms in the United Kingdom and beyond.