Predicting and Analyzing Law-Making in Kenya

TL;DR

This paper develops machine learning models to predict legislative outcomes in Kenya's bicameral parliament, revealing that temporal and categorical features are more predictive than bill text.

Contribution

It introduces a novel approach to analyze and predict law-making in a developing democracy using machine learning on legislative data.

Findings

Year and month of bill introduction are strong predictors.

Bill category significantly influences enactment likelihood.

Text content of bills is less predictive than temporal and categorical features.

Abstract

Modelling and analyzing parliamentary legislation, roll-call votes and order of proceedings in developed countries has received significant attention in recent years. In this paper, we focused on understanding the bills introduced in a developing democracy, the Kenyan bicameral parliament. We developed and trained machine learning models on a combination of features extracted from the bills to predict the outcome - if a bill will be enacted or not. We observed that the texts in a bill are not as relevant as the year and month the bill was introduced and the category the bill belongs to.