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"Eeny, Meeny, Miny, Moe" — What Do Italian Political Elites Choose and Why? Automated Text Analysis of Italian Environmental Executives’ Speeches

Elites
Environmental Policy
Political Engagement
Big Data
Sanja Hajdinjak
Ludwig-Maximilians-Universität München – LMU

Abstract

ABSTRACT. Climate change and environmental concerns represent one of the biggest and longest looming issues on the political horizon. The citizens of developed countries express care for environment and interest in contributing to solving the critical issues, forcing their national governments to also seek solutions to environmental problems. In the European context, Italy represents a sort of a laggard in implementing the EU mandated environmental standards. Not only are its environmental scores lower compared to other developed economies, some of some of its regions have serious and well publicized issues the type of which its European neighbours have solved long time ago. Delivering in the field of the environmental policy is obviously important, but when the implementation capacities are somewhat lacking, what can political elites do? Which areas of the environmental policy do they prioritize? Are there differences between different types of governments? To answer these questions, we analyze Italian federal level, environmental policy executives’ speeches. Our analysis (2008/2020) encompasses the period of the financial and economic crisis and the slow and painful recovery that followed as well as the Paris Agreements. We also include a full range of governments – including a mainstream center-right and a center-left government, but also a technocratic one and finally two populist governments. Methodologically, we rely on automated text analysis, more specifically on structural topic modelling. This approach allows us to examine what do the Italian political executives choose to prioritize, how do they frame important topics and whether there are differences between them. Methodologically, we expand the existing research on political elites by using non-supervised machine learning to understand what executives’ priorities are and what determines them. Theoretically, we hope to contribute to the literature on policy prioritization and framing among top political executives.