Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.746251
Title: Multidimensional voting models : theory and applications
Author: Dotti, V.
ISNI:       0000 0004 7230 7108
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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Abstract:
In this thesis I study how electoral competition shapes the public policies implemented by democratic countries. In particular, I analyse the relationship between observable characteristics of the population of voters, such as the distribution of income and age, and relevant public policy outcomes of the political process. I focus on two theoretical issues that have proved difficult to tackle with existing voting models, namely multidimensionality of the policy space and non-convexity of voter preferences. I propose a new theoretical framework to deal with these issues. I employ this new framework to address three popular questions in the Political Economy literature for which a multidimensional policy space is deemed to be a crucial element to capture the underlying economic trade-offs. Specifically, I analyse (i) the relationship between income inequality and size of the government, (ii) the causal link between population ageing and the ’tightness’ of immigration policies, and (iii) the role played by the income distribution in shaping public investment in education. I compare the predictions derived under the new theoretical tool with those that prevail in the existing literature. I show that the interaction among multiple endogenous policy dimensions helps to explain why several studies in the literature - in which the analysis in restricted to a unique endogenous policy choice - deliver empirically controversial or inconsistent predictions. For all three questions, the approach proposed in this thesis is shown to be helpful in reconciling the theoretical predictions with empirical evidence, and in identifying the economic channels that underpin the patterns observed in the data.
Supervisor: Preston, I. P. ; De Paula, A. D. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.746251  DOI: Not available
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