Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511744
Title: Essays in incomplete insurance and frictional labour markets
Author: Oikonomou, Rigas
Awarding Body: London School of Economics and Political Science
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 2010
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Abstract:
This thesis consists of three chapters that investigate the importance of frictions in insurance and labour markets and their effects on macroeconomic outcomes. It asks how the behavior of aggregate employment and unemployment are affected, or the behavior of a planner who sets benefits to maximize welfare, when agents possess a number of risk sharing opportunities and luck in the labour market is the principal component of idiosyncratic risks. Chapter one deals with the technical aspects of this question. I introduce wealth accumulation in a battery of familiar search models and explore the implications for wages, allocations and the amount of risk sharing that firms can provide to their workforce. The second chapter investigates how the government should optimally set unemployment benefits depending on the range of private insurance opportunities in the economy. I consider a class of models that feature heterogeneous agents and wealth accumulation and contrast their properties with another where firms can provide additional insurance to their workforce. I show that the role of public policy is substantially different between the two economies. The third chapter is joint work with Jochen Mankart. We consider another margin of insurance, namely family self insurance, whereby household members can adjust jointly their labour supply to insure against income losses. We investigate how this feature can affect the cyclical behavior of key labour market statistics. In the US data we find that insurance within the family is important in explaining why the labour force is acyclical and not volatile but when we turn to the model we get the converse prediction. We then evaluate what important additions need to be made to our framework to make the model consistent with the data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.511744  DOI: Not available
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