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dc.contributor.advisorPedersen, Karl Rolf
dc.contributor.authorFinstad, John Anders
dc.date.accessioned2017-09-11T08:43:22Z
dc.date.available2017-09-11T08:43:22Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/11250/2453976
dc.description.abstractIn recent years, a funding of the Norwegian National Insurance Scheme has been discussed as a sustainability-improving measure in the wake of the financial crisis and an ageing population due to baby boomers entering retirement age. Using an extended overlapping generations model, the thesis examines the long-run relationship between the defined-contribution rates of return of the current pay-as-you-go scheme and a fully funded alternative in 81 different scenarios. The scenarios differ with respect to demographic development, real return on pension savings, productivity growth, and retirement age. Projections of the real return for 2016-2100 suggests that the fully funded alternative is likely to yield a significantly larger rate of return, and therefore also a significantly smaller defined-benefit tax rate. A funding is found to increase the rate of return of the pension scheme in the long run in 51 of these 81 scenarios. Furthermore, funding is found to yield a higher rate of return than the current pay-as-you-gofinancing in 7 of the 9 most probable scenarios. Additionally, for a given rate of return the current pay-as-you-go scheme implies an annual tax rate 22% larger than the alternate fully funded scheme in years 2044-2100 in the expected scenario. A gradual funding is discussed as a solution to the challenges related to transitioning from one form of financing to the other. Multiple approaches to a possible implementation of a gradual funding are examined, including examples from other countries that have starter similar systemic reforms.nb_NO
dc.language.isoengnb_NO
dc.subjecteconomicsnb_NO
dc.titlePopulation ageing in extended overlapping generations models : a numerical analysis of the financing of the Norwegian national insurance schemenb_NO
dc.typeMaster thesisnb_NO
dc.description.localcodenhhmasnb_NO


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