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  A long-term model of the German Economy: lagom d_sim

Jaeger, C. C. (2005): A long-term model of the German Economy: lagom d_sim, (PIK Report ; 102), Potsdam : Potsdam-Institut für Klimafolgenforschung, 37 p.

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 Creators:
Jaeger, Carlo C.1, Author           
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1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Most models used in climate economics so far are full-employment models. In Germany, as in other countries suffering from persistent unemployment, climate policy can only be successful if it is embedded in an economic policy that generates additional jobs at a large scale. To investigate the possibility for such embedding, a long-term model of the German economy is needed. lagomd_sim is such a model. It is a dynamic stochastic general equilibrium model; its dynamics is based on a sequence of temporary equilibria with incomplete markets. The model architecture works without needing the problematic fictions of a macro-economic representative agent or a Walrasian auctioneer. The mechanisms of demand and supply on different markets display the frictions of real markets, where price adjustments take time. First simulation results suggest that it is possible to reduce German unemployment by half in a decade by means of suitable incentives for financial markets. This possibility resembles past experiences of Sweden and Austria. If poorly managed, it would result in a massive increase of greenhouse gas emissions. At the same time, it offers opportunities for a Pareto improving climate policy that combines higher growth with lower emissions. To investigate these opportunities in more detail, further model developments are recommended.

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Language(s): eng - English
 Dates: 20052005
 Publication Status: Finally published
 Pages: 37
 Publishing info: Potsdam : Potsdam-Institut für Klimafolgenforschung
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 2691
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Title: PIK Report
Source Genre: Series
 Creator(s):
Potsdam-Institut für Klimafolgenforschung, Editor              
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Pages: - Volume / Issue: 102 Sequence Number: - Start / End Page: - Identifier: ISSN: 1436-0179