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The research catalogue is an archive of ESRC-funded grants and outputs. Links, files and other content will no longer be maintained or updated after April 2014.

New Directions in Monetary and Fiscal Policy Analysis at the Macroeconomic Level

Grant reference: RES-062-23-1436

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Discussion paper details

Electoral uncertainty, the deficit bias and the electoral cycle in a new Keynesian economy
Recent attempts to incorporate optimal fiscal policy into New Keynesian models subject to nominal inertia, have tended to assume that policy makers are benevolent and have access to a commitment technology. A separate literature, on the New Political Economy, has focused on real economies where there is strategic use of policy instruments in a world of political conflict. In this paper we combine these literatures and assume that policy is set in a New Keynesian economy by one of two policy makers facing electoral uncertainty (in terms of infrequent elections and an endogenous voting mechanism). The policy makers generally share the social welfare function, but differ in their preferences over fiscal expenditure (in its size and/or composition). We use this model to examine three issues that arise from either literature. First, we consider the extent to which electoral competition gives rise to a debt or deficit bias, as one party seeks to win elections and tie the hands of a potential successor, when all debt is defined in nominal terms. Second, we examine the extent and nature of the electoral cycle introduced by having two parties reflecting different preferences over either the composition or amount of government spending. Third, we examine whether electoral competition has any impact on the conventional business cycle stabilisation policy, compared to the standard analysis that assumes a single benevolent government.

Primary contributor

Author Campbell Leith

Additional contributors

Co-author Simon Wren-Lewis

Additional details

01 November 2009
Oxford University department of economics discussion paper


paper460b.pdf (.pdf / 468kb)