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Cabinet Durability and Fiscal Discipline

Published online by Cambridge University Press:  05 September 2018

DAVID FORTUNATO  and
MATT W. LOFTIS
DAVID FORTUNATO*
Affiliation:
Texas A&M University
MATT W. LOFTIS*
Affiliation:
Aarhus University
*
David Fortunato is an Associate Professor, Texas A&M University, 2010 Allen Building, 4348 TAMU, College Station, TX 77843-4348 (fortunato@tamu.edu).
Matt W. Loftis is an Assistant Professor, Aarhus University, Bartholins Allé 7, 8000 Aarhus, Denmark (mattwloftis@ps.au.dk).
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Abstract

We argue that short government durations in parliamentary democracies increase public spending by driving a political budget cycle. We present a revision of the standard political budget cycle model that relaxes the common (often implicit) assumption that election timing is fixed and known in advance. Instead, we allow cabinets to form expectations about their durability and use these expectations to inform their spending choices. The model predicts that (1) cabinets should spend more as their expected term in office draws to a close and (2) cabinets that outlive their expected duration should run higher deficits. Using data from 15 European democracies over several decades, we show that governments increase spending as their expected duration withers and run higher deficits as they surpass their forecasted life expectancy.

Type
Research Article
Information
American Political Science Review , Volume 112 , Issue 4 , November 2018 , pp. 939 - 953
Copyright
Copyright © American Political Science Association 2018 

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Footnotes

In addition to the editorial team and four anonymous reviewers, we are grateful to Despina Alexiadou, Timm Betz, Martin Bisgaard, Bill Clark, Jason Eichorst, Carsten Jensen, André Kaiser, Peter B. Mortensen, Oli Proksch, Guy Whitten, and Georg Vanberg, as well as participants of the 2016 Annual Meetings of the Public Choice Society and Southern Political Science Association for helpful comments and feedback. Replication files are available on the American Political Science Review Dataverse: https://doi.org/10.7910/DVN/HHMXU3.

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