Political forecast cycles
Политические циклы прогнозирования
2020-09-06
SCID: 54.1/u4r49e55
Discuss with AI
Portuguese municipalitieselectoral fiscal manipulationmoral hazardpolitical forecast cyclesrevenue forecasts
Figures from the paper
Abstract (AI)
A moral hazard model is used to show why overly optimistic revenue forecasts prior to elections can be optimal: Opportunistic governments can increase spending and appear more competent; ex post deficits emerge in election years, thereby producing political forecast cycles – as also found for US states in the empirical literature. Additionally, we obtain three theoretical results which are tested with panel data for Portuguese municipalities. The extent of manipulations is reduced when (i) the winning margin is expected to widen; (ii) the incumbent is not re-running; and/or (iii) the share of informed voters (proxied by education) goes up.
Key Findings
1
A moral hazard model shows that overly optimistic pre-election revenue forecasts can be optimal because they enable opportunistic governments to increase spending and appear more competent.
2
Forecast manipulation decreases when the expected winning margin widens, the incumbent does not seek reelection, or the share of informed voters—proxied by education—increases.
3
Panel-data evidence from Portuguese municipalities tests three predictions about forecast manipulation.
4
The model predicts political forecast cycles in which election-year spending increases generate ex post deficits, consistent with empirical evidence from U.S. states.
Research Object
Political revenue forecasts and election-year fiscal behavior of governments, with evidence from US states and Portuguese municipalities
Research Subject
Election-related manipulation of revenue forecasts and its effects on government spending and ex post deficits, including the roles of expected winning margins, incumbent reelection, and voter education
Publication Details
Publication Date
2020-09-06
Journal
Publisher
ISSN
Open access PDF
Access Type
Author Information
Download PDF
Subscribe to digest