What drives a successful fiscal consolidation?

Abstract

Fiscal consolidations are currently in the agenda of fiscal authorities in many countries. Using Bayesian Model Averaging to overcome the problem of model uncertainty, we find that growth-enhancing policies and cuts in public wages are the most appropriate ingredients for successfully reducing debt levels and budget deficits.

Keywords:

• fiscal policy
• fiscal consolidation
• budget deficit
• Bayesian model averaging

JEL Classification:

• H30
• H62

Acknowledgements

We would like to thank Cristian Bartolucci, Eric Leeper, and seminar participants at the Spanish Economic Association Meeting in Málaga and Banco de España for useful comments and suggestions. We also thank Silvia Ardagna for kindly sharing the data. The opinions and analyses are the responsibility of the authors and, therefore, do not necessarily coincide with those of the Banco de España or the Eurosystem.

Notes

¹ In particular we consider the Bayesian averaging of classical estimates approach discussed in Sala-i-Martin et al. (2004) and based on Raftery (1995). See Moral-Benito (2011) for a recent overview of BMA methods.

² The rationale for this result comes from the investment channel described in Alesina et al. (2002) who emphasize that deficit reductions achieved through spending cuts from the wage bill (rather than tax increases) are more likely to be successful. This is so because cutting public wages might generate downward wage pressures in the private sector that result in higher levels of investment.

³ Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom and the United States.

⁴ Successful consolidations are isolated from the pool of fiscal consolidations identified by Alesina and Ardagna (2010). According to their definition, a fiscal consolidation episode takes place in a given year if the Cyclically Adjusted Primary Balance (CAPB) improves by at least 1.5% of GDP.

⁵ Note here that some of the results are not exactly replicated because either the sample period or the variables' definition is not equal to the original papers. Nevertheless, these results are only an illustration of the model uncertainty problem for the sake of motivation.

⁶ Ardagna (2004) concludes that stabilizations implemented by cutting public spending lead to higher GDP growth rates and also that the success of fiscal adjustments in reducing debt-to-GDP ratio depends on the size of the contraction and less on its composition.

⁷ Since the number of potential proxies for the four candidate theories (i.e. fiscal situation, consolidation size, consolidation composition and macroeconomic situation) is enormous, the universe of potential regressions to estimate given all the possible combinations of proxies is very difficult to work with.

⁸ See Raftery (1995) or Sala-i-Martin et al. (2004) for more details.

⁹ This is a commonly used criterion for labelling variables as robust/nonrobust in the BMA literature. We assume a priori that all variables are equally robust (i.e. prior inclusion probability of 0.5) and we label as robust those variables for which the PIP is higher than 0.5. This would imply that the data support these variables more than the rest of the regressors. Other criteria such as the scale of evidence in Raftery (1995) or the t-ratio interpretation in Masanjala and Papageorgiou (2008) reinforce our reading of the results.

¹⁰ Which are the best policies for increasing GDP is a controversial question that is beyond the scope of this article.