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Fiscal institutions: different classifications and their effectiveness

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Abstract

Fiscal institutions have been increasingly recognized as being useful for improving fiscal performance. These institutions are defined in different ways, but when a narrower definition is used, they correspond to independent fiscal institutions (IFIs), also called fiscal councils. In this paper, we employ three different classifications of fiscal institutions: from the European Commission, the International Monetary Fund, and one adapted from Calmfors and Wren-Lewis. Our main aim is to assess the impact of fiscal institutions on fiscal performance within a panel composed of the 28 countries of the European Union during the period of 1999–2016, using the bias-corrected least squares dummy variable dynamic panel estimator. We also explore the complementarity between fiscal institutions and the other elements of fiscal governance: numerical fiscal rules, medium-term budgetary frameworks, and the Stability and Growth Pact deficit and debt rules. The results confirm that fiscal institutions improve the discretionary implementation of fiscal policy: policy measures are less procyclical and more concerned with the sustainability of public debt. We also conclude that there is a complementary relationship between IFIs and the Stability and Growth Pact deficit rule, and that the performance of discretionary fiscal policy seems higher in countries with fiscal institutions than in countries with a predisposition for fiscal prudence.

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Notes

  1. Henceforth in this paper, we only use the expression IFIs, because many of the institutions have a more traditional hierarchy with a single head.

  2. The full case studies (Australia, Austria, Belgium, Canada, Denmark, Finland, France, Ireland, Italy, Korea, Mexico, Netherlands, Portugal, Slovak Republic, Spain, Sweden, United Kingdom and United States) can be accessed in Von Trapp et al. (2016).

  3. The latest vintage used in the paper comprises 15 new IFIs that were operational as of the end of 2016, while five IFIs from the original dataset were removed (see Debrun et al. 2017).

  4. A decision was made not to consider the OECD Independent Fiscal Institutions Database (2017) in this comparison because there are nine EU countries that are not member countries of the OECD.

  5. The Fiscal Council in Hungary is not included in our classification, because Wren-Lewis stated on his website (which was last updated in June 2012) that ‘in early 2011 the Hungarian government replaced the Council with a three-person body that was widely perceived as a less effective fiscal policy watchdog’.

  6. The seminal papers by Galí and Perotti (2003) and Fatás and Mihov (2003) follow different approaches to construct a measure for a government fiscal stance. Since there is no consensus and it is not our intention to contribute to that long-standing issue in the literature, we opted for the approach of Galí and Perotti (2003) as it is usual in the empirical papers assessing the effectiveness of fiscal institutions.

  7. Table 5 in the Appendix provides details on the dependent and explanatory variables used and on their data sources.

  8. The results for IV and GMM estimators indicate that when the lagged debt increases, the CAR changes to improve the budgetary situation, but the size of their estimated coefficients is also very small.

  9. Other political variables, such as cabinet composition, type of government, and number of changes in government per year from the database of Armingeon et al. (2018) were not included in our baseline estimations because their estimated coefficients are not statistically significant.

  10. When the LSDVC estimator is used, the MTBF variable is not significant in explaining the CAPE, but it is significant and has a negative estimated coefficient in the case of IV and GMM estimators. Hence, there is no certainty about the role that these medium-term budgetary frameworks play in restraining discretionary decisions that affect primary expenditure.

  11. When the GMM estimator is applied, the coefficient of the SGP deficit rule is not significant in explaining the CAR, which it is an unexpected result. In all the other estimations presented in Table 6, this rule is significant in explaining the CAPB, CAPE and CAR, respectively.

  12. These results are not reported in order to save space.

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Acknowledgements

This work is supported by national funds, through the FCT – Portuguese Foundation for Science and Technology, under the project UIDB/SOC/04011/2020. We would like to thank the editor of this journal as well as two anonymous referees, for their constructive comments and helpful suggestions. We would also like to thank the participants at the 29th EBES 2019 Conference, where a previous version of this paper was presented. All errors and omissions are our own responsibility.

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  1. Centre for Transdisciplinary Development Studies (CETRAD), Department of Economics, Sociology and Management (DESG), University of Trás-Os-Montes and Alto Douro (UTAD), Quinta de Prados, 5000-801, Vila Real, Portugal

    Patrícia Martins & Leonida Correia

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  1. Patrícia Martins
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  2. Leonida Correia
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Appendix

Appendix

See Tables 4, 5 and 6.

Table 4 Different classifications of IFIs in the EU
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Table 5 Variables and data sources
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Table 6 Cyclically adjusted fiscal variables: alternative estimators
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Martins, P., Correia, L. Fiscal institutions: different classifications and their effectiveness. Eurasian Econ Rev 11, 159–190 (2021). https://doi.org/10.1007/s40822-020-00155-0

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