Abstract
Enforcement of European fiscal rules, to a large extent, hinges on fiscal forecasts prepared by the European Commission (EC). The reliability of these forecasts has received little attention in the literature, despite the fact that (i) the forecasts have potentially far-reaching consequences for national governments, especially in the euro area while (ii) the EC depends on information supplied by national officials in preparing its forecasts. We hypothesize that the EC’s forecasts are biased upwards when national governments expect European fiscal rules to bind. Reconstructing this expectation using real-time information, we show that for euro area countries the EC’s fiscal forecasts are indeed biased upwards when the budget deficit threatens to exceed the critical value of 3 % of GDP. For non-euro area countries, which do not face the risk of fines, this bias cannot be established. Our results are robust to various ways of controlling for crisis-induced budgetary problems and the exclusion of various country groups. We offer suggestive evidence that the presence of independent fiscal councils at the national level helps to attenuate the bias induced by the 3 % threshold.
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Notes
Since 2007 these have been supplemented by interim forecasts presented in February/March and September. These were merely updates of the more elaborate, official forecasts. From 2013 onwards, an official Winter forecast is presented annually.
Treaty on European, article 104c-5.
In 2005, member state non-compliance and the perceived rigidity of the rules led to a first reform of the SGP (Claeys et al. 2016). The adjustments aimed at enhancing the economic rationale underlying the rules and improving their flexibility (Andrle et al. 2015) but did not fundamentally alter the centrality of the 3 % threshold.
For member states that were subject to an EDP on 8 November 2011 a 3-year transition period applies, starting in the year following the correction of the excessive deficit, before the debt reduction benchmark becomes relevant. This means that in our sample period, only Estonia, Finland, Luxembourg and Sweden have been subject to the debt rule, and only for 1 year.
Our dataset thus contains 1 year of data in which the reformed governance framework was applicable. For most countries, this was of little relevance due to the fact that they still were in an EDP (so that the preventive arm was not applicable) and because a transition period applied for the debt criterion (see footnote 3). To be sure that 2012 does not unduly affect our results, we have repeated our analysis excluding 2012. The results are similar to those reported in the paper, and are available upon request.
Council Regulation 1467/97—Sect. 2, article 2.
Merola and Pérez (2013) focus on 1999–2007. Note that for our sample period, every euro area country except Estonia (which joined the euro area only in 2011) has been in an EDP at least once, rendering this identification strategy infeasible.
From 2015 onwards, Spring Forecasts are in ESA2010 format. Comparing fiscal forecasts in ESA95 with realizations in ESA2010 is not informative, as this would imply assessing forecasts against realization numbers which are not constructed in the same way. Our sample therefore ends in 2012, for which we use the latest ESA95 realization data available (from the 2014 Spring Forecasts).
As financial sector support data are revised over time, we match the vintages of the financial sector support data to our realization vintages for the other variables where possible. Financial sector support data for 2012 are taken from the April 2014 vintage of the financial sector support tables, data for 2011 from the April 2013 vintage, data for 2010 and before from the April 2012 vintage.
We have two missing observations for Luxembourg.
Throughout the paper, countries are assigned to either group on a year by year basis. For example, in 2004–2007 Cyprus is part of the non-EMU sample, while from 2008 onwards it is counted as a euro area member state.
Financial sector support has a direct effect on government gross debt, but only affects the budget balance in case of capital transfers.
As pointed out by Gandrud and Hallerberg (2014), the timing of financial sector support could be driven by political considerations, e.g. related to elections. However, this is not likely to affect our analysis since financial sector support was generally not anticipated at the time of the Spring Forecast and, moreover, we control for the effect of financial sector support on the forecast error.
Effectively, we test if forecasts are on average more optimistic in case the rules of the SGP bind. This is the most general way to test for an SGP-induced bias, as it does not require us to specify an exact functional form for the expected bias. Averages can, however, be disproportionally affected by extreme values. We show in Sect. 5 that, qualitatively, this does not drive our results.
Growth surprises are potentially endogenous to the extent that unforeseen changes in the fiscal stance lead to a budget balance forecast error while simultaneously affecting GDP growth. This effect is likely to be small, as we focus on current year forecasts published in spring. By then, most policy measures will be known. Nevertheless, caution is required in interpreting the effect of the GDP forecast error on the budget balance forecast error as causal. Under the assumption of conditional mean independence, potential endogeneity of GDP growth does not prevent a causal interpretation of other coefficients.
Throughout our analysis, we focus on planned elections (elections following the end of the government’s term in office), as truly unplanned elections will not induce strategic behavior. However, as noted by one of the referees, “unplanned” (snap) elections are in fact not always completely unplanned. We have repeated our analysis controlling for all elections. Our main results are unaffected, with the coefficient on elections falling somewhat in size compared to when we only include unplanned elections-suggesting that on average, planned elections induce more of a bias than unplanned ones. Results are available upon request.
Our data do not allow us to distinguish between fiscal councils in charge of macro-economic and/or budgetary forecasts.
Such as directly plugging in fitted values from a probit model into the main regression, which is only consistent if the probit model is correctly specified.
The output gap has in fact been used as an explanatory variable for budget balance forecast errors in for example Frankel and Schreger (2013). They show the level of the output gap to be predictive of the GDP forecast error, and thereby of the budget balance forecast error. We control for the GDP forecast error directly however, so as to close off this channel.
In columns 1–4 all instruments are already lagged once; in columns 5–8 they are lagged twice so as to make them exogenous even in case of first-order autocorrelated forecasting errors.
Greece is the individual country with the largest effect on our estimation results.
Chang (2006) argues that under the SGP large countries have had an easier time than small ones, with the most obvious beneficiaries of ‘favoritism’ being France and Germany.
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Acknowledgments
The views expressed in this paper are those of the authors and not necessarily the views of De Nederlandsche Bank. We would like to thank Peter Keus, Paul Mul and Rob Vet for help in constructing the dataset and Diederik Dicou, Peter van Els, Jakob de Haan, Jos Jansen, Cherry Muijsson, Andreas Pick, Maarten van ’t Riet, Wim Suyker, as well as the editor, William F. Shughart II, the anonymous referees, participants of the 30th Annual Congress of the European Economic Association in Mannheim, participants of the 2015 ‘Dutch Economists’ Day’(Nederlandse Economendag) in Amsterdam, and seminar participants at the Dutch central bank, for valuable comments and suggestions.
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Appendix
Appendix
See Tables 4, 5, 6, 7, 8, 9, and 10.
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Gilbert, N., de Jong, J. Do European fiscal rules induce a bias in fiscal forecasts? Evidence from the Stability and Growth Pact . Public Choice 170, 1–32 (2017). https://doi.org/10.1007/s11127-016-0372-1
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DOI: https://doi.org/10.1007/s11127-016-0372-1