ESTIMATING A FISCAL REACTION FUNCTION FOR THE EU: SOME TECHNICAL

ASPECTS

The approach presented in this Chapter is inspired by a strand of the literature on public debt sustainability which adopts the FRF as the main empirical tool to test whether governments fulfil intertemporal solvency requirements. Key papers

are Bohn (1998) and (2005); IMF (2003); Mendoza and Ostry (2008); Celasun et al., (2006), Ghosh et al., (2010), with the main reference being the seminal paper by Bohn (1998) which argues that governments are solvent under very general conditions as long as the primary balance increases with the outstanding stock of debt. A practical advantage of such a criterion is that it is independent of information regarding the discount rates, interest rates on government bonds and inter- temporal preferences.

The equations estimated in these papers normally include further (non-debt) determinants of the primary surplus on the right-hand-side. Examples of frequently used determinants are the output gap, to account for business cycle effects, and the temporary component of public expenditure (e.g. military outlays during wars). Depending on the specific features of countries considered, other variables such as inflation, trade openness, commodity (or oil) prices and the quality of the budgetary framework may also be included. A further refinement is to test for the possibility of a non-linear relationship between debt and the primary surplus, i.e. to see whether the magnitude of the fiscal response changes with the level of debt. This could occur if the surplus rises with debt only when the debt ratio exceeds a certain threshold. A number of papers find evidence of such a non-linear response although with partly conflicting results between advanced and emerging economies. A stronger response of the primary surplus with greater debt levels is found among industrialised countries in Bohn (1998), for the US case, and IMF (2003), for a broader sample of countries, whereas Abiad and Ostry (2005), IMF (2003), Celasun et al. (2006) and Mendoza and Ostry (2008) find that such response tends to weaken among emerging economies when debt exceeds 50% of GDP. However, Mendoza and Ostry (2008) find no evidence of a positive response of the primary surplus to debt within a sub-sample of advanced economies with high debt. Similar evidence is found in Ostry et al. (2010).

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The specification chosen

As the focus of this exercise is on debt sustainability and one of the aims is to have a benchmark for standard sustainability indicators expressed in terms of primary balance, the overall primary balance (as a % of GDP) is chosen as dependent variable.(114)

In line with the existing literature explanatory variables include:

• The variables used in the Barro model (see Barro (1979),) which constitutes the theoretical basis for the econometric specification, i.e. the lagged debt to GDP ratio, the output gap to account for business cycle effects on fiscal policy, the cyclical component of government final consumption expenditures to account for tax-smoothing considerations in setting fiscal policy;(115)

• a number of political and institutional control variables like the occurrence of elections, the size of the government's parliamentary majority, political stability, ideology and fragmentation of government, the strength and coverage of fiscal rules,(116) and dummy variables for the years in run-up to EMU, and those of the latest economic downturn (2009– 10) and for whether the country belongs to the euro area or is a recently acceded Member State.

(114_{) Robustness checks have been carried out by also}

considering CAPB, but the results remain essentially unchanged. The latter is commonly used in papers focusing on the discretionary response of fiscal policy to the business cycle (see Gali and Perotti (2003), European Commission (2006), Ayuso et al. (2010) and Turrini (2008),) thereby capturing the existence of an output stabilisation motive in setting fiscal policy.

(115) See Bohn (1998) and Mendoza and Ostry (2008). The chosen specification controls for the effect of temporary fluctuations of GDP and government expenditures. Whereas the former is measured by the output gap, the latter is captured by de-trending the series for final government consumption expenditures (as a share of GDP) using the Hodrick-Prescott filter, with the smoothing parameter set at 100.

(116_{) This information is summarised by a Fiscal Rules Index}

(FRI) assigning greater scores to more stringent rules and/or rules with a broader coverage. The index is compiled by Commission services.

• the current account balance (as a share of GDP).(117)

The main model is estimated for the EU using a Fixed Effect (FE) estimator and includes the lagged dependent variable among the regressors. As data for fiscal rules (FRI) are only available for the period 1990–2008, Table IV.4.1 presents results for two different time periods, i.e. 1990– 2008 and 1975/80–2010, with the longer time period excluding FRI and including the dummy for the recent economic downturn. Instrumental variables are used for the output gap and for the current account.(118) The regression presented in column (1) of the table only includes the lagged debt and economic controls, whereas the one in column (4) also includes FRI and the whole set of political variables and dummies mentioned above. Column (2) retains FRI and the most significant political variables. Column (5) presents the same regression as in column (2) with the exclusion of FRI which allows a larger number of observations to be used.

Finally, column (3) presents the same regression as column (5) while replacing the ESA95 primary surplus data with linked (i.e. ESA95 and non-ESA) data allowing a further rise in the number of observations. (119)

The coefficient of the lagged debt to GDP ratio is positive and significant in all the above regressions, and is robust to the introduction of political and institutional variables and dummies.(120) The primary balance appears to have

(117_{) This controls for possibilities of 'twin deficits', i.e. external}

deficit being associated to fiscal deficit (Mendoza and Ostry (2008)).

(118_{) For both variables, causality can run in both directions, i.e.}

from the variable in question to the primary balance and vice versa. In other words the two variables may be endogenous. Therefore, to avoid risk of biased estimations, they are instrumented by the lagged output gap and the contemporaneous US output gap and by the lagged current account, respectively. See Ayuso et al. (2008) and Gali and Perotti (2004).

(119_{) In this regression the output gap as a percentage of trend}

output (i.e. ogtrend), instead of potential output, is used given its longer time coverage which allows to match the longer time coverage of linked primary balance series. (120_{) The debt coefficient retains its sign and significance also in}

the model excluding the lagged dependent variable, with a larger coefficient in absolute value. As the literature does not provide clear cut indications as to whether the lagged dependent should be excluded or not, it was included given its strong statistical significance suggesting a certain degree of time persistence in primary balance.

Part IV Debt sustainability in the EU

a strong degree of time persistence as the lagged dependent variable is always positive and highly significant.

Among the other explanatory variables, the cyclical component of government consumption expenditures has a negative and very robust effect on the primary balance, as expected. The output gap only has a significant coefficient in the regressions covering a longer time span (columns (3) and (5)) with a negative sign, suggesting some degree of pro-cyclicality of fiscal policy. The current account balance has a positive and significant coefficient. Among political and institutional variables, the strength and coverage of fiscal rules and the size of government majority have a positive effect on primary balance, whereas during election years the balance is ceteris paribus

lower. Finally, the dummy for crisis years has a strong negative impact on primary balance (columns (3) and (5)) whereas the enlargement dummy has a positive sign, albeit significant only in regressions covering a longer time span (columns (3) and (5).) The specifications in columns (2) and (3) have been chosen as benchmark regressions (highlighted in bold) to be used later on in the analysis for the calculation of

debt thresholds. The former regression includes FRI and the more robust political variables, i.e. the size of government majority and the occurrence of elections, and covers the 1990–2008 period. The latter retains the majority and election dummies while excluding FRI and using linked ESA and non-ESA series for the primary balance allowing a much larger number of observations to be used in the estimation.(121)

The robustness of the results has been tested by repeating the FRF estimates using different estimation methodologies. For simplicity only the specification of the first benchmark regression has

(121_{) Covering a longer time span allows to significantly reduce}

the inconsistency of FE estimators in dynamic panel data models. The regression in column (3) covers an average of 25.7 observations per country, thereby fulfilling comfortably the rule of thumb that T should be larger than 20 to overcome the inconsistency of FE estimators in dynamic panels (Bond (2002)). Hence, the robustness of estimates of regression in column 2 (i.e. the one with a shorter time coverage) can be checked by comparison with the regression in column (3). As the results of the two regressions are qualitatively similar (except for the output gap) estimates of the regression in column 2 can be considered as reliable.

Table IV.4.1: FRF for EU27, dependent variable: primary balance (% of GDP) - fixed effect estimator

-1 -2 -3 -4 -5

VARIABLES pbal_gdp_esa pbal_gdp_esa pbal_gdp_lnkd pbal_gdp_esa pbal_gdp_esa

ogpot 0.0114 -0.0328 0.0825 -0.134** ca_gdp 0.121*** 0.131*** 0.0893*** 0.142*** 0.140*** L.pbal_gdp_esa 0.654*** 0.466*** 0.483*** 0.614*** L.debt_gdp 0.0314*** 0.0327*** 0.0271*** 0.0377*** 0.0314*** cyc_govcons_gdp -1.193*** -0.861*** -1.016*** -0.831*** -1.056*** fri 0.335** 0.144 maj 4.097*** 2.198** 4.580*** 2.509** checks -0.0649 stabs 0.226 legelec -0.373** -0.406** -0.336* -0.407** govfrac -0.745 polariz 0.0662 gov1rlc -0.00296 dummy_runup_EMU -0.0976 dummy_ea -0.195 0.0477 -0.31 -0.0112 dummy_enlarg 0.348 0.677** 0.477 0.820** dummy_crisis -3.129*** -3.444*** ogtrend_fa10 -0.103** L.pbal_gdp_lnk 0.614*** Constant -1.345*** -3.194*** -2.038*** -3.347*** -2.370*** R-squared 0.6063 0.5143 0.6594 0.5665 0.6509 Observations 564 414 693 378 563 Number of cn_num 27 27 27 27 27

Notes: *** p<0.01, ** p<0.05, * p<0.1: variable statistically significant at the 1%, 5% and 10% level, respectively. Definition of variables: Ogpot=output gap (% of potential GDP); ca_gdp= current account balance (% of GDP); L_pbal_gdp_esa=Lagged Primary balance (% of GDP) – ESA series only; L.debt_gdp = lagged government debt-to-GDP ratio: cyc_govcons_gdp = temporary fluctuations of government final consumption expenditures (as a % of GDP); FRI = fiscal rules index; Maj= size of government parliamentary majority (in %); checks = Checks and balances (number of decision-makers with veto power on government decisions); legelec = dummy taking the value of 1 if the country has legislative elections in that year, 0 otherwise; govfrac = government fragmentation (number of parties in government coalition); polariz = government polarization (ideological range between the two government parties ideologically more distant); gov1rlc = ideology of main government party; ogtrend = output gap as % of trend GDP; L.pbal_gdp_lnk = lagged primary balance (% GDP) – linked (ESA and non-ESA) series.

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been considered.(122) Estimations are run alternatively with pooled OLS, a Random Effect estimator and a GMM estimator, to account for the possible inconsistency of FE estimators in dynamic panel data. Overall, coefficient estimates remain qualitatively similar to those in Table IV.4.1, (123) with the exception of government majority becoming insignificant in the GMM estimates. Further robustness checks have been conducted. First, the model has been tested separately for the EU15 and the EU12, with the latter including the new Member States. Only the two benchmark regressions have been considered for simplicity. The coefficient of the debt term remains positive and significant for both subsamples even if it is larger for the EU12 (around 0.06) sample. The size of the government majority and fiscal rules only have a large positive impact on the primary balance among the EU12 while being positive but insignificant among the EU15. Similarly the election variable, albeit negative in all regressions, is significant only among EU15 countries.

Second, the literature has found evidence that the response of the primary balance to debt may be non-linear (see Section IV.4.1;) its size may vary with the level of outstanding debt with primary balance reacting more strongly when the debt ratio exceeds a given threshold, at least in advanced economies. Tests have been made on a spline for the debt ratio at 60% of GDP– in order to test whether a change in the response of primary balance occurs when debt exceeds this level – and on a quadratic and cubic term for debt. The test points to insufficient robustness of the evidence for a non-linear response to debt, so the linear specification is retained for the benchmark regressions.(124)

(122_{) As explained above, the risk of inconsistent estimates is}

substantially reduced in the second benchmark regression given the longer time coverage.

(123_{) This is true in particular for GMM. Pooled OLS and RE}

estimates are likely to be biased given that they do not control for unobserved (country) heterogeneity (pooled OLS) or assume country effects to be uncorrelated with the regressors, which is highly unlikely (RE).

(124) Detailed results of FRF with different estimation techniques, of EU15 vs. EU12 and with non-linear response to debt are available upon request.

4.3. PREDICTIONS OF PRIMARY BALANCE IN