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What Drives Successful Fiscal Consolidation

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					 What Drives a Successful Fiscal Consolidation?∗
                             a
                   Pablo Hern´ndez de Cos                       Enrique Moral-Benito

                                                  May 2012

                                                    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.
        JEL Codes: H30, H62.
        Keywords: Fiscal policy, Fiscal consolidation, Budget Deficit, Bayesian model averaging.




1       Introduction
In order to ensure fiscal sustainability many governments are currently undertaking fiscal consoli-
dation efforts. A crucial issue from a policy perspective is how to succeed in terms of deficit and/or
debt reductions when a fiscal consolidation is carried out. According to the different criteria con-
sidered in the literature, a fiscal consolidation is successful if the reduction in the debt-to-GDP
ratio (or the primary budget balance) is sufficiently large and persistent (see for instance Alesina
and Ardagna, 2010; Alesina and Perotti, 1995).
    The empirical analysis of the main factors driving success of a fiscal tightening manoeuvre
is controversial because there is no theoretical model of reference. The usual approach in the
literature investigating the determinants of success in fiscal consolidation exercises is based on a
regression of a dummy variable identifying successful consolidations on a set of candidate determi-
nants (e.g. Alesina and Ardagna, 1998; Giudice et al., 2007). Due to the lack of clear theoretical
guidance, results typically depend on the particular variables included in the regression. In order
to empirically overcome this model uncertainty problem, we avoid the model selection challenge
and consider Bayesian model averaging —henceforth BMA— techniques.1
    ∗
                             n
     Contact: Banco de Espa˜a. pablo.hernandez de cos@bde.es and enrique.moral@gmail.com. We would like to
thank Cristian Bartolucci, Eric Leeper, and seminar participants at the Spanish Economic Association Meeting
      a                        n
in M´laga and Banco de Espa˜a 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
                                         n
coincide with those of the Banco de Espa˜a or the Eurosystem.
   1
     In particular we consider the Bayesian averaging of classical estimates (BACE) 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.


                                                         1
    Empirical results indicate that growth-enhancing policies are found to be the only relevant
factor that generates successful (in terms of reducing debt-to-GDP ratios) fiscal consolidations.
If the focus is on the persistence of the primary deficit reductions, cutting public wages seems to
be the only crucial factor explaining successful fiscal adjustments.2 These findings indicate that,
in broad terms, only two determinants already identified in the literature (i.e. macroeconomic
factors and spending-based compositions) are robust to model uncertainty.


2       Data
The data used in this paper are from the OECD Economic Outlook No. 84. The sample includes
annual information for 21 OECD countries3 from 1980 to 2007.
    The fiscal variables considered as potential determinants of successful fiscal consolidations can
be classified in three different groups: (i) those variables related to the fiscal situation of the
country (e.g. government debt and primary budget balance as a share of GDP); (ii) variables
capturing the composition of the fiscal consolidation (or stimulus) program (e.g. current primary
expenditure, government wage and non-wage expenditures, subsidies, income taxes, social security
contributions...); and finally, (iii) the change in cyclically adjusted primary deficit as a proxy for
the size of the fiscal manoeuvre. The dataset also incorporates a set of macro variables such as the
GDP growth rate, the output gap, the exchange rate, and the short-term interest rate. Additional
information on the variables considered can be found in the Appendix.


3       Determinants of Successful Consolidations
Despite other criteria have been employed in the literature (see below), we first focus on the
debt reduction criterion considered in Alesina and Ardagna (2010) among others (i.e. successful
fiscal adjustments are those in which the cumulative reduction of the debt-to-GDP ratio three
years after the beginning of the adjustment is greater than 4.5 percentage points). Understanding
under which circumstances a fiscal consolidation might succeed in reducing the level of debt (or
the primary deficit) is relevant from an empirical point of view. One common approach in the
literature is based on constructing a successful consolidation dummy that takes the value one
for those country-years in which the consolidation succeeded in terms of the previous definition,
and zero otherwise.4 Then, they run a regression of this dummy on a set of macro and fiscal
variables capturing the environment in which the successful consolidation took place. Depending
    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, 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 per cent of GDP.


                                                        2
on the t-statistics of this regression, they conclude which are the most relevant characteristics
surrounding a successful fiscal consolidation.
    In broad terms, researchers aim to disentangle the importance of four competing explanations
of successful consolidations proposed in the literature: (i) the country’s fiscal situation prior to
the consolidation, proxied by, for example, the government debt as a share of GDP in the previous
year (e.g. Perotti, 1999); (ii) the size of the adjustment proxied by the change in primary deficit
during the episode (e.g. Giavazzi and Pagano, 1996; Giavazzi et al., 2000); (iii) the composition
of the adjustment in terms of the change in the different items of the public bill as a share of the
whole change in the primary deficit (e.g. Alesina and Perotti, 1995; McDermott and Wescott,
1996); (iv) the macroeconomic situation captured through the output gap or the growth rate of
GDP (e.g. Lambertini and Tavares, 2003).
    The papers by Ardagna (2004) — henceforth A04 —, Alesina and Ardagna (1998) — hence-
forth AA98 —, and Giudice et al. (2007) — henceforth G07 — are good examples of this approach.
Table 1 presents the results we obtain replicating the different specifications considered in these
papers.5

                                                  [Table 1 here]

    In view of the results presented in the AA98 column in Table 1, one might conclude that
the composition in terms of wage expenditures does not affect the success of the consolidation
program. However, according to the A04 column, a consolidation based on cutting public wages is
expected to reduce the probability of success in terms of debt reduction.6 On the other hand, the
statistical significance of the impact of macroeconomic conditions also varies depending on the
specification considered. Moreover, these estimates might again change if we consider alternative
proxies such as other items of the public bill (e.g. non-wage expenditures, transfers, business
taxes, income taxes...).7 This situation implies that estimates based on single model approaches
are always conditional on the empirical specification considered.
    To overcome these issues we consider BMA methods which represent a promising alternative
to the previous approach (i.e. the approach considered in Table 1 based on selecting a single
regression and deciding which variable is important depending on its associated t-ratio) when
model uncertainty is present. The key idea of BMA is to consider and estimate all the possible
regressions given by different combinations of regressors, and then report a weighted average as the
estimate of interest. Moreover, BMA allows a ranking to be constructed of the variables ordered
   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.




                                                         3
by their relative importance in the contribution to the model fit, i.e. the Posterior Inclusion
Probability (PIP).8

                                                  [Table 2 here]

    Table 2 presents the results when applying BMA to estimate all the candidate regressions in
order to investigate which regressors are robust determinants of successful consolidations. Output
gap is the only variable for which the posterior inclusion probability (PIP) is higher than the prior
inclusion probability,9 whereby the main conclusion emerging from Table 2 is that the output
gap is the only robust determinant of successful fiscal consolidations in terms of debt reduction.
Moreover, its posterior standard error is smaller than its posterior mean. Therefore we can also
conclude that the output gap positively affects the probability of success of a fiscal consolidation
package. This result implies that whatever the composition or the size of the adjustment, the most
relevant policies in times of fiscal consolidation must be oriented toward the objective of sustained
and higher rates of GDP growth. Those consolidations not accompanied by economic reforms
aimed at increasing employment and productivity may have more difficulties in the reduction of
debt-to-GDP ratios.10


3.1     Alternative Definitions of Successful Consolidations
How to define a successful consolidation is not straightforward, and the literature has considered
different criteria. In addition to the debt-to-GDP ratio criterion considered in the previous section
(which is usually the most common approach in the literature), we also explore the determinants of
successful fiscal consolidation according to two additional criteria. First, we consider a persistence
criterion which identifies as successful those consolidations in which the primary cyclically adjusted
budget balance improves by at least three percentage points of GDP over three consecutive years
(i.e. between t − 2 and t, between t − 1 and t + 1 or between t and t + 2), and in each year
the change in the primary cyclically adjusted budget balance cannot be below −0.5 percentage
points of GDP. Second, we also consider an expansionary criterion which indicates that a fiscal
consolidation succeeds if average trend growth between t and t + 2 is greater than between t − 1
and t − 2 (see Giudice et al., 2007).

                                                  [Table 3 here]

   BMA results when considering these alternative definitions of success are presented in Table 3.
Regarding the persistence criterion, only the change in wage expenditures as a share of the total
   8
     See Raftery (1995) or Sala-i-Martin et al. (2004) for more details.
   9
     This is a commonly-used criterion for labeling variables as robust / non-robust 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
paper.


                                                          4
change in the primary deficit is a robust determinant of success. This implies that the higher the
proportion of the consolidation conducted via reducing public wages, the higher the probability
of the adjustment being successful in terms of persistence in the deficit reduction. Cutting public
wages is a very costly political decision and, therefore, governments will only reduce the public
wage bill when they take seriously the fiscal consolidation program, and they are thus more likely
to achieve the deficit reduction objective.
    With respect to the expansionary criterion, given that the prior inclusion probability for each
variable is 0.5 and all the PIPs are below this threshold, we conclude that there is no variable
robustly affecting the probability of a fiscal consolidation being expansionary. In addition, all
the variables considered as candidate determinants have posterior standard errors larger than the
corresponding posterior means, which reinforces the previous conclusion of no variable robustly
correlated with expansionary fiscal consolidations.


4     Concluding Remarks
In this paper we analyse which factors are the most relevant in generating successful consolidations
via model averaging techniques. Successful consolidations are those in which the reduction of the
debt-to-GDP ratio three years after the beginning of the adjustment is greater than 4.5%. Our
results indicate that, in order to succeed in reducing debt levels, economic growth is the crucial
ingredient. Without economic recovery, fiscal consolidations may have huge difficulties in this
respect. On the other hand, we also find that cuts in public wages are the key ingredient of fiscal
consolidations in which persistent reductions in primary budget deficits were achieved.


A      Data Appendix
This section describes the data employed in the paper. All data are from the OECD Economic
Outlook Database no. 84.

    • Government debt level: government gross debt as a share of GDP.

    • Deficit level: cyclically adjusted primary deficit as a share of GDP (i.e. primary expenses
      minus total revenue)

    • Consolidation size: Change in the cyclically adjusted primary balance as a share of GDP.

    • ∆Wage expenditures: Change in government wage bill expenditures.

    • ∆Non-wage expenditures: Change in government non wage bill expenditures.

    • ∆Subsidies: Change in subsidies to firms.

    • ∆Transfers: Change in cyclically adjusted transfers as a share of GDP.

    • ∆Government investment: Change in the gross government consumption on fixed capital.

                                                 5
   • ∆Income taxes: Change in cyclically adjusted direct taxes on household as a share of GDP.

   • ∆Business taxes: Change in cyclically adjusted direct taxes on businesses as a share of GDP.

   • ∆Indirect taxes: Change in cyclically adjusted indirect taxes as a share of GDP.

   • ∆Other taxes: Change in cyclically adjusted other taxes (different from income, business or
     indirect) as a share of GDP.

   • ∆S.s. contributions: Change in cyclically adjusted social security contributions paid by
     employers and employees as a share of GDP.

   • GDP growth: Yearly growth rate of real per capita GDP for each country.

   • Output gap: % of potential GDP.

   • ∆Interest rate: Change in the real short-run interest rates between t + 1 and t.

   • ∆Exchange rate: Change in the exchange rate between t + 1 and t.

    Note that all the regressors belonging to the public bill (e.g. ∆Wage expenditures, ∆Subsidies,
∆Indirect taxes,...) are divided by the total change in the primary deficit (∆Item/∆Deficit) to
focus on the proportion of the adjustment which was due to a particular item as proxies of
the composition. More concretely, an increase in these variables means that a larger share of
the change in the primary deficit is due to a change in the particular item of the public bill.
For the spending items, an increase in these variables means that a larger share of the increase
(reduction) of the primary deficit is obtained by increasing (cutting) the particular spending item.
For the revenue items, an increase in these variables means that a larger share of the increase
(reduction) of the primary deficit is obtained by cutting (increasing) the particular revenue item
of the government budget.


References
 [1] Alesina, Alberto, and Silvia Ardagna (1998) “Tales of Fiscal Adjustment.” Economic Policy,
     27: 487-545.

 [2] Alesina, Alberto, and Silvia Ardagna (2010) “Large Changes in Fiscal Policy: Taxes versus
     Spending.” Tax Policy and the Economy, Editor: R. Brown, vol. 24. NBER.

 [3] Alesina, Alberto, and Roberto Perotti (1995) “Fiscal Expansions and Adjustments in OECD
     countries.” Economic Policy, 21: 205-248.

 [4] Alesina, Alberto, Silvia Ardagna, Roberto Perotti, and Fabio Schiantarelli (2002) “Fiscal
     policy, profits, and investment.” American Economic Review, 92: 571-589.

 [5] Ardagna, Silvia (2004) “Fiscal Stabilizations: When Do They Work and Why.” European
     Economic Review, 48: 1047-1074.

                                                6
 [6] Giavazzi, Francesco, Tullio Jappelli, and Marco Pagano (2000) “Searching for non-linear
     effects of fiscal policy: evidence for industrial and developing countries.” European Economic
     Review, 44: 1269-1289.

 [7] Giavazzi, Francesco, and Marco Pagano (1996) “Non-keynesian Effects of Fiscal Policy
     Changes: International Evidence and the Swedish Experience.” Swedish Economic Policy
     Review, 3: 67-103.

 [8] Giudice, Gabriele, Alessandro Turrini, and Jan in’t Veld (2007) “Non-Keynesian Fiscal Ad-
     justments? A Close Look at Expansionary Fiscal Consolidations in the EU.” Open Economies
     Review, 18: 613-630.

 [9] Lambertini, Luisa, and Jose Tavares (2003) “Exchange Rates and Fiscal Adjustments: Evi-
     dence from the OECD and Implications for EMU.” Boston College Working Paper 576.

[10] Masanjala, Winford, and Chris Papageorgiou (2008) “Rough and Lonely Road to Prosperity:
     a Reexamination of the Sources of Growth in Africa using Bayesian Model Averaging” Journal
     of Applied Econometrics, vol. 23, pp. 671-682.

[11] McDermott, John, and Robert Wescott (1996) “An Empirical Analysis of Fiscal Adjust-
     ments.” IMF Working Paper No.96/59.

[12] Moral-Benito, Enrique (2011) “Model Averaging in Economics.” Bank of Spain Working
     Paper 1123.

[13] Perotti, Roberto (1999) “Fiscal Policy In Good Times And Bad.” The Quarterly Journal of
     Economics, 114: 1399-1436.

[14] Raftery, Adrian (1995) “Bayesian Model Selection in Social Research” Sociological Method-
     ology, vol. 25, pp. 111-163.

[15] Sala-i-Martin, Xavier, Gernot Doppelhofer, and Ronald Miller (2004) “Determinants of Long-
     Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach.” American
     Economic Review, 94: 813-835.




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     Table 1: Characteristics of Sucessful Fiscal Consolidations
                                   AA98          A04          G07
 Dependent variable is the successful consolidation dummy
 GDP growth                                  12.47
 (t-ratio)                                   (4.67)
 Government debt in t − 1                    −0.02      −0.23
 (t-ratio)                                  (−0.17)    (−1.58)
 Deficit level in t − 1                        0.34
 (t-ratio)                                   (0.31)
 Consolidation Size              1.91         2.52       2.44
 (t-ratio)                      (0.61)       (0.90)     (0.76)
 ∆ wage expenditures            −0.03        −0.41       0.01
 (t-ratio)                     (−0.18)      (−2.07)     (0.05)
 ∆ interest rate                                        −0.01
 (t-ratio)                                             (−0.22)
 ∆ exchange rate                                         0.01
 (t-ratio)                                              (0.32)
 Output gap                                              0.02
 (t-ratio)                                              (0.82)
 R2                              0.01         0.29       0.06
 Obs.                             73           73         73
Notes: This Table presents the results from estimating three OLS regressions of the successful
(in terms of debt reduction) consolidation dummy on the determinants suggested in Alesina
and Ardagna (1998) [AA98], Ardagna (2004) [A04], and Giudice et al. (2007) [G07]. The
change in wage expenditures is relative to the change in primary budget deficit (i.e. ∆wage
expenditures is one of the items in ∆itemit /∆Defit ; an increase in this variable means that
a larger share of the reduction of the primary deficit is obtained by cutting the public wage
bill.).




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       Table 2: Characteristics of Successful Consolidations via Model Averaging
                                                   PIP                     P. Mean                       P. Std.
 Output gap                                        0.60                      0.05                         0.02
 ∆S.s. contributions                               0.42                     −0.58                         0.30
 ∆Other taxes                                      0.28                     −0.57                         0.37
 ∆Transfers                                        0.25                      0.28                         0.21
 GDP growth                                        0.21                      4.50                         3.64
 ∆Business taxes                                   0.20                      0.31                         0.27
 ∆Government investment                            0.16                      0.15                         0.16
 Deficit level                                      0.15                     −1.36                         1.61
 ∆Non-wage expenditures                            0.14                     −0.32                         0.43
 Government debt level                             0.13                     −0.15                         0.18
 ∆Wage expenditures                                0.13                     −0.19                         0.31
 ∆Indirect taxes                                   0.12                     −0.13                         0.26
 ∆Interest rate                                    0.11                     −0.02                         0.03
 ∆Income taxes                                     0.11                     −0.03                         0.22
 Consolidation size                                0.11                     −1.87                         3.84
 ∆Subsidies                                        0.11                     −0.24                         0.56
 ∆Exchange rate                                    0.10                     −0.01                         0.02
 Prior Inclusion Probability                                                   0.5
 Number of models estimated                                                  131, 072
Notes: PIP refers to the posterior inclusion probability of a particular regressors. Given the prior inclusion probability
is equal for all the variables (i.e. 0.5), those variables with PIP higher than 0.5 are labeled as robust determinants
of successful consolidations. All the regressors belonging to the public bill (e.g. ∆Subsidies, ∆Indirect taxes,...) are
divided by the total change in the primary deficit to focus on the proportion of the adjustment which was due to a
particular item as proxies of the composition. P. Mean refers to the posterior mean conditional on inclusion of a given
regressor in the empirical model, which is a weighted average of model-specific coefficient estimates with weights given
by the model-specific R-squares. P. Std. is the square root of the posterior variance which is a weighted average of
model-specific variances also including the variance of the estimates across different models. The sample is formed by
73 country-year pairs in which a consolidation took place. The 131, 072 estimated models come from all the possible
combinations of the 17 regressors (217 = 131, 072).




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     Table 3: Characteristics of Successful Consolidations: Alternative Definitions
                                                Persistence Criterion                   Expansionary Criterion
                                           PIP       P. Mean      P. Std.            PIP     P. Mean      P. Std.
                                            (1)         (2)          (3)              (4)       (5)         (6)
 ∆Wage expenditures                        0.72        0.38         0.15             0.10      0.05        0.22
 ∆Income taxes                             0.34        0.19         0.11             0.11     −0.07        0.16
 GDP growth                                0.17        2.13         2.00             0.10     −0.51        2.81
 ∆Indirect taxes                           0.14       −0.12         0.15             0.10     −0.06        0.20
 ∆Government investment                    0.13       −0.07         0.10             0.13     −0.09        0.13
 ∆S.s. contributions                       0.13        0.13         0.19             0.16      0.28        0.27
 Output gap                                0.12        0.01         0.01             0.17     −0.02        0.02
 ∆Other taxes                              0.12       −0.12         0.25             0.10     −0.10        0.31
 ∆Transfers                                0.12       −0.06         0.12             0.14      0.14        0.16
 ∆Business taxes                           0.12       −0.08         0.16             0.11     −0.08        0.22
 ∆Non-wage expenditures                    0.11        0.12         0.24             0.12      0.25        0.34
 Government debt level                     0.10        0.04         0.11             0.10      0.04        0.15
 Deficit level                              0.10       −0.29         0.82             0.19      1.46        1.20
 ∆Interest rate                            0.10        0.00         0.02             0.10      0.00        0.03
 ∆Subsidies                                0.10        0.06         0.31             0.10      0.11        0.46
 ∆Exchange rate                            0.10        0.00         0.01             0.10      0.00        0.01
 Consolidation size                        0.10        0.11         2.19             0.26      4.84        3.20
 Prior Inclusion Probability                             0.5                                     0.5
 Number of models estimated                            131, 072                                131, 072
Notes: PIP refers to the posterior inclusion probability of a particular regressors. Given the prior inclusion probability
is equal for all the variables (i.e. 0.5), those variables with PIP higher than 0.5 are labeled as robust determinants
of successful consolidations. All the regressors belonging to the public bill (e.g. ∆Subsidies, ∆Indirect taxes,...) are
divided by the total change in the primary deficit to focus on the proportion of the adjustment which was due to a
particular item as proxies of the composition. P. Mean refers to the posterior mean conditional on inclusion of a given
regressor in the empirical model, which is a weighted average of model-specific coefficient estimates with weights given
by the model-specific R-squares. P. Std. is the square root of the posterior variance which is a weighted average of
model-specific variances also including the variance of the estimates across different models. The sample is formed by
73 country-year pairs in which a consolidation took place. The 131, 072 estimated models come from all the possible
combinations of the 17 regressors (217 = 131, 072).




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