"Some New Results on the Estimation of Structural Budget Balance for Spain"; Guido Zack, Pilar Poncela, Eva Senra, Daniel Sotelsek

Some New Results on the Estimation of Structural Budget
Balance for Spain
GUIDO ZACK*
Alcalá University
PILAR POnCELA**
Universidad Autónoma de Madrid
EvA SEnRA
Alcalá University
DAnIEL SOTELSEK***
Alcalá University
Received: April, 2014
Accepted: July, 2014
Summary
The recession that started in 2008 caused a sharp deterioration of the budget balance of Spain. This de-
cline was not fully anticipated by the structural budget balance due to some methodology limitations.
In this article, we calculate an alternative structural balance for Spain in the years prior to the subprime
crisis that includes residential investment as an explanatory variable. This estimate shows that by 2004
the Spanish fiscal situation was not as strong as presumed. This fragility was hidden by the extraordi-
nary revenue from the real estate bubble and the construction boom.
Keywords: Crisis, structural budget balance, real estate bubble, construction boom, Spain.
JEL Classification: C20, E32, E63, H62.
1. Introduction
Until 2006 Spain seemed to have one of the most countercyclical fiscal policies in Eu-
rope. In fact, the observed budget balance (OBB) improved from a deficit of 6.5% of GDP in
1995 to a surplus of 2% in 2006. nevertheless, the crisis revealed the fragility of this improve-
ment, since between 2007 and 2009 the OBB fell by 13 percentage points (p.p.) of GDP.
* Guido Zack acknowledges the support of the Institute of Latin American Studies (IELAT), Alcalá Universtity.
** Pilar Poncela acknowledges financial support from the Spanish government, Ministerio de Economía y Compet-
itividad, contract grant ECO2012–32854.
*** Daniel Sotelsek acknowledges the support of SIPA-ILAS (Columbia University), during his 2013–2014 visit-
ing scholar.
Hacienda Pública Española / Review of Public Economics, 210-(3/2014): 11-31
© 2014, Instituto de Estudios Fiscales
DOI: 10.7866/HPE-RPE.14.3.1

This strong fall is also shown in the structural budget balance (SBB), however neither
the consolidation until 2006 nor the deterioration between 2007 and 2009 can be explained
only by discretionary measures. Situations like that not only happened in Spain, but they
have been occurring in several countries (Larch and Turrini, 2009). Thus, since 2000 there
are several proposals to improve the SBB.
The main objective of this paper is to adapt some of these proposals to the Spanish case
in the years prior to the subprime crisis. In particular, the fiscal impact of the real estate bub-
ble and the boom in the construction industry are incorporated, showing that the SBB is not
capable to identify completely the cycle effect on the budget balance. The improvement of
the SBB methodology is essential in Spain, given that its new budgetary stability law is
based on this indicator.
The article is structured as follows. Section 2 describes the evolution of the Spanish
budget within the European context, identifying a particularly volatile observed and struc-
tural budget balanced trend. This would have been a reflection of the volatile evolution of
residential investment. The SBB was not able to identify the transitory component of the rev-
enue and expenditure. Consequently, in the third section, several improvements of the SBB
traditional methodology proposed by the literature are reviewed. The fourth section adapts
some of these improvements to the particular case of Spain and shows the results obtained.
Finally, conclusions and policy recommendations are presented in section 5.
2. Descriptive analysis of the case of Spain within the European context
2.1. Evolution of public accounts
During the 90s, the budgetary policy in Eurozone countries was dominated by compli-
ance with the Stability and Growth Pact, which established limits on both government deficit
and public debt (3% and 60% of GDP, respectively). In this context, Spain seemed to pio-
neer a counter-cyclical policy. As shown in figure 1, the country progressed from a chroni-
cally high fiscal deficit in the first half of the 90s to a moderate deficit by the end of the
decade, and finally achieved a surplus of 2% of GDP in the years leading up to the reces-
sion. nevertheless, Spanish apparent solvency disappeared with the subprime crisis. Al-
though the recession punished the budgets of all European countries, Spain was particularly
affected. Indeed, between 2007 and 2009 the balance fell from a surplus of 1.9% of GDP to
a deficit of 11.1%, a decline of 13 p.p. in just two years, when the European Union average
(15 countries) fell only by 6 p.p.
Spanish fiscal decline was the result of the evolution of both expenditure and revenue.
The weight of expenditure in the GDP increased by 7 p.p. due to the combined effect of dis-
cretionary policies aimed at maintaining aggregate demand and automatic stabilisers in the
form of unemployment benefits. Revenue, meanwhile, fell by 6 p.p. of GDP, from slightly
more than 41% in 2007 to less than 35% in 2009, the lowest of all Eurozone countries. As
12 GUIDO ZACK, PILAR POnCELA, EvA SEnRA y DAnIEL SOTELSEK

13Some new Results on the Estimation of Structural Budget Balance for Spain
Figure 1. Observed and structural budget balance
(as a % of GDP and potential GDP)
Source: European Commission.
shown figure 2, the main reason for this was the loss of revenue from corporate tax (2.4 p.p.),
added value tax (2.3 p.p.), personal income tax (0.9 p.p.) and capital transfer tax and stamp
duty (0.9 p.p.).
The evolution of Spanish SBB in these years did not differ substantially from the OBB.
Indeed, from the mid-90s the SBB also showed a process of fiscal consolidation, reaching a
surplus of 1% of potential GDP in 2006 1. When the subprime crisis hit the market, the SBB
returned to negative figures, with a deficit of 8.6% of potential GDP in 2009 (figure 1).
This shortfall in the Spanish SBB was not seen in other European countries. Thus, it is
essential to ascertain what was different in Spain, moreover considering that its new budg-
etary stability law included in the Spanish Constitution is based on this indicator 2. The an-
swer can be found in the collapse of residential investment, since most revenues were direct-
ly and indirectly linked to the construction industry, while this sector was mainly responsible
for the loss of employment and, therefore, the increase in related expenditure (REAF, 2007).

2.2. Evolution of residential investment
In the years leading up to the 2007 crisis, housing prices rose sharply across most Euro-
pean countries, particularly in the UK and Spain, where they increased threefold, and to a
lesser extent in other countries such as France, Italy and Portugal (figure 3). Although price
variations in Spain were among the greatest, this would not seem enough to explain the no-
table difference in public accounts.
The greatest difference is found in the number of units built: while the GDP share of res-
idential investment remained either steady or even declined in most EU countries, in Spain
it rose sharply until 2006 (figure 4). This is because the increase in housing prices, instead
of discouraging buyers fearful of a reversal of the trend, had the opposite effect based on the
belief that prices would continue to raise, a clear indication of the creation of a bubble 3 (Gar-
cía Montalvo, 2003).
Figure 2. Spanish government revenue and expenditure
(as a % of GDP)
Source: Spanish Ministry of the Finance and Public Administrations.

Accordingly, residential investment in Spain grew at an average rate of 8% per year
in real terms over the 1998-2007 period, twice the rate of the economy as a whole and
accounting for one fifth of the rise in GDP. Thus, by 2006 it accounted for 12.5% of
nominal GDP in Spain, when historically it had stood between 5% and 8%. In the
labour market, the construction sector accounted for 13.5% of all jobs in Spain, and
nearly a quarter of the growth in employment over the same period. When the bubble
burst, residential investment shrank to 4.4% of nominal GDP in 2013, while the con-
struction sector was directly responsible for the loss of 60% of the jobs between 2007
and 2011.
To sum up, the evolution of the construction sector and the housing market, not only in
terms of prices but also in the number of units built, could be responsible for the volatility
of Spanish public accounts.
Figure 3. Housing prices in Europe
(1996=100)
Source: European Central Bank.

3. SBB traditional methodology limitations
The SBB is estimated by the European Commission (EC) and the International Mone-
tary Fund (IMF). Both organisations base their calculation on the cyclical position of the
economy, i.e., the output gap (OG), and on the relationship between the cycle and the differ-
ent balance components (revenue and expenditure) 4. The cycle is linked to the budget
through the elasticities between the main revenue items (direct taxation of individuals and
companies, indirect taxes and social security contributions) and the OG, while in terms of
expenditure, only unemployment expenditures are considered cyclical. Then, the structural
components are calculated by multiplying the observed revenue or expenditure net of one-
off and temporary measures (Mourre et al., 2013) by the OG to the corresponding elasticity
power. In the case of expenditure, the IMF approach differs from the EC in that instead of
basing their calculations on the OG and the elasticity, they use a simple proportional rule be-
tween observed unemployment expenditures, the rate of unemployment and the non-Accel-
erating Wage Rate of Unemployment (nAWRU). Finally, the structural revenue and expen-
Figure 4. Residential investment in Europe
(as a % of GDP, 2005 prices)
Source: European Commission.

diture are subtracted and the result is divided by the potential GDP (Girouard and André,
2005; Hagemann, 1999).
The objective of estimating the SBB is twofold: first, to assess the cyclical behaviour of
discretionary fiscal policies; and second, to attempt to measure the sustainability of such
policies by showing whether a fiscal balance is the result of the cyclical components of rev-
enue and expenditure, or whether it is based on a more stable trend (Blanchard, 1990). nev-
ertheless, in several cases, this indicator failed to explain both, the cyclicality and the sus-
tainability of fiscal policy. The reasons that led to these problems are the following.
First of all, SBB real-time estimation could be biased not because an error in its method-
ology, but because a bias in the OG real-time estimation. Kempkes (2012) shows that the OG
real-time estimation is biased. This bias points towards a more negative OG between 0.5%
and 0.6% of potential output. Thus, if a fiscal rule is based on this indicator, the real-time
admissible deficit will exceed the final value by about 0.5 percentage points per year 5.
Assuming an unbiased OG, SBB could still be biased because of the decoupling between
the tax base and GDP (Kremer et al., 2006; European Commission, 2013, Part III). This can
be partially corrected by taking into account the effects of the composition of demand
through more disaggregated indicators than the OG, such as the tax bases (European Com-
mission, 2007, Part II). But, each tax has sometimes several bases and it is not always pos-
sible to consider all of them. Thus, it is desirable to take into consideration at least the most
important ones and the assets that can be affected by a bubble. If not, a significant bias in
elasticity estimations could be generated 6.
In fact, de Castro et al. (2008) show that output elasticities of revenues and expenditures
are not constant across the cycle, but rather pro-cyclical. This could cause the cyclical pat-
tern in the revenues windfalls and shortfalls (Morris et al., 2009). That is why, in times of
economic growth, part of the cyclical revenue is usually considered structural (Girouard and
Price, 2004; Joumard and André, 2008). Therefore, based on real time data, fiscal policies
seem to be counter-cyclical, while after-the-event input suggests pro-cyclicality (Cimadomo,
2008; Golinelli and Momigliano, 2007; Forni and Momigliano, 2004).
Taking into account the SBB limitations to measure the fiscal policy cyclicality, the Eu-
ropean Commission (2013, part III) has developed a new indicator to measure the discre-
tionary fiscal effort (DFE). It consists of a narrative (bottom-up) approach on the revenue
side and a traditional (top-down) approach on the expenditure side. A comparison of both in-
dicators shows that, during booms, DFE indicates a more pro-cyclical policy than real time
estimation of SBB, in line with the evidence presented in the previous paragraph.
However, the DFE does not aim at measuring the sustainability of the fiscal policy. To
do so, and taking into account the SBB limitations reviewed in this section, the impact of
bubbles and booms in the cyclical components needs to be considered. In fact, de Castro et
al. (2008) and Morris et al. (2009) recognize that the housing boom could be responsible for

the notable revenues windfalls presented in Spain between 1999 and 2007. So, this paper
main objective is to include the real estate asset in the Spanish SBB estimation during the
years prior to the subprime crisis at the aim of improving the fiscal policy sustainability
measure.
4. Estimated structural budget balance for Spain
4.1. Methodology
First of all, tax collection must be disaggregated into revenue categories. As we are deal-
ing specifically with Spain, we can achieve a more accurate disaggregation instead of rely-
ing on the EC general categories. Revenue is therefore disaggregated into: social security
contributions (SSC), personal income tax (PIT), corporate tax (CT), property tax (PT; in-
cludes inheritance and gift tax and the discontinued wealth tax), value added tax (vAT), cap-
ital transfer tax and stamp duty (CTT-SD) and regional governments revenue (RG) 7.
Tax base proxies are used instead of the OG as explanatory variables. Thus composition
effects are considered. The tax base proxies used for each revenue item are as follows: for
the SSC, the average wage (W), the number of people employed (EM), and unemployment
benefits (UB); for PIT, the gross disposable household income (GDHI) and the average tax
rate (RatePIT); for CT, the gross disposable corporate income (GDCI) and the average tax
rate (RateCT) 8; for vAT, private consumption (PRC). Finally, for the PT, the CTT-SD and
the RG the residential investment (RI) is the only explanatory variable. RI is also included
in all the equations as an additional tax base because it takes into accounts not only prices,
but also quantities. Therefore, it can be considered as a proxy of economy-wide effects, since
the number of units built may affect employment in the construction sector, wage increases
and consumption rises, among others.
Annual data from 1986 to 2010 are used and the sources can be found at the annex A.
Following Girouard and Price (2004), Kremer et al. (2006), Morris and Schuknecht
(2007), and Price and Dang (2011), an error correction model was estimated for each tax rev-
enue category to capture both, short- and long-term relationships. Accordingly, we have es-
timated a long-term equation (1) linking the levels of the variables (in logarithms) for each
revenue category. Equation (2) shows the short-term dynamics between the revenue varia-
tion rate, due to long-term deviations from equilibrium, the variation rate of the respective
tax base proxies and (nominal) residential investment.
(1)
(2)
ln ln ln, , , ,T B RIi t i i j
j
N
i j t i tlt lt
i
lt
= + + +
=
∑α β γ
1
εεi t,
∆ = + ∆ + ∆
=
∑ln ln ln, , , ,T B Ri t i i j
j
N
i j t ist st
i
st
α β γ
1
II et i i t i t+ +−δ ε , ,1

where Ti is the observed revenue of the “i” revenue category; Bi,j is the “j” tax base proxy of
the “i” revenue category; Rl is the nominal residential investment; bi,j is the elasticity 9 of “i”
revenue to the “j” tax base; gi is the elasticity of the “i” revenue items to nominal residential
investment; subscripts “lt” and “st” stand for the long-term and short-term equations respec-
tively; di is the error correction factor; ∆ is the first difference operator; and ln is the natural
logarithm.
Once the coefficients have been obtained, the equation yields the structural value of each
revenue item based on the long-term values of the tax bases and residential investment prox-
ies. In the former case, following Kremer et al. (2006), tax bases proxies were smoothed
using a Hodrick – Prescott filter (λ=30) 10. In the latter case, the long-term value is taken to
be a fixed percentage of nominal GDP. various scenarios are developed based on the evo-
lution over time in Spain, ranging from 5% to 8% 11. Therefore, the structural value of each
revenue item is obtained from the following equation, adapted from the EC and IMF
methodology based on the OG (Girouard and André, 2005; Hagemann, 1999):
(3)
where the superscript “*” indicates structural value.
Adding up all structural revenue and tax revenue not included in the quantitative calcu-
lation (basically excise tax, foreign trade and gambling tax, that represent less than 10% of
total revenue and are taken to be unrelated to the evolution of the construction industry), we
obtain the total structural revenue.
(4)
where the superscript “*” indicates structural value; T is the total revenue; Ti is the revenue
stemming from residential investment; and NIT are the revenue items not related to residen-
tial investment.
According to the EC and IMF approaches, the only cyclical item of expenditure is un-
employment benefits (Girouard and André, 2005; Hagemann, 1999). The nAWRU, estimat-
ed by the EC, is used as the structural unemployed rate. However, the methodology of this
indicator (Mc Morrow and Roeger, 2000) affirms that it cannot be considered structural,
since it estimates the unemployment rate that stabilises the inflation rate, while the long-term
rate should stabilise both inflation and unemployment. Consequently our methodology does
not use the annual nAWRU value, but rather its 20-year moving average 12. This increases
the unemployment expenditure that is considered cyclical because it allows a greater differ-
ence between the long-term and observed unemployment rate. For the sake of simplicity, we
have followed the IMF method of calculating cyclical expenditure, i.e., a proportional rule
considering the monetary value of unemployment benefits, divided by the unemployment
rate and multiplied by the nAWRU moving average.
T T
B
B
RI
RI
i i
i j
i jj
Ni
i jlt
* ,
*
,
*,
=









=
∏
1
β



γilt
T T NITi
* *
= +∑

(5)
where the superscript “*” indicates the structural value; E is the total public expenditure; Ec
is the cyclical public expenditure; Eu is the unemployment expenditure; and UR is the unem-
ployment rate.
Finally, SBB is obtained by subtracting revenue from structural expenditures.
SBB* = T* – E* (6)
4.4. Results
First of all, we perform Dickey-Fuller (1981) unit roots tests to identify the order of in-
tegration of all variables. As shown in table B.1 in the annex B, we did not reject the unit
root hypothesis for the levels of the variables (in logarithms) in any case.
Table 1 shows short- and long-term coefficients obtained from the error correction
model estimated on each tax revenue using as covariates their tax base and nominal residen-
tial investment proxies, together with the error correction term. The annex B shows evidence
that confirms the suitability of the models used. Specifically, table B.2 shows the statistics
of the unit root test on the residuals of the long-term equations and MacKinnon’s (1991) crit-
ical values. It can be seen that, with the exception of the RG variable, the unit root null hy-
potheses is rejected for the residuals of all long-term equations (1), and as such they can be
considered stationary. In the case of RG, we concluded that there is no cointegration rela-
tionship between this variable and residential investment. Table B.3 shows the autocorrela-
tion value of the first two lags of the residuals from the long-term equations (1) and the error
correction models (2). The Ljung-Box (1978) statistic shows that the residuals in all short-
term equations are white noise, which confirms the suitability of the models. The residuals
of the long-term equations or cointegration relationships can be considered white noise for
all variables, except for IS. In this case, the efficiency of the cointegration vector estimation
can be improved using Stock and Watson’s (1993) dynamic ordinary least squares (DOLS)
technique, that shows very similar results 13.
Going back to table 1, it can be seen that residential investment turned out to be a very
significant variable to explain the evolution of all the revenue items considered, and that the
coefficients signs are as expected. Likewise, the remaining variables are also significant, and
their signs as expected too 14. Error correction terms, which show the percentage of devia-
tion corrected for each period, are significant, being in all cases between 39% and 87%, ex-
cept for SSC, which seems to over-correct the deviation by 9%.
It can also be seen that there are no RG values in either the long-term equation or the
error correction term. This is because, as discussed previously, the residuals from its long-
E E E E E E E E E
UR
UR
E Ec u u u u u
* *
*
( )= − = − − = − −





 = − 1−−






UR
UR
*

term equation were not stationary. Therefore, in this case, we supposed that there is no coin-
tegration relationship between variables, meaning that the coefficient used to estimate
structural revenue is taken from the short-term equation in which no error correction was
included.
Table 1
TAX REVENUE ELASTICITIES
Error
RI W EM UB GDHI RatePIT GDCI RateCT PRC C correction R2
term
Short-term
SSC 0.15** 0.82*** 0.59** 0.07** 0 –1.09*** 0,90
–0,06 –0,1 –0,22 –0,03 –0,01 –0,26
PIT 0.20*** 0.43*** 0.18*** 0.03*** –0.69*** 0,98
–0,03 –0,14 –0,01 –0,01 –0,2
CT 0.39*** 0.42** 0.33*** 0 –0.39** 0,92
–0,08 –0,12 –0,03 –0,01 –0,15
PT 0.99*** 0 –0.87*** 0,58
–0,25 –0,04 –0,21
vAT 0.73*** 1.58*** –0.11*** –0.89*** 0,99
–0,13 –0,45 –0,02 –0,16
CTT–SD 1.39*** 0 –0.82*** 0,66
–0,19 –0,03 –0,21
RG 0.29*** 0.06*** – 0,47
–0,06 –0,02 –
Long–term
SSC 0.13*** 0.87*** 0.61*** 0.08*** 0,16 0,99
–0,04 –0,04 –0,16 –0,02 –0,85
PIT 0.14*** 0.90*** 0.14*** –3.62*** 0,99
–0,02 –0,04 –0,01 –0,32
CT 0.28** 0.65*** 0.38*** –1,30 0,98
–0,12 –0,15 –0,05 –1,00
PT 0.85*** –0,38 0,93
–0,05 –0,86
vAT 0.46*** 0.33*** 0,00 0,99
–0,04 –0,04 –3,95
CTT–SD 1.05*** –2.98*** 0,97
–0,04 –0,65
note: *** significant at 1%; ** significant at 5%; * significant at 10%.
Figures 5 and 6 show the evolution of total revenue and unemployment expenditure, re-
spectively, both in terms of observed and structural values. In the case of revenue, different
long-term residential investment scenarios are presented. It can be seen that up to 2002 the
observed revenue did not differ significantly from the structural revenue as expected. How-
ever, from 2002 to 2008 the observed revenue exceeded all long-term residential investment
scenarios, peaking in 2007 with a difference of 3.3% of GDP compared with the average sce-
nario (6.5%). Since 2009, after the bubble burst, the observed value has returned to the struc-
tural one.

Regarding unemployment expenditures, the observed and the traditional structural ones
are very close to each other, given the small difference between the unemployment rate and
the nAWRU. On the contrary, our structural estimation presents lower values until 1998 and
higher values from that year on. The highest difference in comparison with the observed
value is 1.1% of GDP in 2006 and 2007. Since 2009, our estimation is lower again, given
that it does not take into account completely the extremely high unemployment rate of these
years and due to the reduction in the unitary benefits.
Finally, figure 7 shows the OBB, the SBB estimated by the EC and IMF, our own esti-
mate of the SBB for the average scenario (where the nominal GDP share of residential in-
vestment is 6.5%) and other estimates from earlier papers (Price and Dang, 2011; Morris and
Schuknetch, 2007). First, it can be seen that all the SBB are relatively similar until 1998.
From this year onwards, our own estimate starts to show a downward trend that dips more
sharply after 2001, while the remaining estimations maintain their upward trend for several
more years.
Figure 5. Observed and structural revenue
Source: Own estimations and Spanish Ministry of the Finance and Public Administrations.

With regard to the SBB estimated by the EC and IMF, it is interesting to note how sim-
ilar they are to the OBB, particularly in the case of EC estimates. IMF estimates differ to a
certain extent due to subsequent corrections, but the unrevised estimates are very similar to
those of the EC and the OBB. As discussed above, it is obvious that these indicators need to
be adjusted, since the advantage of a long-term indicator lies in its capacity to differ from
observed data in order to present an overview of the economy that is unaffected by circum-
stantial factors.
This is the aim of the articles written by Price and Dang (2011) and Morris and
Schuknetch (2007). To achieve this, both incorporated the price of financial and property as-
sets in their balance estimates. As indicated above, the methodology used is similar to that
used in this article. Total revenue is divided in different categories, for each category an error
correction model is developed that includes the tax base of the different taxes and the asset
price as explanatory variables. Price and Dang (2011) obtain the structural value of the fore-
going prices in two ways: one based on fundamentals, and the other by means of a Hodrick
Figure 6. Observed and structural unemployment expenditure
Source: Own estimations, Spanish Ministry of the Finance and Public Administrations and European Commission.

and Prescott filter. Morris and Schuknetch (2007) only use the filter. Despite these changes,
it is interesting to note that their structural estimates do not differ to any great extent; at least
they are not capable of identifying fiscal difficulties in advance of observed balances.
The main advantage of the approach suggested here, therefore, is that it shows that long-
term public accounts had started to deteriorate in 1999, with a 3% potential GDP deficit by
2002, and below 4% from 2004 onwards. This would have identified Spanish potential fis-
cal troubles several years in advance, and measures could have been taken to correct them
in real time. The estimation is improved because the general methodology for all countries
is adapted to a particular case. The main feature of Spain over the period studied, as dis-
cussed above, is that the real estate bubble was accompanied by a construction boom, which
means that the process not only affected prices but also the number of units built, a factor
that was not taken into consideration in earlier papers. In other words, instead of discourag-
ing buyers, the rise in housing prices had the opposite effect due to the belief that the upward
trend would continue. This is why it was decided to include nominal residential investment
in our SBB calculation in order to capture the effect of both the prices and units built.
Figure 7. Observed and structural budget balance
(as a % of GDP and potential GDP, respectively)
Source: Own estimations, Spanish Ministry of the Finance and Public Administrations, EC, IMF and referred papers.

As a robustness check, we have estimated our model with data up to 2004 and 2007. We
obtain similar results and none of our conclusions are altered 15. The scenario after the crisis
may have changed. We were able to estimate a model with data up to 2004 and 2007 because
we had enough data. Unfortunately, we only have few observations after the crisis and it is
not possible to estimate a model with these few data. But, in any case, the current SBB esti-
mation is not the final word on the future outlook for public finances in Spain. In fact, the
lower growth rates that are foreseen for Spain in the next years should be taken into account
in future research.
5. Conclusions
This article attempts to make a contribution to understand the extremely deterioration in
the Spanish budget balance. This situation could not be anticipated by the SBB estimated by
the EC and the IMF, given that they showed a structural surplus until 2007. Based on these
figures, the Spanish authorities took a series of decisions to fight against the recession of
2008 on the assumption that public accounts were structurally sound and had a large enough
buffer to allow counter-cyclical policies to be implemented. However, it later became evi-
dent that the buffer was nowhere near as robust as expected, forcing them to withdraw tax
incentives before the private sector was ready to spearhead the aggregate demand.
Such situations might arise due to the failure to include the effect of bubbles in SBB cal-
culations. nevertheless, estimates carried out by other authors who did include such vari-
ables in their calculations were not capable of improved significantly Spanish SBB either, at
least not enough to identify the deterioration of the fiscal situation in time. The main reason
for this is that the studies were carried out for a group of countries or a particular region, and
as such the particular characteristics of each country are not factored in.
In this regard, Spain was in a unique situation because the real estate bubble affected not
only prices but also the number of units built, which means that including property prices as
the sole explanatory variable of tax revenue was not enough. In this article we have used
nominal residential investment as a way of including the number of units built too. It is in-
teresting to note that this type of investment has, historically, represented around 6.5% of
nominal GDP, but it reached 12.5% in the years leading up to the crisis.
The results obtained confirm how important residential investment was in Spanish tax
revenue, at least, until the bubble burst. Indeed, it can be seen that including this variable in
the equation, and assuming different scenarios concerning its long-term share in GDP, re-
veals that Spanish SBB started to show a strong deficit as far back as 2004. Taken this data
into consideration, different fiscal policy decisions would probably have been taken not only
from 2008 onwards, since the start of the crisis, but also in preceding years.
To conclude, it is essential to include bubbles and booms in SBB estimates. This is es-
pecially important in Spain, given that its fiscal rule is based on this indicator.

Annex A
Serie Source
Observed budget balance
Total revenues
Social security contributions
Total expenditures
Unemployment benefits
Gross domestic product
Gross disposable corporate income
Gross disposable household income
Private consumption
Employment
Unemployment
Wages
Personal income tax
Corporate tax
Inheritance and gift tax
Wealth tax
value added tax
Capital transfer tax
Stamp duty
Real Property Tax
Motor vehicle Tax
Urban Land value Increase Tax
Tax on Economic Activities
Tax on Building, Installations and
other Work
Local rates
Structural budget balance
Potential GDP
Structural revenues
Structural expenditures
nAWRU
Residential investment
Housing prices
Average personal income tax rate
Average corporate tax rate
Ministerio de Hacienda y Administraciones Públicas
de España
http://www.sepg.pap.minhap.gob.es/sitios/sepg/es-ES/Presu-
puestos/Documentacion/paginas/bdmacro.aspx
de España
http://www.minhap.gob.es/es-ES/Estadistica%20e%20Infor-
mes/Impuestos/Paginas/Impuestos.aspx
de España
http://serviciosweb.meh.es/apps/EntidadesLocales/
European Commission
http://ec.europa.eu/economy_finance/ameco/user/serie/Se-
lectSerie.cfm
International Monetary Fund
http://www.imf.org/external/ns/cs.aspx?id=28
European Commission
http://ec.europa.eu/economy_finance/ameco/user/serie/Se-
lectSerie.cfm
European Commission
http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/s
earch_database
European Central Bank
http://sdw.ecb.europa.eu/browse.do?node=2120781
OECD
http://stats.oecd.org/

AnnexB
TableB.1
UNITROOTTESTS(Dickey-Fullercriticalvalues,1981)
LevelsFirstDifferencesSeconddifferences
Variable
Nointercept,Withintercept,Withintercept,Nointercept,Withintercept,Nointercept,
notrendnotrendandtrendnotrendnotrendnotrend
“t”statisticP-value“t”statisticP-value“t”statisticP-value“t”statisticP-value“t”statisticP-value“t”statisticP-value
Socialsecuritycontributions0,2770,757–0,7770,807–2,5250,314–1,1570,218–1,8260,359–4,6650,000
Personalincometax3,7771,0002,1441,000–0,9670,9250,5130,817–4,2660,003––
Corporatetax–0,3180,560–1,3990,566–1,7320,697–3,5340,001–2,0340,271––
Propertytax–0,2400,589–1,4840,524–2,8840,185–4,6110,000–4,5700,002––
valueaddedtax0,9580,905–1,0110,732–2,8500,197–4,0010,000–4,0080,007––
Capitaltransfertaxand
stampduty–0,8680,329–1,8810,335–0,7340,956–2,6240,011–3,1880,035––
Regionalgovernmentrevenue3,9401,0003,6221,0001,3661,000–0,6840,408–2,0410,268–5,2650,000
Residentialinvestment3,3400,9991,9421,000–0,5740,971–1,3450,160–4,2650,003––
Wage1,1650,932–1,0450,718–3,2090,114–0,7010,401–3,4930,018––
Employed1,5800,968–1,3800,574–2,8690,190–1,7940,070–2,7210,087––
Unemploymentbenefits3,1200,9992,0781,0000,7550,999–2,4700,016–2,7980,074––
Consumption2,7070,9972,0581,000–2,0310,555–1,4190,141–3,2460,030––
Householdincome21,1201,0006,4911,0002,6591,000–0,9220,306–1,0520,713–5,9630,000
Corporateincome3,3390,9990,6270,987–2,0000,566–2,1640,032–3,9220,008––
PITrate0,5340,824–1,6940,421–2,1580,489–6,2630,000–6,0740,000––
CTrate–0,4220,520–1,7720,385–2,7600,225–4,0050,000–3,9030,007––

Table B.2
UNIT ROOT TESTS OF THE RESIDUALS OF LONG-TERM EQUATIONS
(MacKinnon critical values, 1991)
“t” statistic
Critical values
a = 0,05 a = 0,1
Social security contributions –4,759 –4,998 –4,567
Personal income tax –4,980 –4,564 –4,152
Corporate tax –4,562 –4,564 –4,152
Property tax –4,218 –3,591 –3,218
value added tax –5,357 –4,098 –3,706
Capital transfer tax and stamp duty –3,363 –3,591 –3,218
Regional government revenue –1,920 –3,591 –3,218
Table B.3
AUTOCORRELATIONS OF THE FIRST TWO LAGS AND p-values
ASSOCIATED TO THE LJUNG-BOX STATISTICS (1978) FOR THE RESIDUAL
OF THE LONG- AND SHORT- TERM EQUATIONS
Equation Lags
Long term Short term
Auto
P-value
Auto
P-value
correlations correlations
Social security contributions 1 –0,021 0,913 –0,042 0,827
2 –0,062 0,939 –0,082 0,887
Personal income tax 1 –0,036 0,847 0,223 0,245
2 –0,007 0,981 0,087 0,457
Corporate tax 1 0,401 0,040 –0,022 0,911
2 –0,228 0,060 –0,147 0,747
Property tax 1 0,126 0,504 –0,029 0,879
2 0,266 0,284 0,245 0,422
value added tax 1 0,070 0,709 0,107 0,577
2 –0,231 0,426 –0,076 0,788
Capital transfer tax and stamp duty 1 0,355 0,060 0,073 0,704
2 –0,142 0,126 –0,282 0,301
Regional government revenue 1 – – 0,137 0,476
2 – – –0,095 0,682
Notes
1. Alternative SBB estimations, which are analyzed afterwards (Price and Dang, 2011; Morris and Schuknetch,
2007), also showed a surplus in the years prior to the crisis (see figure 7).
2. For an analysis of the changes introduced, the rationale and shortcomings of the new budgetary stability law
see Hernández de Cos and Pérez (2013).

3. According to Stiglitz (1990) a bubble exists when the price of an asset grows only because investors believe
it will continue to increase in the future, although fundamental factors do not justify this belief.
4. While the OG is an essential driver of the SBB, its estimation remains outside the scope of this paper. For the
OG estimation, see Denis et al. (2006) and D’Auria et al. (2010).
5. This real-time bias in the admissible deficit would be added to another bias identified by de Castro et al.
(2013), related to political issues such as unfavorable economic conditions or elections.
6. For example, if a financial or real estate bubble raises the price of assets, it would affect certain tax bases, but
not the OG, affecting the elasticities (Joumard and André, 2008).
7. This category is the sum of Real Property Tax (Impuesto sobre bienes inmuebles, or “IBI”), Motor vehicle
Tax (Impuesto sobre vehículos tracción mecánica, or “IvTM”), Urban Land value Increase Tax (Impuesto
sobre incremento valor terrenos naturaleza urbana, or “IIvTnU”), Tax on Economic Activities (Impuesto
sobre actividades económicas, or “IAE”), Tax on Building, Installations and other Work (Impuesto sobre con-
strucciones, instalaciones y obras, or “ICIO”), and local rates.
8. RatePIT and RateCT are calculated by the OECD as the ratio between the personal income tax and the corpo-
rate tax revenues, respectably, and the GDP. Therefore, these are not statutory rates.
9. Equations (1) and (2) should take on board legislation changes in order for β and γ to be interpreted as proper
tax elasticities in the sense in which the literature uses them. As we focus on the fit of the equations, we use
these coefficients as proxies of the real elasticities.
10. The choice of the value of the parameter is discussed in Bouthevillain et al. (2001).
11. In the pre-bubble two decades (between 1980 and 2000), residential investment had never been less than 5%
or more than 8% of GDP.
12. Results are not significantly modified when different moving averages are used.
13. Results are available upon request.
14. We attempted to include lagged variables in short-term equations, but none were significant. This was expect-
ed since annual data are used. Thus, only contemporary dynamics are shown.
15. We thank both anonymous referees for suggesting this robustness check.
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gaps”, European Commission Economic Papers, 420.
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aproximación al componente transitorio del saldo público en España”, Boletín Económico del Banco
de España, 6: 71-81.

de Castro, F.; Pérez, J. and Rodríguez-vives, M. (2013), “Fiscal Data Revisions in Europe”, Journal
of Money, Credit and Banking, 45 (6): 1187-1209.
Denis, C.; Grenouilleau, D.; Mc Morrow, K. and Röger, W. (2006), “Calculating potential growth rates
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MPRA Paper, 4315.
García Montalvo, J. (2003), “La vivienda en España: desgravaciones, burbujas y otras historias”, Fun-
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tries”, OECD Economics Department Working Papers, 434.
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ances”, OECD Economics Department Working Papers, 391.
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Resumen
La recesión iniciada en 2008 generó un fuerte deterioro del resultado fiscal de España. Este deterioro
no fue completamente anticipado por el resultado fiscal estructural debido a ciertas limitaciones meto-
dológicas. En este artículo se calcula un resultado estructural alternativo para España durante los años
previos a la crisis subprime, que incluye la inversión en vivienda como variable explicativa. Esta esti-
mación muestra que desde 2004 la situación fiscal española no era tan sólida como se creía y que la
fragilidad estuvo oculta gracias a los recursos extraordinarios resultantes de la burbuja inmobiliaria y
del boom en la construcción.
Palabras clave: crisis, resultado fiscal estructural, burbuja inmobiliaria, boom de la construcción, Es-
paña.
Clasificación JEL: C20, E32, E63, H62.

"Some New Results on the Estimation of Structural Budget Balance for Spain"; Guido Zack, Pilar Poncela, Eva Senra, Daniel Sotelsek

"Some New Results on the Estimation of Structural Budget Balance for Spain"; Guido Zack, Pilar Poncela, Eva Senra, Daniel Sotelsek

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