Towards an effective structural budget balance for economic stability

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DOCUMENTOS DE TRABAJO UC-CIFF-IELAT Nº 13
Junio 2013
Towards an effective structural budget balance for
economic stability
Guido Zack,
Pilar Poncela,
Eva Senra y
Daniel Sotelsek

Towards an effective structural budget balance for
economic stability
Guido Zack, Pilar Poncela, Eva Senra y Daniel Sotelsek

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Zack, Poncela, Senra y Sotelsek. “Towards an effective structural budget balance for economic stability” Junio 2013
UC - CIFF – IELAT | 1
Towards an effective structural budget balance for economic
stability
Guido ZackI
Pilar PoncelaII
Eva SenraIII
Daniel SotelsekIV
Abstract
The recession that started in 2008 caused a sharp deterioration of the budget balance
of many developed countries, not only as a result of discretionary policies but also due
to the loss of revenue caused by the decline in gross domestic product. The latter
effect should have been captured by the cyclically adjusted budget balance, but certain
methodological limitations prevented this from occurring. Consequently, in this article
we raise the need to include asset revaluation in the calculation of the cyclically
adjusted budget balance. To do this, we estimate a structural budget balance for Spain
in the years prior to the sub-prime crisis that includes residential investment as an
explanatory variable. This estimate shows that by 2004 the fiscal situation in Spain was
already tenuous, but this fragility was hidden by the extraordinary revenue from the
real estate bubble.
Keywords
Crisis, structural budget balance, real estate bubble, Spain.
Resumen
La recesión iniciada en 2008 generó un fuerte deterioro del resultado fiscal de muchos
países desarrollados, no solo por las políticas discrecionales, sino también por los
menores ingresos como consecuencia de la caída del producto bruto interno. Este
último efecto debería haber sido captado por el resultado fiscal cíclicamente ajustado,
pero ciertas limitaciones metodológicas lo impidieron. Es por ello que en este artículo
se plantea la necesidad de introducir la revalorización de activos en su cálculo. Al
respecto, se desarrolla una estimación del resultado fiscal estructural de España en los
años previos a la crisis sub-prime que incluye como variable explicativa la inversión en
vivienda. Esta estimación muestra que la situación fiscal de España era frágil desde
2004, pero que la fragilidad estuvo oculta gracias a los recursos extraordinarios
resultantes de la burbuja inmobiliaria.
Palabras clave
Crisis, resultado fiscal estructural, burbuja inmobiliaria, España.
I
PhD Candidate, U. Alcalá. zack.gui@gmail.com
II
Department of Economic Analysis: Quantitative Economy, U. Autónoma de Madrid.
pilar.poncela@uam.es
III
Department of Economics, U. de Alcalá. eva.senra@uah.es
IV
Department of Economics, U. de Alcalá. daniel.sotelsek@uah.es

1. Introduction
Usually, total or primary budget balance is consulted when the fiscal situation of a
given country needs to be determined. These indicators can be useful for a preliminary
calculation, but they have the drawback of reflecting circumstantial, volatile revenues
and expenditures that are the product of a particular phase in the economic cycle. In
order to determine the long-term fiscal situation, these cyclical components must be
separated from other government transactions.
In order to achieve this, a number of alternative measures have been developed that
aim to capture permanent budget balance components. These estimates have two
main objectives: first, to analyse 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 revenue and
expenditure, or whether it is based on a more stable trend (Blanchard, 1990: 5).
The need for a good indicator for the latter objective has been presumed even more
acutely in Europe since the economic and monetary union. Although the Stability and
Growth Pact had, until 2005, based its objectives solely on observable results
(medium-term budgetary balance or surplus with a total deficit limit of 3% of gross
domestic product (GDP) and a public debt-to-GDP ratio of not more than 60%), both
the European Commission (EC) and the International Monetary Fund (IMF) make
estimates of the cyclically adjusted budget balance (CABB). The sub-prime crisis
highlighted certain limitations in these calculations since their values did not differ
significantly from the observed budget balance (OBB), while in many countries the
estimates were subsequently corrected.
A prime example of this is Spain which, in the years leading up to the crisis, seemingly
had one of the most evidently countercyclical fiscal policies in Europe, which
apparently left it in a strong economic position. In 2006 and 2007, it had a surplus of
2% of GDP, while the CABB did not differ substantially from these figures. Public debt,
meanwhile, following the Excessive Deficit Procedure protocol, had fallen from 67.5%
of GDP in 1996 to 36.3% in 2007. Nevertheless, the crisis revealed the fragility of these
improvements, since between 2007 and 2009 the balance fell by 13 percentage points
(p.p.) of GDP, a trend that was echoed in the cyclically adjusted indicators, while debt
had risen to 84.1% of GDP by 2012.
In this article, we show that Spanish structural budget balance (SBB1
) was not as solid
as it seemed. The difficulty in using EC and IMF estimates to identify this situation
stems from the fact that they do not consider the effect of asset revaluation on tax
1
In this article we have taken CABB to be the calculation that corrects OBB solely in terms of cyclical
effect, while SBB also corrects OBB in terms of asset revaluation.

revenue. What made Spain unique is that tax revenue was not only affected by the rise
in housing prices, but also by the great number of homes built. After taking both
effects into consideration, it can be seen that the apparent solvency of the Spanish
economy in the years leading up to the crisis was due to the extraordinary revenue
obtained from the real estate bubble.
To achieve our goal, we have structured the article as follows. The following section
describes the evolution of the Spanish budget in the wider European context,
identifying a particularly volatile OBB trend. This would have been a reflection of the
volatile evolution of residential investment. Although related government revenue and
expenditure, being transitory, should have been differentiated by the CABB, it proved
to be beyond the scope of this indicator. Consequently, in the third section of this
article, we analyse both the approach on which the EC and IMF base their estimates
and the improvements put forward, which mainly involve including asset revaluation in
the formula used to calculate the SBB. The fourth section adapts this methodology to
the particular case of Spain and shows the results obtained. Finally, we present our
conclusions and policy recommendations.
2. Descriptive analysis of the case of Spain in the wider European context
Evolution of public accounts
During the 90s, budgetary policy in Eurozone countries was dominated by compliance
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
pioneered a counter-cyclical policy. As shown in Figure 1, the country progressed from
a chronically high 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
recession. Up to the beginning of 2000 this consolidation process was the result of
reduced expenditure and slightly higher revenue. From that time on, expenditure more
or less kept pace with GDP, while revenue spearheaded the process. Public debt,
meanwhile, fell from an average of nearly 60% of GDP in the 90s to 36.3% in 2007, the
sharpest reduction of all Eurozone countries. It is particularly interesting to note that
Spain is the only European Union (EU) member state where OBB continued to improve
even after the "dot com" crisis.
Nevertheless, the country apparent solvency disappeared with the "sub-prime" crisis.
Although 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%, in other words, a decline of 13 p.p. in just two years,
when the EU average (15 countries) was just 6 p.p.. Public debt, meanwhile, started to
grow, reaching 84% of GDP by 2012.

Figure 1. Observed and cyclically adjusted budget balance
(as a % of GDP and potential GDP, respectively)
Source: European Commission
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 discretionary 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 that 35% in 2009, the lowest of all
Eurozone countries. As shown in Figure 2, the main reason for this was the loss of
revenue from corporate tax (CT; 2.4 p.p.), added value tax (VAT; 2.3 p.p.), personal
income tax (PIT; -0.9 p.p.) and capital transfer tax and stamp duty (CTT-SD; -0.9 p.p.)2
.
2
Although the crisis had a striking effect on the job market, social security contributions (SSC) remained
largely unchanged because unemployment benefits continued to contribute. Therefore, the impact of
unemployment on the budget is centred on expenditure.
-12
-10
-8
-6
-4
-2
0
2
4
1995 1997 1999 2001 2003 2005 2007 2009 2011
Differential
Observed budget balance
Cyclically adjusted budget balance
Spain
-10
-8
-6
-4
-2
0
2
1995 1997 1999 2001 2003 2005 2007 2009 2011
Differential
Germany
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
1995 1997 1999 2001 2003 2005 2007 2009 2011
Differential
France
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
1995 1997 1999 2001 2003 2005 2007 2009 2011
Differential
Italy
-12
-10
-8
-6
-4
-2
0
2
1995 1997 1999 2001 2003 2005 2007 2009 2011
Differential
Portugal
-12
-10
-8
-6
-4
-2
0
2
4
1995 1997 1999 2001 2003 2005 2007 2009 2011
Differential
United Kingdom

Figure 2. Spanish government revenue and expenditure
(as a % of GDP)
Source: Spanish Ministry of the Finance and Public Administrations
Given the volatility of the Spanish public accounts, however, it is essential to ascertain
what set it apart from other European countries. The answer can be found in the
collapse of residential investment, since the construction sector is mainly responsible
for the loss of employment and, therefore, the increase in related expenditure, while
revenue fell due to the loss of taxes linked directly to this sector (REAF, 2007).
Evolution of residential investment
In the years leading up to the 2007 crisis housing prices rose sharply across most
European 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 notable difference in public accounts.
The greatest difference is found in the number of units built: while the GDP share of
residential 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
0
5
10
15
20
25
30
35
40
45
50
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
CTT-SD VAT CT
PIT SSC Other revenue
Total revenue Total expenditure

opposite effect based on the belief that prices would continue to raise, a clear
indication of the creation of a bubble3
(García Montalvo, 2003: 47).
Figure 3. Housing prices in Europe
(1996=100)
Figure 4. Residential investment in
Europe (as a % of GDP, 2005 prices)
Source: European Commission and Central European Bank
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 is had stood at around 7%. 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 around 6% of nominal GDP, while the construction
sector was directly responsible for the loss of 60% of 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. However, for the past fifty years experts have
been developing fiscal indicators aimed at separating transitory from permanent
components in order to identify a country long-term economic soundness. The most
widely used indicator of this type is the CABB published simultaneously by the EC and
the IMF. We will now analyse the results of this index.
Evolution of the cyclically adjusted budget balance
Figure 1 shows the CABB together with the OBB. At first glance it can be seen that the
indicators do not differ substantially. Over the 1995 - 2012 period, the difference never
exceeds 2 p.p. of GDP in the countries considered, a figure that is not high enough to
change the conclusions reached on both the financial solvency of the countries or the
cyclical nature of the policy in question.
3
According to Stiglitz (1990: 13) 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.
80
100
120
140
160
180
200
220
240
260
280
300
320
1996 1998 2000 2002 2004 2006 2008 2010
Germany
Spain
France
Italy
Portugal
United Kingdom
2
4
6
8
10
12
14
1996 1998 2000 2002 2004 2006 2008 2010
Germany Spain France
Italy Portugal United Kingdom

Spain is a particularly interesting case as the volatility of its OBB is hardly diminished by
its CABB. Indeed, from the mid-90s to 2007 the CABB also showed a process of fiscal
consolidation. During this period, the country went from a deficit of over 7% of
potential GDP to a 1% surplus in the closing years. When the sub-prime crisis hit the
market, the CABB returned to negative figures, with a deficit of 9.4% of potential GDP
in 2009. It is a decline of 10.4 p.p., which is similar to the results of the OBB.
This decline can be explained by an increase of 4.5 p.p. of potential GDP in cyclically
adjusted spending, while revenue fell by nearly 6 p.p.. The increase in expenditure,
meanwhile, is less pronounced than in the observed indicator because the cyclical
adjustment does not take into account a large portion of the rise in unemployment-
related expenditure. It is, however, significant to note that the loss of revenue is
identical in comparison with the observed indicator, suggesting that the fall in revenue
has absolutely nothing to do with the cyclical effect.
This is borne out by comparing the observed and cyclically adjusted revenue series as a
percentage of observed and potential GDP, respectively, published by the EC4
. As
shown in Figure 5, cyclically adjusted revenue is practically the same as observed
revenue since in no country do these two elements differ by more than 0.3 p.p.. In
other words, the EC methodology is not capable of adjusting revenue by cycle, and
therefore does not capture the transitory revenue derived from the real estate bubble
in Spain. Consequently, the difference between OBB and CABB is due entirely to the
adjustment made to unemployment expenditure, since this is the only outlay
considered cyclical in this methodology (Girouard and André, 2005: 13; Hagemann,
1999: 7-8), and does not usually represent more than 3% of total primary expenditure.
It is also interesting to note that the crisis did not only affect public accounts but also
forced analysts to correct the CABB figures for the years immediately preceding onset.
In most European countries, prior to the collapse of Lehman Brothers CABB projections
for 2008 showed a deficit of no more than 3% of potential GDP, while estimates were
far more pessimistic after the event. Spain is, again, an extreme case, since an
estimated surplus of around 1.5% of potential GDP in the first half of 2008 fell to a
deficit of over 4% just one year later, finally ending up at nearly 5%. Spain accordingly
adopted fiscal measures to fight the crisis based on the belief that the country had a
cyclically adjusted surplus of 1.5% of potential GDP, when in fact it had a deficit of 5%
(Figure 6).
4
Unfortunately the IMF data cannot be disaggregated in the same way as they are not published.

Figure 5. Observed and cyclically adjusted public revenue
In other words, the limitations of CABB estimates have driven analysts to put forward
alternative methodologies capable of capturing transitory components in public
accounts. This is the focus in the next section.
-0.15
-0.1
-0.05
0
0.05
0.1
32
34
36
38
40
42
1995 1997 1999 2001 2003 2005 2007 2009 2011
Differential (right axis)
Observed revenues
Cyclically adjusted revenues
Spain
-0.02
-0.01
0
0.01
0.02
0.03
0.04
41
42
43
44
45
46
47
1995 1997 1999 2001 2003 2005 2007 2009 2011
Observed revenues
Germany
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
47
48
49
50
51
52
53
1995 1997 1999 2001 2003 2005 2007 2009 2011
Observed revenues
France
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
43
44
45
46
47
48
49
1995 1997 1999 2001 2003 2005 2007 2009 2011
Observed revenues
Italy
-0.15
-0.1
-0.05
0
0.05
0.1
36
38
40
42
44
46
1995 1997 1999 2001 2003 2005 2007 2009 2011
Observed revenues
Cyclically adjustes revenues
Portugal
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
37
38
39
40
41
42
43
1995 1997 1999 2001 2003 2005 2007 2009 2011
Observed revenues
cyclically adjusted revenues
United Kingdom

Figure 6. Evolution of the 2008 cyclically adjusted budget balance
(as a % of potential GDP)
3. From the cyclically adjusted to the structural budget balance
The approaches used by the EC and IMF to estimate CABB are very similar. 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 different balance
components (revenue and expenditure).
Originally, the cyclical position was obtained by applying a Hodrick-Prescott filter to
the GDP series. The advantage of this method lies in its simplicity and transparency,
but it is criticised for not using information from other relevant macroeconomic
variables, and because it is hard to identify break points in time (Kuttner, 1994: 262).
As a result, analysts are gradually adopting production function-based methods that
attempt to incorporate output fundamentals (Denis et al., 2006; D´Auria et al., 2010).
This approach, however, is not without its drawbacks, particularly its use of the
Hodrick-Prescott filter to calculate trends in total factor productivity, the job offer, and
to soften the non-accelerating wage rate of unemployment (NAWRU), and the fact
that estimates are more sensitive to baseline statistics, which can lead to different
institutions obtaining different results (Corrales et al., 2002).
-7
-6
-5
-4
-3
-2
-1
0
1
2
Alemania España Francia Italia Portugal Reino Unido
Spring-07 Autumn-07 Spring-08 Autumn-08

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 costs
are considered cyclical. Each elasticity is, in turn, the sum of two elasticities: the
elasticity between the tax base and the OG, and the elasticity of tax collections to tax
base. Then, the cyclically adjusted components are calculated by multiplying the
observed revenue or expenditure 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
between observed unemployment costs, the rate of unemployment and the NAWRU.
Finally, revenue and expenditure is subtracted and the result is divided by the
potential GDP (Girouard and André, 2005; Hagemann, 1999).
Algebraically:
∗
=
∑ ∗
− ∗
∗
(1)
∗
= ∗
∗ ;
(2)
; = ; ∗ ;
: ∗
= ∗
∗ ;
= ∗
∗ ;
(3)
; = ;! ∗ !; =
"
∗ !;
IMF: ∗
= − " + " ∗
∗
(4)
where superscript "*" is the cyclically adjusted value or the potential GDP; BB is the
budget balance; Ti is the "i" category tax revenue; E is the primary public expenditure;
Eu is the unemployment expenditure; Y is the GDP; U is the unemployment rate; ; is
the elasticity of the "i" revenue category with respect to the OG; ; the elasticity of
the "i" revenue category tax base with respect to the OG; ; is the elasticity of the
"i" revenue with respect to its tax base; ; is the elasticity of the primary public
expenditure with respect to the OG; ;! is the elasticity of the primary public
expenditure of the unemployment gap; and !; is the elasticity of unemployment
with respect to OG.
One of the best features of this calculation is its simplicity, given that the figures can be
easily obtained by purely mechanical rules that can be applied to all countries.
Nevertheless, by 2000 certain errors started to appear in the CABB to explain the
discretionary measures that the tax authorities of various countries claim to have

taken5
(Larch and Turrini, 2009: 10-11). Two factors are responsible for these
shortcomings. The first is the difficulty of calculating the real time OG, even using
production function methods (Orphanides and van Noorden, 2002). In particular, in
times of economic growth there is a trend to overestimate the potential product,
causing part of cyclical revenue to be considered structural (Girouard and Price, 2004:
6-8; Joumard and André, 2008: 8-11). Therefore, based on real time data, fiscal policies
seem to be counter-cyclical, while after-the-event input suggests pro-cyclicality (Forni
and Momigliano, 2004; Cimadomo, 2008; Golinelli and Momigliano, 2007). The second
is the significant fluctuation of tax elasticities in respect of GDP, partly due to the
failure to consider the effects of the composition of demand through an aggregate
indicator such as the OG (European Commission, 2007: 103).
The variability of OG and elasticity estimates would in part be explained by asset
revaluation, a factor which is not considered in the methodology used by international
organisations. A financial bubble, for example, would raise the price of assets, and
these would affect certain tax bases (capital gains or wealth tax), but would not
directly affect the OG, which is why the elasticities would be affected (Joumard and
André, 2008: 10). Difficulties would also arise in the calculation of the potential
product due to indirect effects on the real economy such as wealth effects on
consumption or the effect of balance sheets on investment and the labour market
(Eschenbach and Schuknecht, 2002a: 11, and 2002b: 13-4). A real estate bubble,
meanwhile, when accompanied by a construction industry boom, would also have a
direct effect on real economy due to the number of units built (Addison-Smith and
McQuinn, 2010).
To avoid bias in OG estimations, the first attempt involved excluding this from CABB
calculations and replacing it with variables more directly related to public finance, such
as the tax bases of the same revenue and expenditure items considered by the EC and
IMF6
. In this way, cyclically adjusted tax bases are no longer obtained through an
elasticity that links them to the OG, but are estimated directly (Bouthevillain et al.,
2001; Morris et al., 2009).
This is a better approach because it considers the effects of the composition of
aggregate demand. Nevertheless, it is still hard to identify transitory fiscal revenue,
which is usually derived from an overestimation of the value of assets. Due to this, the
5
Spain, between 2007 and 2009, would also illustrate the divergence between the government
discretionary fiscal policy and that shown in the CABB. Indeed, both the Spanish authorities (Informe
Presidencia del Gobierno de España, 2010: 40-42) and the OECD (2009) estimated Spanish tax incentives
over that period to be around 4% of GDP. However, as discussed above, the CABB declined by 10.4%.
6
The average wage and rate of employment is used as the tax base for personal income tax and social
security contributions; a company profit proxy is used for corporate tax; private consumption is used for
indirect taxes; while the unemployment rate is used for the cyclic component of expenditure.

concept of the structural budget balance (SBB) was developed. This differs from the
CABB in that it does not only extract cyclical components from the OBB, but also
transitory components related to the value of financial and property assets.
Eschenbach and Schuknecht (2002a: 31) show the importance of these variables by
including them in the estimated tax revenue of several European countries, thereby
improving the adjustment criteria.
The approach is based on estimating, using an error correction model, the short- and
long-term elasticities of different public revenue and expenditure items to their tax
bases and to the price of financial and property assets. The elasticities thus obtained
are applied to the estimated structural value of the tax bases and asset prices7
, to
obtain the SBB. This is usually less volatile than the CABB estimated by the EC and IMF
(Girouard and Price, 2004; Morris and Schuknecht, 2007; Price and Dang, 2011).
These analyses are made for groups of countries, and as such are better in terms of
comparison and regional analysis, albeit at the price of accuracy, since the specificity of
each country is not considered. For this reason, similar specific studies on a country-
by-country basis are called for, as in the case of Ireland (Kanda, 2010; Addison-Smith
and McQuinn, 2010). In the following, we adapt the SBB approach to the case of Spain.
This enables us to more accurately disaggregate tax revenue and to include, as
explanatory variables of these revenue, not only the revaluation of housing, but also
the sharp increase in the number of units built, as discussed above.
4. Estimated structural budget balance for Spain
Methodology
Based on the aforementioned papers, which introduce asset revaluation into SBB
calculations (Girouard and Price, 2004; Morris and Schuknecht, 2007; Price and Dang,
2011), the first step is to disaggregate tax collection into revenue categories. As we are
dealing specifically with Spain in this article, we can achieve a more accurate
disaggregation instead of relying on the EC general categories. Revenue is therefore
disaggregated into: social security contributions (SSC), personal income tax (PIT),
corporate tax (CT), property tax (PT; includes inheritance and gift tax and the
discontinued wealth tax), value added tax (VAT), capital transfer tax and stamp duty
(CTT-SD) and revenue from regional authorities (RA). An error correction model has
been developed for each of these categories that uses the tax base proxies of each
7
Generally speaking, filters such as Hodrick-Prescott are used to obtain these structural values. Asset
market fundamentals, such as in Price and Dang (2011), are rarely used.

category and nominal residential investment as explanatory variables8
. This gives us
the short- and long-term collection elasticity of each explanatory variable.
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); for VAT, private consumption (PRC). Finally, for the PT, the CTT-SD and
the RA the residential investment (RI) is the only explanatory variable9
.
We have included nominal residential investment in all our equations because in real
estate bubbles, unlike their financial equivalent, prices rise very often accompanied by
an increase in the number of units built (Addison-Smith and McQuinn, 2010). As
discussed above, Spain could be a reference case. One way of combining both effects
into a single variable is by using residential investment in nominal terms.
We use error correction models to capture both, short- and long-term relationships.
Accordingly, we have estimated a long-term equation (5) linking the levels of the
variables (in logarithms) for each revenue category. Equation (6) shows the short-term
dynamics between the revenue change of rate, due to long-term deviations from
equilibrium, the change of rate of the respective tax base proxies, and (nominal)
residential investment.
Long-term equation:
'( ,* = + ,-
+ . / ,0,-
∗ '( ,0,*
1
0
+ 2 ,-
∗ '(34* + ,* (5)
Short-term equation:
∆'( ,* = + 6-
+ . / ,06-
∗ ∆'( ,0,*
1
0
+ 2 6-
∗ ∆'(34* + 7 ∗ ,*8 + 9 ,* (6)
where is the observed revenue of the “i” revenue category; ,0 is the “j” tax base
proxy of the “i” revenue category; 34 is the nominal residential investment; subscripts
“lr” and “sr” stand for the long-term and short-term equations respectively; / ,0 is the
elasticity of “i” revenue to the “j” tax base; 2 is the elasticity of the “i” revenue items
to nominal residential investment; 7 is the error correction factor; ∆ is the first
difference operator; and '( is the natural logarithm.
8
Figure A.1 of the annex shows the evolution of residential investment and total public revenue in
Spain.
9
Figures of all public revenue items and their corresponding tax bases are given in the annex.

Once the elasticities have been obtained, the equation yields the structural value of
each revenue item based on the structural values of the tax base and residential
investment proxies. In the latter case, the structural value is taken to be a fixed
percentage of nominal GDP. Various scenarios are developed based on the evolution
over time in Spain, ranging from 5% to 8%. Based on these, we aimed to capture the
direct effect of residential investment on government revenue. We attempted to
capture indirect effects, meanwhile, by smoothing the proxies using a Hodrick –
Prescott filter. Therefore, the structural value of each revenue item is obtained from
the following equation, adapted from the equation used by the EC to obtain the
cyclically adjusted tax revenue based on the OG (Girouard and André, 2005: 7):
∗
= : ;
,0
∗
,0
<
= ,>?@
1
0
∗
34∗
34
A ?@
(7)
where superscript “*” indicates structural value.
Adding up all structural revenue and tax revenue not included in the quantitative
calculation (NIT, 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.
∗
= . ∗
+ B4 (8)
where superscript “*” indicates structural value; T is total revenue; is revenue
stemming from residential investment; and B4 are revenue items not related to
residential investment.
Structural expenditure, according to the EC and IMF approach, is obtained by
considering only unemployment expenditure to be cyclical (Girouard and André, 2005:
13 and 19-20; Hagemann, 1999: 7-8). The EC NAWRU estimate is used to calculate the
number of unemployed in structural terms. However, the methodology of this
indicator (Mc Morrow and Roeger, 2000: 10-11) says 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 the
20-year moving average of this rate10
. Using the NAWRU average, instead of its annual
value, increases the unemployment expenditure that is considered cyclical because it
allows a greater difference between the long-term and observed unemployment rate.
10
Figure A.4 of the annex shows the unemployment rate, the NAWRU and its corresponding 20-year
moving average. It is interesting to note that the results vary very little when longer or shorter averages
(10 to 30 years) are used.

For the sake of simplicity, we have followed the IMF method of calculating cyclical
expenditure, namely, a proportional rule considering the monetary value of
unemployment benefits, divided by the unemployment rate and multiplied by the
NAWRU moving average11
.
∗
= − D = − " − "
∗ (9)
∗
= − " − " ∗
3∗
3
(10)
where superscript “*” indicates the structural value; is total public expenditure; D is
cyclical public expenditure; " is the unemployment expenditure; and 3 is the
unemployment rate.
Finally, SBB is obtained by subtracting revenue from structural expenditures.
BB∗
= T∗
− E∗ (11)
Results
The following section sets out the results for all the revenue items considered. The
sample used comprises annual data from 1986 to 2010. First, we perform Dickey-Fuller
(1981) unit roots tests to identify the order of integration of all the variables. As shown
in Table A.1 in the annex, 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 elasticities obtained from the error correction
model estimated on each tax revenue using as covariates their tax base and nominal
residential investment proxies, together with the error correction term. The annex
shows evidence that confirms the suitability of the models used. Specifically, Table A.2
shows the statistics of the unit root test on the residuals of the long-term equations
and McKinnon's (1991) critical values. It can be seen that, with the exception of the RA
variable, the unit root null hypotheses is rejected for the residuals of all long-term
equations (5), and as such they can be considered stationary. In the case of RA, we
concluded that there is no cointegration relationship between this variable and
residential investment. Table A.3 shows the autocorrelation value of the first two lags
of the residuals from the long-term equations (5) and the error correction models (6).
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
11
Despite the improvement of including a long-term unemployment rate proxy, this calculation
continues to be biased because it lacks an estimation of the long-term economically active population
(EAP) and total population. Indeed, a real estate bubble does not only bring down unemployment, it also
forces up the EAP rate, and even impacts population figures through migratory flows.

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 results12
.
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 elasticities signs are as expected. Likewise, the remaining variables are
also significant, and their signs as expected too13
. Error correction terms, which show
the percentage of deviation corrected for each period, are significant, being in all cases
between 39% and 87%, except for SSC, which seems to over-correct the deviation by
9%.
Table 1. Tax revenue elasticities14
*** significant at 1%; ** significant at 5%; * significant at 10%
It can also be seen that there are no RA values in either the long-term equation or the
error correction term. This is because, as discussed previously, the residuals from its
long-term equation were not stationary. Therefore, in this case, we supposed that
there is no cointegration relationship between variables, meaning that the elasticity
used to estimate structural revenue is taken from the short-term equation in which no
error correction was included.
Figure 7 shows the evolution of different revenue items and of the total, both in terms
of observed values and the different structural residential investment scenarios. It can
be seen that up to 2002 observed revenue did not differ significantly from structural
revenue as expected. From that year on, however, most of the observed revenue items
already exceeded what was considered to be their long-term values. It is interesting to
12
Results are available upon request.
13
We attempted to include lagged variables, in short-term equations, but none were significant. This was
expected since annual data is used. Thus, only contemporary dynamics are shown.
14
The definition of the names of the different revenue items and their respective variables is explained
in section 3.1 above (Methodology).
RI W EM UB GDHI RatePIT GDCI RateCT PRC C
Error
correction
term
R
2
Short-term
SSC 0.15** 0.82*** 0.59** 0.07** 0 -1.09*** 0.90
PIT 0.20*** 0.43*** 0.18*** 0.03*** -0.69*** 0.98
CT 0.39*** 0.42** 0.33*** 0 -0.39** 0.92
PT 0.99*** 0 -0.87*** 0.58
VAT 0.73*** 1.58*** -0.11*** -0.89*** 0.99
CTT-SD 1.39*** 0 -0.82*** 0.66
RA 0.29*** 0.06*** 0 0.47
Long-term
SSC 0,13 0,87 0,61 0,08 0,16 0.99
PIT 0,14 0,90 0,14 -3.62 0.99
CT 0,28 0,65 0,38 -1,30 0.98
PT 0,85 -0,38 0.93
VAT 0,46 0,33 0,00 0.99
CTT-SD 1,05 -2.98 0.97
RA - - -

remark that PIT collection only exceeded the structural value from 2005 onwards. With
regard to SSC, the observed value only exceeds what is considered the permanent
value in 2008, and to a lesser extent in 2009. This was due to the fact that the decline
of the revenue derived from loss of jobs was offset by higher contributions from
unemployment benefits, which contribute to social security. Accordingly, the total
revenue from 2002 to 2008 is higher than all the residential investment scenarios,
peaking in 2007 with a difference of 3.3% of GDP compared with the average scenario
(6.5%), and an accumulated difference of 18% of GDP15
.
Finally, Figure 8 shows the OBB, the CABB estimated by the EC and IMF, our own
estimate of SBB for the average scenario (where the nominal GDP share of residential
investment 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 estimated cyclically
adjusted or structural balances 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.
With regard to CABB, it is interesting to note how similar 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 must be adjusted,
since the advantage of a structural indicator lies in its capacity to differ from observed
data in order to present an overview of the economy that is unaffected by
circumstantial 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 assets 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 foregoing prices in two ways: one based on
fundamentals, and the other by means of a Hodrick and Prescott filter. Morris and
Schuknetch (2007) only use the filter. Despite these changes, it is interesting to note
that their cyclically adjusted estimates do not differ to any great extent, at least they
are not capable of identifying fiscal difficulties in advance of observed balances.
15
We should clarify that the scenarios used should not be considered fixed as it would be very restrictive
to consider the structural value for the GDP share of residential investment constant along time. On the
contrary, the structural value of residential investment can change over time, between 5% and 8%.

Figure 7. Observed and structural observed tax revenue by category
Source: Own estimations and Spanish Ministry of the Finance and Public Administrations
The main advantage of the approach suggested here, therefore, is that it shows that
structural 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 fiscal 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
30
33
36
39
42
45
1986 1989 1992 1995 1998 2001 2004 2007 2010
Observed 8,0%
7,5% 7,0%
6,5% 6,0%
5,5% 5,0%
Total revenue
11,5
12,0
12,5
13,0
13,5
14,0
1986 1989 1992 1995 1998 2001 2004 2007 2010
Observed 8,0%
7,5% 7,0%
6,5% 6,0%
5,5% 5,0%
Social security contributions
4,5
5,0
5,5
6,0
6,5
7,0
7,5
8,0
1986 1989 1992 1995 1998 2001 2004 2007 2010
Observed 8,0%
7,5% 7,0%
6,5% 6,0%
5,5% 5,0%
Personal income tax
1,5
2,0
2,5
3,0
3,5
4,0
4,5
5,0
1986 1989 1992 1995 1998 2001 2004 2007 2010
Observed 8,0%
7,5% 7,0%
6,5% 6,0%
5,5% 5,0%
Corporate tax
0,0
0,1
0,2
0,3
0,4
0,5
0,6
1986 1989 1992 1995 1998 2001 2004 2007 2010
Observed 8,0% 7,5% 7,0%
6,5% 6,0% 5,5% 5,0%
Property tax
0
1
2
3
4
5
6
7
1986 1989 1992 1995 1998 2001 2004 2007 2010
Observed 8,0%
7,5% 7,0%
6,5% 6,0%
5,5% 5,0%
Value added tax
0,4
0,6
0,8
1,0
1,2
1,4
1,6
1,8
2,0
1986 1989 1992 1995 1998 2001 2004 2007 2010
Observed 8,0%
7,5% 7,0%
6,5% 6,0%
5,5% 5,0%
Capital transfer tax and stamp duty
1,5
2,0
2,5
3,0
3,5
4,0
1986 1989 1992 1995 1998 2001 2004 2007 2010
Observed 8,0% 7,5% 7,0%
6,5% 6,0% 5,5% 5,0%
Revenue from regional authorities

over the period studied, as discussed 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 discouraging buyers, the rise in housing
prices had the opposite effect due to the belief that upward trend would continue.
This is why it was decided to include nominal residential investment in the SBB
calculation in order to capture the effect of both the prices and units built.
Figure 8. Observed, cyclically adjusted and structural budget balance
Source: Own estimations, Spanish Ministry of the Finance and Public Administrations, EC, IMF
and referred papers.
5. Conclusions
This article attempts to address the obvious need to improve CABB estimates, given
the similarity of the latter to OBB and the significant corrections applied. Between
2007 and 2009, Spanish CABB deteriorated by 10.4 p.p. of potential GDP, largely in
parallel with OBB. With regard to corrections, in 2008 both the EC and the IMF
estimated a surplus of around 1% of potential GDP for the 2008-09 period, in line with
the expected fiscal balance. Just 12 months later, however, both institutions reduced
their estimates to a deficit of more than 4% in 2008 and between 5% and 7% in 2009,
to finally stabilise them at deficits of 5% and 9%, respectively.
-14
-12
-10
-8
-6
-4
-2
0
2
4
1986 1989 1992 1995 1998 2001 2004 2007 2010
Observed Own estimation (6,5%)
European Commission IMF
Price-Dang (2011) Fundamental Price-Dang (2011) HP
Morris-Schuknecht (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 evident that the buffer was not nearly as
robust as expected, forcing them to withdraw tax incentives before the private sector
was ready to spearhead the aggregate demand.
A review of the literature shows that such situations usually arise due to the failure to
include asset revaluation factors in CABB calculations. Nevertheless, estimates carried
out by other authors who did indeed include such variables in their calculations were
not capable of significantly improving Spanish CABB 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 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. It is interesting to note that this type of investment has, historically, represented
around 7% of nominal GDP, but it reached 12% in the years leading up to the crisis.
The results obtained confirm the importance of residential investment in Spanish tax
revenue. Indeed, it can be seen that including this variable in the equation, and
assuming different scenarios concerning its long-term share in GDP, reveals that
Spanish tax balance 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 achieve this goal in the future, we believe it is essential to include asset revaluation,
both, in terms of prices and number of units, in OBB estimates. It is also important to
draw up a counter-cyclical fiscal rule based on this estimate. This would prevent public
expenditure from deviating so significantly from structural revenue, making far less
likely a new insolvency public position.

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Annex
Figure A.1. Total public revenue and residential investment in Spain
(in billions of euros at current prices)
Source: Spanish Ministry of the Finance and Public Administrations and the Spanish Statistics
Office
0
20
40
60
80
100
120
0
50
100
150
200
250
300
350
400
450
1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Total public revenue
Residential investment (right axis)

Figure A.2. Public revenue disaggregated into categories
(in thousands of euros at current prices)
Office
2.00E+07
4.00E+07
6.00E+07
8.00E+07
1.00E+08
1.20E+08
1.40E+08
1.60E+08
86 88 90 92 94 96 98 00 02 04 06 08 10
Socialsecurity contributions
0.0E+00
1.0E+07
2.0E+07
3.0E+07
4.0E+07
5.0E+07
6.0E+07
7.0E+07
8.0E+07
9.0E+07
86 88 90 92 94 96 98 00 02 04 06 08 10
Personal income tax
0.0E+00
1.0E+07
2.0E+07
3.0E+07
4.0E+07
5.0E+07
86 88 90 92 94 96 98 00 02 04 06 08 10
Corporate tax
0
1000000
2000000
3000000
4000000
5000000
6000000
86 88 90 92 94 96 98 00 02 04 06 08 10
Property tax
0.0E+00
1.0E+07
2.0E+07
3.0E+07
4.0E+07
5.0E+07
6.0E+07
7.0E+07
86 88 90 92 94 96 98 00 02 04 06 08 10
Value added tax
0.00E+00
4.00E+06
8.00E+06
1.20E+07
1.60E+07
2.00E+07
86 88 90 92 94 96 98 00 02 04 06 08 10
4.00E+06
8.00E+06
1.20E+07
1.60E+07
2.00E+07
2.40E+07
2.80E+07
3.20E+07
3.60E+07
4.00E+07
86 88 90 92 94 96 98 00 02 04 06 08 10
2.00E+07
2.50E+07
3.00E+07
3.50E+07
4.00E+07
4.50E+07
5.00E+07
86 88 90 92 94 96 98 00 02 04 06 08 10
Other revenue

Figure A.3. Tax base applied to revenue categories
(in thousands of euros, thousands of people and %)
Office
8000
12000
16000
20000
24000
28000
32000
86 88 90 92 94 96 98 00 02 04 06 08 10
Average wage
11000
12000
13000
14000
15000
16000
17000
18000
19000
20000
86 88 90 92 94 96 98 00 02 04 06 08 10
Number of employed
0.00E+00
5.00E+06
1.00E+07
1.50E+07
2.00E+07
2.50E+07
3.00E+07
3.50E+07
86 88 90 92 94 96 98 00 02 04 06 08 10
Unemploymentbenefits
1.0E+08
2.0E+08
3.0E+08
4.0E+08
5.0E+08
6.0E+08
7.0E+08
86 88 90 92 94 96 98 00 02 04 06 08 10
Private consumption
1.0E+08
2.0E+08
3.0E+08
4.0E+08
5.0E+08
6.0E+08
7.0E+08
8.0E+08
86 88 90 92 94 96 98 00 02 04 06 08 10
Gross disposable household income
4.8
5.2
5.6
6.0
6.4
6.8
7.2
7.6
8.0
86 88 90 92 94 96 98 00 02 04 06 08 10
Average PIT rate
2.00E+07
4.00E+07
6.00E+07
8.00E+07
1.00E+08
1.20E+08
1.40E+08
1.60E+08
86 88 90 92 94 96 98 00 02 04 06 08 10
Gross disposable corporate income
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
86 88 90 92 94 96 98 00 02 04 06 08 10
Average CT rate

Figure A.4. Observed and structural unemployment rate
(as a % of EAP)
Source: Spanish Statistics Office and European Commission
6
8
10
12
14
16
18
20
22
24
26
1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Unemployment rate
NAWRU
NAWRU (20 years moving average)

Table A.1. Unit root tests
(Dickey-Fuller critical values, 1981)
"t" statistic P-value "t" statistic P-value "t" statistic P-value "t" statistic P-value "t"statistic P-value "t" statistic P-value
Social security contributions 0,277 0,757 -0,777 0,807 -2,525 0,314 -1,157 0,218 -1,826 0,359 -4,665 0,000
Personal income tax 3,777 1,000 2,144 1,000 -0,967 0,925 0,513 0,817 -4,266 0,003 - -
Corporate tax -0,318 0,560 -1,399 0,566 -1,732 0,697 -3,534 0,001 -2,034 0,271 - -
Property tax -0,240 0,589 -1,484 0,524 -2,884 0,185 -4,611 0,000 -4,570 0,002 - -
Value added tax 0,958 0,905 -1,011 0,732 -2,850 0,197 -4,001 0,000 -4,008 0,007 - -
Capital transfer tax and stamp duty -0,868 0,329 -1,881 0,335 -0,734 0,956 -2,624 0,011 -3,188 0,035 - -
Revenue from regional authorities 3,940 1,000 3,622 1,000 1,366 1,000 -0,684 0,408 -2,041 0,268 -5,265 0,000
Residential investment 3,340 0,999 1,942 1,000 -0,574 0,971 -1,345 0,160 -4,265 0,003 - -
Wage 1,165 0,932 -1,045 0,718 -3,209 0,114 -0,701 0,401 -3,493 0,018 - -
Employed 1,580 0,968 -1,380 0,574 -2,869 0,190 -1,794 0,070 -2,721 0,087 - -
Unemployment benefits 3,120 0,999 2,078 1,000 0,755 0,999 -2,470 0,016 -2,798 0,074 - -
Consumption 2,707 0,997 2,058 1,000 -2,031 0,555 -1,419 0,141 -3,246 0,030 - -
Household income 21,120 1,000 6,491 1,000 2,659 1,000 -0,922 0,306 -1,052 0,713 -5,963 0,000
Corporate income 3,339 0,999 0,627 0,987 -2,000 0,566 -2,164 0,032 -3,922 0,008 - -
PIT rate 0,534 0,824 -1,694 0,421 -2,158 0,489 -6,263 0,000 -6,074 0,000 - -
CT rate -0,422 0,520 -1,772 0,385 -2,760 0,225 -4,005 0,000 -3,903 0,007 - -
Variable
Levels First differences Second differences
No intercept,
no trend
With intercept,
no trend
With intercept
and trend
No intercept,
no trend
With intercept,
no trend
No intercept,
no trend

Table A.2. Unit root tests of the residuals of long-term equations
(Mac Kinnon critical values, 1991)
Table A.3. Autocorrelations of the first two lags and p-values associated to the Ljung-
Box statistics (1978) for the residuals of the long- and short-term equations
α = 0,05 α = 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
Revenue from regional authorities -1,920 -3,591 -3,218
"t" statistic
Critical values
Auto
correlations
P-value
Auto
correlations
P-value
1 -0,021 0,913 -0,042 0,827
2 -0,062 0,939 -0,082 0,887
1 -0,036 0,847 0,223 0,245
2 -0,007 0,981 0,087 0,457
1 0,401 0,040 -0,022 0,911
2 -0,228 0,060 -0,147 0,747
1 0,126 0,504 -0,029 0,879
2 0,266 0,284 0,245 0,422
1 0,070 0,709 0,107 0,577
2 -0,231 0,426 -0,076 0,788
1 0,355 0,060 0,073 0,704
2 -0,142 0,126 -0,282 0,301
1 - - 0,137 0,476
2 - - -0,095 0,682
Social security contributions
Personal income tax
Corporate tax
Property tax
Value added tax
Equation Lags
Long term Short term

Colección de Documentos de Trabajo Conjuntos IELAT-CIFF (UAH) y UC
DTI Nº1: Carlos Marichal, Crisis financieras y debates sobre la globalización: reflexiones
desde la historia económica latinoamericana. Junio 2011.
DTI Nº 2:Renaldo Antonio Gonsalves, A construção dos mercados monetários e
financeiros no Brasil: A história da moeda que transitou da hiperinflação para a
valorização. Agosto 2011.
DTI Nº 3: José Luis Machinea, Desaceleración o recesión global: los márgenes de la
política económica. Octubre 2011.
DTI Nº 4: Alejandro Izquierdo y Ernesto Talvi (coordinadores), One Region, Two
Speeds? Challenges of the New Global Economic Order for Latin America and the
Caribbean. Diciembre 2011.
DTI Nº 5: Rosa Matilde Guerrero, Kurt Focke y Ana Cristina M. de Pereira, Supervisión
con base en riesgos: precisión del marco conceptual. Febrero 2012.
DTI Nº 6: Pablo Sanguinetti, Acceso a servicios financieros, desarrollo y bienestar en
América Latina. Abril 2012.
DTI Nº 7: Pablo López y Marcelo Rougier, Los Bancos de Desarrollo en América Latina
en los años de la industrialización por sustitución de importaciones. Junio 2012.
DTI Nº 8: Manuel Lucas Durán, Fiscalidad y libre circulación de capitales y pagos en el
Derecho de la Unión Europea: análisis jurisprudencial. Agosto 2012.
DTI Nº 9: José Marcelino Fernández Alonso, La República Argentina y las inversiones
extranjeras directas. Análisis sobre el impacto de los procesos de
expropiación/nacionalización sobre la reputación internacional del país (2002-2012).
Octubre 2012.
DTI Nº 10: Gilmar Masiero y Luiz Carlos Zalaf Caseiro, State Support for Emerging
Market Multinationals: The Brazilian and Chinese experiences, Diciembre 2012.
DTI Nº 11: Diego Coatz, Política pyme en América Latina a la luz de los cambios
globales, Febrero 2013.
DTI Nº 12: José Esteban Castro, Apuntes para una ecología política de la integración en
América Latina y el Caribe, Abril 2013.
DTI Nº 13: Guido Zack, Pilar Poncela, Eva Senra y Daniel Sotelsek, Towards an effective
structural budget balance for economic stability, Junio 2013.

_______________________________________________________
Todas las publicaciones están
disponibles en las páginas Web de las
tres instituciones participantes: IELAT,
CIFF y Universidad de Cantabria.
http://www.ielat.es/
http://www.ciff.net/
http://www.unican.es/
Los documentos de trabajo
desarrollados contienen información
analítica sobre distintos temas y son
elaborados por diferentes miembros de
las instituciones participantes u otros
profesionales colaboradores de las
mismas. Cada uno de ellos ha sido
seleccionado y editado por IELAT-CIFF-
UC, tras ser aprobado por la Comisión
Académica correspondiente.
Desde este ámbito de reflexión
animamos a que estos documentos se
utilicen y distribuyan con fines
académicos indicando siempre la
fuente. La información e interpretación
contenida en los documentos son de
exclusiva responsabilidad del autor y no
necesariamente reflejan las opiniones
del IELAT-CIFF-UC.
Universidad de Alcalá
Universidad de Cantabria
Colegio de Trinitarios
C/Trinidad 1 – 28801
Alcalá de Henares (Madrid)
España
34 – 91 885 5278
guido.zack@uah.es
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