FISCAL FEDERALISM AND REGIONAL INEQUALITY: THE SPANISH CASE
(1986-2007)
Montero-Granados, Roberto
Jiménez-Aguilera, Juan de Dios
Barrilao-González, Pedro E.
Villar-Rubio, Elena
University of Granada
Abstract
From the perspective of fiscal decentralisation, the decentralization of public
services must be accompanied by the decentralisation of taxation because, otherwise,
the amounts offered by the sub-central units may be inefficient. So if there are regional
inequity in tax revenue (i.e. in per capita terms) and the decentralization level is high,
horizontal transfers may be needed. If we assume that horizontal transfers are very
difficult then we have a limit for fiscal decentralization. Moreover, horizontal transfers
have two faces: a equality face, very known, but a significant efficiency face too. As
some regions can collect more taxes than would correspond to them in terms of their
actual economic situation, then the difference between real revenue (what is actually
collected in the territory) and potential revenue (the taxable transactions taking place in
the territory) will place limits on the capacity of economic decentralisation in terms of
efficiency. We draw up an index to measure the intensity of this taxation shift from the
perspective of both direct and indirect taxes and illustrate the problem within the
framework of the Spanish case.
Keywords: decentralisation, interregional tax displacement, concentration indices,
equity and efficiency in tax collection.
JEL Codes: H72, H73.
1. Introduction
The theory of decentralisation (Oates 1972, 1999) predicts that fiscal
decentralisation processes can increase the efficiency of public management. However,
it has been detected (Brandforf and Oates, 1971; Quigley and Smolnensky, 1993; Hines
and Thaller, 1995; Rodden, 2002, etc.) that local governments tend to overspend when
they are financed by transfers from the central unit of government and are not directly
responsible for collecting taxes. This phenomenon, commonly known as the "flypaper
effect", is intended to be corrected by the transfer of tax-collecting competences to the
sub-central governments in order to cover necessary expenditures. Hence, processes to
decentralise expenditure should be accompanied by parallel processes to decentralise
tax revenue.
However, in many times, i.e. when there is great economic disparity between
regions, it is necessary transfers resources from one region to another (horizontal
transfers) or from a central government level to a sub-central level (vertical transfers).
Oates (2008) states that the literature justifies such transfers for two reasons:
1
a) The existence of spillover or external profits in public investment. These
transfers are referred to as "pro-efficiency" transfers.
b) The existence of poorer regions that require greater resources. These transfers
are referred to as "pro-equity" transfers. According to Oates (2008), pro-equity
transfers should be transparent, while Padovano (2007) and Rodden et al. (2003)
state that they must be limited so as not to affect convergence.
We detect yet another reason for making transfers that is also based on
efficiency grounds. Some regions can collect more taxes than correspond to them by
their economic activity, in example because some taxpayers established their tax
residence (but not their activity) and pay their taxes in political capitals and wealthy
regions, the revenue of these regions exceeds their economic capacity. Taxpayers can
have different reasons for domiciling their firms in important economic or
administrative centres such as increased promotion, more professional contacts and
easier tax evasion, among others. Then, transfers also serve as to return each region its
authentic tax resources. We believe that these transfers are also pro-efficiency transfers
because their absence would lead to inefficient tax competition between regions and
thus multiply the cost of tax compliance by firms. For example, if the firms are required
to pay income or consumption tax in each region and not in the region where they have
established their tax domicile).
Moreover, in practice and whatever their motives, regions with higher levels of
revenue have incentives and often have budgetary resources to prevent or limit
horizontal transfers. This issue is evident in the case of Spain regarding the fiscal
competition of the Basque country, the claims of contour regions and the vast literature
on regional fiscal balances (Barberán, 2004, Castells et al., 2000, De la Fuente, 2001,
Uriel, 2001, among others)
The aim of this paper is to quantify economic inequalities between regions and
their tax collection capacity with a view to arguing, ceteris paribus, that greater
economic inequality between regions allow a lower level of fiscal decentralisation and
vice versa because regions with higher tax revenues do not promote horizontal transfers.
Data on the Spanish case is used as Spain is an example of an economy with marked
regional differences that has embarked on a far-reaching fiscal decentralisation process
in recent years.
2. Methodology
We use a panel of data on the Spanish Autonomous Communities (AACC) that
reflects the dynamic evolution of revenue collected from the three taxes with highest
potential revenue: personal income tax (PIT), value added tax (VAT) and other
consumption taxes; the so-called excise duties (ED). We analyse these three taxes
because they are the most important taxes established under the Spanish regional
financial agreement. We use relevant macroeconomic variables from the 1986-2007
period for PIT and VAT, and from the 2000-2005 period for ED. The beginning and end
of the period are determined by the availability of both revenue and macroeconomic
2
data. Data on revenue were obtained from AEAT1 Annual Reports on Tax Revenue,
while the macroeconomic data were obtained from the Spanish Regional Accounts
(CRE) databases, specifically the databases of 1986 (CRE-86), 1995 (CRE-95) and
2000 (CRE-00) (available at www.ine.es). Due to their special fiscal regulations, we
have excluded the Canary Islands, the Basque Country and Navarre.
The independent variables (regional macroeconomic data) were selected
according to strictly normative criteria derived from the legislation on each tax. Of the
different alternative models, (state, logarithms, aggregates, per capita, etc.) the best fit
was obtained with the elasticity ratio.
The variables forming the panel are shown below. All the variables have been
transformed into their corresponding natural logarithms:









pit: Territorialised revenue (by AACC) for personal income tax (residents and nonresidents)
vat: Territorialised revenue for value added tax
ed: Territorialised revenue for excise duties
wages: Wages and salaries
income: Income from fixed and mobile property
profits: Gross operating surplus and gross mixed income. Company profits and the
profits of family businesses.
ct: Territorialised revenue for the tax on capital transfers. This tax is levied on
transfers of assets between individuals
cons_terr: End consumption, including public consumption, in the territory
dummy: A dichotomised variable for the PIT econometric model. This variable is
used due to changes in heterogeneity between the macroeconomic data drawn from
CRE-86 and CRE-2000, but has not been included in the econometric model as it is
not stastically significant.
For the purposes of this article, we refer to the effective revenue obtained in each
region as ‘real revenue’ and the revenue that should be obtained according to the
macroeconomic indicators of each region as ‘potential revenue’. Since one of the
objectives of this study is to detect differences between potential revenue and real
revenue, the former must be estimated. To do so, we use the predictions of a regression
analysis that is performed according to the following linear2 models:
pitit = βo + β1 wagesit + β2 incomeit + β3 profitsit + β4ctit + β5 dummyit + ui + eit [1]
vatit = β’’o+ β’’1cons_territ + u’i + e’it
[2]
In the case of ED, it is not possible to use regression analysis due to the lack of
degrees of freedom (five years). Hence, we estimate the potential revenue for each
region as a ratio of the territorialised consumption of the assets which form the basis on
which to assess this tax (tobacco, wine, beer, gasoline and fuel, etc.).
1
AEAT stands for Agencia Estatal de la Administración Tributaria. It is the Spanish tax authority and is
in charge of collecting centralised state taxes.
2
The linear specification is correct even in the case of the PIT, which is a progressive tax. This is because
the estimation is not individual, but aggregated by AACC.
3
To measure the differences between real and potential revenue, we adapted the
granted methodology developed in the Health Economics field by Kakwani, Wagstaff
and van Doorslaer (Kakwani et al. 1997; Wagstaff et al 1999 and van Doorsaler et al.
2000a and 2000b). An index of tax inequality (TI) was then estimated for each tax and
for each year of the sample.
One way to measure the degree of inequality in regional revenue is by means of
the Lorenz curve which represents the cumulative percentage of real revenue as Lr. A
greater gap between the Lr curve and the line of equiproportionality (diagonal) will
indicate greater inequality and vice versa. The concentration index (CI) doubles this gap
so it is constrained between 0 (minimum inequality) and 1 (maximum inequality).
There are many ways of measuring this index. One of the simplest (Kakwani et
al, 1997) is:
2 n
1
CI r   fi ( r ,i   r )(Ri  )
 r i 1
2
Where fi is the relative population of each region i, τe.i is the real revenue (r) per
n
capita;  r   fi r ,i is the weighted average of income per capita of all regions,
i 1
Ri 
i 1
f
i
i 0

1
f i is the fractional relative order of each region i (0 ≤ Ri ≤ 1) (i.e. the
2
accumulated percentage of population that is over the median of each interval after the
regions are sorted by revenue per capita) and  R2 is the variance of Ri. In a similar
manner, we obtained a concentration index of potential revenue (CIp) for each tax. CIr
measures the area between Lr and the equidistribution line twice and is an indicator of
inequality that indicates the degree of difference in the real revenues of different
regions. CIp measures the area between Lp and the equidistribution line twice and is an
indicator of the inequality in the distribution of regional revenue relative to the
macroeconomic potential of each region. The difference between both indices (CIr-CIp),
what we call the index of tax inequality (TI), can also be seen as a measure of the
amount of compensatory transfers required to match the actual collection of each region
to its potential revenue.
3. Results
3.1. Regression analysis
The functional econometric model reflected in expression [1] underwent
successive Breuch-Pagan and Hausman tests (Table 6). The results show that the ideal
estimator is the most consistent (the fixed effects estimator). The cointegration test is
also positive. The general results are shown in Table 1.
The general fit of the model is very high, while the general determination
coefficient exceeds 90% of the fit. The significance of the variables included in the
model is also high except for ct. Since an elasticity ratio was constructed, the regression
model shows that, in Spain, a 100% increase in wages and salaries increases PIT tax
4
revenue by 52.7%. On the other hand, a 100% increase in the profits of professionals
and business owners leads to a 34.1% increase in revenue.
TABLE 1. ESTIMATED PARAMETERS OF REVENUE FROM PIT
Variable
wages
income
ct
profits
dummy
constant
fit R2:
Wald χ2
N:
groups:
estimation
0.527
0.386
0.045
0.341
-0.137
-3.625
intra:
94.36 %
between: 91.60%
overall: 91.90%
753.64
308
14
p-value
0.000
0.000
0.375
0.000
0.000
0.000
0.000
In light of the results of the Hausman test, we use the random effect estimator
for the econometric model for the VAT potential revenue estimation. The cointegration
tests are also positive. The general results are shown in Table 2 below.
TABLE 2. ESTIMATED PARAMETERS OF REVENUE FROM VAT
Variable
cons_territ
constant
fit R2:
Wald χ2
N:
groups:
estimation
0.976
-2.826
intra:
82.3 %
between: 82.7%
overall: 81.8%
1273.46
280
14
p-value
0.000
0.000
0.000
3.2. The tax inequality index
This section presents the results obtained for the tax inequality index. While CIr
should be the entire revenue inequality, CIp can be interpreted as the need by equity
redistribution and IT can be interpreted as the need by efficiency redistribution.
Table 3 shows the data estimated for the last year of the sample. These results
are similar across the study period, and also for other taxes and methodological
alternatives not considered in this study. The results indicate that revenue from PIT is
the most stable and behaves homogeneously. Inequality varies from 12.85% in 2002 to
15.26% in 2000. VAT revenue, on the other hand, shows a greater disparity between
the concentration index for real and potential revenue. The inequality index reaches a
minimum in 2004 at 37.69% and a maximum in 2000 at 41.87%. The ED revenues
show the greatest inequality. The concentration index of the real revenue shows more
than 70% inequality in all cases, while the concentration index of potential collection,
with a slight downward trend, is about 10%.
5
TABLE 3. TAX REVENUE INEQUALITY INDEX IN SPAIN (2000-2007)
CIr PIT
CIp PIT
TIPIT
2000
38.05
22.79
15.26
2001
35.45
21.92
13.54
2002
34.34
21.49
12.85
2003
34.76
21.24
13.53
2004
36.37
21.44
14.93
2005
33.80
21.26
12.54
2006
33.89
21.47
12.42
2007
33.67
20.59
13.09
Meanpit
35.04
21.53
13.52
CIr VAT
CIp VAT
TIVAT
2000
49.28
7.41
41.87
2001
47.21
7.02
40.19
2002
46.33
6.81
39.52
2003
44.91
6.49
38.42
2004
44.06
6.37
37.69
2005
44.36
6.32
38.05
Meanvat
46.03
6.74
39.29
CIr ED
CIp ED
TIED
2000
70.84
11.53
59.31
2001
71.98
11.19
60.78
2002
76.90
11.18
65.72
2003
77.46
10.52
66.94
2004
76.01
9.69
66.32
2005
75.21
9.21
66.01
Meaned
74.73
10.55
64.18
In the case of PIT (00-07 mean), 35% of total tax revenue would be
redistributed. Specifically 13.5% due to a tax shift by tax residence of taxpayers and
21.5% due to economic inequality. In the case of VAT and ED, the shift in tax revenue
due to taxpayer residence is 39.2% and 64.18%, respectively.
These results suggest that taxpayer fiscal residence may be a significative
influence on regional revenue and the horizontal or vertical transfers required to correct
such revenue must also be large.
If fiscal decentralisation must be joined by tax decentralisation, assuming that
the application of horizontal transfers are difficult, then the concentration index of real
revenue places constraints on tax decentralisation. Moreover, if transfers do not match,
at least, the amount of the tax inequality index (TI=CIr-CIp), fiscal decentralisation may
adversely affect efficiency.
6
4. Conclusions
The decentralisation of tax resources is a controversial matter. The most
important practical problem involved in decentralising taxes is transfers. According to
the literature, this is due to two reasons: to guarantee the efficiency of an investment
that provides externalities or spillovers to other regions and to ensure equal access to
essential public services by residents in the poorest regions.
In this paper we have argued that there is another reason for making regional
transfers consisting in returning to each region taxes that should have been collected
according to their economic potential, but were not collected, among other reasons,
because the taxpayers may set up their tax residence in a region and all or some
economic activity in other.
For various reasons, taxpayers tend to established their tax residence in the
richest regions. As such, the above transfers are mixed and confused with transfers for
equalization. These transfers must be applied prior and separately to transfers for
equalization or the equity transfers will otherwise appear to be greater than they actually
are.
In Spain, the amount of these transfers is significant. There is evidence that in
the case of income tax, 38.6% of transfers for equalization are taxes that should be
levied in poorer regions. This percentage jumps to 85% in the case of VAT and ED.
We also argue that since rich regions are reluctant to provide horizontal
transfers, it is more difficult to efficiently decentralise public services in an economy in
which regional inequality is greater. The tax revenue inequality index can be understood
as a proxy of the inverse of an economy’s capacity for fiscal decentralisation.
5. References
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Papeles de Economía Española, 99, págs. 40-76
Brandfort, DF. Oates, WE., (1971) “The analysis of revenue sharing in a new
approach to collective fiscal decisions”. Quarterly journal of economics. 85. 416439.
Castells, A.; Barberán, R.; Bosch, N.; Espasa, M., Rodrigo, F. Y Ruiz-Huerta, J.
(2000): “Las balanzas fiscales de las Comunidades Autónomas (1991-1996).
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Administración Central”. Ariel. Barcelona.
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fiscal para las regiones españolas». Estudios de Economía Española, 91. FEDEA.
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Dickey, GEP. Fuller, WA. (1979): “Distribution of the estimators for autoregressive
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Elliot, G. Rothenberg, T. Stock, JH. (1996): “Efficient test for an autoregressive unit
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7
Hines, J.R. y Thaller, R.H. (1995): “The flypaper effect”, Journal of economics
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Kakwani, N. Wagstaff, A., van Doorslaer, E., 1997. Socioeconomic inequalities in
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Oates, WE., 1972. Fiscal Federalism. Harcourt Brace Jovanovich.
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Oates. W.E. (2008): “On The Evolution of Fiscal Federalism: Theory and
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Rodden, J., Gunnar, A. Eskeland, S. Litvack. J (2003): Fiscal Decentralization and
the Challenge of Hard Budget Constraints. Cambridge, MA: MIT Press.
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estable de financiación autonómica, Madrid: Fundación BBVA, 109-378.
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Geurts, J. O’Donnell, O. Propper, C. Puffer, F. Rodríguez, M. Sundberg, G.
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Duvhesne, I. Gertham, U. Gerfin, M. Geurts, J. Gross, L. Hakkinen, U. John, J.
Klavus, J. Leu, R. Nolan, B. O'Donnel, O. Propper, C. Puffer, F. Schellhorn, M.
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Gertham, UG. Gerfin, M. Gross, L. Häkinnen, U. Johnson, P. John, J. Klavus, J.
Lachaud, C. Lauritsen, J. Leu, R. Nolan, B. Perán, E. Pereira, J. Propper, C. Puffer,
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6. Appendices
Appendix 1. Cointegration tests
The cointegration of the variables was tested by Engle Granger methodology
(Engle and Granger, 1987) in two steps: a) determine the order of integration of the
variables, and b) determine the order of integration of residuals. In both cases the order
of integration was estimated by the augmented Dickey-Fuller method (ADF) (Dickey
and Fuller, 1979). Lags in ADF are estimated based on the DF-GLS test (Elliott et al.,
1996), AIC and Perron. The cointegration test was conducted on the time series of the
aggregate variables, not the panel data. The results of these tests should be interpreted
with caution given the few degrees of freedom in a sample of 22 observations.
8
TABLE 4. PIT MODEL: AUGMENTED DICKEY-FULLER AND COINTEGRATION TESTS
variable
lags
pit
0-1
wages
6-7
income
4-8
ct
0-1
profits
0-6
residuals
0-3
ADF
-1.793
(0.384)
-1.317
(0.621)
-0.258
(0.931)
-0.938
(0.775)
-0.570
(0.877)
-5.595
(0.000)
diagnosis
I(1)
I(1)
I(1)
I(1)
I(1)
I(0)
The first part of the ADF test shows that all the variables are non-stationary. The
second part of the test, conducted on the estimated residuals, concludes that the
variables are stationary and I(0).
TABLE 5. VAT MODEL: AUGMENTED DICKEY-FULLER AND COINTEGRATION TESTS
variable
lags
vat
0-1
cons_terr
0-1
residuals
1-6
ADF
-1.806
(0.378)
-0.931
(0.778)
-3.421
(0.010)
diagnosis
I(1)
I(1)
I(0)
In the case of the VAT model, the three tests on the variables indicate
that the variables are first-order non-stationary. Regarding the residuals, the ADF
test indicates that the variables are stationary and I (0) with 99% certainty.
Appendix 2. Specification test
TABLE 6 SPECIFICATION TESTS
(LM) Breusch-Pagan (χ2)
Hausman (χ2)
PIT
1053.83
(0.0000)
74.67
(0.000)
VAT
2376.17
(0.0000)
2.86
(0.240)
Appendix 3. Collinearity test
TABLE 7. PIT MODEL: COLLINEARITY TEST
Variable
wages
profits
income
ct
dummy
Mean VIF
VIF
1/VIF
100.34 0.010
55.46 0.018
16.85 0.059
14.55 0.069
2.25 0.444
37.89
9
Collinearity exists in a variable when the value VIF (variance inflation factor)
exceeds 30. The PIT model results may be influenced by this problem and it can affects
increasing the standard errors of estimated parameters.
10