2
Tax Structure, Trade Taxes, and
Economic Development: An Empirical
Investigation
Theo Hitiris*
Tax structures vary because countries have different economic
structures and governments have diverse economic, social, and polit­
ical objectives and varying abilities tO use rhe tax system. During
economic development the objectives of tax policy change, a wider
range of possible tax bases becomes accessible, and the capability of
countries ro administer broad-based tax systems improves. Economic
development changes the economic structure, the aims of economic
policy, and the tax structure. In theory and practice, the develop­
ment of tax structure means a rise in the share of direct taxes and
thus an increase in the progressivity of the tax system.
The evolution of trade taxes is frequently presented as typical of
the changes of the tax system during economic development. Import
and export taxes have an easily identifiable and accessible tax base
that provides an administratively convenient and effective source of
tax revenue for countries in their early stages of economic develop­
ment (Musgrave ( 1 969)). As the economy grows, government expen­
diture as a ratio of GOP tends to rise. At the same time, the principal
role of foreign trade taxes also changes from revenue raising ro
protecting domestic production against foreign competition and pro­
moting exports. Consequently, the importance of trade taxes as a
source of tax revenue is, arguably, inversely related to the relation­
ship between economic development and tax structure, with particu­
lar emphasis on foreign trade taxes (Carden ( 1 974), Dixit ( 1985)).
'Theo Hitiris is Senior Lecturer in economics at the University of York. England. He
was formerly a research economist at the Center of Planning, Athens, Greece. His main
writings and research interests are in international trade theory and policy and the
economics of the European Community. He is grateful 10 the editor, two referees, and
). P. Hutton for constructive comments on this paper. The remaining errors are his alone.
29
©International Monetary Fund. Not for Redistribution
30
THEO HITIRIS
T h i s paper studies the qua n t i tat ive aspects of an obse rved set o f
c ross-se c t i o n data in a n attempt to d iscover empi rical regu l a r i t ies i n
the economic developme n t . T h e next section i nvestigates the fiscal
importance of trade taxes during economic development
<tnd how
t hese taxes re late to other macroeconomic variables . The final sec­
tion offers a summary of the res u l t s and conclu ding observa t ions.
Tax Structure and Economic Development
The analysis is based on a set of observed cross- ect ion data from
l 05 industrial and develop i ng cou n t ries. 1 In the data se t , each cou n ­
t r y ' s t a x st ruct u re is desc r i bed by t h e al location of tota l tax revenue
among ten di fferent taxe s , comprising fou r broad tax types :
Income taxes, I T: personal, IP; social secu rity, IS· corporation a n d
o t her, /C.
Domestic taxes on goods and services, S T: sa les, SS; excises, SX;
other SO.
Trade taxes, TT: import dut ie s , TM; export t axe s , TX .
Other taxes, O T; weal t h and p r o pe r t y taxe s , O W; o t her, 00.2
In addition to t hese data, the fol lowing variables of t h e cou n t r ies in
the sample are also conside red: G O P per capita (in
.
S . dol lars ) , Y, as
a proxy for t h e le ve l of economic developme n t ; t he share of i m ports ,
exporrs, a n d total trade
effe c t i ve t rade tax rat e , V
in GDP (M.
=
X, and
T, respect ively); t he
trade tax/trade val u e ; and t h e total tax
burde n , G = t ot a l tax revenue/G D P.
' h is in f�ct the dt:taik:d sa mp lt: of developing coumries published in Tanzi ( 1 987).
augmented hy t h e ad d i t io n of t h e industrial count r i c� - Tanl.i 's sample of 86 developing
hascd nn bot h publ ished �nd u npublished data collected by the I nterna­
Fund's B u rea u of tatist ics. From t his sample we h;n·e omit tt:d one
nu n , which is considered an oil-exporting economy. and added 20 i ndus­
<.:oun t ry,
trial o.:ou n t rit:s for wh ich data comparable 10 t hose of t he developing cou m rit:s are
ava i lable (data for japan arc not ;!Vailablc). For cc:nain nf the: countries in the: sam p k
the dar� refer to central government revenue only (sn: T:mzi ( 1 987)). For t h t: devel­
oped count ries, the t:tx revenue data come from I nternational Monetary Fund. Cor­
emmenl Financial Statistics Vearbook. Vol . VI (Was h i ngton, 1 9R2), :mel art: :.t\'erage.
of t h e years 1 979- 1 982: the sha re of t rad<.: in G O P. effect ive t;tx r:ne. and total tax
countries is
tional Monetary
IJu rdc:n come from International Monet:try Fund, International Financial Stat istics:
7i'ade Statistics, Suppl<.:ment
o . -i (Wasl1ingto n . 1 98-), and Supple·
me111 011 Co1•emnumt Filla liCe, Supplement N o . I I (Was h i ngto n . 1 981l). For ;til the.: I 06
coumries r h e G P per head v�riable, l', for 1 98 1 at 1 980 m:uket p r i ces in l ' . S . doll:trs,
(mid-year popuhn ion) cornt:s from l n te rn a t i on:t l Monetary Fund, lntematio11al Finan­
talislics. Su ppkmt:nt
o. H ( Wa�hington.
cial SU/Iislics. Supplemenl 011 Output
1 984). F o r t h e i nd u s t rial count ries a n d most of t he non industrial coumries the data are
averaged around 1 91l I.
2 0 t her t a xes ;rrc wxes w h i ch cannot be a l l o ca t ee! 10 specific subcategories or are not
imporwnt cnough revenue sources to be s t u d i ed on t h e i r own (see Tanzi ( 1 ')87)).
Supplement 011
©International Monetary Fund. Not for Redistribution
Tax Structure and Economic Development
31
The cross-section set of data has been arranged in ascending order
of the level-of-development variable, Y. This ordered sample places
each country in a unique position in the spectrum of development
stages, from the least developed (Chad, Y=93) to the most devel­
oped (Switzerland, Y= 16,231 ).
Comparative studies of tax structures seem to confirm that, al­
though there may be specific reasons why in some countries particu­
lar taxes have large revenue shares, tax structure is generally associ­
ated with the level of economic development. One notable feature of
this pattern is that in developing economies indirect taxes on domes­
tic sales and foreign trade are more important than personal income
taxes. In contrast, income taxes are important in industrial econo­
mies, where traditionally they are used as a major instrument for
income redistribution. The following analysis examines whether the
assumed differences in tax structures can be confirmed by quantita­
tive methods, and whether countries can be classified in distinct
groups according to similarities in tax structure and stage of eco­
nomic development.
In the comparison of different national tax structures we are con­
cerned with relationships between observed variables-the set of tax
revenue shares-and latent processes presumed to be generating the
observations. This information is not readily available at the level of
the data set, which occupies a matrix of I 0 x 105 elements. What is
re�:(Uired is that the underlying factors represented by the set of tax
shares be reduced to a comprehensive summary set, but without any
substantial loss of the information contained in the original data. This
will enable us to identify the important characteristics of each coun­
try's tax revenue structure and to investigate the interrelationships
between the tax shares for the presence of common elements. A
statistical technique for exploring interdependencies among variates
is the principal components analysis that transforms a large set of
correlated variables into a smaller set of uncorrelated principal con1ponents that account for the total variance among the original data in
the system. The principal components are ordered hierarchically in
terms of their variance (Theil ( 1 9 7 1 )). The first principal component
is the normalized linear combination of the original variables that
contributes a maximum to their variance. The second principal com­
ponent contributes a maximum to the residual variance, and so on
until the total variance is accounted for. The weight of each variable
in a principal component is the regression coefficient of that variable
on rhe principal component; the square of each weight is the propor­
tion of the variance of the original variable accounted for by the
corresponding principal component.
The results of the principal components analysis are presented in
Table I , which gives the variable weights in the composition of each
©International Monetary Fund. Not for Redistribution
32
THEO HITIRIS
Table l . Tax Revenue Structure: Variable Weights in the Principal
(Components)
Component
Cl
C2
C3
C4
C5
C6
.56
-.40
.68
.45
.00
. 14
-.75
-.47
.27
02
.69
. 14
.25
-.05
.12
-.33
-.49
.04
.28
-.46
-.19
.68
.487
.69
.16
-.37
. 14
-.38
-.3 3
.59
.02
.23
- 28
.06
-.21
.06
.22
-.42
.67
-.26
.78 1
0.924
Tax Variable
IP
IC
IS
ss
sx
so
TM
TX
ow
00
R·SQR
Eigenvalues'
Tax Type
Income, IT
-.07
.202
2.019
.95
04
-.87
Sales, ST
-.
Trade, TT
Other, OT
R-SQR
-.33
Eigenvalues'
.442
1 .770
.
.24
-.53
- .6 1
-.24
.1 5
- .47
-
-.3 5
.365
1.630
1 .220
.3 1
-.97
. 12
-.21
.44
-. 1 7
.758
1 .260
-.21
93
-.37
.35
-.30
-.19
.03
.02
.02
.590
1 .026
-.15
-.46
.18
.18
.08
.26
.689
0.994
1 .000
0.969
'Eigenvalue is the sum of squared weights. The eigenvalue divided by the number of variables
gives thl' proportion of the total variance accounted for by that principal component, C,. R-SQfl is the
cumulative eigenvalue divided b)• the number of variables.
principal component, first, for all the ten taxes and, after aggrega­
tion, for the four broad types of taxes: income (/7), domestic sales
(57), trade (TT), and other taxes (07). The size of the weights in the
principal components seems to indicate that the structure of tax
revenues is primarily determined by the interrelationships between
three groups of tax variables: income, domestic sales, and trade
taxes. I n the analysis of ten taxes, component 1 (C 1 ) is dominated by
a large negative contribution of import taxes (TM), and by two large
positive contributions of social security (IS), and personal income
(IP) taxes. Although the signs of the weights have no fundamental
significance in themselves, as all may be reversed without affecting
any of the results, they indicate a classification of the variates into
two subgroups (Maxwell ( 1 987)). Then, what C I tells us is that in the
total tax revenue of the countries in the sample there is a tendency
for high import and export tax shares, TM and TX, to be associated
with low personal and social security tax shares, IP and IS, and vice
versa. This point accords with the stylized facts and confirms the
©International Monetary Fund. Not for Redistribution
Tax Structure and Economic Development
33
findings of previous studies,:\ that trade tax shares are imponant in
countries characterized by low income tax shares. Component 2 (C2)
is dominated by another of the income taxes, the corporation tax,
IC, with a positive effect, and by domestic excise and other taxes, SX
and SO, with a negative effect. This again implies that in the data set,
countries with high corporation tax shares, /C, display low sales tax
shares, SX and SO, and vice versa. The wealth and property taxes,
OW, and other taxes, 00, contribute large weights funher down the
list of principal components. The R-SQR statistic shows the propor­
tion of variance explained cumulatively by successive principal com­
ponents. Its values show that in general the proponion of variance
explained by each principal component is relatively low. This may
be taken as evidence that tax structures differ substantially between
countries.
Table I also shows that at the aggregate level of four tax variables,
the total variance is explained completely by just three principal
components. C 1 is now dominated by the large effects of two types
of taxes operating in opposite directions, income taxes, IT, and trade
taxes, TT. This again indicates that in countries of high income tax
shares in total tax revenue, the trade tax shares are small. Domestic
sales taxes, ST, dominate C2, while the other taxes, OT, dominate
component 3 (C3).
It is now interesting tO examine whether the identified interrela­
tionships in the structure of tax revenue are in any way associated
with the level of economic development and other determinants of
the effective tax base. Our problem is to examine whether the sam­
ple of l 05 countries, of which each is described by a number of
variables, can be segmented into a number of constituent groups in a
way that the members of each group differ from one another as little
as possible. This will reduce the data set and thus enable us tO
recognize common factors in the composition of total tax revenue
which might go some way toward explaining the similarities of tax
structures among countries, thus leading tO the formulation of test­
able hypotheses. This objective we pursued by application of cluster
analysis, which involves a process of classification based on measure­
ment of the similarities between the countries in the sample with
respect to the given set of variates 4 The number of partitions and
the elements on which they are based are determined subjectively.
We applied the cluster analysis for separation of the sample accord­
ing tO similarities in the set of variables describing the tax revenue
structure, the level of economic development, and the degree of
openness tO international trade. The analysis shows that the strongest
'On principal components and their interpretation see Maxwell ( 1987).
'For a differem approach to a similar problem see Tait and others ( 1979).
©International Monetary Fund. Not for Redistribution
34
THEO HITIRJS
influence in the determination of country groupings is exercised by
the GOP-per-head variable, Y, tO the extent that the sample is seg­
mented in blocks across the ordered data series. 5 Accordingly, the
results of the analysis show that there are basically four major groups
of similar countries (A = underdeveloped, B = developing, C =
semideveloped, D = developed), and that further segmentation of
the sample to five or six classes results in subdivisions of the first and
second groups only (A 1 , A2, B 1 , 82), leaving the last two groups
intact. That is, the clusters are shown to be relatively robust. How­
ever, nOte that the sample splits into two groups at observation 87,
corresponding to underdeveloped countries, 1-87, and developed/
industrial countries, 88- 105.
The variables on which the segmentation of the sample has been
based display interrelationships consistent with those detected in the
principal components. Thus, on the basis of the means of variables,
the comparison between groups in Table 2 shows that, moving from
the lower-income tO the higher-income groups: the import tax share
in tOtal revenue falls consistently, from 33.2 percent in group A I to
3 . 3 percent in group D; the social security tax share increases consis·
tently, from 3.0 percent in group A 1 to 27.3 percent in group 0; the
share of direct taxes (personal and corporate income taxes and social
security) rises consistently, from 26.6 percent in group A tO 67.6
percent in group 0; and none of the other taxes (including the
domestic sales taxes) seem to display a discernible pattern associated
with the level of economic development. The results reached by
principal components analysis are thus confirmed by the cluster
analysis with the addition of a development perspective: the tax
revenue structure displays high trade tax shares and low income tax
shares in the group of low income countries; in groups of higher­
income countries, the trade tax share falls, and the income tax share
rises; and the highest-income group displays the lowest trade tax
share and one of the highest income tax shares. These points accord
with the observation that economic development entails the substitu­
tion of direct taxes for trade taxes, and therefore a rising share of
direct taxes in total tax revenue, reflecting structural changes in the
economy and, probably, social and political trends toward redistribu­
tion (Musgrave ( 1 969)).
Trade Taxes and Economic Development
The statistical analysis so far performed has hinted that trade taxes
make a diminishing contribution to total revenue of higher-income
'Without the Y variable. the clusters become funr and mixed.
©International Monetary Fund. Not for Redistribution
35
Tax Structure and Economic Development
Table 2. Tax Revenue Structure: Cluster Classification of Countries
(Means of Variables)
Group
Observ:uions
AI
1-5R
A2
59-62
65-71
71-!12
83-89
90-IOS
10.2
1 3.4
3.0
12.2
13.3
3.7
.33.2
6.3
1 .8
:U
.B3
30.7
I 5.6
12.2
19.7
5.4
I 1.1
12.0
5.0
2 1 .8
7.0
2.5
2.9
1 ,026
34.3
25.1
13.1
1 1 .6
14.2
8.5
I 1.2
8.6
196
4.0
33
5.8
1 ,891
34.7
1 8.4
7.9
20.-1
18.0
1.3.9
9.0
5.1
1 7.6
1 .0
3.5
3.2
3.133
49.7
53.9
32.6
16.9
18.1
9.4
7.3
4.2
7.0
0.0
4.1
0.1
5.675
62.4
49.6
26.9
6.9
27 3
17.7
8.9
.3.3
3 3
0.0
2.2
:1.5
1 1 .729
29.8
29.7
Bl
fl2
c
0
Tmal
1 - )(IS
Variable'
IP
IC
IS
ss
SX
so
TM
TX
OW
00
y
M
X
1;.7
1;.6
10.8
12.5
1 1 ..3
21.5
...6
-1.3
? -)
3.2
3.01 I
36.7
26.7
'The vari:tbles are: //'=personal income tax. IC=corpor:ne and mher income taxes. IS=social
secu rity taxes. SS=domcstic s:llcs tax. SX=domcstic excises. SO=other domestic t:txes on goods :mc.l
services, 7�\l=impon t:txcs, TX=cxpon taxes. Olfl=weahh :tnd property t:txes. ()()=other tax.-s.
!'=income per head (in U.S. dollars). M=imJ>ort v:tlue/GOP. X=cxpon value/GOP.
The groups correspond roughly to: ( I ) underdeveloped countries= A I :1nd A2. (2) de\'cloping
countries= B l and 02, (5) scmidevcloped countrics=C, and (4) developed countries= D.
economies. In this section we explore this point further by cluster
and regression analysis.
The trade tax share in total revenue, TT, is identically equal to the
product of three variables: the effective tax rate, V = trade tax/trade
value; the share of trade in GOP, T = trade value/GOP; and the
inverse of toral tax burden, G = total tax/GOP:
TT = (V X 7)/G.
(I)
The variables on the right-hand side of the identity in ( I ) are, of
course, themselves endogenous. We are now interested in whether the
countries in our data set can be classified in distinct groups according
tO latent interrelationships between the variables in ( 1 ) and the level of
economic development, Y. Application of cluster analysis separated
the ordered sample at exactly the same points as the previous analysis,
proving once more that the formation of clusters is strongly influ­
enced by the level of per capita GOP. The results for six-group and
two-group splits are presented in Table 3. The move from the lower·
to higher-income groups shows that the share of trade taxes in total
tax revenue, TT, and the effective trade tax rate, V, decline consis­
tently, that the total tax burden, G, rises and that the openness to
trade, T, displays no discernible pattern. These points, which are
©International Monetary Fund. Not for Redistribution
36
T H EO HITIRJS
Table 3. Trade Tax Shares: Cluster Classification of Countries.
(Groups and Mea ns of Variables)
Groups
Six Groups
AI
A2
62
c
D
Observations
1 -38
59-62
63-
Il l
I
2-1!2
f!.;-!!9
90- 1 0 �
Variable
TT
If
T
c
39.5
2 .9
23. I
29.9
28.8
18 3
29.7
1 8. 5
23 6
1 6.9
26.6
1 8.7
1 8 .6
1 2 .6
53.9
2 2 .4
7.0
6.3
56.0
30 8
3 . :1
4.4
29.7
344
Two G roups
nden.leveloped
1 -87
TT
30.5
If
1 8. 1
T
2 10
c
3 1 .8
Y' (In dollars)
1 ,3 1 7
S1andard deviation ( I ,355 .4)
r
0.538
0 238
- 0 405
' T he variab le.< are 7T= t rade raxlrmal t:ox.
Develo p�<.l
1:!8- 1 0 5
3.1
37. 1
4.2
29. 1
Tmal
r
I - I OS
25.8
1 8. 1
0.962
- 0.265
3 13
2 1 .:1
- 0.665
3,01 1
1 1,19
(2 , 3 7 .4) (4 ,054.7)
r
0.664
0 209
- 0 .6 1 3
lf= r rade raxlt radc value, T= trilde value/G OP. G = tmal
wxiGDP. Y = pe r capita G D P; r denotes the corrcl:uion coefficient between TT and the other vari·
able,. "11<.1 the st• n<.lard deviation is uf mc;m Y.
standard propositions in the l i te ra t u re on taxation (M usgrave ( I 969))
and trade (Kindle berger ( 1 962)), are also confirmed by the size and
sign of the partial correlation coefficients between TT and the righ t·
hand side variables in ( 1 ) , which are presented in Table 3.
We can now exam ine whethe r t he pattern of changes in the vari­
able TT t h rough d i ffe rent income count ries can be "explained" by
regression analysis. On t h e basis of ide n t ity ( l ) and the i n formation
gathe red by data analysis, a reasona b l e hypothesis to make would be
that the c rade tax share depends o n t h ree variables, the level of
economic developme n t , Y, the measure of openness to trade, T, and
the share of income taxes in total tax revenue, IT:
TT = f ( Y
T, IT ) .
(1 . 1 )
The principal components a n d cluster analysis would also imply that
(1 . 1 ) is expected to b e : Jy < 0,
h > 0, f1r< 0 . The correlation coefficients between the variables V
the d i rec t ion of t he relationship in
and
Y,
r
=
0 .6, and G and I T,
r
=
0 . 7 , also suggest that the
spec ificat ion in ( 1 . 1 ) is a relati vely close approximation of identity
( I ) . However, in attem pting ro estimate ( 1 . 1 ) by econometric m e t h­
ods, two problems must be considered: fi rst , the distr i b u t ion of the
©International Monetary Fund. Not for Redistribution
Tax Structure and Economic Development
37
variables in ( 1 . 1 ), and second, the continuity of the relationship
between countries at different stages of economic development.
The variables in equation ( 1 . 1 ) are expressed in values which by
definition are non-negative. Moreover, the trade taxes and income
tax shares, TT and IT, are percentages, that is, positive numbers
taking values within the range 0-100. Hence, the values of all the
variables in ( l . l ) are constrained and cannm strictly follow a normal
distribution. Therefore, the conditions of the classical linear regres­
sion model, that is, homogeneity of variance and normality of errors,
are a priori not satisfied (Atkinson ( 1 985)). This means that the esti­
mates of regression coefficients would still be unbiased and best, but
their statistical reliability cannot be assessed by the classical tests of
significance based on normal distributions. A way out of this prob­
lem is to transform the original data, for example, by taking the logs
of the positive variables Y and T and normalizing the proportional
variables by the logit transformation, logit(x)
log[x/(1 -x)]. The
lOglinear form of function ( 1 . 1 ) is:
=
It = a + b,y + bzy + b3y,
b, < 0 , b2>0, b3 <0,
(2)
where the small case letters in equation (2) denote logs or logit
transformations of the variables in ( 1 . 1 ).
As we have seen, the sample can be split into several groups
characterized by different trade tax ratios. If the differences between
the groups are significant, the sample is nonhomogeneous. Conse­
quently, it is possible that structural breaks may exist in the regres­
sion relationship (2) that would render the estimation from the entire
set of observations invalid. Therefore, we begin by investigating this
possibility, using exploratory methods of data analysis. If signs of
departure from constancy become evident, they will be borne in
mind during subsequent analysis and assessed by formal statistical
tests.
A technique for exploring the continuity of a data set is the estima­
tion of cusum and cusum-squares of recursive residuals (Brown and
others ( 1 975)). This method generates statistics, which, if they are
significant, indicate some departure from constancy over the sample,
and provides a likelihood ratio statistic as the means for locating the
probable point of break in the stability of the regression (Quandt
( 1 960)). Application of this method tO the ordered sample of I 05
observations6 provided evidence of departure from stability in re''The cusum and cusum-squares methods are applicable to ordered data in time
series sequence, that is 10 time series data. We have made use of these techniques here
on data ordered in a sequence of income values under the :1ssumption that economic
development follows a sequence of stages. Hence, a departure from constancy signi·
fies a break i n the ordered series of stages, that is, a significant gap between levels of
economic development.
©International Monetary Fund. Not for Redistribution
38
THEO HITIRIS
gression (2) at about observation 86 or 87, suggesting the possibility
that the sample is not formed by a homogeneous set of observations.
Note that cluster analysis has also indicated that, if the data set
consists of two groups of countries, the split occurs at observation
87. As will be seen later, the break point identified by these descrip­
tive methods appears to be significant on the basis of standard tests.
As a starting point we estimated equation (3) from the entire data
set by application of ordinary least squares (OLS):
tt = 1 . 561 - 0.754y + 0.5831 - 0.655it,
0. 61 5 , F(3 , 1 0 1 ) = 56.35, s = 1 . 1 64 , RSS = 136.83,
n = 105 (Observations 1 - 1 05), x-2(2) = 56.646,
F, (6,94) = 4 . 5 1 9 , F2(9,9 1 ) = 3.32, F�(2 ,99) = 4.61,
R2
(3)
=
where in addition to the usual goodness of fit statistics, RSS is the
residual sum of squares, and r(.), F1(.), F1(.), F,(.) are diagnostic
statistics of model adequacy for testing, respectively, the normality,
heteroscedasticity, functional form, and variable specification of the
estimated equation. The estimated values of all four diagnostic tests
are beyond the acceptance levels, making it clear that there are
problems with equation (3) so that it does not seem possible to
obtain a satisfactory estimate of the model suggested from the entire
set of data. Testing further for the possibility of functional misspecifi­
cation, we failed to find an acceptable form of equation (3) (linear,
semi-log, or the like) that could accommodate the entire data set and
be judged statistically adequate.
Fitting the maintained model to the subset of the first 87 observa­
tions appears to provide satisfactOry results on the basis of the diag­
nostic tests:
It = -0.527 - 0.400y + 0.5731 - 0.549il,
(0.818) (4.406) (5.023) (5.527)
R2 = 0.582, F(3.83) = 38.56, s = 0.680, RSS = 38.78,
n = 87, (Observations 1 -87)
0.842,
0,324, F2(9,73)
.x2(2) = 4 , 6 5 1 , F1 (6,76)
F,(2, 8 1 ) = 0.850.
=
(4)
=
The figures in parentheses below the coefficients are estimated
/-ratios. The values of :x...! , F,, Fl. and F,, which show that the esti­
mated equation passes all the diagnostic tests, confirm that the model
is statistically adequate, and this tends to corroborate the indication
of a structural break just after observation 87.
The addition of more explanatory variables results in estimations
which do not pass the diagnostic tests of model adequacy. For exam­
ple, adding the domestic sales taxes, ST, among the explanatory
variables results in rejection of homoscedasticity, functional form,
and variable specification, suggesting that in the data set examined
©International Monetary Fund. Not for Redistribution
Tax Structure and Economic Development
39
sales taxes are neither close substitutes nor complements of trade
taxes.
The regression relationship from the last 1 9 observations provided:
(5)
It = -26,164 + 2.781y - 1 . 193/ - 1 .027it,
(I ,443) ( 1 .433) ( 1 .276) ( 1 .745)
R2 = 0.338, F(3 , 1 4) = 2,39, s = 1 .784, RSS
44,54, n = 1 9 ,
(Observations 88-1 05),
=
with the F-statistic and most of the coefficients statistically nonsignif­
icant, those of income and openness to trade, Y and T, also wrongly
signed. These results do not mean that in the data set the group of
developed countries has a uniform tax structure (e.g., there are no
social security taxes in Australia) nor that trade taxes are negligible in
every developed economy (e.g., they account for 2 1 .92 percent of
total tax revenue in Iceland).
For further evidence concerning the difference between the two
groups of countries and the statistical significance of the idemified
structural break, we estimated the likelihood ratio (LR) statistic,
which tests the equality on both regression coefficients and variances
between the two subsamples.7 Its estimated value is LR = 35.913,
which, against critical value x1(5) = 1 1 .070 at the 0.05 level, shows
that the break at observation 87 is statistically significant and that the
structure of the estimated model from the first 87 observations is
different from that derived from the last 1 8 observations. Therefore,
with regard to the fiscal importance of trade taxes, the data set of
1 05 observations comprises two different subsamples, those of un­
derdeveloped and developed or industrial countries.B These subsam­
ples are heterogeneous, and therefore their data subsets cannot have
been originated from the same data generating process.
The economic interpretation of these results is that trade taxes are
assigned diverse functions in the two different groups of countries.
The estimates of equation (4) show that the use of trade taxes for
raising tax revenue is associated positively with the relative size of
the foreign trade sectOr and negatively with the level of economic
development (Corden ( 1 974)). During the process of economic de­
velopment, in their role of raising tax revenue, trade taxes are gradu-
"The form of the likelihood ratio test estimated here is;
LR
=
[n ln(RSS/n)-n 1 l n(RSS,!n,)- n,ln(RSSJn!)l
=
.�5.913.
"Not surprisingly, the group of underdeveloped countri<::s detected he;;re;; consists of
all the countries traditionally classified under this label. for example. in ·ranzi ( 1 987).
with the addition of Ireland and Sp<tin. The cutoff point is Italy's lcvd of incomc, l' =
6,637, which is .3.85 standard deviations above the me;m income of the unclerdevel­
opecl group of countries.
©International Monetary Fund. Not for Redistribution
40
THEO HITIRIS
ally replaced by income taxesY Equation (5) shows that, in contrast
to the developing countries, industrial countries do not use trade
taxes for raising tax revenue: they display neither substitution be­
tween trade taxes and income taxes nor a statistically significanr
relationship between trade tax revenue and openness to international
trade. These comparisons suggest that, with reference to the fiscal
aspects of trade taxes, the developing countries form a group of their
own, clearly distinct from that of the industrial countries. The statis­
tical evidence supports the hypothesis that there is a clearly identi­
fied threshold level of economic development that separates two
dissimilar groups of countries characterized by different tax struc­
tures and different interrelationships between taxes.
Conclusions
In this paper, we have examined a cross-section data set in an
attempt to identify by quantitative methods the main differences
between the tax structures of different countries and to assess the
fiscal importance of trade taxes at different stages of economic
development.
In general, none of the results obtained by our analysis is contrary
to predictions. The novelty of the present exercise is that it provides
objectively concrete evidence to a number of issues previously con­
sidered to be subjective general statements or speculations.
Bearing in mind that cross-section analysis describes general
trends, identical terms in tax systems are given different contents in
different countries, and the individual characteristics and peculiari­
ties of tax systems cannot be completely captured by purely statisti­
cal summaries (Theil ( 1 9 7 1 )), the present study gives rise to four
main conclusions.
First, certain taxes or rates of taxes go with the level of economic
development and therefore constitute the principal characteristics
that make the tax structures of industrial countries different from
those of developing countries. We have found that the effective trade
tax rate and the trade tax share in total tax revenue fall, while the
"1The inverse relationship between trade and income tax shares in tOtal tax revenue
i mpl ies that it should be possible w e;:stimate an income tax relationship for the group
of less developed coumries. Our data provided:
it = 2.904 + 0.271y - 0.41011,
(5.702) (3.326) (5.057)
R' = 0.478, F(2.84) = 38.47, s = 0.65, n = 87,
(Observations 1-87)
.\.! = 0.594, F1(4,79) = 1 . 1 9 , F,(5,78) = 1 .76, F,(2,83) = 1 .040,
which passes all the econometric tests. As in the case of trade taxes, the generat ion of
income tax data of the group of industrial countries cannot be explained by a relatiOn·
ship similar to t he one :�bove.
©International Monetary Fund. Not for Redistribution
Tax Structure and Economic Development
41
total tax burden and the share of income taxes in total tax revenue
rise with economic developmenl.
Second, countries can be classified in distinct groups according to
tax structure and level of economic development. We have found
that four main groups of economic and tax development can be
distinguished. The country membership of these groups is deter­
mined by the level of per capita income and the shares of trade taxes
and income taxes in tOtal tax revenue; the Iauer two characteristics
are related inversely. A second classification of countries according
10 level of economic development and size and composition of trade
tax shares in tOtal tax revenue provided an identical classification and
country membership.
Third, the degree of developing countries' fiscal dependence on
trade taxes is shown 10 be associated positively with the relative size
of the trade sector in the national economy and negatively with the
level of per capita income and the share of income taxes in total tax
revenue.
Fourth, the statistical evidence seems to support the hypothesis
that there is a threshold level of economic development-level of
per capita income-beyond which countries cease 10 be fiscally de­
pendent on trade tax revenue.
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____
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