Policy, Research, and External Affairs
WORKING PAPERS
Pubilc Economics
Country Economics Department
The World Bank
December 1990
WPS 556
Taxing Choices
in Deficit Reduction
John Baffes
and
Anwar Shah
To control their deficits, Brazil, Mexico, and Pakistan should try
to raise revenues and curtail spending simultaneously. In
Argentina and Chile, the first priority should be to control public
spending.
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Policy, Research, and Extern I Affairs
WPS 556
This paper - a product of the Public Economics Division, Country Economics Department - is part of
a larger effort in PRE to investigate the causes and consequences of macroeconomic imbalances. Copies
are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Ann
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Baffes and Shah use the cointegration approach revenues and spending to control the deficit and
to determine whether deficits are more effec- that spending and taxes tend to feed each other in
tively controlled by raising taxes or controlling those countries.
expenditures - or both. They use long-term
historical time series data for Argentina, Brazil, In Argentina and Chile, they found the
Chile, Mexico, and Pakistan. deficit to be explosive - and caused by spend-
ing. There was no empirical evidence of efforts
Many studies have examined causality in the to adjust revenues to control the deficit.
relationships between taxes and spending in
developed countries. Some have found evidence They recommend that to control the deficit
that higher spending tends to lead to higher Brazil, Mexico, and Pakistan should try to raise
taxes. Some have found that higher taxes lead to revenues and curtail spending simultaneously.
more spending. Some find that causality runs In Argentina and Chile, however, the first
both ways. priority should be to control spending.
Baffes and Shah find that Brazil, Mexico,
and Pakistan have continuously tried to align
The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research, and External
AffairsComplex. Anobjective oftheseries is to getthese findings outquickly, even if presentations are less than fully polished.
The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.
Produced by the PRE Dissemination Center
TAXING CHOICES IN DEFICIT REDUCTION
by
John Baffes and Anwar Shah
Table of Contents
,. Introduction ...... 1
II. Theoretical Considerations . . 2
III. Data, Estimation, and Results . . 7
i. Cointegration. 7
ii. Causality .9
iiI. Variance Decompositions ............ ............ 10
iv. Impulse Responses .......... 11
IV. Summary and Conclusions .... .. .... 12
Endnotes ... 14
Tables ................................. 16
Figures ................................. 20
References ...................... 23
The views expressed in this paper are those of the authors alone and should
not be attributed to the World Bank. The authors are grateful to Bela
Balassa, Johannes Linn, Martha de Melo, Zmarak Shalizi, Javad K. Shirazi, and
Vinaya Swaroop for helpful comments.
TAXING CHOICES IN DEFICIT REDUCTION
I. Introduction
Government deficits especially in developing countries are rising at an
alarming pace. Further, it is believed that chronic deficits often discourage
economic growth, and adversely affect other macroeconomic aggregates.
Controlling deficits involves raising taxes, or reducing expenditures.
Raising taxes has adverse effects on the private sector and on economic growth
in general. Reducing expenditures is also a difficult task because It
involves long-run commitments. Further, if public spending is primarily
devoted to development of basic Infrastructure, as in many developing
countries, then avenues for reducing spending might be quite limited (see
Shah, 1990). In either case however, the problem Is that raising taxes may
induce higher spending or reducing spending may induce lower taxes, without
necessarily affecting the deficit. The latter result obtains if expenditure
reduction results in unacceptably low standards of public services and
thereby unleash strong anti-public sector sentiment creating political
pressures to lower taxes. This paper focuses on qualitative and quantitative
effects of spending on revenues and vice-versa for five developing countries.
While many studies have examined relationships between taxes and spending
In developed countries from a causality point of view, no common agreement
exists as to the direction of the causality. For example, Anderson, Wallace
and Warner (1986) and Von Furstenberg, Green, and Jeong (1985) have found
evidence that higher spending tends to lead taxes. Manage and Marlow (1986)
and Ahiakpor and Amirkhalkhali (1989), on the other hand, have found that
causality runs the opposite direction. In a more recent study, Miller and
Russek (1990) found bidirectional causality between expendltures and revenues
for the U.S. (their study Included federal, state, and local level data).-
1
The most Important element which differentiates the present study from
previous ones is that it carries out a formal test of whether governments make
consistent attempts to align revenues with spending. In doing so we are also
able to r.ake inferences as to whether non-tax revenues play an important role
in determining the level of the deficit.2 Further, this Is the first study of
its kind for developing nations.
The specific objectives of this study are: (a) to test whether a long-
run relationship between revenues and expenditures exists; (b) If such a
relationship exists, what is the direction of the causality; and (c) quantify
those causality effects by estimating the error correction representation and
subsequently calculating variance decompositions and impulse responses. The
paper is structured in the following manner. The next section discusses the
theoretical model and the concepts which are required for the development of
the test. Section III describes the data, the estimation procedure, and the
empirical results. The last section presents conclusions along with some
policy implications and directions for future research.
II. Theoretical Considerations
Consider a government whose objective Is to maximize welfare by choosing the
level of public goods and services to be consumed. The instantaneous indirect
welfare function, V(p,E), is defined as:
(1) V(p,E) a Max (W(z): px = E),
x
where x denotes che vector of goods and services, p represents the exogenously
determined price vector, and E denotes expenditures. W(x) Is a twice
continuously differentiable concave welfare function. In order to finance
expenditures the government uses revenues, denoted as T. In general it would
be expected that T = E so that the government solely covers expenditures
2
through taxation. In most cases however the governments do not operate on a
balanced budget schedule so that T * E, and hence,
(2) Dt = T - Er.
The natural question arising at this moment Is whether Dt represents short-run
deviations from zero or whether It consistently deviates from zero even in the
long run.3
An intuitive way to test whether revenues and expenditures drift apert
In the short run only, would be to form the regression
(3) Tt = 90 + gIEt +Ct
where 10 and 3 denote parameters to be estimated. Then test Ho $0 = C' and
1li = 1 against HI: o0 * O and P I 1, where acceptance of Ho would Imply that
the government has been making consistent attempts to equate revenues to
spending, at least in the long run. Notice that if H Is true, (3) collapses
to (2) where t = Dt, which means that the government finances expenditures
entirely through revenues.
This test however presents some shortcomings. First, the test is very
restrictive in the sense that it Is rather unlikely for the governments to run
balanced budgets on an annual basis, so H Is likely to be rejected. Second,
this procedure fails to take into consideration certain properties of time
series variables which sometimes may Invalidate standard regression results,
namely that the variables being considered are stationary. To circumvent
those shortcomings we take the alternatlive of testing wnether Tt and Et are
cointegrated. Notice that cointegration not only takes Into account
stationarity properties of the variables being considered, but also It
examines whether T and Et move together in the long run, allowing for short-
run deviations.4
Cointegration requires that all variables are of the same order of
3
integra.lon. Let ; first give an intuitive-explanation of the concept of the
order of Integration. If a series has a finite mean and variance It is called
integrated of order zero, and is denoted as I(O). If the series needs to be
differenced once to become I(O), it is then called integrated of order one,
I(1). In general a series that is required to be differenced d times to
become I(O) is called I(d). If two series are I(d) and there exists a linear
combination of those series which is I(b) with b < d, then the series are to
be cointegrated, denoted as CI(d,d-b). For practical purposes we generally
consider I(1) series since most economic variables become I(O) after being
differenced once and hence the cointegrated system is CI(1,1). Ir what
follows, the terms stationarity (or stationarity in levels) and I(O) will be
used interchangeably.
First, the order of integration of the variables under consideration must
be determined. Three prominent procedures to determinr tile order of
Integration are: (a) Dickey-Fuller (DF), (b) augmented Dickey-Fuller (ADF),
and (c) Durbin-Watson (DW). The'DF test is based on the regression: AXt = A
+ 1lXt + et, where Xt denotes the variable of interest and a denotes the
difference operator; p and $ denote parameters to be estimated. The null
hypothesis (H ) is: X is not I(O). Ho is rejected if the estimate of t3 is
negative and significantly different from zero. The ADF test is based on:
AXt =i+ 3X + E r 7 AX + c , where xr is selected so that e is white
t-I 1=1 I tI t .
noise; I, $, and V denote parameters to be estimated as before. Again Ho is
rejected if $ is negative and significantly different from zero. Finally, the
DW test is based on the Durbin-Watson statistic of: Xt = I + £t. Low DW
t. ~~t
statistic indicates that X is not I(O).
t
If the variables of interest are all say I(1), to test for cointegration
we regress one variable on the other and then test whether the estimated
residual is I(O). In other words we estimate (3) and then test for
4
stationarity of ce. Notice that since one of the primary objectives of this
study Is to test whether the government m.kses consistent attempts to equate
revenues with spending, we first test for stationarity of Dt which implicitly
imposes the balanced budget constraint as a long-run restriction.5 Therefore,
testing for coir.tegration between Et and Tt assuming that a long-run
relationship between revenues and spending exists, becomes a simple unit root
test In the univariate process (Engle and Yoo, 1987). In the case that Dt is
not I(O) we proceed to estimate (3). Notice that since the cointegration
parameter is unique in the bivariate case, if we find cointegration by
restricting 10 = 0 and $ = 1 then the regression in (3) should produce tl.e
same outcome .6
As a second step we test to see whether there exists causality between
expenditures and revenues (Granger, 1969). Notice that if the lndividual
series are I(1) (which Is the case as it will be shown in the next section) we
have to take differences to induce stationarity. Hence, we estimate the
following relationship:
i. T
(4a) AT + AEt = + E aIATt-I + E $t Et i + Ut.
~~~~~ t
(4b) AE~t + ATt v + EIAEt + E AT t + V,
where 1, of, a .t, v, al, 1l, I, and S denote parameters to be estimated; 'r
denotes the number of lags which is not necessarily tne same for all
variables. ut and vt are assumed to be mutually uncorrelated white noise
processes. If $0 = a = 0 and some 's and 8 's have non-zero values,
(4a-4b) implies a simple causal relation with feedback (i.e. simple
bidirectional causality). If a0 0 O and 60 0 O and some Si's and 6I's have
non-zero values, then (4a-4b) implies instantaneous bidirectional causality.
Finally, unidirectional causality is implied if the above relations hold for
5
one equation only. In terms of Tt and Et. simple causality means that past
Et only affects Tt, while instantaneous causality means that both past and
current Et affect Tt and vice-versa. Such causal relationships can be
detected with conventional F-tests.
One other Important element we consider in this study Is the relation
between cointegration and causality. If the variables being considered are
cointegrat_d, there exists causality in at least one direction (Granger,
1986). Further, an additional implication of cointegration is that if there
exists cointegration then the system can be represented by an error correction
mechanism (ECM).7 The implication of such representation is that we quantify
the causality effects by constructing the Vector Autoregressive (VAR)
representation of the bivariate system as defined by Tt and Et and also
incorporate the long-run relationship as follows (for a complete
characterization of VAR processes see Sims (1980)):
T T
(5a) ATt = v 0Dt_ + E (Xi AT + 1fE +,t- to
(5b) AE = 8 D + F XAT + E 8AE + V
where Dt 1 is the lagged level of deficit. The remaining variables and
parameters are defined in (4a-4b). It Is interesting to notice that if we
replace AEt and ATt by Dt In the causality regressions (4a-4b) we arrive at
the ECM representation; so cointegration unifies ECM and conventional.
causality models. (Sa-Sb) is sometimes called restricted VAR, where the
restriction is the residual from the cointegration regression (in this case
the observed deficit or the error term of (3)). The advantage of the ECM as
opposed to the unrestricted VAR is that by including the deiicit in the
equations we retain information in levels, without distorting the stationarity
properties of the variables involved in the system, since, because of
6
cointegrati.on Dt_1 Is I(0). From the estimated VAR system we can then
calculate Impulse responses and variance decompositions as means of
quantification of the causality effects.
III. Data, Estimation, and Results
Data In the current study Include total government expenditures and total
revenues for the countrien of Argentina, Brazil, Chile, Mexico, and Pakistan.
The data series for Argentina cover the 1904-1983 time period and were
obtained from unpublished World Bank Tables. Data for Brazil cover the
1905-1983 period and were obtained from Estatisticas Historicas do Brasil.
Data for Mexico cover the 1895-1984 time period and were obtained from
Estatisticas Historicas de Mexico. Data for Pakistan cover the 1947-1989
period and were obtained from various publications of the Central Statistical
Office, Government of Pakistan. Finally, data for Chile cover the 1960-1985
time period and were obtained from Banco Central De Chile. Because of some
missing observations, the data set for Argentina and Mexico contain 71 and 78
observations respectively. All series were adjusted by the respective GDP
deflators in view of the high inflation rates experienced by the Latin
Arerican countle3 Included in this study. The .ATS package was used to
estimate the models.
The remainder of this section, which is divided in four parts, will
discuss and analyze results regarding cointegration (Table 1), causality
(Table 2), as well as variance decompositions (Table 3) and impulse responses
(Figures 1, 2, and 3).
(1) Cointegration
The first step regarding cointegration is the determination of the order of
integration of revenues and spending. Table 1 reports such results. In all
7
cases both revenues and spending were found to be nonstationary and hence the
first differences had to be considered. Stationarity tests for the first
differences Indicated that both variables In all countries were I(1).
The second step was to determine the order of integration of D . For
Argentina all three tests indicated that Dt is nonstationary (D% level of
significance). This means that the Argentinean government ha- not been able
to equate revenues and spending In the long run. The next step was to test
for cointegration by considering the unrestricted version of the cointegration
regression (i.e. relationship (3)). All three tests indicated that there
exists strong evidence of cointegration. (The cointegration regression - not
reported here - gave a coefficient of 0.65). To summa.ize, cointegration
tests for Argentina indicate that although revenues and spending move together
In the long run there Is an unexplained component in spending since revenues
account for 65% of spending only.
Cointegration tests for Brazil showed evidence of cointegration. In
particular the DW and ADF test indicate mild evidence of cointegration while
the DF test Indicates strong evidence. In what follows we consider that
revenues and spending in Brazil are cointegrated.
For Chile, all three cointegration tests showed that revenues and
spending are not cointegrated (5% level of significance). The conclusion was
the same even when relationship (3) was estimated (estimates not reported
here). The implication of such result is that there exist a nonstationary
component (such as seigniorage or borrowing) that causes explosiveness of the
deficit which is not being taken into censideration.
For Mexico, the results are the same as in Brazil. Notice that all three
tests supported stationarity of the deficit at the 1% level of significance.
For Pakistan the results are almost Identical. The only differencc is that
the results hold for the 5% level of significance. Notice that the ADF test
8
for Pakistan indicates that Et and Tt are I(2). However, the deficit Is still
I(O), thus supporting cointegration.
(ii) Causality
Table 2 reports causality results. In order to conserve space we do not
report parameter estimates and other statistics of the system. To facilitate
comparability with other studies we report results for all three types of
causality (i.e. simple causality, instantaneous causality, and causality with
the cointegration restriction Imposed).
For all three countries for which cointegration was found instantaneous
causality runs both directions (i.e. revenues cause spending and spending
causes revenues). Notice that the finding of at least one direction causality
is consistent wlth the existence of cointegration between revenues and
spending foi those three countries. On the other hand, while simple causality
runs from spending to revenues for Pakistan no simple causality in any
direc.tion was found for Brazil and Mexico. When the colntegration restriction
is imposed, Mexico exhibits bidirectional causality. In Brazil, revenues
cause spending while the opposite is true for Pakistan.
Since cointegration was not found for Argentina and Chile, the
possibility that the deficit causes (or is caused by) expenditures and/or
revenues was examined. Thus, we tested all possible causal relations among T,
E, and D. Specifically, the results for Argentina show that Instantaneous
causality runs in all directions (i.e. among deficit, revenues, and spending).
On the other hand, simple causality runs from spending to revenues and from
deficit to revenues.
It is of interest to notice that while simple causality was found in a
few cases only, instantaneous ca"isality was found in almost any case examined.
The implication of this is that decisions to reduce/increase spending are
being made simultaneously with decisions to Increase/reduce revenues.
9
Finally, it should be noticed that the qualitative nature of the causality
results did not change wnen other lag structures were considered. Single
exceptien to this constitutes Pakistan, in which case ihen more than two lags
were considered no causality was found in any direction.
(iii) Variance Decompositions
Variance decompositions exhibit the contribution of each source of innovation
to the variance of the k-year ahead forecast error for each of the variables.
Stated otherwise, variance decompositions refer to a breakdown of the change
in the value of the variable in a given year arising from changes in the same
variable as well as other variables in previous years. Table 3 gives
estimates of variance decompositions for Brazil, Mexico, and Pakistan. As it
was mentioned earlier, for Chile and Argentina T and E do not fully describe
the deficit so we did not form the ECM representation. The results can be
summarized as follows. 95% of the variation of expenditures in Mexico is
accounted by past expenditures, while only 5% is accounted by past revenues.
This pattern seems to be consistent through the whole period examined. The
same picture is presented when we consider revenues, i.e. 96% of the variation
in revenues is accounted by past expenditures while 4% only is accounted by
past revenues.
In Brazil, changes in expenditures account for most of the variation in
future expenditures as in the case of Mexico. On the other hand, changes in
revenues and expenditures equally account for the variance of revenues. Again
this pattern is consistent through the whole period examined.
In Pakistan, changes in expenditures account for most of the variation in
future expenditures. On the other hand, changes in revenues account for abcut
75% of the variation in future revenues throughout the period examined. To
summarize, it appears that in most cases expenditures account for most of the
variation in both future expenditures and future revenues.
10
(iv) Impulse Responses
Impulse responses give the dynamic response of each variable to policy changes
affecting this variable as well as of the ocher variables included In the
system. In other words, impulse responses describe whether a shock of one
variable has a persistent or transitory effect on the other variables as well
as on the variable itself. Figure 1 depicts impulse responses for Brazil. In
general all four impulse responses presert similar picture. A one-standard
deviation shock on spending induces more spending as well as more revenues in
the first period while after the third period both spending and revenues
return to the pre-shock level. The same picture is presented when we consider
the own-effect of revenues. On the other hand revenue shock has no effect on
spending in the first period, while after a negative effect in the second
period spending returns to Its pre-shock level.
Figure 2 glves impulse responses for Mexico. The effect of a shock of
spending on both spending and revenues as well as the effect of revenue shock
on revenues are similar: after an increase In the first period the variables
tend to return to the pre-shock levels following an oscillatory process.
Revenue shock on spending however has no effect in the first period, while
after a negative effect in the second period it oscillates before it returns
to its pre-shock level.
Figure 3 gives impulse responses for Pakistan. Spending shock has the
same effect on both revenues and spending. I.e. there is a large positive
effect in the first period. After that, the variables have a tendency to
return to the pre-shock levels. To some extent spending responds the same way
to revenues shock. Comparing Pakistan with Brazil and Mexico however we
observe that in the Pakistani case the shocks tend to be persistent, that is,
it takes longer for the variables to return to the pre-shock level. This
result is consistent with the fact that for Brazil no simple causality but
11
strong instantaneous causality was found while both types of causality were
found for Pakistan.
To summarize, it seems that both Brazil and Mexico present the same
picture in the sense that the shocks have short run effects only. On the
contrary, in Pakistan the shocks have persistent effects.
IV. Summary and Conclusions
In this paper an attempt was made to: (a) determine whether governments have
continuously attempted to align revenues or spending to control the deficit.
(This was done by testing whether there exists cointegration between revenues
and spending); (b) test for causality between taxes and spending; and (c)
quantify the causality effects by (f) estimating an error correction model and
(ii) calcuiating variance decompositions and impulse responses. The tests
were carried out for the countries of Argentina, Brazil, Chile, Mexico, and
Pakistan. The availability of long data series primarily determined the
selection of those countries.
The results can be summarized as follows: The governments of Brazil,
Mexico, and Pakistan seem to have successfully aligned revenues and spending
as means of controlling the deficit over the time period examined while a
similar deduction for Argentina and Chile could not be made. For Brazil,
Mexico, and Pakistan strong instantaneous causality runs both directions. In
Argentina and Chile deficit was found to cause and be caused by expenditures.
Impulse responses for Mexico and Brazil were found to have short-run effects
only while for Pakistan the effects were more persistent. In terms of
variance decompositions it was found that variations In both revenues and
spending are explained in most part by past spending. The above results
suggest that to control the deficit, Brazil, Mexico, and Pakistan should
attempt to raise revenues and curtail expenditures simultaneously. In
12
Argentina and Chile, on the other hand, controlling public expenditures should
be the first priority.
An important qualification Is in order here. The results stated earlier,
are based on long term relationships that may differ from the present
situation. A case in point is Chile, that has succeeded in eliminating the
budget deficit since the end of the period of observations in our sample. In
contrast, the deficit has increasedl in Argentina and Brazil in more recent
years.
13
ENDNOTES
Other studies Include Furstenberg, Green, and Jeong (1986); Ram (1988a,
1988b); Ahsan, Kwan, and Sahni (1989); Holtz-Eakin, Newey, and Rosen (1989);
Miller and Russek (1990). In particular Miller and Russek tested for
causality by imposing the cointegration restriction as defined in (3) of this
study. The present study makes an explicit distinction between the
cointegration restriction defined in (2) and the cointegration restriction
defined In (3).
2 An Important element of non-tax revenue, especially in Latin America, is
seigniorage (i.e. inflation tax). See Kiguel and Neumeyer (1989) for a
treatment of seigniorage in Argentina.
3 To be precise, the government is constrained to run balanced budget In
present value terms. So the constraint to test would be, 7 o(1+8) t lT, -
co (1+8) Et = 0, where a denotes the discount rate. We chose to test the
balanced budget constraint associated with (2) because It is convenient to
form the ECM representation and subsequently carry out the causality tests.
See Hamilton and Flavin (1986) for a test regarding the balanced budget
restriction In present value terms.
4 Ihe theory of cointegration Is discussed extensively in Engle and Granger
(1987), Hendry (1986), and Granger (1986).
5 Several studies have considered theoretical restrictions In testing
cointegration. For example, Campbell and Shiller (1987) tested the term
structure of the interest rate; Corbae and Oullaris (1988) tested the
Purchasing Power Parity. Other studies of similar nature include Hall (1986)
and Ambler (1989).
14
6 It is true that the cointegratlon regression gives an excellent estimate of
the cointegratlon parameter. However this Is the case In large samples only.
In small samples the regression does not guarantee that the cointegration
parameter will be found.
The equivalence between cointegration and ECM comes directly from Granger
representation theorem (Engle and Granger, p. 225). The ECM type of models
were first Introduced by Phillips (1957).
15
TABLE 1: Stationarity Tests
DW DF ADF
ARGENTINA (71 observations)
Et 0.075 -0.396 -0.511
AE 1.939 -8.030 -5.786
Tt 0.105 -1.171 -0.946
AT 2.581 -11.429 -6.199
Dt 0.352 -1.975 -2.047
BRAZIL (78 observations)
Et 0.038 0.861 1.926
AEt 2.463 -11.179 -7.816
T 0.028 1.430 2.608
t
ATt 2.481 -11.165 -7.170
Dt 0.625 -3.621 -2.977
CHILE (26 observations)
Et 0.318 -1.292 *-1.362
AEt 1.979 -4.638 -3.224
T 0.114 -0.867 -1.008
ATt 1.402 -3.418 -3.196
D t 0.636 -2.046 -2.470
MEXICO (78 observations)
E t 0.053 0.381 0.206
AEt 1.856 -7.994 -4.889
Tt 0.089 -0.647 -0.388
ATt 2.262 -9.817 -3.174
Dt 0.963 -4.091 -8.289
continued
16
PAKISTAN (42 observations)
Et 0.024 4.018 3.442
AE 1.358 -4.420 -2.872
t
T 0.023 6.066 4.744
t
AT 1.047 -2.921 -1.027
t.
D 0.783 -3.081 -3.257
t.
NOTE: Et and Tt denote spending and revenues; Dt denotes deficit; A
represents the difference operator (i.e. AEt = Et - Et t and AT = T - T ).
The critical values for Argentina, Brazil, and Mexico are: DW = 0.259 (5%) and
0.376 (1%) (from Sargan and Bhargava (1983), Table 1); DF and ADF = -2.89 (5%)
and -3.59 (1%) (from Fuller (1976), TA statistic, Table 8.5.2).
The critical vOlues for Pakistan are: DW = 0.493 (5%) and 0.705 (1%)
(from Sargan and Bhargava (1983), Table 1); DF and ADF = -2.93 (5%) and -3.58
(1%) (from Fuller (1976), T statistic, Table 8.5.2).
The critical values for Chile are: DW = 0.770 (5%) and 1.081 (1%) (from
Sargan and Bhargava (1983), Table 1); DF and ADF = -3.00 (5%) and -3.75 (1%)
(from Fuller (1976), TA statistic, Table 8.5.2.). These statistics are based
on Monte Carlo experiments made on 100, 50, and 25 observations, respectively.
The number of lags in the ADF test was determined by the Akaike
information criterion.
17
TABLE 2: Causality Tests
ARGENTINA CHILE
I II I II
T does not cause E 0.07 6.83 0.18 0.46
E does not cause T 6.47 10.64 0.05 0.39
T does not cause D 0.10 28.68 0.91 1.27
D does not cause T 6.47 34.58 0.05 0.83
D does not cause E 0.07 5.49 0.18 40.02
* *
E does not cause D 0.10 5.51 0.91 41.74
BRAZIL
I II III
T does not cause E 0.08 32.67 4.54
E does not cause T 0.06 32.65 0.28
MEXICO
I II III
T does not cause E 0.01 660.41 13.65
E does not cause T 0.15 661.78 6.30
PAKIST.'
I II III
T does not cause E 2.65 4.05 2.34
E does not cause T 3.25 4.38 3.52
NOTE: I means simple causality, II means instantaneous causality, while III
means causality with the cointegration restriction imposed. One star (*)
Indicates rejection of no causality at the 5% level of signlflcance while two
stars (**) Indicate rejection of no causality at the 1% level. All tests were
carried out with one lag.
18
TABLE 3: Proportion of Forecast Error Variance K Periods Ahead Produced by
Each Innovation.
Ixinovation In:
MEXICO BRAZIL PAKISTAN
Error
in K E T E T E T
E 1 1.000 .000 1.000 .000 1.000 .000
2 .946 .054 .966 034 .955 .045
3 .952 .048 .961 .039 .939 .061
4 .949 .051 .961 .039 .935 .065
5 .950 .050 .961 .039 .934 .066
6 .950 .050 .961 .039 .933 .067
7 .950 .050 .961 .039 .933 .067
8 .950 .050 .961 .039 .933 .067
9 .950 .050 .961 .039 .933 .067
10 .950 .050 .961 .039 .933 .067
T 1 .966 .U34 .496 .504 .207 .793
2 .954 .046 .494 .506 .250 .750
3 .954 .046 .494 .506 .262 .738
4 .953 .047 .494 .506 .265 .735
5 .952 .048 .494 .506 .266 .734
6 .952 .048 .494 .506 .267 .733
7 .952 .048 .494 .506 .268 .732
8 .952 .048 .494 .506 .267 .733
9 .952 .048 .494 .506 .267 .733
10 .952 .048 .494 .506 .267 .733
NOTE: K indicates years. E and T denote spending and revenues respectively.
One lag was used to estimate the ECM model. No major differences in the
results were observed when other lag lengths were considered.
19
SPENDING SHOCK/SPENDING RESPONCE SPENDING SHOCK/REVENUE RESPONSE
8-
4
3
4
2
21
-2 -2
1 2 3 4 5 e 7 8 9 10 1 2 3 4 5 6 7 8 9 10
N
REVENUE SHOCK/SPENDING RESPONSE REVENUE SHOCK/REVENUE RESPONSE
5 5
4
0 3
2
-6
-10~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- 10~~~~~~~~~~~~~~~~~~~~~-
-15 -2
1 2 3 4 5 8 7 8 9 10 1 2 3 4 5 6 7 8 9 10
FIGURE 1: Brazil
SPENDING SHOCK/SPENDING RESPONSE SPENDING SHOCK/REVENUE RESPONSE
30 -40
20 30
20
10
10
0
0
-10-1
- 10
-20 -20
-30~~~~~~~~~~~~~~~~~~2
_;30 -30l
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 e 7 8 9 10
N}
REVENUE SHOCK/SPENDING RFSPONSE REVENUE SHOCK/REVENUE RESPONSE
4 8
26
0 ~~~~~~~~~~~~~~~~~~~~~4
2
-2
-4
-2
-4
-8 -6
-10 -8
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
FIGURE 2: Mexico
SPENDING SHOCK/SPENDING RESPONSE SPENDING SHOCK/REVENUE RESPONSE
26 10
20 8
16 6
10 4
5 2
1 3 4 6 6 8 9 10 1 2 3 4 S 7 8 9 10
N
REVENUE SHOCK/SPENDING RESPONSE REVENUE SHOCK/REVENUE RESPONSE
20
S
15
4
3 10
2
0-
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
FIGURE 3: Pakistan
REFERENCES
Ahiakpor, J.C.W. and S. Amirkhalkhali. "On the Difficulty of Eliminating
Deficits with Higher Taxes." Southern Economic Journal. 56(1989):24-31.
Ahsan, S.M., A.C.C. Kwan, and B.S. Sahni. "Causality between Government
Consumption Expenditure and National Income: OECD Countries." Public
Finance. 44(1989):205-224.
Ambler, S. "Does Money Matter In Canada? Evidence from a Vector Error
Correction Model." Review of Economics and Statistics. LXXX(1988):
651-658.
Anderson, W., MOS. Wallace, and J.T. Warner. "Government Spending and
Taxation: What Causes What?" Southern Economic Journal. 52(1986):
630-639.
Banco Central De Chile. Indicatores Economicos y Sociales: 1960-1985.
Direccion de Estudios, Chile, 1985.
Campbell, J.Y. and R.J. Shiller. "Cointegration Tests of Present Value
Models." Journal of Political Economy. 95(1987):1061-1088.
Central Statistical Ozfice, Government of Pakistan. 25 Years of Pakistan In
Statistics: 1947-1972. Karachi, 1972. Also supplementary issues.
Corbae, D. and S. Ouliaris. "Cointegration and Tests of Purchasing Power
Parity." Review of Economics and Statistics. LXX(1988):508-511.
Engle, R.F. and C.W.J. Granger. "Co-Integration and Error Correction:
Representation, Estimation and Testing." Econometrica. 55(1987):
251-276.
Engle, R.F. and B.S. Yoo. "Forecasting and Testing in Co-integrated Systems."
Journal of Econometrics. 35(1987):143-159.
Estatisticas Historicas do Brasil. Series Estatisticas Retrospectivas, Vol 3.
Fundacao Instituto Brasileiro de Geografia e Estatistica, 1987.
Estadisticas Hist;ricas de Mexico. Tomo I, II. Instituto National De
Estadisticas Geografia e Informatica. 1986.
Fuller, W.A. Introduction to Statistical Time Series. John Wiley & Sons, New
York, 1976.
Granger, C.W. "Investigating Causal Relations by Econometric Models and
Cross-Spectral Methods." Econometrica. 37(1969):424-438.
Granger, C.W. "Developments in the Study of Cointegrated Economic Variables."
Oxford Bulletin of Economics and Statistics. 48(1986):213-228.
Hall, S.G. "An Application of the Granger & Engle Two-Step Estimation
Procedure to United Kingdom Aggregate Data." Oxford Bulletin of
Economics and Statistics. 48(1986y:229-240.
23
Hamilton, J.D. and M.A. Flavrin. "On the Limitations of Government Borrowing:
A Framework for Empirical Testing." American Economic Review. 76(1986):
808-819.
Hendry, D. "Econometric Modelling with Cointegrated Variables: An Overview."
Oxford Bulletin of Economics and Statistics. 48(1986):201-212.
Holtz-Eakin, D., W. Newey, and H.S. Rosen. "The Revenues-Expenditures Nexus:
Evidence from Local Government Data." International Economic Review.
30(1989):415-429.
Kiguel N.A. and P.A. Neumeyer. "Inflation and Seigniorage In Argentina." PPR
Working Paper. Macroeconomic Adjustment and Growth Division, Country
Economics Department, The Woirld Bank, WPS 289, October 1989.
Manage, N. and M.L. Marlow. "The Causal Relation between Federal Expenditures
and Receipts." Southern Economic Journal. 52(1986).617-629.
Miller, S.M. and F.S. Russek. "Co-Integration and Error-Correction Models:
The Temporal Causality between Government Taxes and Spending." Southern
Economic Journal. 57(1990):221-229.
Phillips, A.W. "Stabilization Policy and the Time-Forms of Lagged Responses."
Economic Journal. 67(1957):265-277.
Ram, R. "Additional Evidence on Causality between Government Revenue and
Government Expenditure." Southern Economic Journal. 54(1988a):763-769.
Ram, R. "A Multicountry Perspective on Causality Between Government Revenue
and Government Expenditure." Public Fiz- nce. 43(1988b):261-269.
Sargan, J.D. and A. Bhargava. "Testing Residuals from Least Squares
Regression for Being Generated by the Gaussian Random Walk."
Econometrica. 51(1983):153-174.
Shah, A. "Dynamics of Public Infrastructure and Private Sector Productivity."
Paper presented at the 6th World Congress, Econometric Society,
Barcelona, Spain, 1990.
Sims, C. "Macroeconomics and Reality." Econometrica. 48(1980):1-49.
Von Furstenberg, G.M., R.J. Green, and J-H. Jeong. "Have Taxes Led Government
Expenditures? The United States as a Test Case." Journal of Public
Policy. 5(1985):321-348.
Von Furstenberg, G.M., R.J. Green, and J-H. Jeong. "Tax and Spend, Or Spend
and Tax." Review of Economics and Statistics. LXVIII(1986):179-188.
24
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