Directory UMM :Data Elmu:jurnal:I:International Review of Economics And Finance:Vol10.Issue1.2001:

International Review of Economics and Finance
10 (2001) 95 ± 108

The government spending and private consumption: a panel
cointegration analysis
Tsung-wu Ho*
Department of Economics, Shih Hsin University, No. 1, Lane 17, Section 1, Mu-Cha Road, Taipei, 11602,
Taiwan, ROC
Received 8 October 1999; revised 14 April 2000; accepted 20 April 2000

Abstract
In this article, whether an increase in government spending will crowd out the private consumption
is re-examined. This article augments the empirical literature by extending this issue to panel data. The
empirical framework applies the panel cointegration model, dynamic OLS (DOLS), proposed by Kao
and Chiang [On the estimation and inference of a cointegrated regression in panel data. Working
Paper, Economics Department, Syracuse University, 1999.]. Evidence from 24 OECD countries
indicates a significant degree of substitutability between government spending and private
consumption when the real disposable income is included, which rejects the permanent income
hypothesis. The existence of crowding out renders the Keynesian plea for expansionary fiscal policy
unconvincing. D 2001 Elsevier Science Inc. All rights reserved.
JEL classification: C22; E21

Keywords: Panel cointegration; Dynamic OLS; Fiscal multiplier; Crowding-out

1. Introduction
The multiplier process causes an increase in government spending, or any other exogenous
increase in spending, to have a greater ultimate effect on the nominal level of income through
price increases, real income increases, or both, depending on where the economy is, relative
to full employment. This observation makes expansionary fiscal policy attractive to those
* Tel.: +886-2-236-8225 ext. 542; fax: +886-2-236-1658.
E-mail address: tsungwu@cc.shu.edu.tw (T. Ho).
1059-0560/01/$ ± see front matter D 2001 Elsevier Science Inc. All rights reserved.
PII: S 1 0 5 9 - 0 5 6 0 ( 0 0 ) 0 0 0 7 3 - 3

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T-w. Ho / International Review of Economics and Finance 10 (2001) 95±108

who believe in government intervention to control the economy. However, Bailey (1971) first
indicates that there may be a degree of substitutability between government spending and
private consumption, or the crowding-out effect. Barro (1981) incorporates it into a general
model to examine the direct effect of government purchases of goods and services on

consumption utility. Aschauer (1985) and Kormendi (1983) employ the permanent-income
approach and find a significant degree of substitutability between private consumption and
government spending for the United States. Ahmed (1986) estimates the effects of UK
government consumption in an intertemporal substitution model and finds that government
expenditures tend to crowd out private consumption. Recently, Aiyagari, Rao, Christiano, and
Eichenbaum (1992) and Baxter and King (1993) explore the effect of government spending
shocks on various economic aggregates in a one-sector neoclassical growth model with
constant returns to scale and variable labor supply. They find that increases in government
spending significantly led to a decline in private consumption, showing negative relation
between government spending and private consumption. Amano and Wirjanto (1997) apply a
relative-price approach to estimate the intratemporal elasticity of substitution between
government spending and private consumption of US to be about 0.9. In other words, this
group of research indicates that the increase in government spending has fiscal crowding-out
effect on private consumption.
However, some empirical studies have found different results. In terms of a neoclassical
model with increasing returns to scale and monopolistic competition, Devereux, Head, and
Lapham (1996) examined the impact of government spending shocks and found that an
increase in government consumption generates an endogenous rise in aggregate productivity.
The increase in productivity raises the real wage sufficiently, that there is a substitution away
from leisure and into consumption. Thus, an increase in government expenditures leads to an

increase in private consumption. Karras (1994) examines the change of private consumption
in response to increases in government spending across a number of countries and finds that
public and private consumption are better described as complementary rather as substitutes.
The strength of this complementary relationship is shown to be negatively affected by the
government size. These findings are robust across all specifications. In other words, in the
aggregate, they are best described as complementary goods in the sense that an increase in
government spending tends to raise the marginal utility of private consumption. These
findings imply that private consumption cannot be responsible for any crowding-out effects
that government spending might have on aggregate demand. On the contrary, private
consumption is probably ``crowded-in.''
Given the wide range of empirical results, there appears to be no clear consensus among
research works on this issue. Methodologically, the statistical inference of the abovementioned research works relies on univariate time series data of single country over a long
time span. However, using a long time series data usually involves possible regime changes
and structural breaks, which are both economically and empirically relevant, and can severely
affect the properties of inferential procedures. For example, Carruth, Hooker, and Oswald
(1998) indicate that the two oil crises in the 1970s severely affected the employment level and
the national income. Expensive oil raises producers' costs and squeezes their profit margins.
To restore these margins, employers strive to cut labor costs. At the aggregate level, and for
any given pressure of demand, higher unemployment and the subsequent decrease in

T-w. Ho / International Review of Economics and Finance 10 (2001) 95±108

97

aggregate consumption are the result. Carruth et al. also point out that the oil shocks in the
late 1970s played a stronger and statistically more significant role in driving American
unemployment than interest rates. In other words, once the sampling period spans the 1970s,
then the inferential result may be spurious.
Alternatively, when using smaller samples, applied econometric analysis appeals to panel
data where additional information from cross-sectional units helps identify the parameters of
concern. The benefits of using panel data model have been discussed extensively by Baltagi
(1995) and Hsiao (1996). The fundamental advantage of a panel data set over a cross-section
is that, it will provide the researcher far greater flexibility in modeling differences in behavior
across individuals.
In light of this, this article extends this line of research to panel data. In addition, since our
data involves significant nonstationarity, a panel cointegration method is applied as an
alternative to traditional time series and cross-sectional regressions. One of the original
motivations in applying tests for stationarity in panel data is due to the lack of power of
conventional univariate unit root tests against persistent alternatives, typically for sample
sizes that occur in practice. Recognizing data of longer span may lead to more reliable

inferences, researchers then employ the amount of available information as much as possible
in empirical time series works. This has been proven quite satisfactorily in improving the
power of unit root tests.
Recent developments in panel cointegration methods have sparked a large body of
literature. Quah (1994) pioneers the research by proposing the tests that exploit information
from cross-sectional dimensions in inferring nonstationarity from panel data. Moreover,
Pedroni (1996) proposes a fully modified estimator for heterogeneous panels; Pedroni
(1997) derives asymptotic distributions for residual based tests of cointegration for both
homogeneous and heterogeneous panels. Im, Pesaran, and Shin (1999) and Levin, Lin, and
Chu (1997) constitute further important contributions along the line. McCoskey and Kao
(1998) proposed LM-statistic to test the null hypothesis of cointegration in panel data. Kao
(1999) studies the asymptotic theory of cointegration in the panel data. The empirical
framework of this article applies the dynamic OLS model (henceforth, DOLS) proposed by
Kao and Chiang (1999) to examine cointegration in the panel data. The DOLS was
originally developed by Phillips and Loretan (1991) and Saikkonen (1991), which is also
known as nonlinear single equation ECM; Kao and Chiang extend it to panel data.
Data of 24 OECD countries are derived from the AREMOS/OECD, which is a database
compiled by the Ministry of Education (Taiwan, ROC) from IMF International Financial
Statistics. The sample period extends from 1981 to 1997, which avoids the possible effects of
the two oil crises in the 1970s. Although there was another oil crisis in 1990±1991, this one

was a not-so-noticed case. The private consumption includes consumer spending on goods
and services. The government consumption consists of government spending on goods,
services, and investment. The disposable income is calculated by subtracting total government tax revenue from national income. The real variables are calculated according to 1980
current price and exchange rate, and the unit is expressed in millions of US dollars. All per
capita variables are obtained by dividing the aggregate measure by total population.
This article is organized as follows. Section 2 develops a theoretical model and analyzes
basic time series properties. Section 3 conducts the analysis of panel data. Section 4 concludes.

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T-w. Ho / International Review of Economics and Finance 10 (2001) 95±108

2. The model
2.1. The theoretical modeling
In this section, the theoretical structure of the model is summarized. The standard Keynesian
effective consumption C* is assumed to consist of two components, as specified below (Eq. (1))
C  ˆ Ct ‡ aGt

…1†

where Ct is the real per capita private consumption, Gt is the real per capita government
consumption, and a is the parameter measuring the relationship between them. Assume a
representative individual maximizing the lifetime utility given below (Eqs. (2) and (3))
"
#
1
X
t

…2†
Max E0
b UCt
tˆ1

fS:t: At‡1 ˆ At ‡ Yt ÿ Ct ÿ …1 ÿ a†Gt …1 ‡ r†g

…3†

where the utility function is concave, E0 is the expectations operator based on information of
period 0, and b is the subjective discount factor. Eq. (3) is the intertemporal budget constraint,

where At is the real financial assets net real government debt at the beginning of period t, and
r is a time invariant real rate of interest. Finally, we assume that U is increasing and concave
in its arguments, and that @U(0)/@C * ! 1. Based on Barro (1981) and Christiano and
Eichenbaum (1992), a function of G can be added to the utility function so that the
government consumption's marginal utility becomes positive. Hence, the Lagrangean
function for the optimization problem is given by Eq. (4):
"
#
1
X
…4†
bt U …Ct † ‡ lt fAt‡1 ÿ …1 ‡ r†‰At ‡ Yt ÿ Ct ÿ …1 ÿ a†Gt Šg
E0
tˆ1

where lt is the Lagrange multiplier associated with the budget constraint equation above,
which measures the marginal utility of wealth. The first order necessary conditions for period
t include the following equations
@Ut =@Ct ˆ lt

…5†

E0 ‰b…1 ‡ r†lt‡1 Š ˆ lt

…6†

for t = 1,2, . . ., where @Ut/@Ct* = @Ut(Ct*)/@Ct*. Substituting Eq. (5) for lt and lt + 1 into Eq.
(6), the Euler equation between periods t and t + 1 can be derived below:
E0 ‰b…1 ‡ r†…@Ut‡1 =@Ut †Š ˆ 1:

…7†

To investigate the empirical implications of the model, we assume that the change in
marginal utility is negligibly small over time, so that Eq. (7) can be written as
E0Ct + 1*=[b(1 + r)]sCt*, where s = ÿ U0C*/{C * U(C*)} is the intertemporal elasticity of
substitution. Hence, the econometric relationship below is derived:

ˆ gCt ‡ nt
Ctÿ1

…8†

T-w. Ho / International Review of Economics and Finance 10 (2001) 95±108

99

where nt  i.i.d. Eq. (8) can be rewritten as:
Ct ‡ aGt ˆ g…Ctÿ1 ‡ aGtÿ1 † ‡ nt :

…9†

Rearranging them (Eqs. (8) and (9)), we obtain:
…Ct ÿ gCtÿ1 † ˆ ÿa…Gt ÿ gGtÿ1 † ‡ nt

nt  i:i:d:

…10†

If both Ct and Gt are I(1), then they are cointegrated in the sense of Engle and Granger
(1987) with cointegrating vector A. Hence, Eq. (10) implies an error correction mechanism

that can be consistently estimated by the procedures suggested by Park (1992), Phillips
(1991), Phillips and Hansen (1990), Phillips and Loretan (1991), and Wickens and Breusch
(1988). In other words, it also implies that g = 1 and Ct* is integrated of order 1, or I(1).
Moreover, Graham (1993) has shown that the robustness of the relationship between
government spending and private consumption will be weakened when real disposable
income is excluded from the model. Thus, letting Y d denote the real disposable income, we
estimate the two models [Eqs. (11A) and (11B)] below:
Model 1 :

Ct ˆ a0 ‡ a1 Gt ‡ nt

Model 2 :

Ct ˆ a0 ‡ a1 Gt ‡ bYtd ‡ nt :

…11A†
…11B†
d

Eq. (11B) can be easily derived by simply adding Y to Eq. (1).
2.2. The time series properties of individual country
We first test whether there is a unit root in each series. To this end, we conduct ADF
(Dickey & Fuller, 1981; Said & Dickey, 1984) and the KPSS-statistic proposed by
Kwiatkowski, Phillips, Schmidt, and Shin (1992). The ADF tests the null hypothesis that
there is a unit root and the KPSS-statistic tests the opposite. Table 1 presents the estimation
result of individual country. Reading across the rows of the table is individual country by
country results for each of the 24 countries, and the presence of a unit root is confirmed.
Banerjee, Dolado, Hendry, and Smith (1986), Phillips (1987, 1991), and Phillips and Ouliaris
(1990) have shown that conventional tests in multivariate regressions with integrated
processes cannot be applied asymptotically. In this case, classical asymmetric theory breaks
down and the presence of nuisance parameter dependencies in the limiting distribution theory
raises similar issues to panel data with I(1) processes. To expose this problem, assume that the
generating mechanism for Yt is a cointegrating system (Eqs. (12) and (13)):
Yt ˆ A ‡ BXt ‡ u1t ;
DXt ˆ u2t :

t ˆ 1; 2; . . . ; T

…12†
…13†

Phillips and Durlauf (1986) showed that under appropriate centering and scaling, OLS
estimation of the cointegrating vector in Eq. (12) are asymptotically non-normal. The weak
convergence of appropriately scaled sample moments with random matrices rather than
constant matrices results in this non-normality.
Moreover, OLS leads to estimators that are asymptotically biased, and whose distributions
involve unit root asymptotics and nontrivial nuisance parameters (see Phillips and Loretan,

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T-w. Ho / International Review of Economics and Finance 10 (2001) 95±108

Table 1
Unit root tests of individual country
Yd

Ct
KPSS

Gt
KPSS

KPSS

Country

ADF

Level

Trend

ADF

Level

Trend

ADF

Level

Trend

Argentina
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Greece
Iceland
Ireland
Italy
Japan
Luxembourg
Netherlands
New Zealand
Norway
Portugal
Spain
Sweden
Switzerland
Turkey
UK
USA
OECD Europe

0.07
ÿ 0.94
ÿ 0.44
ÿ 0.79
0.37
ÿ 0.98
ÿ 1.08
ÿ 1.26
ÿ 0.59
ÿ 0.97
1.99
ÿ 1.63
ÿ 1.71
ÿ 0.93
0.24
0.98
2.06
ÿ 2.01
ÿ 1.06
ÿ 0.91
ÿ 1.56
ÿ 1.13
ÿ 1.27
0.31
ÿ 0.90

0.5434
0.5421
0.5311
0.5232
0.5471
0.4716
0.5315
0.5306
0.5405
0.5112
0.5341
0.5239
0.5258
0.5335
0.5393
0.5146
0.5455
0.5179
0.5273
0.5139
0.4962
0.5475
0.5278
0.5433
0.5358

0.081
0.082
0.084
0.117
0.088
0.121
0.121
0.122
0.124
0.117
0.158
0.126
0.13
0.097
0.126
0.118
0.114
0.110
0.118
0.125
0.127
0.134
0.112
0.097
0.123

1.547
ÿ 1.924
ÿ 1.293
ÿ 0.711
0.120
ÿ 2.041
ÿ 3.571
ÿ 2.622
ÿ 0.62
ÿ 1.511
1.722
ÿ 1.863
ÿ 3.711
ÿ 1.552
0.142
0.541
ÿ 0.512
ÿ 1.393
ÿ 1.361
ÿ 2.283
ÿ 2.081
0.642
ÿ 1.563
ÿ 0.352
ÿ 1.841

0.429
0.463
0.449
0.461
0.477
0.375
0.414
0.404
0.464
0.390
0.416
0.395
0.416
0.522
0.534
0.529
0.527
0.512
0.508
0.44
0.514
0.56
0.52
0.544
0.529

0.159
0.092
0.093
0.118
0.096
0.126
0.127
0.111
0.095
0.118
0.163
0.127
0.132
0.095
0.114
0.086
0.085
0.093
0.094
0.119
0.133
0.089
0.120
0.118
0.118

ÿ 1.906
ÿ 0.674
ÿ 0.021
ÿ 0.816
ÿ 1.507
ÿ 1.089
ÿ 1.130
ÿ 1.060
ÿ 0.865
ÿ 1.252
ÿ 1.014
ÿ 0.671
ÿ 0.487
ÿ 1.603
ÿ 2.585
ÿ 0.881
ÿ 0.924
ÿ 0.500
ÿ 1.228
ÿ 0.688
ÿ 1.098
ÿ 1.854
ÿ 1.647
ÿ 1.790
ÿ 1.667

0.1743
0.1893
0.2333
0.1813
0.1629
0.1420
0.1341
0.1377
0.1855
0.3077
0.3599
0.3590
0.3111
0.3960
0.3132
0.1857
0.3204
0.3262
0.2965
0.2615
0.2357
0.2759
0.1954
0.2643
0.4066

0.1485
0.1416
0.1512
0.1390
0.1413
0.1405
0.1322
0.1302
0.1419
0.1666
0.1539
0.1468
0.1695
0.1565
0.1548
0.1580
0.1675
0.1685
0.1834
0.1726
0.1791
0.1806
0.1830
0.1665
0.1453

ADF is calculated by one lag, including trend and intercept. The MacKinnon (1991) critical values for ADF at
1%, 5%, and 10% significance level are ÿ 4.044, ÿ 3.451, and ÿ 3.151. For KPSS, the approximate asymptotic
critical values at 10%, 5%, and 1% significance level for the level model are, respectively, 0.347, 0.463, and
0.739, and for the trend model, they are 0.119, 0.146, and 0.216, respectively.

1991, p. 426). Phillips (1991) and Phillips and Ouliaris (1990) have proven that standard tests
statistics, such as the Wald test, no longer generate asymptotically distributed c2 criteria.
Because the limiting distribution of the regression coefficients is non-normal, the metric
underlying the Wald test is no longer valid. In other words, statistical estimation and inference
in these models require a methodology that accounts for the nonstationarity of the underlying
time series.
Roughly at the same time, Phillips and Loretan (1991) and Saikkonen (1991) propose an
estimator that can produce asymptotically efficient estimates of the long-run vector B in the
absence of contemporaneous correlation of u1t and u2t. It is specified below:
Yt ˆ BXt ‡ d1 …L†…Yt ÿ BXt † ‡ d2 …L†Xt ‡ et
…14†
1
P
j
where dk …L† ˆ
dkj L , and k = 1,2. Estimation of B in Eq. (14) is achieved through a simple
jˆ1

T-w. Ho / International Review of Economics and Finance 10 (2001) 95±108

101

nonlinear LS regression. For simplicity, we call it NLS estimator. The limiting distribution of
the NLS estimate of B is free of nuisance parameters, and asymptotically normal t ratios and
asymptotically c2 criteria constructed in the usual fashion can be used for inferential purposes.
Unfortunately, in the NLS modeling, valid conditioning on the regressors always fails. With
feedback from ui1 to ui2 (the presence of simultaneous equation bias), the limiting distribution
of the estimator of B is biased, asymmetric, and without scaled nuisance parameters.
To deal with the long-run endogeneity of Xt, Phillips and Loretan (1991) included leads
and lags of Xt in the regression so that et is asymptotically orthogonal to the entire history of
(DXt) 1
ÿ 1 . The modified specification has the form below
Yt ˆ BXt ‡ d1 …L†…Yt ÿ BXt † ‡ d2 …L†DXt ‡ d3 …Lÿ1 †DXt ‡ nt

…15†

where d3(L ÿ 1) is similarly defined as previous equation. The nonlinear least square estimate
of B from Eq. (15) is fully efficient in the limit, asymptotically median unbiased, and
asymptotically equivalent to the full system MLE and spectral regression estimator.
Conventional c2 criteria for hypothesis testing, with respect to all coefficients in Eq. (15), can
be applied. Parameter estimates in Eq. (15) are obtained by nonlinear least square that is
equivalent to maximum likelihood estimation when the errors are normally distributed. The
issue of applicability of these asymptotic results to small samples has also been addressed by
Phillips and Loretan (1991). Simulations with a small-scale cointegrated system showed that
the small-sample properties are consistent with its asymptotic distribution theory. Phillips and
Loretan (1991) concluded that the limit distribution theory, with respect to their proposed
estimator, provides good approximations in sample sizes that are typical in economic time
series data. Therefore, the model we are going to estimate is
Ct ˆ a0 ‡ a1 Gt ‡ bYtd ‡ d1 …Ct ÿ a0 ÿ a1 Gt ÿ bYtd † ‡ d2 …L†DGt
‡ d3 …Lÿ1 †DGt ‡ d4 …L†DYtd ‡ d5 …Lÿ1 †DYtd ‡ et :

…16†

Because the nonlinear error correction is limited to 1, Eq. (16) is expressed by
NLECM( p,q), where p and q denote the number of leads and lags, respectively. The
economic mechanism underlying the specification in Eq. (16) is one where countries react
to departures from long-run equilibrium through the adjustment coefficient d1. The disequilibrium correction takes place only if d1 is negative. Table 2 presents the results of individual
country estimated by NLECM(1,1). To save space, we only report four major parameter
estimates of Model 2. Examining a1 and b, not surprisingly, on a country-to-country basis,
the data can hardly obtain consistent results of cointegration and substitutability. This is
largely consistent with previous studies. It motivates the subsequent study in panel data.

3. The panel DOLS model
Pooling time series has traditionally involved a substantial degree of sacrifice in terms of
the permissible heterogeneity of the individual time series. In order to ensure broad
applicability of any panel cointegration test, it will be important to allow for as much
heterogeneity as possible among the individual members of the panel. Table 3 offers a list of

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T-w. Ho / International Review of Economics and Finance 10 (2001) 95±108

Table 2
NLECM(1,1) estimates for individual country
Country

a0

a1

b

d

AIC

ADF

Argentina
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Greece
Iceland
Ireland
Italy
Japan
Luxembourg
Netherlands
New Zealand
Norway
Portugal
Spain
Sweden
Switzerland
Turkey
UK
USA

ÿ 3.426 ( ÿ 1.48)
0.45 (0.56)
ÿ 1.36 ( ÿ 1.86)
ÿ 2.28 ( ÿ 3.45)
ÿ 0.98 ( ÿ 1.99)
ÿ 2.268 ( ÿ 5.26)
ÿ 0.39 ( ÿ 0.98)
ÿ 4.27 ( ÿ 24.85)
ÿ 5.09 ( ÿ 7.83)
1.84 (2.12)
2.31 (1.004)
ÿ 7.82 ( ÿ 1.45)
0.081 (0.356)
3.001 (13.26)
ÿ 0.09 ( ÿ 0.77)
ÿ 4.23 ( ÿ 19.2)
2.949 (42.82)
ÿ 1.387 ( ÿ 2.87)
ÿ 1.34 ( ÿ 1.56)
ÿ 5.86 ( ÿ 1.41)
2.34 (5.454)
2.72 (1.039)
ÿ 5.88 ( ÿ 2.23)
ÿ 1.34 ( ÿ 2.18)

0.01 (0.65)
ÿ 0.011 ( ÿ 1.99)
0.007 (1.87)
0.003 (0.57)
ÿ 0.003 ( ÿ 0.35)
ÿ 0.05 ( ÿ 4.26)
0.002 (0.39)
0.005 (1.143)
0.005 (0.318)
0.0003 (0.05)
ÿ 0.019 ( ÿ 0.71)
ÿ 0.037 ( ÿ 0.98)
0.009 (3.543)
0.06 (3.86)
ÿ 0.001 ( ÿ 0.57)
ÿ 0.018 ( ÿ 3.74)
ÿ 0.0045 ( ÿ 3.3)
0.011 (1.34)
ÿ 0.01 ( ÿ 0.659)
ÿ 0.048 ( ÿ 1.76)
0.005 (1.39)
0.042 (0.35)
0.042 (0.85)
ÿ 0.004 ( ÿ 0.31)

1.206 (6.9)
0.9 (14.36)
1.05 (18.09)
1.11 (23.66)
1.004 (27.4)
1.17 (24.9)
0.97 (32.87)
1.24 (102.1)
1.401 (20.7)
0.696 (6.36)
0.72 (2.92)
1.542 (3.658)
0.93 (55.0)
0.523 (15.67)
0.947 (85.36)
1.35 (55.56)
0.662 (107.8)
1.05 (20.8)
1.05 (13.5)
1.44 (3.99)
0.74 (19.93)
0.687 (5.16)
1.348 (8.49)
1.046 (32.3)

0.002 (0.26)
0.004 (1.76)
ÿ 0.002 ( ÿ 1.38)
ÿ 0.001 ( ÿ 0.32)
0.003 (0.72)
0.033 (3.86)
ÿ 0.004 ( ÿ 1.67)
ÿ 0.74 (4.41)
ÿ 0.003 ( ÿ 0.74)
ÿ 0.003 ( ÿ 0.64)
0.005 (0.606)
0.009 (0.765)
ÿ 0.003 ( ÿ 3.12)
ÿ 0.02 ( ÿ 2.95)
0.002 (1.14)
0.01 (4.88)
0.005 (9.68)
ÿ 0.004 ( ÿ 1.26)
0.007 (1.11)
0.02 (2.408)
ÿ 0.001 ( ÿ 0.14)
ÿ 0.034 ( ÿ 0.8)
ÿ 0.02 ( ÿ 0.78)
0.0013 (0.2)

ÿ 8.91
ÿ 11.66
ÿ 10.69
ÿ 10.41
ÿ 9.82
ÿ 10.86
ÿ 10.12
ÿ 12.18
ÿ 9.414
ÿ 9.605
ÿ 7.455
ÿ 9.334
ÿ 12.63
ÿ 8.67
ÿ 12.4
ÿ 12.1
ÿ 13.55
ÿ 9.79
ÿ 9.935
ÿ 8.11
ÿ 11.57
ÿ 6.626
ÿ 8.865
ÿ 11.21

ÿ 6.62
ÿ 5.27
ÿ 4.73
ÿ 7.46
ÿ 7.93
ÿ 6.81
ÿ 6.29
ÿ 6.42
ÿ 2.779
ÿ 4.335
ÿ 2.625
ÿ 3.565
ÿ 3.314
ÿ 2.27
ÿ 3.41
ÿ 7.218
ÿ 5.472
ÿ 6.54
ÿ 2.34
ÿ 2.29
ÿ 2.201
ÿ 1.808
ÿ 2.97
ÿ 2.678

the 24 economies and country annual averages over 1981±1997 for the three variables. It is
apparent from Table 3 that these variables vary substantially across economies. If the
theoretical predictions are verified empirically, this large difference suggests that the
cointegrating relation is expected to differ considerably across countries.
To further study the problem in panel data, we test for the presence of a panel unit root. We
apply the ADF-based test (tNT, thereafter) proposed by Im et al. (1999) to test the null
hypothesis whether there is a panel unit root. Table 4 reports the outcomes of panel unit root
test. To avoid the small-sample bias, we calculated the critical values of the panel-based test
by using Monte Carlo simulations calibrated for our sample size. These critical values are
reported in Table 5. The presence of a panel unit root in three variables is confirmed. Kao and
Chiang (1999) show that the DOLS estimator can produce asymptotically efficient estimates
of the long-run multipliers vector B in the absence of contemporaneous correlation of u1,it and
u2,it. They have also shown that the DOLS outperforms both bias-corrected OLS and fully
modified OLS. Technically, the panel DOLS is slightly different from the NLS mentioned
above; it is specified according to the equation below (Eq. (17)):
Cit ˆ ai0 ‡ a1 Git ‡ bYitd ‡

q
X

jˆÿq

c1ij DGi;t‡j ‡

q
X

jˆÿq

d
‡ eit :
c2ij DYi;t‡j

…17†

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T-w. Ho / International Review of Economics and Finance 10 (2001) 95±108
Table 3
Sample means: 1981 ± 1997, millions of US dollars
Countries

Yd

Ct

Gt

Australia
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Greece
Iceland
Ireland
Italy
Japan
Luxembourg
Norway
Netherlands
New Zealand
Portugal
Spain
Sweden
Switzerland
Turkey
UK
USA

0.0131
0.0168
0.0157
0.0159
0.0207
0.0199
0.0164
0.0174
0.0072
0.0157
0.0107
0.0152
0.0189
0.0221
0.0159
0.0119
0.0175
0.0054
0.0099
0.0205
0.0265
0.0185
0.0134
0.0177

0.0093
0.0111
0.0118
0.0105
0.0123
0.0122
0.0115
0.0115
0.0063
0.0113
0.0072
0.0108
0.0130
0.0155
0.0111
0.0069
0.0127
0.0041
0.0073
0.0123
0.0185
0.0148
0.0097
0.0139

0.0026
0.0038
0.0027
0.0042
0.0063
0.0050
0.0036
0.0041
0.0012
0.0036
0.0019
0.0031
0.0021
0.0034
0.0027
0.0029
0.0033
0.0010
0.0018
0.0067
0.0044
0.0024
0.0033
0.0035

Source: AREMOS/OECD Data Set, Ministry of Education, Taiwan, ROC, 1999.

To test for panel cointegration, we apply McCoskey and Kao's (1998) panel LM-statistic,
which is defined below (Eq. (18)):

LM ˆ

N P
T
P

Sit2
iˆ1 tˆ1
NT 2 v2

;

Sit ˆ

t
X

…18†

eij

jˆ1

where v2 is a consistent estimator of se2 under the null hypothesis. To avoid the small-sample
bias, the critical values of the LM are also calculated by using Monte Carlo simulations
calibrated for our sample size; Table 5 reports the simulation results. In addition to LM, we

Table 4
Tests for panel unit root
Test statistic

Ytd

Ct

Gt

tNT for nonstationarity

ÿ 0.5669

ÿ 1.121

ÿ 1.158

Critical values are listed in Table 5 below.

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T-w. Ho / International Review of Economics and Finance 10 (2001) 95±108

Table 5
Critical values for the panel unit root tests
Significance level (%)

tNT

LM

1
5
10

ÿ 2.387
ÿ 2.218
ÿ 1.674

1.42
1.25
1.13

10,000 replications with N = 25 and T = 17.

also employ tNT-statistic to test the presence of a panel unit root in the residuals of
cointegrating equation.
Table 6 presents the results of the two models and the two tests for panel
cointegration estimated by DOLS(1,1). Because the analysis of panel data always
removes individual effect by subtracting individual means, the result does not include
the intercept term. We first examine Model 1, which does not include the real per capita
disposable income. In this, a1 is 2.035, and the two numbers in the parenthesis are,
respectively, the standard deviation and the corresponding t-statistic. The a1 is significantly positive, which implies that a US$1 increase in government spending will
cause a US$2.035 increase in private consumption. That is, the temporary conclusion so
far indicates that the government spending multiplier increases private consumption.
Although the tNT-statistic can reject the null hypothesis that Eq. (16) is not a
cointegrated panel at 5% significance level, the LM-statistic substantially rejects the
null hypothesis that Eq. (16) is a cointegrated panel. Examining the goodness-of-fit
statistics, the adjusted R2 is .49 and AIC is ÿ 2.3; therefore, Model 1 may not be an
appropriately specified model. The underlying reason that causes this result may be
model instability indicated by Graham (1993). Unfortunately, conventional stability tests
for univariate series cannot directly extend to panel data. We are unable to prove this
point here unless its asymptotic properties are proven.
Second, let us examine Model 2. In this case, a1 is ÿ 0.5387 and b = 0.77. Both estimates
are significant. In sharp contrast to the previous model, a1 here implies a crowding-out effect,
rather than crowding-in effect. It implies that a US$1 increase in government spending will

Table 6
DOLS(1,1) estimates for panel cointegration
Parameters
a1
b
Adjusted R2
AIC
tNT
LM

Model 1
2.035 (0.156, 13.05)
.4859
ÿ 2.2912
ÿ 2.29
2.69

Model 2
ÿ 0.5387 (0.2525, ÿ 2.134)
0.77 (0.03, 24.87)
.9611
ÿ 5.684
ÿ 4.466
0.652

Critical values are listed in Table 5. The long-run covariance matrix and true covariance of DOLS estimates
are available from the author. The numbers in the parenthesis are, respectively, standard errors and
corresponding t-statistic.

T-w. Ho / International Review of Economics and Finance 10 (2001) 95±108

105

cause a US$0.5387 decrease in private consumption. b = 0.77 implies that a US$1 increase in
disposable income will cause a US$0.77 increase in private consumption. The adjusted R2 is
.96 and AIC is ÿ 5.689; both goodness-of-fit statistics and significant b not only show the
importance of real disposable income, but also the fact that Model 2 is better specified than
Model 1. Most importantly, the tNT-statistic is ÿ 4.47, which significantly rejects the null
hypothesis that Eq. (16) is not a cointegrated panel; and the LM-statistic is 0.652, which
significantly accepts the null hypothesis that Eq. (16) is a cointegrated panel. They
consistently show that the government spending and private consumption are cointegrated
in this panel. Therefore, Model 2 is better than Model 1. It concludes that there is a significant
degree of substitutability between government spending and private consumption in the
presence of real disposable income, which also rejects the permanent income hypothesis.

4. Conclusion
The crowding-out phenomenon describes the process whereby an increase in government
spending decreases other components of aggregate demand, thus reducing the government
spending multiplier effect on stimulating aggregate demand. In this article, we employ the
Kao and Chiang's (1999) panel DOLS to examine this issue in nonstationary panel data.
The panel cointegration test proposed by McCoskey and Kao (1998) is used to test the
cointegration hypothesis. In order to have a comparative perspective, we estimate two
specifications of the model. We find several interesting results.
First, the model with real disposable income exhibits desirable result. It implies the
important role of real disposable income in the model specification. When econometricians provide reliable test, we can formally show this point. Current evidences are
sufficient to drop the model that excludes the real per capita disposable income. In a
nutshell, the permanent income hypothesis is rejected.
Second, in sharp contrast to individual country results as shown in Table 2, we find
substantial evidence to accept the hypothesis of crowding out. The reason for these
findings can be interpreted as: The power of the panel unit-root test is higher than that of
conventional tests.
The existence of crowding-out effect would make the government spending multiplier
smaller than it is anticipated. Besides the level of employment, the crowding-out effect is
related to the means used to finance an increase in government spending. If taxes are
used to finance an increase in government spending, then this multiplier is called the
balanced-budget multiplier, reflecting the fact that the fiscal action has no impact on the
size of the government's budget deficit or surplus. In this case, consumers reduce
consumption spending to be able to pay the higher taxes. The decrease in consumption
demand partially offsets the increase in government spending, reducing the size of the
multiplier. The offset is only partial because not all the financing for extra taxes comes
from reducing consumption. Some come from reducing saving, which is not a component
of aggregate demand.
Moreover, the multiplier process usually assumes that government sells bond to finance
an increase in its spending, in this case, extra crowding out comes about in two ways.

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T-w. Ho / International Review of Economics and Finance 10 (2001) 95±108

First, it raises the rate of interest. To sell bonds, the government must make them attractive,
so it must raise the interest rate. The higher interest rate crowds out all components in
aggregate demand.
Second, when the bonds mature, interest and principal must be paid to the bondholders.
According to the Ricardian equivalence, people would expect that future taxes will be higher
because of this, and react by increasing saving to build up a reserve, so that those anticipated
higher taxes can be paid without disrupting future consumption levels.
During the multiplier process, several factors crowd out aggregate demand. Financing an
increase in government spending by increasing taxes or by selling bonds to the public causes
crowding-out forces to reduce the magnitude of the multiplier. Although financing by printing
money may avert crowding-out effect temporarily, it will cause inflation and raise the interest
rate in the future. However, whether the increase in national income is due to the increase in
the money supply or the increase in government spending, is still a debatable issue. This
article implies that the existence of crowding out renders the Keynesian plea for expansionary
fiscal policy unconvincing.

Acknowledgments
I am deeply indebted to the editor and two anonymous referees for helpful comments and
suggestions. Helpful discussions with Dr. Biing-shen Kuo and Dr. River Huang are most
appreciated. I am also grateful to Chiang and Kao (2000) who offer the NPT 1.0 as public
program. All errors are mine.

Appendix A
This appendix summarizes the simulation procedure. Interested readers are referred to Im
et al. (1999) for detail. In the work of Im et al., the series is specified according to the
following time-series representation (Eq. (A1)):
zit ˆ d0 ‡ dit zitÿ1 ‡ zit ;

i ˆ 1; 2; . . . ; N; t ˆ 1; 2; . . . ; T

…A1†

where xit is assumed to be composed of two random components (Eq. (A2)),
xit ˆ qt ‡ hit

…A2†

qt is a stationary time-specific common effect that allows for a degree of dependency across
groups. hit is independently distributed across groups. To correct the possible serial
correlation in hit, we estimate the following model (Eq. (A3)):
Dzit ˆ bi ‡ bi zitÿ1 ‡ b2 t ‡

p
X

dij Dzitÿk ‡ xit ;

…A3†

jˆ1

where p is selected to make xit uncorrelated over time. The null hypothesis for the presence of
unit root is bi = 0.

T-w. Ho / International Review of Economics and Finance 10 (2001) 95±108

107

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