A1PS 23 98
POLICY RESEARCH WORKING PAPER 2398
Determinants of Current In developing countries,
increases in current account
Account Deficits in deficits tend to be associated
Developing Countries with a rise in domestic output
growth and shocks that
increase the terms of trade
Cesar Calderon and cause the real exchange
Alberto Chong rate to appreciate. Higher
Norman Loayza savings rates, higher growth
rates in industrial economies,
and higher international
interest rates tend to have the
opposite effect.
The World Bank
Latin America and the Caribbean Region
Regional Studies Program
July 2000
POLICY RESEARCH WORKING PAPER 2398
Summary findings
Calder6n, Chong, and Loayza examine the empirical Among their findings:
links between current account deficits and a broad set of * Current account deficits in developing countries are
economic variables proposed in the literature. moderately persistent.
To accomplish this, they complement and extend * A rise in domestic output growth generates a larger
previous research by using a large, consistent set of current account deficit.
macroeconomic data on public and private domestic * Increases in savings rates have a positive effect on
savings, external savings, and national income variables; the current account.
focusing on developing economies by drawing on a panel * Shocks that increase the terms of trade or cause the
data set for 44 developing countries and annual real exchange rate to appreciate are linked with higher
information for the period 1966-95; adopting a current account deficits.
reduced-form approach rather than holding to a * Either higher growth rates in industrial economies
particular structural model; distinguishing between or higher international interest rates reduce the current
within-country and cross-country effects; and employing account deficit in developing economies.
a class of estimators that controls for the problems of
simultaneity and reverse causation.
This paper-a product of the Regional Studies Program, Latin America and the Caribbean Region-is part of an effort in
the region to understand the determinants of external sustainability. Copies of the paper are available free from the World
Bank, 1818 H Street NW, Washington, DC 20433. Please contact Hazel Vargas, room 18-138, telephone 202-473-8546,
fax 202-522-2119, email address hvargas@worldbank.org. Policy Research Working Papers are also posted on the Web
at www.worldbank.org/research/workingpapers. The authors may be contacted at crcn@troi.cc.rochester.edu,
achong@worldbank.org, or nloayza@condor.bcentral.cl. July 2000. (37 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this
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countries they represent.
Produced by the Policy Research Dissemination Center
DETERMiNANTS OF CURRENT ACCOUNT DEFICITS IN
DEVELOPING COUNTRIES
CESAR CALDERON, ALBERTO CHONG, AND NORMAN LOAYZA*
JEL Classification: F30, F32, F40.
Keywords: Current Account, Within-Country, Cross-Country Panel Data Models.
*
Calder6n: University of Rochester, Chong: World Bank, and Loayza: Central Bank of Chile.
For comments and suggestions we are grateful to Hamid Faruqee, Malcolm Knight, Aart Kraay,
Klaus Schmidt-Hebbel, Luis Serven, Alan Stockman, Rodrigo Valdes, Luisa Zanforlin, and
seminar participants at the Central Bank of Chile and the 1999 Latin American Meetings of the
Econometric Society. Many thanks to Stephen Bond for providing the software to estimate
dynamic models of panel data using GMM methods. Correspondence: Chong, World Bank,
Development Research Group, 1818 H Street, NW, MC 3-620, Washington, DC 20433. Email:
achong@worldbank.org. This paper is part of the Latin America Regional Studies Program of
the World Bank. The views are the authors' and should not be attributed to the World Bank or the
Central Bank of Chile.
DETERMINANTS OF CURRENT ACCOUNT DEFICITS IN DEVELOPING COUNTRIES
1. INTRODUCTION
Recent macroeconomic crises in developing countries have once again underscored the
need for a clear understanding of the factors underlying a country's current account position.
Despite the relatively extensive body of theoretical literature on the subject, there are only a few
comprehensive cross-country studies that empirically analyze the effect of macroeconomic
variables on the current account deficit.' This lack of cross-country empirical evidence is
surprising given the fact that the position of the current account is typically used as one of the
main leading indicators for future behavior of an economy and is part of the everyday decision
process of policy makers. The objective of this paper is to examine the empirical linkage
between current account deficits and a broad set of economic variables proposed by the
theoretical and empirical literature. In order to accomplish this task, we intend to complement
and extend previous empirical research by:
* Using a large and consistent macroeconomic data set on public and private saving rates, as
well as other national income variables (the World Saving Database; see Loayza, L6pez,
Schmidt-Hebbel, and Serven, 1998).
* Focusing on developing countries by drawing on a panel data set consisting of 44 developing
countries and annual information for the period 1966-95.
* Adopting a reduced-form approach (instead of holding to a particular structural model) that
includes a "pool" of determinants of current account deficits identified in the literature of
international economics.
* Estimating separately the within-country and cross-country relationships between the current
account deficit and its determinants.
* Employing a class of estimators that controls for the problems of joint endogeneity of the
explanatory variables (simultaneity and reverse causation) and correlated unobserved
1
country-specific effects (i.e. country heterogeneity) [see Arellano and Bond, 1991; and
Arellano and Bover, 1995].
Unlike typical developed countries, most developing countries are credit constrained.
Both the behavior and response of the current account deficit to changes in internal and external
conditions are thus likely to be different in the latter. We acknowledge this possible different
behavior and also take into account the scarcity of empirical research on developing countries,
and thus concentrate our study on them. The paper is organized as follows. The next section
presents a brief review of the theoretical and empirical literature. Section 3 describes the data.
Section 4 presents the econometric methodology to estimate within-country and cross-country
effects. Section 5 discusses the results. Section 6 concludes.
2. REVIEW OF THE LITERATURE
According to the intertemporal approach, the current account deficit is the outcome of
forward-looking dynamic saving and investment decisions driven by expectations of productivity
growth, government spending, interest rates, and several other factors. Within this framework,
the current account balance behaves as a buffer against transitory shocks in productivity or
demand (Sachs, 1981; Obstfeld and Rogoff, 1995, 1996; Ghosh, 1995; Razin, 1995). One of the
main lessons from this literature is that the impact of economic changes on the current account
balance may vary according to their origin, persistence and timing of such changes. With respect
to their origin, shocks may be country-specific or global. Telling them apart is important since
the literature finds that, for instance, global productivity shocks have a smaller impact on current
account deficits than country-specific shocks (Glick and Rogoff, 1995; Razin, 1995). Similarly,
the persistence of the shocks, whether transitory or permanent, may produce a different response
of the current account balance. For example, a permanent productivity shock may widen the
current account deficit as it may generate a surge in investment and a decline in savings (given
that it causes consumption to rise by more than gross output). On the other hand, transitory
productivity shocks may move the current account into surplus, as there may be no investment
2
response to a purely temporary shock (Glick and Rogoff, 1995; Obstfeld and Rogoff, 1995).
Finally, the timing of shocks, in particular, the extent to which they are expected or unexpected
by agents in the economy, may also matter in current account outcomes. In this paper, we take
into account how the nature of economic changes impact on the current account by distinguishing
effects due to overtime changes within a country, the evolution of the world economy, and
structural differences across countries. The first two mostly capture dynamic effects where
transitory shocks are predominant, while current account outcomes due to cross-country
differences mostly capture structural effects, where long-run factors play a large role.
In the context of a real business cycle model, the intertemporal approach has been widely
used to evaluate the impact on the current account balance of fiscal policy (Leiderman and Razin,
1991; Frenkel and Razin, 1996), real exchange rate (Stockman, 1987), terms of trade fluctuations
(Obsfeld, 1982; Svensson and Razin, 1983; Greenwood, 1983; Mendoza, 1995; Tornell and Lane,
1998; Mansoorian, 1998), capital controls (Mendoza, 1991) and global productivity shocks (Glick
and Rogoff, 1995; Razin, 1995 .)2 In assessing the effects of these variables, the RBC literature
has been careful to recognize that dynamic general equilibrium models imply the existence of
simultaneity between the current account deficits and its determinants. The same care has not
been exercised in most traditional econometric studies. Although primarily used to explain
current account fluctuations at business cycle-frequencies, the intertemporal approach has
attempted to introduce life-cycle implications to explain trend developments. In this regard, the
literature on current account sustainability (Milesi-Ferreti and Razin, 1996) has proved to be a
useful complement.3 However, there are still unsolved issues regarding the factors that could
trigger a policy reversal in situations of unsustainability. Events that might generate policy shifts
are different across countries, and might reflect different degrees of vulnerability to external
shocks, or differences in the ability to undertake policy adjustments.4
So far the empirical literature has focused on particular aspects only. Moreover, most of
the studies are mainly focused on industrial countries, either as a group or individually, and
3
typically with emphasis on the response of the current account balance to shocks in one specific
determinant (see Table 1 for a summary of the findings of the empirical literature). An example
of this emphasis on specific variables is given by the several studies that deal with terms of trade
shocks. Its influence has been evaluated using different econometric techniques (Marquez et al.,
1988; Marquez, 1990, 1991; Rose and Yellen, 1989; Debelle and Faruqee, 1996) and using
calibration and simulation of RBC models for both industrial economies (Backus, Kehoe, and
Kydland, 1994) and developing countries (Mendoza, 1995; Senhadji, 1998). Another example is
fiscal policy. Not only has it been evaluated with impulse-response functions from simulations of
dynamic general equilibrium models (Leiderman and Razin, 1991; Frenkel, Razin, and Yuen,
1996), but also with econometric techniques -VAR and panel data analysis (Glick and Rogoff,
1995; Debelle and Faruqee, 1996).
As important as the above studies are, comprehensive cross-country empirical studies on
the determinants of the current account balance are quite scarce. An early attempt to provide a
more comprehensive characterization of the current account behavior was performed by Kahn
and Knight (1983). They use a "pooled" time-series cross-section data for 32 non-oil developing
countries during over the period 1973-805. They find that external factors (captured by rising
foreign real interest rates, slowdown in the growth rate of industrial countries, and the secular
decline in the terms of trade) as well as domestic factors (as represented by increasing fiscal
deficits and real exchange rate appreciation) were relevant in explaining the deterioration of the
current account of non-oil developing countries. Similarly, Marquez (1990), and Hooper et al.
(1998) systematically compute aggregate income and price elasticities that are consistent with
bilateral trade elasticities for both developing and developed countries. However, the work that is
closest in spirit to our research is Debelle and Faruqee (1996). They use a panel of 21 industrial
countries over 1971-93 and an expanded cross-sectional data set that includes an additional 34
industrial and developing countries. Their paper attempts to explain long-term variations and
short-run dynamics of the current account by specifying cross-section and panel data models,
4
respectively. Debelle and Faruqee find that the fiscal surplus, terms of trade and capital controls
do not play a significant role on the long-term (cross-sectional) variations of the current account,
while relative income, government debt and demographics do. Furthermore, with the purpose of
estimating short-run effects, Debelle and Faruqee estimate both a partial-adjustment model with
fixed-effects and an error-correction model (to account, respectively, for the possibilities of
stationarity or non-stationarity of the ratio of net foreign assets to GDP). In both cases, they find
that short-run changes in fiscal policy, movements in terms of trade, the state of the business
cycle, and the exchange rate affect the current account balance. We complement Debelle and
Faruqee's approach by applying recent econometric techniques to control for joint endogeneity
and by distinguishing between within-country and cross-country effects. Our aim is to take a
rather comprehensive approach with emphasis on LDCs, as our expanded data set allows.
3. DATA
We use an unbalanced panel of 753 annual observations from 44 developing countries
over the period 1966-95. In order to ensure a minimum time-series dimension and allow
adequate implementation of our econometric methodology, we only consider countries that have
at least six consecutive annual observations. The following are the key variables used6:
Income, Current Account, and Saving. The measure of income employed to construct and
normalize both the current account balance and national saving is gross national disposable
income (GNDI). This corresponds closely to the concept of total income available for
consumption and saving of national residents and is equal to gross national product (GNP) plus
all net unrequited transfers from abroad. Gross national saving (GNS) is computed as GNDI
minus consumption expenditure, and the current account deficit (CAD) is the difference between
gross domestic investment (GDI) and gross national savings (GNS). We normalize the current
account deficit and public and private saving by dividing each of them by GNDI. Data on
income, saving, and investment is taken from the World Saving Database (Loayza et al., 1998).
5
Public and Private Saving. We employ a broad definition of the public sector that includes
central and local governments as well as non-financial public enterprises. Furthermore, we use
adjusted saving data for capital gains and losses that accrue to the public and private sectors as a
result of inflation (that is, the erosion of the real value of non-indexed public debt). The source of
these variables is the World Saving Database (Loayza et al., 1998).
Exchange Rate. The effective real exchange rate was calculated as:
TCR=- (Pie) ,
FJ (Pk Ilek)'
k
where P is the consumer price index of the domestic country, e is the exchange rate (price of the
US dollar in units of local currency), Pk and ek are the consumer price index and exchange rate for
the trading partners, and 8k represent the IMF-generated weights based on both bilateral trade
shares and export similarity. An increase in the real exchange rate implies a real appreciation of
the domestic currency.
Balance of Payments Controls and Black Market Premium on Foreign Exchange. Grilli and
Milesi-Ferreti (1995) construct dummy variables on three forms of BoP restrictions: (i) payments
for capital transactions; (ii) multiple exchange rate practices; and (iii) restrictions on current
account transactions.' We use a simple average of (i), (ii), and (iii) as a first proxy of BoP
restrictions. Following Dooley and Isard (1980), we use the black market premium on foreign
exchange as an alternative measure of capital and current account controls. Employing this
variable may be particularly important in empirical analysis that uses relatively high (annual)
frequency data. Data on black market premium is obtained from Wood (1988) and International
Currency Analysis Inc. (various years).8
Industrialized Output Growth Rate and International Interest Rates. The first is computed
from dollar-denominated real GDP of OECD countries. For the second, we use the nominal
6
Eurodollar London rate, adjusted with the CPI percentage change for industrial countries. The
source is the IMF International Financial Statistics.
4. ECONOMETRIC METHODOLOGY
We work with pooled time-series and cross-country data. Taking advantage of the nature
of this data set, we identify and differentiate within-country and cross-country effects. Whereas
the former emphasize the current-account response to over-time changes in a given country, the
latter consider how the differences in current-account deficits across countries are driven by their
respective characteristics. Within-country effects are dynamic in nature and require relatively
high-frequency data to be identified. Cross-country effects focus on trends and are best identified
using relatively low-frequency data, which dampens the importance of business-cycle
fluctuations.
In addition, our model considers inertial properties in the current account deficit by
allowing for an independent effect from its lagged value. Finally, our estimation method relaxes
the common assumption of strong exogeneity of the explanatory variables, thus allowing for
(limited) reverse causality and simultaneity.
4.1. Within-country and cross-country Effects
We estinate the within-country effects with a model that controls for country-specific
factors. This model allows us to de-emphasize the cross-sectional variation of the data in favor of
its time-series counterpart. In this sense, our method is akin to the common fixed-effects
estimator (Mundlak 1978, Anderson and Hsiao 1982); in contrast, however, our method allows
for joint endogeneity, as we discuss below. For the estimation of the within-country effects, the
frequency of the time-series data is annual, which is the highest available for our set of variables
and countries. The regression equation for the within-effects model is given by,
Yit = PlYit-1 + P2Xit + i + it (1)
7
where, yit is the current account deficit, as a ratio to national income, of country i in year t; X, is
a set of its economic determinants; and r1i represent country-specific factors.
The estimation of cross-country effects is based on a regression on time-averaged data.
In order not to minimize the cross-country variation, country-specific factors are not controlled
for. Furthermore, using period averages allows us to concentrate on the cross-sectional variation
and mostly abstract from business-cycle fluctuations. However, we do not work with averages
over the whole 1966-95 period; rather we work with non-overlapping five-year periods. We
break the sample period in order to, first, allow for inertial effects and, second, implement our
method to control for joint endogeneity (which, as explained below, is based on using lagged
values of the variables as instruments.)9 The regression equation for the estimation of cross-
country effects is given by,
yi,r = a 1Yir 1 + a 2Xir + J ir (2)
where the index r denotes a given five-year period.
4.2. Joint Endogeneity
Our models of within-country and cross-country effects are dynamic (i.e., the set of
explanatory variable includes a lag of the dependent variable) and include some explanatory
variables that are potentially jointly endogenous (in the sense of being correlated with the error
tern). In addition, the model of within-country effects presents an unobserved country-specific
factor, which is correlated with the explanatory variables. Our preferred method of estimation is
the Generalized Method of Moments estimator for dynamic models of panel data introduced by
Arellano and Bover (1995) and Blundell and Bond (1997). In what follows, we describe the
methodology used to consistently and efficiently estimate the within-country effects model. The
estimation of the cross-country effects model follows similar lines but is simpler given that it
does not control for country specific factors. At the end of this section we highlight the
differences in estimation between the two.
8
To control for country-specific factors and joint endogeneity, we use Arellano and
Bover's system GMM estimator. This estimates in a system the regression equations in
differences and levels, each with its specific set of instrumental variables. For ease of exposition,
we discuss each section of the system, though actual estimation is performed using the whole
system jointly. Specifying the regression equation in differences allows direct elimination of
country-specific factors. First-differencing equation (1) yields,
Yit Yt-i = PI(Y - Y,t-2 )+ 2 (Xi, - Xi,t )+ (E - E,t_, (3)
The use of instruments is required to deal with two issues: first, the likely endogeneity of
the explanatory variables, X, which is reflected in the correlation between these variables and the
error term; and, second, the new error term, (s, - - is correlated by construction with the
differenced lagged dependent variable, (Y,tIl - Yi,-2). Instead of assuming strict exogeneity (that is,
the explanatory variables be uncorrelated with the error term at all leads and lags), we allow for
the possibility of simultaneity and reverse causation. We adopt the more flexible assumption of
weak exogeneity, according to which current explanatory variables may be affected by past and
current realizations of the dependent variable but not by its future innovations. Under the
assumptions that (a) the error term, £, is not serially correlated, and (b) the explanatory variables
are weakly exogenous, the following moment conditions apply:
E[y, ts * (£,,t - Ei,.-I )| = 0 for s > 2; t 3,...,T (4)
E[Xit, 1 * (£,,, - et_- )] = 0 for s > 2; t =3,..., T (5)
The GMM estimator simply based on the moment conditions in (4) and (5) is known as
the differences estimator. Although asymptotically consistent, this estimator has low asymptotic
precision and large biases in small samples, which leads to the need to complement it with the
regression equation in levels.10
9
For the regression in levels, the country-specific factor is not directly eliminated but must
be controlled for by the use of instrumental variables. The appropriate instruments for the
regression in levels are the lagged differences of the corresponding variables if the following
assumption holds. Although there may be correlation between the levels of the right hand side
variables and the country-specific effect, there is no correlation between the differences of these
variables and the country-specific effect. This assumption results from the following stationarity
property,
E[yi,+P 7i j]= E[yi,+, -n, j and E [Xi,+p ni,= E[X,,+q *n,] for all p and q (6)
Therefore, the additional moment conditions for the second part of the system (the
regression in levels) are given by the following equations:'"
E[(yi,t-s -Yi,t-s-1) (qi +i,it)] = 0 for s=] (7)
E[(Xi,t-s - Xi, t-s- ) * (t) i + -i,'t)] = 0 for s = 1 (8)
Using the moment conditions presented in equations (4), (5), (7) and (8), and following
Arellano and Bond (1991) and Arellano and Bover (1995), we employ a Generalized Method of
Moments (GMM) procedure to generate consistent estimates of the parameters of interest.12 The
consistency of the GMM estimator depends on whether lagged values of the explanatory
variables are valid instruments in the current account deficit regression. We address this issue by
considering two specification tests suggested by Arellano and Bond (1991) and Arellano and
Bover (1995). The first is a Sargan test of over-identifying restrictions, which tests the overall
validity of the instruments by analyzing the sample analog of the moment conditions used in the
estimation process. Failure to reject the null hypothesis gives support to the model. The second
test examines the hypothesis that the error term £j,, is not serially correlated. We test whether the
differenced error term (that is, the residual of the regression in differences) is first-, second-, and
third-order serially correlated. First-order serial correlation of the differenced error term is
10
expected even if the original error term (in levels) is uncorrelated, unless the latter follows a
random walk. Second-order serial correlation of the differenced residual indicates that the
original error term is serially correlated and follows a moving average process at least of order
one. If the test fails to reject the null hypothesis of absence of second-order serial correlation, we
conclude that the original error term is serially uncorrelated and use the corresponding moment
conditions. Finally, given that the cross-country effects model must not control for country-
specific factors, estimation is performed with a levels specification for both the regression
equation and the instrumental variables. Allowing for weak endogeneity of the explanatory
variables entails the use of instruments but, since there is no country-specific effect to control for,
these instruments can simply be the lagged levels of the explanatory variables. The two tests of
specification outlined in the previous section can be applied to the estimation of this model, with
the modification that, for the serial correlation test, the model will be misspecified if we find
evidence offirst-order serial correlation.
5. RESULTS
The dependent variable is the current account deficit as ratio to gross national disposable
income (GNDI). The set of core explanatory variables is chosen on the basis of their relevance in
the literature. They are the lagged current account deficit, the domestic output growth rate, private
and public saving ratios with respect to GNDI, the share of exports in GNDI, the real effective
exchange rate, the terms of trade, the extent of balance of payment controls, the black market
premium, the output growth rate of industrialized countries, and the international real interest
rate. The explanatory variables are allowed to be jointly (weakly) endogenous, except for the
terms of trade, the industrialized output growth rate, and the international real interest rate,
variables which in our developing-country sample are most likely exogenous. Table 2 shows
summary statistics on all variables for both the sample of developing countries and the sub-
sample of heavily-indebted countries.
11
5.1. Within-Country Effects
We now present the estimation results of the within-country effects regarding the
relationship between the current account deficit and its domestic and international determinants.
First, we discuss the results obtained with the full sample of developing countries. Then, we
compare the results obtained for a sample of highly indebted countries. Table 3 reports the current
account regressions using alternative estimators on the sample of developing countries and
employing the core specification. For the reasons outlined in the previous section, our preferred
estimation method is the GMM system estimator. The other two alternative estimators have their
particular shortcomings. Thus, the fixed-effects OLS estimator eliminates the country-specific
effect but does not account for the joint endogeneity of the explanatory variables.13 The
differences GMAM estimator accounts for both joint endogeneity and country-specific factors but
eliminates valuable information and uses weak instruments. Notice that the specification tests
support the system GMM panel estimator. The test of over-identifying restrictions (i.e. Sargan
test) can not reject the null hypothesis that the instruments are uncorrelated with the error term.
Moreover, serial correlation tests do not reject the hypothesis that the differenced error term is not
second- or third-order serially correlated (while rejecting that it is not first-order serially
correlated). The two specification tests support the use of (appropriate) lags of the explanatory
variables as instruments for estimation.14 The Sargan test only marginally supports the
specification of the differences GAM estimator. In the case of the fixed-effects OLS estimator,
there is no counterpart to the Sargan test given that they do not rely on instrumental variables.
Below we discuss the effects of each "core" explanatory variable on the current account deficit
(Table 3). For each variable, the system GMM estimator is discussed first and then compared
with those obtained under alternative techniques. We also discuss the effects of a few additional
variables (Table 4), partly to allow comparison with the model of cross-country effects and partly
to test for robustness of the "core" variables.
12
Persistence. The coefficient of the lagged current account deficit (as ratio to GNDI) is positive
and significant, estimated at around 0.36. The size of this coefficient reveals moderate persistence
of transitory shocks, implying that the half-life of these shocks on the current account deficit is
about 1.67 years. The finding of moderate persistence is in line with the notion that, controlling
for country-specific factors, the current account deficit is stationary.15 The alternative estimators
obtain quite similar results regarding the size and significance of the lagged current-account
deficit.
Internal Economic Conditions:
Public and Private Saving. An increase in either public or private saving rates contributes to
decrease the current account deficit. However, whereas the coefficient on the public saving rate
is strongly statistically significant, the one on the private saving rate is only marginally so.
According to the estimated coefficients reported in column 5, the effect of an increase in the
public saving rate of 1 percentage point leads to a CAD fall of 0.35 percentage points; the
corresponding figure for the private rate is 0.13, that is, almost three times smaller. Then, it
appears that shocks in private saving rates are accompanied almost one-to-one by investment rate
shocks, whereas shocks in public saving rates are only partially offset by increases in the
investment rate. A practical implication derived from this result is that when short-run
improvement of the current account deficit is needed, an increase in public saving is a mildly
effective policy option. The impact of rises in private and public saving rises on the current
account deficit is robustly negative and significant across all considered estimators. Although the
size of these two estimated coefficients varies across estimators, a robust result is that the
coefficient on the public saving rate is larger than the corresponding one on private saving.
Domestic output growth. An increase in the domestic output (GDP) growth rate has the effect of
enlarging the current account deficit. A 1 percentage point rise in the GDP growth rate leads to
an increase of about 0.21 percentage points in the current account deficit. Although a rise in
growth may be associated with an increase in the saving rate, it seems that its correlation with the
13
investment rate is somewhat larger, thus leading to a worsening of the current account deficit. If
the increase in growth rates were solely the result of a temporary productivity surge, then it would
be expected to move the current account towards surplus (see Glick and Rogoff, 1995). The
coefficient on domestic output growth is robustly positive and significant across all estimators.
The size of this estimated coefficient seems to be larger when weak endogeneity is allowed and
accounted for (differences GMIM and system GMA4). This is consistent with the notion that a
larger current account deficit brings about poorer growth performance; this negative effect would
be controlled for through the use of the GMM estimators.
In Table 4, we examine the effect of two other variables dealing with internal economic
conditions. The first is the ratio of liquid liabilities to GDP, whose high-frequency changes
measure mostly monetary and credit expansions. Its effect on the current account deficit is
positive and significant. Its likely mechanism is through the interest rate: a monetary expansion
leads to an interest rate drop, which in turn encourages investment and, in the absence of an
important saving effect, a rise in the current account deficit. The second variable is the standard
deviation of inflation, which serves as a measure of macroeconomic uncertainty. Its effect on the
current account deficit is negative and significant. This is consistent with the notion that
macroeconomic uncertainty both lowers investment and, through a precautionary saving motive,
rises saving -- both effects leading to a lower current account deficit (see Gosh and Ostry, 1997).
External Economic Conditions:
Exports. A temporary increase in exports, relative to GNDI, has the effect of lowering the
current account deficit, most likely through its positive impact on the trade balance. This result is
robust across alternative estimators. However, although this effect is statistically significant, its
economic impact is quite small. An increase in the ratio of exports to GNDI of 5 percentage
points leads to a CAD reduction of about 0.2 percentage points.
Real Exchange Rate. We find a significant relationship between the real exchange rate and the
current account deficit that is consistent with the predictions of the Mundell-Fleming model. A
14
depreciation of the domestic currency (that is, a fall in the real effective exchange rate) has the
effect of reducing the current account deficit, though by a small amount. Thus, a 10%
depreciation of the real exchange rate leads to a temporary current account deficit reduction of
0.34 percentage points. Recent evidence argues that the relationship between the real exchange
rate fluctuations and current account deficits may not be monotonic. 16 Thus, we study the
delayed effects of the real exchange rate on the current account deficit in Table 4 by including the
RER lagged one year as an additional regressor. First, we find no evidence in support for the J-
curve hypothesis (as it applies to yearly data; regarding higher frequencies, clearly we have
nothing to say)."7 Second, the contemporaneous positive impact of changes in the RER is offset
by about half the following year. The "net" effect (adding the coefficients on contemporaneous
and lagged RER in Table 4, column 4) is quite similar to the coefficient of the RER in the core
specification. Regarding the alternative estimators, none of them obtain statistically significant
coefficients for the real effective exchange rate.
Terms of Trade. We find a negative and significant relationship between changes in the terms of
trade and current account deficits, which is consistent with the Harberger-Laursen-Metzler effect
(Obstfeld, 1982; Svensson and Razin, 1983; Greenwood, 1983; Mendoza, 1992, 1995).18 Hence,
according to our preferred estimation, an increase of 10% in the terms of trade will reduce the
current account deficit in 0.44 percentage points. Only the estimators that both control for
country-specific effects and allow for (weak) joint endogeneity obtain significant (and negative)
coefficients for the terms of trade.
Balance of Payments Controls. Raising BoP controls has no significant effect on the current
account deficit; Debelle and Faruqee (1996) obtain a similar result. One caveat to consider in
interpreting this result is that the proxies on BoP controls we use vary very little over time and do
not measure accurately the intensity of controls, but only their presence (as stressed by Grilli and
Milesi-Ferreti, 1995). The lack of significance of the coefficient on BoP controls seems to be
robust across alternative estimators.
15
Black Market Premium on Foreign Exchange. In contrast to the BoP controls examined above,
controls on the exchange rate manifested in the size of the black market premium have the effect
of decreasing the current account deficit. The effect is statistically significant, although
economically rather small. Imposing foreign exchange controls that result in an increase in the
black market premium from 0 to 20% lead to a decrease in the current account deficit of 0.6
percentage points. The fixed-effects OLS estimator obtains similar results in size and
significance, but the difference GMM estimator does not.
Evolution of the World Economy:
Output Growth Rate of Industrialized Countries. An increase in the growth rate of
industrialized countries leads to a reduction in the current account deficits of developing
countries. This can be explained by both a rise in the demand for the exports of developing
countries and increased capital flows between industrialized countries at the expense of flows to
developed countries. Our estimates indicate that a 1 percentage point rise in the growth rate of
industrial countries would generate a reduction of 0.46 percentage points in the current account
deficit. This result is quite robust, in sign, size, and significance, across alternative estimators.
International Real Interest Rate. We find a negative association between the international real
interest rate and the current account deficit in developing countries. This result is in line with the
argument that net debtor countries, as most developing countries are, widen their demand for
international capital in response to interest rate reductions (Reisen, 1998). On the side of the
supply of capital, lower real interest rates induce international investors to look for investment
opportunities in developing countries (Milesi-Ferreti and Razin, 1996 and 1998). According to
our estimates, a rise in international real interest rates of 1 percentage point leads to a current
account deficit reduction of about 0.18 percentage points. In contrast to the industrialized
countries growth rate, the estimated coefficient on the international real interest rate varies
considerably across alternative estimators.
16
5.2. External Indebtedness
A country's current account deficit is likely to be affected by its stock of foreign assets.
More specifically, it is likely that the stock of foreign assets affects the response of the current
account deficit to changes in various economic variables. . We would like to study this conjecture.
Unfortunately, data on foreign asset positions are mostly unavailable for a large sample of
developing countries. However, we do have data on total external debt (mostly from the World
Bank), which can be used as indicator of a country's net foreign asset position (NFA). For most
of our sample, external debt is a good indicator of NFA given that by far external financing has
taken the form of debt issues; this assumption is less appropriate in the most advanced developing
countries and in the most recent years. Our approach to analyze the influence of external
indebtedness is to estimate our core model on the sample of "heavily" indebted developing
countries and, for comparison purposes, on the sample of all developing countries with external
debt data available. We follow the World Bank criterion (in the World Development Indicators)
by which a "heavily" indebted country/year is one that has either the ratio of external debt to
GDP higher than 50% or the ratio of total debt service to exports greater than 25%. We need to
account for the fact that being a heavily indebted country has repercussions that extend beyond
the year at which the criterion is met; furthermore, we need to smooth the (over time) country
composition of both samples in order to be able to use our dynamic panel procedures. Therefore,
we modify the World Bank criterion in the following way: a country is classified as heavily
indebted in a given year if it meets the above condition in any two years of the five year window
surrounding the year in question. The results are presented in Table 5. The first thing to notice is
that the heavily-indebted country sample is almost 80% of the sample containing all developing
countries. Most developing countries have suffered of long periods of high external
indebtedness. Not surprisingly, the results for both samples are quite similar. There are,
however, a couple of noteworthy differences. First, an increase in the private saving rate lowers
the current account deficit only in the case of highly indebted countries. It appears that in non-
17
heavily indebted countries, which are likely to face less stringent external borrowing constraints,
an increase in private saving is accompanied by a corresponding rise in domestic investment.
Second, in contrast to the result for all developing countries, a fall in international real interest
rates does not have a significant effect on the current account deficits of heavily indebted
countries have. From the perspective of the supply of capital, this result indicates that
international investors tend to avoid putting their capital in debt-ridden countries, even if real
interest rates fall in developed countries.
5.3. Cross-Country Effects
Table 6 shows the results related to the estimation of cross-country effects for both the
full sample and the sample of heavily indebted countries. Here the discussion of results follows a
different format with respect to the previous sub-section; we now emphasize how the cross-
country effects compare with the within-country effects. Also, we compare the results obtained
with the sample of heavily indebted countries. As expected, the lagged current account deficit has
a positive and highly significant coefficient. The finding of a moderate degree of persistence in
the sample is consistent with the observation that while some countries tend to stay at certain
current account levels for long periods of time, others experience sudden changes. Note that the
level of persistence is much smaller in the case of heavily indebted countries, a group prone to
current account reversals. Other variables that have similar effects in the within-country and
cross-country models are the domestic and industrialized growth rates and the international real
interest rate. Countries with a higher domestic growth rate have larger current account deficits,
though the statistical significance of this effect is marginal. On the other hand, in periods when
the industrialized output growth rate or the international real interest rate are larger, the current
account deficit of developing countries is reduced. Given that these international variables do not
vary across countries but only over time, it is natural that their effects be similar for the within-
country and cross-country models. Conversely, the cross-country results related to the private
and public saving rates differ from those of within-country effects: Countries with higher saving
18
rates do not appear to have higher or lower current account deficits. In other words, countries
with higher saving rates also have higher investment rates. An exception of this result occurs in
the sample of heavily indebted countries, for which countries with higher private saving present
lower current account deficits. This result can be explained by considering that heavily-indebted
countries must destine increases in available resources to paying off their debts.
Countries with larger exports (relative to GNDI) present bigger current account deficits;
this result contrasts with the effect of exports in the within-country model. It seems that while an
increase in exports from one year to the next lowers the current account deficit through a direct
effect on the trade balance, having a large export sector indicates an improved capacity to repay
external debts and, thus, leads to an expansion of the current account deficit (Milesi-Ferreti and
Razin, 1996).19 Again in contrast to the results related to the within-country effects model, the
black market premium on foreign exchange and the measure of BoP restrictions have,
respectively, positive and negative coefficients, both statistically significant. It appears that
countries having a larger black market premium also have larger current account deficits. Thus,
although foreign currency restrictions may limit the expansion of the current-account deficit in
the short run, they are associated with macroeconomic mismanagement and higher external
imbalances in the long run. On the other hand, countries with stricter BoP restrictions appear to
limit the size of their current account deficit. The sign and size of the coefficients related to
exports, black market premium, and BoP restrictions estimated using the full sample are quite
similar to those using the sample of heavily indebted countries; however, the latter are estimated
with less precision. Regarding the real exchange rate and the terms of trade, neither have a
significant coefficient in the cross-country effects model. The non-significance of the
coefficients on the real exchange rate and terms of trade in this model is not surprising for two
reasons. First, changes in these variables mainly affect the inter-temporal allocation of saving
and investment; and second, their low frequency variation is quite small, particularly when
compared to their annual fluctuations. Both inter-temporal changes and high-frequency variation
19
are considered in the within-country effects model, where both the real exchange rate and the
terms of trade are found to be significant determinants of the current account deficit.
5.4. Additional Cross-Country Results
In Table 7, we consider some popular hypothesis regarding the determinants of current
account deficits. The first column of Table 7 examines the stages of development hypothesis,
which states that the size of current account deficits decreases as a country develops in relation to
the rest. In other words, a poor country would tend to run large current account deficits because
its investment needs cannot be met with its limited saving, but as the country develops, it requires
less external financing and starts devoting resources to pay back its external debt. Our proxy for
the (relative) stage of development of a given country is the log of the ratio of per capita GDP of
such country to the (weighted average of) per capita GDP of industrialized countries. This ratio
is expressed in logs to account for likely non-linear effects. As the first column shows, we do
find a negative and significant effect of relative per capita GDP on the current account deficit,
which gives support to the stages of development hypothesis. In the next two columns of Table 7,
we assess the relevance of demographic variables in driving the current account deficit. We do
this by adding to the set of explanatory variables, first the age dependency ratio, and second, its
components, the young and old dependency ratios, separately. Although their estimated
coefficients are consistently negative, they all fail to be statistically significant. We conclude that
demographic variables do not affect a country's propensity to run current account deficits beyond
their effect through private saving.
Table 8 examines the effects of additional financial variables. The first column considers
the effect of the ratio of liquid liabilities to GDP. While this ratio mostly captures monetary and
credit expansions in the short run for a given country, it represents financial depth when
compared across countries and in the long run (see King and Levine 1993). The estimated
coefficient is negative but not statistically significant; its negligible impact may be due to
contrasting effects of financial depth on the current account deficit. On the one hand, countries
20
with stronger financial depth are better prepared to accommodate larger external financing; but on
the other hand, these countries are also likely to have higher income and internal resources for
investment. In the second column, we address the issue of macroeconomic uncertainty, proxied
by the standard deviation of (monthly) inflation. We do not find a significant coefficient in the
cross-country effects model. Again, this could be due to contrasting effects: on the one hand,
macroeconomic instability decreases domestic investment and increases saving; but on the other
hand, an aspect of deficient macroeconomic policy is excessive borrowing from abroad. Finally,
the last column of Table 8 considers external debt as ratio to GDP as an additional explanatory
variable for current account deficits across countries. We fail to find a statistically significant
coefficient. The effect of the stock of debt on its flow (which to a large extent is given by the
current account deficit) is a complex relationship marked by non-linearities, asymmetries, and
threshold effects. Our simple linear specification does not capture the complexity of this
relationship, but such purpose is beyond the scope of this paper.
6. CONCLUSIONS
In this paper we study the empirical relationship between the current account deficit (as
ratio to GNDI) and the main economic variables proposed by the theoretical and empirical
literatures. Taking advantage of the pooled (time-series and cross-country) nature of our sample,
we distinguish between the effects due to changes over time in the explanatory variables and
those derived from cross-country differences in the same variables. We call them within-country
and cross-country effects, respectively. Furthermore, taking into account that most relevant
variables are jointly endogenous with the current account deficit, we implement an econometric
methodology that controls for simultaneity and reverse causation. This methodology is an
application of the GMM estimator proposed by Arellano and Bond (1991) and Arellano and
Bover (1995) for dynamic models employing panel data.
Our sample consists of an unbalanced panel of 44 developing countries for the period
1966-95. We use annual data and non-overlapping five-year averages in the study of within-
21
country and cross-country effects, respectively. We concentrate on developing countries because
the response of their current account deficit to changes in internal and external conditions is likely
to be different from that of industrialized countries: whereas the latter largely face unobstructed
access to financial markets, most developing countries are credit constrained. In addition, there
are comparatively few studies focusing on developing countries. Our main findings are:
* There is a moderate level of persistence in the current account deficit beyond what can be
explained by the behavior of its determinants. The level of persistence is much smaller in
heavily-indebted countries.
* The domestic output growth rate has a positive effect on the current account deficit both
within a country and across countries, indicating that the domestic growth rate is associated
with a larger increase in domestic investment than in national saving.
* The growth rate of industrialized countries contributes to reduce the current account deficits
of developing countries. This may occur through either an increase in the demand for
developing countries' exports or a rise in investment going to other industrialized countries at
the expense of external financing to developing countries. The negative effect on the current
account deficit is stronger in the sample of heavily indebted countries.
* Whereas within-country changes in private and public saving rates contribute to a moderate
decrease in the current account deficit, cross-country differences in either saving rate do not
affect the current account deficit. This is consistent with the notion that saving differences
across countries are accompanied by similar differences in domestic investment. An
interesting departure of this finding is obtained for the sample of highly-indebted countries.
In this group of countries, those that have larger private saving rates exhibit lower current
account deficits, which may reflect the need to destine any increase in available resources to
debt repayment.
22
* While an increase in exports for a given country lowers the current account deficit (likely
through a direct effect on the trade balance), cross-country differences in the size of exports.
are positively linked to differences in current account deficits. The latter effect may be due to
the fact that a bigger export sector signals an improved debt repayment capacity.
* Short-frequency (annual) changes in the level of restrictions on balance of payments flows do
not have a significant impact on current account deficits for a given country; however, across
countries and in lower frequencies, they are linked to smaller current account deficits. On the
other hand, short-frequency changes in the black market premium is deficit-reducing for a
given country, while across countries the black market premium is linked with higher current
account deficits.
* An appreciation of the real exchange rate or a worsening of the terms of trade generate an
increase in the current account deficit
- Reductions in international real interest rates generate an increase in current account deficits
in developing countries. This is consistent with both an increased demand for foreign
financing and a rise in the supply of foreign capital when international real interest rates are
low. This result applies to the sample of all developing countries; in contrast, for the sample
of heavily indebted countries, a fall in international real interest rates does not have a
significant effect on the current account deficit.
* Finally, the stages of development hypothesis receives support from the result that countries
whose per capita GDP is closer to that of industrialized countries tend to run lower current
account deficits.
23
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26
Table 1
Determinants of Current Account Deficits
Category Variable Expected Sign Empirical Sign
Persistence Current Account Deficit lagged one period _ _ +0.67 for CA/GDP [21
+0.50 for CA/GDP [12]
Income Domestic Output Gap + + [1]
Country-Specific Productivity Shock: + / + [3,4,11,12]
Transitory/Permanent
Global Specific Productivity Shock: + / 0 0 1123
Transitory/Permanent
_______ __ Domestic Output Growth + _ [8,9]
Saving/ Saving: National / Private
Investment
Investment + + [2,4,121
Fiscal Policy Public Saving -[5]
Budget Surplus - [2]
Government Spending Shocks: Temporary / + /0 0 [4]
Perrnanent
External Degree of Openness Ambiguous - [8,9]
Indicators
Real Effective Exchange Rate Marshall-Lerner: + _ _21
Intertemporal: 0 [11]
______________ _ __________________________________ Ambiguous
_____________________________________ Non-Monotonic J-Curve: 0 [13]
Terms of Trade Harberger-Laursen- - [2,7,11,12]
.____________________ Metzler: -
Non-Monotonic J-Curve: [6,15]
S-Curve: [1,141
Exchange Controls + 0 [2]
Foreign Industrialized Countries Growth Rate - [8,9]
Indicators
World Real Interest Rate Net Debtor: - 0 [12]
_______________ ______________________ ___________ __ Net Creditor: +
Note: The empirical findings in this table summarizes: [1] Backus, Kehoe and Kehoe (1994); [2] Debelle and Faruqee
(1996); [3] Elliot and Fatas (1996); [4] Glick and Rogoff (1995); [5] Leiderman and Razin (1991); [6] Mansoorian
(1998); [7] Mendoza (1995); [8] Milesi-Ferreti and Razin (1996); [9] Milesi-Ferreti and Razin (1998); [10] Razin and
Rose (1992); [I 1] Razin (1995); [1 2] Reisen (1998); [1 3] Rose and Yellen (1989); [1 4] Senhadji (1998); [1 5] Tornell and
Lane (1998).
27
Table 2
Current Account Deficit Determinants in Developing Countries: Summary Statistics
Annual Data, 1966-1995
A. Sample of Developing Countries
Variable Mean Std.Dev. Minimum Maximum
Current Account Deficit (% GNDI) 0.0327 0.0468 -0.1224. 0.1704
Internal Conditions:
Domestic Output Growth 0.0370 0.0464 -0.1963 0.2400
Private Saving(%GNDI) 0.1329 0.0647 -0.1368 0.3133
Public Saving (% GNDI) 0.0554 0.0444 -0.1255 0.3762
External Sector:
Exports (%GNDI) 0.2524 0.1481 0.0442 0.9619
Real Effective Exchange Rate a/ 4.7483 0.3314 3.5211 6.2032
Terms of Trade a/ 0.0424 0.1848 -0.5764 0.9342
BlackMarketPremium b/ 0.1831 0.2675 -0.3314 1.7918
BoP Controls 0.5811 0.3388 0.0000 1.0000
Evolution of the World Economy:
OECD's Output Growth 0.0281 0.0331 -0.1342 0.0624
International Real Interest Rate b/ 0.0197 0.0226 -0.0406 0.0563
B. Sample of Heavily-Indebted Developing Countries
Variable Mean Std.Dev. Minimum Maximum
Current Account Deficit (% GNDI) 0.0345 0.0486 -0.1224 0.1687
Internal Conditions:
Domestic Output Growth 0.0427 0.0828 -0.1335 0.9209
Private Saving (% GNDI) 0.1309 0.0656 -0.1368 0.3133
Public Saving (% GNDI) 0.0560 0.0448 -0.1255 0.3762
External Sector:
Exports(0/oGNDI) 0.2622 0.1313 0.0515 0.7881
Real Effective Exchange Rate a! 4.7354 0.2993 3.6480 5.6846
Terms of Trade a/ 0.0312 0.1849 -0.3741 0.8901
BlackMarketPremium b/ 0.1911 0.2571 -0.3314 1.7918
BoP Controls 0.6482 0.3370 0.0000 1.0000
Evolution of the World Economy:
OECD's Output Growth 0.0281 0.0331 -0.1342 0.0624
Intemational Real hnterest Rate b/ 0.0197 0.0226 -0.0406 0.0563
C. Simple Correlation of Current Account Deficit with Determinants
Developing Heavily-Indebted
Variable Countries Developing Countries
Persistence:
Current Account Deficit (% of GNDI) lagged I year 0.66 0.67
Internal Conditions:
Domestic Output Growth -0.04 0.03
Private Saving (% GNDD -0.34 -0.38
Public Saving (% GNDI) -0.17 -0.20
External Sector:
Exports (% GNDI) 0.11 0.07
Real Effective Exchange Rate a/ 0.11 0.25
Temns of Trade a/ -0.03 -0.01
Black Market Premium b/ 0.03 0.04
BoP Controls -0.07 -0.06
Evolution of the World Economy:
OECD's Output Growth -0.17 -0.03
International Real Interest Rate b/ -0.07 -0.06
a/Expressed in logs.
bl The variable is expressed in log(l + Variable).
28
Table 3
NVthin-4ountry Effects: Vanous Estion Techniques
Dependent Variable: CuwrentAccowzt Deficit as apercentage of jNDI ((AD)
(t-Statistics are presented below their corresponding coefficients)
Type of Model: Within Differices (D) Sysm D-L
Estimation Techmique: OLS (MM-IV (iMM-IV
Constant - -0.1560
-2.6146
Persistence:
CAD lagged I period 0.3495 0.3084 0.3559
7.7365 5.5698 7.6818
Internal Conditions:
Domestic Output 0.1318 0.3397 0.2128
(kovCh Rate 3.6790 4.0703 4.3595
Private Saving -0.3215 -0.4318 -0.1265
(as %of (ND1) -7.1298 -2.6246 -1.5727
Public Saving -0.3714 -0.6075 -0.3451
(as %of (iNDl) -6.1612 -4.5213 -5.4781
External Sector:
Exports -0.0170 -0.0389 -0.0362
(as % of GNDI) -1.7173 -1.7403 -2.8576
Real Effective Exchange -0.0036 -0.0290 0.0361
Rate (in logs) -0.5034 -0.9893 3.4071
Tenns ot Trade (m logs) -0.0059 -0.0670 -0.0465
-0.5164 -3.1956 -3.8810
Black Maaket Prenum (BMP) -0.0094 0.0033 -0.0327
(inlog[l+BMP]) -1.8326 0.1943 -2.8429
Balance of Payments -0.0095 0.0023 -0.0034
controls -1.4483 0.1792 -0.3803
Evolution of the World Economy:
Inhuiaimd Output -0.5679 -0.3883 -0.4641
(iowth Rate -7.0668 -4.0653 -6.6942
WorldReal Intaerest -0.0711 0.1177 -0.1790
Rate (in log[l+r*) -1.2553 0.8523 -2.3612
No. Counties 44 44 44
No. Observations 709 709 709
SPECIFICATION TESTS (P-Values)
(a) San Test 0.158 0.224
(b) Seijal Contaon:
First-Order 0.000 0.003 0.000
Seond-Order 0.550 0.533 0.624
Third-Order 0.696 0.879 0.789
Observations: The Arellano-Bover (1995) S)ystem Estimator is ourpreferred estimator.
This combines regressions in levels and dfferences (column 5). In addition, th definition
used to define private andpublic saving is the consolidated non-financialpublicsector,
adyustedfor inflationavy capital gains or losses.
29
Table 4
Within-Country Effects: Additional Financial Variables
Dependent Variable: Current Account Deficit as a percentage of 'GNDI (CAD)
Estimation Technique: GMM System Estimator
(t-Statistics are presented below their corresponding coefficients)
Variable [1] [21 [3i [41
Constant -0.1132 -0.1552 -0.1996 -0.1687
-2.0589 -2.5294 -2.7158 -2.7402
Persistence:
CAD fagged I period 0.3504 0.3699 0.4070 0.3873
7.6106 8.5724 7.1465 8.5252
Internal Conditions:
DomesticOutput 0.2043 0.2386 0.1620 0.1553
Cirowth Rate 4.0352 4.8639 2.5472 3.0232
Private Saving -0.1917 -0.1228 0.0714 -0.0160
(as % of UNDI) -2.2494 -1.3885 0.8289 -0.1929
Public Saving -0.3863 -0.3120 -0.2399 -0.2489
(as % otf NDI) -5.8476 4.3606 -3.4985 -4.2711
External Sector:
Exports -0.0411 -0.0598 -0.0363 -0.0455
(as % of GNUI) -2.5828 -3.4622 -2.6254 -3.5259
Real Eflective Exchange 0.0267 0.0225 0.0369 0.0652
Rate(inlogs) 2.4164 1.8823 3.1733 2.7379
Real Ettective Exchange -0.0339
Rate lagged I period -1.4136
Terms of Trade -0.0405 -0.0636 -0.0576 -0.0629
(in logs) -3.5785 -4.8917 -4.8326 -4.6784
Black Market Premium (BME -0.0333 -0.0372 -0.0315 -0.0315
(in log[ 1+3MPJ) -2.9413 -3.0383 -2.6157 -2.6741
Balance of Payments -0.0025 -0.0005 0.0086 -0.0012
Controls -0.3278 -0.0542 1.6384 -0.1278
Evolution oj'the World Economy:
Industrialized Output -0.4208 -0.4647 -0.5531 -0.4335
Cirowth Rate -6.6350 -5.6041 -6.4108 -5.7344
World Real Interest -0.1222 -0.1372 -0.1977 -0.1827
Rate (in log[l+r4') -1.9064 -1.6711 -2.9473 -2.3283
Additional Financial Variables:
Standard Deviation -0.0007
of Inflation -2.1529
Liquid Liabilities as a 0.0631
percentage of GDP 3.1356
External Lebt 0.0181
(as % of GNP) 1.2870
No. Countries 42 44 40 44
No. Obs. 670 672 557 709
SPECIFICATION TESTS (P-Values)
(a) Sargan lest 0.519 0.345 0.229 0.267
(b) Serial Correlation:
First-Order 0.001 0.001 0.000 0.000
Second-Order 0.537 0.706 0.797 0.581
'Ihird-Order 0.747 0.959 0.998 0.496
30
Table 5
Within-Country Effects:
Heavily-Indebted vs. Non-Heavily Indebted Countries a/
Dependent Variable: Current Account Deficit as a percentage of GNDI (CAD)
Estimation Technique: GMM System Estimator
(t-Statistics are presented below their corresponding coefficients)
All Reavily-inaebtea
Variable Countries Developing Countries
Constant -0.1572 -0.1772
-2.6363 -2.5305
Persistence:
CAD lagged I period 0.3954 0.4148
7.2639 8.1906
Internal Conditions:
Domestic Output 0.1369 0.3318
Growth Rate 1.9854 4.3298
Private Saving 0.0231 -0.1667
(as % of GNDI) 0.3200 -2.0052
Public Saving -0.2374 -0.2917
(as % of GNDI) -3.2528 -4.2124
External Conditions:
Exports -0.0394 -0.0561
(as % of GNDI) -2.4505 -5.4291
Real Effective Exchange 0.0300 0.0365
Rate (in logs) 2.7215 2.7563
Terms of Trade -0.0544 -0.0760
(in logs) -4.6649 -5.2339
Black Market Premium (BMP) -0.0336 -0.0492
(in logll+BMPJ) -2.1879 -4.3229
Balance of Payments 0.0087 -0.0015
Controls 1.2925 -0.3367
Evolution oJ the World Economy:
Industrialized Output -0.4985 -0.6423
Girowth Rate -6.6804 -4.0851
World Real Interest -0.1829 -0.0979
Rate (in 0og[l+rT]) -2.3070 -1.1333
No. Countries 40 35
No. Obs. 557 434
SPECIFICATION TESTS (P-Values)
(a) Sargan Test 0.123 0.193
(b) Serial Correlation:
First-Order 0.000 0.007
Second-Order 0.855 0.705
Third-Order 0.957 0o959
a/A country is classified as "heavily indebted" in a given year #'it meets the
Jollowing criterion in any two years of aJiv-year window: the country has
either the ratio of external debt to GNP higher than 50% or the ratio oJ'total
debt service to exports greater than 25%.
31
Table 6
Cross-Country Effects: Heavily-Indebted vs. All Developing Countries a/
Dependent Variable: Current Account DeJicit as a percentage of GNDI (CAD)
Estimation Technique: GMM System Estimator
(t-Statistics are presented below their corresponding coefficients)
All Developing Heavily Indebted
Variable Countries Developing Countries
Constant 0.1400 0.1513
1.5689 0.9052
Persistence
CAD lagged I period 0.4684 0.2079
4.4050 1.4785
Internal Conditions:
Domestic Output 0.4383 0.3565
(irowth Rate 1.4385 1.3884
Private Saving -0.0417 -0.2307
(as % of UNDI) -0.4652 -2.6212
Public Saving 0.0319 -0.1885
(as % ot GNDI) 0.2165 -1.1898
External Conditions:
Exports 0.0142 0.0155
(as % ofGNDI) 2.4410 1.4944
Real Effective Exchange -0.0159 -0.0036
Rate (in logs) -0.8973 -0.1199
Terms of Trade -0.0183 0.0206
(in logs) -0.8073 0.4697
Black Market Premium (BMP) 0.0655 0.0619
(in log[l+BMPJ) 1.7460 1.0947
Balance of Payments -0.0254 -0.0188
Controls -3.0165 -0.9839
Evolution of the World Economy:
Industrialized Output -0.7787 -1.6470
Growth Rate -1.5611 -2.3895
World Real Interest -0.6590 -0.4840
Rate (in logIl+r]) -4.0337 -2.7797
No. Countries 41 26
No. Obs. 126 68
SPECIFICATION TESTS (P-Values)
(a) Sargan Test 0.817 0.232
(b) Serial Correlation:
First-Order 0.220 0.436
Second-Order 0.267 0.470
Third-Order 0.766 0.642
a/For the estimation oJ the cross-country effects model, we use non-overlapping
five-year averages of all variables.
32
Table 7
Cross-Country Effects: Testing Some Popular Hypothesis
Dependent Variable: Current Account Deficit as a percentage of GNDI ('CAD)
Estimation Technique: GMM System Estimator
(t-Statistics are presented below their corresponding coefJicients)
Variable III 121 131
Constant 0.1591 0.2232 0.2535
1.8739 1.4799 1.2922
Persistence:
CAD lagged I period 0.4204 0.5632 0.5538
3.8088 3.0360 2.8617
Internal Conditions:
Domestic Output 0.3918 0.4456 0.3761
(irowth Rate 1.1539 1.4354 0.8618
(UDP in (GDP per capita with -0.0075
respect to OECD a/ -1.7915
Private Saving -0.0402 -0.0629 -0.0879
(as % of 'GNDI) -0.4696 -0.6515 -0.5793
Public Saving 0.0714 -0.0261 -0.0304
(as % oftGNDI) 0.4897 -0.1803 -0.2009
External Sector:
Exports 0.0186 0.0119 0.0121
(as % of CiNDI) 3.0017 1.8890 1.8525
Real Etfective Exchange -0.0223 -0.0125 -0.0104
Rate (in logs) -1.2990 -0.6839 -0.4896
Terms of Trade -0.0089 -0.0202 -0.0160
(in logs) -0.3894 -0.7800 -0.5047
Black Market Premium (BMP) 0.0486 0.0776 0.0726
(in log[l+BMPJ) 1.2896 1.5113 1.2579
Balance of Payments -0.0263 -0.0281 -0.0300
Controls -2.8727 -2.3101 -2.0628
Evolution of the World Economy:
Industrialized Output -0.4272 -1.0101 -0.9609
(Growth Rate -0.9594 -1.6598 -1.4554
World Real Interest -0.6200 -0.7038 -0.6889
Rate (in log[l+r*]) -3.5739 -3.7719 -3.4191
Demographic Variables:
Age Dependency Ratio -0.0974
-0.7732
Young Dependency Ratio -0.1124
-0.7241
Old Dependency Ratio -0.0186
-0.4273
No. Countries 41 41 41
No. Obs. 126 126 126
SPECIFICATION TESTS (P-Values)
(a) Sargan Test 0.513 0.885 0.801
(b) Serial Correlation
First-Order 0.219 0.329 0.374
Second-Order 0.164 0.256 0.333
Third-Order 0.910 0.763 0.714
a/ The gap in GDP per capita is computed as the log oJ the ratio oJ the GDP per
capita in any developing country to the weighted average of the OECD economies.
33
Table 8
Cross-Country Effects: Additional Financial Variables
Dependent Variable: Current Account Deficit as a percentage of GNDI (CAD)
Estimation Technique: GMM System Estimator
(t-Statistics are presented below their corresponding coefficients)
Variable 111 121 131
Constant 0.12508 0.14365 0.32473
1.27373 1.54335 1.48303
Persistence:
CAD lagged I period 0.49429 0.46963 0.13144
3.99316 4.34362 0.39207
Internal Conditions:
Domestic Output 0.40880 0.45888 0.82144
Growth Rate 0.77543 1.51927 1.13807
Private Saving -0.03695 -0.04066 -0.25474
(as % of GNDI) -0.29744 -0.41187 -1.20911
Public Saving -0.00124 -0.00821 -0.08934
(as % of (NDI) -0.00809 -0.05126 -0.32611
External Conditions:
Exports 0.01184 0.01694 0.02527
(as % of GNDI) 1.60694 1.92344 2.05732
Real Effective Exchange -0.01293 -0.01202 -0.05064
Rate (in logs) -0.66304 -0.64844 -1.17348
Termns of Trade -0.01894 -0.01242 -0.00301
(in logs) -0.59543 -0.56086 -0.09396
Black Market Premium (BMP) 0.05894 0.05552 0.03666
(in log[l1I+BMPJ) 0.90917 1.56397 0.92387
Balance of Payments -0.02295 -0.02171 -0.01879
Controls -1.42767 -2.73084 -1.10362
Evolution oJ the World Economy:
Industrialized Output -0.86004 -1.10338 0.41191
Growth Rate -1.60246 -2.06057 0.29639
World Real -0.62693 -0.55730 -1.08473
Interest Rate -2.80038 -3.13778 -1.78899
Additional Financial Variables:
Standard Deviation of 0.00004
(monthly) Inflation 0.01025
Liquid Liabilities -0.02908
(as % of GDP) -0.75374
Extemal Debt 0.02918
(as % of GNP) 0.95963
No. Countries 39 40 36
No. Obs. 119 119 92
SPECIFICATION TESTS (P-Values)
(a) Sargan Test 0.779 0.836 0.525
(b) Serial Correlation:
First-Order 0.170 0.163 0.876
Second-Order 0.240 0.331 0.741
Third-Order 0.649 0.816
34
Appendix
Sources for Ancillary Variables
External Debt. To characterize the external debt position of a country we draw the ratios of total
external debt to gross national product (EDT/GNP) and total debt service to exports of goods and
services (TDSIXGS) from the World Bank's World Development Report. Relying on these
coefficients, we define a country as heavily-indebted if either its ratio of total external debt to
GNP exceeds 0.50 or its ratio of total debt service to exports of goods and services exceeds 0.25
in at least two years within a window of 5 years. Finally, for our nested model, we construct a
dummy variable that takes the value of 1 for any country and period satisfying the previous rule
of thumb.
Demographics. To assess the generational accounting effects on current account, we use the age
dependency ratio (number of total dependents over total population), and its components, say, the
young and old dependency ratios. The data were taken from the World Bank's World
Development Indicators.
Financial Deepening and Uncertainty. From Levine, Loayza and Beck (1998) we used the ratio
of liquid liabilities as a percentage of GDP, while we construct the standard deviation of monthly
inflation rates as a measure of uncertainty from the IMF's International Financial Statistics.
35
Endnotes
Among them we have Kahn and Knight (1983) and Debelle and Faruqee (1996).
2 We present the response of the current account to changes in some of its determinants in Table 1.
3 Milesi-Ferreti and Razin (1996) define a current account position as unsustainable if the continuation of
the current policy stance and/or the private sector behavior entails the need of a drastic policy shift or leads
to a crisis.
4 Based on the analysis of solvency and willingness to lend considerations, Milesi-Ferreti and Razin
propose several operational indicators of current account sustainability, classified in the following groups:
(i) structural features (investment/savings, economic growth, openness, composition of external liabilities,
and financial structure); (ii) macroeconomic policy stance (exchange rate policy, fiscal policy, trade policy
and capital account regime); (iii) political economy factors (i.e. political instability); and, (iv) market
expectations.
5 Kahn and Knight simply use OLS in their regression analysis and control for time-effects by including a
time trend. They do not control for endogeneity in the regressors.
6 Appendix I provides information on the additional variables used and on the data sources.
7 Their dummy variables take the value of one when a restriction is in place for a given country and year
(and zero otherwise).
8 We use the black market premium as log(l-+BMP).
9 We chose this period length for two additional reasons. The first one is that is our sample size is quite
limited in the time-series dimension; if we were to consider longer periods, the lack of sufficient degrees of
freedom would prevent us from implementing our dynamic panel data procedures. The second reason is
that, in using five-year periods, we are following the empirical literature on endogenous growth, where this
period length is customarily used to average out cyclical fluctuations (see Caselli, Esquivel, and Lefort
1996; and, Easterly, Loayza, and Montiel 1997).
10 Alonso-Borrego and Arellano (1996) and Blundell and Bond (1997) show that when the lagged
dependent and the explanatory variables are persistent over time, lagged levels of these variables are weak
instruments for the regression equation in differences. This weakness has repercussions on both the
asymptotic and small-sample performance of the differences estimator. As persistence increases, the
asymptotic variance of the coefficients obtained with the differences estimator rises (i.e., deteriorating its
asymptotic precision). Furthernore, Monte Carlo experiments show that the weakness of the instruments
produces biased coefficients in small samples. This is exacerbated with the variables' over time persistence,
the importance of the specific-effect, and the smallness of the time-series dimension. An additional
problem with the simple differences estimator relates to measurement error: Differencing may exacerbate
the bias due to errors in variables by decreasing the signal-to-noise ratio (Griliches and Hausman, 1986).
Blundell and Bond (1997) suggest that the use of Arellano and Bover's (1995) system estimator that
reduces the potential biases and imprecision associated with the usual differences estimator.
1' Given that lagged levels are used as instruments in the differences specification, only the most recent
difference is used as instrument in the levels-specification. Other lagged differences would result in
redundant moment conditions. (Arellano and Bover 1995)
12 The weighting matrix for GMM estimation can be any symmetric, positive-definite matrix, and we obtain
the most efficient GMM estimator if we use the weighting matrix corresponding to the variance-covariance
of the moment conditions. Since this variance-covariance is unknown, Arellano and Bond (1991) and
Arellano and Bover (1995) suggest the following two-step procedure. First, assume that the residuals, Ej,,
are independent and homoskedastic both across countries and over time. This assumption corresponds to a
specific weighting matrix that is used to produce first-step coefficient estimates. We construct a consistent
estimate of the variance-covariance matrix of the moment conditions with the residuals obtained in the first
step, and we use this matrix to re-estimate our parameters of interest (i.e. second-step estimates).
Asymptotically, the second-step estimates are superior to the first-step ones in so far as efficiency is
concerned. In this paper the moment conditions are applied such that each of them corresponds to all
available periods, as opposed to each moment condition corresponding to a particular time period. In the
former case the number of moment conditions is independent of the number of time periods, whereas in the
latter case, it increases more than proportionally with the number of time periods. Most of the literature
dealing with GMM estimators applied to dynamic models of panel data treats the moment conditions as
applying to a particular time period. This approach is advocated on the grounds that it allows for a more
36
flexible variance-covariance structure of the moment conditions (see Ahn and Schmidt 1995). Such
flexibility is achieved without placing a serious limitation on the degrees of freedom required for estimation
of the variance-covariance matrix because the panels commonly used in the literature have both a large
number of cross-sectional units and a small number of time-series periods (typically not more than five).
We have, however, chosen to work with the more restricted application of the moment conditions (each of
them corresponding to all available time periods) because of a special characteristic of our panel, namely,
its large time-series dimension (for some countries in our sample, we work with as many as 20 time-series
observations). This approach allows us to work with a manageable number of moment conditions, so that
the second-step estimates, which rely on estimation of the variance-covariance matrix of the moment
conditions, do not suffer from over-fitting biases (see Altonji and Segal 1994, and Ziliak 1997).
13 Given that our model is dynamic, the data transfornation involved in the within estimator also introduces
a correlation between the transformed error term and the lagged dependent variable, which may lead to
significant biases when the time-dimension of the data is not large.
14 As explained in the section on methodology, the fact that the differenced error term is first-order but not
higher-order serially correlated implies that the error term in levels does not follow a random walk and is
not serially correlated.
'5 For further empirical evidence on CAD stationarity, see Sheffrin and Woo, 1992; Ghosh and Ostry,
1995; and Debelle and Faruqee, 1996.
16 Theoretically, this non-monotonically relationship (consistent with the J-curve pattern) could be derived
from models with voracity effects (Tormell and Lane, 1998) or models of consumption with habits
developed over the flow of services of durable goods (Mansoorian, 1998).
17 Empirical evidence on the J-curve for developed countries is also mixed. Rose and Yellen (1989) found
no support for the J-curve, whereas Marquez (1991) and Backus et al. (1994) found evidence in favor of the
J-curve.
la According to the Harberger-Laursen-Metzler effect, adverse transitory terms of trade shocks produce a
decline in current income that is greater than that in permanent income. Hence, a decline in savings
follows and, thus, a deterioration in the CA position ensues.
'9 The size of the export sector leads to a greater willingness to honor debt commitments since the
possibility of trade disruptions raises the cost of debt default for the more open economies. Likewise, a
weak export sector hinders the ability of the country to sustain external imbalances.
37
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