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IMPORT DEMAND UNDER A FOREIGN EXCHANGE CONSTRAINT
Angelos A. Antzoulatos, and Simone Peart
Federal Reserve Bank of New York
Research Paper No. 9810
April 1998
This paper is being circulated for purposes of discussion and comment.
The views expressed are those of the author and do not necessarily reflect those
of the Federal Reserve Bank ofNew York of the Federal Reserve System.
Single copies are available on request to:
Public Information Department
Federal Reserve Bank of New York
New York, NY 10045
IMPORT DEMAND UNDER A FOREIGN EXCHANGE CONSTRAINT
Angelos A. Antzoulatos*
Simone Peart
Economist
International Research Department
Federal Reserve Bank of New York
33 Liberty Street
New York, N.Y. 10045
aa.antzoulatos@ny.frb.org
Ph.D. Student
Department of Economics
University of California, Berkeley
Berkeley, CA 94720
simone@econ.Berkeley.EDU
Abstract.
This paper develops a forward-looking model for import demand under a foreign exchange constraint, in which import growth is an increasing function of contemporaneous and expected future
export growth. Unlike existing models which stress the role of foreign exchange reserves and
contemporaneous export earnings for countries that have limited access to foreign borrowing,
this one stresses the importance of the expected time path of future export earnings. The implications of the model are tested and confirmed with data from three East Asian developing
countries for which relevant time series are available at quarterly intervals.
J.E.L. Classification Numbers: F32, F47, G15
Keywords: Import Demand, Foreign Exchange Constraint, Error Correction Models
*Corresponding author. We thank, without implicating, Linda Goldberg, Leonardo Bartolini, Thomas
Klitgaard and KeiMu Yi for many insightful discussions on the subject; and Jennifer Bale for able research
assistance. As usual, the authors are responsible for any remaining errors.
The views expressed here are those of the authors and do not necessarily reflect the views of the Federal
Reserve Bank of New York or the Federal Reserve System.
1
1 Introduction
Countries that have limited access to foreign borrowing ought to take into account the expected
time path of future export earnings in deciding how much to import today. And they ought to
do so even when the foreign exchange constraint, the product of the limited access to foreign
borrowing, does not bind; i.e., even when the sum of current export earnings, foreign exchange
reserves and the amount that can be borrowed in the international financial markets exceeds
current desired imports. Otherwise, they would expose themselves to the perils of fluctuations
in export earnings, and in particular to the need to curtail imports, even of basic goods, at
some point in the future at which their foreign exchange reserves and limited access to foreign
borrowing could not compensate for a fall in these earnings.
Yet, most of the existing models of import demand largely overlook the expected time path
of future export earnings. Consequently, they are not likely to take sufficiently into account the
impact of the constraint, nor to be correctly specified. Reminiscent of the consumption literature,
these models typically consider one of two polar cases. In the first, with notable examples the
. intertemporal models of import demand, only the long term budget constraint matters. That is,
the present value of the stream of future imports must be less than or equal to the present value
of the stream of future exports plus the country's current net foreign assets (Hemphill [1974),
Winters [19871). In this case, imports are completely decoupled from the expected time path of
future export earnings.
In the second polar case, imports are tied to the contemporaneous availability of foreign exchange, implicitly assuming that the foreign exchange constraint binds every period. Building
upon the ideas of Khan (1974), models falling into this case augment typical import-demand
equations with the stock of real reserves (Khan and Knight [19871), contemporaneous foreign
exchange receipts and the stock of foreign exchange reserves (Moran [19881), or with contemporaneous export receipts (Mazarei [19951). Parenthetically, the aforementioned equations usually
consider relative prices and some contemporaneous measure of economic activity -the latter
proxied by real GDP and GNP- as main determinants of import demand (see, among others,
Bahmani-Oskooee [1986), Khan [1974). Khan and Knight [1987). and Reinhart [19951).
This paper, in turn, develops a model of import demand under a foreign exchange constraint
that formalizes the ideas outlined in the introductory paragraph. Specifically, the paper develops
an error-correction model for (log) imports in which import growth (log-change) is increasing
in contemporaneous and expected future export growth. (Imports and exports appear to be
stationary in log-differences.) The two building blocks are a function for desired (log) imports
that is increasing in (log) contemporaneous and expected future export earnings; and a typical
2
partial adjustment mechanism that is driven by country-specific import restrictions,
as well as
by the inertia in adjusting trade flows similar to that assumed in the J-curve literature
. The
first building block additionally includes reserves, the proxy for the foreign exchang
e constraint
considered before, and other variables suggested by the existing studies.
Two implications of the model are particularly useful in checking the consistency
of the results and their robustness to alternative interpretations. First, across countries, the
coefficients
of the export-growth terms should be decreasing in the maximum amount that can
be borrowed
in addition to existing debt. (These coefficients are increasing in the coefficients of
contemporaneous and future export earnings in the function for desired imports.) A high amount
effectively
reduces the importance of the expected time path of future export earnings as more
imports can
be financed -if need arises- by foreign borrowing at any point in time. Second, the
coefficient of
the error-correction term, which is equal to the speed of adjustment, should be lower
(in absolute
value) for countries with more severe import restrictions. Such restrictions, as Faini
et al. (1988)
note, diminish the responsiveness of imports to price and income incentives.
Developing countries are likely to be subject to a foreign exchange constraint
and, thus,
natural candidates for the empirical analysis. From the East Asian ones, only Indones
ia, Korea,
the Philippines and Thailand have appropriate series at quarterly intervals. Korea,
however,
whose error-correction term is non-stationary, is not included in the sample. Latin
American
countries, whose import decisions were greatly distorted during the previous decade
by the "debt
crisis", were not considered.
The econometric results for the three sample countries are largely consistent with
the model.
Specifically, for all of them import growth is increasing in contemporaneous and
future export
growth and decreasing in the error-correction term. In addition, the relative across-c
ountries
magnitude of the estimated coefficients is as expected. Notably, the Philippines,
which -as
indicated by historical data- probably had the lowest access to foreign borrowing
and the most
severe import restrictions during the sample period, has the highest coefficients for
the exportgrowth terms and the lowest for the error-correction term (in absolute value). Also,
Thailand,
which probably had the highest access to foreign borrowing, has the lowest coefficie
nts for the
export-growth terms.
The results are also robust to the alternative interpretation that the significance of
the contemporaneous and future export-growth terms is due to imports of intermediate goods
that are
re-exported after further processing. Had this interpretation been true, Thailand,
which had the
highest share of imports of intermediate goods, should have the highest coefficie
nts for these
terms. The opposite, however, is true.
3
The remainder of the paper is organized as follows. Section 2 develops the error-correction
model and explores the implications of the external financing constraint and of import restrictions.
Section 3 describes the data and presents the econometric results, while Section 4 discusses
briefly their implications for import dynamics. Finally, three appendix tables provide information
that can be used to check the consistency of the results with the model and their robustness to
alternative interpretations.
2
A Forward-Looking Model of Import Demand
Equation (1) formalizes the idea that desired imports at t,
imf, should be increasing in contem-
poraneous and expected future exports, ex 1 and E 1ex 1+k (k :::: 1). Equation (1) also includes the
real interest rate, rert, while other variables that could affect import demand are subsumed into
the vector zt, Among the latter are foreign exchange reserves carried over from the previous
period, RESt-1, and contemporaneous income. E is the usual expectations operator,
u1
the
error term, while small and capital letters denote respectively logs and levels.
(1)
Equation (1) encompasses the existing models of import demand under a foreign exchange
constraint. As noted earlier, these models augment typical import-demand equations, which
include some measure of economic activity -subsumed into z 1- and the relative price of imports
-captured by rert, with reserves (Khan [1974], Khan and Knight [1988], Moran [1988]), and
contemporaneous foreign exchange (Moran [1988]) or export receipts (Mazarei [1995]). Khan
and Knight (1988), for example, justify the inclusion of the stock of real reserves arguing that the
capacity of many developing countries to import is constrained by their limited reserves. This
argument, however, implicitly assumes that these countries are always up against their borrowing
limit in the international financial markets and, hence, the foreign exchange constraint binds every
period. A similar assumption is implicit in the models that attempt to capture the effect of the
constraint with contemporaneous foreign exchange or export receipts. In econometric terms, the
existing models impose the restriction
cxk
= o for k
> o.
Equation (1) also encompasses the intertemporal models of import demand which postulate
that only the long-term budget constraint binds. The latter effectively assume that, as long as
the long-term budget constraint is satisfied, developing countries can borrow as much as they
wish to ride a temporary decline in export earnings or in anticipation of higher future earnings.
4
As a consequence, the intertemporal models decouple import demand from the
expected time
path of export earnings. In any event, their determinants of import demand are
included in the
zt vector of equation (1 ).
The inequality below shows the foreign exchange constraint. It states that actual
imports
at t, IM,, cannot exceed the sum of contemporaneous exports receipts, EXt, reserves
carried
over from the previous period, RESt-1 , and the maximum amount that can be borrowe
d in the
international financial markets in addition to existing debt, u,. This constraint, which
is stricter
than the long-term budget constraint assumed in intertemporal models of import demand
, drives
the significance of the expected time path of future export earnings and thus justifies
the inclusion
of ext+k (k 2: 0) in equation (1). (See below for details.)
I Mt:<::; EXt
+ RESt-1 + U,
(2)
Ut derives from the sovereign credit ceiling often used in the literature. This
ceiling, as in
• Adda and Eaton's (1997) typical specification, is expressed as a floor on the borrowin
g country's
net external assets, with a lower floor corresponding to a higher ceiling. Ut, therefor
e, is equal
•. to the ceiling minus existing debt. Nevertheless, both u, and the ceiling express
the idea that
lenders impose limits on how much countries can borrow at a particular period.
The theoretical
reasons for this behavior by lenders is beyond the scope of this paper, but intereste
d readers can
refer to Adda and Eaton (1997). It should be noted, however, that this credit rationing
imposed on
a country by the international financial markets is different from the import restrictio
ns national
governments often impose for balance of payments considerations. Such restrictio
ns include
import surcharges, advance import deposits and restrictions on current transactions.
An important implication of the forward-looking nature of import demand postulated
in equation
(1) is that the foreign exchange constraint can affect I MP even when it does not bind
contemporaneously; i.e., even when (2) holds as a strict inequality, IM,< EX,+R ES,_1 +UtThe reason
is that the possibility of a binding constraint in the future, i.e., I Mt+k = EX t+k + RE
St+k-1 + Ut+k
for some k 2: 1, will affect current desired imports. To illustrate this, a decline in E1EXt+
k (k 2: 1)
increases the probability that (2) will hold as an equality and, hence, the constrai
nt will bind at
t
+ k.
As a consequence, it may induce lower desired imports and higher reserves at
t. The
higher reserves will be used to reduce the aforementioned probability and the associa
ted risk of
a forced curtailment of imports even of basic goods.
The above argument can be shown rigorously using an explicit intertemporal optimiza
tion
framework. With a caveat though: There are a lot of conceptual difficulties related
to the utility
function for total imports (for a pertinent discussion, see Faini et al. [1988, pp. 11-12]).
Assuming
5
for simplicity -but without loss of generality- that the (constant) interest rate is equal to the
time discount rate, and abstracting from relative prices and income, the first-order condition
(f.o.c.) is u'(IMt+k)
= Et+ku'(IMt+k+1) + >-t+k, where u(.)
is the time-separable utility function,
u' (.) denotes the marginal utility, and >-t+k is the Lagrange multiplier associated with the foreign
exchange constraint -inequality (2)- at period t
it does not, >-t+k
= o.
+ k.
If the constraint binds at t
In the first case, marginal utility at t
+k
+ k,
>-i+k
> o; if
would be higher and I Mt+k lower
than in the case the constraint (2) did not bind (or [2] did not exist and only the long-term budget
constraint applied).
If, as oft+ k - l, the constraint is expected to bind at t + k for some states of nature (exports
are stochastic), expected marginal utility Et+k-iu'(Mt+k) will be higher than if the constraint is
not expected to bind. In response, an optimizing agent, say, a social planner, will reduce imports
at t + k- 1 even when the constraint does not bind at t + k -1. To see it, the corresponding f.o.c.
when >-i+k-1
=
0, u'(Mt+k-1)
=
Et+k-1u'(Mt+k), implies that as Et+k-iu'(M,+k) increases,
so does u'(Mt+k-1), which, in turn, implies that Mt+k-l decreases. Iterating backwards, the
probability of a binding constraint at some t
The f.o.c. u'(I Mt+k)
+ k (k ?:
= Et+ku'(I Mt+k+1) + >-t+k
1) will lead to lower imports at t.
also helps illustrate the importance of the
expected time path of future export earnings. A decrease in EXt+k increases the severity of the
binding constraint and the value of >-t+k and thus leads to lower I Mt+k• Iterating backwards, the
decrease in EtEXt+k decreases optimal -desired, in the terminology of equation (1 )- imports at
t. Most important, this will hold even when the decrease in EtEXt+k (k ?: 1) is coupled with an
increase in some E,Ext+k+J (J > 0) which would leave the present value of the expected stream
of future export earnings unchanged. In contrast, when only the long-term budget constraint
binds, the above changes in EtEXt+k and EtEXt+k+J would not affect IMP.
Lagged reserves are expected to have a positive coefficient in equation (1 ), as an increase in
RES,_ 1 will be allocated between contemporaneous and future imports. Reserves are essentially
the analog of precautionary savings in the consumption literature. Further, the real exchange
rate at t, rer,, should have a negative coefficient. An increase in ,·ert, denoting in this framework
a real depreciation, would make foreign goods more expensive and, as a result, discourage
imports.
Across countries, the relative magnitude of the ak (k ?: 0) coefficients should be inversely
related to Ut. As
u,
increases, a country can borrow more, if it so desires. Thus, the possibility
of a binding constraint at any point in the future decreases. And along with it decreases the
significance of the expected time path of export earnings. At the extreme, for a country that
faces only its long-term budget constraint the expected time path of exports will not matter and
6
its imports can be completely decoupled from it (<>k = 0, tor all k 2". 0). Such
a country can
borrow to ride a temporary decline in export earnings or in anticipation of higher future
earnings,
using these earnings as collateral. This is reminiscent of the argument by Hemphi
ll (1974) and
Winters (1987) that ultimately long-run imports must be equal to long-run receipts
.
To complete the model, actual imports, im, are assumed to follow the partial
adjustment
mechanism described by equation (3). This typical mechanism, which is similar to
that in Khan
and Knight (1988) and Moran (1988), postulates that the actual change in (log) imports,
Aim,=
im, - imt-I, is proportional to the difference between current desired and
lagged actual (log)
imports, imf - imt-I• fl measures the speed of adjustment, o < fl < 1, while ,, is
an i.i.d.
error
term. The adjustment mechanism can be justified by the import restrictions countrie
s facing a
foreign exchange constraint often impose (Faini et al. [1988]), as well as by the time
lags typically
assumed in the J-curve literature. Provided the lags related to the J-curve are sufficien
tly similar
across countries, the countries with low restrictions should have a higher fl.
imt - imt-I
=
fl(imf - imt-I)
+ •t
(3)
Substituting equation (1) into (3) yields the error-correction model for (log) imports
shown in
equation (4). The corresponding error-correction term, EC Mt-I, is shown in equation
(5).
p
Aim,= ,j,
+ L ,PiAext+ i + 1rArert -
flEC Mt-I+ ''fZt
+ T/t
(4)
i=O
EC Mt-I
= imt-1
- AO - AieXt-1 - A2rer,-1
(5)
A is the usual difference operator. The terms ,;,k tor o ::; k < p are given by the
recursive
formula ,;,k = ,;,k+I + flak, with the boundary condition ,Pp= flap, Given that fl and
all "k (k 2". 0)
are positive, all ,Pk (k 2". 0) should be positive as well. The coefficients of rert and
EC Mt-1, 1r
and -fl, are negative. Moreover, tor the same country it should be ,;,i > ,;,k tor J
< k; i.e., less
distant export-growth terms should have bigger coefficients. Across countries, the
coefficients
,;,k (k 2". o), which are increasing in the <>n's (n 2". k) and fl, should be decreas
ing in U1. -as the
cxn's are.
Equations (4) and (5) can be used to test the model. Note, however, that there
is no need
to convert exports, imports and reserves to "real", as long as they are expressed
in a common
international currency. This implication derives from equation (1) which, essentia
lly, states that,
in deciding how much to import, a country has to take into account existing savings
in foreign
currency (reserves) plus foreign exchange receipts from exports - both current
and future ex-
7
peeled. For consistency, though, with the existing studies, "real" figures are used. Nevertheless,
the results are virtually the same with nominal ones.
3 Empirical Analysis
3.1
Data & Preliminary Results
Most series are collected from the International Financial Statistics (IFS) database. Exports,
EXt, correspond to total export receipts net of factor payments, in millions of U.S. dollars; i.e.,
to the sum of exports of goods and services, plus income and transfer receipts, minus income
and transfer payments; lines 78aad, 78add, 78agd, 78ajd, 78ahd and 78akd in the Balance of
Payments block of IFS. Imports, I Mt, correspond to the sum -in millions of U.S. dollars- of
imports of goods and services; lines 78abd and 78aed in the same IFS block. To convert EXt
and I Mt to "real", both series are divided with the U.S. consumer price index (CPI); line 11164 in
the IFS. The unit value of imports and exports, two alternative price indices for such a conversion,
could not be used for the lack of data in the IFS. Specifically, both indices end in 1991 for the
Philippines, while the first is not reported for Indonesia.
The real exchange rate, RE Rt, is calculated as the weighted average of the country's bilateral
real exchange rates -normalized to 100 in 1976:01- with the G-7 countries. For these rates, the
consumer price index (CPI) (line 64 in IFS) and the quarterly averages of the bilateral nominal
exchange rates (line OOrf in IFS) are used. The weights correspond to the average of the export
and import share of the sample countries with each G-7 country, and are calculated with data
from the "Direction of Trade Statistics" database of the International Monetary Fund. Another
measure of the real exchange rate, the ratio of the unit values of exports and imports, could
not be used for the lack of data in the IFS. In mathematical terms, the real exchange rate for a
sample country is given by
Pt and et stand for the sample country's CPI and nominal exchange rate vis-a-vis the U.S.
dollar; and Pi,t and ei,t for the same series for the i country from the G-7 group (for the U.S.,
ei,t
= 1).
The ratio etfe;,t is equal to the nominal exchange rate of the sample country with the i
country. The second fraction in the square brackets normalizes the bilateral real exchange rates
to 100 at the first quarter of 1976, the first period in the sample. Lastly, wf.t is the average trade
8
share with the i country. Following a standard practice, the shares are
normalized so that they
sup to one; i.e., wf.t =
wi,t are the raw shares.
I;;::·~,.,;
The sample includes Indonesia, the Philippines and Thailand, Standard
unit-root tests indicate that their imt, ext and rert are stationary in first differences. Their
error-correction terms
are stationary too. (The critical values for the unit-root tests were taken
from Charemza and
Deadman [1992]). Together with Korea, these are the only East Asian
countries for which appropriate series are available at quarterly intervals. But Korea, whose
error-correction term is
non-stationary, is not included in the sample. Moreover, Korea is not include
d in the 1997 version of the Global Development Finance database, from which some data
on external debt are
retrieved (see below). Also, Latin American countries, whose import decisio
ns were greatly distorted during the previous decade by the "debt crisis", were not conside
red. Lastly, the sample
period is dictated by data availability. It extends from 1981:1 to 1995:4
for Indonesia, 1977:1 to
1995:4 for the Philippines, and 1976:1 to 1995:4 for Thailand.
Following a standard practice in models with unobserved expectations,
Etflext +k (k ;::: O)
and Etflrer t are instrumented with lagged variables. Table 1, however,
which exhibits several
correlation coefficients of interest, indicates that finding good instruments
is not a trivial task.
Briefly, Llrert exhibits very little autocorrelation. The same applies to Llext,
with the exception
of Thailand. Also, the correlation coefficients of Llimt with leads of Llext
and Llrert are very
small. On the positive side, none of the three series exhibits strong season
ality, as indicated
by their relatively fourth-order autocorrelation coefficients. By the way,
to avoid cluttering the
table, the correlation coefficients of Llext and Llrert with Llimt+k (k = 0,
4) are not shown; these
coefficients are not needed because Llimt is not instrumented. As for
Llimt's autocorrelation
coefficients, they are shown to evaluate the magnitude of seasonality.
Insert Table 1 Here
Furthermore, three tables in the appendix provide information that can
help check the consistency of the results with the model and their robustness to alternative
interpretations. Table
A-1 shows qualitative information on import restrictions that can be used
to compare the speed
of adjustment -coeffi cient f)- across the sample countries. Although hard
to quantify, it seems
that the Philippines had the most severe restrictions during the sample
period. In particular, it
had restrictions on payments for current transactions for every year except
1986. It also imposed import surcharges from 1984 through 1992 and required advanc
e deposits until 1992.
Indonesia's and Thailand's restrictions appear about the same and less
severe than those of the
Philippines. Thus, provided the lagged import adjustment related to J-curve
factors is sufficiently
9
similar across the three countries, the lagged adjustment postulated in equation (3) should be
slower for the Philippines relative to the other two countries. In econometric terms, the Philippines should have a lower (3 than the other two countries. The latter should have approximately
equal (J's.
Table A-2 reports three statistics that can be used to gauge the relative magnitude of Ut
across the sample countries: total external debt as percent of GNP, plus interest and principal
arrears in thousands of U.S. dollars. For almost every single year during the period 1977 to 1995,
the Philippines had the highest debt-to-GNP ratio and Thailand the lowest. The Philippines also
had both interest and principal arrears for most of the previous decade, Indonesia had some
arrears from 1985 to 1988, while Thailand had none. Provided that high existing debt and past
debt-servicing difficulties affect adversely a country's access to foreign borrowing, the figures in
Table A-2 suggest that the Philippines had the lowest Ut (and the highest <>k's), and Thailand
the highest Ut (and the lowest <>k's).
Finally, to check the robustness of the results to the alternative interpretation that the significance of ti.ext+k (k 2: 0) may be due to imports of intermediate goods that are re-exported
after further processing, Table A-3 reports the share of intermediate goods in total imports. The
working hypothesis is that a higher share is associated with higher re-exports and, hence, higher
a k and ,pk ( k
2: o) coefficients. (This hypothesis is made out of necessity, because data on
re-exports are not readily available for the sample countries.) Table A-3 shows that Indonesia
and the Philippines had approximately the same average share, 61.0 versus 61.4 percent, which
is below Thailand's 67 percent. Thailand, also, had a higher share than the Philippines for every
year except 1991. (For the second country, 1991 appears to be an outlier. Specifically, its share
jumped from 59.1 in 1990 to 73.5 percent in 1991, and subsequently fell to the normal below
60.0 percent.) Thus, if the significance of ti.ext+k (k 2: O) is due to re-exporting, Thailand should
have higher ,pk (k 2: O) coefficients than the Philippines. The comparison with Indonesia is not
as straightforward because this country had a higher share than the other two for several years.
This, however, is not a major hindrance for the interpretation of the results.
To summarize the expectations, the Philippines is expected to have the highest "'k (k 2: O)
coefficients and Thailand the lowest. In addition, the Philippines should have the lowest (3, while
the other two countries should have similar ones. Provided that the cross-country differences
are bigger for the <>k's than the (J's, and keeping in mind that the ,pk coefficients are increasing
in both (3 and "'k, the Philippines should have the highest ,µk's and Thailand the lowest. Lastly,
if the significance of EtCi.ext+k (k 2: O) is driven by re-exports, Thailand should have the highest
,Pk'S.
10
3.2
Main Results
The paper follows Engle and Granger's two-step estimation approach (Engle and Granger [1987]).
At the first step, im, is regressed on a constant, ex, and rer,. The lagged residuals of this regression correspond to EC M,_1, equation (5). At the second step, equation (4) is estimated.
However, the difficulty of finding good instruments for Aext+k (k ~ O) restricts its terms to only
two; i.e., k = o, 1. Thus the equation to be estimated becomes as shown in (6). In it, the omission of 6ex1+k fork ~ 2 is not expected to bias significantly the coefficients of Aexi+k (k = o, 1)
and Arer, because the first series exhibits very little auto-correlation as well as little correlation
with the second (see, Johnston, 1984, p.260). Nevertheless, 6ex 1+ was not significant when
2
included in the estimated equation.
Aim,= ,p
+ ,/JoAex, + ,J,1Aex1+1 + rrArer, + (JEC Mt-1 + w(RESt-1) + •yz, + 1/t
(6)
In equation (6), reserves appear as a general function, w(RES,_1) , to allow for maximum
flexibility. Specifically, w was set equal to reserves over imports, 1'.{;;~~1 , and to "real" reserve
growth, Ares,_ 1 = log(RES,_ ifCPI~ 1) - log(RES,_ 2/CPI~ ). Both measures are lagged
2
because the reserves reported in the IFS (line 11.d) correspond to end-of-period figures. They
z,
are also tested for stationarity. Further, income is subsumed into the
vector. For the lack
of quarterly GNP data, income is proxied with (end-of-quarter) domestic credit; line 32 of the
Monetary Survey section of the IFS for Indonesia and the Philippines, and line 52 for Thailand.
Two proxies are used: domestic credit converted to U.S. dollars with the end-of-quarter nominal
exchange rate (line 00ae in IFS), and domestic credit converted to "real" with the consumer price
index. In equation (6),
corresponds to the growth rate at t of the income proxy.
z,
But instrumenting variables dated t + 1 with instruments dated t - 1 may induce an AR(1)
term in 11 ,. Thus, and in line with Carroll et al. (1994), equation (6) is estimated with non-linear
instrumental-variable least squares (IV-NLLS) in which the error term is assumed to follow the
process 11 , = p 111,_ 1 +u,. IV-NLLS negates the need to lag the instruments two periods, a practice
that would exacerbate the already-difficult task of finding good instruments for 6ex +k (k = 0, 1)
1
and Arer,. If, however, p 1 is insignificant, the more efficient results with instrumental-variable
ordinary least squares (IV-OLS) are reported.
Table 2 summarizes the empirical evidence. Starting from the left, it shows the name of
the country; the estimated coefficients ,i, 0, ,i, 1 , rr, fJ and -y; the R 2 and D.W. statistics; the
instruments used; and the adjusted R 2's of the projection of Aext+k (k = 0, 1) and Arer, on the
instrument sets. The extensive sets reflect the aforementioned difficulty of finding good predictors
11
for the instrumented variables. To give an idea of this difficulty, reducing the lags for Indonesia's
instruments from eight to six reduces the
jp
of Aex, from 0.272 to -0.120, and of Aext+1 and
Arer, to about half that reported in Table 2. For Thailand, reducing the instrument lags from six
to four does not affect perceptibly the
il. 2 of Aex,
and Aex 1+1 , but reduces substantially that of
Arer1 (from 0.033 to -0.054).
Insert Table 2 Here
The evidence is largely consistent with expectations. To begin with, both contemporaneous
and future export growth affect positively and significantly import growth for all three countries.
In addition, the inequality ,/;o > ,/;1 holds for all. (It is statistically significant though for Indonesia
only.) Further, the error-correction term is significant and has the correct sign. But the (%) change
of real exchange rate, Arer,, is not significant for the Philippines and Thailand. Nevertheless,
the real exchange rate affects their import growth through the error-correction term.
Even more interesting, the relative magnitude of the estimated coefficients across the sample
countries is largely as expected, too: Briefly, (3 is inversely related to the severity of import
restrictions, while ,/;o and ,/;1 are inversely related to the presumed u,. In greater detail, (3 is
lowest (/3
= 0.284)
for the Philippines, the country that had the most severe restrictions. The
(3's of the other two countries, 0.372 for Indonesia and 0.364 for Thailand, are virtually the same.
Also, ,/;o decreases from the Philippines to Indonesia to Thailand, despite that the first country
has the lowest (3. The Philippines also has the highest ,/; 1 . Indonesia's slightly lower ,/; 1 than
Thailand's, 0.177 versus 0.187, is not consistent with the model. But the difference is too small
to reject the model on this evidence only.
Moreover, the relative across-countries magnitude of ,/;o and ,/;1 indicates that the significance
of Aext+k (k
= 1, 2)
is not likely due to re-exporting. Had this interpretation been true, Thailand,
which had a higher share of imports of intermediate goods than the Philippines for almost every
single year, should have higher ,/;o and ,/; 1 . The opposite, however, holds.
The above results and the corresponding conclusions are robust to the instrument set used.
Specifically, although the parameter estimates are somewhat sensitive to the instrument set,
their significance and relative across-countries ranking is not. To give an example, including
four lags of the ratio log(RES,! I M 1 ) in the Philippines' instrument set increases substantially the
first-stage
jp of
Arer, (from 0.080 to 0.117) and marginally that of Aex, and Aex1+1• Yet, Arer,
remains insignificant, while the coefficients of the other variables remain virtually the same. The
same happens when the number of instrument lags is increased from four to six. For Thailand,
reducing the number of lags from six to four leaves the coefficients of Aex, and EC M,-1 virtually
12
unchanged and increases slightly that of .6.ext+ 1 (from 0.187 to 0.211).
~ff.~~•
Lastly, both functions of reserves,
and .6.res,_ 1 , and both proxies of income were
insignificant for all countries and all instrument sets. Worth also pointing out, the results are
qualitatively the same when exports and imports are not divided by the U.S. CPI. This indicates
that the correlation between Aim, and Aext+k (k = o, 1) is driven by the value of exports and
not by the U.S. CPI. This, in turn, provides further support for the paper's forward-looking model.
4 Discussion
To summarize, the empirical results indicate that the expected time path of future export earnings
does affect import demand, and more so for countries that have low access to foreign borrowing.
Also, the speed of import adjustment is lower for countries with more severe import restrictions.
These findings are consistent with the paper's forward-looking model of import demand under
a foreign exchange constraint. As such, they have important policy implications. Notably, a
real depreciation may not succeed in reducing imports. If exports are sufficiently price elastic,
the resultant upward revision of expected future export earnings may dominate the effect of the
depreciation.
In addition, as access to foreign borrowing and import restrictions vary over time, so should
the coefficients 1Pk (k 2 0) and f3 and, along with them, the short-run dynamics of imports. (The
short-run dynamics is captured by the error-correction model, equation [4], and the long-run
dynamics by the error-correction term, equation [5]). Briefly, the coefficients 1Pk (k 2 0) should
be higher (lower) during periods of low (high) access to foreign borrowing, and f3 lower (higher)
during periods of more (less) severe import restrictions. This implication is not affected by
the possibility that import restrictions evolve endogenously in response to balance of payments
considerations (Faini et al. [1988]; see, also, Bartolini and Drazen [1997] for a related theoretica
l
discussion.). It is instructive, though, to note that the sample country with the lowest access to
foreign borrowing, the Philippines, had the most severe restrictions. In any event, exploring the
presumed time variability of the above coefficients is a worthwhile research endeavor, though
beyond the scope of this paper.
Lastly, econometric specifications of import demand that do not take into account the expected
time path of future export earnings are likely to be misspecified. Hence, another worthwhile
research endeavor is to explore whether the paper's intuition can help improve econometric
specifications of import demand by developing countries.
13
REFERENCES
1. Adda, Jerome and Jonathan Eaton (1997). "Borrowing with Unobserved Liquidity Constraints: Structural Estimation with an Application to Sovereign Debt." mimeo, Boston Uni-
versity (October).
2. Bahmani-Oskooee, Moshen (1986). "Determinants of International Trade Flows: The Case
of Developing Countries." Journal of Development Economics 20, pp. 107-123.
3. Bartolini, Leonardo and Allan Drazen (1997).
'When Liberal Policies Reflect External
Shocks, What Do We Learn?." Journal of International Economics 42, pp. 249-273.
4. Carroll, Christopher D., Jeffrey C. Furher, and David W. Wilcox ( 1994). "Does Consumer
Sentiment Forecast Household Spending? If So, Why?." American Economic Review 84,
pp. 1397-1408 (December).
5. Charemza, Wolciech W. and Derek F. Deadman (1992). New Directions in Econometric Practice,
Edward Elgar Publishing Limited, U.K..
6. Engle, Robert F. and C.W.J. Granger (1987). "Co-Integration and Error Correction: Representation, Estimation, and Testing." Econometrica Vol. 55, No. 2, pp. 143-159.
7. Faini, Ricardo, Land Pritchett, and Fernando Clavijo (1988). "Import Demand in Developing
Countries." PPR Working Paper 1, The World Bank (November).
8. Hemphill, William L. (1974). ''The Effect of Foreign Exchange Receipts on Imports of Less
Developed Countries." IMF Staff Papers 21, pp. 637-677 (November).
9. International Monetary Fund. Exchange Arrangements and Exchange Restrictions, various
issues.
10. International Monetary Fund (1995). Direction of Trade Statistics 1988-1994.
11. Johnston, J .. Econometric Methods. McGraw Hill Book Company, 1984.
12. Khan, Mohsin S. (1974). "Import and Export Demand in Developing Countries." IMF Staff
Papers 21, pp. 678-693 (November).
13. Khan, Mohsin S. and Malcolm D. Knight (1988). "Import Compression and Export Performance in Developing Countries." Review of Economics and Statistics 70, pp. 315-321.
14. Mazarei, Adnan (1995). "Imports Under a Foreign exchange Constraint: The Case of the
Islamic Republic of Iran." IMF Working Paper No. 97 (October).
14
15. Moran, Cristian (1988). "Import Demand Under a Foreign Exchange Constra
int." PPR
Working Paper 1, The World Bank (March).
16. Reinhart, Carmen M. (1995). "Devaluation, Relative Prices, and International
Trade: Evidence from Developing Countries." IMF Staff Papers Vol. 42, No. 2, pp. 290-312
(June).
17. United Nations (1976). Classification by Broad Economic Categories, Statistic
al Papers,
Series M, No. 53.
18. United Nations. International Trade Statistics Yearbook, various issues.
19. Winters, Alan L. (1987). "An Empirical lntertemporal Model of Developing
Countries' Imports." Weltwirtschaftliches Archives 123, pp. 58-80.
15
TABLE 1.
Correlation Coefficients
Country
Indonesia
Philippines
Thailand
k=O
li.ext
li.rert
li.imt
li.ext
li.rert
li.imt
li.ext
li.rert
li.imt
1.00
0.12
0.32
1.00
0.05
0.36
1.00
0.06
0.14
k=l
0.03
0.10
-0.02
-0.15
-0.19
0.18
-0.02
0.14
0.04
li.ext+k
k=2 k=3
-0.18
-0.09
-0.08
-0.05
0.04
-0.11
-0.54
0.06
0.12
-0.02
0.16
0.02
-0.11
-0.15
-0.03
0.05
-0.06
0.25
k=4
k=O
k = I
0.21
0.21
0.13
0.11
0.11
0.27
0.55
0.11
0.04
0.12
1.00
-0.37
0.05
1.00
0.11
0.06
1.00
-0.07
-0.16
0.17
0.18
-0.04
0.15
-0.04
-0.12
0.18
-0.22
li.rert+k
k=2 k=3
-0.37
0.04
-0.27
0.03
0.07
-0.08
-0.12
0.05
0.09
-0.26
0.03
-0.01
-0.17
-0.11
-0.08
0.04
0.04
-0.19
k=4
-0.05
0.10
-0.25
0.02
0.03
-0.06
0.05
0.18
-0.06
li.imt+k
k=2 k=3
k=O
k = I
1.00
-0.28
0.11
-0.08
0.28
1.00
-0.03
-0.22
0.24
0.39
1.00
-0.03
0.37
0.09
0.09
k=4
Notes:
1. ti.: first-difference operator.
2. Variable Definitions & Sources:
• ext= log(EXtfCPif8).
EXt corresponds to total export receipts net of factor payments, in millions of U.S. dollars; i.e., to the sum of exports of goods
and services, plus income and transfer receipts, minus income and transfer payments; lines 78aad, 78add, 78agd, 78ajd, 78ahd
and 78akd in the Balance of Payments block of the "International Financial Statistics" (IFS). Exports are converted to "real" with
the U.S. Consumer Price Index (CPI), c P r;:s; line 11164 in IFS.
• imt = log(IMtfCPif8).
I Mt corresponds to the sum -in millions of U.S. dollars- of imports of goods and services; lines 78abd and 78aed in the Balance
of Payments block of the IFS. Imports are also converted to "real" with the U.S. CPI.
• rert = log(RERt)RERt is the CPI-based real effective exchange rate. RE Rt is calculated as the weighted-average of the country's bilateral real
exchange rates with the G-7 countries. For these rates, which are normalized to 100 at the first quarter of 1976, the consumer
price index (CPI) (line 64 in IFS) and the quarterly averages of the bilateral nominal exchange rates (line OOrf in IFS) are used.
The weights are equal to the average of the export and import share of the sample countries with each G-7 country; they are
calculated with data retrieved from the International Monetary Fund's "Direction of Trade Statistics" database.
TABLE 2.
Main Results
Aim,= 1/; +,/;all.ext+ 1/;1Aex1+1 + 1rtl.rer1
7/t = PI'lt-l + Ut
Country
'PO
'Pl
7r
/3
Indonesia
0.463
(5.86)***
0.177
(1.82)*
-0.399
(-2.21 )**
-0.372
(-3.22)***
Philippines
0.688
(2.99)***
0.514
(3.20)***
-0.284
(-3.10)***
Thailand
0.273
(2.04)**
0.187
(1.69)*
-0.364
(-4.24)***
'Y
0.235
(2.37)**
+ f3ECMt-I +Vt+ 7/t
First stage f/, 2 of
tl.ex, tl.ext+I Arert
R2
D.W.
Instruments
0.495
1.75
Aext-k, Aimt-k,
tl.rert-k, k = 1, 8
EC Mt-I
0.272
0.278
0.423
0.242
1.72
Aext-k, ilimt-k,
Arert-k, k = 1, 4
EC Mt-I
0.136
0.127
0.080
0.229
1.88
flext-k, ilimt-k,
0.406
0.468
0.033
tl.rert-k, k = 1, 6
EC Mt-I
Notes:
1. Sample Periods (dictated by data availability):
• Indonesia: 1981:1-199 5:4
• Philippines: 19n: 1-1995:4
• Thailand: 1976:1-1995:4.
2. All equations and instrument sets include a consta.nt.
3. The first stage R2 refers to the adjusted R 2 of the projection of tl.ex , tl.ext+I and Arer on the instruments
1
. For other variable
1
definitions & sources, see Table 1 and the main text.
4. Estimation Method: Instrumental-Variable Ordinary Least Squares (IV-OLS) because p is insignificant for
all countries. The
1
estimated coefficients are virtually the same with Instrumental-Variable Non-Linear Least Squares.
5. The t-statistics are estimated with a heteroskedaslicity-consistent variance/covariance matrix, using the
RATS.
ROBUSTERRORS
option in
6. For the Philippines, z 1 correspond s to tl.im,_4_ tl.imt-4 proved more efficient lit correcting fourth-order autocorrela
tion in the
resi~uals than using the process 71, = °Lti Pi'lt-i + "t• In this process, all Pi were insignificant.
APPENDIX.
TABLE A-1.
Import Restrictions
Year
19n
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
Current
Payments
Indonesia
Import
Surcharges
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Advance
Deposits
•
•
•
•
•
Philippines
Current
Import
Payments Surcharges
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Advance
Deposits
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Current
Payments
Thailand
Import
Surcharges
Advance
Deposits
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Notes:
1. Source: Exchange Arrangements and Exchange Restrictions (International Monetary Fund), various issues.
2. Following the conventions in the Exchange Arrangements and Exchange Restrictions, a bullet(•) denotes the existence of a restriction;
a blank space its absence.
3. Variable Definitions:
(a) "Current Payments": restrictions on payments for current transactions.
(b) "Import Surcharges": cost-related restriction.
(c) "Advance Deposits": advance import deposits.
APPENDIX (continued).
TABLE A-2.
Selected Debt Statistics
Indonesia
Year
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
Total Debt
% of GNP
34.6
33.9
35.3
28.0
25.5
27.9
36.9
38.8
44.4
56.5
73.0
63.9
61.3
64.0
64.9
66.2
58.7
57.2
56.9
Interest
Arrears
,000 US$
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
300.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Philippines
Principal
Arrears
,000 US$
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
8800.0
500.0
500.0
500.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Total Debt
% of GNP
41.9
47.8
48.3
53.7
58.5
66.2
74.0
80.0
89.1
96.5
91.5
77.3
68.2
68.7
70.5
60.7
64.9
60.8
51.5
Interest
Arrears
,000 US$
0.0
0.0
0.0
0.0
0.0
0.0
14700.0
54000.0
64200.0
2600.0
3800.0
38600.0
500.0
46600.0
89200.0
300.0
0.0
0.0
0.0
Source: Global Development Finance - 1997database (World Bank).
Thailand
Principal
Arrears
,000 US$
100.0
200.0
500.0
500.0
500.0
700.0
68200.0
388500. 0
761600.0
12900.0
660800.0
155200.0
2600.0
167100.0
214300. 0
6400.0
0.0
0.0
0.0
Total Debt
% of GNP
17.0
21.1
24.6
25.9
31.6
34.0
35.0
36.3
45.9
43.8
40.9
35.8
32.9
33.2
39.0
38.3
34.9
34.4
34.9
Interest
Arrears
,000 US$
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Principal
Arrears
,000 US$
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
APPENDIX (continued).
TABLE A-3.
Imports of Intermediate Goods
(% of Total Imports)
Year
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
Indonesia
50.9
51.7
57.8
60.0
58.1
58.4
60.7
64.5
66.8
67.3
65.7
66.0
66.5
59.7
55.4
56.5
62.0
65.6
65.3
Philippines
68.6
64.9
63.5
66.2
65.4
64.7
65.2
62.0
64.3
63.2
55.6
59.8
61.2
59.1
73.5
56.7
53.1
50.1
49.8
Thailand
72.1
69.6
72.0
68.5
71.9
73.2
68.8
68.0
68.8
64.8
66.6
65.2
66.9
64.6
63.9
61.6
60.6
62.4
62.7
Notes:
1. Source: International Trade Statistics Yearbook, various issues, (United Nations).
2. Following the Classification by Broad Economic Categories (1976), intermediate goods were
calculated as the sum of categories
(a) 111 Food and beverages, primary, mainly for industry
(b) 121 Food and beverages, processed, mainly for industry
(c) 2 Industrial supplies not elsewhere specified
(d) 31 Fuels and lubricants, primary
(e) 322 Fuels and lubricants, processed (other than motor spirit)
(f) 42 Parts and accessories of capital goods (except transport equipment)
(g) 35 Parts and accessories of transport equipment.
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