POLICY RESEARCH WORKING PAPER 1412
Foreign Technology Imports developingcountry's
economic growth rate
and Economic Growth - increases as foreign
technology imports increase.
DeVelOping Countries In developing countries,
increases in productmty
Xiaoming Zhang depend not on innovation
Heng-fu Zou but on importing foreign
plants and equipment and on
borrowing foreign
technology.
The World Bank
Policy Reseach Department
Public Econom.ics Divion.
January 1995
POL.ICY RIESE-ARCH WORKING PAPEI 1412
Summary findings
Zhang and Zou investigate the rclationship between They run eegressions with data for about S0
foreign technology imports and economic growth in devcloping countries, using different econometric
developing countries. methods and time spans. These cmpirical tests confirm
They develop an intertemporal endogenous growth the hypothesis that foreign technology transfers boost
model that explicitly accepts foreign tcchnology imports incomc growth rates.
as a factor of production. The model establishes a link Moreover, economic dcveloping in developing
between the growth rate of productivity in a developing countries differs from that in industrial countries. In
country and the country's intensity of learning to use developing countries, increases in productivity depend
foreign technologies. not on innovation but on importing foreign plants and
T hey hypothesize that a developing country's equipment and on borrowing foreign technology.
economic growth rate increases as foreign technology
imports increasie.
This paper-a productof the Public Economics Division, Policy Research Dcpartmcnt-is partofa largereftort in the department
to understand economic growth and foreign trade. Copies of the papcr are availablc free from the World Bank, 1818 H Street
NW, Washington, DC 20433. Please contac Carlina Jones, room NlO-063, extension 37754 (38 pag,.s). January 199S.
The PO IKy Resacnh Working Paper Seoies dsmminates the findings of work in press to encourage the exchange of ideas about
deuelopment issu An obecative of the seris is to get the findings out quickly, een if the presentations ae ess than fully polishecL The
papers cany the names of the authors and should be used and cited accordingly. The findings, interpretations, and condusions are the
autbors' own and should not be attributed to the World Bank its Fxecutive Board of Directors, or any of its member countries.
Produced by the Policy Research Dissemination Center
FOREIGN TECHNOLOGY IMPORTS AND
ECONOMIC GROWTH IN DEVELOPING COUNTRIES
Xiaoming Zhang
and
Heng-fu Zou
Policy Research Depariment
The World Bank
Washington, D.C. 20433
Mailing Address:
Mr. Heng-fu Zou
Economist
The World Bank
Room N1O-075
Washington, D.C. 20433
We thank Lance Taylor, Richard Eckaus and Shantayanan Devarajan for very helpful comments.
FOW-NTCEO.DN BO ADW sNI
GILOW1Z IN DSVEOI!ING CtNRE
By
Xiaoming Zhang
and
Heng-fu Zou
1. NTRODUCTION
The relationship between dhe transfer of foreign technology and economic growth in
developing countries has long been studied by economists. In the gap-model approach, Chenery and
Bruno (1962), McKinmon (1964), Bacha (1984), and Taylor (1990, 1993) focus on foreign exchange
resources as of the most important constraint on economi growth in developing countries. Their
argument is based on the idea that most developing counties, becase they camot produce the
eeded technology-embodied capital goods domestically, rely on imported capitl goods n acquirng
advanced technology; thus, imported capital goods and intermediate goods are indispensable inputs;
and if there is not sufficient foreign exchange to fmance the desired technology-embodied foreign
capital goods and intmediate goods, the economy cannot operate properly, not to mention achieve
high growth. 1
Some economists even claim that foreign technology imports are the most important factor
in explaiinig the rapid economic growth of Japan, Taiwan, South Korea, and other newly
industrialized countries. For example, Amsden (1989, p. V) argues that the common character of
the economic development process of all the 'late industialiUs (i.e., developing coantries) is that
their industrialization is based on learng. Such countries as Japan, South Korea, Brazl, Turkey,
India, and Mexico "all industrializedby borrowing foreign technology rather tan by generating new
lBochove (1982) also argues that many imports are indspensable inputs in developing economies,
therefore imports should be treated explicitly as a factor of production in long-run growth models.
- 2 -
products or processes[.)" She suggests that a growth model appropriate for late industrializers should
incorporate not technological innovation, but foreign technology imports.
While the idea of imports as a factor of production has been put forward in some simple
models, to our knowledge, there does not exist an intertemporal endogenous growth model
incorporating this idea, nor are there any systematic studies to test ihis hypothesis. Although many
new growth models try to tackle the important issue of endogenous productivity growth, they fail to
explain the important linkage between foreign technology transfer and the phenomenal economic
growth in countries like Japan, South Korea, Taiwan, and many others. Thus the conventional
growth model is inappropriate for developing countries because it throws away v aluable information
on the source of productivity increase in these countries: borrowed foreign technology through
import and transfer.
On the empirical side, there are few studies based on the two-gap model approach in testing
the linkage between imports and growth. For example, Esfahani (1991) conducted a simultaneous-
system analysis testing the relationship among exports, intermediate Worts, and economic growth
using a sample of 31 semi-industrialized countries, and found that 'export promotion policies in these
countries can be quite valuable in supplying foreign exchange, which relieves import shortages and
permits output expansion." (p. 114) However, there exists no empirical studies directly testing the
hypothesis that foreign technology imports are the most important factor in explaining economic
growth process in developing countries.
In this paper, we first develop an intertemporal endogenous growth model that explicitly links
foreign capital imports to economic growth in developing countries. Then we conduct an empirical
test on the model using a sample of about 50 developing countries.
- 3 -
In section 2, we present a two-goods model of optimal growth along the lines of the
technology argument by dividing capital accumulation in a typical developing country into two parts:
the accumulation of traditionally, home-produced capital and the accumulation of imported foreign
technology. Revenues from exports are used to purchase foreign consumer goods and foreig
technology imports. We formally show that a developing country's economic growth rate increases
as foreign technology imports increase. In section 3, we conduct empirical tests of the hypotheses
generated by the model, using panel data from developiL, countries. Section 4 concludes.
2. AN ENDOGENDUS GROWT0H MOE0M
FOREIGN TECHIOLOGY IMPORTS
The model developed in this section has its orgins m the neoclassical growth model. The
standard version of the neoclassical growthi model developed first by Solow (1956) has the property
that the only potential sources of growth are susained exogenous increases in factor supplies (e.g.,
population growth) and exogenously given technological change (see, for example, Jones & Mamuelli
1989). Thus, except for the possibility of exogenous tecbnical change, ihese models of growth lead
to the starting conclusion that there is no per capita growth in the long rn. Rather, depending on
inital conditions, there is growth until the capital stock reaches a steady state where things settle
down permanenty. The fundamental problem with he neoclassical growth model, as Solow (1970)
acknowledged, is that it is not able to explain the wide differences in rates of productivity growth
across countries. Faced with the phenomenal sustined growth in per capita output that many
developing couries have experienced, the only explanation the model has to offer is exogenous
technological change, which sheds no new light on cross-country differences.
*4 -
Since the mid- 1980s, many economists have tried to endogenize the process of technological
change. Three different groups of models have been proposed to deal with this problem. The first
group relies on externality and increasing returns to scale (Romer 1986). In the second group are
the models of human capital formation pioneered by Lucas (1988). The third focuses on the
introduction of new goods with learning by doing advanced by Grossman and Helpman (1990).
However, aL of these models fail to explain the important linkage between foreign technology
transfer and the phenomenal economic growth in countries such as Japan, South Korea. Taiwan, and
many other developing countries. As Amsden (1989) points out, the conventional growth model is
inappropriate for developing countries because it trows away valuable information on the source
of productivity increase in these countries: borrowed foreign technology through import and transfer.
We construct a model that addresses this shortcoming. Two features distinished our model
from all the other growth models. First, we explicitly assume that foreign capital goods are
indispensable inputs in developing countries' production. Foreign capital goods are not perfectly
substitutable by home capital goods. Second, we build into the model a direct linkage between
foreign technological imports and productivity increases in developing countries by assuming that the
rate of technological growth is a positive function of foreign capital imports.
2.1 The Model
There are two economies in this model: the home country and the foreign country. The home
country is a developing economy, and foreign country is a developed one. There are two goods -
the home good and the foreign good; and the home good price in the foreign market is Px, which,
as will be discussed later, is a negative function of the quantity exported.
- 5 -
At time t there are N(t) identical persons in the home country producing the home good with
a technology given by the production function,
Y(t) = Kh(t)aKf(t)O [A(t)N(t)] -cr+, (a +0 < 1)
(1)
Where Kh(t) is home capital stock at time t and, K(t) is foreign capital. While allowing substititiori
between home capital and foreign capital in production, in general, foreign capital through its
embodiment of modem technology is more efficient than home capital. The idea of putting foreign
capital into the production function as an input is taken from the paper by Devarajan and Zou (1993).
N(t) is the total population in the home country. The population is growing at a constant rate n, i.e.,
N/N = n.
A(t) is an index of labor-augmenting technology at time t. A t) is growing at rate 4: A(t) =
ewt. We can defne N(t)e'O as the effective labor force at time t, and denote it as . Thus,2
k=N(O)e(f+O)t, because N=N(O)et (2)
The effective labor force grows at the rate of n + k. For a given size of physical
population, there will be more effective units of labor as time passes. But the number of physical
bodies increases at the constant rate n. Now we can rewrite the production function as follows:
Y=pKKRFA -(cx+0) (3)
h f
2For notational simplicity, we will drop the time index for all the current variables from here on.
So unless specified otherwise, N is equivalent to N(t).
-6-
Dividing both sides of (3) by h, and defining y = Y/I, kh - KhIAT, and kf= Kf 1, the constant
return assumption implies:
h -f ~~~~~~~~~~~~~~(4)
Note that here y, kh, kf are all variables measured in effective units of labor.
Now we need to fiuther examine the technology index A = eWt. In the standard neoclassical
growth model, 4 is assumed to be exogenously given. In our model, in order to capture a stylized
fact of developing countries, we assume that the technological growth rate is a function of the
imported foreign capital stock, i.e.,
|t b(k(t-l)) #SYh< Yf (5)
'Of if Yh 2Yf
where ky(t -1) is the foreign capital stock measured in efficient units of labor at time t - 1, and $'(.)
> 0, r(.) < 0. f is the developed country's technological growth rate, which is assumed to be
exogenously given and constant for simplicity. Equation (5) says that the growth rate of the labor-
augmenting technology in the developmg country at tuMe t is a positive fimction of the stock of
imported foreign capital goods at time t - 1. This is an inportant assumption in our model. It
establishes a direct lik between foreign technology transfer and home country's techological
growth.
We can identify hee chamnels through which foreign capital import affects the growth rate
of technology in developing countries: first, foreign plant and equipment investments generally
embody advanced foreign echnology, advanced designs and advanced management methods. More
-7 -
investment in importing foreign plant and equipment will raise the home country's technology by
having more embodied foreign technology. Furthermore, investment in foreign plant and equipment
involves training technicians in the foreign country, so a higher stock of foreign capital means a
proportionally larger number of people being trained in a foreign country. The second channel by
which the stock of foreign capital affects the growth rate of technology is by scale economies. A
higher level of foreign capital imports makes it more likely for the home country to operate foreign
technology on a scale sufficient to minimize unit costs. The third channel is through experience
accumulation. How efficiently foreign technology is used will depend on the experience of the user.
The higher the level of foreign capital imports, the more intensively people have to learn to operate
foreign equipment, and hence the faster experience accumulates. That is to say, learning-by-doing,
which is one critical aspect of learning in general, depends on the accuulation of foreign capital.
The assumption that the growth rate of technology in the developed country is exogenously
fixed is for analytical simplicity. Alternatively, we could have used an endogenous growth
formulation along the lines of Lucas (1988) or Grossman & Helpman (1991). However, the focus
of this paper is on the developing country's "catching-up" process; what happens after the developing
country becomes a developed one is not important here.
We assume that there is no foreign direct investment in the home country. To obtain foreign
technology, the home country relies on its export earnings. This assumption is for simplicity; it can
be relaxed without changing the results in our model. The home country's foreign earnings are:
E = PxX (6)
where Px is the price of the home good in the foreign market.
Let Ch and Cf be the aggregate home good consumption and foreign good consumption at
time t, respectively. The dynamic equations for the a lation of home capital and foreign capital are:
- 8 -
kh KaKOf(eOfN)l (1*I) ~ Kh - X,(8
hh = :f(XNl(t Ch-kX (8)
lff =PxX-Cf-Kf. (9)
Expressed in effective units of labor, the dynamic equations for the accumulation of home
capital and foreign capital become:
kh =kak -Ch - (n+O)kh - (10)
h f
kf = Px,x -cf- (n+O)kf. (11)
Note that we have assumed away capital depreciation for simplicity.
Consumers maximize an instantaneous udlity function specified as:
log (1gCh /NeO) +0 log (Cf Ne Otfle -Ptdt. (12)
The separability of utility function is also purely for analytical simplicity. The constnt 0 is positive
and measures the preference for foreign good consumption.
Note that the utility function is defined in consumption per capita (per physical body) terms
while the dynamic equations are defined in terms of consumption per effective labor unit. We can
transform the utility function using the equality CNe't0= c.
J° [lg0ch +Ologcf] tdt (13)
The representative agent in the home country chooses ch and cf so as to maximize (13)
subject to the dynamic constraints (10) and (11), and the initial values of home capital and foreign
capital (kh(O). kf (O)).
-9-
The current value Hamiltonian function is:
H * k-Ptkogc^.iogc [ c - x - (fn+j)kh]
+Xf[Pxx - Cf - (n + )kfJ (14)
Note that although 4 changes in each period, the representative agent takes + as given because it is
an externality as in Romer (1986) and Lucas (1988).
The necessary conditions for maximization are:
Cf PxcCh, (15)
_ akfk k - (n+*+p), (16)
__- O .k If h- (n+4)+P), (17)
kh =AO - ch - (n+4O)kh - x, (18)
kf P-x - Cf- (n+ )kj (19)
In the steady state, th = ef = kd = kf 0 O. So the necessary conditions for optimization
in equilibrium are:
Cf - Pxch =° (20)
-,,j3-a-1
akf kh - (n+4+p) -O (21)
-cf k -(n+ +P) = 0 (22)
- 10-
khkf -ch-(n+O)kh- x = 0 (23)
Pxx- Cf- (n+O)kf = 0 (24)
where a bar over a variable denotes its steady-state value, and all derivatives are evaluated at the
steady state.
Condition (20) gives the optimal relationship between home good consumption and foreign
good consumption. Conditions (21) and (22) are the modified golden rules. Condition (23) gives
the steady-state level of per effective unit of labor consumption of home good. Condition (24) says
that exports is the only sources of income for purchasing the foreign consumption good and foreign
capital good.
2.2 Growth rate at the steady state
We define the steady state (or balanced growth path) as the state where all the variables grow
at a constant rate. Thus we rule out paths with ever increasing growth rates.
Equations (21), (22), (23), and (24) tell us that in the steady state, the consumption of home
and foreign goods, and the home capital stock and foreign capital stock measured at per effective
labor unit are constant, i.e.,
Ch=C, cf= Cf. kh = kh, kf= kf
Hence the growth rates of all per effective unit of labor variables are zero. Knowing this, we can
find the growth rate of all the variables measured in per capita (i.e., per physical body) from the
relation between per capita variables and per effective unit variables. Taking time derivative of both
sides of equation (25) and then dividing the result by (25), we get the growth rate of per capita home
good consumption at steady state:
d(ChIN)Idt )
Ch = 4]= (kfp (26)
Similarly, we can show that all the per capita variables grow at the same rate when the economy is
at the steady state:
d(CfIN)Idt d(Kh IN)/dt d(KfIN)ldt = d(YlN)ldt= (27
Cf/N Kh IN Kf IN YIN
Equation (26) and (27) say that in the steady state, per capita consumption of home good and
foreign good, and per capita home and capital stocks, and thus per capita income, are growing at the
same rate 4( z +(kf)), which is determined by the steady-state foreign capital stock per effective
labor unit. If a country has a higher steady-state per effective labor unit foreign capital stock, its
per capita income growth rate in the steady state is higher. It is conceivable dtat given the right
parameters the home country's growth rate can be higher than that in the developed country, i.e.,
*(kf) >.O. Then the income gap between the two countries will decrease, unti the home
country's per capita income level is equal to that of the foreign country at which point there wiUl be
no particular advantage of importing foreign technology and the growth rate of the home economy
will be the same as the foreign country O = gy This scenario captures the essence of the catching-
up experience by many late industrializers (Japan, South Korea, Taiwan, Singapore), which was
based on learning and borrowing foreign technology from developed countries.
The aggregate variables of this economy-aggregate income Y, total consumptions of home
good Ch and foreign good Cf, and total home capital stock Kh and foreign capital stock K, - are
all growing at the rate of 4,+n, until the home country's aggregate income level reaches that of the
foreign country, at which point the growth rates of the two economies will converge.
- 12 -
The above result is a very powerful one. It links a developing country's long-run economic
growth rate with its efforts in learning advanced technology from foreign technological imports. The
model can explain why some developing counties succeeded in catching up with the developed
countries while others lagged behind.
We pause for a moment and compare the model constructed here with all existing growth
models. There are three distinctive features which set this model apart from new growth models.
As mentioned in the beginning, in the existing growth theory, the growdh rate of technology is eitier
assumed to be exogenously determined (Solow, 1956), or to be determined endogenously by
postulating some externality effects (Lucas 1988, Romer 1986). All of them have one dting in
common: they assume away the important fact that developing countnes can usually take advantage
of the existing advanced technology in the developed countries by intensive learning, instead of by
investing in R&D and innovation. Although in the models developed by Grossman and Helpman
(1991), the technological difference between the North and South is a central focus, they model the
learning process as a rather mechanical one: the North always creates new products and the South
always imitates. The developing countries can never catch up and surpass the income and
technological level of the developed ones. Our model is a drastic departure from growth models on
technological progress. In our model, it is the quality gap between the developing country's home
technology and imported technology from developed country that propels the former to catch up with
the latter. Through active learning, the developing country can reduce the technological gap and
eventually become a "NIC". By explicitly linking productivity growth with increases in output, our
model is a long distance descendant of models developed by Kaldor (1967, 1978).
Another important feature of this model is that the steady state is given a new meaning here.
In most growth models, the steady state means an ideal state existing only in the futre. All
- 13 -
developing countries are usually assumed not to be in such a state, as if the long histories of these
countries do not count. In our model, we do not assume that economic growth starts from the
beginning of the 20th century or the end of World War II or some arbitrary date. After all, most
developing countries have several hundred years history; many even have several thousand years of
civilization. If after such a long history a country is still in some mid-way to the steady state, then
what is the use of studying the steady state? In our model, we postulate that all developing countries
are in their steady state development. Each country's steady state per capita income is growing at
a rate determined within the economic system. The different growth rates we observe are the results
of each nation's different preferences and tastes (which are related to culture and history) and
different foreign exchange resources.
3. EMPIRICAL TESTS OF THE RELATIONSHIP BETWEEN FOREIGN
TECHNOLOGY IMPORTS AND ECONOMIC GROWTH
In this part, we conduct enpirical tests on the predicdons generated by the model in the
previous section. We test the relationship between the economic growth rate and foreign technology
imports. We first develop statistical model specifications, then discuss the data and the empirical
results, and then discuss policy implications and suggestions for future research.
3.1 The Model and Statisfical Specification
Our empirical model specification follows the general approach used in the study by Mankiw,
Romer, and Weil (M-R-W thereafter) (1992), although we do not adopt many of their assumptions.
Let the production function be:
Y(t) = K(t)K((28)(t)N(t))
Let Sk be the fraction of income invested in Foreign capital imports. The dynamics of the
economy is given by:
- 14 -
K(t)h = S(Oh Y(t) -K(Q)h (29)
ke(tyf= S(t)hY(t) -K(t)f (30)
Equations (29) and (30) imply that the economy converges to a steady state given by:
K(th = A(t)N(t) | -a-
(31)
K(tof = A(t)N(t) |Q)ftS(f I-a-
(32)
Substituting (31) and (32) into the production fumntion, and taking logs, we get an equation
for per capita income:
InY(t) = lnA(t)+InN(t)- a-'43 a 1 h+ a I nf (33)
1-a1-U -li -11
Equation (33) relates a country's level of income with the rate of home capital investment
and that of foreign capital investment, and its population. This equation will be the basis of our
empirical study. Our model predicts that the coefficients on home capital investment and on foreign
capital import are positive, and the latter should be bigger than the former in magnitude. We have
to first make assumptions on the parameters before we can test the model. We assume that a is
country specific but constant over time within the same country. Thus by taking first differences of
individual country observations over time we can eliminate the 6:
- 15 -
bnYr-InYt-I = [IUnA(t)-nA(t - 1)] + l (nSht- InSh,t-1)
+ 1 6 (InSf,b-SfnSyt- ) +[InN() - InN(r- 1)]
i.e.,
y-[bMA(t) - A(t-1)]+ 1-. s| + l-a-ti[fI + (35)
The term A(.) in equation (35) is in fact an all encompassing variable. It reflects not only
technology, but also many unobservable factors. These include resource endowments, climates,
social institutions, and oiter random effects. In M-R-W's study, they assume that
IA (t) = a + f (36)
where a is assumed to be a constant both cross-country and over time, and e is a random shock
including all country-specific factors that are independent of the rate of investment and population
growth. In growth form, their assumption means:
LnA (t) - nA(t - )= -t -f t_ (37)
That is, all the unobserved institutional variables are assumed away in this formulation. This
assumption allows them to proceed with the simple OLS estimation.
M-R-W provide three reasons for this assumption. First, this assumption is made not only
by Solow, but also in many other growth models. They also argue that in models where investment
is endogenous but preferences are isoleastic, Sh and Sf are independent of e. Second, this
- 16 -
assumption is necessary for testing different hypothese in their paper. Third, because the model
predictions are very precise, they can use the result to test whether the OLS is a mis-specification.
Many economists have questioned this assumption and the three supporting arguments. For
example, Islam (1992) argues that investment and fertility behavior is apparently affected by the
variables included in the A(t). Indeed a theoretical case can be made against M-R-W's assumption.
By standard formulation, M(t) - bzA(t - 1) is the technical growth rateD , which is country
specific. In fact, 4 can be decomposed into:
'O = xci + git (38)
Where i denotes countries in the sample, t is index for time. C* is a country-specific constant, and
Pft is all the unobserved variables that are not correlated with the explanatory variables, and is i.i.d.
2
with variance equal to cr;. Substituting (37) and (38) into equation (35), we have:
y c + shl +____ [4 + I+Ait (39)
y1XC1+ i-a-li |hJ 1-a-# | S[] N
Equation (39) specifies a model with heterogeneous inmtercepts, homogeneous slopes. If this
specification is true, then M-R-W's specification of an independent e is equivalent to a restriction that
all intercepts are the same. And their estimates would be biased.
In what follows, we will use the specification in equation (39) to study the relationship
between a country's level of income and its foreign capital import share in GDP, albeit expressed
in growth rate form. The dependent vanable is the income growth rate, the independent variables
are the growth rates of dte share of foreign capital imports in GDP, of home investment is share in
GDP, and of population. The term Ct is an unobservable constant for each couty. We will use
- 17 -
variable-intercept models with panel data to deal widh this issue. By assuming that the effects of the
numerous omitted country-specific variables are each individually unimportant but collectively
significant and possess the property of a random variable that is uncorrelated with all other included
and excluded variables, we can specify our model as having common slopes for all countries but the
intercept varies over individual countries. This method is called the variable-intercept method.3
We will also run simple OLS regressions based on M-R-W's assumption and compare the
results from different methods, which would provide a test on their assumption.
3.2 The Data and Samples
The data are assembled from the United Nations Statistical Office, the World Bank, Summers
and Heston (1991), and some other sources. Definitions for all variables and data sources appear
in Appendix 1. The data do not include OECD countries, since many development economists argue
that the development process in developing countries is different from that of developed countries.
We also exclude major oil producers fron our sample (as defined by World Bank in World
Development Report). Countries with a population less than 1 million in early 1980s are excluded
the sample because the determination of their real income may be dominated by idiosyncratic factors.
The data include annal variables and cover the period of 1965-1988. The panel data set
albows us to conduct tests based on variable-intercept models, which can control for unobserved
country-specific effects. We measure Sf as the share of current foreign capital goods imports in
current GDP. The data on foreign capital imports are obtained from the United Nation's
3See Hsiao (1986) for the details of the variable-intercept method in panel regression.
- 18 -
International Trade Statistics.4 Sh is calculated as the difference between the share of current total
investment in current GDP minus Sf. The data on current total investment share in GDP are from
the Summers and Heston (1991) data set. We measure SfISf as changes in the share of foreign
capital import in GDP, Sh ISh the change in the share of home investment in GDP. Y/Y is real
annual growth rate of GDP, which are from the World Bank's World Tables (1990). The population
growth rate N/N is from the population data in the 1990 World Tables. Table 1 lists all the
countries in our sample and the mean values of Sf Sf, ShISh, YlIY,and NIN. We also list the quality
rating of the data by Summers and Heston, since many of our variables are taken from their data set.
This information should be useful in helping readers make judgement on the reliability of the
statistical inferences from the data.
The number of developing countries included in our empirical study varies among different
model specifications, depending on the variables included in a specification. Some countries may
not have information on certain important variables so we have to exclude them from a particular
equation.
3.3 The Result
3.3.1 Initial regressions
Table 2 presents three different regressions of the growth rate of income on the growth rate
of foreign capital import, growth rate of home investment, and growth rate of population. Before
4We divide the SITC two digit level import commodity data into three main categories: capital
equipment iWports (including SITC commodities 71,72, 73, part of 86, 87, and part of 9), intermediate
good inport (including SITC commodities 2, 3, 4, 5, 6, and part of 9), and final consumtion good
import (including commodities in the SITC groups 0, 1, 81-85, part of 86, 89, and part of 9).
- 19 -
Table 1: The list of Countries In the Sample
(All the variables are averages over the period 1965-1988)
Country Sf/Sf Sh/Sh Y/Y N/ N DataQuality
Greece 0.071 0.186 0.041 0.007 a-
Portugal 0.065 0.176 0.043 0.004 a-
Israel 0.098 0.152 0.052 0.024 b
Hong Kong 0.144 0.060 0.079 0.020 b-
South Korea 0.051 0.223 0.086 0.018 b-
Kenya 0.043 0.101 0.052 0.038 C
Costa Rica 0.050 0.097 0.045 0.026 c
Dominican Rep. 0.036 0.138 0.014 0.025 C
El Salvador 0.031 0.045 0.021 0.023 C
Guatemala 0.027 0.059 0.035 0.028 c
Honduras 0.058 0.077 0.016 0.033 c
Jamaica 0.051 0.106 -0.002 0.014 C
Mexico 0.023 0.178 0.046 0.027 C
Panama 0.060 0.176 0.050 0.024 c
Argentina 0.021 0.097 0.021 0.015 c
Bolivia 0.049 0.119 0.023 0.025 c
Chile 0.038 0.089 0.024 0.017 C
Colombia 0.023 0.143 0.045 0.022 C
Ecuador 0.045 0.205 0.025 0.028 C
Paraguay 0.043 0.081 0.027 0.029 C
Peru 0.034 0.124 0.028 0.026 c
India 0.007 0.163 0.037 0.022 C
Indonesia 0.027 0.195 0.060 0.022 C
Malaysia 0.075 0.227 0.064 0.025 c
Philippines 0.025 0.171 0.042 0.027 c
Singapore 0.179 0.105 0.109 0.019 c
Turkey 0.019 0.201 0.051 0.024 c
- 20 -
(Continued)
Table 1: The list of Countries In the Sample
(All the variables are averages over the period 1965-1988)
Country S/ Sf Sh / Sh Y/Y N / N Data Quality
Cameroon 0.047 0.059 0.052 0.027 c-
Ivory Coast 0.063 0.044 0.049 0.040 c-
Morocco 0.034 0.060 0.043 0.025 c-
Senegal 0.045 0.028 0.021 0.026 c-
South Africa 0.084 0.192 0.008 0.022 c-
Tanzania 0.067 0.156 0.033 0.033 c-
Brazil 0.011 0.179 0.059 0.024 c-
Uruguay 0.024 0.146 0.011 0.004 c-
Pakistan 0.016 0.119 0.056 0.030 c-
Sri Lanka 0.015 0.202 0.044 0.018 c-
Thailand 0.028 0.129 0.065 0.025 c-
Egypt 0.041 0.022 0.055 0.024 d+
Ethiopia 0.023 0.023 0.024 0.026 d+
Madacascar 0.030 0.057 0.012 0.027 d+
Malawi 0.038 0.092 0.045 0.032 d+
Mali 0.035 0.035 0.038 0.022 d+
Mauritius 0.037 0.094 0.052 0.014 d+
Sierra Leon 0.017 0.001 -0.031 0.021 d+
Zambia 0.106 0.240 0.013 0.030 d+
Ghana 0.042 0.029 0.007 0.024 d
Sudan 0.026 -0.008 0.028 0.027 d
Uganda 0.112 -0.072 0.003 0.026 d
Zaire 0.063 0.030 0.001 0.029 d
Haiti 0.014 0.063 0.035 0.018 d
Nacaragua 0.031 0.146 0.009 0.030 d
- 21 -
Table 2: Panel Data Regresions (Annual Data)
Dependent variable: annual growth rate of income
Method of Esti mte Pooled OLS Fixed-effects Random-effects
Countries: 53 53 53
Observations: 989 989 989
Sf/Sf 0.059 0.051 0.053
(0.007) (0.006) (0.006)
SI/Sf 0.012 0.013 0.013
(0.004) (0.004) (0.004)
NIN 0.421 0.529 0.430
(0.169) (0.311) (0.240)
C 0.033 0.032
(0.004) (0.006)
RF 0.090 0.088 0.085
F2 0.087 0.034 0.031
Test of Restrictions: F(52,933)=4.33 X2(3)= 17.00
Note: Standard errors are in parenthesis.
we discuss empirical findings, we explain the different econometric methods used in the three
regressions. The first column is the result from a simple OLS regression using pooled data. The
second and third columns are results from panel data regressions using variable-intercepts method.
The difference between the second column and the third cohlmn is that we use a fixed-effects model
for the regression in the second column, and a random-effects model in the third. That is, in the
- 22 -
second column, we assume that the omitted country-specific variable (C,) are fixed over time, while
in the third column regression we treat the country-specific effects, like the error term, as random
variables. Generally the two types of specifications produce quite different results.5
At the bottom of the third column, we provide the chi-square statistic which can be used to
test whether the data favor a fixed-effects model or a random-effects one. The null hypothesis is that
the true model is a random-effects model. If the computed chi-square statistic is larger than the
critical value at a predetermined significance level, the null hypothesis should be rejected. From
Table 2 we see the computed Chi-square statistic is 17.0, which well exceeds the critical value for
the 1 percent significance level at 3 degrees of freedom, which is 11.34. Thus we should reject the
random-effects specification in the third column and accept the fixed-effects model in the second
rolumn. At the bottom of the second column, we also provide the F-statistic for testing the
hypothesis that the intercepts for different countries are different (i.e., the pooled OLS model is mis-
specified). The computed F value is 4.33, which is much larger than the I percent critical value,
This indicates that the pooled OLS regression, which is based on the M-R-W's assumption, is indeed
mis-specified. We should reject the result in column one and accept the result from the second
column. However, if we look at the estimated coefficients across Table 2, we find that, econometric
theory notwithstanding, the results from all the different regression are very similar. That, is to say,
the pooled regression produces results similar to the panel data regression.
Now consider at the estimated coefficients. Both estimated coefficients on foreign capital
imports and home investment are positive and statistically very significant. Furthermore, the
estimated coefficient on foreign capital imports is indeed much higher than the one on domestic
5For a detailed discussion about the difference between fixed effects and random effects models, see
Hsiao 1986.
- 23 -
capital investment, as is predicted by our model. Thus the empirical data from 53 countries shows
that the level of foreign capital imports has a positive impact on the growth rate of income. The
estimated coefficient on the population growth rate is positive but not statistically significant in the
fixed-effects model (th,e second column of Table 2), which is the favored model.
Although the results from Table 2 produce the right signs for the coefficients on the
investment of foreign capital equipment and that of home capital, there are several problems. Fir st,
as mentioned above, the estimated coefficient on population growth turns out to be insignificant.
Second, the magnitudes of the estimated coefficients on the three variables (Sf 1Sf, Sh hSh, and kl/N)
are too small. The implied a and (3, which are the reladve share of home capital and imported
capital in production, are smaller than 0.02 and 0.06 respectively. And the estimated coefficient on
population growth is also much smaller than 1, as the model predicted. The third problem is that
the independent variables in all three regressions explain very little of the variation of the dependent
variable, as indicated by the extremely low i2s. 6
3.3.2 Omitted variable problem
We suspect that the above problems may arise because of the many omitted variables. As
mentioned in the last section, our model specification are based on strong neoclassical assumptions
that are not true in the real world. In reality, the economic development process in developing
countries is affected not only by factors of production, but also by many social and institutional
factors. These omitted variables may cause biased estimates in our m6del.
6Please note that the smaller R2s in the variable-intercept models are due to the fact that a large
number of constants are used in these models.
- 24 -
Thus, in Table 3, we present the regression results with more exogenous variables included
in the model. The new variables introduced into the regressions are: annual inflation rate (INFLAT),
black market foreign exchange rate premium (EXCHPREM), changes in the terms of trade (TOT),
primary school enrollment rate in the population (SCHOOL), growth rate of export (EXPORT).
All these variables are widely used by other economists in empirical studies growth. Fischer
(1993) has argued that the inflation rate is a good measure of the long-run economic growth rate,
because it is the best indicator of the overall ability of the government to manage and stabilize the
economy. If macroeconomic stability is good for growth, then a high inflation rate tends to lower
growth rate. Levine and Renelt (1992) show that high growth countries are also lower inflation
countries, and have lower black market exchange rate premia. The negative impact of adverse terms
of trade shocks on developing countries's economic growth has been a widely accepted fact. Th-%
inclusion of the SCHOOL variable was introduced first by M-R-W (1992), and has become a
standard variable in growth studies ever since. Many studies have found a positive relationship
between the growth rate of export and economic growth. Zhang (1994) found that different
sectors of export (i.e., primary exports and macturi exports) have different impacts on the
long-run growth rate. However, because we do not have annual sectoral cross-country data on
developing countries' exports, we will only use a single export variable in this study.
Now we look at the results in Table 3. It once again contains three regressions. The first
one is the simple OLS regression, and the last two are panel data regressions. Note that the sample
size of regressions in Table 3 are smaller than these in Table 2. Nine countries which were in the
Table 2 sample do not have information on some of the new variables, so they are excluded in Table
3 regressions.
- 25 -
Table 3: Panel Data Regressions (Annual Data) With Added Variables
Dependent variable: annual growth rate of income
Method of Estimate Pooled OLS Fixed-effects Random-effects
Sf/Sf 0.058 0.058 0.058
(0.007) (0.007) (0.007)
Sf/S (t -1) 0.024 0.023 0.024
(0.007) (0.007) (0.007)
S/ (t - 2) 0.017 0.016 0.016
f J (0.007) (0.007) (0.007)
Si/Sf ft-3J 0.011 0.007 0.010
(0.007) (0.007) (0.007)
S,/Sh 0.017 0.016 0.016
(0.004) (0.004) (0.004)
N/N 0.901 1.170 0.908
(0.2176) (0.510) (0.271)
INFLAT -0.023 -0.023 -0.024
(0.005) (0.006) (0.005)
EXCHPREM -0.022 -0.010 -0.014
(0.005) (0.004) (0.004)
TOT 0.030 0.028 0.030
(0.011) (0.011) (0.011)
SCHOOL 0.00008 -.00027 -0.00004
(0.00008) (0.0002) (0.0001)
EXPORT 0.056 0.045 0.051
(0.009) (0.009) (0.009)
DEASIA 0.005 0.006
(0.006) (0.008)
DSAS1A 0.0006 0.0005
(0.007) (0.010)
DLATIN -0.0097 -0.009
(0.0052) (0.007)
DSAFRIC -0.017 -0.018
(0.006) (0.008)
C 0.025 0.028
(0.010) (0.013)
R2 0.325 0.262 0.286
1?2 0.310 0.192 0.218
Countries: 44 44 44
Observations: 772 772 772
Test of Restrictions: F(43,614)= 1.96 X2(16)=28.37
Note: Standard errors are in parenthesis.
- 26 -
The F and chi-square statistics are shown at the bottom of the table. The F-test once again
rejects the pooled OLS regression in favor of variable-intercept models. The chi-square statistic,
however, indicates that the fixed-effects model should be rejected in favor of the random-effects
model. But once again we find similarities among the results in the three regressions.
Results in Table 3 show several improvements over the regressions in Table 2. First, after
introducing new explanatory variables, both R2 and R2 are indeed much higher than the
corresponding regressions excluding new variables. Second, the estimated coefficients on population
growth (NIN) are very significant and close to I in magnitude in all three regressions. The
estimated coefficients on the other key variables - foreign capital imports and home investment -
are again positive and very signficat, and are larger than those in Table 2 in magnittde.
All the newly added variables except the SCHOOL variable have the expected signs and are
statistically significant. The SCHOOL variable is a proxy for human capital, which should be
positively contributing to growth. But in Table 3, the SCHOOL variable is either insignificant (first
column), or has a wrong sign (in the second and third columns). One possible reason for this result
is that primary-school enrollment rate in a country is not a good proxy for the measurement of
human capital.7
We also include several lagged foreign capital imports as exogenous variables in the Table
3 regressions. The estimated coefficients for these lagged foreign capital imports provide a very
interestng result. They show that the current change in foreign capital imports has the strongest
positive impact on income growth, and the impacts become weaker as one goes back further. This
can also serve as a test of the causal relationship between income growth and foreign capital imports.
7Tbis negative sign has appeared in many other recent studies; see Jorgenson and Fraumeni (1992)
and Benhabib and Spiegel (1994) for more discussions.
- 27 -
Since both one-year and two-year lagged foreign capital investment have positive impacts on income
growth, the causal relationship is likely to be from the former to the latter, rather than the other way
around.
Finally, notice that we put regional dummy variables for different regions in the equation
(East Asia, South Asia, Latin America, and Sub-Saharan Africa).8 The countries in the base group
are non-OECD European countries (Greece, Portugal, Turkey), North African countries (Egypt,
Morocco), and South Africa and Israel. Table 3 shows that only the coefficient for Sub-Saharan
Region is significantly negative.
3.3.3 Annual data vs. longer time span
Although Table 3 results show a significant improvement than those in Table 2, there remains
the problem that the estimated coefficients on the growth rate of foreign capital imports and on that
of home capital investment are still too small in magnitude. Furthermore, the reported R2's are still
not very high relative to the ones in other similar studies (for example, see Levine and Renelt 1992).
We suspect that the problem may arise from the use of the annual data, which contain too
much noise and short term disturbances that do not reflect long-run trends, and are not captmred in
the exogenous variables in the model. One way to smooth these short term disturbances is to use
a longer time span. We thus divide the total period of 1965-88 into several 5-year time intervals.
More specifically, we will have four observations for each country, i.e., 1970, 1975, 1980, and
1985. When t = 1985, t - 1 is 1980. All the growth rate variables are averages over the five year
time span. This set-up would also reduce the serial correlation between the JiA's.
8The fixed-effects model does not provide estimates for regional dummies because the fixed individual
country-specific intercepts already account for these individual country effects.
- 28 -
Table 4: Panel Data Regressions (5-Year Time Interval)
Dependent variable: 5-year average growth rate of income
Method of E timate Pooled OLS Fixed-effects Random-effects
St/Ssf 0.165 0.147 0.155
(0.020) (0.016) (0.015)
ShiSh 0.024 0.051 0.030
(0.019) (0.020) (0.016)
N/N 0.715 1.207 0.833
(0.271) (0.533) (0.312)
C 0.026 0.023
2 *(0.007) (0.080)
0.400 0.609 0.498
R2 0.385 0.335 0.147
Countries: 49 49 49
Observations: 125 125 125
Test of Restrictions: F(48,73)=3.29 X2(3)=5.52
Note: Standard errors are in parenthesis.
Table 4 presents the regression results using 5-year time interval data. The regressions in
Table 4 use the basic model without added variables, corresponding to these in Table 2. The first
apparent result in Table 4 is that the R2's are improved greatly compared to the corresponding
results in Table 2 or even the larger-variable regressions in Table 3. The second thing to notice is
that how once again similar the estimates from the three regressions are.
The most important result in Table 4 regressions is that the estimated coefficients
on SfISf and Sh ISh are not only positive and very significant, they are also much larger in
magnitude than those estimated with annual data.
- 29 -
Table 5 shows the fixed-effects panel data regressions using 5-year time intervals data with
different groups of added explanatory variables. Since we have seen in all the previous tables that
the results from fixed-effects model and random-effects model are very similar, we do not present
the results from the random-effects regressions. Again, all the estimated coefficients on foreign
capital imports are positive and significant. This result strongly supports our model prediction that
foreign technology transfer is one of the most important factors in explaining the different economic
growth rates among developing countries.
For comparison, in Table 6, we present the pooled OLS regressions with the same exogenous
variables as in Table 5. One can see that the results in Table 6 are very similar in those in Table
5. Thus we have demonstrated that for practical purposes, pooled regressions produce results similar
to results from panel regressions.
4. CONCLUSIONS
In summary, we first developed a model specification that links the growth rate of income
with that of foreign capital imports' share in GDP and home investment's share in GDP. Then we
ran regressions with a sample of around 50 developing counties, using different econometric
methods and different time spans. Several conclusions can be drawn from this study. First, our
empirical tests confirm our theoretical model prediction that foreign technology transfer has a
positive impact on the income growth rate in developing counties. All the results confirm the
hypothesis that foreign technology imports are a key element in explaining the differences in the
growth rates of income among developing countries. The economic development process in
developing countries is different from that in developed countries. More specifically, the increases
- 30 -
Table S: Fixed-Effects Panel Data ReMressions (5-Year Time Intervafl
Dependent variable: 5-year average growth rate of income
0.132 0.111 0.115 0.102 0.197
(0.016) (0.018) (0.017) (0.018) (0.036)
Sh / Sh 0.037 0.027 0.036 0.028 0.030
(0.019) (0.018) (0.019) (0.018) (0.017)
N/N 1.107 0.819 1.157 1.394 2.260
(0.559) (0.492) (0.596) (0.643) (1.140)
INFLAT -0.036 -0.020 -0.021 0.004
(0.011) (0.011) (0.012) (0.016)
EXCHPREM -0.021 -0.028 -0.024 -0.012
(0.010) (0.009) (0.010) (0.019)
TOT 0.031 -0.051 -0.124
(0.046) (0.045) (0.063)
F.CONSUM -0.011 -0.004 -0.018
(0.015) (0.016) (0.018)
GDP(t - 1) -0.000009 -0.00001 -0.00002 -0.000003
(0.000004) (0.0000D4) (0.000006) 0.00001
SCHOOL -0.0003 -0.0002 -0.0002
(0.0003) (0.00032) (0.0004)
EXPORT 0.060 0.040 -0.012
(0.030) (0.030) (0.019)
Sf/ Sf (t -1) 0.120
(0.032)
R2 0.721 0.735 0.681 0.794 0.926
R2 0.495 0.518 0.439 0.562 0.634
Countries: 42 44 46 40 37
Observations: 106 112 119 101 65
Test of Restrictions: F(41,58) F(43,61) F(45,67) F(39,47) F(36,12)
=2.81 =3.26 =2.61 -1.83 =1.91
Note: Standard errors are in parenthesis.
- 31 -
Table 6: Pooled OLS Regressions (5-Year Time Interval)
Dependent variable: 5-year average growth rate of income
Sf/J Sf 0.137 0.135 0.126 0.113 0.145
(0.018) (0.020) (0.019) (0.017) (0.021)
Sh / Sh 0.047 0.032 0.050 0.039 0.028
(0.029) (0.020) (0.019) (0.017) (0.016)
N/N 0.717 0.805 1.076 0.948 .998
(0.250) (0.265) (0.282) (0.287) (0.338)
INFLAT -0.027 -0.031 -0.025 -0.022
(0.009) (0.010) (0.009) (0.009)
EXCHPREM -0.028 -0.028 -0.014 -0.014
(0-.008) (0.009) (0.009) (0.010)
TOT -0.013 -0.0003 -0.018
(0.044) (0.039) (0.050)
F.CONSUM 0.019 -0.0003 -0.036
(0.018) (0.016) (0.020)
GDP(t -1) 0.000002 -0.000002 -0.000006 -0.000004
(0.000002) (0.000002) (0.000002) (0.000003)
SCHOOL 0.0002 -0.0002 -0.00009
(0.00009) (0.00010) (0.0001)
EXPORT 0.150 0.124 0.146
(0.028) (0.027) (0.038)
Sf/ Sf (t-1) 0.054
(0.024)
DEASIA 0.0016 -0.0003
(0.0067) (0.007)
DSASIA -0.0027 -0.010
(0.0076) (0.008)
DLATIN -0.0022 -0.012
(0.0057) (0.006)
DSAFRIC -(0.0149) -0.022
(0.0067) (0.007)
C 0.036 0.033 -0.0056 0.022 0.026
(0.007) (0.009) (0.011) (0.013) (0.015)
R2 0.582 0.579 0.563 0.744 0.842
R2 0.557 0.550 0.540 0.703 0.793
Countries: 42 44 46 49 37
Observations: 106 112 119 101 65
Note: Stndard errors are in parenthesis.
- 32 -
of productivity in developing countries do not rely on innovation but on inporting foreign plant and
equipment and on borrowing foreign technology. Second, although econometric theory shows that
M-R-W's OLS assumption would produce a biased result, for all practical purpose, OLS regression
results are as good as panel regression results. However, one thing to note is that in all our
regressions, the F-tests demonstrate that although the heterogeneous intercept and homogeneous
slopes specification is a better model than the simple OLS, it should be rejected in favor of models
allowed for hetergeneity of intercept and slopes. That is, the data call for individual country
regressions. However, since we do not have enough observations for each country to allow
individual country regressions, ibis is a candidate for future study of the relationhip between imcome
growth and foreign technology imports.
- 33 -
Amsden, Alice. 1989. Asia's Next Giant: South Korea and Late Industrializadon. Oxford
University Press.
Arrow, K.J. 1962. "Tbe Economic Implication of Learning by Doing. Review of Economic
Studies, 29 (June). pp. 155-173.
Bacha, Edar L. "Growth with Limited Supplies of Foreign Exchange: A Reappraisal of the Two-
Gap Model" in Moshe Syrquin, Lance Taylor, and Lany Wespal, eds., Economic
Structure and Performance: Essays in Honor of Hollis B. Chenery. New York: Acadmc
Press, 1984.
Balassa, Bela 1978. "Exports and Growth: Further Evdence", Journal ofDevelopment Economics
5, no. 2, 181-189.
Barro, R. 1991. 'Economic Growth in a Cross Secdon of Countries." Quartery Jouma of
Economis. May.
Benhabib, J. and M. Spiegel. 1994. 'The role of hma capital in econmic development: Evidence
from aggregate cross-country data.' Joura of Monetary Econoics 34, pp. 143-173.
Bochove, Conelis A. van. 1982, Imports and Economic Growth. The Haue: Marims Nijhoff
Publishers.
Chenery, Hollis, and Micheal Bnmo. 1962. 'Development Alternative in an Open Economy: tbe
Case of Israel,' Economic Journal. Vol. 57, March 1962, 79-103.
de Melo, Jaime and Sherma Robinson. 1990. "Productivity and Externis: Models of Exrt-led
Growth," Working paper no. 387, The World Bank.
- 34 -
Devarajan, Shanta and Heng-fu Zou. 1993. "Exports, Foreign Technology Imports, and Long-run
Growth", mimeo, Policy Research Department, The World Bank.
______. 1994. "Export Externalities, Export Pricing and Endogenous Growth", mimeo, Policy
Research Department, The World Bank.
Esfahani, Hadi S. 1991. "Exports, Imports, and Economics Growth in Semi-industrialized
Countries," Journal of Development Economics, 35(1), pp. 93-116.
Fischer, Stanley. 1993. "The role of macroeconomic factors in growth." Unpublished paper,
Department of Economics, MIT.
Grossman, G.M., and Helpman, E. 1991. Innovation and Growth in the Gla Economry.
Cambridge, MA: MIT Press.
Hsiao, Cheng. 1986. Analysis of Panel Data. Cambridge University Press.
Islam, Naznul. 1992. "Growth Empirics: A Panel Data Approach." Unpublished paper, Deparment
of Economics, Harvard University.
Jones, L. and R. Manuelli. 1990. " A Convex Model of Equilibrium Growth.' Journal of
Poliical Economy, October.
Jorgenson, Dale W. and Barbara M. Fraumeni. 1992. "Investment in Education and U.S. Economiic
Growth." The Scandinavian Journal of Economics, Vol. 94, Supplement, pp. 51-70.
Kaldor, N. 1967. Strategic Factors in Economic Development. Ithaca, NY: Cornell University
Press.
_ . 1978. "Causes of the Slow Rate of Growth in U.K." Essays in Economic Theory.
London: Duchworth.
- 35 -
King, R. and Rebelo, S. 1990. 'Public Policy and Economic Growth.' Journal of Political
Economy, October.
Levine, Ross, and Renelt David. 1992. 'A Sensitivity Analysis of Cross-Country Growth
Regressions," 7he American Economic Review, 82:942-63.
Lucas, R.E., Jr. 1988. "On the Mechanics of Economic Development." Journal of Monetary
Economics. No. 22, pp. 3-42.
Mankiw, G., D. Rommer, and D. Weil. 1991. "A Contribution to the Empirics of Economic
Growth." Quarterly Journal of Economics.
McKinnon, Ronald. 1964. "Foreign Exchange Constraints in Economic Development and Efficient
Aid Allocation," Economic Journal, Vol. 74, p. 388-409.
Romer, P. 1986. "Increasing Returns and Long-Run Growth." Joumal of Politcal Economy, 94,
pp. 1002-1037.
Solow, R. 1956. "A contribution to the theory of economic growth." The Quarterly Journal of
Economics, 70 (February): 65-94.
*957. "Technical Change and The Aggregate Production Function." The Review of
Economics and Statistics 39, pp. 312-320.
______* 1970. Growth T7heory: An Exposition. New York and Oxford: Oxford University Press.
Summers, Robert, and Alan Heston. 1991. "The Penn World Table (Mark 5): an Expanded Set of
Iernational Comparisons, 1950-1988," The Quarterly Journal of Economics (May), pp.
327-68.
Taylor, Lance. 1991. Income Distribution, Inflaion, and Growth. Cambridge, MA: MIT Press.
- 36 -
Tyler, WilLiam. 1981. "Growth and Export Expansion in Developing Countries," Journal of
Development Economics 9, 1981, pp. 121-130.
United Nations Conference on Trade and Development. Handbook of International Trade and
Development Statisftcs. New York: United Natinns Publicaton. Various years.
United Nations. Internaonal Trade Stadstical Yearbook. 1992. United Nations Statistical Office.
Uzawa, H. 1965. "Optimum Technological Change in an Aggregative Model of Economic
Growth.' Internatonal Economic Review 6 (January). pp. 18-31.
The World Bank. World Development Report. Various years.
The World Bank. 1990. World Tables of Economic and Social Indicators. International Economics
Department, Computer file. Am Arbor, MI: Inter-university Consortium for Political and
Social Research (distributor).
Zhang, Xiaoming. 1994. "Different Sectors of Exports and Economic Growth.' Mimeo,
Department of Economics, MIT.
Zou, Heng-fu. 1993. "Product Inovation, Capital Accumulation and Endogenous Growth."
Mimeo, Policy Research Dqetment, The World Bank.
Deflnltlons and Sources of Variables
Y/Y: Average annual growth rate of GDP. Source: World Tables, World Bank, 1992.
Sf / Sf: Average annual change of the share of foreign capital import in GDP. This variable
is calculated based on data from two sources: data on the dolUar value of foreign
capital are from United Nations' Intrtional Trade Statistical Yearbook; and the
share of total import in GDP is from Summers and Heston (1991).
Sh / Sh: Average annual change of the share of home investment in GDP. This is calculated
as the log difference of share of home investment in GDP, and the share of home
investment in GDP in urV in calculated by subtracting Sf from the share of total
investment in GDP. The latter is frm Summers and Heston data set.
i
N I N: Average annual growth rate of population. Soure: Summers and Heston data set.
INFLAT: Average amual inflation rate, computed as the log-difference of CPI. Source:
Intermational Fmawcial Statisdics, CD-ROM, June, 1993. GDP deflator data from
the World Bank were used to extend inflation senes for Malawi.
EXCPREM: Average black market exchange rate premium. Source: World Bank, World
Development Report, 1991. [Computer file]
TOT: Change in terms of trade, calculated as the log difference of the net terms of trade
in a time period. Sources: World Bank, World Development Report, 1991
dataset.
F.CONSUM: Annual average change in the share of foreign consumption iMport in GDP.
Sources: UN's International Trade Statistical Yearbook, and Summers and Heston.
- 38 -
GDP(t-l): Real GDP 5 years before current 5-year period. This variable is used here as an
substitution for the initial GDP level in Mankiw's single period regression model.
Source: Summers and Heston (1991).
SCHOOL: Primary school enrollment as percentage of age group. Source: same as above.
EXPORIT: Average annual growth rate of export, weighted by the share of export in GDP.
Source: Summers and Heston.
Policy Research Working Paper Series
Contact
Tbtle Author Date for paper
WPS 1397 Are Prvate Capital Flows to Un Dadush December1994 J. Queen
Developing Countries Sustainable? Ashok Dhareshwar 33740
Ron Johannes
WPS 1398 The Cost of Air Pollution Abatement Raymond S. Hartman December 1994 E. Schaper
David Wheeler 33457
Manjula Singh
WPS1399 How Important to India's Poor is the Martin Ravallion December 1994 P. Cook
Urban-Rural Compositon of Growth? Gaurav Datt 33902
WPS1400 Technical and Marketing Support Brian Levy with December 1994 D. Evans
Systems for Successful Small and Albert Berry, Motoshige Itoh, 38526
Medium-Size Enterprises in Four Unsu Kim, Jeffrey Nugent,
Countries and Shujiro Urata
WPS1401 Colombia's Small and Mediurn-Size Albert Berry December 1994 D. Evans
Exporters and Their Support Systems Jose Escandon 38526
WPS1402 Indonesia's Small and Medium-Size Albert Berry December 1994 D. Evans
Exporters and Their Support Systems Brian Levy 38526
WPS1403 Small and Medium-Size Enterprise Motoshige Itoh December 1994 D. Evans
Support Policies in Japan Shujiro Urata 38526
WPS1404 The Republic of Korea's Small and Linsu Kim December 1994 D. Evans
Medium-Size Enterprises and Their Jeffrey B. Nugent 38526
Support Systems
WPS1405 Growth and Poverty in Rural India Marlin Ravallion January 1995 WDR
Gaurav Datt 31393
WPS1406 Structural Breaks and Long-Run Javier Le6n January 1995 R. Luz
Trends in Commodity Prices Raimundo Soto 31320
WPS1407 Pakistan's Agriculture Sector Rashid Faruqee January 1995 F. Willie
Is 3 to 4 Percent Annual Growth 82262
Sustainable'
WPS1408 Macroecon' c Management and Jun Ma January 1995 C.Jones
lntergoverr ntal Relations in 37754
China
WPS1409 Restructurir ,lUganda's Debt Kapil Kapoor January 1995 E. Spano
The Commercial Debt Buy-Back 35538
Operation
WPS1410 Macroeconomic Effects of Terms- Nikola Spatafora January 1995 J. Queen
of-Trade Shocks: The Case of Oil- Andrew Wamer 33740
Exportinig Countries
Policy Research Working Paper Series
Contact
Title Author Date for paper
WPS1411 Income Inequality, Wellarm, and Nanak Kakwanl January 1995 G. Evans
Poverty: An Illustration Using 85783
Ukrainian Data
WPS1412 Foreign Technology Imports and XiaomingZhang January 1995 C.Jones
Economic Growth in Developing Heng-iu Zou 37754
Countries