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Pergamon WorldDevelopment, Vol. 25, No. 9. pp. 1443-1452, 1997
0 1997 Elsevier Science Ltd All rights reserved. Printed in Great Britain
0305-750x/97 $17.00 + 0.00 PII: SO305-750X(97)00044-2
Tropics and Economic Development: An Empirical
Investigation
RAT1 RAM Illinois State University, Normal, U.S.A.
Summary. - Following Kamarck’s (1976) reasoning, crosscountry regressions of income, life expectancy, schooling, human development, population increase, capital accumulation, and economic growth are estimated with the country’s distance from the equator as an explanatory variable. The distance-parameter shows high statistical significance and is quantitatively substantial in all cases. Several versions of the Solow-models formulated by Mankiw et al. (1992) are also reestimated after including the distance variable, which shows high significance in these cases too. Some implications of the regression estimates are noted. 0 1997 Elsevier Science Ltd
Key words - economic development, tropics, climate
1. INTRODUCTION
The diversity in the levels of income and well- being across various countries of the world is tremendous. Lucas (1988, pp. 334) noted that the crosscountry income variation was “...literally too great to be believed” and that the living standards differed by a factor of 40 in the richest and the poorest countries. The variation has become even more incredible now since Human Development Report 1996 (pp. 135-137) indicates a ratio of 1:80 in the real (PPP) GDP
P er capita for the poorest and
the richest countries. An understanding of the sources of this diversity, and of the processes of economic growth and development that generate such differences, is of obvious importance. An enormous amount of research effort has, therefore, gone into the study of growth and development, and many models have been suggested to explain the observed differences2 In a “provocative” inquiry into the poverty and affluence of nations, Kamarck (1976) proposed that a country’s geographical location has an important bearing on its ability to develop and thus to achieve economic affluence in the contemporary economic context. He noted that countries located in the temperate zone are at a big advantage, and those in the tropics are at a substantial disadvantage. The main reasons given by him include (a) erratic patterns of rainfall in the tropics can play havoc with agriculture, (b) contin- uous heat and absence of frost lead to a wide variety of weeds, insects, fungi, and other microbes that affect both crops and human life, (c) due to the
aforesaid climatic characteristics, the tropics abound in various types of enemies of agriculture, (d) for the same reasons, the human health hazards are tremendous in the tropics, and labor productivity and accumulation of human and nonhuman capital tend to be low, (e) with some exceptions, tropical soils are poor in organic material, have turned into laterite over large areas, and are characterized by shifting cultivation, and (f) it is more difficult to find mineral resources in tropical countries. In a some- what similar vein, in their crosscountry studies of demand systems, Theil and Finke (1983, p. 357) used distance from the equator as an instrument for per capita income on the basis that “poverty is... approximately a tropical problem.” More recently, Theil (1996, pp. 28-37) has described the cross- country latitude-income relationship in terms of the “equatorial grand canyon.”
While one can raise several types of questions about the position articulated by Kamarck (1976). the general character of the patterns suggested by him is perhaps not hard to perceive. At any rate, irrespective of one’s views on Kamarck’s reasoning, it seems important to quantify the relationship between a country’s geographical location, or the degree of its “tropicality,” and the various measures of its income, well-being and economic growth over the last several decades. The main purpose of this work is to undertake such an investigation by relating a country’s distance from the equator with various dimensions of its affluence and well-being.
Final revision accepted: March 25, 1997.
1443
1444 WORLD DEVELOPMENT
The main finding is that the relationship of almost every measure of a country’s well-being with its distance from the equator appears remarkably strong. In many cases, the distance variable alone can explain nearly half of the crosscountry variation in income and other measures of well-being.
2. THE MODEL, DATA, AND THE MAIN RESULTS
A simple model is used to study the relation between a country’s geographical location and its level of income and well-being. The postulated relationship is of the following form
Y; = a + b LIIST, + t(j (I)
where Yi is a measure of income or some other indicator of well-being of country i, DZSTi is its distance from the equator, and ui is a well-behaved stochastic term that justifies the use of the ordinary least-squares (OLS) procedure.
Out of the many possible measures of national affluence, well-being, factor accumulation, and economic growth, this study considers the follow- ing.
Real GDP per adult in 1960 is included because it reflects the income level at a relatively early point in the postwar experience of many developing coun- tries. It is taken directly from the data provided by Mankiw et al. (1992, pp. 434-436), and is measured in international (PPP) dollars at 1985 prices.
Real GDP per adult in 1985, which also is a part of the Mankiw-Romer-Weil (MRW) data, provides an opportunity to see if the relationship between income and location has changed over the 25year period 1960-85. This variable too is expressed in international dollars at 1985 prices, and is directly comparable with the 1960 measure.
To consider a more recent year, real GDP per capita in PPP dollars for 1993 is taken from Human Development Report 1996 (UNDP, 1996, pp. 135- 137) prepared by the United Nations Development Programme (UNDP). Since this is expressed in per capita terms and is probably in 1993 prices, it is not directly comparable with the income measures for 1960 and 1985, but is useful in providing an estimate of the relationship for a more recent year.
Since a focus on income alone may not give a fair indication of the state of human well-being, life expectancy at birth for the year 1993 is included. Life expectancy is usually considered to be a good indicator of the health status and overall well-being of the population. This is taken from UNDP (1996, pp. 135-137).
One measure of human-capital formation is taken
from MRW, who report the average percentage of working-age population that was in secondary school during 1960-85.
Since there may be some uneasiness about school enrollment being treated as a measure of the country’s human-capital stock, average number of years of schooling of the population aged 25 and over is taken from the compilation of Barro and Lee (1993). The numbers for 1985 are used so as to cover more observations and a relatively recent period.
The position indicated by a single measure is partial, and it seems better to include a composite indicator also. Therefore, human development index for 1993 is taken from UNDP (1996). This variable is scaled from zero to one.
Rate of growth of the working-age population over 1960-85 is also included. This seems useful since population change is often considered to be a relevant factor in development and growth. The conceptual linkage between a country’s geographical location and its recent population growth appears uncertain, however, and the effect of location probably occurs largely through other variables, especially income and education. Information on population growth is taken directly from the MRW data, and the variable is expressed in percentage points at the annual rate for 1960-85.
As an indicator of the rate of accumulation of physical capital, investment-GDP ratio is taken from MRW. This is in percentage terms and is the average for 1960-85.
The proportionate increase in real GDP per adult during 1960-85 is taken as a measure of economic growth over the period. This is based on the information given by MRW. and, following their procedure (1992, pp. 425-426), is computed as LRYSS-LRY60, where LRY60 and LRY85 are the logarithms of real GDP per adult in 1960 and 1985 respectively. Thus the variable does not express growth at the annual rate, but indicates the proportionate increase in real GDP per adult for the entire period. Use of the variable in this form enables a comparison of some of the estimates with MRW’s.
The distance variable (DZST) is measured in terms of the country’s latitude in degrees.3 This is taken from Goode’s World Atlas (Rand McNally, 1995, pp. 254-370).4
The basic sample considered is MRW’s non- oil group of 98 countries. This seems reasonable since this is the largest sample used by them, and as explained later, some of their regression models are reestimated after including the distance variable, and use of this sample enables a direct comparison of some of the estimates. The Appen- dix provides descriptive statistics for the basic variables.
TROPICS AND ECONOMIC DEVELOPMENT I445
Table I. Crosscount~ regressions of income, life expectancy. education, human development. capital accumulation und growth on distance from the equator (DIST)‘“’
Dependent variable Constant Coefficient of R’ N (t-statistics) DIST (SEE)
(t-statistics)
Real GDP per adult, 1985 (PPP dollars of 1985) -38.999 235.890* 0.54 98 (-0.06) (10.79) (3564.3)
Real GDP per capita, 1993 (PPP dollars) -320.316 312.658* 0.53 98 (-0.42) (11.77) (4836.5 i
Real GDP per adult, 1960 (PPP dollars of 1985) 433.501 I 12.962* 0.42 98 (1.35) (9.18) (2177.1)
Life expectancy at birth, 1993 (years) 54.142* 0.432* 0.40 98 (32.79) (10.14) (8.646)
Average schooling of population aged 25 and over, 1985 (years) 2.082* 0.117* 0.46 86 (6.27) ( 1 1.05 I (2.132)
Percentage of working age population in secondaryschool. 1966 2.604* 0.123* 0.34 98 85 average (5.28) (8.65) (2.8201
Human Development Index 1993, scale 0 to 1 0.4 19* 0.010* 0.39 98 (11.45) (10.08) (0.196)
Working-age population growth (percentage, 196&85 mean) 3.034% -0.037* 0.46 98 (29. I81 (-10.45) (0.653)
Investment-GDP ratio (percentage, 196@85 mean) 12.184* 0.243* 0.2s 98 (9.82) (6.36) 16.X63)
Proportionate increase in real GDP per adult, 1960 to 1985 0.241* 0.009* 0.10 98 (3.05) (4.18) (0.422)
‘=‘Data on real GDP per capita, human development index, and life expectancy are from UNDP ( 1996). Average schooling of the population is from Barro and Lee (1993). Distance from the equator is from Rand McNally’s Goode’s World At1a.y (1995). and is the country’s latitude in degrees. All other variables are from Mankiw et al. (1992). SEE denotes standard error of the regression. The t-statistics are based on White’s (1980) heteroskedasticity-consistent standard errors. See note 7 about the parameters for the first two regressions. *Statistically significant at least at the 5% level.
Table 1 contains OLS estimates of equation (1) with each of the 10 alternative dependent variables. Although White’s (White, 1980) test does not indicate significant heteroskedasticity in most cases, the reported t-statistics are based on White’s (1980) heteroskedasticity-consistent standard errors, and not the usual OLS standard errors.5
The most notable aspect of Table 1 is that the distance-parameter shows strong statistical signifi- cance in every regression, and the explanatory power of these simple models is high. In six of the 10 cases, the distance variable alone can explain nearly one- half of the crosscountry variation in such important measures as real GDP per adult (or per capita). schooling, life expectancy, and population growth. Even for the human development index, the adjusted- R2 is just a little under 40%. It is remarkable that the distance from the equator should have such a high explanatory power over a wide variety of indicators of economic performance and human well-being. It might be of some interest to note that the explanatory power of the distance-variable relative to real GDP per adult in 1985 is very close to that of the
“textbook” Solow-model estimated by Mankiw et al. (1992. p. 414).6
The estimates also indicate that the quantitative effect of the distance-variable is substantial in most cases. For example, an increase of one degree in a country’s latitude is associated with an increase of about $113 in real GDP per adult in 1960 and about $236 in 1985. Therefore, an increase of 20 degrees in its latitude tends to raise a country’s GDP per adult in 1985 by nearly $4,500.’ The effect on real GDP per capita in 1993 is also large, and a 20-degree increase in the latitude raises real GDP per capita by about $6,000. Similarly, a 20-degree increase in the latitude raises average adult schooling by nearly 2.5 years, increases life expectancy by almost nine years, improves the human development index by nearly 0.200, and lowers working-age population growth by three-fourths of a percentage point. The effects on capital formation and economic growth are also sizable.
A comparison of the parameter estimates for real GDP per adult in 1960 and 1985 shows that the income-advantage of being located away from
1446 WORLD DEVELOPMENT
the equator more than doubled over 1960 to 1985.’ This is an indication of the (“absolute” or “unconditional”) divergence in the income-status of the sample countries. While a divergence in income is to be expected since the distance- variable has a significantly positive effect on growth, the size of the increase in the distance- parameter appears large.
Some further reflections on the interpretation of the parameter estimates should be useful. It is obvious that a country’s distance from the equator is one prime example of an econometrically exogenous variable, and there is little possibility of any “feedback” from any of the dependent variables. One might ask, however, whether the estimates reflect some statistical artifact caused by the historical coincidence of a group of temperate-zone countries being presently affluent, and the large bunch of the tropical countries being poor. Such a possibility seems unlikely. First, Kamarck’s (1976) reasoning would suggest that the distance from the equator is an indication of the differences in several types of natural endowments caused by (a) the degree of heat and humidity and its effect on productivity and accumulation, (b) human health hazards related to various types of bacteria, viruses and parasites, (c) soil quality, (d) growth and strength of enemies of agriculture and crops, and (e) availability of mineral resources. At a qualitative level, Streeten noted in his foreword to Kamarck (1976, pp. ix-x), “development efforts (in the tropical
20,000
18.000
16,000
l 4
z 12,000
k
fk 10,000
z
z 8000 l
6000
South) have to be substantially greater to achieve results similar to those realized in the North,” and elaborated, “A hot and humid climate reduces the efficiency of man, cattle, and land”, “...debilitating or killing tropical diseases...present major obstacles to development,” “Land erosion...is much more serious in tropical countries,” and there is “inade- quate rainfall in arid tropical zones.” It is, therefore, likely that the distance-parameter in the reported regressions shows quantitatively the endowment- advantage of being located away from the tropics. Second, Figure 1, which is a plot of real GDP per adult in 1985 against the country’s distance from the equator, shows that it is not the case that there are two groups of countries in the southwest and the northeast comers that could be joined by a somewhat artificial positively sloped regression line. Although there are some deviant cases, the continuity of the relationship across the wide ran
0 e of distance and
income values seems remarkable. Moreover, even if the 19 countries that are traditionally treated as “developed” and are classified by World Bank (1994, p. 163) as “high-income economies,” which are located far away from the tropics, are excluded, the distance-parameter retains high statistical sig- nificance in almost all cases.” Therefore, it is reasonable to believe that the regression estimates largely show the disadvantage of being located closer to the equator in terms of a wide variety of endowment factors that affect productivity, accumu- lation and growth.
* l
4
* I I I
0 10 20 30 40 50 60
DIST (degrees)
Figure 1. ScatterplotofrealGDPperadultin 1985 (RY85)againstthecountry’sdistancefromtheequator(DIST).
TROPICS AND ECONOMIC DEVELOPMENT 1447
One may be uneasy about the simple linear specification in equation (1), and may ask whether semi-log or double-log models do better. I ’ In almost all cases, they do not, and the linear specification is the best in terms of adjusted-R* and standard error of the regression (SEE). For instance, if both real GDP per adult in 1985 and the distance-variable are used in the logarithmic form, the adjusted-R* is 0.34 instead of 0.54 for the linear model reported in Table 1.” Similarly, it might seem that using a quadratic in distance might be more informative. That is not true, however, and in every regression that uses both linear and quadratic terms for the distance-variable, one or both of the distance- parameters lack statistical significance. At any rate, in no case is there an indication of a significant increase or decline in the effect of the distance- variable. ’ 3
Some worry about the possibility of “omitted” variables affecting the estimates in Table 1 is not unreasonable. Two observations, however, are relevant here. First, since life expectancy, educa- tional attainment, human development index, and investment rate are likely to be positively related with income, and the distance-variable has a strong positive effect on income, some effect of income is reflected in the distance-parameter in these equations. But, this is not “spurious,” and is likely to be the “true” effect of the distance variable, a large part of which probably occurs through income. Second, noting that a country’s distance from the equator is clearly an exogenous variable, it is difficult to think of other “omitted” variables whose effect may be picked up in the distance- parameter in the income-equations themselves.14 While the usual factors of labor, capital and technology easily come to mind, Table 1 indicates that all these are likely to be affected by the distance-variable.
At any rate, one way to explore the possible consequence of “omitted” variables is to include the distance-variable in multiple regression models. The study by Mankiw et al. (1992) is helpful in that exploration. They estimate several variants of “Solow models” where the dependent variable is the logarithm of real GDP per adult in 1985. They also conduct tests of “convergence,” which amount to estimation of growth equations. It is easy to add the distance-variable in these models and to study the partial effect of the variable in the presence of conventional production factors and determinants of growth. Table 2 reports estimates of three models included in the MRW study both with and without the distance-variable.‘5 The models are (i) their “textbook” Solow model (p. 414), (ii) the “augmen- ted” Solow model (p. 420), and (iii) “test for conditional convergence” with human capital (p. 426, Table V).Ih
It is clear from Table 2 that the distance-variable shows high statistical significance in every case even when investment rate, labor input and human capital are held constant. Of course, distance from the equator is likely to affect income through these variables also. While noting the high statistical significance of the distance-parameter in these multiple regression models, some consideration of its quantitative magnitude is also useful. Although its size is expected to be smaller than in Table 1 since a part of the effect of the distance-variable occurs through the inputs, a comparison with the estimates in Table 1 should be useful. Considering the textbook Solow-model. it is seen that a one-unit (one degree) increase in the country’s latitude raises real GDP per adult roughly by a proportion of 0.033 or approximately 3.30%. Using the sample mean of $5,310 for GDP. one-degree increase in the latitude raises GDP per adult by roughly $150 to $175, when other inputs are held constant. This is in harmony with the estimate of $236 in Table 1. which shows the total effect, including that through the increase in investment-GDP ratio and reduction in population growth. It is also interesting to note that the addition of the distance-variable lowers the investment- parameter by more than 20%. and causes a dramatic change in the parameter of the composite term (“LNGD”) and turns it from a highly significant negative to an insignificant positive number. Similar remarks apply to the augmented Solow-model. The estimates show that, in addition to the effect of the distance-variable through investment, population growth, and schooling, one-degree increase in a country’s latitude increases GDP per adult roughly by 1.90% or something like $75 to $100. Here, again, the parameters of the investment and the schooling variables drop, and there is a big change in the parameter for the composite variable whose high statistical significance disappears. Therefore, if there is an omitted-variable problem, it seems more likely in the conventional models that exclude DIST than in regressions of income and other measures of well- being on a country’s distance from the equator. as done in Table 1.” In the growth equation, the scenario is similar. Not merely is the distance- parameter statistically significant, even in the pre- sence of the initial-income variable, which should have picked up most of its effect, but its size is broadly of the same order as in (the last row of) Table 1. These considerations lead to the conclusion that the estimates in Table 1 indicate a genuine effect of the distance-variable on income and other measures of well-being; it is unlikely that any “omitted variables” have caused a major bias in these estimates, and the size and the significance of the distance-parameter suggest a pervasive effect of a country’s geographical location on various dimen- sions of its performance and well-being.
Tab
le
2. C
ross
coun
try
esti
mat
es
of S
olow
-mod
els
wit
h an
d w
itho
ut
dist
ance
fr
om
the
equa
tor
(DIS
T)‘
“’
Con
stan
t C
oeff
icie
nt
of
R*
(SE
E)
L.lY
L
NG
D
LSC
H
L&
Y60
D
IST
“T
extb
ook”
So
low
M
odel
D
epen
dent
va
riab
le:
loga
rith
m
of r
eal
GD
P pe
r ad
ult
in
1985
(L
RY
85)
With
out
DIS
T
-1.1
28
1.42
4*
-1.9
90*
.59
(-0.
79)
(9.9
5)
(-3.
53)
(0.6
89)
With
D
IST
5.
760*
1.
105*
0.
567
0.03
3*
.70
(3.4
0)
(8.2
3)
(0.8
7)
(5.9
0)
(0.5
92)
“Aug
men
ted”
So
low
M
odel
(a
fter
in
clud
ing
hum
an
capi
tal)
D
epen
dent
va
riab
le:
LR
Y85
W
ithou
t D
IST
0.
622
0.69
7*
-1.7
45*
(0.5
8)
(5.2
4)
(-4.
20)
With
D
IST
4.
383*
0.
632*
-0
.280
(3
.23)
(5
.09)
(-
0.53
)
Gro
wth
(“
Con
verg
ence
”)
Mod
el
(with
hu
man
ca
pita
l)
Dep
ende
nt
vari
able
: pr
opor
tiona
te
incr
ease
in
rea
l G
DP
per
adul
t ov
er
1960
-85
With
out
DIS
T
-0.4
55
0..5
24*
-0.5
06**
(-
0.65
) (6
.03)
(-
1.75
) W
ith
DIS
T
1.18
3 0.
508*
0.
023
(1.2
2)
(5.9
8)
(0.0
6)
0.65
4*
(9.0
0)
0.54
3*
(7.4
6)
0.01
9*
(4.0
5)
.78
(0.5
08)
.81
k?
(0.4
7 1)
s z
0.23
I *
-0
.288
* .4
6 3
(3.8
9)
(-4.
68)
(0.3
27)
0.21
3*
-0.3
35*
0.00
8*
.49
(3.6
3)
(-5.
29)
(2.3
6)
(0.3
19)
(a) T
he
desc
ript
ion
of t
he
mod
els
is a
dapt
ed
from
M
anki
w
et a
l. (1
992)
. L
RY
60
and
LR
Y85
ar
e,
resp
ectiv
ely,
lo
gari
thm
s of
rea
l G
DP
per
adul
t in
19
60
and
1985
(i
n 19
85
PPP
dolla
rs).
L
NG
D
deno
tes
MR
W’s
co
mpo
site
va
riab
le
that
in
clud
es
popu
latio
n gr
owth
, te
chno
logi
cal
chan
ge
and
depr
ecia
tion,
th
e su
m
of t
he l
ast
two
bein
g a
ssum
ed
to
be 5
% (
.05)
. L
IY d
enot
es
the
loga
rith
m
of i
nves
tmen
t-G
DP
ratio
(p
erce
ntag
e)
aver
aged
fo
r 19
60-8
5.
LSC
H
stan
ds
for
the
loga
rith
m
of t
he p
erce
ntag
e of
the
w
orki
ng-a
ge
popu
latio
n th
at
is i
n se
cond
ary
scho
ol
(ave
rage
d fo
r 19
60-8
5).
See
note
s to
Tab
le
1 fo
r da
ta
sour
ces.
R
elev
ant
t-st
atis
tics
are
in p
aren
thes
es
belo
w
the
para
met
er
estim
ates
, N
equ
als
98 i
n al
l ca
ses,
an
d S
EE
den
otes
st
anda
rd
erro
r of
the
reg
ress
ion.
*S
tatis
tical
ly
sign
ific
ant
at l
east
at
the
5%
le
vel.
** S
tatis
tical
ly
sign
ific
ant
at t
he
10%
lev
el.
TROPICS AND ECONOMIC DEVELOPMENT 1449
3. CONCLUDING REMARKS
Following Kamarck’s (1976) provocative reason- ing, this study provides some quantitative estimates of the consequences of a country’s location, relative to the equator, for its income, well-being and economic growth. The estimates indicate a highly significant and quantitatively substantial impact on income, life expectancy, educational attainment, human development, capital accumulation and economic growth. While some of the effect on the non-income variables may certainly be transmitted through income, results from several standard multi- ple regression models of income and growth indicate that the estimated effects of the distance-variable are unlikely to be biased to any major extent due to “omitted” variables, and that these effects are likely to be quite pervasive.
Although the suggested effects of a country’s geographical location on its affluence and develop- ment may seem somewhat deterministic, the opera- tional implications are perhaps as easy to perceive as the is;emingly implicit developmental determin- ism. Many of these implications have been articulated by Paul Streeten in his foreword to Kamarck (1976) and by Kamarck himself. By way of supplementing those observations, five points are mentioned here very briefly.
First, the locational factor seems to deserve much greater attention in crosscountry studies of income, well-being, growth. and development. Although Streeten noted in Kamarck (1976, p. xi) the neglect of this aspect ‘not only in academic literature but also in development plans,” and suggested some possible reasons, it is surprising that this factor is still so rarely discussed, or even mentioned, in scholarly research, textbooks and policy studies.”
Second, even if, for some reason, the design of a study does not include the distance-variable (or something similar), the consequences of its omission might be kept in view. Table 2 provides a vivid illustration of the parametric upheaval that can occur if this variable is omitted.
Third, given the strong econometric exogeneity of a country’s distance from the equator, its use as an
instrumental variable merits consideration. Theil and Finke (1983) have provided a nice example of its usefulness for that purpose.
Fourth, in the context of policy discussions, the tremendous variation in the degree of disadvan- tage (or advantage) that different countries face in terms of the endowment patterns related to their geographical location may be treated as an impor- tant factor. Kamarck (1976) lucidly explained various dimensions of the crosscountry heterogene- ity related to this aspect, and suggested that country-specific factors caused by such endow- ment-differentials be an important part of the discussions on economic development. In his fore- word to Kamarck (1976, pp. ix-xii), Streeten also recapitulates the special difficulties faced by the tropical countries, discounted the implicit determin- ism, and noted “But a good deal can be done, and has not been done, to find solutions (to the these difficulties)...” due perhaps to “the...mythol- ogy that all countries tread inexorably the same path.” The discernible impact of these observations still appears meager. It may be hoped that the quantification of the role of geographical location provided in this paper will lead to a greater focus on the study of development problems that affect the tropical countries more than others. Sub- Saharan Africa, much of which falls in this category. has remained far behind and seems to deserve a special attention along this dimension.
Last, the policy prescriptions that international organizations provide to developing countries, and the operational help that is given to these countries, could benefit from a consideration of the tremendous heterogeneity in the nature and the magnitude of the obstacles to development faced by different coun- tries. In that sense. the apparent uniformity of the basic prescriptions given by these organizations might be seriously flawed. From this perspective also, while the well-known “policy” explanations abound, the dismal development performance in much of Sub-Saharan Africa might reflect the point that these prescriptions have not been based on an adequate consideration of crosscountry heterogeneity in problems of economic development.
1450 WORLD DEVELOPMENT
NOTES
1. The variation in conventional dollar GNP per capita, which is what Lucas (1988) considered, is, of course, greater. For example, World Bank (1996, pp. 18-19) shows the lowest GNP per capita in 1994 as $80 to $130 (in Mozambique and Ethiopia), and the highest as $39,850 in Luxembourg and $37,180 in Switzerland, leading to a ratio of around 1:300 in the lowest and the highest.
2. It is not possible or useful to mention here even a tiny sample of this research. By way of a few arbitrary examples, see, besides Kamarck (1976), Barre and Sala-i- Martin (1995). Landes (1990). Lucas (1988). Mankiw et al. (1992), and Olson (1996).
3. It is possible to express the distance approximately in miles by multiplying the latitude in degrees with 69. But, such a linear resealing of the variable does not add anything to the analysis.
4. Goode’s World Atlas (Rand McNally, 1995, p. 354) states that “Latitude and longitude coordinates for... extensive areal features, such as countries...are given for the position of the type as it appears on the map”. Note that the variable used in this paper is slightly different from, that of Theil and Finke (1983) who used the distance of a country’s capital from the equator as a measure of its tropicality. The estimates based on the country’s distance, however, are very similar to those based on the distance of the capital. Theil (1996) used the latitude of the most populous city of the country. As an aside, it might be noted that the countries located to the north and the south of the equator are treated symmetrically.
5. The patterns are, however, the same whether the standard errors used are heteroskedasticity-consistent or the ordinary ones. Additional details are available from the author.
6. See the first row of Table 2 also.
7. The negative estimates for the constant term in the equations for real GDP per adult in 1985 (RY85J and real GDP per capita in 1993 (RY9.3) are not plausible. Although the estimated parameters are not statistically significant at any meaningful level, even a zero value for the constant term is not quite plausible. But, if the constant terms in these regressions are constrained to 400 and 300 respec- tively, which reflect approximately the sample minima for RY85 and RY93, the restriction is not rejected at almost any sensible level, and the estimated slope parameters remain virtually unchanged as Table 4 in the Appendix (a comparison of the unconstrained and the constrained estimates for the slope parameters) indicates. Therefore, the numbers mentioned in the text about the effect of a 20- degree increase in the latitude apply to the constrained as well as the unconstrained estimates.
8. Recall that real GDP per adult for 1960 and 1985 are in the same (1985) prices and the parameters are directly comparable.
9. The most blatant outlier on the positive side is Singapore. But, it is a country of less than 3 million people. As a perceptive referee noted, being a city-state, it escapes most of the damaging effects of the tropics on agriculture. Other major (positive) outliers in the equation for GDP per adult in 1985 (RY85) include Hong Kong and Trinidad and Tobago. Hong Kong, like Singapore, is also a city-state, and escapes most of the damage inflicted by the tropics on agriculture. Trinidad and Tobago, which is a small economy of some 1.3 million people, is largely oil- dependent and does not really belong in MRW’s non-oil group. As a somewhat interesting aside, the “miracle” of South Korea shows as a relatively weak performance here in terms of the residuals in the equation for RY85.
10. The 19 countries are Australia. Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Spain, Sweden, Switzerland, United Kingdom, and United States. After excluding these 19 countries, the estimated distance- parameters in a few models are shown in Table 5 in the Appendix. It may be seen that while the parameter in the income equation is expectedly smaller than in Table 1, the effect on life expectancy and human development index is of the same order, and the effect on growth is larger.
11. In addition to the aspects discussed in the text, it might be asked how the estimates look in a different sample. Although the 98-country sample used in this study is quite large, the patterns appear very similar in larger samples. For example, the following are the estimates of the equation for real GDP per capita in 1993 (RY93) from a sample of 137 non-transition non-oil countries (with ordinary t-statistics in parenthesis): RY93=-564.73 I + 305.663 (11.86) DIST
(-0.81) (11.86) Adj.-R’: 0.5 I. SEE: 4724.5
Additional details are available from the author.
12. If only the distance-variable is entered logarithmi- cally, adjusted-R2 is even lower at 0.29. Complete details of these estimates are available on request. The patterns are, however, similar whether the model is specified linearly or in semi-log or double-log format.
13. Additional details of the quadratic-form estimates are available from the author.
14. It is, of course, possible to think of some very general “omitted variable” such as “colonialism.” Casual ob- servation suggests that until the middle of this century, countries located away from the tropics colonized a large number of countries located in or around the tropics. Like many other scholars, Kamarck (1976) discounts the view on development implied by such a thought. Another variable that is omitted here, as in most research on income and growth, relates to “policies” or “institutions,” which seem to have been emphasized in recent years in the context of economic growth. Olson’s (1996) lecture is one example of the fairly widely-shared view that discounts variations in endowments and stresses differences in “policies” and “institutions.” Given the estimates reported in this paper, it
TROPICS AND ECONOMIC DEVELOPMENT 1451
would be interesting to consider whether such policies or institutions are systematically related to a country’s distance from the equator. The point here is not that policies do not matter, but that the emphasis placed on this aspect seems reductionist.
15. The distance-variable is entered linearly. As noted for Table 1, models with linear DIST are clearly superior, in terms of adjusted-R* and SEE, to those that specify the variable logarithmically.
16. The parameter estimates without the distance-vari- able are very close to MRW’s, except for the constant term, which probably reflects some scaling differences.
17. MRW’s sample and some of their models are used only as a matter of convenience and ease of data availability and replicability. These illustrations are not intended to evaluate or criticize their work. which has received considerable attention.
18. Some evidence of the “mutability” of the disadvan- tage of being located in the tropics is indicated by a substantial change in the latitude-parameter. over 1929- 1989. in several preliminary regressions of income on latitude for the US states.
19. See also Kamarck’s (1976, pp. 4-5. 8-9) observa- tions about the neglect of this aspect in the textbooks and elsewhere.
REFERENCES
Batro, R. J. and Lee, J. (1993) International comparisons of
educational attainment. Journal of Monetary Economics
32, 363-394. Barro, R. J. and Sala-i-Martin, X. (1995) Economic Growth.
McGraw-Hill, New York. Kamarck, A. M. (1976) The Tropics and Economic
Development. Johns Hopkins University Press, Balti-
more, MD. Landes, D. S. (1990) Why are we so rich and they so poor?
American Economic Review, Papers and Proceedings
80, I-13. Lucas, R. E., Jr. (1988) On the mechanics of economic
development. Journal of Monetary Economics 22,342. Mankiw, N. G., Romer, D. and Weil, D. N. (1992) A
contribution to the empirics of economic growth.
Quarterly Journal of Economics 107, 407437. Olson, M., Jr. (1996) Distinguished lecture on economics in
government: Big bills left on the sidewalk: Why some
nations are rich. and others poor. Journal of Economic Perspectives 10. 3-24.
Rand McNally ( 1995) Goode’s World Atlas, 19th edn. Rand McNally, New York.
Theil, H. (1996) Studies in Global Econometrics. Kluwer Academic Publishers, Boston, MA.
Theil, H. and Finke, R. (1983) The distance from the equator as an instrumental variable. Economic7 Letters 13, 357-360.
United Nations Development Programme (1996) Human Development Report 1996. Oxford University Press, New York.
White, H. (1980) A heteroskedasticity-consistent covar- iance matrix estimator and a direct test for hetero- skedasticity. Econometrica 48, 8 17-838.
World Bank (1994) World Development Report 1994. Oxford University Press. New York.
World Bank (1996) The World Bank Atlas 199ti. World Bank, Washington, DC.
[Appendix - overleajj
1452 WORLD DEVELOPMENT
APPENDIX A
Table 3. Descriptive statistics for the basic variables’“’
Mean Std. Dev. Minimum Maximum N (unweighted) (unweighted) value value
1. Real GDP per adult, 1960, international (PPP) dollars, 1985 prices 2. Real GDP per adult, 1985, international (PPP) dollars, 1985 prices 3. Real GDP per capita, 1993, PPP dollars 4. Working-age population growth, 1960-85, annual, percentage 5. Life expectancy at birth, 1993, years 6. Average schoohng of population aged 2.5+, 1985, years 7. Percentage of working-age population in secondary school, 196&85 average 8. Human development index, 1993, scale: 0 to 1 9. Investment-GDP ratio, percentage, 196@85 average 10. Proportional increase in real GDP per adult from 1960 to 1985 (LRY85-LRY60) 11. Country’s distance from the equator, latitude in degrees
2,995
5,310 412 19,723 98
6,769 2.20
2,863
5,277
7,070 0.89
300 24,680 98 0.30 4.30 98
63.95 11.19 39.20 79.60 98 4.86 2.91 0.54 12.04 86
5.40 3.47 0.40 11.90 98
0.635 0.251 0.204 0.951 98 17.67 7.92 4.10 36.90 98
0.45 0.45
22.67 16.57
383 12,362 98
-0.68
0
1.66 98
63.80 98
‘a) Items 1, 2,4,7,9 and 10 are based on Mankiw et al. (1992, pp. 434-436). Items, 3,5, and 8 are from UNDP (1996, pp. 135-137). Item 6 is from Barre and Lee (1993). Item 11 is from Goode’s World Atlas (Rand McNally, 1995, pp. 254370).
Table 4. Comparison of unconstrained and constrained estimates for the distance parameters’“’
Table 5. Estimated distance parameters for some dependent variables after excluding 19 high-income countries@’
Unconstrained Constrained RY93: (Table 1) estimate estimate
114.5 (2.76) Life expectancy 0.403 (4.11) in 1993:
Model for RY85 235.9 (10.80) 223.2 (17.42) Model for RY93 312.7 (10.55) 294.8 (16.95)
HDI for 0.008 (3.57) Growth of GDP 0.011 (2.32) 1993: per adult 1960-85:
(a) OLS t-statistics in parenthesis (*) t-statistics in parenthesis
