-Caveat Lector-
its interesting that the WB is taking the same line as S Huntington in his
clash of civilisations
John
POLICY RESEARCH WORKING PAPER
.
Africa's Growth Tragedy A Retrospective, 1960-89
William Easterly
Ross Levine
The World Bank
Policy Research Department Macroeconomics and Growth Division
and Finance and Private Sector Development Division August 1995
POLICY RESEARCH WORKING PAPER
Summary findings
Africa's economic history since 1960 fits the classical Definition of
tragedy: potential unfulfilled, with Disastrous consequences Easterly and
Levine use one methodology -cross- Country regressions - to account for
Sub-Sahara Africa's growth performance over the past 30 years and suggest
policies to promote growth over the next 30 years.
They statistically quantify the relationship between Long run growth and a
wider array of factors than any previous study. They consider such standard
variables as
initial income to capture convergence effects, schooling, political
stability, and indicators of monetary, fiscal, trade, exchange rate, and
financial sector policies. They
consider such new measures as infrastructure
development, cultural diversity, and economic spillovers
from neighbors' growth.
Their analysis:. improves substantially on past attempts to account for the
growth experience of Sub Saharan Africa
* Identifies spillovers of growth performance between neighboring countries.
The spillover effects of growth have implications for policy strategy.
Improving policies alone boosts growth substantially, but if neighboring
countries act together, 'the effects on growth are much greater.
Specifically, the results suggest that the effect of neighbors' adopting a
policy change is 2.2 times greater than if a single country acted alone.
Shows that low school attainment, political instability, poorly developed
financial systems, large black-market exchange-rate premia, large government
deficits, and inadequate infrastructure are associated with slow growth.
Finds that Africa's ethnic diversity tends to slow growth and reduce the
likelihood of
adopting good policies
Produced by the Policy Research Dissemination Centa
Introduction
Africa's economic history since 1960 fits the classical definition of
tragedy: potential
unfulfilled, with disastrous consequences. In the 1960s. a leading
development textbook ranked
Africa's growth potential ahead of East Asia's* and the World Bank's chief
economist listed seven African countries that "clearly have the potential to
reach or surpass" a 7 percent growth rate.'Yet, these hopes went awry.
Much of Africa has suffered negative per capita growth since 1960,
*and the seven promising countries identified by the World Bank's chief
economist were among
those with negative growth,
This failure has indeed had dreadful consequences. In terms of
GDP per capita, Sub-
Saharan Africa averaged about $1,132 during the 1980s. while GDP per capita
averaged $3,356 in Latin America and $5,048 in East Asia.2 Out of the
20 poorest countries in the world, 16 are
in Sub-Saharan Africa. Africa's growth tragedy is also reflected in painful
human scars.
The typical African mother has only a 30 percent chance of having all of her
children survive to age 5, average life expectancy for a person born in 1980
in Sub-Saharan Africa is only 48 years compared with 65 in Latin America,
and daily calorie intake is only 70 percent of Latin America's and East
Asia's
Not only is Sub-Saharan Africa poor, growth has been the slowest of
any region of the
world.
On average, real per capita GDP did not grow in Africa over the
1965-1990 period, while, in East Asia and the Pacific, per capita GDP growth
was over five percent and Latin America grew at almost two percent per year.
Figure l's map of the world illustrates this distressing point.
Shaded countries suffered negative real per capita GDP growth over the
1960-88 period. Almost all of these. countries fall in Sub-Saharan Africa.
This tragedy has drawn considerable attention. In addition to numerous
journal articles,' a
stroll through the Africa section of the library reveals an abundance of
books with titles such as
----------------------------------------------------------------------------
-----------------------------------
l References and to Enke (1963) and Kamarck (1%7), respectively.
2 These figures are Purchasing Power Parity adjusted terms.
3 See Woldi Bank (1981.1989,1994a). bevan, Collier, and Gunning
(1993). Collier, and Gunning (1992). Soludo (1993). Husain and Farwee
(1994), pack (1993). Lewis (1994). Wheeler (1984). Ndulu (1991). Elbadawi
(1992). ElLahi and Ndulu (1994). Hielleiner (1986). Fosu (1992a.b.c).
Gyimah-Bhempong (1991). Killick(1991). Berg (l993), Picktt (1990).
Hadjimichael et al. (1994), and Rimmcr (1991). Chhibber and Fischer (1992)
edited a book on econoomic reform in Sub-Sahara Africa which discusses
changes in exchange rate, fiscal, financial
sector,trade educational and regional integration policies that could
potentially stimulate sustained growth in Africa
----------------------------------------------------------------------------
-----------------------------------
Economic Crisis m Africa,. The Destruction of a Continent. The Crisis and
Challenge of African Development. Africa in Economic Crisis, Africa:
Dimensions of the Economic
Crisis, and Africa: What Can Be Done,4 Furthermore, the World Bank recently
produced two studies. Adjustment in Africa: Reforms, Results, and the Road
Ahead
(World Bank, 1994a) and Adjustment r in Africa: Lessons from Country Cases
(Husain and Faruquee, 1994). that examine the linkages between policy
reforms and economic performance over the past decade's.
These rigorous country-studies identify a diverse set
of potential causes of Sub-Saharan Africa's ills ranging from bad policies,
to poor education, to political instability, to inadequate infrastructure,
to ethnic strife, etc. Clearly, if economists are to claim any success in
explaining why some countries are rich and others poor, Africa's tragedy
must be part of the explanation. Similarly, a great challenge for
policy analysts is to derive policy recommendations and strategies that will
ignite sustained development in Africa.
This paper uses one methodology - cross-country regressions - to
examine cross-country growth experiences, with special attention to
Sub-Saharan Africa, over the last 30
years We contribute to the literature by statistically quantifying the
empirical association relationship between economic growth and a wider array
of factors than any existing study, In addition to standard variables
such as initial income to capture convergence
effects, schooling, political stability, and indicators of monetary, fiscal,
trade, exchange
rate, and financial sector policies, we consider new measures of
infrastructure
developmet& cultural diversity, and economic spillover from neighbors'
growth. The analysis:
(1) improves substantially upon past attempts to account for the growth
experiences of subsaharan African countries,
----------------------------------------------------------------------------
--------------------------------
4 The authors of these books are, in order Blomstrom and Lundahl(l993),
Begin & corbett (1982).
Glickman (1988). Ravenhill (1986). sadip Ali and Gupta (1987). Turok (1987).
5 The former has recently Updated in Bouton & Jones. and Kiguel(l994).
----------------------------------------------------------------------------
--------------------------------
(2) affirms that low school attainment, political instability, poorly
developed financial systems, large black market exchange rate premia, large
government deficits, and inadequate infrastructure are associated with stow
growth,
(3) Finds that Africa's ethnic diversity tends to slow growth and reduce the
likelihood of adopting good policies, and
(4) identities a strong web of geographic connections: many policies in
country A are closely associated with growth in country A; policies in
neighboring country B are correlated with policies in country A; and country
A's growth rate is strongly correlated with neighboring country B's growth
rate, even after controlling for policies incolmtlyk
The relationship between particular policy indicators in one country
and growth in
its neighbors' economy suggests that there may be growth spillovers with
strategic policy implications. While requiring much additional work to
establish causal relationships, this
paper's results are consistent with the view that improving policies alone
boosts growth
substantially, but if neighboring countries act together, the growth effects
are much larger.
Specifically, the coefficients suggest that a policy change by a set of
neighbors will have an elTect on growth that hi 2.2 times larger than if a
single country had acted alone.
The cross-country regression methodology has numerous shortcomings
and should
not be the only method used to study growth or draw conclusions about
Africa.
6 Cross-country regressions do not establish the direction of
causality between growth and the policy and political indicators that we
study. We do not estimate structural models and the coefficients should
not be interpreted as elasticities. Although we sometimes use the
coefficient estimates to exemplify the strength of the association between
growth and policy indicators, these examples should be interpreted as
suggestive illustrations, not as exploitable elasticities. We view the
cross-countty regressions as examining the strength
----------------------------------------------------------------------------
--------------------------------
6 For a discussion of the analysis of the weakness with cross country
regressions see. Levine and Renelt(l991) and Levin and zervos(1993).
----------------------------------------------------------------------------
--------------------------------
of the partial correlation between economic growth and a variety of economic
and political indicators. As such, cross-country regressions offer
complementary information to the rigorous country studies mentioned above by
permitting a uniform statistical assessment of growth across a wide array of
countries.
II Using Cross-Country Regressions to Explain Growth
Since we are focusing on long-run growth, we attempt to abstract
from business de fluctuations and study economic performance over decades.
Specifically, the explanatory variable in our regressions is the average
annual growth rate of GDP per capita in the 1960s. 1970s. and 1980s for all
countries with data (excluding Gulf oil states). Thus, each coutry has three
observations, data permitting. We typically have193 obsavations.
A. Core Regression: Description
To explain long-run growth, we begin with a "core" regression that
includes a
fairly standard set of right-hand-side variables and then expand this set in
subsequent
sections. This subsection describes why we include each "core" variable.
In addition to different intercept terms for each decade, we include
dummy variables for Sub-Saharan africa and Latin America and the Caribbean
called AFRICA and LATINCA respectively.Barro (1991) found significant,
negative coefficients on both A.FRICA and LATINCA in cross-countty
regressions.
These dummy variables reflect the inability to explain the poor
performance of Africa and Latin America with variables designed to control
for politic, economic, and other measurable characteristics.7
Further, we include two Variables to control for initial income (at
the start of each decade) and thereby capture the convergence effect
highlighted by Barre and Sala-I-
Martin (1992). The economic reasons underlying this convergence effect are
based on the
assumption that - all else equal - lower income countries will enjoy a
higher marginal
productivity of capital. This should stimulate domestic investment by
residents and foreigners that will raise the capital/labor ratio and
generate output growth and higher
wages. However, Baumol et al. ( 1992). Easterly (1994), and others show that
this
convergence result is generally non-linear, first rising and then falling
with per capita
income. To capture the potential non-linear relationship between initial
income and future growth, we include two terms: the logarithm of GDP per
capita at the start of the decade (INCOME) and the square of the logarithm
of initial income at the start of each decade (INCOMESQ).
The core regression also includes a measure of human capital. We
use the
logarithm of the average educational attainment variable constructed by
Barr0 and Lee
(1993a), and call this variable SCHOOL. Countries with better educated
workers should have greater growth opportunities than countries with
citizens with less education. Also,
we attempt to control political instability by including a measure of
political assassinations, ASSASS, which Barre (199 1) found to be negatively
associated with growth Although not presented, we used other indicators of
political instability that did not alter the results.*
Finally, we include three policy indicators in the core regression.
We include a
measure of financial development, DEPTH, which equals liquid liabiities of
the financial system divided by GDP.9 For many countries the ratio equals
M2/GDP. King and Levine (1993b) show that DEPTH responds to financial sector
policies in prediotable ways, and King and Levine (1993a,b) show that DEPTH
is closely associated with long-run growth. Also, given the findings by
numerous authors, we include a measure of the black market exchange rate
premium, BLACK. Finally, we measure the fiscal stance of the country by
----------------------------------------------------------------------------
--------------------------------
8 For example, we used measures of civil liberties, the number revolutions
and coups, and the number of cassualties by war. Also, see Barro
(1994).
9 Liquid liabilitiss including demand and interest bearing liabilities of
banks and non banks On finance and economic development also see Collier
and Map (1989).
----------------------------------------------------------------------------
--------------------------------
including the central government surplus to GDP ratio, SURPLUS.10" We
experimented with including a measure of inflation and with including the
ratio of exports plus imports to GDP. Inflation and trade indicators,
however, typically did not enter significantly, nor did they alter the
following results 11.
B. Core Regression Results
Table 1 presents the core regressions. All of the variables are
significant at the
0.05 significance level and of the anticipated sign. Countries with
greater financial development, larger &al surpluses, and lower black market
exchange rate premia grew significantly faster than countries with more
shallow financial systems, large fiscal deficits, and &able black market
premia. The regression also indicates that political assassinations are
negatively correlated with long-run growth, while educational attainment is
positively linked to growth.
The coefficients on the catch-up variables, 0.096 on INCOME and
-0.007 on
INCOMESQ, imply that the catch-up effect will be weaker for very poor
countries and
strongest the middle-income countries. Specifically, the catch-up effect is
a concave
function of initial income. For the given parameter values, the catch-up
effect is strongest for countries with incomes of about 51,6OO.rs
Africa's average initial per capita income is below $1,600. Thus, the
regression indicates that Africa should enjoy a catch-up effect, but this
effect will, on average be less pronounced for Africa because of the
non-linear association between initial income and growth whereby very poor
countries enjoy less of a catch-up effect than countries with incomes of
around
$1,600.
----------------------------------------------------------------------------
-------------------------------
10 Negative relationship between deficits and growth has reported earlier
11 Trade or export shares are generally not significant explanatory
variables in cross country growth studies
12 to compute this take the derivativ of the core regression with respect to
INCOME
----------------------------------------------------------------------------
-------------------------------
The dummy variables for both Sub-Saharan African countries and Latin America
and Caribbean countries are significant and negative. These two regions of
the world grow significantly more slowly that predicted by the cross-country
growth regressions.
However, when we do a Chow test to see whether the coefficients of the core
regression are significantly different for only the sample of Sub-Saharan
African countries, we cannot.
reject the hypothesis that there are no differences. This implies that
the difficulty in
accounting for the tragedy of Africa does not lie in different sensitivities
to policy
variables. Nonetheless, although regression's R2 is a bit over 50 percent
and the coefficients have the expected signs, we are unable to account
adequately for the poor
growth performance of Africa and Latin America.
C. Assessing Africa's Performance
Using the core regression results presented in Table I, we now
decompose Africa's
performance and compare it to other regions of the world (following a
similar exercise by Barro and Lee (1993 b), which was also emulated for Mica
by Elbadawi and Ndulu (1994)). Table 2 gives average values of the variables
in the core regression for different groups of countries. Africa had
worse policy indicators than other regions of the world.
For example, financial depth in tica is less than half of financial depth in
East Asia and Pacific. Africa's black market premium is 50 percent larger
than the black market
premium in the rest of the developing country world, and, on average, Africa
has larger government deficits than non-African countries. Furthermore,
average school attainment is about 50 percent higher in other developing
countries. Thus, poor policy indicators and low human capital, as
measured by school attainment, link closely with growth in Africa.
One can formally decompose the core regression results by computing
that part of
the growth difference between Africa and other countries accounted for by
each of the tight-hand-side variables of the core regression. For example,
consider Africa versus non African countries.
. Subtracting Africa's growth rate from non-African country growth
rates the difference in growth rates is 2.3 percentage points.13 By
subtracting Africa's value for each explanatory variable from non-African
country values and multiplying this difference by the regression
coefficient, we can compute that part of the difference in growth rates
between non-African countries and African countries associated with by each
explanatory variable. The decomposition results are presented in Table 3,
The core regression attributes
1.5 of the 2.3 percentage point difference in growth rates between
non-African and
African countries to the Africa dummy variable. All of three policy
indicators (black
market premium, financial depth, budget surplus) combined account for about
0.9
percentage points of the 2.3 percentage point difference. Table 3
provides comparisons
between Africa and non-Africa, non-Africa developing countries, and East
Asian and pacific countries. The most remarkable feature of Table 3 is
how much of the difference is associated with the Africa dummy variable.
Since the Africa dummy variable really just measure our ignorance our
inability to explain Africs growth - this decomposition highlights that the
Variables commonly used in cross-country regressions do not account for much
of Africa's economic performance
Figure 2 provides an illustrative decomposition and comparison of the growth
performance of Africa versus East Asia, where policy differences are
greater. In 1960, Africa's GDP per capita was about $800 while East
Asia's was about $1500. By 1989,Africa's GDP per capita was still only about
$900, while East Asia's had grown to about $5,000. Figure 2 uses the
core regression to decompose the difference in GDP per capita
----------------------------------------
13 The difference between African and non Africanreal per capita GDP is
1.81% We adjust this figure to take account of the decade and Latin american
dummy variables and arrive at a difference of 2.3 % that must be accounted
for by political and other explanatory variables.
----------------------------------
between these two regions in 1989. About $850 of the $4,100 gap is due to
the original percentage gap in GDP per capita. Policies (financial depth,
black market premium, and
the government surplus) account for $1750 of the large gap that emerged over
the 1960-89 period. Initial income and schooling in each decade are
associated with S450 of the gap
(the disadvantage of lower African schooling more than offsets the advantage
of lower initial income in Africa). About $1,050 of the $4,100 gap between
East Asia and Africa
remains unexplained. It is to this gap that we now turn. We attempt to
reduce the size of
this unexplained gap by introducing non-traditional explanatory variables
into the core regression.
III Two Other Explanations For African Growth
ln this section, we attempt to account more fully for Africa's poor
performance.
Although we examined the effects of institutions 14, wars, 15 terms of
trade.16 infrastructure, and ethnic conflict, we concentrate on the links
between growth and both infrastructure and ethnic conflict due to data
availability. Furthermore, since data are scarce and the SURPLUS variable
reduces the sample considerably, we consider the effects of incorporating
indicators for infrastructure and ethnic conflict with and without the
government SURPLUS variable in the core regression.
-------------------------------------------------------------
14-Many studies of Africa cite the hostile environmental environment for
private business as a factor in the growth outcome.
15 Over the past 3 decades , 13 of the 20 worst military conflicts have been
in Africa. However this variable is not significant in the pool growth
regressions. This may be because the most disruptve wars interrupt data
collection. We do not have complete data on 10 of the 20 worst war
experiencing countries
16 Africa's trade shocks were no worse than other LDC's which have confirmed
in our data
------------------------------------------------------
A Infrastructure
Many studies of Africa cite the poor state of infrastructure.
Infrastructure
variables have the same rationale for inclusion in the growth regression as
human capital
variables: they raise the marginal product of private investment in physical
capital, and thus the growth potential. An influential study by Aschauer
(1988) claimed that infrastructure had large effects on US productivity
growth; Canning and Fay (1993) and Easterly and Rebel0 ( 1993) have similar
findings for the cross-country sample. 17 Easterly and Rebel0 used
consolidated public sector investment in transport and communications; these
data are available for too few African countries to be of use here.
Canning and Fay (1993) present data on physical measures of
ittf+asttucture,
such as kilometers of roads and railways per worker, electricity-generating
capacity per
worker, and telephones per worker. Table 4 shows the averages of the
1960, 1970, and 1980 values of these infrastructure variables for Africa and
the rest of the sample. We insert the Canning and Fay variables into our
core regression and the results are presented in Table 5. The initial
stock of roads/railways and initial electricity generation are not
significantlyy correlated with future economic growth. r*
We do, however, find a strong link between growth and telephones per
worker
as shown in regressions (2) and (5) in table 5. The coefficient on
telephones per worker indicates that it is associated with perhaps 1
percentage point of Africa's 2.3 percentage point lower growth relative to
the rest of the sample. We are dubious that the direct effect of phones is
ready this large, but it may be a good indicator of the poor state of
infrastructure in general. To use the East Asia benchmark once again,
Hong Kong had more telephones in 1960 than Nigeria, even though Nigeria's
population was 37 times larger. By 1980, Hong Kong had more telephones than
all of Sub-Saharatt Africa.
----------------------------------------------------------------------------
-----
17 this study used only an indirect measure of infrastructure investment
18Fixed effects are inappropriate here
----------------------------------------------------------------------------
-------
The data shown here may even understate the extent of the infrastructure gap
between Africa and the rest of the world, as they do not correct for
quality of
infrastructure. For example, Chad is shown as having 15 thousand telephones,
but 91 percent of all local phones calls are unsuccessful.Uganda has two
thousand kilometers of
paved roads, but only 10 percent of them are in good condition. 19
Although infrastructure seems to matter, the Africa dummy remains
significant in
the regression including telephones. Africa grows more slowly than
accounted for by the tight-hand-side variables.
B Ethnic Diversity
Wars, institutional weakness, and even bad policies may reflect a
more
fundamental characteristic of African societies, great ethnic diversity.
High ethnic diversity may lead to increased civil strife, political
instability, and destructive competitions for rents by ethnic factions.
Shleifer and Vishny (1993) shows how
corruption is most damaging when different groups are competing for payoffs.
It may be more difficult to achieve a consensus for good policies in a
polarized environment as indicated by Alesina and Drazen (1991), Alesina and
Rodrik (1994), Alesina and Tabellini (1989). and Ale&a and Perotti (1994).
We suspect that ethnically fragmented societies are prone to competitive
rent-seeking by the different ethnic groups and have difficulty agreeing on
public goods like infrastructure, education, and good policies. Furthermore,
ethnic diversity may favor policies destructive to long-run growth like
financial repression and overvalued exchange rates if such policies create
rents for the group in power at the expense of other groups.
To examine the effects of ethnic diversity, we use a variable
constructed by Mauro (1993) based on data orightally collected by an
institute in the Soviet Union in the 1960s.
-----------------------
19 Sante: World Bank (1994b). World Development Indicators, Table 32. These
data are not available for earlier years, so we cannot insert them into the
regression.
The variable, ETHNIC, measures the probability that two randomly selected
individuals in a country will belong to different ethnolinguistic groups.
ETHNIC will increase with the number of ethno linguistic groups and wig
increase the more equal is the size of the
groups. Canning and Fay (1993) use a related measure based on the same
original data:
the proportion of the population belonging to the largest ethno linguistic
group and find tit growth is positively related to size of the largest
ethnic group.
Table 6 shows the most and the least ethnically diverse societies in
the world in
1960 in Mauro's data Fourteen out of the fifteen most ethnically diverse
societies in the world are in Africa; three of the East Asian fast growers
are among the most ethnically
homogeneous.
Table 5 regressions (3),(4), (6), and (7) present evidence on the
empirical
association between ethnic diversity and economic growth. ETHNIC is
significantly
correlated with growth, controlling for other factors. The coefficient on
the ethnic
diversity variable implies that it accounts for 0.8 percentage points of the
2.3 percentage point gap between Africa's growth and the rest of the sample,
i.e., Africa's greater than
a- ethnic diversity accounts for about 35% of its growth differential with
the rest of the world. While ethnic diversity is negatively associated with
growth and Sub-Saharan Africa has great ethnic diversity, the Sub-Saharan
Africa dummy variable tends to remain significant in the Table 5 regressions
that include the ethnic diversity variable. we still
can not account for Sub-Saharan Africa's slow growth.
Importantly, the ethnic diversity variable has a high correlation
with the other
right-hand-side variables. Table 7 shows that ethnic diversity is negatively
correlated with schooling attainment, with financial depth, and with all
three in6astructure indicators: roads, telephones, and electricity.
It is positively correlated with the black market premium.
quantitatively, the data imply, as noted above, that ethnic diversity
independently accounts for about 35% of Africa's growth differential with
the rest of the world, but when the effects of ethnic diversity on policies
is also considered this figure rises to 45% of Africa's growth differential.
Thus, ethnic diversity slows growth directly d retards growth
indirectly by making
the adoption of good policies more difficult.
IV. Troubles with the Neighbors
. The frequent use in the literature of a dummy variable for
Africa indicates that the poor growth performance of Africa is usually
thought to be a fixed effect (e.g. Barre, 1991). What is striking in
the data is the regional concentration of both failure (in Africa) and
success (in East Asia), as well as the variation across decades (Africa had
done bettter in the 1960s; Latin America had a synchronized crisis in the
1980s) 20.
Recently, an insightful pair of papers has suggested that there are general
spillovers across borders from unfavorable characteristics of one's
neighbors, like low investment or high political
instability, to one's own growth performance (Chua, 1993, Aries and Chua,
1993). These authors report that the Africa dummy variable becomes
statistically insignificant when controlling for spillovers from one's
neighbors.
A. Estimating Neighbor SpiIlovers
This paper extends the work of these papers in two ways.
Firsti we change the
Chua (1993) definition of neighbor effects by weighing each neighbor by the
size of its total GDP, as opposed to Chua's equal weights. It seems
plausible that Mexico would be
affected more by the US than by Belize, and Cameroon would be affected more
by gigantic Nigeria than by tiny EquatorialGuinea.21 Second, instead of
putting the averages
of the neighbors' right-hand side variables into the growth regression, we
put the average
----------------------------------------------------------------------------
-
20 It is eaey to forget that a number of African countries were considered
success stories well into the
1970s (Cote d'lvoire and Kenya. for example). In fact, la every decade.
there were some African countries with respectable per capita growth rates
-even In the disastrous 1980's. 3 African countries grew in excess of 3
percent per capita. But few African countries sustained healthy growth over
time. hence the low average growth for the continent
. 21 We explore further different weighting schemes for spillover effects
from other countries. We find that weighting by distance (which was
unsuccessful in an earlier paper by De Long and Summers(1992) performs
poorly in identifying county spillover effects
----------------------------------------------------------------------------
-
.of the neighbors' growth rate itself into the regression. This
allows us to test for direct
contagion effects of growth successes and failures. Because there is
simultaneity in this case -- you affect your neighbor and your neighbor
affects you back -- we instrument for the neighbors' growth rate with the
neighbors' regressors from the core regression. We will then perform a test
of the over identifying restrictions that the neighbors right-hand-side
variables have no direct effect on growth (i.e. other than through the
growth contagion channel), which will allow US to test our contagion
hypothesis against the policy spillovers hypothesis.
Table 8 shows two-stage least squares with the neighbors' weighted
average
growth rate included in the core regression that excludes the government
surplus. We use the neighbors' weighted average right-hand side
variables as instruments. Each country's neighbors' growth rate has a
surprisingly large and statistically significant effect on each countries
own growth: one percentage point more growth by the neighbors in a given
decade translates into higher Own growth of .55% percentage points.
While the Latin American dummy variable remains uncomfortably significant,
the African dummy becomes insignificant once the neighbors' growth rate is
included.sa
We also test whether a country A's neighbors policies, educational
attainment,
initial income, and political stability independently affect A's growth
after allowingg for its neighbors growth rates. A test of the over
identifying restrictions that all of the
neighbor's right-hand side variables have zero direct effect on the
countries own growth rate once its neighbors' growth is considered fails to
reject this set of restrictions The test statistic is TR2 where T is the
number of observations and the R3 is from the regression of the residuals in
the regression shown in Table 8 on the set of ah exogenous variables,
including the neighbors' right-hand side variables. The test statistic,
which is distributed
as x2 with 5 degrees of freedom (six excluded exogenous variables -- the
neighbors' right-hand-side variables -- minus one included endogenous
variable), has a value of 8.35 and is not significant at the 5 percent level
in the regression excluding the government surplus. In the regression
including SURPLUS, the test statistic has 6 degrees of Freedom
.and has a value of 10.65, still not significant at the 5 percent level.
Thus, the data.do not
reject our econometric specification of using two-stage least squares with
the neighbors weighted average growth rate.
B. Where Do Neighbor Spillovers Come From?
Unfortunately, we can only speculate about where neighbor spillovers
come from.
For example, if adapting a technology to a local environment is risky and
involves fixed
costs, then a direct foreign investor who has had success in one country may
find it easier and more attractive to move next door to a neighboring
country. Thus, success in one
country could spillover to neighboring countries. In addition to
potentially lowering the
risk and cost of foreign investment, neighbor success may have demonstration
effects. Governments that attain high growth with a given set of policies
provide a valuable model of the efficacy of such policies to the government
and citizenry of neighboring countries"
We have empirically examined one channel. International trade
does not appear to
be a very plausible mechanism for spillovers. African countries do not
trade much with each other. Moreover, when we construct a spillover variable
using trade weights, the international trade spillover variable performs
very poorly.
-------------------------------------------
23 The growth literature of course features much speculation and (a little)
evidence about externalities and strategic complementarities, which finds
external focus of human or physical capital across industries (Caballero,
and Lyons, 1989, 1990). and within cities (Raoch, 1992). Strategic
complementaritie have also been suggested as a factor that explains booms
and busts in business cycles (see Hall, 1991, Haltiwaagu. 1993).
Borjas (1994) and Case and Katz (1991) find contagion from
individuals' neighbors in socioeconomic outcomes in American cities. Calvo
and Reinhart (1995) show how thee is contagion in capital flows from large
Latin American countries to their small neighbors.
-------------------------------------------
What about the transmission of growth failures across borders? Governments
do
not necessarily maximize growth; they may maximize rent-seeking
opportunities. Even policies that are bad for growth could be imitated by
neighbors ifthey are demonstrated to be good for creating rent-seeking
opportunities or some other non-growth objective that is desired by policy
-making elite's.
We find that our observable policy indicators and the other
right-hand side
variables hm Table 8 are indeed highly correlated across neighbors (Table
9). This give a hint that unobservable government or private sector behavior
contained in the residual my be correlated as well.
We acknowledge that the replacement of the Africa dummy by a growth
spillover
effect really only changes the kind of mystery. More research is needed to
go inside the black box. Our results suggest that research on growth
interactions between countries would be another fruitful area to add to the
study of countries' individual characteristics.
c. Neighbour multipliers
The implications of a growth contagion effect are very different
from an Africa
dummy effect. If we presume a particular causal direction for illustrative
purposes, the contagion effect says that Africa's lagging growth relative to
policy variables wig ww if a critical mass of countries improve their
policies. The Africa dummy effect
said that Africa's growth would always be worse for a given set of
policies.The good
news about the contagion effect - if one assumes that causality runs from
policies to
growth - is that the negative contagion effect of the last 30 years could be
changed to a
contagion effect in the next 30 years: a huge policy change in unison would
have a multiplier etkt on the countries in the region that is even larger
than the strong, direct
effect ofa country's policies on its own growth rate.
If a country reforms alone, there will be a small spillover to its
neighbor's growth
me, which in turn spills back into the country's own growth rate. Given that
most
countries have 4 or more neighbors, these spillover effects are fairly small
as shown in the
Appendix. From our estimates, the median country changing policies in
isolation has a neighbor multiplier of 1.04 1; that is, the total effect of
one's policies on one's own growth rate taking into account neighbor
feedback is only 4 percent larger than the direct effect of one's own
policies on one's own growth.
.
However, if ail countries act together, the neighbor multiplier is much
larger. This
is because all of the home country's neighbors are acting together to
increase their own
growth, which increases the home country's growth by a large amount in
addition to the direct effect of the home country's policy change. If
we suppose that policy changes are
identical for a closed set of neighbors, the multiplier will be [l/( I-b)],
or 2.2 where b is the estimated coefficient on one's weighted average of
neighbor growth rates, estimated by us at .55 That is, a set of neighbors
adopting a set of policy changes that would have raised growth by 1.04
percentage points if they had each acted alone will see growth increase by
2.2 percentage points if they act together. This also works in the other
direction: with a set of neighbors all simultaneously adopting bad policies
like exchange rate controls leading to a high black market premium, the
negative effect on all of them would be magnified.
It is important to emphasize that our results do not imply that
countries would be better off free-riding on their neighbors' good policies
rather than making their own policy changes. The typical free rider
problem arises because one's own actions have only a
negligible effect on the benefit one obtains; here, one's own policies still
have a stronger effect on one's own growth than they do on the neighbor's
growth. Nor is there any
incentive to wait for the other country to move first, since with our
additive specification the marhinal growth benefit of changes in one's own
policies is the same regardless of whether the neighbors have good or bad
policies. These results do suggest that acting in unison has magnified
effects for good or evil.
V. Conclusions
This paper sheds additional light on accounting for long-run growth
across all
countries with a particular emphasis on understanding Africa's growth
tragedy. In short, we find that poor growth is strongly associated
with (I) low schooling, (2) political
instability, (3) under-developed financial systems, (4) distorted foreign
exchange market, as measured by the black market premium, (5) high
government deficits, (6) low
infrastructure, (7) ethnic fractionalization, (8) spillovers from neighbors
that
magnify (1) - (7).
The two most novel features of our results are our findings on
ethnic diversity and
contagion. Both findings require further investigation into the mechanisms
at work. What are the mechanisms by which ethnic diversity results in high
black market premia and low
spending on public goods? What other mechanisms explain the link from ethnic
diversity to growth? what cases neighboring countries to inmate each
others' policies? why is there a spillover to your growth from your
neighbor's growth? The findings on the role of
etb& diversity and contagion in Africa point towards interesting directions
in further research on both the fundamental determinants of bad policies and
the interactions
between neighboring country policies and growth performance.
Table 6: Ethnolinguistic Fractionalization Index (ETHNIC) 66 Countries, 1960
Countrv ETHNIC countly ETHNIC
I5 MOST FraclionaIized: 15 Least Fractionalized
Tanzania 93 Korea 0
Uganda 90 Haiti 1
Zaire 90 Japan 1
India 89 Portugal 1
south Africa 89 Hong Kong 2
Cameroon 88 Yemen 2
Nigeria 87 Germany 3
Ivory coast 86 Burundi 4
CAR 83 Dominican Rep 4
Kenya 83 Egypt 4
Liberia 83 Ireland 4
Zambia 82 Italy 4
Angola 78 Norway 4
Mali 78 Jamaica 5
Sierra Leone 77 Jordan 5
Note: ETHNIC measures probability that two randomly selected persons from a
given-country will not belong to the same ethnolinguistic group.
The more groups there are, higher the ETHNIC. The more equally distributed
the groups, the higher ETHNIC.
Source: Taylor and Hudson, World Handbook of Political and Social Indicators
( 1972).
DECLARATION & DISCLAIMER
==========
CTRL is a discussion and informational exchange list. Proselyzting propagandic
screeds are not allowed. Substance—not soapboxing! These are sordid matters
and 'conspiracy theory', with its many half-truths, misdirections and outright
frauds is used politically by different groups with major and minor effects
spread throughout the spectrum of time and thought. That being said, CTRL
gives no endorsement to the validity of posts, and always suggests to readers;
be wary of what you read. CTRL gives no credeence to Holocaust denial and
nazi's need not apply.
Let us please be civil and as always, Caveat Lector.
========================================================================
Archives Available at:
http://home.ease.lsoft.com/archives/CTRL.html
http:[EMAIL PROTECTED]/
========================================================================
To subscribe to Conspiracy Theory Research List[CTRL] send email:
SUBSCRIBE CTRL [to:] [EMAIL PROTECTED]
To UNsubscribe to Conspiracy Theory Research List[CTRL] send email:
SIGNOFF CTRL [to:] [EMAIL PROTECTED]
Om
