The impact of remittances on economic growth and development in Africa.
Fayissa, Bichaka ; Nsiah, Christian
I. Introduction
For more than half a century, there have been heated debates on the
sources of economic growth of developing economies (Lewis, 1954: Solow,
1956: Chenery and Strout, 1966: Denison, 1967; Myrdal, 1968:
Harris-Todaro, 1970: Schultz, 1979: Fields, 1980; Romer, 1986: Lucas,
1988; Barro, 1991: and Easterly, 2001). The perceived sources of
economic growth have ranged from surplus labor to physical capital
investment and technological change, foreign aid, foreign direct
investment (FDI), investment in human capital, increasing returns from
investment in new ideas and research and development. Other researchers
such as Owens (1987), Sen (1990), and Kaufmann, Kray, and Mastruzzi
(2006) have also focused on the impact of institutional factors such as
the role of political freedom, political instability, voice and
accountability on economic growth and development.
For many developing countries, however, remittances represent a
major part of international capital flows, surpassing foreign direct
investment (FDI), export revenues, and foreign aid (Giuliano and
Ruiz-Arranz, 2005). A recent World Bank (2006) study suggests that
recorded remittances have grown faster than foreign direct investment,
or official development assistance (See Figure 1 below).
Consequently, recent financial flows into developing countries in
the form of remittances are receiving increased attention because of
their size and impact on the economies of recipient countries. According
to Gupta, et al. (2007), estimated official remittances reached a total
of $188 billion in 2005 which is twice the amount of development
assistance received by emerging economies. Informal and unreported
remittances could even easily add another $94 billion to the above
figure. Between 2000 and 2005, remittances to Sub-Sahara Africa (SSA)
rose 55 percent to nearly $7 billion in comparison to 81 percent
increase for all developing countries as a group. In terms of the ratio
of remittances to GDP, the top ten countries are: Lesotho (38%), Cape
Verde, Guinea-Bissau, Togo, Senegal, Sudan, Swaziland, Comoros, Mali,
and Nigeria (about 5%) (See Figure 2 below).
Despite the increasing importance of remittances in total
international capital flows, the relationship between remittances and
growth, especially in SSA, has not been adequately studied. This study
explores the aggregate impact of remittances on the economic growth of
African countries within the conventional neoclassical growth model,
using panel data spanning from 1980 to 2004 for 36 African countries.
(1)
[FIGURE 2 OMITTED]
We also account for the traditional sources of economic growth
using estimation methods that are based on simple fixed-effects and
random-effects models which allow us to account for the heterogeneity of
African economies and the differences in the traditional sectors'
contributions to the economic growth of African economies. The
contribution of our work to the empirical literature is that we provide
evidence of the extent to which the remittances can spur economic growth
while accounting for the conventional sources of economic growth using
standard theory. Our empirical results show that remittances have
statistically significant contribution to both the current level of
gross domestic product and the economic growth rate of African countries
as do investments in physical and human capital. Our findings suggest
that remittances play a role in the economic growth of African economies
by augmenting the dwindling external sources of capital in the form of
foreign aid, foreign direct investment, and/or private investments to
Africa.
The rest of the paper is organized as follows. Section II provides
a review of selected literature. In section III, we specify a
conventional neoclassical growth model which incorporates remittances as
one of the sources of growth. Section IV presents estimation results for
both the fixed and random effects regressions accounting for both the
country and time effects and the Arellano-Bond (2002) dynamic panel data
estimates for reflecting both the dynamic nature of the data and
endogeneity of some of the conventional growth sources. The last section
summarizes the results, draws conclusions, and makes some policy
recommendations for promoting remittances as a growth and development
strategy.
II. A Review of Selected Literature
Based on household survey data from various African countries, few
empirical studies have investigated the role of remittances in reducing
poverty (Lucas and Stark, 1985; Adams, 1991; Sander, 2004; Azam and
Gubert, 2005; Adams, 2006). The macroeconomic impacts of remittances,
however, may have been overlooked for at least two reasons. One
theoretical strand suggests that workers' remittances are mainly
used for consumption purposes and, hence, have minimal impacts on
productive investment that would spur long-run economic growth (Giuliano
and Ruiz-Arranz, 2006). In other words, remittances are widely viewed as
compensatory transfers between family members who lost skilled workers
due to migration with limited or no discernable impact of the national
economy.
Nevertheless, Stahl and Arnold (1986) argue that the use of
remittances for consumption may have a positive effect on growth because
of their possible multiplier effect. Moreover, remittances respond to
investment opportunities in the home country as much as to charitable or
insurance motives. Many migrants invest their savings in small
businesses, real estate or other assets in their own country because
they know local markets better than in their host countries, or probably
expecting to return in the future. The second reason why empirical
macroeconomic impacts of remittances on economic growth have been
limited in the literature has to do with the lack of macro-level data.
Partly, the availability of macro-level data problem arises from the
informality of such transfer of capital from the host to the receiving
country since money is typically sent through family members and other
friends unlike the formal transfer schemes (e.g. Western Union, Money
Gram) which enter the national accounts of the receiving countries
(Loser, et al., 2006).
There are two possible motives why migrants might remit money to
home country. The first motive may be for altruistic reason, i.e. when
the economic condition in the migrant's home country is bad, money
is sent as compensatory transfers between family members and they are
motivated by welfare and insurance considerations (Lueth and
Ruiz-Arranz, 2006). The second motive of remittances is to take
advantage of investment opportunities when the economic condition in the
home country is healthy. In about two-thirds of developing countries,
remittances are mostly profit-driven and increase when economic
conditions improve back home (Giuliano and Ruiz-Arranz, 2006). Such
external monetary flows are particularly used for investment where the
financial sector does not meet the credit needs of local entrepreneurs
(Ruiz-Arranz, M., 2006). Thus, we cannot, a priori, predict the
direction of the impact of remittances ([REM.sub.it]) on the economic
growth of African economies based on the above discussions.
III. An Empirical Model of Economic Growth with Remittances
In the economic growth literature, researchers have been interested
in the rate at which countries close the gap between their current
positions and their desired long-run growth path. To determine the
responsiveness of income growth rate to remittances and the traditional
sources of economic growth such as investment in physical and human
capital, an external source of capital represented by foreign aid,
openness of the economy as measured by the ratio of the sum of imports
and exports to the GDP, often proxied by terms of trade, foreign direct
investment, a measure of an institutional factor often represented by
the economic and political freedom index, and the impact of the initial
per capita income, we first specify a simple double log-linear
Cobb-Douglass production function as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)
Where in [GDP.sub.it] is the natural log of real GDP per capita and
In [REM.sub.it] is log of remittances per capita in US$; In [GCF.sub.it]
is the log of gross fixed capital formation as a percent of real GDP
used as a proxy for investment in physical capital (2). In [ENR.sub.it]
is log of secondary and tertiary enrollment rates used as measure of
investment in human capital which is expected to have a positive effect
on the economic growth of developing countries (Schultz, 1980; Romer,
1986; Lucas, 1988; and Barro, 1990).
The next three variables are used to capture the impact of external
sources of capital on economic growth. Proponents of aid argue that
overseas capital flows are necessary for the economic growth of
developing countries (Chenery and Strout, 1966; Papenek, 1973; Levy,
1987; and Islam, 1992). On the other hand, opponents of foreign aid
argue that it has a negative effect on domestic savings and economic
growth in less developed countries (see, Heller, 1975 and Boone, 1994).
The log of foreign aid (ln [AID.sub.it]) denotes the sum of official
development assistance. The log of other official flows (ln
[OFI.sub.it]) is used to capture the impact of foreign portfolio
investments and other foreign financial flows except foreign direct
investment. In [FDI.sub.it] is the log of foreign direct investment used
to capture the effect of external sources of capital on growth; In
[TOT.sub.it] is the log of the terms of trade for each country under
consideration, measured by the ratio of the export to import price
indices (See, Table 1 for detailed definitions) to capture the impact of
trade, or openness of the economy on economic growth.
In [EFI.sub.it], is log of the economic freedom index. Owen (1987)
and Sen (1999) argue that freedom (political, economic, social,
transparency and security) is a necessary condition for economic growth
and development. Thus, we use the log of the economic freedom index (ln
[EFI.sub.it]) to capture the effect of this institutional factor. The
EFI is designed to measure the consistency of a nation's
institutions and policies with economic freedom in the spirit of La
Porta et al. (1997). (3) It ranges from 1 to 10, with higher numbers
denoting more freedom. Hence, we expect a positive coefficient for the
economic freedom index.
The impact of the initial level of GDP per capita ([INY.sub.it]) on
economic growth has been controversial at best. On the one hand,
Blomstrom (1996) and Casseli, et al. (1996) have found a positive
relationship between growth rate and the initial level of GDP per capita
growth rate through its positive impact on capital formation. On the
other hand, Barro (1997) has found a negative relationship between the
initial GDP per capita and the GDP growth rate in a cross-country
empirical study which he interprets to imply a case of conditional
convergence. Consequently, we cannot, a priori, predict the sign of the
initial level of GDP per capita ([INY.sub.it]) coefficient. TFP is a
measure of total factor productivity. It is measured by the log of gross
domestic product divided by the logs of the product of gross capital
formation and labor force. The more productive an economy is the more
per capita income they will observe, thus we expect a positive
relationship between productivity and per capita income.
To estimate the parameters corresponding to variables of interest
from the data under consideration, we employ a panel data estimation, an
empirical exposition of which is provided in equation (2) below.
[Y.sub.it] = [[lambda].sub.i] + [[gamma].sub.t] + ([X.sub.it])[phi]
+ [[psi].sub.it] (2)
where [Y.sub.it] is the natural logarithm of real GDP per capita in
country i at year t, and [X.sub.it] is a vector of the explanatory
variables (remittances, investment in physical and human capital,
foreign aid, openness, foreign direct investment, economic freedom
index, and initial income) for country i = 1, 2..., m and at time t = 1,
2, ..., T, [phi] a scalar vector of parameters of [[beta].sub.1]....,
[[beta].sub.7]; [[psi.sub.it] is a classical stochastic disturbance term
with E[[[psi].sub.it]] = 0 and var [[[psi].sub.it]] =
[[sigma].sub.[??].sup.2], [[lambda].sub.i] and [[gamma].sub.t] are
country and time specific effects, respectively. Instead of a priori
decision on the behavior of [[lambda].sub.i] + [[gamma].sub.t] different
types of assumptions are separately imposed on the model and the one
that gives robust estimates is chosen.
If we assume the "country specific" effects to be
constant across countries and the "time specific" effects are
not present [i.e. [[lambda].sub.i] = [[gamma].sub.t] and [[gamma].sub.t]
= 0)], then model (2) is estimated by the Ordinary Least Squares (OLS)
method, or restricted OLS method. The second estimation technique
assumes that the country specific effects are constant, but not equal
(i.e. [[lambda].sub.i] = [[gamma].sub.t] and [[gamma].sub.t] = 0 which
yields a one-way fixed effects model). The third assumption is a
situation where the country effects are not constants, but rather are
disturbances: the time effects are not present [i.e. [[lambda].sub.i] =
[[gamma].sub.t] + [w.sub.i] and [[gamma].sub.t] = 0] where E[[w.sub.i]]
= 0 and var[[w.sub.i]] = [[sigma].sub.w.sup.2] and cov[[psi].sub.i],
[w.sub.i]] = 0. In this case, model (2) is estimated by the generalized
least squares (GLS) which yields random-effects model.
Given that some of the traditional factors that explain growth are
either pre-determined, or endogenous, or both, and current period growth
could depend on its values in the past, a dynamic variant of the fixed
and random effects provided in Equation (2) above, known as the
Arellano-Bond estimation (1991) is specified as follows:
[DELTA][Y.sub.it] = [[alpha].sup.'][DELTA][Y.sub.it-1] +
[[beta].sup.][DELTA][X.sub.it-1] + [[gamma].sup.'][Z.sub.it] +
[v.sub.i] + [[epsilon].sub.it] (3)
Where [DELTA][Y.sub.it] is first difference of the natural log of
per capita income growth in country i during time t: [DELTA][Y.sub.it-1]
is lagged difference of the dependent variable, [DELTA][X.sub.it-1] is a
vector of lagged level and differenced predetermined and endogenous
variables, [Z.sub.it], is a vector of exogenous variables, and [alpha],
[beta], and [gamma] are parameters to be estimated, [v.su.i] and
[[epsilon].sub. it] are assumed to be independent over all time periods
in country i. The term [v.sub.i] represents "country specific"
effects which are independently and identically distributed over the
countries while [[epsilon].sub.it] noise stochastic disturbance term and
is also assumed to be independently distributed. We estimate the
coefficients of the variables using the Arellano-Bond (1991) Generalized
Method of Moments (GMM) estimator to evaluate the joint effects of
remittances and the other explanatory variables on economic growth in
African countries while controlling for the potential bias due to the
endogeneity of some of the regressors including the lagged dependent
variable.
All data, except for the economic freedom index (which is taken
from the Fraser Institute's Economic Freedom of the World Index),
and the foreign financial flow data (which are taken from the UNCTAD
Handbook of Statistics) are from the World Bank Development Indicators
(WDI, 2006) CD. The definitions and descriptive statistics of each
variable included in the model are provided in Tables 1 and 2,
respectively.
IV. Empirical Results and Interpretations
Several versions of equation 2 are tested in order to obtain a
model which yields robust results and best fits the data. Accordingly,
columns 1 and 2 of Table 3 present the estimation results of a quasi
fixed-effects panel with heteroskedasticity corrected standard errors,
whereas column 3 and 4 present the estimation results for the random
effects model with bootstrap standard errors. The correction for
heteroskedasticity and the presence of the initial income converts the
pooled regression with heteroskedasticity corrected standard errors into
a quasi fixed-effects model. Apart from the magnitude of the
coefficients, the results reported in models 1 and 2 are comparable.
Since the composition of countries receiving remittances in our sample
consists of those which may be suspected to be outliers such as Lesotho
and Cape Verde, we report regression results in columns 2 and 4 of Table
3 without these outliers for the quasi-fixed and random effect models,
respectively. While the results are found to be similar, the magnitude
of the impact of remittances on per capita income appears to improve.
A comparison of the consistent quasi fixed-effects model with the
efficient random-effects model using the Hausman specification test,
rejects the random effects estimates at p < 0.05 in favor of the
quasi fixed effects estimates. We thus base the discussion of our
findings on the more robust quasi fixed-effects results reported in
column 1 of Table 3. Broadly, the results reveal the expected
relationship between the GDP per capita income ([GDP.sub.it] and the
explanatory variables i.e., the variables representing the sources of
growth have the expected signs according to the a priori predictions.
All the coefficients represent elasticities since we estimated a
double-logarithmic model.
The results from our model of choice indicate that the remittance
variable has a positive and statistically significant effect on the GDP
per capita (at p < .01) of African countries. Accordingly, we find
that a 10 percent increase in the remittances of a typical African
economy would result in about 0.4 percent increase in the average per
capita income. Similarly, a 10 percent increase in investment in human
capital (ENR) increases GDP per capita by 1.1 percent. Consistent with
the findings of Solow (1956), Barro (1990), and Temple (1999), we also
find that investment in physical capital (GCF) measured by the gross
fixed capital formation as a percent of GDP has a positive and
statistically significant impact on the real per capita GDP i.e., we
observe that a 10 percent increase in investment in the physical capital
will lead to about 1.6 percent increase in the GDP per capita, by far
the main variable which spurs economic growth.
Our results also indicate that foreign aid (AID) has a negative and
statistically significant effect on economic growth, confirming the
position of the opponents of aid (Heller, 1975; Boone, 1994). The
results also show that the other financial flows (OFI), foreign direct
investment (FDI), the terms of trade (TOT), and the total factor
productivity measure (TFP) have a positive, but not significant effect
on the economic growth of African countries in our sample.
On the other hand, the institutional variable (EFI) used to capture
the effect of economic freedom shows that good governance promotes the
economic growth performances of African economies. Consistent with
arguments made by Sen (1990), Owen (1987), and La Porta et al. (1997),
our estimates indicate that a 10 percentage increase the economic
freedom index leads to about 1.55 percent increase in per capita income.
In their celebrated article, "Africa's Growth Tragedy:
Policies and Ethnic Divisions," Easterly and Levine (1997) have
empirically demonstrated that economic growth is affected by quality of
governance (ethnic divisions, political instability), schooling, state
of the financial system, exchange markets distortions, high government
deficits, and insufficient infrastructure. Finally, we find that the
coefficient of the initial per capita income (GDPPC 1980) has a
positively and statistically significant effect on the current level of
economic growth of countries. In fact, a coefficient value of 0.9 for
the initial GDP per capita (INY) implies that a 1 percent increase in
INY increases the current GDP per capita by 0.9 percent. This indicates
a case of non-convergence, this is to say, the countries with higher
per-capita income in 1980 are the ones experiencing higher growth.
While results based on the fixed and random effects models in which
we simultaneously account for the heterogeneity and time to time
fluctuations in the economic performance of African economies are
appealing, we note that some of the explanatory variables of growth are
endogenous, thus confounding the results. For example, while FDI and
investment in human capital (ENR) have often been credited for their
role in the economic growth of a country, there is also ample evidence
(Hansen and Rand, 2006; de Mello, 1999) that the level GDP and its
growth rate have feedback effects on the amount of FDI a country
receives and the rate of investment in human capital formation. Given
that we are mainly interested in analyzing the effect of remittances on
African economic growth while accounting for the traditional growth
explanatory factors that are either pre-determined (e.g., schooling) or
endogenous (e.g., FDI), or both, we employ the Arellano-Bond dynamic
panel General Method of Moments (GMM) estimator to obtain robust
estimates by using levels lagged one period to serve as instruments for
the endogenous variables. The Arellano-Bond dynamic GMM estimates are
reported in Table 4.
In our case, the Sargan test as treated in the Arellano and Bond
(1991) fails to reject the null hypothesis that the over-identifying
restrictions are valid while the Arellano-Bond test rejects the null
hypothesis of no-first autocorrelation in the differenced residuals
AR(1), and accepts the null hypothesis of no second order
autocorrelation in the differenced residuals. Consequently, the
estimated coefficients reflect the true (efficient and unbiased)
relationship between growth in African per capita GDP and remittances
(our variable of interests) and the traditional growth determinants that
are either pre-determined, or endogenous, or both.
Based on the results from our model, we observe that the
coefficients of the lagged values of GDP per capita (PCI) and changes in
remittances (REM) have a significant and positive impact on the growth
rate of African GDP per capita. Accordingly, a 10 percent increase in
remittances would lead to a 0.05 percent growth in the GDP pre capita of
African economies. Accounting for the endogenous nature of the
traditional growth explaining factors, we find that while foreign direct
investment (FDI), the terms of trade (TOT), and the institutional
variable proxied by the political rights index (EFI) were not
significant, investment in physical capital (GCF), and the lag of human
capital (ENR), have significant growth enhancing roles.
V. Conclusion
The main goal of this study is to investigate the effect of
remittances relative to the other external sources of capital such as
foreign aid and foreign direct investment on the economic growth and
development of African countries. The results show that remittances do
positively impact the economic growth of African countries. We have
found that a 10 percent increase in remittances lead to a 0.4 percent
increase in the GDP per capita income.
According to Gupta et al. (2007), remittances are neither a panacea
nor a substitute for a sustained and domestically engineered development
endeavor for curing the problems of low-income countries. Furthermore,
large-scale migration can have a deleterious effect on domestic labor
markets in specific sectors such as higher education, government
services, science and technology, and the manufacturing and services,
especially where those migrating to other countries are largely skilled
workers who are difficult and expensive to replace. Migrant transfers in
the form of remittances can ease the immediate budget constraints of
families by bolstering crucial spending needs on food, health care, and
schooling expenses for their children. Such an unharnessed market in
money transfers is, not only a source of small scale saving, but it can
also be expected to pave a way for the development of a formal financial
sector which is essential for the economic growth and development of
African countries in line with King and Levine (1993) and Beck, et al.
(2000), Giuliano and Ruiz-Arranz (2006), and Gupta, et al. (2007).
In addition, the results show that the conventional sources of
growth such as investment in physical and human capital and the ability
of households to have the wherewithal of spending on health, housing,
nutrition, and other household items can enhance their productivity and
spur their economic growth. A policy implication which may be drawn from
this study is that African countries can improve their economic growth
performance, not only by investing on the traditional sources of growth
such as investment in physical and human capital, trade, and foreign
direct investment, but also by strategically harnessing the contribution
of remittances by ensuring their efficient and reliable transfers and
reducing cost of transfers by improving their governance performance.
APPENDIX.
Variable Means by Country in Sample
Country-Name GDPPC REM ENR GCF AID
Algeria 1791.248 31.070 77.230 30.721 216.759
Benin 296.854 13.709 17.409 16.377 206.190
Botswana 2243.048 38.041 58.857 29.056 95.694
Burkina Faso 218.344 12.827 10.532 19.528 350.288
Cameroon 669.895 1.450 32.962 19.482 428.166
Cape Verde 927.560 156.548 36.854 26.086 96.526
Comoros 401.250 18.960 27.163 20.974 39.369
Congo, Rep. 1074.509 1.904 42.499 28.066 131.438
Cote d'Ivoire 682.286 5.460 31.310 13.224 502.693
Egypt, Arab 1234.931 61.005 98.384 22.846 2024.138
Rep.
Ethiopia 97.360 0.400 16.941 15.354 819.509
Gabon 4138.969 0.288 85.995 29.470 76.178
Gambia, The 319.751 10.411 28.451 19.880 64.982
Ghana 224.610 0.996 35.378 15.940 543.388
Guinea-Bissau 167.502 9.885 17.045 25.635 94.423
Kenya 424.471 7.777 20.423 19.963 601.519
Lesotho 405.708 207.451 29.328 43.637 97.084
Madagascar 254.891 0.758 20.857 12.932 381.740
Malawi 144.185 4.252 15.009 17.212 362.108
Mali 197.619 9.069 12.206 20.617 392.485
Mauritania 367.552 2.800 19.491 22.334 223.627
Mauritius 2781.911 110.703 70.546 25.486 38.652
Mozambique 178.140 3.912 12.501 20.378 839.731
Namibia 1802.632 7.003 52.414 21.128 98.621
Niger 178.394 1.485 8.323 12.395 299.505
Nigeria 352.128 5.365 47.880 19.019 181.344
Rwanda 252.161 0.820 20.928 15.603 313.202
Senegal 412.455 18.884 18.943 15.316 525.129
Sierra Leone 230.990 5.122 21.034 10.043 150.843
South Africa 3153.716 4.137 90.198 19.319 389.784
Sudan 313.316 13.855 28.094 16.500 585.466
Swaziland 1208.405 91.131 46.682 23.350 36.695
Togo 264.578 7.028 28.011 18.055 124.216
Tunisia 1674.134 71.502 69.814 27.993 242.610
Uganda 200.429 -5.380 13.968 13.822 545.627
Zimbabwe 601.519 1.589 49.424 16.832 300.343
Country-Name OFI FDI TOT EFI TFP
Algeria 240.427 0.443 95.206 3.963 64.593
Benin 6.312 1.430 91.586 5.044 58.624
Botswana 7.630 2.554 89.202 6.164 40.934
Burkina Faso 0.575 0.283 102.115 5.000 60.040
Cameroon 52.543 0.457 87.281 5.682 65.483
Cape Verde 0.149 1.979 105.090 5.198 29.048
Comoros 0.141 0.433 83.929 4.881 33.718
Congo, Rep. 53.944 3.038 83.344 4.748 46.046
Cote d'Ivoire 86.648 1.123 156.881 5.490 77.637
Egypt, Arab 298.971 1.734 131.972 5.387 78.597
Rep.
Ethiopia 0.204 1.460 116.391 5.698 82.845
Gabon 74.106 0.080 168.186 5.122 39.841
Gambia, The 1.162 3.966 106.115 4.941 39.731
Ghana 7.609 1.141 122.495 4.576 78.773
Guinea-Bissau 1.333 0.905 138.170 3.839 37.323
Kenya 18.646 0.322 90.669 5.690 71.687
Lesotho 5.164 8.668 99.896 5.184 34.816
Madagascar 19.576 0.510 87.634 4.844 75.066
Malawi 0.323 0.755 131.729 5.020 62.919
Mali 2.376 1.339 113.187 5.590 58.877
Mauritania 5.474 2.939 94.508 4.765 44.990
Mauritius 1.550 0.864 90.701 6.502 41.641
Mozambique 27.515 2.321 162.758 5.299 66.418
Namibia 25.670 0.825 93.283 5.587 43.903
Niger 5.470 0.418 158.191 5.191 71.463
Nigeria 342.246 2.926 97.088 4.363 86.378
Rwanda 1.410 0.541 78.835 5.130 61.746
Senegal 24.882 0.830 144.339 5.153 65.862
Sierra Leone 0.772 0.436 103.278 4.511 69.168
South Africa 158.111 0.530 106.502 5.939 83.777
Sudan 38.302 1.370 118.640 4.356 73.781
Swaziland 3.440 4.706 99.313 5.130 36.501
Togo 6.777 1.527 104.470 4.916 53.330
Tunisia 184.406 2.139 106.041 5.484 55.329
Uganda 2.672 1.255 169.218 4.147 78.855
Zimbabwe 32.222 0.600 94.750 4.578 67.976
Country-Name INY
Algeria 1877.760
Benin 298.331
Botswana 1242.416
Burkina Faso 200.094
Cameroon 747.953
Cape Verde 681.447
Comoros 422.229
Congo, Rep. 1217.315
Cote d'Ivoire 849.253
Egypt, Arab 952.812
Rep.
Ethiopia 103.111
Gabon 4670.099
Gambia, The 328.563
Ghana 203.932
Guinea-Bissau 161.847
Kenya 423.011
Lesotho 301.762
Madagascar 297.393
Malawi 152.528
Mali 202.367
Mauritania 352.259
Mauritius 1666.669
Mozambique 158.910
Namibia 1940.005
Niger 222.918
Nigeria 350.663
Rwanda 283.166
Senegal 414.549
Sierra Leone 314.398
South Africa 3436.783
Sudan 281.070
Swaziland 947.317
Togo 304.277
Tunisia 1377.847
Uganda 177.806
Zimbabwe 620.412
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Notes
(1.) We planned to use data for all African countries, however,
only 36 countries had data for the variables employed by this study for
the time period under consideration.
(2.) Our specification in Eq(1) is based on the empirics in the new
growth theory (Lucas, 1988; Barro, 1990; Benhabib and Spiegel, 1994;
Grossman and Helpman, 1991; Barro and Sala-i-Martin, 1992b; Barro and
Lee, 1994; and Temple, 1999).
(3.) Whereas La Porta et al. (1997) looked at the "ease of
doing business" and how "investor protection" impact
capital markets, we use a more general measure of economic freedom
(Economic Freedom of the World data) which range from 1-10 with higher
numbers indicating more freedom.
Bichaka Fayissa, Corresponding Author: Department of Economics and
Finance, Middle Tennessee State University, Murfreesboro, TN 37132, USA,
Tel: (615) 898-2385, Fax: (615) 898-5596, Email: bfayissa@mtsu.edu
Christian Nsiah, Department of Accounting and Economics, Black
Hills State University, Spearfish, SD-57799, USA, Tel (605) 642-6286,
Email: ChristianNsiah@bhsu.edu
TABLE 1.
Variable Description and Source Information
Variable Description
GDPPC Gross domestic product per capita, measured
in constant (2000) US dollars. From the 2007
World Development Indicators Data Set.
REM Per capita workers' remittances and
compensation of employees, received (nominal
US dollars). From the 2007 World Development
Indicators Data Set.
ENR Gross enrollment rate secondary and tertiary
(%c of gross). From the 2007 World
Development Indicators Data Set.
GCF Gross fixed capital formation as a % of
gross domestic product. From the 2007 World
Development Indicators Data Set.
AID Total Official Development Assistance
(Millions $) includes grants or loans which
are: (a) undertaken by the official sector;
(b) with promotion of economic development
and welfare as the main objective; (c) at
concessional financial terms (if a loan, have
a grant element of at least 25 per cent).
From the United Nations Conference on Trade
and Development (UNCTAD) Handbook of
Statistics.
OFI Other Official Flows (Millions $) are
transactions by the official sector whose
main objective is other than development
motivated, or, if development motivated,
whose grant element is below the 25%
threshold which would make them eligible to
be recorded as ODA. The main classes of
transactions included here are official
export credits, official sector equity and
portfolio investment, and debt reorganization
undertaken by the official sector at
non-concessional terms (irrespective of the
nature or the identity of the original
creditor). From the United Nations Conference
on Trade and Development (UNCTAD) Handbook of
Statistics.
FDI Foreign direct investment, net inflows (% of
GDP). From the 2007 World Development
Indicators Data Set
TOT Net barter terms of trade (2000 = 100). From
the 2007 World Development Indicators Data
Set.
EFI Economic Freedom Index obtained from Economic
Freedom of the World project data the ranges
from 1 to 10 with higher numbers denoting
more freedom. It is designed to measure the
consistency of a nation's institutions and
policies with economic freedom. From the
Fraser Institute.
TFP Total factor productivity, measured as the
log of GDP divided by the product of labor
force and gross capital formation. From the
2007 World Development Indicators Data Set.
INY Initial Per Capita Income (GDPPC 198(1)
measured in constant U.S. dollars. From the
2007 World Development Indicators Data Set.
TABLE 2.
Descriptive Statistics
Variable Mean Std. Dev. Min Max
GDPPC 828.25 950.83 85.79 4670.10
REM 27.72 48.46 0.00 250.01
ENR 36.63 25.06 5.85 117.11
GCF 20.86 7.84 4.84 59.00
AID 352.44 407.51 0.01 3817.46
OFI 48.91 158.25 0.00 1096.55
FDI 1.52 2.77 0.00 26.75
TOT 111.26 37.38 44.40 320.94
EFI 5.09 0.78 2.89 7.43
EFI 59.63 17.44 26.44 117.48
INY 777.82 926.49 103.11 4670.10
Notes: All data are transformed into logs for our analysis.
Data is averaged over 5 year periods between 1980
and 2004 for 36 countries.
TABLE 3.
Results for Random Effects and Quasi-Fixed Effects Models
Model 1 Model 2
Variables Whole-Sample Sub-Sample Whole Sample Sub-Sample
Constant -0.664 ** -0.619 * -0.786 ** -0.726 *
(0.333) (0.338) (0.373) (0.377)
REM 0.039 *** 0.042 *** 0.037 *** 0.039 ***
(0.008) (0.008) (0.011) (0.011)
ENR 0.111 *** 0.115 *** 0.109 *** 0.113 ***
(0.032) (0.032) (0.040) (0.041)
GCF 0.174 ** 0.177 ** 0.211 *** 0.196 **
(0.075) (0.081) (0.075) (0.082)
AID -0.022 *** -0.024 ** -0.028 ** -0.028 **
(0.010) (0.010) (0.012) (0.013)
OFI 0.007 0.009 0.007 0.009
(0.006) (0.006) (0.009) (0.009)
FDI 0.007 0.006 0.003 0.002
(0.013) (0.014) (0.010) (0.010)
TOT 0.053 0.050 0.052 0.052
(0.042) (0.042) (0.049) (0.049)
EFI 0.155 * 0.162 * 0.185 * 0.189 *
(0.091) (0.092) (0.109) (0.110)
TFP 0.011 0.011 0.015 * 0.014
(0.007) (0.007) (0.008) (0.009)
INY 0.857 *** 0.846 *** 0.849 *** 0.843 ***
(0.027) (0.030) (0.035) (0.037)
Number of 180 170 180 170
Observations
Number of 36 34 36 34
countries
R-Squared 0.980 0.981 0.979 0.981
Notes: Model 1 presents estimates for the quasi-fixed model with
panel specific heteroskedasticity corrected standard errors;
Model 2 presents estimates for random effects with bootstrap
standard errors; The sub-sample leaves out Lesotho, and Cape
Verde. The standard Errors In Parenthesis; and * indicate
significance at p < 0.01, p < 0.05, and p < 0.1 levels,
respectively.
TABLE 4.
Arellano-Bond Dynamic Panel-Data Estimation-Results
(1) (2)
Whole Sample Sub Sample
Estimates Estimates
(One-Step and (One-Step and
Variables One-Year Lag) One-Year Lag)
GDP (LD) 0.8671 *** 0.8698 ***
(0.0120) (0.0110)
REM (D(1)) 0.0050 ** 0.0060 ***
(0.0022) (0.0019)
ENR (D(1)) 0.0077 *** 0.0064 ***
(0.0025) (0.0012)
ENR(LD) 0.0053 *** 0.006l ***
(0.0014) (0.0020)
GCF (D(1)) 0.0165 *** 0.0131 *
(0.0060) (0.0074)
AID (D(l)) 0.0009 * 0.0006
(0.0005) (0.0006)
OFI (1)(1)) -0.0003 ** 0.0000
(0.0001) (0.0001)
OFI (LD) 0.0006 *** 0.0006 ***
(0.0001) (0.0001)
FDI (D(1)) -0.0012 -0.0010
(0.0008) (0.0003)
FDI (LD) 0.00121 *** 0.0002 ***
(0.0003) (0.0002)
TOT (D(l)) 0.0247 *** 0.0271 ***
(0.0050) (0.0027)
EFI (D(1)) 0.07021 *** 0.0684 ***
(0.0108) (0.0052)
PRO (D(1)) 0.0023 *** 0.0025 ***
(0.0006) (0.0007)
Constant 0.6039436 0.6979399
(0.4165) (0.4389)
Number of 900 850
Observations
Number of 36 34
Countries
Arellano-Bond Test -2.279 ** -2.412 **
of the null of No
AR(1) Residual
Errors
Arellano-Bond test 0.651 1.401
of the null of No
AR(2) Residual
Errors
Sargan Test of the 22.072 17.258
Validity of the null
of over-identifying
Restrictions
Notes: Standard Errors In Parenthesis; ***, **, and * indicate
significance at 1) < 0.01, h < 0.05, and h < 0.1 levels,
respectively. While the suffix D(1) after each variable denotes
the number of times the specific variable was differenced. LD
denotes the lagged difference. The variable ENR is treated as
predetermined, while FDI and OFI are treated as endogenous
variables. The sub-sample model excludes the countries of Lesotho
and Cape Verde.
FIGURE 1. International Capital Flows to Africa.
Millions of Dollars
Workers Remittances 1994 2004
Foreign Direct Investment 1994 2004
Official Development Assistance 1994 2004
Source: World Development Indicators Dataset 2007, and
UNCTAD Handbook of Statistics
Note: Table made from bar graph.
