Abstract
Aggregate economic performance in sub-Saharan Africa during the past decade has remained unsatisfactory, in contrast to robust performance of developing countries elsewhere. Both domestic and external factors have contributed to this disappointing overall performance. The external environment, characterized by sharp declines in world commodity prices and substantial losses in the terms of trade, has been generally unfavorable. For many countries, the effects of these adverse external developments have been compounded by unfavorable weather. Also, virtually all countries in the region have been confronted with deep-rooted developmental constraints—rapid population growth, low human capital development, and inadequate infrastructure—which have constituted major impediments to private sector development and the supply response of the economies in general. In addition, ethnic conflicts, political instability, adverse security conditions, and protracted civil wars have aggravated the economic performance of several countries. Furthermore, governance concerns have been compounded by the legacy of repressive regimes in several African countries, as well as by bloated and inefficient public administrations, ineffective judicial systems, and complex administrative and institutional frameworks. Finally, the inappropriate economic policies pursued by several countries have also contributed to the weak aggregate economic performance.
Aggregate economic performance in sub-Saharan Africa during the past decade has remained unsatisfactory, in contrast to robust performance of developing countries elsewhere. Both domestic and external factors have contributed to this disappointing overall performance. The external environment, characterized by sharp declines in world commodity prices and substantial losses in the terms of trade, has been generally unfavorable. For many countries, the effects of these adverse external developments have been compounded by unfavorable weather. Also, virtually all countries in the region have been confronted with deep-rooted developmental constraints—rapid population growth, low human capital development, and inadequate infrastructure—which have constituted major impediments to private sector development and the supply response of the economies in general. In addition, ethnic conflicts, political instability, adverse security conditions, and protracted civil wars have aggravated the economic performance of several countries. Furthermore, governance concerns have been compounded by the legacy of repressive regimes in several African countries, as well as by bloated and inefficient public administrations, ineffective judicial systems, and complex administrative and institutional frameworks. Finally, the inappropriate economic policies pursued by several countries have also contributed to the weak aggregate economic performance.
Nevertheless, the overall poor growth performance of sub-Saharan Africa has masked the important progress made by several countries, particularly since the mid-1980s, in lowering internal and external imbalances and addressing structural rigidities, and, thus, in establishing the necessary conditions for resuming sustainable growth. On average, countries that have adopted and effectively implemented broad-based macro-economic and structural reforms have done better than others.1 A recent paper by Hadjimichael and others (1995), using cross-section data during 1986–92, has demonstrated that, after population growth and unfavorable weather, inappropriate macroeconomic policies were the most important factors contributing to the poor per capita growth performance of sub-Saharan African countries. A number of other studies have provided evidence in support of the beneficial impact on growth of a stable macroeconomic environment.2
The current paper extends the empirical investigation of the determinants of growth in sub-Saharan Africa in three different ways. First, following Knight, Loayza, and Villanueva (1993), an extended version of the Mankiw, Romer, and Weil (1992) framework is applied to panel data for 29 countries in the region during 1981–92.3 Second, following the papers by Khan and Kumar (1993) and Khan and Reinhart (1990), the contribution of private and government investment to growth is investigated. Finally, the effects on growth of macroeconomic policies, structural reforms, changes in the terms of trade, human capital, the weather, and political freedom are investigated. The results indicate that growth is stimulated by public policies that lower the budget deficit in relation to GDP (without reducing government investment), reduce the rate of inflation, maintain external competitiveness, promote structural reforms, encourage human capital development, and slow population growth. The paper also finds that an increase in private investment has a relatively large positive impact on per capita growth, and that per capita income converges after controlling for human capital development and public policies.
The rest of this paper is organized as follows. Section I discusses briefly some theoretical considerations in the growth literature. Section II presents the empirical model, and Section III outlines the empirical framework and summarizes the estimation results. The last section summarizes the main conclusions and draws some policy implications.
I. Some Theoretical Considerations
In the Solow-Swan growth model, “steady state” growth depends on technological progress and population growth, both of which are exogenous to the model; in the absence of technological progress, steady state per capita output does not grow. In this framework, an increase in the savings rate raises per capita economic growth in the short run; however, owing to diminishing returns to capital, per capita output in the long run grows at the rate of exogenously given technological progress. As such, economic policies do not affect steady state economic growth, although they can affect the level of output or its growth rate when the economy is in transition from one steady state to another.
An important prediction of neoclassical growth models is that the output levels of countries with similar technologies should converge to a given level in the steady state. A number of recent papers have shown that this unconditional convergence hypothesis does not appear to be consistent with the empirical evidence. Nevertheless, support for conditional convergence is usually found when account is taken of the effects on per capita growth of the rate of investment and public policies.4
The dependence of growth on exogenous technological progress in the neoclassical model, as well as the apparent inconsistency of the unconditional convergence hypothesis with actual data, has prompted investigation of alternative growth models. Endogenous growth models are able to generate a linkage between public policies and growth in the long run by assuming aggregate production functions that exhibit nondecreasing returns to scale.5 The remainder of this section provides a brief review of the main theoretical considerations in the current literature on growth.
Human Capital
Recent endogenous growth models have shown that human capital accumulation can be an important source of long-term growth, either because it is a direct input into research (Romer (1990)) or because of its positive externalities (Lucas (1988), and Becker, Murphy, and Tamura (1990)). For example, Lucas (1988) proposes a model in which investment in human capital enhances the productivity of both the recipients of such capital and the society at large, thereby creating positive externalities. The decision of individuals to invest in human capital enhances technological progress. Hence, policies that enhance public and private investment in human capital promote long-run economic growth. Using a neoclassical framework, Mankiw, Romer, and Weil (1992) have shown that the contribution of human capital to growth is also consistent with the predictions of the Solow-Swan model.
Macroeconomic Stability
Macroeconomic policies may affect economic growth, either directly through their effect on the accumulation of factors of production, namely, capital, or indirectly through their impact on the efficiency with which factors of production are used, Macroeconomic stability—reflected in low and stable inflation, sustainable budget deficits, and appropriate exchange rates—sends important signals to the private sector about the direction of economic policies and the credibility of the authorities’ commitment to manage the economy efficiently. This stability, by facilitating long-term planning and investment decisions, encourages savings and private capital accumulation. A lack of macroeconomic stability, by creating an atmosphere of uncertainty, makes it difficult for economic agents to extract the correct signals from relative prices, such as real returns to investment, and thus leads to inefficient allocation of resources (Barro (1976 and 1980)). An appropriate level of the real exchange rate and an appropriate structure of relative prices and economic incentives in general are key ingredients of a stable macroeconomic environment.
The effect of inflation on growth is ambiguous in the theoretical literature. According to the Tobin-Mundell effect, higher anticipated inflation leads to a lower real interest rate and causes portfolio adjustments away from real money balances toward real capital; hence, a higher anticipated inflation would be expected to raise real investment and growth. However, portfolio adjustments in developing countries with underdeveloped domestic capital and financial markets would most likely be from real money balances to real assets, which are not usually included in private investment, or to assets denominated in foreign currency through capital flight. Thus, higher anticipated inflation in these countries would be expected to lower private investment and growth. In the cash-in-advance models (for example. Stockman (1981)), anticipated inflation, by raising the cost of capital, lowers capital accumulation and economic growth.
Fiscal policy and the extent of government involvement in the economy have received considerable attention in the development literature.6 Other things being equal, a larger budget deficit will crowd out the private sector as a result of lower access to bank credit, higher real interest rates, and a more appreciated real exchange rate. Government investment has also been used in empirical studies as a direct proxy of the government’s contribution to capital accumulation, as well as an indicator of the adequacy of basic economic and social infrastructure. Some studies have included government consumption to allow for the concern of supply-side theories that higher government spending creates expectations of future tax liabilities that, in turn, distort incentives and lower economic growth (for example, Kormendi and Meguire (1985)). Endogenous growth models have shown that fiscal policy can have significant effects on economic growth in the long run. For example, in a model that assumes constant returns to scale with respect to government inputs and private capital combined, but diminishing returns with respect to private capital alone, Barro (1989 and 1990) has shown that high levels of government taxation distort savings decisions, which, in turn, lower economic growth in the steady state.
Trade Policy
Outward-oriented trade policies are conducive to faster growth because they promote competition, encourage learning-by-doing, improve access to trade opportunities, raise the efficiency of resource allocation, and enhance positive externalities resulting from access to improved technology (Grossman and Helpman (1989a, 1989b, and 1991), Khan (1987), Lucas (1988), and Romer (1986 and 1990)).7 Also. Krueger (1974) has argued that import quotas divert productive resources to rent-seeking activities and reduce growth.
Structural Policies
Structural and institutional reforms are essential to enhance economic incentives and improve the allocation of resources, as well as to remove the impediments to private sector development.8 Policies aimed at improving the efficiency of economic resources involve measures to reduce the wedges between prices and marginal costs, which typically arise from price controls, imperfect competition, subsidies and tax exemptions, distortive taxes, and exchange and trade restrictions (Khan (1987)). Policies to expand capacity include reforms aimed at raising savings and investment.
Other structural reforms include the liberalization of administrative procedures for private sector activity and legal reforms. A common characteristic of countries with limited political rights and civil liberties is a lack of well-defined property rights and market-friendly legal institutions. The absence of these rights and institutions lowers the security for life and property and consequently reduces the rate of accumulation and the efficiency of factors of production.
II. The Empirical Mode)
The paper assumes a Cobb-Douglas production function of the following form:9
where Y is real output; L is labor; Kp and Kh are the physical and human capital stock, respectively; A0 is an overall index of technology and efficiency in the economy; and Ap, Ah, and AL are the physical- and human-capital-augmenting technology and the labor-augmenting technology, respectively. Defining
equation (1) can be rewritten as
where A encompasses the factor-augmenting, economy-wide levels of technology and efficiency.10
Labor and labor-augmenting technology are assumed to grow according to the following functions:
and
where n is the exogenous rate of growth of the labor force; t is a time index; g is the exogenous rate of technological progress; X is a vector of policy and other factors that can affect the level of technology and efficiency in the economy; and θ is a vector of coefficients related to these policy and other variables.
Let Spand Sh, be the fractions of income invested in physical and human capital, respectively; for simplicity, it is assumed that both types of capital stock depreciate at the same rate δ. Thus, physical and human capital are accumulated according to the following functions:
and
Next, let kp and kh be the stock of physical and human capital in terms of effective labor units, that is, kp= Kp/A·L and kh= Kh/A·L. Let also y be the level of output per effective unit of labor, that is, y =Y/A·L. Rewriting the production and accumulation functions in terms of quantities per effective labor unit gives
and
In the steady state, the levels of physical and human capital per effective labor unit are constant. Thus, setting equations (6’) and (7’) to zero and solving the resulting equations gives
and
Substituting equations (8a) and (8b) in equation (3’) and taking natural logarithms gives the steady state output per effective labor unit:
where ξ = (α+ β).
An empirical counterpart of equation (9) can be obtained by taking the natural logarithm of y= Y/A·L and substituting for A from equation (5):
The terms ξ/1 – ξ, α/1 – ξ, and β/1 – ξ in the above equations are the elasticities of per capita income with respect to population growth, the fraction of income invested in physical capital, and the fraction of income invested in human capital, respectively. This model predicts that the sum of the elasticities with respect to Spand Shis equal to the elasticity with respect to (n + g + δ).
Finally, following Mankiw, Romer, and Weil (1992), the transition of actual output per effective labor unit to its steady state level is approximated by
where λ = (η + g + δ)(1 –ξ) is the speed of convergence, y is the actual output per effective labor unit, and the other variables are defined as before. Equation (II) implies that11
where y0 is output per effective labor unit at time t0. Subtracting y0 from both sides of equation (12) and substituting In y* from equation (10) gives:
where T is the length of time under consideration.
An empirical counterpart of equation (13) for the ith sub-Saharan African country considered in this study is written as follows:
where YGPC is per capita real GDP growth rate; Y0 is a measure of initial income; PG is the population growth rate; PIY and GIY are the ratios of private and government investment to GDP. respectively; HCI is an indicator of human capital development, measured either by gross primary and secondary school enrollment ratios, or by life expectancy at birth; INFL is the rate of inflation; DEFYE is the ratio of the government budget deficit (excluding grants) to GDP; RERG is the percentage change in the real effective exchange rate; XG is the growth of export volume; STRUC is a dummy variable to account for countries that implemented significant structural reforms during 1986–92; TTG is the percentage change in the external terms of trade; DRY is a proxy for inadequate rainfall: PR is an index of political rights; and ui, vt, and ei.t are the country-specific, time-specific, and overall error terms, respectively. Note that the variables HCI and YO vary only across countries. The Appendix gives the definitions and sources of the variables. Following Mankiw, Romer, and Weil (1992), it is assumed that (g+ δ) - 0.05. Small variations of this assumed figure do not alter the results of estimation significantly.
From equation (14), the elasticities of per capita income with respect to population growth, and the fraction of income invested in public and human capital (that is, ξ/1 – ξ, α/1 – ξ, and β/1 – ξ) are obtained from the expressions η1/–η0, η2/–η0, and η3/–η0, respectively. Also, the prediction that the sum of the elasticities on Sp and Sh, is equal to that on (n + g + δ) can be tested by the following null hypothesis: η1+ η2+ η3= 0. Furthermore, if the null hypothesis η2– η3= 0 were not rejected, it would imply that the elasticities of growth with respect to the ratios of private and government investment are equal. In addition, the speed of convergence is obtained by the formula
III. Empirical Results
Equation (14) is estimated with panel data for 29 countries in sub-Saharan Africa. Using annual data for the period 1981–92, four observations are constructed for each country by taking three-year nonoverlapping averages of the variables during the subperiods 1981–83, 1984–86, 1987–89, and 1990–92, generating a total of 116 observations.12 The time period 1981–92 was chosen because data for several explanatory variables, such as private and government investment and comparable indicators of macroeconomic policies, are available only since 1980 in levels, and, hence, only since 1981 in growth rates for the group of countries considered. The choice of countries was dictated by the availability of data for the complete set of variables for each country during the full period 1981–92 (see Table 1 for a list of countries included). Because panel data are used, the error term for equation (14) has three components: ui and vt, which capture country- and time-specific effects, respectively, and ei.t, which is an error term common to all countries.
In order to deal with time effects, the data are processed to remove the time means from the series, and the resulting model is estimated without an intercept. The use of the least-squares dummy variables (LSDV) procedure is quite common in the treatment of country heterogeneity: however, with the inclusion of time-invariant variables (YO, HCL and STRUC) in the regression analysis, the LSDV procedure cannot be implemented because the vector of dummy variables is perfectly collinear with these variables. Instead, the country-specific effects are captured though the country-specific information imbedded in the indicators for the level of human capital development, the stance of economic policies, changes in the terms of trade, and the degree of political freedom, as well as through the proxy for the weather. In addition, dummy variables for subgroups of countries (CFA8183, CF48486, CFA8789, and CFA9092), as well as STRUC, are used to account for possible fixed effects stemming from a priori information regarding country characteristics and institutional arrangements.
Table 1 gives the averages of the variables for each country during 1981–92. For convenience, the sample of countries is classified into four subgroups: high-growth countries, with per capita growth greater than or equal to 1 percent; medium-to-low-growth countries, with per capita growth less than 1 percent but greater than or equal to 0 percent; weak-growth countries, with per capita growth of less than 0 percent but greater than or equal to minus 1 percent; and very-weak-growth countries, with per capita growth of less than minus 1 percent. The data indicate that, in general, countries with higher growth rates had higher investment ratios, lower population growth rates, higher primary and secondary school enrollment ratios and life expectancy at birth, lower inflation rates, lower budget deficit ratios, higher export volume growth, and a higher degree of political rights. However, the countries with faster growth rates did not necessarily experience a more favorable external environment (in terms of changes in their terms of trade) than did those with poor growth performance. Nevertheless, these broad trends are not clear-cut at the individual country level over the 12-year period. For example, Lesotho had an average ratio of private investment of 37 percent during 1981–92 yet experienced negative average growth during the same period. Also, Uganda, with a high average rate of inflation, experienced positive per capita growth in 1981–92.
Period Average of Variables by Country. 1981–92a
See Appendix for definitions and sources of variables.
Only sub-Saharan African countries with complete data series during 1981–92 for all the variables used in the regressions are included.
Countries with average per capita growth rates greater than or equal to 1 percent during 1981 –92.
CFA franc country.
Countries with average per capita growth rates of less than 1 percent but greater than or equal to 0 percent during 1981–92.
Countries with average per capita growth rates of less than 0 percent but greater than or equal to minus 1 percent during 1981–92.
Countries with average per capita growth rates below minus 1 percent during 1981–92.
Unweighted averages.
Period Average of Variables by Country. 1981–92a
| Countriesb | YGPC | TIY | PIY | GIY | PG | PRI | SEC | LIFE | Y0 | INFL | DEFYI | RERG | XG | TTG | PR | YGFD | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| High-growth countriesc | |||||||||||||||||
| Botswana | 5.51 | 29.9 | 20.4 | 9.6 | 3.6 | 107.5 | 32.0 | 66.4 | 780 | 11.1 | –12.8 | –1.0 | 9.2 | 2.0 | 0.67 | –1.7 | |
| Mauritius | 4.00 | 25.0 | 17.0 | 8.0 | 0.9 | 102.0 | 48.0 | 68.4 | 1180 | 9.2 | 5.3 | –l.8 | 6.8 | 5.1 | 0.54 | 2.6 | |
| Congod | 1.81 | 27.5 | 16.4 | 11.1 | 3.1 | 161.5 | 85.0 | 51.3 | 850 | 6.3 | 9.6 | 0.1 | 9.0 | –3.6 | 0.16 | –0.3 | |
| Cape Verde | 1.79 | 41.0 | 24.8 | 16.2 | 2.5 | 115.5 | 13.5 | 65.9 | 530 | 10.8 | 7.8 | 1.2 | 0.6 | 1.7 | 0.28 | –0.9 | |
| Burundi | 1.50 | 16.7 | 5.1 | 11.7 | 2.9 | 46,5 | 4.5 | 48.1 | 200 | 7.5 | 7.3 | –2.9 | 7.2 | –2.0 | 0.14 | –0.1 | |
| Mediam-to-low-growth countriese | |||||||||||||||||
| Uganda | 0.90 | 9.3 | 6.7 | 2.6 | 2.6 | 60.5 | 9.0 | 46.7 | 280 | 92.7 | 3.1 | –15.0 | 6.7 | –9.4 | 0,20 | 0.6 | |
| Burkina Fastod | 0.86 | 19.2 | 11.4 | 7.8 | 2.6 | 27.5 | 5.5 | 46.8 | 240 | 1.9 | 9.5 | –2.8 | –1.4 | 1.1 | 0.16 | 3.0 | |
| Malid | 0.51 | 18.6 | 8.0 | 10.7 | 1.9 | 25.5 | 8.0 | 46.1 | 240 | 1.5 | 5.4 | –2.9 | 13.2 | –2.2 | 0.18 | –0.2 | |
| Nigeria | 0.40 | 20.4 | 11.2 | 9.1 | 3.4 | 88.0 | 19.5 | 50.2 | 930 | 21.9 | 7.4 | –8.8 | –0.5 | –5.0 | 0.26 | 1.9 | |
| Swaziland | 0.38 | 23.4 | 15.6 | 7.8 | 3.5 | 105.5 | 42.5 | 55.2 | 820 | 12.7 | 0.1 | –2.3 | 7.8 | –3.0 | 0.!9 | –1.7 | |
| Kenya | 0.14 | 19.6 | 11.8 | 7.8 | 3,7 | 105,0 | 24.5 | 57.5 | 420 | 13.8 | 5.7 | –2.6 | –2.0 | –2.4 | 0.18 | –0.2 | |
| Senegald | 0.13 | 13.4 | 10.0 | 3.4 | 2.9 | 52.5 | 13.5 | 47.1 | 510 | 4.9 | 3.3 | –1.0 | 4.2 | 2.6 | 0,28 | 2.9 | |
| Weak-growth countriesf | |||||||||||||||||
| Zimbabwe | –0.19 | 20.6 | 17.3 | 3.3 | 3.3 | 101.0 | 29.0 | 59.3 | 710 | 17.1 | 4.7 | –5.9 | –1.7 | 6.1 | 0.23 | –3.2 | |
| Lesotho | –0.31 | 56.6 | 37.4 | 19.2 | 3.0 | 109.5 | 22.0 | 58.3 | 410 | 14.2 | 9,4 | –0.5 | 11.0 | 1.7 | 0.18 | –2.8 | |
| Tanzania | –0.32 | 25.9 | 19.9 | 6.0 | 3.1 | 81.0 | 6.5 | 51.2 | 290 | 27.3 | 5.5 | –5.7 | 2.1 | –5.8 | 0.17 | –1.3 | |
| Ghana | –0.61 | 9.2 | 3.8 | 5.4 | 3.4 | 78.5 | 39.5 | 54.0 | 410 | 41.5 | 3.5 | –4.7 | 6.1 | –5.4 | 0.22 | 0.2 | |
| Cameroond | –0.73 | 21.8 | 12.8 | 9.0 | 3.1 | 99.5 | 23.0 | 53,5 | 760 | 7.3 | 4.7 | 0.7 | 10.1 | –8.2 | 0.17 | –1.6 | |
| The Gambia | –0.80 | 20.6 | 11.6 | 9.0 | 3.6 | 53.5 | 11.5 | 43.1 | 350 | 16.5 | 3,8 | –2.2 | 12.9 | 9.1 | 0.44 | 1.0 | |
| Gabond | –0.95 | 32.8 | 24.7 | 8.1 | 2.1 | 116.5 | 22.0 | 51.7 | 3830 | 5.0 | 3.8 | –1.9 | 6.3 | –4.0 | 0.19 | –1.6 | |
| Very-weak-growth countriesg | |||||||||||||||||
| Togo” | –1.08 | 23.6 | 14.7 | 8.9 | 3.5 | 114.5 | 28.0 | 52.8 | 410 | 3.5 | 4.1 | –2.1 | 0.5 | –1.3 | 0.16 | –1.2 | |
| Malawi | –1.17 | 14.7 | 8.5 | 6.2 | 3.2 | 63.0 | 4.0 | 45.2 | 180 | 16.0 | 8.8 | –0.9 | 2.1 | –1.0 | 0.16 | –5.4 | |
| Rwanda | –1.26 | 15.7 | 8.4 | 7.3 | 3.3 | 67.0 | 5.5 | 45.1 | 240 | 6.1 | 5.3 | –0.6 | –5.0 | 2.0 | 0.17 | –1.3 | |
| Central African Republicd | –1.83 | 12.1 | 2.1 | 10.0 | 2.7 | 69.5 | 13.0 | 47.0 | 320 | 5.1 | 5.9 | –0.9 | 9.6 | 0.5 | 0.15 | –1.2 | |
| Ethiopia | –1.89 | 11.4 | 5.7 | 5.8 | 3.3 | 33.0 | 11.0 | 46.1 | 120 | 7.1 | 7.1 | 2.6 | –5.3 | –2.0 | 0.15 | –1.2 | |
| Madagascar | –2.37 | 10.6 | 3.6 | 7.0 | 2.5 | 117.0 | 16.5 | 50.5 | 430 | 16.8 | 4 8 | –5.2 | –6.7 | –2.2 | 0.20 | –1.8 | |
| Sierra Leone | –2.42 | 12.0 | 8.7 | 3.3 | 3.0 | 50.0 | 15.0 | 41.0 | 320 | 74.7 | 8.7 | –0.5 | –2.5 | 1.0 | 0.19 | –1.3 | |
| Zambia | –3.01 | 9.1 | 5,0 | 4.1 | 3.8 | 91.0 | 18.0 | 50.3 | 600 | 64.8 | 9.6 | 0.4 | 3.4 | 0.9 | 0.24 | –2.1 | |
| Côte d’lvoired | –3.61 | 14.5 | 8.7 | 5.8 | 3.7 | 75.0 | 21.0 | 55.1 | 1180 | 4.6 | 9.3 | 0.1 | –5.2 | –7.7 | 0.17 | –0.7 | |
| Nigerd | –4.20 | 12.1 | 2.9 | 9.2 | 3.4 | 27.0 | 5.5 | 44.4 | 44Í) | 2.2 | 6.0 | –5.3 | 0.9 | –0.6 | 0.15 | –1.1 | |
| Group averagesh | |||||||||||||||||
| High-growth countries | 2.92 | 28.0 | 16.7 | 11.3 | 2.6 | 106.6 | 36.6 | 60.0 | 708 | 9.0 | 3.4 | –0.9 | 6.6 | 0.7 | 0.36 | — | |
| Medium-to-low-growth countries | 0.47 | 17.7 | 0.7 | 7.0 | 2.9 | 66.4 | 17.5 | 50.0 | 491 | 21.4 | 4,9 | –5.0 | 4.0 | –2.6 | 0.21 | 0.9 | |
| Weak-growth countries | –0.56 | 26.8 | 18.2 | 8.6 | 3.1 | 91.4 | 21.9 | 53.0 | 966 | 18.4 | 5.1 | –2.9 | 6.7 | –0.9 | 0.23 | –1.3 | |
| Very-weak-growth countries | –2.29 | 13.6 | 6.8 | 6.8 | 3.2 | 70.7 | 13.8 | 47.8 | 424 | 20.1 | 7.0 | –1.2 | –1.5 | –1.0 | 0.17 | –1.7 | |
| CFA franc countries | –0.91 | 19.6 | 11.2 | 8.4 | 2.9 | 76.9 | 22.S | 49.6 | 878 | 4,2 | 6.2 | –1.6 | 4,7 | –2.3 | 0.18 | –0.2 | |
| Non-CFA franc countries | 0.01 | 20.6 | 12.8 | 7.9 | 3.1 | 82.9 | 19.6 | 52.8 | 484 | 25.4 | 5.0 | –3.0 | 2.4 | –0.4 | 0.25 | –1.0 | |
| All countries | –0.30 | 20.3 | 12.2 | 8.0 | 1.0 | 80.8 | 20.6 | 51.7 | 620 | 18.1 | 5.4 | –2.5 | 3.2 | –1.1 | 0.23 | –0.7 | |
See Appendix for definitions and sources of variables.
Only sub-Saharan African countries with complete data series during 1981–92 for all the variables used in the regressions are included.
Countries with average per capita growth rates greater than or equal to 1 percent during 1981 –92.
CFA franc country.
Countries with average per capita growth rates of less than 1 percent but greater than or equal to 0 percent during 1981–92.
Countries with average per capita growth rates of less than 0 percent but greater than or equal to minus 1 percent during 1981–92.
Countries with average per capita growth rates below minus 1 percent during 1981–92.
Unweighted averages.
Period Average of Variables by Country. 1981–92a
| Countriesb | YGPC | TIY | PIY | GIY | PG | PRI | SEC | LIFE | Y0 | INFL | DEFYI | RERG | XG | TTG | PR | YGFD | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| High-growth countriesc | |||||||||||||||||
| Botswana | 5.51 | 29.9 | 20.4 | 9.6 | 3.6 | 107.5 | 32.0 | 66.4 | 780 | 11.1 | –12.8 | –1.0 | 9.2 | 2.0 | 0.67 | –1.7 | |
| Mauritius | 4.00 | 25.0 | 17.0 | 8.0 | 0.9 | 102.0 | 48.0 | 68.4 | 1180 | 9.2 | 5.3 | –l.8 | 6.8 | 5.1 | 0.54 | 2.6 | |
| Congod | 1.81 | 27.5 | 16.4 | 11.1 | 3.1 | 161.5 | 85.0 | 51.3 | 850 | 6.3 | 9.6 | 0.1 | 9.0 | –3.6 | 0.16 | –0.3 | |
| Cape Verde | 1.79 | 41.0 | 24.8 | 16.2 | 2.5 | 115.5 | 13.5 | 65.9 | 530 | 10.8 | 7.8 | 1.2 | 0.6 | 1.7 | 0.28 | –0.9 | |
| Burundi | 1.50 | 16.7 | 5.1 | 11.7 | 2.9 | 46,5 | 4.5 | 48.1 | 200 | 7.5 | 7.3 | –2.9 | 7.2 | –2.0 | 0.14 | –0.1 | |
| Mediam-to-low-growth countriese | |||||||||||||||||
| Uganda | 0.90 | 9.3 | 6.7 | 2.6 | 2.6 | 60.5 | 9.0 | 46.7 | 280 | 92.7 | 3.1 | –15.0 | 6.7 | –9.4 | 0,20 | 0.6 | |
| Burkina Fastod | 0.86 | 19.2 | 11.4 | 7.8 | 2.6 | 27.5 | 5.5 | 46.8 | 240 | 1.9 | 9.5 | –2.8 | –1.4 | 1.1 | 0.16 | 3.0 | |
| Malid | 0.51 | 18.6 | 8.0 | 10.7 | 1.9 | 25.5 | 8.0 | 46.1 | 240 | 1.5 | 5.4 | –2.9 | 13.2 | –2.2 | 0.18 | –0.2 | |
| Nigeria | 0.40 | 20.4 | 11.2 | 9.1 | 3.4 | 88.0 | 19.5 | 50.2 | 930 | 21.9 | 7.4 | –8.8 | –0.5 | –5.0 | 0.26 | 1.9 | |
| Swaziland | 0.38 | 23.4 | 15.6 | 7.8 | 3.5 | 105.5 | 42.5 | 55.2 | 820 | 12.7 | 0.1 | –2.3 | 7.8 | –3.0 | 0.!9 | –1.7 | |
| Kenya | 0.14 | 19.6 | 11.8 | 7.8 | 3,7 | 105,0 | 24.5 | 57.5 | 420 | 13.8 | 5.7 | –2.6 | –2.0 | –2.4 | 0.18 | –0.2 | |
| Senegald | 0.13 | 13.4 | 10.0 | 3.4 | 2.9 | 52.5 | 13.5 | 47.1 | 510 | 4.9 | 3.3 | –1.0 | 4.2 | 2.6 | 0,28 | 2.9 | |
| Weak-growth countriesf | |||||||||||||||||
| Zimbabwe | –0.19 | 20.6 | 17.3 | 3.3 | 3.3 | 101.0 | 29.0 | 59.3 | 710 | 17.1 | 4.7 | –5.9 | –1.7 | 6.1 | 0.23 | –3.2 | |
| Lesotho | –0.31 | 56.6 | 37.4 | 19.2 | 3.0 | 109.5 | 22.0 | 58.3 | 410 | 14.2 | 9,4 | –0.5 | 11.0 | 1.7 | 0.18 | –2.8 | |
| Tanzania | –0.32 | 25.9 | 19.9 | 6.0 | 3.1 | 81.0 | 6.5 | 51.2 | 290 | 27.3 | 5.5 | –5.7 | 2.1 | –5.8 | 0.17 | –1.3 | |
| Ghana | –0.61 | 9.2 | 3.8 | 5.4 | 3.4 | 78.5 | 39.5 | 54.0 | 410 | 41.5 | 3.5 | –4.7 | 6.1 | –5.4 | 0.22 | 0.2 | |
| Cameroond | –0.73 | 21.8 | 12.8 | 9.0 | 3.1 | 99.5 | 23.0 | 53,5 | 760 | 7.3 | 4.7 | 0.7 | 10.1 | –8.2 | 0.17 | –1.6 | |
| The Gambia | –0.80 | 20.6 | 11.6 | 9.0 | 3.6 | 53.5 | 11.5 | 43.1 | 350 | 16.5 | 3,8 | –2.2 | 12.9 | 9.1 | 0.44 | 1.0 | |
| Gabond | –0.95 | 32.8 | 24.7 | 8.1 | 2.1 | 116.5 | 22.0 | 51.7 | 3830 | 5.0 | 3.8 | –1.9 | 6.3 | –4.0 | 0.19 | –1.6 | |
| Very-weak-growth countriesg | |||||||||||||||||
| Togo” | –1.08 | 23.6 | 14.7 | 8.9 | 3.5 | 114.5 | 28.0 | 52.8 | 410 | 3.5 | 4.1 | –2.1 | 0.5 | –1.3 | 0.16 | –1.2 | |
| Malawi | –1.17 | 14.7 | 8.5 | 6.2 | 3.2 | 63.0 | 4.0 | 45.2 | 180 | 16.0 | 8.8 | –0.9 | 2.1 | –1.0 | 0.16 | –5.4 | |
| Rwanda | –1.26 | 15.7 | 8.4 | 7.3 | 3.3 | 67.0 | 5.5 | 45.1 | 240 | 6.1 | 5.3 | –0.6 | –5.0 | 2.0 | 0.17 | –1.3 | |
| Central African Republicd | –1.83 | 12.1 | 2.1 | 10.0 | 2.7 | 69.5 | 13.0 | 47.0 | 320 | 5.1 | 5.9 | –0.9 | 9.6 | 0.5 | 0.15 | –1.2 | |
| Ethiopia | –1.89 | 11.4 | 5.7 | 5.8 | 3.3 | 33.0 | 11.0 | 46.1 | 120 | 7.1 | 7.1 | 2.6 | –5.3 | –2.0 | 0.15 | –1.2 | |
| Madagascar | –2.37 | 10.6 | 3.6 | 7.0 | 2.5 | 117.0 | 16.5 | 50.5 | 430 | 16.8 | 4 8 | –5.2 | –6.7 | –2.2 | 0.20 | –1.8 | |
| Sierra Leone | –2.42 | 12.0 | 8.7 | 3.3 | 3.0 | 50.0 | 15.0 | 41.0 | 320 | 74.7 | 8.7 | –0.5 | –2.5 | 1.0 | 0.19 | –1.3 | |
| Zambia | –3.01 | 9.1 | 5,0 | 4.1 | 3.8 | 91.0 | 18.0 | 50.3 | 600 | 64.8 | 9.6 | 0.4 | 3.4 | 0.9 | 0.24 | –2.1 | |
| Côte d’lvoired | –3.61 | 14.5 | 8.7 | 5.8 | 3.7 | 75.0 | 21.0 | 55.1 | 1180 | 4.6 | 9.3 | 0.1 | –5.2 | –7.7 | 0.17 | –0.7 | |
| Nigerd | –4.20 | 12.1 | 2.9 | 9.2 | 3.4 | 27.0 | 5.5 | 44.4 | 44Í) | 2.2 | 6.0 | –5.3 | 0.9 | –0.6 | 0.15 | –1.1 | |
| Group averagesh | |||||||||||||||||
| High-growth countries | 2.92 | 28.0 | 16.7 | 11.3 | 2.6 | 106.6 | 36.6 | 60.0 | 708 | 9.0 | 3.4 | –0.9 | 6.6 | 0.7 | 0.36 | — | |
| Medium-to-low-growth countries | 0.47 | 17.7 | 0.7 | 7.0 | 2.9 | 66.4 | 17.5 | 50.0 | 491 | 21.4 | 4,9 | –5.0 | 4.0 | –2.6 | 0.21 | 0.9 | |
| Weak-growth countries | –0.56 | 26.8 | 18.2 | 8.6 | 3.1 | 91.4 | 21.9 | 53.0 | 966 | 18.4 | 5.1 | –2.9 | 6.7 | –0.9 | 0.23 | –1.3 | |
| Very-weak-growth countries | –2.29 | 13.6 | 6.8 | 6.8 | 3.2 | 70.7 | 13.8 | 47.8 | 424 | 20.1 | 7.0 | –1.2 | –1.5 | –1.0 | 0.17 | –1.7 | |
| CFA franc countries | –0.91 | 19.6 | 11.2 | 8.4 | 2.9 | 76.9 | 22.S | 49.6 | 878 | 4,2 | 6.2 | –1.6 | 4,7 | –2.3 | 0.18 | –0.2 | |
| Non-CFA franc countries | 0.01 | 20.6 | 12.8 | 7.9 | 3.1 | 82.9 | 19.6 | 52.8 | 484 | 25.4 | 5.0 | –3.0 | 2.4 | –0.4 | 0.25 | –1.0 | |
| All countries | –0.30 | 20.3 | 12.2 | 8.0 | 1.0 | 80.8 | 20.6 | 51.7 | 620 | 18.1 | 5.4 | –2.5 | 3.2 | –1.1 | 0.23 | –0.7 | |
See Appendix for definitions and sources of variables.
Only sub-Saharan African countries with complete data series during 1981–92 for all the variables used in the regressions are included.
Countries with average per capita growth rates greater than or equal to 1 percent during 1981 –92.
CFA franc country.
Countries with average per capita growth rates of less than 1 percent but greater than or equal to 0 percent during 1981–92.
Countries with average per capita growth rates of less than 0 percent but greater than or equal to minus 1 percent during 1981–92.
Countries with average per capita growth rates below minus 1 percent during 1981–92.
Unweighted averages.
Table 2 gives the matrix of correlation coefficients between pairs of variables. A number of the conventional and policy variables are significantly correlated with per capita growth. The empirical linkage between private investment and growth is stronger than that between government investment and growth. Interestingly, although the measures of human capital development—life expectancy at birth (LIFE) and primary and secondary school enrollment ratios (PRI and SEC)—are highly correlated. LIFE has a stronger statistical relationship with growth than either PRI or SEC. Therefore, the variable LIFE is used in the regressions. In addition, there is a significant positive correlation between increases in political rights and per capita growth. Furthermore, there is no support for unconditional convergence, as the correlation coefficient between per capita growth and the initial income level is insignificant, albeit positive. Also, the simple correlations between growth, on the one hand, and inflation, the percentage change in the real effective exchange rate, and the change in the terms of trade, on the other, are not significant. As regards the CFA franc countries, their significantly lower rates of inflation during 1981–92 did not translate into higher growth rates than those of the other sub-Saharan African countries; in fact, the CFA franc countries registered on average significantly lower per capita growth rates during 1987–92.
Matrix of Correlation Coefficients for Pairs of Variablesa
See Appdndix for definitions and sources of variables. Panel data are used to calculate these correlation coeficents. Three asterisks, two asterisks, and one asterisk beside the estimated coefficients denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.
Matrix of Correlation Coefficients for Pairs of Variablesa
| In TIY | In PIY | In GIY | In(PG+.05) | In PRI | In SEC | In LIFE | In Y0 | INFL | DEFYE | RERG | XG | TTG | PR | DRY | STRUC | CFA1 | CFA2 | |
| YGPC | 0.36*** | 0.33** | 0.23** | –0.24** | 0.14 | 0. 15 | 0.31*** | 0.08 | –0.10 | –0.36*** | –0.14 | 0.20** | 0.15 | 0.21** | –0.29*** | 0.28*** | 0.09 | –0.25*** |
| In TIY | 1 | 0.85*** | 0.65*** | –0.19* | 0.40*** | 0.25*** | 0.52*** | 0.34*** | –0.38*** | –0.12 | 0.09 | 0.12 | 0.03 | 0.19** | –0.01 | 0.14 | 0.09 | 0.06 |
| In PIY | 1 | 0.21** | –0.14 | 0.47*** | 0.35*** | 0.52 *** | 0.41*** | –0.15 | –0.15* | 0.03 | 0.12 | 0.02 | 0.27*** | –0.08 | 0.18* | –0.1 | –0.03 | |
| In GIY | 1 | –0.14 | 0.008 | –0.01 | 0.24*** | 0.01 | –0.44*** | — | 0.18** | 0.01 | –0.01 | 0.01 | 0.03 | 0.01 | 0.25*** | –0.17* | ||
| In(PG+.05) | 1 | –0.01 | –0.09 | –0.25** | –0.20** | 0.1 | –0.01 | — | — | –0.06 | –0.19** | –0.02 | –0.07 | –0.05 | –0.03 | |||
| In PRI | 1 | 0,72*** | 0.66*** | 0.61*** | 0.06 | –0.14** | 0.02 | 0.01 | –0.04 | 0.19** | 0.03 | –0.05 | –0.13 | –0.13 | ||||
| In SEC | 1 | 0.61*** | 0.66*** | — | –0.20** | 0.04 | 0.09 | 0.01 | 0.28*** | –0.09 | –0.01 | 0.02 | 0.02 | |||||
| In LIFE | 1 | 0.52*** | –0.16* | –0.27*** | 0.06 | 0.0.1 | 0.03 | 0.43*** | –0.12 | 0.09 | –0.13 | –0.13 | ||||||
| In Y0 | 1 | –0.11 | –0.17* | 0.02 | 0.07 | –0.04 | 0.27*** | –0.08 | –0.09 | 0.16* | 0.16* | |||||||
| INFL | 1 | 0.02 | –0.08 | –0.06 | 0.03 | –0.02 | –0.03 | –0.19** | –0.37*** | |||||||||
| DEFYE | 1 | 0.09 | –0.24*** | –0.04 | –0.41*** | –0.02 | –0.36*** | — | 0.14 | |||||||||
| RERG | 1 | –0.17* | 0.10 | 0.12 | 0.12 | 0.03 | 0.11 | 0.05 | ||||||||||
| XG | 1 | –0.14 | 0.05 | –0.13 | 0.11 | 0.06 | 0.07 | |||||||||||
| TTG | 1 | 0.1 | –0.22 | –0.13 | 0.04 | –0.09 | ||||||||||||
| PR | 1 | –0.05 | 0.18* | –0.19*** | –0.17 | |||||||||||||
| DRY | 1 | 0.11 | –0.07 | 0.02 |
See Appdndix for definitions and sources of variables. Panel data are used to calculate these correlation coeficents. Three asterisks, two asterisks, and one asterisk beside the estimated coefficients denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.
Matrix of Correlation Coefficients for Pairs of Variablesa
| In TIY | In PIY | In GIY | In(PG+.05) | In PRI | In SEC | In LIFE | In Y0 | INFL | DEFYE | RERG | XG | TTG | PR | DRY | STRUC | CFA1 | CFA2 | |
| YGPC | 0.36*** | 0.33** | 0.23** | –0.24** | 0.14 | 0. 15 | 0.31*** | 0.08 | –0.10 | –0.36*** | –0.14 | 0.20** | 0.15 | 0.21** | –0.29*** | 0.28*** | 0.09 | –0.25*** |
| In TIY | 1 | 0.85*** | 0.65*** | –0.19* | 0.40*** | 0.25*** | 0.52*** | 0.34*** | –0.38*** | –0.12 | 0.09 | 0.12 | 0.03 | 0.19** | –0.01 | 0.14 | 0.09 | 0.06 |
| In PIY | 1 | 0.21** | –0.14 | 0.47*** | 0.35*** | 0.52 *** | 0.41*** | –0.15 | –0.15* | 0.03 | 0.12 | 0.02 | 0.27*** | –0.08 | 0.18* | –0.1 | –0.03 | |
| In GIY | 1 | –0.14 | 0.008 | –0.01 | 0.24*** | 0.01 | –0.44*** | — | 0.18** | 0.01 | –0.01 | 0.01 | 0.03 | 0.01 | 0.25*** | –0.17* | ||
| In(PG+.05) | 1 | –0.01 | –0.09 | –0.25** | –0.20** | 0.1 | –0.01 | — | — | –0.06 | –0.19** | –0.02 | –0.07 | –0.05 | –0.03 | |||
| In PRI | 1 | 0,72*** | 0.66*** | 0.61*** | 0.06 | –0.14** | 0.02 | 0.01 | –0.04 | 0.19** | 0.03 | –0.05 | –0.13 | –0.13 | ||||
| In SEC | 1 | 0.61*** | 0.66*** | — | –0.20** | 0.04 | 0.09 | 0.01 | 0.28*** | –0.09 | –0.01 | 0.02 | 0.02 | |||||
| In LIFE | 1 | 0.52*** | –0.16* | –0.27*** | 0.06 | 0.0.1 | 0.03 | 0.43*** | –0.12 | 0.09 | –0.13 | –0.13 | ||||||
| In Y0 | 1 | –0.11 | –0.17* | 0.02 | 0.07 | –0.04 | 0.27*** | –0.08 | –0.09 | 0.16* | 0.16* | |||||||
| INFL | 1 | 0.02 | –0.08 | –0.06 | 0.03 | –0.02 | –0.03 | –0.19** | –0.37*** | |||||||||
| DEFYE | 1 | 0.09 | –0.24*** | –0.04 | –0.41*** | –0.02 | –0.36*** | — | 0.14 | |||||||||
| RERG | 1 | –0.17* | 0.10 | 0.12 | 0.12 | 0.03 | 0.11 | 0.05 | ||||||||||
| XG | 1 | –0.14 | 0.05 | –0.13 | 0.11 | 0.06 | 0.07 | |||||||||||
| TTG | 1 | 0.1 | –0.22 | –0.13 | 0.04 | –0.09 | ||||||||||||
| PR | 1 | –0.05 | 0.18* | –0.19*** | –0.17 | |||||||||||||
| DRY | 1 | 0.11 | –0.07 | 0.02 |
See Appdndix for definitions and sources of variables. Panel data are used to calculate these correlation coeficents. Three asterisks, two asterisks, and one asterisk beside the estimated coefficients denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.
The regression results are summarized in Table 3. All regressions are corrected for heteroscedasticity by a feasible generalized least-squares (GLS) procedure implemented in two steps. First, an ordinary least-squares (OLS) procedure was used to estimate each regression equation with pooled data; the residuals from this step were used to calculate the standard deviation for each country. Second, the country-specific standard deviations were used to scale all the included variables, and an OLS procedure was applied again to the pooled transformed data to obtain the feasible GLS estimators. It should be noted that, under the GLS estimation procedure used in this paper, the conventional coefficient of determination (R2) loses its usual interpretation (for details, see Judge and others (1988. pp. 31-2)). Buse (1973) has suggested an alternative measure for the adjusted goodness of fit for GLS models, calculated as the proportion of weighted variation in the dependent variable explained by the regression.
Estimates of the Growth Equationa
The numbers in parentheses below the estimated coefficients are the absolute values of the t-ratios. Three asterisks, two asterisks, and one asterisk beside the estimated coefficients denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.
See Appendix for definitions of variables used.
This column reports the results of the re-estimation of regression (6) using a generalized two-stage least-squares estimation procedure.
Adjusted R2 is an adjusted goodness of fit for heteroscedastic models (Buse (1973)).
Fl is the statistic for the lest of the null hypothesis that the joint effect of all the variables included on the right-hand side of the estimated equation is zero.
Using annual data during 1981–92, four observations are constructed for each of the 29 countries by taking three-year nonoverlapping averages of the variables during the subperiods 1981–83, 1984–86, l987-89, and 1990–92.
F2 is the statistic of the null hypothesis that either the sum of the coefficients for In TIY and In LIFE or the sum of the coefficients for In PIY, In GIY, and In LIFE is equal to the coefficient on ln(PG +.05).
Estimates of the Growth Equationa
| Explanatory variablesb | Regression | ||||||||
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (6’)c | |
| Conventional variables | |||||||||
| In Y0 | 0.0037 | –0.0034 | –0.0048 | –0.0097** | –0.0091** | –0.0085** | –0.0083** | –0.0081** | –0.0182*** |
| (1.14) | (1.14) | (1.48) | (2.62) | (2.20) | (2.07) | (1.99) | (1.99) | (3.40) | |
| In(PG+.05) | … | –0.0867*** | –0.1010*** | –0.0955*** | –0.0853*** | –0.0756*** | –0.0700*** | –0.0868*** | –0.0678*** |
| (3.96) | (4.93) | (4.96) | (4.29) | (4.51) | (4.13) | (4.80) | (2.95) | ||
| In TIY | … | 0.0258*** | … | … | … | … | 0.0189*** | … | … |
| (6.76) | … | … | … | … | (3.89) | … | … | ||
| In PIY | … | … | 0.0146*** | 0.0121*** | 0.0126*** | 0.0113*** | … | … | 0.0292*** |
| (5.36) | (4.18) | (4.29) | (3.99) | (2.95) | |||||
| In GIY | … | … | 0.0082** | 0.0024 * | 0.0095** | 0.0082** | … | … | 0.0126 |
| (2-16) | (1.79) | (2.46) | (2.25) | (1.60) | |||||
| In LIFE | … | … | … | 0.0545*** | 0.0339* | 0.0410** | 0.0435** | 0.0581*** | 0.0499** |
| (2.75) | (1.85) | (2.25) | (2.44) | (3.21) | (2.01) | ||||
| Policy-related | |||||||||
| variables | |||||||||
| INFL | … | … | … | … | –0.004 | –0.001 | –0.005 | –0.021*** | 0.0154 |
| (0.35) | (0.12) | (0.39) | (2.74) | (0.47) | |||||
| DEFYE | … | … | … | … | –0.118*** | –0.109*** | –0.107*** | –0.080** | –0.121* |
| (3.20) | (2.93) | (3.01) | (2.32) | (1.90) | |||||
| RERG | … | … | … | … | –0.039*** | –0.033** | –0.033** | –0.030** | –0.139** |
| (2.86) | (2.50) | (2.49) | (2.61) | (2.41) | |||||
| XG | … | … | … | … | 0.055*** | 0.045*** | 0.042*** | 0.048*** | 0.048 |
| (3.59) | (3.10) | (2.77) | (3.24) | (1.35) | |||||
| STRUC | … | … | … | … | 0.0111*** | 0.0151*** | 0.0156*** | 0.0184*** | 0.0109* |
| (2.87) | (3.60) | (3.83) | (4.48) | (1.66) | |||||
| Other variables | |||||||||
| TTG | … | … | … | … | … | 0.047*** | 0.046*** | 0.050*** | 0.054* |
| (2.71) | (2.69) | (2.81) | (1.95) | ||||||
| PR | … | … | … | … | … | –0.0092 | –0.0052 | 0.0059 | 0.0009 |
| (0.58) | (0.33) | (0.38) | (0.44) | ||||||
| DRY | … | … | … | … | … | –0.0064** | –0.0057** | –0.0073*** | –0.0068 * |
| (2.31) | (1.99) | (2.69) | (1.85) | ||||||
| CFA8183 | 0.0049 | –0.0043 | 0.0004 | 0.0013 | –0.0015 | 0.0013 | 0.0013 | 0.0063 | 0.0040 |
| (0.52) | (0.55) | (0.05) | (0.16) | (0,22) | (0.19) | (0.17) | (0.77) | (0.46) | |
| CFA8486 | — | — | 0.0017 | 0.0042 | 0,0015 | 0.0082 | 0.0057 | 0.0035 | 0.0267*** |
| (0.23) | (0.53) | (0,23) | (1.17) | (0.76) | (0.43) | (2.67) | |||
| CFA8789 | –0.0251*** | –0.0223*** | –0.0186** | –0.0172*** | –0,0230*** | –0.0215*** | –0.0237*** | –0.0288*** | –0.0084 |
| (2.65) | (2.85) | (2.56) | (2.21) | (3.02) | (2.76) | (2.94) | (3.43) | (0.71) | |
| CFA9092 | –0.0110 | –0.0076 | –0.0070 | –0.0028 | –0.0075 | –0.0039 | –0.0038 | –0.0105 | 0.0102 |
| (1.16) | (0.96) | (0.97) | (0.37) | (0.97) | (0.48) | (0.44) | (1.21) | (0.83) | |
| Adjusted R2e | 0.008 | 0.498 | 0.486 | 0.501 | 0,689 | 0.694 | 0.700 | 0.659 | 0.972 |
| F1e | 1.85 | 19.23*** | 13.96*** | 13.08*** | 16.14*** | 13.36*** | 14.67*** | 15.43*** | 7.88*** |
| Nf | 116 | 116 | 116 | 116 | 116 | 116 | 116 | 116 | 116 |
| Implied rate of | |||||||||
| convergence | –0.0036 | 0,0035 | 0.0050 | 0.0101 | 0,0097 | 0.0091 | 0.0088 | 0.0085 | 0.0191 |
| F2g | … | 6.37** | 12.33*** | 0.66 | 1.28 | 0.22 | 0.04 | … | 0.40 |
The numbers in parentheses below the estimated coefficients are the absolute values of the t-ratios. Three asterisks, two asterisks, and one asterisk beside the estimated coefficients denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.
See Appendix for definitions of variables used.
This column reports the results of the re-estimation of regression (6) using a generalized two-stage least-squares estimation procedure.
Adjusted R2 is an adjusted goodness of fit for heteroscedastic models (Buse (1973)).
Fl is the statistic for the lest of the null hypothesis that the joint effect of all the variables included on the right-hand side of the estimated equation is zero.
Using annual data during 1981–92, four observations are constructed for each of the 29 countries by taking three-year nonoverlapping averages of the variables during the subperiods 1981–83, 1984–86, l987-89, and 1990–92.
F2 is the statistic of the null hypothesis that either the sum of the coefficients for In TIY and In LIFE or the sum of the coefficients for In PIY, In GIY, and In LIFE is equal to the coefficient on ln(PG +.05).
Estimates of the Growth Equationa
| Explanatory variablesb | Regression | ||||||||
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (6’)c | |
| Conventional variables | |||||||||
| In Y0 | 0.0037 | –0.0034 | –0.0048 | –0.0097** | –0.0091** | –0.0085** | –0.0083** | –0.0081** | –0.0182*** |
| (1.14) | (1.14) | (1.48) | (2.62) | (2.20) | (2.07) | (1.99) | (1.99) | (3.40) | |
| In(PG+.05) | … | –0.0867*** | –0.1010*** | –0.0955*** | –0.0853*** | –0.0756*** | –0.0700*** | –0.0868*** | –0.0678*** |
| (3.96) | (4.93) | (4.96) | (4.29) | (4.51) | (4.13) | (4.80) | (2.95) | ||
| In TIY | … | 0.0258*** | … | … | … | … | 0.0189*** | … | … |
| (6.76) | … | … | … | … | (3.89) | … | … | ||
| In PIY | … | … | 0.0146*** | 0.0121*** | 0.0126*** | 0.0113*** | … | … | 0.0292*** |
| (5.36) | (4.18) | (4.29) | (3.99) | (2.95) | |||||
| In GIY | … | … | 0.0082** | 0.0024 * | 0.0095** | 0.0082** | … | … | 0.0126 |
| (2-16) | (1.79) | (2.46) | (2.25) | (1.60) | |||||
| In LIFE | … | … | … | 0.0545*** | 0.0339* | 0.0410** | 0.0435** | 0.0581*** | 0.0499** |
| (2.75) | (1.85) | (2.25) | (2.44) | (3.21) | (2.01) | ||||
| Policy-related | |||||||||
| variables | |||||||||
| INFL | … | … | … | … | –0.004 | –0.001 | –0.005 | –0.021*** | 0.0154 |
| (0.35) | (0.12) | (0.39) | (2.74) | (0.47) | |||||
| DEFYE | … | … | … | … | –0.118*** | –0.109*** | –0.107*** | –0.080** | –0.121* |
| (3.20) | (2.93) | (3.01) | (2.32) | (1.90) | |||||
| RERG | … | … | … | … | –0.039*** | –0.033** | –0.033** | –0.030** | –0.139** |
| (2.86) | (2.50) | (2.49) | (2.61) | (2.41) | |||||
| XG | … | … | … | … | 0.055*** | 0.045*** | 0.042*** | 0.048*** | 0.048 |
| (3.59) | (3.10) | (2.77) | (3.24) | (1.35) | |||||
| STRUC | … | … | … | … | 0.0111*** | 0.0151*** | 0.0156*** | 0.0184*** | 0.0109* |
| (2.87) | (3.60) | (3.83) | (4.48) | (1.66) | |||||
| Other variables | |||||||||
| TTG | … | … | … | … | … | 0.047*** | 0.046*** | 0.050*** | 0.054* |
| (2.71) | (2.69) | (2.81) | (1.95) | ||||||
| PR | … | … | … | … | … | –0.0092 | –0.0052 | 0.0059 | 0.0009 |
| (0.58) | (0.33) | (0.38) | (0.44) | ||||||
| DRY | … | … | … | … | … | –0.0064** | –0.0057** | –0.0073*** | –0.0068 * |
| (2.31) | (1.99) | (2.69) | (1.85) | ||||||
| CFA8183 | 0.0049 | –0.0043 | 0.0004 | 0.0013 | –0.0015 | 0.0013 | 0.0013 | 0.0063 | 0.0040 |
| (0.52) | (0.55) | (0.05) | (0.16) | (0,22) | (0.19) | (0.17) | (0.77) | (0.46) | |
| CFA8486 | — | — | 0.0017 | 0.0042 | 0,0015 | 0.0082 | 0.0057 | 0.0035 | 0.0267*** |
| (0.23) | (0.53) | (0,23) | (1.17) | (0.76) | (0.43) | (2.67) | |||
| CFA8789 | –0.0251*** | –0.0223*** | –0.0186** | –0.0172*** | –0,0230*** | –0.0215*** | –0.0237*** | –0.0288*** | –0.0084 |
| (2.65) | (2.85) | (2.56) | (2.21) | (3.02) | (2.76) | (2.94) | (3.43) | (0.71) | |
| CFA9092 | –0.0110 | –0.0076 | –0.0070 | –0.0028 | –0.0075 | –0.0039 | –0.0038 | –0.0105 | 0.0102 |
| (1.16) | (0.96) | (0.97) | (0.37) | (0.97) | (0.48) | (0.44) | (1.21) | (0.83) | |
| Adjusted R2e | 0.008 | 0.498 | 0.486 | 0.501 | 0,689 | 0.694 | 0.700 | 0.659 | 0.972 |
| F1e | 1.85 | 19.23*** | 13.96*** | 13.08*** | 16.14*** | 13.36*** | 14.67*** | 15.43*** | 7.88*** |
| Nf | 116 | 116 | 116 | 116 | 116 | 116 | 116 | 116 | 116 |
| Implied rate of | |||||||||
| convergence | –0.0036 | 0,0035 | 0.0050 | 0.0101 | 0,0097 | 0.0091 | 0.0088 | 0.0085 | 0.0191 |
| F2g | … | 6.37** | 12.33*** | 0.66 | 1.28 | 0.22 | 0.04 | … | 0.40 |
The numbers in parentheses below the estimated coefficients are the absolute values of the t-ratios. Three asterisks, two asterisks, and one asterisk beside the estimated coefficients denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.
See Appendix for definitions of variables used.
This column reports the results of the re-estimation of regression (6) using a generalized two-stage least-squares estimation procedure.
Adjusted R2 is an adjusted goodness of fit for heteroscedastic models (Buse (1973)).
Fl is the statistic for the lest of the null hypothesis that the joint effect of all the variables included on the right-hand side of the estimated equation is zero.
Using annual data during 1981–92, four observations are constructed for each of the 29 countries by taking three-year nonoverlapping averages of the variables during the subperiods 1981–83, 1984–86, l987-89, and 1990–92.
F2 is the statistic of the null hypothesis that either the sum of the coefficients for In TIY and In LIFE or the sum of the coefficients for In PIY, In GIY, and In LIFE is equal to the coefficient on ln(PG +.05).
From regression (1), the hypothesis of unconditional convergence is rejected, confirming the results obtained by Barro and Sala-i-Martin (1992) and Mankiw, Romer, and Weil (1992) for a diverse group of countries, and by Ghura (1995a) for sub-Saharan African countries. Regressions (2) and (3) give the estimation results of the Solow-Swan model, excluding human capital. These results imply a slow but statistically insignificant rate of convergence of about 0.5 percent a year.13 Also, population growth exerts a large adverse impact on per capita growth. Furthermore, the effect of investment is positive and significant, as expected. Regression (3) indicates that the impact of the private investment ratio is larger than that of the government investment ratio, confirming a similar result by Khan and Reinhart (1990). However, on the basis of an F-test. the hypothesis that the estimated coefficients on these two forms of investment are equal could not be rejected. The impact of an increase in the private investment ratio of one standard deviation is estimated to raise growth by about 1 percentage point, and an increase in the government investment ratio of one standard deviation is estimated to raise growth by about 0.5 percentage point. Finally, from regressions (2) and (3), which do not control for human capital, the null hypothesis (given by F2 in Table 3) of the equality of the absolute values of the coefficients of physical capital and population growth is rejected, indicating perhaps the lack of an important component of capital, namely, human capital.
Regression (4) reports the results of the Solow-Swan-type growth model, augmented by human capital. The coefficient on initial income is now negative and statistically significant, implying that, after controlling for human capital, poorer countries tend to grow faster than the less poor ones (conditional convergence). This finding confirms a similar result obtained by Barro (1991) for a diverse set of countries.14 The estimated speed of convergence is about 1 percent a year.15 The measure of human capital is positively and significantly correlated with per capita growth, confirming similar results obtained by Barro (1989) and Mankiw, Romer, and Weil (1992) for a diverse group of countries, and by Ghura (1995a) for sub-Saharan African countries during 1970–90. As in regressions (2) and (3), population growth lowers per capita growth with an elasticity that is much larger than that reported by Mankiw, Romer, and Weil (1992) or Knight, Loayza, and Villanueva (1993). Thus, it appears that increases in population have a much larger adverse impact on pet capita growth in sub-Saharan African countries than in other regions. One way to attenuate this adverse effect would be to raise investment in human capital, as suggested by the significant negative correlation between human capital development and population growth (Table 2). The estimated coefficient on human capital from regression (4) is 0.055 (with a standard deviation of 0.02); the data imply that an increase in life expectancy of one standard deviation (which corresponds to about seven years, for the data sample used in this paper) would raise, on average, per capita growth by about 0.7 percentage point a year. This result is indicative of the potential gains that could be realized from improvements in human capital in sub-Saharan African countries.
Finally, in regression (4), the null hypothesis of the equality between the absolute values of the sum of the coefficients on physical and human capital, on the one hand, and that on population growth, on the other (as given by F2 in Table 3), cannot be rejected. Nevertheless, the estimated share in total income of human and physical capital from the restricted regression is 0.89, which is substantially higher than the share of 0.67 found by Mankiw, Romer, and Weil (1992) for a diverse group of countries.16
The results of regression (5) indicate that the policy environment matters for growth. The estimated coefficients on the budget deficit ratio and the changes in the real effective exchange rate are negative, the coefficient on export volume growth is positive, and are all highly significant. Thus, countries experienced faster growth rates (than other countries in the sample) if their budget deficit ratios were lower,17 their export volume growth rates were higher,18 their structural reform programs were deeper, or their actual real effective exchange rates converged faster toward the respective equilibrium levels. During the period under investigation, most sub-Saharan African countries experienced large deteriorations in their terms of trade, which caused the equilibrium real exchange rate to depreciate, other things being equal. In this context, a real depreciation (that is, a decline in the real effective exchange rate) can be considered as a narrowing of the gap between actual and equilibrium real exchange rates.19
The coefficient on the variable STRVC has a positive and significant effect on growth in regression (5). supporting the view that broad-based structural reforms alleviate the impediments to private sector development and stimulate economic growth. The variable STRUC is significant after controlling for the effects of the other variables, indicating that it is capturing the independent effects of structural reforms. However, inflation did not have a significant, independent direct effect on per capita growth when included with the other policy-related variables, although its effect is negative; it is argued below that the impact of inflation was registered indirectly through its effect on the volume of investment.
It must also be noted that the null hypothesis that the estimated coefficients on the policy-related variables are jointly equal to zero is rejected in regression (5). This result points to the potential benefits of implementing a comprehensive package of policies rather than piecemeal policy actions. Broad-based adjustment policies are more likely to be self-reinforcing and durable than isolated policy reforms, thus enhancing their credibility and the potential response of the private sector.20
Regressions (6) and (7) investigate the effects of some additional factors on growth. The external environment seems to have exerted a statistically significant influence on growth in sub-Saharan Africa during 1981–92. The estimated effect of changes in the terms of trade is positive and significant, supporting the notion that terms of trade losses contributed in part to the poor growth performance of sub-Saharan African countries during 1986–92.21 However, the adverse effects of terms of trade losses on per capita growth seem to have been offset to some extent by declines in the real effective exchange rate. Once account is taken of other determinants of growth, the effect of the variable measuring political rights (PR) is not significant, contrary to the significant effects reported by Fosu (1992) and Sav-vides (1995) for Africa. Finally, inadequate rainfall appears to have reduced per capita growth on average significantly in sub-Saharan Africa during 1981–92.
Regression (8) attempts, albeit in a somewhat crude way, to investigate the effects of economic policies on growth through either the volume or the efficiency of investment. As noted by Kormendi and Meguire (1985), if a policy variable works mainly through the efficiency channel, the inclusion of the investment ratio in the growth equation would raise the significance of the coefficient of the policy variable without substantially changing its value. However, if a policy variable works mainly through the volume of investment channel, the inclusion of the investment ratio would lower the significance and magnitude of its coefficient. The results indicate that both channels are at work for the group of countries included in this study. The significant effect of inflation on growth in regression (8) suggests that this influence is effected through the volume channel.22 The effects of the other policy-related variables, however, are registered mainly through the efficiency channel.23
Another result of interest concerns the dummy variables for the CFA franc countries.24 The average per capita growth rate of the CFA franc countries was about 2 percentage points a year lower than the average for sub-Saharan Africa as a whole during 1987–89, despite the significantly lower levels of inflation recorded in the CFA franc region. The CFA franc countries as a group had significantly lower average government investment rates and measures of human capital development during 1987–89 than the other sub-Saharan African countries. Also, in contrast to the other countries in the region, the decline in the real effective exchange rate of the CFA franc countries during 1987–89 was very modest in comparison to the recorded reduction in their terms of trade. Because the regressions control for all these factors, the dummy for the CFA franc countries could be capturing the effects of the private sector’s low confidence in the thrust of these governments’ policies, as well as the impact of structural rigidities.
The results presented in regressions (2)–(8) might be subject to simultaneity bias, owing to the endogeneity of certain explanatory variables, namely, private investment as a ratio to GDP (PIY). the rate of inflation (INFD, the budget deficit as a ratio to GDP (DEFYE), the percentage change in the real effective exchange rate (RERG), and export growth (XG). One way to correct for this problem would be to use the lagged values of the endogenous explanatory variables in the regressions. However, given this study’s use of period-average data, this methodology would entail a large loss in degrees of freedom. Alternatively, an instrumental variables estimation method could be used to address this problem, even though, as noted by Fischer (1991), good instruments are in practice difficult to find in this type of analysis. Regression (6) in Table 3, which represents the complete growth model given by equation (14), was re-estimated using a generalized two-stage least-squares (G2SLS) estimation method to correct for both simultaneity bias and heteroscedasticity.25 A number of interesting observations can be made from the estimation results, which are shown in the last column of Table 3 as regression (6’).
First, the overall results are broadly consistent with those reported in regression (6). Second, the quantitative impact of the ratio of private investment on growth is higher in regression (6’) than in regression (6). An increase in the private investment ratio of one standard deviation raises growth by 1.8 percentage points, instead of the 1 percentage point estimated earlier. This result reinforces the crucial role played by private investment in the growth process in sub-Saharan Africa. Third, the effect of the ratio of government investment on growth, although positive and greater than reported earlier, is no longer significant at the conventional levels, although it is still significant for the one-tail test. Fourth, the direction of the effects of the policy-related variables are maintained (with the exception of inflation), although these effects are less significant statistically. The fact that the impact of changes in the real effective exchange rate is much greater than estimated in regression (6) reinforces the positive role played by improved external competitiveness in boosting growth. Finally, the coefficient on initial income is now significantly higher in absolute terms, implying a speed of convergence of about 1.9 percent a year, which is close to the 1.8 percent estimated by Barro (1991) for a diverse group of countries.
IV. Conclusions and Policy Implications
The determinants of economic growth have been widely investigated in a number of recent studies. This paper has extended the investigation to the case of 29 sub-Saharan African countries during 1981–92. The main findings of the paper, which are robust to various empirical specifications and methodology, can be summarized as follows. First, there is evidence of conditional convergence of per capita income. The estimated speed of convergence is about 2 percent a year in the model that corrects for simultaneity bias, a result that is consistent with that reported by Barro (1991) for a diverse set of countries. Second, the impact of private investment on growth is positive and significant. An increase of one standard deviation in the private investment ratio is estimated to raise per capita growth by about 2 percentage points in the model that corrects for simultaneity bias. This result reinforces the crucial role played by private investment in the growth process in sub-Saharan Africa. Third, the effect of increases of the government investment ratio on growth is positive, although not statistically robust. Fourth, macroeconomic policies affect per capita growth through their effects on both the volume and the efficiency of investment. Economic growth is stimulated by public policies that lower the budget deficit in relation to GDP (without reducing government investment), reduce the rate of inflation, maintain external competitiveness, promote structural reforms, encourage human capital development, and lower population growth. Finally, adverse exogenous factors (deteriorations in the terms of trade and droughts) have significant negative effects on per capita growth.
Notwithstanding the significant effects of exogenous factors on sub-Saharan African growth, growth in the region can be enhanced by policies that encourage macroeconomic stability, remove tax and other price distortions, and alleviate the impediments to private sector development through structural reforms. The variables measuring the effects of macroeconomic policies and structural reforms are jointly statistically significant in influencing growth, indicating that implementation of a comprehensive package of policies is more beneficial than piecemeal policy actions. As increases in private investment stimulate growth, governments should formulate and implement appropriate policies to encourage private sector development. Broad-based adjustment policies are more likely to be self-reinforcing and durable than isolated policy reforms, thus enhancing their credibility and the potential response of the private sector. In addition, a stable macroeconomic environment would stimulate private sector saving and raise the efficiency and volume of private investment, thus speeding up the achievement of sustainable growth.
The results also indicate that, although lowering the budget deficit is beneficial to economic growth, doing so by cutting government investment is counterproductive. Thus, alternative ways of lowering budget deficits are needed. As many sub-Saharan African countries are characterized by narrow tax bases, weaknesses in tax administration, and a proliferation of tax exemptions, there is significant potential for raising tax receipts by broadening the tax base, improving the tax administration, and rationalizing the tax system. Such reforms would allow an increase in government revenue and investment without necessarily raising tax rates that would tend to undermine private investment. Increased government expenditure on education and health would help raise human capital and contribute to growth, both directly and—by slowing down population growth—indirectly.
APPENDIX
Definitions and Sources of Variables26
YGPC = Growth in per capita real GDP (in percent).
TIY = Total investment as a ratio to GDP (in percent).
GIY = Government investment as a ratio to GDP (in percent).
PIY = Private investment as a ratio to GDP, measured as TIY-GIY (in percent).
PG = Population growth (in percent).
Y0 = Initial income, as measured by per capita GNP in 1980 (in U.S. dollars). Source: World Bank (1994).
HCI = Indicators of human capital development. Three alternative variables are used to measure this indicator:
PRI = average primary school enrollment ratios for the years 1980 and 1990 (in percent);
SEC = average secondary school enrollment ratios for the years 1980 and 1990 (in percent); and
LIFE = average life expectancy at birth during 1980–92 (in years).
Sources; World Bank (1994); and Moock (1988).
INFL = Annual rate of consumer price inflation (in percent).
DEFYI = Government budget deficit (including grants) as a ratio to GDP (in percent).
DEFYE = Government budget deficit (excluding grants) as a ratio to GDP (in percent).
RERG = Percentage change in the real effective exchange rate (REER). For each country, the REER is a weighted index of the nominal exchange rates, adjusted for the differential between the domestic inflation rate and the rates of inflation in partner countries by a geometric weighting method (see Wickham (1987) for details). A positive value for RERG denotes an appreciation of the REER. Source: International Monetary Fund (IMF), World Economic Outlook data base.
XG = Export volume growth (in percent), defined as total exports deflated by export unit value, both measured in U.S. dollars. Source: IMF, World Economic Outlook data base.
TTG = Percentage change in terms of trade. Source: IMF, World Economic Outlook data base.
YGFD = Percentage change in an index of per capita food production. Source: World Bank (1994).
DRY = A dummy variable as a proxy for the extent of inadequate rainfall. It takes a value of 1 if the value of YGFD is less than 0 (and 0 otherwise).
PR = Index of political rights obtained from Gastil (1987), McColm and others (1991), and the Freedom House in New York. The methodology used to calculate this index entails rating countries on a seven-point (1-7) scale for levels of political rights, with a rating of 1 denoting full political rights. For the purpose of the current study, the inverse of these ratings is used, so that a rise in the index denotes an improvement in the degree of political rights. The latter are defined as rights to participate meaningfully in the political process, such as the right of all adults to vote and compete for public office, and of elected representatives to have a decisive vote on public policies.
STRUC = Index of structural reforms, as measured by a dummy variable that takes a value of 1 (and 0 otherwise) for countries adjudged as strong adjusters during 1987–92. Two categories of countries are covered by this classification. First, countries in the sample that implemented broadly appropriate policies under IMF-supported adjustment programs for at least three years during 1986–92, namely, Burundi, The Gambia, Ghana, Lesotho, Malawi, Mali, Niger, Senegal, Tanzania, Togo, Uganda, and Kenya. Second, countries that implemented appropriate policies during 1987–92 and did not need to adopt major adjustment programs, with or without support from the IMF, namely, Botswana, Mauritius, Swaziland, and Zimbabwe.
CFA8183,
CFA8486,
CFA8789,
and
CEA9092 = Dummy variables for CFA franc countries during the subperiods 1981–83, 1984–86, 1987–89. and 1990–92. respectively. The CFA franc countries included in this study are Burkina Faso, Cameroon, Central African Republic, Congo, Côte d’lvoire, Gabon, Mali, Niger, Senegal, and Togo.
CFA1 and CFA2 = Catenation of dummy variables CFA8183 and CFA8486 (CFA1) and CFA8789 and CFA9092 (CFA2).
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