A. Understanding the Post–1995 Improvement in Growth

The stylized facts of growth during 1960-2003 are sobering. For the region as a whole, real GDP grew at an average rate of 3.7 percent a year, and real GDP per capita grew at 1.1 percent.⁶ Real per capita income is approximately the same as in the mid-1970s. Because of very weak overall growth, Africa’s real GDP per capita has steadily lost ground relative to both industrial and other developing country regions. While there have been periods of fast growth in many individual countries, only five countries (Botswana, Equatorial Guinea, Mauritius, the Gambia, and Swaziland) have registered an average growth rate of at least 5 percent. Equatorial Guinea is a special case of oil boom beginning in the 1990s; only Botswana and Mauritius have consistently grown at rates exceeding the long-run mean for developing countries. Growth rates in Africa also tend to be more volatile than in other regions, particularly at short and medium horizons. Growth-accounting decompositions show that average total factor productivity (TFP) growth for SSA has declined in every decade since 1970⁷, which has been called the primary reason for SSA’s slow growth.⁸

There has been a strong improvement in economic growth since the mid-1990s. SSA’s average real GDP per capita growth increased to 2.0 percent in 1995-99, from –1.1 percent in 1990-94, an improvement shared by all subgroups (Figure 1). The number of countries with real GDP growth rates exceeding 5 percent increased from 4 to 15. However, during 2000–03, growth slackened somewhat for all subgroups except oil producers and resource-intensive countries, where it was driven by the 21.6 percent growth in Equatorial Guinea, and conflict countries, where it was driven by the post-conflict recovery in Sierra Leone. The post-1995 growth recovery has been fueled by a significant increase in TFP growth. We consider below the factors accounting for the strong growth in the fastest-growing economies of the 1990s–––that is, those whose real GDP per capita growth rates place them in the top third of the distributions(See Appendix Table A2).⁹

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Real GDP Per Capita Growth (Percent)

Citation: IMF Working Papers 2005, 195; 10.5089/9781451862140.001.A001

Source: IMF, WEO/Economic Trends in Africa database, 2005.Note: See Appendix Table 1 for country groupings.

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Higher growth rates in the 1990s were accompanied by improved macroeconomic indicators (Figures 2 and 3). The average inflation rate in economies that grew the fastest during the 1990s was 12 percent, compared with an average of 21 percent in the slowest-growing economies.¹⁰ Despite spending roughly the same as slow-growing economies as a ratio to GDP, fast-growing economies exhibit lower fiscal deficits including grants because of their higher revenue collections. There is no doubt that the region’s stronger terms of trade growth since the second half of the 1990s has also contributed to the growth recovery. However, the fastest growers of the 1990s do not appear to have experienced more favorable terms of trade growth. They were, however, more open to trade, as indicated by higher ratios of exports plus imports to GDP (Figure 4). Different aspects of the late 1990s growth recovery give mixed signals about its sustainability. On the negative side, except in the oil-producing countries, total and private investment has, on average, barely increased. Excluding Equatorial Guinea’s unique investment rates of 90 percent of GDP in the late 1990s, the fast-growing economies still had slightly better total investment than the medium or slow growers, and maintained it in 2000-03. Investment rates were also higher in non-CFA franc countries (again excluding Equatorial Guinea), but the differential eroded in the most recent period. The positive news is that TFP growth, although moderating in the most recent period, improved strongly in the second half of the 1990s for the first time since the 1960s.¹¹ The fast growers of the 1990s registered TFP growth of 2.3 percent in the second half of the decade (3.3 percent including Equatorial Guinea), while TFP growth in the other two performance groups was negative, or below 0.7 percent. These increases in TFP growth were significantly influenced by improvement in countries with on-track¹² IMF-supported programs (Appendix Table A3).¹³ It is important to note, however, that standard estimates of TFP growth for oil-producing countries, given the structure of their economies, are problematic. While the progress on TFP growth in SSA is less strong when oil producers are excluded, the positive results for the fast growers of the 1990s and for countries with on-track Fund programs are not affected. (See Box 1 on the varied growth experiences in three countries and challenges relating to productivity improvements, responses to shocks, and management of oil revenues.)

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Annual Inflation Rate (Percent)

Citation: IMF Working Papers 2005, 195; 10.5089/9781451862140.001.A001

Source: IMF, WEO/Economic Trend s in Africa database, 2005.

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Fiscal Balance (Percent of GDP)

Citation: IMF Working Papers 2005, 195; 10.5089/9781451862140.001.A001

Source: IMF, WEO/Economic Trends in Africa database, 2005.

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Trade Openness in the Fast, Medium, and Slow Growers of the 1990s (Percent of GDP)

Citation: IMF Working Papers 2005, 195; 10.5089/9781451862140.001.A001

Source: World Bank, World Development Indicators database 2004.

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Many of the inferences noted above are also supported by robustness analyses of crosscountry growth regressions. Recent papers use a new Bayesian technique to address uncertainty about which explanatory variables belong in the model and to address endogeneity of these variables (in the second reference) (Sala-i-Martin, Doppelhofer, and Miller 2004; Tsangarides, 2005). Using a world sample, an extension of the latter found that, in addition to initial conditions, the following variables were robustly correlated with growth: factor accumulation (investment and education); policy variables (inflation, fiscal balance, government consumption, black market premium); and fixed geographical and exogenous factors (percentage of land in the tropics, arable land, and terms of trade growth).

The results of the cross-country growth analysis suggest that Africa’s growth has been substantially lower than that of other regions on account of weak policies, but lower levels of factor accumulation, particularly investment, have implied extremely large growth losses compared with other regions. Clearly, however, lower factor accumulation in SSA is also partly the consequence of weak policies. Appendix Table A4 shows that SSA’s growth could have been about 2 percentage points higher every year if policies had been as strong as those in other developing country regions, such as Latin America or South Asia, and these shortfalls increased slightly in the 1990s. But, strikingly, the estimates suggest that annual growth in SSA could have been substantially higher if it had been able to achieve the same factor accumulation rates—mainly investment—as other developing countries.

Higher growth in the second half of the 1990s than in earlier periods reflects the contribution of improved policies.¹⁴ Appendix Table A5 shows that the fast growers’ improvement in growth relative to the early 1980s reflected the combined positive impact of the policy variables, as well as a small positive contribution from the terms of trade.¹⁵ The contribution from investment, however, was smaller (and had a negative effect on growth for medium and slow growers). The result showing the contribution of policies to growth for fast growers is propelled by the large improvement in the robust policy variables in countries with on-track IMF-supported programs. However, declines in investment for this group had a negative impact on growth. While investment declines negatively affected growth in CFA franc countries, small increases contributed positively to growth in non-CFA franc countries.

B. Identifying Strong and Weak Growth Performers

Since different methods of averaging growth performance across subgroups should be interpreted differently, it is also useful to examine the distribution of growth rates at the country level. Box 2 shows that comparisons of average growth rates across subgroups differ somewhat depending on whether averages are unweighted, GDP-weighted, or population-weighted. Interpreting the effects of weighting is not an issue when we examine the distribution of growth rates by country. Strong growth in a few countries or a particular subgroup, such as the oil producers, has not been primarily responsible for the region’s improvement in growth performance since the mid-1990s; rather, growth has improved in a wide range of countries. A frequency distribution of countries, charted over different intervals of real per capita growth, shows that the percentage of countries with zero or negative rates declined from 58 percent in the 1980s to 12 percent in 1995–99 and 16 percent in 2000–03. At the other end of the distribution, the share of countries with real per capita growth rates above 3 percent increased from 14 percent in the 1980s to 28 percent in the second half of the 1990s and to 23 percent in 2000–03 (Figure 5).

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Sub-Saharan Africa: Frequency Distribution of Per Capita Real GDP and Real GDP Growth Rates (Percent)

Citation: IMF Working Papers 2005, 195; 10.5089/9781451862140.001.A001

Source: IMF, World Economic Outlook database, 2005.

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The top third of the distribution of per capita growth rates in the 1990s includes some countries that have consistently been relatively strong performers and other countries that have moved up since the 1970s and 1980s, when they were in the lower end of the distribution (Appendix Table A2). Focusing on the latter, notable “new” fast growers in the 1990s include such countries as Benin, Ghana, Mali, Mozambique, and Uganda. However, as discussed in Section III, although a good number of countries have achieved strong growth over some period, sustaining the strong performance is much more difficult.

Another way to identify good and poor growth performers is to benchmark countries’ performance relative to a set of exogenous opportunities. As mentioned in the introduction, a useful framework proposed in recent studies suggests that opportunities for growth vary among African countries (depending on availability of natural resources, location, and other characteristics) and that it is important to understand how their political and policy choices in the face of those opportunities have determined growth outcomes. Collier and O’Connell (2005) note that location and resource availability shape the growth opportunities and constraints of all developing countries, and they suggest a grouping of countries as resource abundant, coastal without resources, and landlocked without resources. Conceptually, O’Connell (2004) suggests that a second important dimension (for SSA) is the extent of regionally based political polarization present at the time of independence, which strongly influenced continuing politics of distributional conflict. For example, some countries emerged at independence with a highly polarized politico-economic structure, with a few large groups struggling for control of resources.¹⁶ Considering countries’ actual growth performance in the face of opportunities can provide a useful lens for evaluating successes and failures. One example is Uganda, whose success as one of the fast-growing SSA countries in the 1990s is more striking given that it is landlocked (and transport costs are thus high) and came to independence with deep regionally based cleavages.

Growth benchmarking is a simple way to evaluate countries’ growth performance relative to their opportunities. Growth performance was assessed above on the basis of actual real GDP per capita growth rates; a fuller assessment can be provided when African countries’ growth is compared with what would have been expected given the structural and external conditions they face. Benchmarking compares predicted growth rates based on country circumstances with actual growth and draws attention to unusually large differences between actual and predicted performance (O’Connell, 2004). Using a set of exogenous growth determinants, we estimate a panel regression for the period 1960–2000¹⁷ for a set of 60 developing countries. In addition to geographic dummies (share of land in the tropics), two cross-sectional explanatory variables are included: (1) a trade gravity measure, the predicted ratio of trade to GDP in 1988, which is an exogenous (since it is aggregated up from a gravity model) determinant of exposure to trade and (2) a settler mortality variable, which may capture institutional quality or current health conditions. These variables are augmented by time-varying but exogenous (at the country level) variables: growth in trade partners, growth in the terms of trade, a set of time dummies, and initial GDP levels (convergence effects).

Big differences between actual and predicted growth as determined by the benchmarking methodology are broadly consistent with what is known about country experiences. But, although the lost opportunities of resource-rich countries are well known, there are also countries that have done relatively well despite, for example, being landlocked or poor in resources. As expected, the full sample results show some tendency for the good and bad performers relative to the benchmark to be the good and bad performers overall. Botswana and Mauritius on the positive side and the Central African Republic and Zambia on the negative side fall into this category. These countries are ranked in both the top (bottom) 10 of actual growth and the top (bottom) 10 of differences between actual and predicted growth (growth surprises).¹⁸ Nevertheless, the technique highlights other countries that outperformed their benchmark over the full period, including The Gambia and the Republic of Congo. Conversely, South Africa and Ethiopia are identified as big underperformers.

For the 1990s, benchmarking identifies several countries whose growth experiences, albeit modest overall, were better than difficult circumstances suggested were possible. The composition of the over- and underperformers changes somewhat for the period 1990–2000 relative to the full sample. Burkina Faso, Ghana, and Mozambique move into the top 10 growth surprises, and Uganda moves to the top of this list overall. In addition to the expected presence of conflict-racked countries in the bottom 10 of the 1990s, Cameroon appears as a very large underperformer of that period. While conflict and resource dissipation play a major role in the negative growth surprises, there is a potentially informative cluster of medium but steady growers in the positive growth surprises (Benin, Ghana, and Mali) in addition to the usual rapid growers in this group. When “success stories” are selected by their rates of overall growth, these cases are not at the top of the list.

A. Identification and Bivariate Correlates of Accelerations

Growth accelerations are identified by a comparison of backward- and forward-looking per capita growth rates calculated over a moving window for each country. HPR compare seven-year forward- and backward-looking growth rates of per capita GDP from a given year. An acceleration is identified when the forward-looking rate exceeds the backward-looking rate by at least 2 percent, and the jump is to a level of at least 3.5 percent, with an additional proviso that post-acceleration GDP level must exceed pre-acceleration GDP level (to exclude crisis recovery periods). In the interest of focusing the analysis on more recent experiences, this study uses IMF World Economic Outlook real GDP per capita (PPP) data from 1980-2004, rather than PWT data (only available until 2000), and shortens the acceleration window to five years, allowing identification of acceleration episodes beginning up to 1999. Given lower average SSA growth rates than in the full developing country sample of HPR, while in our definition an acceleration still requires a jump in per capita growth over a 5 year window of at least 2 percent per annum, the cutoff for the post-jump growth rate is 2 percent (rather than 3.5 percent). The requirement that the level of GDP per capita must exceed the pre-acceleration level is retained.

This method identifies 34 growth acceleration episodes in the region since 1980, with more such episodes in the 1990s than in the 1980s, including several episodes currently under way. Episodes occur in countries at all levels of per capita income. The original HPR cutoffs would produce 22 accelerations in total, of which 8 are in the 1980s (indicated by an asterisk in Table 1). In the benchmark set, 6 countries experience two accelerations over the full period, indicating nonetheless that accelerations are a surprisingly widespread phenomenon. Equally however, the presence of accelerations in 28 separate countries over this period of sluggish overall growth points to the difficulty in sustaining them beyond the five year period. This motivates interest, pursued in the probit models below, in analyzing sustained accelerations as well as examining whether the same factors are associated with the accelerations identified by the tighter HPR filter, as for the benchmark set.

Table 1.

Acceleration Start Dates and Per Capita Growth Rates for 1980s and 1990s

Source: IMF staff calculations from World Economic Outlook database, 2004. Notes: GDP per capita data in U.S. dollars. Acceleration episodes last five years and are identified as described in text. Post-episode growth refers to the annual growth rate in the five years after an episode ends. Since an episode itself lasts five years, post episode growth rates can not be calculated for accelerations after 1994. A sustained acceleration (shaded) is one where the average per capita growth was at least 2 percent for five years after an acceleration ends. All growth rates are calculated by a regression of per capita income on a constant trend. An asterisk indicates accelerations where the growth exceeds the 3.5 percent cutoff of Hausman and others (2004). For Chad this cutoff dates are the acceleration to 1981 while for Rwanda it begins in 1994.

Table 1.

Acceleration Start Dates and Per Capita Growth Rates for 1980s and 1990s

	1980s				1990s
	Start date	Episode growth	Post- episode growth		Start date	Episode growth	Post- episode growth
Botswana*	1986	7.7	1.2	Angola*	1993	4.9	2.6
Burkina Faso	1983	3.3	2.9	Benin	1993	2.2	2.0
Burundi	1983	2.4	-0.1	Botswana*	1996	4.7	..
Chad*	1983	3.3	1.4	Burkina Faso*	1994	4.7	3.2
Congo, Rep. of*	1984	5.2	-2.7	Cape Verde*	1992	4.5	5.1
Gabon	1986	2.9	0.5	Chad*	1999	8.3	..
Ghana	1983	2.9	2.0	Côte d’lvoire*	1993	2.3	-4.2
Kenya	1984	2.5	-1.6	Equatorial Guinea*	1994	29.7	18.5
Lesotho*	1986	4.2	2.8	Ethiopia*	1992	3.8	1.4
Mauritius*	1984	7.3	5.6	Gambia, The	1995	2.2	..
Mozambique*	1986	6.0	2.4	Guinea	1994	2.3	0.0
Seychelles*	1987	5.7	2.6	Malawi*	1994	4.8	-3.5
Tanzania	1985	2.3	-1.6	Mozambique*	1994	7.1	5.1
Uganda*	1986	3.9	4.1	Rwanda*	1996	2.6	..
Zimbabwe	1986	2.6	-1.2	Senegal	1994	2.2	1.5
				Seychelles	1995	7.5	..
				Sierra Leone*	1999	10.9	..
				Tanzania*	1999	4.0	..
				Zambia	1999	2.1	..

Source: IMF staff calculations from World Economic Outlook database, 2004. Notes: GDP per capita data in U.S. dollars. Acceleration episodes last five years and are identified as described in text. Post-episode growth refers to the annual growth rate in the five years after an episode ends. Since an episode itself lasts five years, post episode growth rates can not be calculated for accelerations after 1994. A sustained acceleration (shaded) is one where the average per capita growth was at least 2 percent for five years after an acceleration ends. All growth rates are calculated by a regression of per capita income on a constant trend. An asterisk indicates accelerations where the growth exceeds the 3.5 percent cutoff of Hausman and others (2004). For Chad this cutoff dates are the acceleration to 1981 while for Rwanda it begins in 1994.

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Table 1.

Acceleration Start Dates and Per Capita Growth Rates for 1980s and 1990s

	1980s				1990s
	Start date	Episode growth	Post- episode growth		Start date	Episode growth	Post- episode growth
Botswana*	1986	7.7	1.2	Angola*	1993	4.9	2.6
Burkina Faso	1983	3.3	2.9	Benin	1993	2.2	2.0
Burundi	1983	2.4	-0.1	Botswana*	1996	4.7	..
Chad*	1983	3.3	1.4	Burkina Faso*	1994	4.7	3.2
Congo, Rep. of*	1984	5.2	-2.7	Cape Verde*	1992	4.5	5.1
Gabon	1986	2.9	0.5	Chad*	1999	8.3	..
Ghana	1983	2.9	2.0	Côte d’lvoire*	1993	2.3	-4.2
Kenya	1984	2.5	-1.6	Equatorial Guinea*	1994	29.7	18.5
Lesotho*	1986	4.2	2.8	Ethiopia*	1992	3.8	1.4
Mauritius*	1984	7.3	5.6	Gambia, The	1995	2.2	..
Mozambique*	1986	6.0	2.4	Guinea	1994	2.3	0.0
Seychelles*	1987	5.7	2.6	Malawi*	1994	4.8	-3.5
Tanzania	1985	2.3	-1.6	Mozambique*	1994	7.1	5.1
Uganda*	1986	3.9	4.1	Rwanda*	1996	2.6	..
Zimbabwe	1986	2.6	-1.2	Senegal	1994	2.2	1.5
				Seychelles	1995	7.5	..
				Sierra Leone*	1999	10.9	..
				Tanzania*	1999	4.0	..
				Zambia	1999	2.1	..

Source: IMF staff calculations from World Economic Outlook database, 2004. Notes: GDP per capita data in U.S. dollars. Acceleration episodes last five years and are identified as described in text. Post-episode growth refers to the annual growth rate in the five years after an episode ends. Since an episode itself lasts five years, post episode growth rates can not be calculated for accelerations after 1994. A sustained acceleration (shaded) is one where the average per capita growth was at least 2 percent for five years after an acceleration ends. All growth rates are calculated by a regression of per capita income on a constant trend. An asterisk indicates accelerations where the growth exceeds the 3.5 percent cutoff of Hausman and others (2004). For Chad this cutoff dates are the acceleration to 1981 while for Rwanda it begins in 1994.

Empirical investigation sought to identify determinants of accelerations during the 1980s and 1990s using bivariate analysis. A broad range of explanatory variables covering macroeconomic stability, trade, debt, institutions, capital, and geography were examined, some of which can be thought of as triggering an acceleration, and some of which enable an acceleration to continue. We first examine the correlates of accelerations using bivariate analysis, which is useful to give an overall sense of the relationships in the data. However, given the limitations of bivariate analysis, robustness of the findings are verified using multivariate probit models in subsequent sections.

Findings in Table 2 are based on a comparison of average values of economic variables during the acceleration episodes with those during times when there was no acceleration, as well as relative to the period prior to an acceleration, augmented by formal tests of statistical significance. In interpreting the results, one should bear in mind that the analysis is limited to correlations, not causal determinants; it is difficult to distinguish between the causes and the consequences of accelerations.

Table 2.

Differences Between Sample Averages for Acceleration Episodes: Own Past and Nonepisodes

Source: IMF staff calculations. Note: Asterisk (*) indicates that the difference in means was significant in at least a one-tailed test at 10 percent.

Table 2.

Differences Between Sample Averages for Acceleration Episodes: Own Past and Nonepisodes

		1980s			1990s
		Accelerations vs. nonaccelerations: during	Accelerations: during vs. before	Accelerations vs. nonaccelerations: during	Accelerations: during vs. before
Macroeconomic
	Inflation	-2.7	-5.6 *	-1.9	-2.3
	Central govt. bal. to GDP	2.4 *	1.4 *	-0.9	0.5
	REER, percent change	-6.0 *	-9.9 *	-1.8	-2.0
	REER, percent change, non-CFA	-8.5 *	-14.3 *	-1.0	-1.3
	CPIA	0.3 *	0.3 *	0.2 *	0.03
Trade
	Partner growth	0.3 *	1.1 *	0.3 *	0.3 *
	Sachs-Warner (updated)	—	0.03 *	0.04 *	0.02
	Real export growth	10.2 *	14.4 *	5.8 *	6.5 *
Debt
	Debt service	0.7	9.1 *	-2.4 *	-4.3 *
	Debt/GDP	-39.3 *	27.6 *	5.6	8.8
	NPV of debt growth	0.8	-9.4	-4.0 *	-3.8 *
	NPV of debt/exports	0.3	1.5 *	0.3	0.1
Institutions
	Polity index	1.1 *	-2.1 *	0.2	3.9 *
	Longtime leader change	0.2	0.6	1.1 *	1.1
Capital and productivity
	Investment to GDP	1.8 *	-1.4	6.1 *	6.0 *
	TFP growth	0.03 *	0.03 *	2.3 *	3.3 *

Source: IMF staff calculations. Note: Asterisk (*) indicates that the difference in means was significant in at least a one-tailed test at 10 percent.

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Table 2.

Differences Between Sample Averages for Acceleration Episodes: Own Past and Nonepisodes

		1980s			1990s
		Accelerations vs. nonaccelerations: during	Accelerations: during vs. before	Accelerations vs. nonaccelerations: during	Accelerations: during vs. before
Macroeconomic
	Inflation	-2.7	-5.6 *	-1.9	-2.3
	Central govt. bal. to GDP	2.4 *	1.4 *	-0.9	0.5
	REER, percent change	-6.0 *	-9.9 *	-1.8	-2.0
	REER, percent change, non-CFA	-8.5 *	-14.3 *	-1.0	-1.3
	CPIA	0.3 *	0.3 *	0.2 *	0.03
Trade
	Partner growth	0.3 *	1.1 *	0.3 *	0.3 *
	Sachs-Warner (updated)	—	0.03 *	0.04 *	0.02
	Real export growth	10.2 *	14.4 *	5.8 *	6.5 *
Debt
	Debt service	0.7	9.1 *	-2.4 *	-4.3 *
	Debt/GDP	-39.3 *	27.6 *	5.6	8.8
	NPV of debt growth	0.8	-9.4	-4.0 *	-3.8 *
	NPV of debt/exports	0.3	1.5 *	0.3	0.1
Institutions
	Polity index	1.1 *	-2.1 *	0.2	3.9 *
	Longtime leader change	0.2	0.6	1.1 *	1.1
Capital and productivity
	Investment to GDP	1.8 *	-1.4	6.1 *	6.0 *
	TFP growth	0.03 *	0.03 *	2.3 *	3.3 *

Source: IMF staff calculations. Note: Asterisk (*) indicates that the difference in means was significant in at least a one-tailed test at 10 percent.

Growth accelerations do not come at the expense of macroeconomic stability; inflation and budget deficits are either insignificantly different or better in acceleration episodes than in control groups. Inflation is slightly lower during the episodes of accelerated growth, but not significantly so, and the episodes of the 1980s also feature better central government budget balance, including grants. Furthermore, the results for trade variables (discussed further below) show a real exchange rate depreciation in acceleration episodes, which also suggests that inflation expectations are well contained. The most striking finding here is that policies improve for accelerating countries and are better than for countries that did not experience an acceleration of growth. The World Bank’s Country Policy and Institutional Assessment (CPIA), a broad measure of policy stance, shows a positive association with acceleration episodes in both decades.

There is a strong association between acceleration episodes and trade. Episodes are correlated with strong growth in the economies of a country’s trade partners, export growth, and a more competitive real exchange rate. Exports were also facilitated by real effective exchange rate (REER) depreciations, a result that is nearly as strong as when countries in the CFA franc zone are excluded, pointing to the importance of careful management of competitiveness regardless of the exchange rate regime.

Measures of political and economic liberalization have a robust correlation with accelerations; some plausibly function as measures of reforms that trigger growth, such as trade liberalization and leadership transitions. The Sachs-Warner economic liberalization index displays a small but significant association with accelerations in both decades.²⁰ Broader indices of democracy are likely to capture the enabling environment. The composite measure of the autocracy-democracy mix (polity) captures an association between alignment toward democratic institutions and accelerations.²¹ Consistent with recent research, the 1990s evidence also indicates an expansionary role for a transition to new leadership after the departure of a long-time incumbent.

Accelerations coincide with increases in investment and productivity improvements; both higher investment and TFP growth seem to be required for an acceleration to occur. The results support, in particular, an investment-productivity nexus operating for the more recent accelerations. The most important finding here is the role of TFP growth, which is statistically significant for both decades and of considerable economic magnitude for the 1990s.

The growth of the net present value (NPV) of debt falls significantly for 1990s accelerations, pointing to the important role of debt concessionality in supporting surges in growth in the region. Whereas accelerating countries in the 1980s had increased debt-service ratios, the 1990s episodes saw reduced debt-service ratios, as well as reduced growth in the NPV of debt levels. Although countries that experienced growth accelerations also experienced a general rise in the NPV of debt-to-export ratios in the 1980s, they avoided that problem in the 1990s. Concessionality is important for these results, as the face value of debt-to-GDP ratios increases for accelerating countries. It is plausible that relaxed claims on current fiscal revenues through debt relief and greater debt concessionality have facilitated the investment increases associated with growth accelerations.

When the focus is further narrowed to accelerations sustained over 10 years, the key correlates are robust trade and investment, lower debt burdens, and more democratic institutions. Half of the accelerations analyzed above can be considered sustained over the medium term, because per capita annual growth rates over five years following an acceleration episode were also above 2 percent (Table 1). Analysis of the 5- to 10-year growth rates reveals some disappointments, such as Kenya and Zimbabwe in the 1990s and Côte d’lvoire more recently, but also accelerations that were sustained over the medium term in Uganda, Burkina Faso, and Ghana, among others. The methodology looks for statistically significant differences in averages for these sustained episodes compared with unsustained accelerations (Appendix Table A9). The key finding is a strengthened emphasis on favorable trade and debt alignment along with political institutions and investment as correlates of sustained growth. The analysis also shows that sustained accelerations are associated with increases in aid. In addition, aid combined with a good policy and institutional environment is shown to be a strongly significant correlate of the sustained accelerations.

The strong association between accelerations and trade is consistent with literature suggesting that a lack of openness to trade has substantially reduced Africa’s growth. Crosscountry regressions indicate that Africa’s greater closure to international trade than the average developing country has cost the region 0.4–0.7 percentage points a year in growth. Indeed, being less open is more costly to Africa than to other developing countries. These findings are not surprising given the large body of empirical literature that shows that open economies grow faster than closed ones. While these econometric findings should be treated with caution as the debate on the interpretation of such results continues to evolve, research based on other methodologies also supports the view that trade openness promotes growth in Africa. In general, African countries with lower tariffs tend to have higher TFP growth (Figure 6).

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Tariffs and TFP Growth in Sub-Saharan Africa, 1997-2003 (Percent)

Citation: IMF Working Papers 2005, 195; 10.5089/9781451862140.001.A001

Source: IMF staff estimates.

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B. What Is Different About Acceleration Episodes?

The empirical model estimates the probability of being in an episode, or the probability of an episode beginning in a given year, in a multivariate context, implemented using a probit regression. Since the events of beginning an episode or being in one corresponds a discrete event with two outcomes, the dependent variable is equal to one for a year in an acceleration episode or a year when an episode begins and zero otherwise. Because the probit method estimates a linear equation within a cumulative normal density function, the regression coefficients are most easily understood when transformed to show the marginal change in the probability in response to an infinitesimal change in the explanatory variable, evaluated at the mean. This convention is followed for most of the tables below.²²

Variables consistently and positively associated with a country that is in an acceleration episode are real exchange rate depreciation, investment, total factor productivity, debt burden, and the overall quality of institutions and policy as measured by the International Country Risk Guide (ICRG) index. Results are presented separately for accelerations from the 1980s and 1990s, along with the combined sample for both decades and the corresponding set that would be obtained by the HPR cutoff (a jump of 2 percent growth to a rate of at least 3.5 percent) (Table 3).²³ The most robust correlates are those that emerge as significant in the support a strong association between trade-related factors and acceleration episodes combined regression or in both decades separately—providing the list of five variables above.²⁴

Table 3.

Probit Marginal Estimates for Probability of a Country Being in an Acceleration in a Year, 1980-2004

Source: IMF staff calculations. Notes: The indicated coefficient refers to the probability of an infinitesimal change in the independent variable x, evaluated at the mean value of x. The p-value is the analog of the usual regression test for the probit coefficient being zero. The cutoff probability for the goodness of fit calculations is 0.25. Country risk refers to the index from the International Country Risk Guide (ICRG). Results with a smaller set of accelerations are shown in the rightmost column. These require 3.5 percent growth, as in Hausmann and others (2004).

Table 3.

Probit Marginal Estimates for Probability of a Country Being in an Acceleration in a Year, 1980-2004

			1980s		1990s		All Accelerations		All with 3.5 percent growth
			Marginal coefficient	p-value	Marginal coefficient	p-value	Marginal coefficient	p-value	Marginal coefficient	p-value
Macroeconomic
	Inflation		0.002	0.20	0.001	0.67	0.001	0.72	0.001	0.34
	Deficit		0.006	0.12	0.007	0.23	0.007	0.14	0.01	0.01
	Debt and aid
	Aid		-0.020	0.01	0.001	0.88	-0.005	0.04	-0.002	0.17
	Debt service		-0.001	0.65	0.001	0.65	0.001	0.47	0.001	0.73
	Debt net present value burden		0.030	0.01	0.013	0.12	0.020	0.01	0.02	0.01
Trade
	Terms of trade		0.002	0.10	0.003	0.12	0.002	0.10
	Real exchange rate		-0.002	0.01	-0.003	0.05	-0.003	0.01	-0.001	0.1
	Partner growth		0.033	0.05					0.004	0.69
	Export growth		0.003	0.09	-0.001	0.33
Institutions
	Country risk		0.040	0.17	0.090	0.03	0.060	0.05	0.063	0.01
	Sachs-Warner		0.250	0.31	0.130	0.40	0.080	0.31	0.015	0.02
Geographic
	Coastal		-0.040	0.47	0.080	0.26	0.020	0.77	-0.04	0.28
	Resource rich		-0.180	0.01	0.110	0.20	-0.020	0.77	-0.03	0.41
		Capital and productivity
	Investment		0.011	0.01	-0.005	0.26	0.005	0.09	0.001	0.8
	Total factor productivity		0.47	0.25	1.82	0.01	1.49	0.01	0.66	0.03
Summary statistics and goodness of fit:
p-value for chi-squared test				0.01		0.01		0.01		0.01
Pseudo R-squared				0.29		0.11		0.1		0.22
Percent of acceleration years predicted				79		54		58		59
Percent of predicted acceleration years incorrect				52		61		62		68

Source: IMF staff calculations. Notes: The indicated coefficient refers to the probability of an infinitesimal change in the independent variable x, evaluated at the mean value of x. The p-value is the analog of the usual regression test for the probit coefficient being zero. The cutoff probability for the goodness of fit calculations is 0.25. Country risk refers to the index from the International Country Risk Guide (ICRG). Results with a smaller set of accelerations are shown in the rightmost column. These require 3.5 percent growth, as in Hausmann and others (2004).

View Table

Table 3.

Probit Marginal Estimates for Probability of a Country Being in an Acceleration in a Year, 1980-2004

			1980s		1990s		All Accelerations		All with 3.5 percent growth
			Marginal coefficient	p-value	Marginal coefficient	p-value	Marginal coefficient	p-value	Marginal coefficient	p-value
Macroeconomic
	Inflation		0.002	0.20	0.001	0.67	0.001	0.72	0.001	0.34
	Deficit		0.006	0.12	0.007	0.23	0.007	0.14	0.01	0.01
	Debt and aid
	Aid		-0.020	0.01	0.001	0.88	-0.005	0.04	-0.002	0.17
	Debt service		-0.001	0.65	0.001	0.65	0.001	0.47	0.001	0.73
	Debt net present value burden		0.030	0.01	0.013	0.12	0.020	0.01	0.02	0.01
Trade
	Terms of trade		0.002	0.10	0.003	0.12	0.002	0.10
	Real exchange rate		-0.002	0.01	-0.003	0.05	-0.003	0.01	-0.001	0.1
	Partner growth		0.033	0.05					0.004	0.69
	Export growth		0.003	0.09	-0.001	0.33
Institutions
	Country risk		0.040	0.17	0.090	0.03	0.060	0.05	0.063	0.01
	Sachs-Warner		0.250	0.31	0.130	0.40	0.080	0.31	0.015	0.02
Geographic
	Coastal		-0.040	0.47	0.080	0.26	0.020	0.77	-0.04	0.28
	Resource rich		-0.180	0.01	0.110	0.20	-0.020	0.77	-0.03	0.41
		Capital and productivity
	Investment		0.011	0.01	-0.005	0.26	0.005	0.09	0.001	0.8
	Total factor productivity		0.47	0.25	1.82	0.01	1.49	0.01	0.66	0.03
Summary statistics and goodness of fit:
p-value for chi-squared test				0.01		0.01		0.01		0.01
Pseudo R-squared				0.29		0.11		0.1		0.22
Percent of acceleration years predicted				79		54		58		59
Percent of predicted acceleration years incorrect				52		61		62		68

Source: IMF staff calculations. Notes: The indicated coefficient refers to the probability of an infinitesimal change in the independent variable x, evaluated at the mean value of x. The p-value is the analog of the usual regression test for the probit coefficient being zero. The cutoff probability for the goodness of fit calculations is 0.25. Country risk refers to the index from the International Country Risk Guide (ICRG). Results with a smaller set of accelerations are shown in the rightmost column. These require 3.5 percent growth, as in Hausmann and others (2004).

Although a trade interpretation of acceleration episodes is supported by all samples, the 1980s sample features particularly strong evidence that trade channels during an episode are active. It is notable that growth of terms of trade, exports, and trade partners, which might incline to diminish each other’s explanatory power because of collinearity, are all significantly positive in the regression for this decade. Using a general-to-specific estimation strategy, as warranted by the presence of multiple indicators of the same channel, the most robust of the three measures of trade buoyancy is terms of trade growth, which is significant in the combined regression and has borderline significance in the 1990s regression when the other measures of the role of trade are excluded. Finally, regardless of sample and specification, REER depreciation is always significantly correlated with acceleration episodes.

The regressions show a very strong association between institutional quality and policy stance and acceleration episodes. The significant associations between episodes and the ICRG index across all samples and specifications are a multivariate counterpart to the REO finding of a linkage between episodes and good rankings on the World Bank’s CPIA. As a residual variable picking up unmeasured improvements in the productive climate, TFP growth may also be capturing the institutional improvements that accompany accelerations, explaining the positive link found for this variable. One divergence between the results reported here and in Section II.A is that the ICRG is the empirically dominant measure of country institutions and policy in the multivariate context, whereas the CPIA had performed better in the bivariate analysis.

Checks for robustness using different estimation techniques confirm the importance of real depreciation, external conditions, TFP growth, and debt as correlates of accelerations. The final two columns in Table 3 show the correlates of episodes determined by the original HPR cutoffs. Debt burden, real depreciation, and institutional quality remain significant and positive, and the economic liberalization index and budget balance are added to that list. Conversely, the coefficient on aid, which was negative for the larger set of accelerations, is now insignificantly different from zero. Appendix Table A10 reports the results from the use of alternative estimation methods (for parsimony, results are shown only for the full-period sample). A random effects probit is estimated, whereby the error term is allowed to have a country effect drawn from a distribution. The most noteworthy change from the earlier regressions is the loss in significance of the institutional quality variable (ICRG) and the emergence of significant coefficients on the dummies for coastal or resource-rich countries. The likely explanation is that the random effects absorb much of the explanatory cross-sectional country variation in the ICRG, leaving a role for the subregion dummies. This is consistent with the instrumental variable (IV) estimates,²⁵ whereby the ICRG variable is instrumented by historical settler mortality (which has only cross-sectional variation) and returns to its typical strong level of significance.²⁶ The IV regression also attaches significance to the economic liberalization measure while removing significance from aid.

Despite the reasonable significance levels associated with certain variables, the empirical model should also be assessed in terms of its ability to predict acceleration episodes in the sample. The standard goodness-of-fit tests for probit models can be augmented by examination of how well the estimated model predicts positive outcomes. Since the dependent variable is binary and the estimated probabilities range continuously between zero and one, a threshold must be chosen to convert an estimated probability into a prediction. A conventional threshold is 50 percent (that is, estimated probabilities of 0.5 or greater are taken to predict that the dependent variable equals one). However, when the overall frequency of the event is less than 50 percent, researchers have suggested that this cutoff is not appropriate and recommend adjusting it toward the observed frequency in the sample or optimizing a loss function. The model can then be evaluated in terms of two types of prediction: the proportion of actual positive outcomes that were correctly predicted and the proportion of predicted positive outcomes that were incorrect (“false positives”).

When an estimated probability of 0.25 or greater is taken as predicting an acceleration, the model correctly predicts about half of episode years, but also incorrectly identifies a sizable number of nonepisode years as accelerations (Table 3). The model classifies at least half of episode years correctly. But the model tends to make a sizable proportion of incorrect predictions that an episode is occurring; for instance, in the regression for all accelerations (in the third column), 62 percent of all predicted positive outcomes are incorrect. The performance of the random effects and instrumental variables methods is similar to that of the benchmark estimates (Appendix Table A10).The instrumental variable model does somewhat better in matching observed positive outcomes, but both models fare poorly in terms of false positives.

C. What Triggers An Acceleration?

An empirical model that can predict the timing of accelerations is more challenging, given that determining when an acceleration began is imprecise, and the more difficult objective of linking a discontinuous event like an acceleration to big changes in other variables occurring around the same time. To deal with uncertainty about timing, we follow HPR in labeling each episode as beginning in the originally determined year from Table 1 plus the two adjacent years. In addition, the dating of policy transitions includes three leading-in years (in the case of economic liberalization) or three lagging years (in the case of political transitions) to increase the chance that a transitional event overlaps with the beginning of an episode. Finally, to isolate large triggering events, terms of trade growth is transformed into a dummy variable equal to one whenever the three-year change is in the upper 75 percent of the sample.

The analysis finds that economic liberalization, as measured by the Sachs-Warner index, plays a significant role in explaining the timing of accelerations (Appendix Table A11). The variable is a significant factor in the timing of 1990s accelerations and in the combined sample. This is the major difference between our results and those of HPR, who did not find a significant role for liberalization in their timing regressions.

Political transitions are also a significant determinant of acceleration timing. Using the polity indicator from the Polity IV database (which ranges from -10 for autocracy to +10 for democracy) identifies a regime change by a change of 3 or greater in the indicator. Regime changes can be in the direction of increased autocracy (negative change of 3 or greater) or democracy (positive change of 3 or greater). A second measure of political transitions is provided by the leader tenure variable from Olken and Jones (2004), but it does not emerge as significant in the probit framework.²⁷

Similar to HPR’s findings with a global sample, when regime changes are separated into movements toward democracy and those toward autocracy, the latter are a more robust determinant of acceleration timing than the former. Researchers have hesitated to take this implication too literally, suggesting that it may be a proxy for improved state capacity or restoration of control following a period of disorder or conversely, weakened state capacity following a shift to more participatory government. Among the acceleration episodes matched to the negative change in the polity score are Angola in 1993, Ethiopia in 1992, Chad in 1984, and Zambia in 1999. Nonetheless, when a more direct measure of recent civil conflict was included in the same regression, it was not significant.²⁸

Theory suggests multiple channels linking political transitions to growth and generally implies that broad-based governments are most likely to supply the public good prerequisites for sustained growth. The theory is reinforced by consideration of the region’s relatively high number of resource-rich economies. Collier and O’Connell (2005), whose discussion is highly relevant, describe a complicated interaction of resource rents, political structure, and ethnic diversity. When an autocracy in a resource-rich economy is identified with a single ethnic group in an ethnically diverse country, the prioritization of transfers over growth becomes a dominant influence on economic outcomes. But democracy can also have counterproductive effects on growth if rents are dissipated in by individuals seeking votes from groups when checks and balances are weak. Collier and O’Connell confirm an association between autocracy and weak, uneven growth in resource-rich countries in the region. The seemingly contradictory finding here of a link between moves toward autocracy and the timing of accelerations is not robust to in the refinement of the criteria for an episode or in the consideration of sustained accelerations, as discussed below.

Tightening the criteria for an acceleration strengthens the link of timing to economic variables and weakens the link to the autocracy indicator. When the probit timing model is estimated for the sample that identifies an acceleration using the tighter HPR cutoffs, coefficients are significant for the terms of trade variable and for the positive political regime change variable but not for its negative counterpart. Because the HPR cutoff is a tighter filter of high growth experiences, the results suggest that, whatever the positive growth effects of autocratic transitions, they arise for modest accelerations only. In results not reported, we find no econometric gain to interacting the polity changes with ethnic fractionalization. In a probit model with limited degrees of freedom, it is difficult, however, to fully explore the Collier and O’Connell channels relating autocratic political structures to rent transfers in ethnically diverse and resource-rich economies.

The timing model’s in-sample predictive power is poorer than that of the model for being in an acceleration; the rate of incorrect predictions of the start of an acceleration is particularly high. The overall frequency of acceleration initiations is low, so a 15 percent cutoff probability was used. At best, the model was able to match about one-third of positive outcomes, but this outcome is associated with this is a rate of incorrect predictions of positive outcomes of up to 80 percent (Appendix Table A11). This is consistent with HPR’s message: “A lot of takeoffs take place when [] conditions appear not to be particularly favorable… And growth takeoffs typically fail to materialize when the conditions are indeed favorable.” Indeed, this conclusion is reinforced when the episodes are based on HPR’s stricter cutoff. Now, just one fifth of episode starts are correctly called, while most predicted starts are incorrect.

D. What Sustains An Acceleration?

Separating accelerations that were sustained from those that were not reinforces the emphasis on the role of trade, while also pointing to explanatory roles for location and conflict recovery. First, a model that explains the probability of a country’s being in a sustained acceleration episode is estimated (Appendix Table A12) using a specification similar to that used earlier for being in an acceleration episode. The model includes only acceleration episodes and compares values of the variables (during the five-year initial acceleration episode) for sustained versus unsustained episodes. Noteworthy here is the continued role of real depreciations but also significant positive coefficients on dummies for coastal and recent civil conflict countries. This result is understandable given that Mauritius, Mozambique, Seychelles, and Uganda are included in the set of countries that experienced sustained accelerations. Goodness of fit for this model, assessed at a 50 percent probability threshold, is a little better than for the corresponding model from Table 3. However, the model here is for sustained accelerations within the set of all accelerations, so the underlying sample is quite different.

Sustained acceleration occurrence is now negatively correlated with debt burdens, pointing to debt as a constraining factor on growth. Two measures of debt—the ratios of net present value and of debt service to exports—are negatively associated with sustained episodes. This finding has two complementary implications: that debt relief has the potential to spur growth, and growing economies need to avoid accumulating excessive debt. The finding of a negative association between debt and accelerations survives the narrowing of the set of accelerations to those determined by the HPR cutoff, with aid and investment (both positive) being the only other significant variables. This result for debt is linked to a finding from the bivariate correlations in the REO that 1990s accelerations and sustained accelerations are negatively correlated with debt burdens.

Comparing explanations for the timing of sustained accelerations with that of unsustained accelerations directs attention toward economic variables and away from political transitions (Appendix Table A13). From our benchmark sample of accelerations, the economic liberalization variable is significant, as in the earlier timing regressions (Appendix Table A11), but so also are democratic transitions and the recent conflict dummy. This finding marks an alignment of our results with HPR, who find that economic liberalization and moves toward democracy lead to sustained accelerations, but that big terms of trade shocks do not. When sustained accelerations are defined using the tighter HPR cutoffs, of those variables in the original set, only the democratic transition indicator is significant.

E. Summary

Consistent themes from this section’s more detailed analysis and from the REO are the key roles of trade, investment, productivity, and policy and institutional soundness in supporting growth accelerations. Not unexpectedly, the significant variables within these broad categories change somewhat when the analysis shifts from the bivariate to the multivariate context. For example, the probit regressions tend to find the ICRG index rather than the CPIA index to be the stronger correlate of accelerations among the institutional quality variables. In addition, although real depreciation is a robust correlate of accelerations across all specifications, the effect of trade buoyancy is captured variously by trade partner growth, export growth, and the terms of trade.

There are two important cases where findings from the REO reemerge in a different form in the multivariate analysis. First, although the REO found a strong positive link between the polity indicator and acceleration episodes in bivariate analysis, the probit regressions for being in an episode did not reproduce this finding. Recall, however, that the regressions did find a strong role for the ICRG index, which includes indicators of political freedom that overlap with those measured by the polity indicator.

Second, the REO found that debt rose in the 1980s growth acceleration episodes and fell in the 1990s episodes. In the multivariate analysis, debt tends to rise in episodes from both decades, but falling debt is found to be a characteristic of sustained accelerations. This is an important category because, although a relatively large number of countries in the region have been able to generate a single growth acceleration, only sustained growth will deliver significant progress towards the MDGs.

As others have found, the most difficult challenge is to find what can predict the onset of an acceleration. In other words, the search for factors that change around the same time as the initiation of an episode is a much sharper filter than the search for factors that differ between episodes and nonepisodes. The data support a role for economic liberalization and political changes in triggering accelerations. However, the timing of accelerations is very poorly predicted, indicating that the models fall short of providing guidance on either necessary or sufficient conditions for an acceleration to occur.

The findings regarding acceleration triggers vary with the type of episode considered. For the benchmark set as used in the REO, initiation of accelerations is linked to economic liberalization and big changes in the polity indicator, with a move toward autocracy having a strong role in the latter effect. But when the criteria are refined—limited either to sustained episodes or to those with a higher growth rate—only democratic transitions are significant.

The most encouraging finding for policymakers is the link between the quality of policies across a range of dimensions and the propensity for acceleration. The empirical analysis uses indicators of macroeconomic stability, institutional quality, trade openness and productivity and demonstrates their link to the occurrence of an episode. On the other hand, neither resource availability nor geography was consistently associated with episodes, suggesting that an acceleration is feasible for most countries in the region. Finally, the finding that sustained accelerations are associated with lower debt and higher investment along with trade indicates that Africa’s development partners need to continue to help promote growth in the region.

A. Fiscal Policy³⁰

The literature suggests several propositions about the impact of fiscal policy on growth in low-income countries. First, recent papers have found that the channels through which fiscal policy affects growth in low-income countries are different from those in industrial countries, giving rise to a nonlinear effect of deficits on growth.³¹ One paper found a threshold of 2.5 percent of GDP (deficit including grants) at which further fiscal consolidation does not benefit growth.³² This threshold should be considered more of a range, as the relationships between deficits and growth will vary according to country specifics. Second, in general, fiscal consolidations that reduce reliance on domestic financing enhance growth.³³ Third, the composition of fiscal spending affects growth. A higher share of spending on education and health benefits growth, but with a lag. However, this positive effect is reduced if governance is poor or macroeconomic policies are unsound.³⁴

Recent data support the hypothesis of a threshold in the growth-deficit link in SSA. Although causality runs in both directions, a simple way to highlight the deficit-growth channel is to relate lagged changes in deficits to growth and conduct a separate analysis of the link between the direction of changes in the deficit and growth, depending on whether the country is above or below a particular deficit threshold. While clearly not definitive, the simple calculations in Appendix Table A14 support a stronger association between growth and deficit reduction when the deficit is above the 2.5 percent threshold. For high-deficit countries, average growth is higher when the deficit is reduced, whereas low-deficit countries show much smaller growth improvements. The difference in growth rate changes in the two groups is statistically significant.

Since the early 1990s, SSA has seen an overall improvement in fiscal balances accompanied by a more prudent financing mix. Since the mid-1990s, growth has improved and deficits have declined. Since 2000, growth has moderated slightly, whereas deficits show further improvement, allowing countries to reduce the burden on domestic financing sources. Oil producers switched to making net repayments to both domestic and foreign sources, but the trend of relying less on domestic financing is more general. By 2004, on average, SSA governments were making net repayments to domestic sources (Appendix Table A8).

Social sector spending

Since the mid-1980s, SSA countries have increased their outlays on education and healthcare, with government spending increasing both as a ratio of GDP and as a share of total government spending (Figure 7).³⁵ The only exception to this trend is oil-producing countries, where, since the late 1980s, both measures of social sector spending have been declining. In addition, SSA data support the literature’s prediction that strong governance augments the effectiveness of social sector spending.³⁶ SSA countries were ranked according to the quality of governance (World Bank CPIA data, average over the 1990s), level of social sector spending, and education and health outcomes (net enrollment in primary schools and under-5 child mortality in 2000).³⁷ All seven countries that ranked in the top third of the distribution on both governance and education spending also ranked in the top third on education outcomes. Five of the eight countries that ranked in the top third on governance and health spending also ranked in the top third on health outcomes. In contrast, top outcome rankings were relatively few for countries ranking in the top third on only one of the governance or spending indicators.

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Government Spending on Education and Health (Percent of total spending)

Citation: IMF Working Papers 2005, 195; 10.5089/9781451862140.001.A001

Source: IMF, WEO/Economic Trends in Africa database, 2004.

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Infrastructure spending

Declines in capital expenditures are a cause for concern. Since the mid-1980s, public investment in SSA has declined modestly, both as a ratio of GDP and as a share of total spending. These declines partially reflect poor budget execution—persistently lower than programmed capital expenditures, which tend to be cut when there are fiscal overruns occur—and may also relate to shortfalls in foreign financing.

Higher allocations of public investment spending on infrastructure are likely to contribute to growth. Recently, high-profile efforts such as the UN Millennium Project (2005) have focused attention on the severe infrastructure shortage in Africa as a key obstacle to growth. However, results from studies on the impact of public investment on growth both overall and for Africa in particular, do not give clear-cut results.³⁸ Studies that have focused on infrastructure also have mixed results, but some have found a positive, significant contribution to output and growth. A recent panel study of over 100 countries by Calderon and Serven (2004) found that an infrastructure index measuring telecommunications, power, and transport had a positive effect on growth. The index comprises data on the number of main telephone lines, an economy’s electricity-generating capacity, and the length of the road network.

SSA benchmarks compared poorly with low-income countries in other developing country regions, based on the new data on infrastructure stocks and quality. In addition to infrastructure stocks, Calderon and Serven (2004) also compile an aggregate index of the quality of infrastructure services in the three subsectors. Compared with low-income countries in other regions,³⁹ SSA has the lowest value for the infrastructure stock index, and has experienced the slowed growth in the index since 1980–2000 (Figure 8). The infrastructure quality index has deteriorated slightly for SSA, and South Asia is currently the only region where infrastructure quality ranks lower than that of SSA.

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Infrastructure Development in Low Income Countries

Citation: IMF Working Papers 2005, 195; 10.5089/9781451862140.001.A001

Source: Calderon and Serven (2004).Notes: Infrastructure stock uses data on number of main telephone lines per 1000 workers, electricity generating capacity in MW per 1000 workers, and length of the road network in km. per sq. km. of land area. Infrastructure quality uses three indicators: waiting tim for telephone main lines, the percentage of transmission and distribution losses in electricity production, and the share of paved roads in total roads. The indices of infrastructure stock and quality are calculated based on the weights by which the log values of the indicators enter the first principal component analysis. The negative values reflect the log values of fractions.

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The expected growth benefits of reaching the level of infrastructure development of Mauritius, the regional leader, vary across SSA countries. Based on preferred estimates from Calderon and Serven (2004), payoffs are relatively low for countries like South Africa, whose current infrastructure stock is highly developed, and very large for countries like Niger, whose infrastructure is very limited (Appendix Table A16). Estimates suggest that Nigeria would experience the largest growth gain from improving its infrastructure quality to the level of the regional leader, consistent with its current poor quality of infrastructure services. But these expected large growth benefits would require infeasible growth in infrastructure stocks. For example, to reach the level of the regional leader, Ghana would need a 35-fold increase in the number of main telephone lines, a 5-fold increase in power generating capacity, and a 6-fold increase in the density of the road network. These calculations indicate that, according to this model’s specification, the growth effects of more reasonable increases in infrastructure are actually very modest.⁴⁰

B. Financial Development⁴¹

The economies in SSA with the best-developed financial sectors have experienced a higher per capita growth rate than the average, and the differential has widened since the financial liberalization of the 1990s. However, the development of financial markets, as measured by the ratio of liquid liabilities to GDP, has been slow and uneven.⁴² Differences in growth are wider if the oil producers, which experienced high growth but remained financially underdeveloped, are excluded (Figure 9). The weak financial development-growth link in the oil producers may help explain indications from the literature of a somewhat weaker relation between growth and financial development in Africa.⁴³ Excluding oil producers, the economies that grew fastest over 1960-2003 also are those that are the most financially developed (Figure 9). For financial development to stimulate growth, the policy environment must be favorable. In the early 1990s, the persistence of fiscal imbalances, which tend to crowd out credit flows to the private sector, may have weakened the effects of financial liberalization for some African countries.⁴⁴ Substantial government ownership and interference in the banking sector may reduce the quality of banks’ decisions, lowering investment efficiency and growth. A crude segmentation of African countries into four categories depending on financial sector development and growth suggests that the growth-promoting effects of financial sector development may materialize only in conditions of macroeconomic stability (Appendix Table A17). Among the countries with relatively strong financial development indicators, those that grew faster achieved greater macroeconomic stability; that is, they had much lower budget deficits, including grants and lower inflation. This supportive effect of macroeconomic stability for the financial development-growth nexus was even stronger during 1997–2003.

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Financial Development of Countries Classified by Growth (Percent)

Citation: IMF Working Papers 2005, 195; 10.5089/9781451862140.001.A001

Source: World Economic Outlook database, 2004.Note: The six oil-producing countries are classified separately. The remaining countries are classified by quartiles, according to real growth over 1960-2003.

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C. Institutions⁴⁵

Recent evidence in the literature suggests that institutions are the most important determinant of long-run growth. However, improving basic institutions—the laws, rules, and other practices that govern property rights; the freedom to do business; and the sanctity of contracts—can take a long time. In fact, as causation operates in both directions, spurring large improvements in basic institutions may be difficult without sustained growth.⁴⁶ Policies also seem to play a role in fostering institutional development—for example, strengthening competition through trade openness, expanding the public’s access to information, increasing transparency, providing assistance in building institutional capacity, and creating external incentives, such as the peer pressure mechanisms to be used in the New Partnership for Africa’s Development (NEPAD).⁴⁷

The impact of institutional quality on growth is economically significant. In general, poor policies and institutions have explained a large share of the slow growth in Africa.⁴⁸ Studies have found that the annual growth in Sub-Saharan Africa would increase by 1.7 percent if countries in the region adopted the world average quality of institutions.⁴⁹ Moreover, extensive corruption within the political system and inefficient government bureaucracies are found to increase transaction costs and constrain the efficiency of resource allocation.⁵⁰ Tax revenue to GDP ratio in sub-Saharan Africa would increase by 1.5 percent if corruption improved by one unit (for example, from 3 in Kenya, to 4 in Madagascar).⁵¹

The overall quality of both economic and political institutions in SSA has been improving. However, for SSA as a whole, while the improvement in political institutions continued throughout the 1990s, the strengthening of economic institutions plateaued in the late 1990s.⁵² Fast-growing countries generally had better-quality institutions than slow-growing countries. Also, fast- and medium-growing countries have had more improvement in institutional quality than slow-growing countries (Figure 10). These observations have been confirmed by recent objective measures of countries’ economic institutions. In fast-growing countries, starting a business, registering property, enforcing contracts, and closing a business are less costly; urban and rural land property rights for investors and for the poor are more secure, and there are fewer land-related conflicts (Figure 11).⁵³

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The Evolution of Economic and Political Institutions in Sub-Saharan Africa, 1984-2004

Citation: IMF Working Papers 2005, 195; 10.5089/9781451862140.001.A001

Source: International Country Risk Guide (ICRG).Note: Fast, medium and slow growers refer to 1960-2003 period.

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Objective Measures of Economic Institutions in Sub-Saharan Africa, 2004

Citation: IMF Working Papers 2005, 195; 10.5089/9781451862140.001.A001

Sources: World Bank (1994); Diankov and others (2002); Diankov and others (2003); World Bank database 2004.

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While the quality of economic institutions is correlated with the quality of political institutions, the linkage between changes in political and economic institutions in SSA is weak. Recent evidence shows that the quality of political institutions and the degree of political stability influence economic institutions, which, in turn, affect economic performance.⁵⁴ In levels, measures of the economic and political institutions in SSA tend to be strongly correlated; for example, there is a 30–50 percent difference in the index of security of property rights between countries in SSA that have political freedom and those that do not, as measured by Freedom House. In the long run (1980–2000), however, Johnson and Subramanian (2005) show limited correlation between changes in political institutions (measured by the polity indicator) and changes in economic institutions (from ICRG).⁵⁵ We confirm that this weak correlation is evident using a range of alternative indicators of political and economic institutions. Over time, the country level correlations rise from 1980 up to the early 1990s and then fall.

This weak correlation between changes in political institutions and economic institutions is not surprising given trends in these indicators beginning in the 1990s. Significant improvement has been registered in political indicators such as the Freedom House indexes on political rights and civil liberties, the policy democracy index, and indices of the competitiveness of legislative and executive elections.⁵⁶ As noted, however, region-wide, the ICRG indicator of economic institutions has stagnated since the second half of the 1990s.

The sizable number of resource-rich economies in SSA is also a factor related to the weak correlation between changes in political and economic institutions. Resource-rich countries have seen some improvement in political institutions, but this increasing democratization has not been associated with improvements in economic governance. This is consistent with a recent paper’s evidence on the negative effects of democratization on growth in resource-rich countries.⁵⁷ The authors also found that these adverse effects in resource-rich countries were reversed in political systems with intensified checks and balances, particularly freedom of the press.

The quality of economic institutions is also correlated with other structural characteristics of SSA countries. On average, institutions in SSA tend to be strongest in coastal countries, followed by resource-rich countries, and then landlocked countries.⁵⁸ Institutions also tend to be weaker in oil-producing countries, in members of the CFA franc zone, and in conflict countries. Finally, institutional improvement is stronger in countries with on-track IMF programs than it is in both nonprogram countries and countries with off-track programs. While causality is difficult to determine, a recent paper finds that strong institutions improve IMF program implementation.⁵⁹

A. Meaning and Importance of Pro-Poor Growth

The notion of pro-poor growth captures the extent to which economic growth leads to increased welfare for the less well off in a society. An assessment of whether growth is pro-poor thus requires knowledge of how the distribution of income shifts during growth, and how this affects the welfare of the less well off. Studies of pro-poor growth identify the less well-off group as those who fall below the poverty line for income or consumption. If the welfare of the poor depends on the gap between their income and that of richer households, then pro-poor growth would involve more rapid income growth for the poor than the nonpoor. However, noting that global and national development targets call for reductions in the level of poverty, Ravallion (2004a) has argued that pro-poor growth is best defined as growth that reduces the poverty measure of interest. This logic leads to a focus on the change in the income of the poor, which underlies changes in income poverty measures.

In accounting terms, a change in poverty over time contains components attributable to the rate of economic growth, the response of poverty to that growth, and changes in income distribution. This decomposition of changes in headcount poverty has proved to be very informative about the reasons for variations in the rate of poverty reduction across countries. A method proposed by Datt and Ravallion (1992) allows a decomposition of country-level poverty changes into a growth component and an inequality component. The growth component reflects the rate of growth and poverty response to it (the elasticity of poverty with respect to growth), whereas the inequality component reflects changes in distribution. Kraay (2005a, b) presents a variance decomposition method that further attributes the variation in the Datt-Ravallion growth component to variation in the growth rate and the elasticity.⁶⁰ These two components can then be combined with the inequality component to produce three-part decompositions of changes in poverty.

The key finding that emerges from poverty decompositions is that the bulk of the variation across countries in the rate of poverty reduction is due to variation in overall growth. At medium to long horizons, the proportion of poverty-reduction variation explained by growth is close to 100 percent. This means that, in the long run, whether growth is pro-poor is simply a matter of the rate of overall growth. At shorter horizons, variation in inequality and poverty elasticity can matter, but, on average, the effect is small. Moreover, as emphasized by Kraay (2005a, b), the source of variation in the income distribution and elasticity components is poorly understood. Nonetheless, the elasticity of poverty (which is presumed to be negative) is larger in absolute value when inequality is lower and mean income is higher. The intuition, as explained in Heltberg (2002), turns on the fact that the elasticity refers to the percentage change in poverty and not the change in headcount poverty itself. Thus, when income distribution is more unequal, more households fall below any given poverty line and the percentage changes are thus smaller when the distribution shifts. Similarly, the higher is mean income above the poverty line, the fewer households there are (all other things equal) in poverty, generating bigger percentage changes in poverty from a change in mean.⁶¹

Despite the historically minor role of changes in inequality in explaining changes in poverty, SSA has seen sizable changes in inequality since 1980. This finding is especially surprising given the belief that indicators of inequality are quite stable over time. Consider the Gini coefficient, which measures the skewness in the income shares accruing to groups of equal size in a population.⁶² Using data from the World Bank’s Global Poverty Monitoring database, Figure 12 shows the log change in the Gini coefficient for 27 growth-poverty spells in 20 SSA countries between 1980 and 2001.⁶³ There are cases of both large declines and increases in the Gini, with increases in the Gini predominating (several changes in the Gini are near or above 20 percent). There is no obvious tendency for inequality to increase with growth, as a perusal of specific country episodes reveals. For instance, the largest increases in the Gini were experienced by Mali (1989–94) and Niger (1992–95), where mean income fell over the same period. Conversely, large declines in inequality occurred in Ethiopia (1995–2000) and Kenya (1994–97); in the former, mean income fell and, in the latter, it rose. This lack of correlation between growth and changes in inequality is demonstrated in large samples by Adams (2003) and Ravallion (2004b).

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Changes in Inequality for Sub-Saharan Africa (Log change in Gini)

Citation: IMF Working Papers 2005, 195; 10.5089/9781451862140.001.A001

Source: IMF staff calculations and World Bank Global Poverty Monitoring database.Note: The data spells differ by country with some multiple spells for a single country, and span 1980-2001overall. Countries are indicated by ISO code, plus a number where there is more than one spell for a country.

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Researchers have found that SSA has a low elasticity of poverty with respect to growth by global standards, reflecting high inequality and low per capita income in the region. Besley and Burgess (2003) report a global poverty elasticity to per capita GNP growth of -0.73, with elasticities ranging from a high of -1.14 in transition countries to a low of -0.49 in SSA.⁶⁴ Iradian (2005) constructs a large sample of poverty, growth, and inequality spells from multiple sources, and in a regression of the poverty headcount on GDP growth and the Gini, finds a global elasticity to growth of -1.1 and to inequality of 1.4. For SSA, the corresponding numbers are -0.79 and 1.2.⁶⁵ Epaulard (2003) confirms that the poverty elasticity to growth varies as one would expect with respect to initial equality and mean income in a global sample.

Nevertheless, there is large country variation globally and across SSA in the size of the elasticity around the global and regional averages. In addition, the empirical elasticities are only loosely correlated even with their proximate determinants, and more loosely with other measurable country-specific factors. Figure 13 shows a set of “gross” elasticities derived from the World Bank’s Global Poverty Monitoring data—that is, the percentage change in headcount poverty divided by percentage change in income growth for any country for which at least two household surveys are available. In the right panel of Figure 13, the elasticities are plotted against the initial Gini coefficient for the episode; in the left panel, they are plotted against initial income. Note the huge range of values for the elasticity and its lack of obvious correlation with either variable.

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Elasticity of Poverty in Sub-Saharan Africa

Citation: IMF Working Papers 2005, 195; 10.5089/9781451862140.001.A001

Sources: IMF staff calculations; World Bank Global Poverty Monitoring database.

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Active research seeks to clarify the role of policies and country conditions in explaining the evolution of poverty and inequality over time. However, relatively sparse data and multiple directions of causation linking growth, poverty, and inequality have impeded definitive findings. Kraay (2005a) finds that it is difficult to provide a satisfactory empirical model for the elasticity of poverty or changes in distribution, consistent with the mixed record in explaining income distribution patterns across countries (probably because of the presence of poorly measured country-specific determinants thereof). Such unobserved heterogeneity also introduces the risk of bias in regressions explaining income distribution. Nevertheless, the formalization of pro-poor growth has provided a new set of diagnostic tools for analyzing poverty reduction within and across countries and guidance about where to look for explanations of different rates of progress on poverty reduction.

Despite the small role of inequality in explaining the average extent of pro-poor growth, rising inequality undercuts the ability of growth to reduce poverty. Thus, an indicator of inequality like the Gini coefficient is a valuable tool for monitoring a country’s poverty reduction prospects. In addition, the simple mathematics of income distribution and elasticity formulas implies that if African countries could find a painless way to reduce inequality, they would reap a double benefit: an immediate reduction in poverty and a higher elasticity of poverty to growth, meaning that any given rate of future growth would translate into more rapid poverty reduction than in the past (for example, Heltberg, 2002). As Besley and Burgess (2003) emphasize, this result is not a justification for a static redistribution of resources through taxes and transfers. Instead, it will be more important to address administrative or market imperfections that have an undue impact on the poor; property rights and access to financial services are two leading examples. Furthermore, while decompositions such as those reported here provide information on the proximate sources of variation in pro-poor growth, it is necessary to examine the drivers of growth to understand its distributional impact.

B. Channels of Pro-Poor Growth

Recent studies point to three related determinants of the effectiveness of growth in reducing poverty: generation of growth in the agriculture and rural sectors; enhancement of productive capacity, particularly in infrastructure; and management of aid inflows. The following subsections present the detailed evidence for this assertion, but the main points can be summarized here. Many studies of economic development in Africa highlight that development policy has tilted toward donor priorities in the social sectors and that, in the 1990s, it was assumed that the private sector would provide infrastructure.⁶⁶ Donor and government budgets have tended not to make basic rural infrastructure and other investments to enhance agricultural productivity high priorities. Indeed, the lack of an agricultural productivity boom in Africa such as the one South Asia experienced underscores the different growth paths taken by the two regions. Countries in SSA are further constrained by a weak domestic revenue base, which makes their economies vulnerable to both aid fluctuations and donor prioritization of spending needs.⁶⁷ Thus, the legacy of a weakened agriculture sector as a result of traditional urban-biased development policies has not been fully offset.

Since pro-poor growth concerns the ability of the poor to participate in a country’s growth, an assessment of whether a policy is pro-poor should focus on its impact on the productive opportunities of the poor. This thinking leads to recognition of intertemporal trade-offs in the determination of policies. For instance, many health and education policies have a delayed productivity impact because beneficiaries may not be in the labor force until years after the policies are adopted. In addition, the effectiveness of provision of social services depends on the current productive constraints that hold back other sectors of the economy. Recent studies have demonstrated powerful synergies between the attainment of the poverty and sectoral MDGs when economy-wide links are taken into account. These links center on the role of infrastructure in increasing productivity—including that of the health and education sectors. Similarly, while labor-intensive growth in the sectors where the poor are employed would be associated with poverty reduction, the incomes of the poor also depend on the productivity of these sectors.

The exploration of country variation in the link between poverty, growth, and inequality poses significant methodological challenges. The basic problem is the number of possible explanations for the link between growth and poverty relative to the infrequency of household surveys that provide data on poverty and inequality. Thus, the analytical framework needs to be refined to derive more information from the limited range of experiences. Studies have used a variety of methodologies, from country case studies, structural economic models, statistical modeling, and hybrid statistical-narrative approaches. The case-study approach was the centerpiece of the World Bank’s Operationalizing Pro-Poor Growth (OPPG) research program. Structural models offer the ability to trace poverty levels to specific policy and exogenous factors, but their relevance for country experiences is questionable. Regression approaches offer the prospect of formal testable hypotheses, but will quickly exhaust available data unless very carefully specified. These considerations motivate hybrid studies.

C. Summary

Studies provide two types of guidance for policymakers on the sources of pro-poor growth. First, since growth is the most reliable long-run vehicle for poverty reduction, pro-poor growth policies overlap with growth policies. Second, there is large variation across countries in the rate of poverty reduction from a given rate of growth, and, as discussed above, useful tools and methods are available to better understand the nature of this variation. The complexity of the linkages between poverty, growth, and inequality means that only tentative lessons can be drawn for pro-poor growth. Nevertheless, some broad themes have emerged. The lessons build on the fact that the poor are disproportionately rural and depend on agriculture livelihoods, although past policies tended to center on employment in the urban formal sector. Countries have made substantial progress in unwinding explicit policy biases against the agriculture sector, but public spending on agriculture and rural sectors remains low. Since it is difficult for countries to mobilize additional domestic resources in the short run, they are faced with the closely intertwined challenges of raising agricultural productivity and allocating aid inflows prudently.

Recent studies highlight that countries stand to gain from spending aid inflows on public capital, because the induced increase in supply can protect sectors that would otherwise be constricted by relative price adjustments. To the extent that the economy’s general productivity is enhanced, the efficiency of social sector provision also rises. These findings create a case for front-loading aid expenditures toward building absorptive capacity: for example, “general purpose technologies” like infrastructure and communications as well as human capital.

Clearly, there is no single recipe for pro-poor growth. Because data on poverty and inequality are available only on an infrequent basis, policymakers will require more proximate indicators of whether growth benefits the poor. The evidence here suggests that such measures as growth in agriculture, productivity growth at aggregate and sectoral levels, wage and price differentials, and interest rate spreads may provide indicators of the extent to which the environment supports pro-poor growth. However, strategies for pro-poor growth should be formulated on the basis of an analysis of the factors that limiting the participation of the poor in growth at the country level. Agence Française de Développment and others (2005) provide some guidelines on areas for analysis. The suggested lines of enquiry include, for example, sources of growth; the relationship between growth, changes in poverty and changes in income distribution; comparison with other countries in the region on key initial conditions affecting pro-poor growth (fertility, population density, inequality, climatic instability, role of agriculture); distribution of spending across sectors and benefit incidence of these expenditures; sources of income for the poor; and access of the poor to productive assets. Further country-level study is therefore warranted.