metadata: authors: 0: author: Arbache,Jorge Saba
1: author: Page,John
count: Africa
docna: 0: docna: More growth or fewer collapses ? a new look
            at long run growth in Sub-Saharan Africa
docty: Policy Research Working Paper
subtopic: National Governance,Government Policies,International Trade and Trade Rules,Youth and Governance,Food Security,Inflation,Governance Indicators,Inequality
teratopic: Governance,Health, Nutrition and Population,Macroeconomics and Economic Growth,Poverty Reduction
abstracts: cdata!: Low and highly volatile growth define
            Africa's growth experience. But there is no evidence
            that growth volatility is associated to long term economic
            performance.  This result may be misleading if it suggests
            that volatility is not important for economic and social
            progress. In this paper we use a variant of the method
            developed by Hausmann, Pritchett, and Rodrik (2005) to
            identify both growth acceleration and deceleration episodes
            in Africa between 1975 and 2005.  The authors find that
            Africa has had numerous growth acceleration episodes in the
            last 30 years, but also nearly a comparable number of growth
            collapses, offsetting most of the benefits of growth.  Had
            Africa avoided its growth collapses, it would have grown 1.7
            percent a year instead of 0.7 percent, and its GDP per
            capita would have been more than 30 percent higher in 2005.
            The authors also find that growth accelerations and
            decelerations have an asymmetric impact on human development
            outcomes.  Finally, our results suggest that it is easier to
            identify the likely institutional and policy origins of
            growth decelerations than of growth accelerations.
repnme: repnme: More growth or fewer collapses ? a new look
            at long run growth in Sub-Saharan Africa
display_title: More growth or fewer collapses a new
            look at long run growth in Sub-Saharan Africa
listing_relative_url: /research/2007/11/8665641/more-growth-or-fewer-collapses-new-look-long-run-growth-sub-saharan-africa
topicv3: Agriculture and Food Security m1326205 435,Poverty m1326687 695,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Trade m1326924 698,Governance Indicators m1327488 834
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Governance,Health, Nutrition and Population,Macroeconomics and Economic Growth,Poverty Reduction
no_of_pages: 29
author_keys: 0: author_key: 806932
1: author_key: 91200
model_extractions: WPS4384 Policy ReseaRch WoRking PaPeR 4384 More Growth or Fewer Collapses? A New Look at Long Run Growth in Sub-Saharan Africa _Jorge Saba Arbache John Page_ The World Bank Africa Region Office of the Chief Economist November 2007
Policy ReseaRch WoRking PaPeR 4384

# **Abstract**

Low and highly volatile growth define Africa’s growth experience. But there is no evidence that growth volatility is associated to long term economic performance. This result may be misleading if it suggests that volatility is not important for economic and social progress. In this paper we use a variant of the method developed by Hausmann, Pritchett, and Rodrik (2005) to identify both growth acceleration and deceleration episodes in Africa between 1975 and 2005. The authors find that Africa has had numerous growth acceleration episodes in the last 30 years, but also nearly a comparable number of growth collapses, offsetting most of the benefits of growth. Had Africa avoided its growth collapses, it would have grown 1.7 percent a year instead of 0.7 percent, and its GDP per capita would have been more than 30 percent higher in 2005. The authors also find that growth accelerations and decelerations have an asymmetric impact on human development outcomes. Finally, our results suggest that it is easier to identify the likely institutional and policy origins of growth decelerations than of growth accelerations.

This paper—a product of the Office of the Chief Economist , Africa Region—is part of a larger effort in the department to investigate growth and poverty in Africa. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted a tjarbache@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **More Growth or Fewer Collapses? A New Look at Long Run Growth in Sub-Saharan Africa**

Jorge Saba Arbache and John Page The World Bank Keywords: Growth acceleration and deceleration, Sub-Saharan Africa. JEL Code: O11, O47, O55, O57.

Acknowledgements: We would like to thank Vijdan Korman for excellent research assistance, and Monica Das Gupta for comments and suggestions.

Disclaimer: The findings and interpretations of this paper are those of the authors. They do not represent the views of the World Bank, its Executive Directors or the countries they represent.

Corresponding author: Jorge Saba Arbache, World Bank, 1800 H St, NW, Washington, DC, 20433; email jarbache@worldbank.org.
# **1.  Introduction**

During the last three decades growth in Sub-Saharan Africa (hereafter Africa) has been both low and highly variable (Ndulu et al., 2007). Between 1975 and 2005 per capita income PPP grew by 0.7% per year, by far the lowest figure among developing regions. At the same time country growth rates were highly volatile. Interestingly, however, there is no evidence that growth volatility was associated with Africa’s poor long term economic performance (Arbache and Page, 2007a). This result is unexpected (Ramey and Ramey, 1995, Hnatkovska and Loayza, 2004) and may be misleading. Perhaps because no statistical association exists between Africa’s long term growth rate and its volatility, most attempts to explain Africa’s growth performance have focused on investigating the determinants of growth overtime and across countries using standard models and techniques (Ndulu et al., 2007, O’Connell and Ndulu, 2000, Collier and Gunning, 1999). Instead, given Africa’s high growth volatity, it may be more relevant and rewarding to examine the causes and consequences of medium term deviations from the long run trend – growth accelerations and decelerations.

Building on the work by Hausmann, Pritchett and Rodrik (2005) (hereafter HPR), we propose in this paper an empirical method to identify growth accelerations and decelerations relative to a country’s long term growth trend. Focusing on both good times and bad times gives us a broader view of the growth experience, which we believe to be particularly relevant for Africa. To reflect the heterogeneity of African countries’ long run performance, we endogenize economic conditions into the methodology by defining episodes of growth and decline relative to each country’s long run trend growth. In contrast, HPR and related literature impose common parameters across countries to identify growth accelerations.

Using our methodology, we find that African countries have experienced numerous growth acceleration episodes in the last 30 years, but also a comparable number of growth collapses. In short, Africa’s long run record of slow and volatile growth reflects a pattern of alternating, identifiable accelerations and declines, rather than random variations of growth rates around the long run trend. We also find that growth volatility – when viewed as the product of accelerations and declines – is not neutral and indeed matters for economic and social outcomes. To begin to address the public policy questions posed by these results, we 2
look for correlates associated with acceleration and deceleration episodes and examine the probability that an economy will undergo a growth acceleration or deceleration.

This paper is organized as follows. Section 2 briefly discusses the Africa’s growth experience. Section 3 presents the methodology for identifying growth accelerations and decelerations. Section 4 presents the main results. Section 5 examines whether growth accelerations and decelerations matter for economic and social outcomes and looks at correlates of accelerations and decelerations. Section 6 concludes.

# **2.  Africa’s growth 1975-2005**

Data on GDP per capita (PPP at 2000 international prices) and its growth rate are taken from the World Development Indicators, unless otherwise specified. Our sample includes all Sub-Saharan countries, except Liberia and Somalia, for which there are no GDP per capita PPP data.<sup>1</sup> Because we are primarily interested in examining the representative country, we use unweighted country data in the aggregate analysis unless otherwise stated.

Our time series spans from 1975 to 2005.<sup>2</sup> We thus have an unbalanced panel of data with T=31 and N=45. This period follows the first oil-shock and includes the commodities prices plunge, when many African economies collapsed and several conflicts erupted, the introduction of structural reforms, which brought significant changes in many economies, and the recently observed growth recovery.

Figure 1 shows that mean GDP per capita in Africa had a slow, positive long term trend, consisting of about 20 years of virtual stagnation with a point of inflexion upwards in mid-1990s. Since then, actual income has remained above the trend most of the time and the variance appears to have declined.<sup>3</sup> Figure 2 shows Africa’s growth path over the same period. Trend growth declined until the late 1980s, and increased thereafter, although there is evidence of a slowdown in the last years. Variance has declined since the mid-1990s and actual growth has tended to be above and closer to the trend.

> 1 Our sample accounts for more than 98% of population and 99% of the regional GDP in 2005.

> 2 The WDI’s GDP per capita PPP series starts in 1975.

> 3 We employ the Hodrick-Prescott filter in Figures 1 and 2 to smooth the estimate of the long term trend component of the GDP series.

3
<!-- Start of picture text --> Figure 1: GDP per capita<br>2800<br>2400<br>100<br>2000<br>0<br>-100<br>1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>GDP per capita Trend Cycle<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2: GDP per capita growth<br>8<br>4<br>0<br>8<br>-4<br>4<br>0<br>-4<br>-8<br>1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>GDP per capita growth<br>Trend<br>Cycle<br><!-- End of picture text --> At the aggregate level, growth increased substantially during 1995-2005 and was accompanied by a sharp reduction in the coefficient of variation. The per capita growth rate rose to 1.88% in this period from -0.23% of the previous decade, and the coefficient of variation fell to 3.2% from -25.5%. This shift implies an increase of 2% in growth, which is about three-times the long term growth rate of 0.7%. Income per capita went up to $2,486 in 1995-2005, which represents an increase of about $300, or 11%, as compared to previous periods. Recursive residual estimations, Chow breakpoint tests, and Chow forecast tests, do not reject the hypothesis that a structural break in the growth series occurred between 1995 and 1997 (Arbache and Page, 2007b).

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# **3.  Identifying good times and bad**

We define a growth acceleration as a period that satisfies the following four conditions: _Condition 1_ – The forward four-year moving average growth minus the backward four-year moving average growth > 0 for a given year; i.e., the forward moving average window (t, t+1, t+2, t+3) must be higher than the backward window (t, t-1, t-2, t-3) and above 0. A signal change from (+) to (–) or vice-versa suggests growth trend shift;<sup>4</sup> _Condition 2_ – The forward four-year moving average growth exceeds the country’s average growth, meaning that the pace of growth during acceleration is higher than the country’s trend; _Condition 3_ – The forward four-year moving average GDP per capita exceeds the backward four-year moving average; _Condition 4_ – A growth acceleration episode requires at least three years in a row satisfying conditions 1-3. An episode includes the three subsequent years after the last year that satisfies conditions 1-3; i.e., we attach the moving average window to the years identifying the growth acceleration.<sup>5</sup> Condition 2 endogenizes the country’s economic conditions, because its growth trend is a key parameter for identifying growth acceleration episodes. There is clearly a risk that by identifying a period of modest, sustained growth in a low growth economy as a growth acceleration episode we will assign too much significance to a minor change in economic performance. But it is also true that a period of relatively modest per capita growth, say 2%, may well be a genuine growth boom in a country enduring very low growth rates, and a decline in per capita income of equal magnitude could spell a serious economic collapse in a stagnant economy.

Condition 2 also helps to limit the number of identified accelerations in countries with sustained, long run growth: if a country, for example, is growing rapidly it will lift the growth trend, reducing the number of estimated accelerations. This is particularly significant for countries experiencing very low or very high growth rates. Condition 3 ensures that the growth acceleration episode is not a recovery from a recession.

> 4 The window size may change according to the long term growth volatility of a set of countries and/or region: the higher the volatility, the lower should be the window size if one wants to observe the effects of volatility on economic performance.

> 5 As an example, if conditions 1 to 3 identify growth acceleration during, say, 1991 to 1995, the years 1996, 1997 and 1998 are included as part of the episode. Thus, this growth acceleration episode comprises a period that starts in 1991 and ends in 1998.

5
The identification of growth deceleration episodes requires the following adjustments: in _Condition 1_ , the forward four-year moving average growth minus the backward four-year moving average growth < 0 for a given year; in _Condition 2_ , the forward four-year moving average growth is below the country’s average growth; in _Condition 3_ , the forward four-year moving average GDP per capita is below the backward four-year moving average.

The methodology has three main characteristics that affect the interpretation of the results: first, it identifies good and bad times rather than only focusing on rapid growth spells or deep collapses; second, it carries over inertia when identifying growth accelerations and decelerations; and third, it is sensitive to the length of time series.

Figures 3-5 illustrate the methodology at work. Condition 1 alone would identify 1993-2005 in Tanzania as a growth acceleration episode (Figure 3). But only the period 1998-2005 satisfies Conditions 1-4 and is identified as a growth acceleration. Condition 3 identifies 1993-1998 is a recovery from a recession.

<!-- Start of picture text --> Figure 3 : Tanzania - GDP per capita growth rate and growth acceleration<br>6<br>5<br>4<br>Conditions 1 to 4<br>satisfied<br>3<br>(1998-05)<br>Only condition 1<br>2<br>satisfied<br>(1993-05)<br>1<br>0<br>-1<br>-2<br>-3<br>-4<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> The cases of Senegal and South Africa illustrate the method further. The dotted lines show deceleration periods, while the shaded lines identify acceleration periods. Senegal experienced a contraction between 1988 and 1994; the average growth rate during this period 6
was -1.4%, well below the average of 0.35% (Figure 4). In contrast, average growth in 19942001 – a growth acceleration – was 1.75%.

<!-- Start of picture text --> Figure 4: Senegal - GDP per capita growth rate and growth acceleration and deceleration<br>15<br>10<br>Growth acceleration<br>5<br>(1994-01)<br>0<br>-5<br>Growth deceleration<br>(1988-94)<br>-10<br>1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005<br><!-- End of picture text --> In the case of South Africa, the average growth rates during the downturns 1982-1987 and 1989-1994 were -1.91% and -1.54%, respectively, compared to the overall mean of 0.12% (Figure 5). During the growth acceleration period, 1999-2005, growth was 1.96%.

Table A1 in the Appendix shows the start years of growth acceleration and deceleration episodes and compares our results with those from two closely related studies. HPR in their seminal work identify growth accelerations worldwide using GDP per capita growth from 1950 to 1999 from the Penn World Tables. They compare seven-year forward and backward moving average growth windows and impose a cutoff that the forward moving average window should exceed the backward moving average window by at least 2%. They further require that average growth must be at least 3.5% during the acceleration episode.

7
<!-- Start of picture text --> Figure 5: South Africa - GDP per capita growth rate and growth acceleration and<br>deceleration<br>5<br>4<br>Growth acceleration<br>3 (1999-05)<br>2<br>1<br>0<br>-1<br>-2<br>-3 Growth deceleration<br>(1989-94)<br>-4 Growth deceleration<br>(1982-87)<br>-5<br>1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005<br><!-- End of picture text --> Pattillo, Gupta, and Carey (2005) (hereafter PGC) have applied a similar methodology to Africa only, using GDP per capita PPP growth between 1980 and 2004 from the IMF World Economic Outlook.<sup>6</sup> To address the higher volatility and lower overall rate of growth in Africa the authors use a five-year window and require that growth average be at least 2% during the acceleration.

The main differences between these two methodologies and the one proposed here are that: first, the moving average windows are bigger than ours. Second, they impose a cutoff of at least 2% in the forward minus backward moving average window, whereas we impose a cutoff of zero. Third, they impose a common minimum growth rate to define an acceleration for all countries, whereas we use the country’s growth trend as the cutoff. In general, our calculations accord with those the other two studies, but since our filter is more flexible – and identifies decelerations – it picks up more episodes; we find 32% more episodes for Africa than PGC and 114% more than HPR.

> 6 PGC calculated the growth acceleration episodes using the HPR methodology and PPP growth data. So results in Table A1 are fairly comparable to ours, despite slight differences between the IMF and WDI data. The IMF’s and WDI’s data generally follow the same pattern. However, the WDI’s GDP per capita tends to be slightly lower than the IMF’s. For a discussion on the discrepancies between the IMF and WDI GDP data see Africa Development Indicators 2006 (p. 114).

8
# **4.  Results**

Table 1 shows the frequency of accelerations and decelerations and their associated growth rates during selected periods. For the full period there is a slightly higher probability of a growth acceleration than deceleration: 25% of the 1,243 country-year observations (total of valid observations per country per year) belong to growth accelerations, while 22% are classified as growth decelerations.<sup>7</sup> Between 1975 and 2005 countries in Africa that experienced growth accelerations managed to grow on average by 3.6% during those episodes, compared with the region-wide average of 0.7%. During decelerations countries contracted on average by -2.7%. Given the almost equal probabilities of growth accelerations and decelerations, most of the benefits of growth accelerations in the continent were offset by growth collapses, leading to the region’s overall tepid rate of growth. Had Africa avoided its growth collapses it would have grown at 1.7% a year in per capita terms instead of 0.7%. Figure 6 shows the actual and simulated GDP per capita at these growth rates. Income per capita would have been at least 30% higher in 2005 from avoiding bad times.<sup>8</sup> Growth decelerations matter a great deal for fighting poverty in Africa The relative frequency of good and bad times is reflected in Africa’s long run pattern of growth. Accelerations are more frequent in 1995-2005; decelerations are more common in the two preceding decades. Forty two percent of the 494 country-year observations of 19952005 occur in countries experiencing growth accelerations, and only 12% in countries undergoing growth decelerations. The remaining 46% of observations belong to years in which countries were experiencing neither growth acceleration nor deceleration. In 19751984 growth decelerations were 350% more frequent than accelerations.<sup>9</sup> In 1985-1994 this ratio had dropped to 71%, mainly due to a sharp rise of accelerations to 21% from 4%.

> 7 To check the robustness of our results we also identified growth accelerations and decelerations by replacing 0 with +1% and -1% for acceleration and deceleration, respectively, in condition 1, but the results did not change substantially. We therefore report only the base case results because they are less restrictive.

> 8 The simulated growth rate without collapses takes into account growth rate during all country-years but growth deceleration years. The additional GDP per capita results from the difference in compound growth at 1.7% and 0.7% in 1975-2005.

> 9 Calculated as ((0.18/0.04)-1)*100.

9
|**DP per**<br>**capita**<br>**G**<br>**ion**|1,794|1,765|1,804|1,797|1,794|
|---|---|---|---|---|---|
|**ita**<br>**Growth rate**<br>**owth decelerat**|-2.74|-3.06|-3.18|-1.29|-3.14|
|**d GDP per cap**<br>**Frequency**<br>**(country-**<br>**years)**<br>**Gr**|0.22|0.18|0.36|0.12|0.29|
|**GDP per**<br>**capita**<br>**on**<br>**owth rates, an**|2,598|2,796|2,907|2,449|2,892|
|**Growth rate**<br>**owth accelerati**<br>**deceleration, gr**|3.64|4.61|3.21|3.76|3.39|
|**Frequency**<br>**(country-**<br>**years)**<br>**Gr**<br>**cceleration and**|0.25|0.04|0.21|0.42|0.14|
|**GDP per**<br>**capita**<br>**P**<br>**cy of growth a**|2,299|2,180|2,183|2,486|2,182|
|**GDP growth**<br>**GD**<br>**ble 1: Frequen**|0.70|0.13|-0.23|1.88|-0.07|
|**Observations**<br>**Ta**|1,243|316|433|494|749|
|**Period**|1975-2005|1975-1984|1985-1994|1995-2005|1975-1994|
<!-- Start of picture text --> Figure 6: Actual and simulated GDP per capita<br>3,600<br>3,400<br>3,200<br>3,000<br>2,800<br>2,600<br>2,400<br>2,200<br>2,000<br>Actual GDP per capita<br>GDP per capita growth at the observed average (0.7% pa)<br>GDP per capita growth in the no-collapse scenario (1.7%pa)<br>$3,469<br>$2,639<br>$2,579<br>$2,092<br>1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005<br><!-- End of picture text --> In 1995-2005, the average growth rate for countries during acceleration episodes was 3.8%, the second highest average among the three ten year periods. Interestingly, it was in 1975-1984, a period of very modest regional economic growth, that average growth during accelerations reached its highest rate. This reflects a compositional effect at work. In the last decade even long stagnant economies such as the Central African Republic, Ethiopia, Mali, Mozambique, Sierra Leone, and Tanzania, experienced some sustained growth, pushing down the averages during acceleration episodes, whereas in 1975-1984 the high average growth rate was mainly due to a small number of growth accelerations over all and very rapid growth in the Republic of Congo.

The average (negative) growth rate for countries experiencing growth decelerations in 1995-2005 was less than half that in previous decades, contributing to the more positive overall economic performance of the period. Economic declines had both the highest frequency – double that of the next highest decade – and the greatest impact on countries during the period 1985-1995.

Over the entire 30 year period richer countries have had more growth accelerations and poorer countries more growth collapses. This is of course to some 11
extent endogenous; average income per capita will tend to rise in countries with more frequent growth accelerations and fall in countries with more frequent collapses. But this result also holds in each ten year period, where the compounding effects may be assumed to be less important. This may indicate that richer countries are better able to take advantage of propitious circumstances and that poorer countries are less able to avoid bad times. There is one interesting exception. Income per capita for countries experiencing growth accelerations in 1995-2005 is slightly below the average for the region overall, indicating that growth successes have been spreading to poorer countries in the past decade.

Table 2 shows the frequency of growth acceleration and deceleration episodes by country category and compares them with the mean. In general, there is no substantial difference in the probabilities of growth acceleration and deceleration episodes for a given country category. But, while geography does not appear to matter, geology and conflict do.<sup>10</sup> As might be expected, oil exporters and resource rich countries have more frequent growth accelerations, but somewhat unexpectedly, the same frequency of growth decelerations as the regional average. Conflict is also important in determining good times and bad. Major conflict countries had fewer growth accelerations than the regional average but also fewer decelerations. They also had significantly lower average growth than the regional average. Taken together these results suggest that major conflict countries were trapped in a low level equilibrium. Minor conflict countries have a substantially higher probability of a growth deceleration than the average and are much more likely to experience bad times than good times.

**<u>Table 2: Frequency of growth acceleration and deceleration by country category</u>**

|**Country category**|**Growth ac**|**celeration**|**Growth de**|**celeration**|
|---|---|---|---|---|
||**Frequency (country-**<br>**years)**|**Above/below all**<br>**countries' mean**|**Frequency (country-**<br>**years)**|**Above/below all**<br>**countries' mean**|
|All countries'mean|0.25|-|0.22|-|
|Coastal|0.26|Above|0.22|Equal|
|Landlocked|0.23|Below|0.22|Equal|
|Coastal without resources|0.24|Below|0.23|Above|
|Landlocked without<br>resources|0.22|Below|0.22|Equal|
|Oil exporters|0.29|Above|0.23|Above|
|Non-oil exporters|0.24|Below|0.22|Equal|
|Resource countries|0.30|Above|0.21|Below|
|Non-resource countries|0.23|Below|0.23|Above|
|Major conflict|0.16|Below|0.17|Below|
|Minor conflict|0.19|Below|0.32|Above|

10 See country assignment in Table A2 in Appendix.

12
Table A3 in the Appendix shows the unconditional probabilities of growth acceleration and deceleration at the country level and the growth rates during these episodes. The gaps between growth rates during accelerations and decelerations at the country level tend to be high, generating the high growth volatility observed in Africa. The high average growth rates observed in many economies during acceleration episodes also show the resilience and capacity of the region’s economies to grow when economic and political conditions favor growth. The magnitude of economic contractions during deceleration episodes similarly indicates the severity of the consequences when economic and political conditions are unfavorable.

There are 16 countries in our sample that have avoided growth decelerations altogether. Many – Botswana, Cape Verde, Equatorial Guinea, Lesotho, Mauritius, Mozambique, Uganda – are among the region’s top performers in per capita income growth over the three decades, but not all. Burkina Faso, Guinea, Namibia, São Tomé and Príncipe, and Swaziland are not among the region’s growth leaders. Avoiding growth collapses is important for long run success at the country level, but is not the only factor contributing to robust long term growth.

Seven countries – DRC, Eritrea, Gabon, The Gambia, Madagascar, Mauritania and Niger – have never had a growth acceleration. Of these only Eritrea shows good long term per capita income growth. Four of the seven had long run declines in per capita income.

# **5.  Do growth accelerations and decelerations matter?**

We have shown that growth accelerations and decelerations are an important feature of Africa’s low and volatile long run growth, but do they matter for economic and social outcomes beyond their direct consequences for the rate of growth? If growth accelerations and decelerations have non-neutral impacts, one would expect that economic, social, and governance indicators will be different during such episodes than during normal times. In this section we investigate the hypothesis of non-neutrality of growth volatility by examining differences in mean values in countries experiencing growth acceleration and deceleration episodes and simple correlations between changes in key economic and social variables and the presence or absence of growth 13
accelerations/decelerations. Table 3 shows sample averages during growth accelerations, decelerations, and “normal” times. – defined as the absence of either. Table 4 gives the correlation coefficients between a number of economic, social governance and institutional characteristics and the frequency of acceleration and deceleration episodes.

Table 3 reveals an asymmetric relationship between growth accelerations and decelerations and some economic indicators. The major changes in national accounts during growth episodes take place in investments and savings rather than in consumption. Savings and investments are higher during accelerations as compared with normal times, and substantially lower during deceleration episodes. Foreign direct investment during accelerations is six-times the figure for deceleration episodes.

Table 4 shows that countries that have high savings and investment have a higher probability of growth acceleration and less probability of deceleration. Consumption is relatively lower during growth accelerations, which is consistent with the higher allocation of resources for investment. But consumption is also lower during decelerations, which is probably due to the fall in purchasing power of households.

With regard to the structure of the economy, the share of the agriculture sector is slightly higher in countries experiencing decelerations, while industry’s share is slightly larger in countries going through accelerations. Correlations suggest that countries that rely more on agriculture have more spells of growth deceleration, possibly because of higher exposure to insects, draughts and other natural disasters, but also because of swings in agriculture commodity prices.

Macroeconomic management appears to be an important factor in both good times and bad times. Decelerations are accompanied by high inflation; one recent example is Zimbabwe. There is a positive correlation between inflation and the frequency of growth decelerations. Public debt is higher during both acceleration and deceleration episodes than during normal times, and government consumption falls during both accelerations and decelerations. Correlations suggest that countries that increased their debt also experienced more growth accelerations and decelerations, which may support the view that prudent debt management is important for reducing growth volatility.

14
|**Growth acceleration**<br>**Growth deceleration**|**During**<br>**Otherwise**<br>**t-test**<br>**During**<br>**Otherwise**<br>**t-test**<br>Savings (% GDP)<br>15.2 (.77)<br>10.2 (.43)<br>*<br>7.46 (.61)<br>12.7 (.45)<br>*<br>Investments (% GDP)<br>23.7 (.71)<br>19.4 (.32)<br>*<br>15.9 (.43)<br>21.9 (.36)<br>*<br>Private sector investment (% GDP)<br>13.71 (.66)<br>11.22 (.29)<br>*<br>9.20 (.42)<br>12.8 (.34)<br>*<br>Foreign direct investments net flow (% GDP)<br>4.12 (.63)<br>1.97 (.17)<br>*<br>.77 (.15)<br>3.05 (.26)<br>*<br>Consumption (% GDP)<br>88.5 (.99)<br>92.3 (.59)<br>*<br>89.4 (.74)<br>91.9 (.62)<br>*<br>Agriculture value added (%GDP)<br>28.6 (.91)<br>30.4 (.49)<br>*<br>31.9 (.89)<br>29.4 (.46)<br>*<br>Industry value added (% GDP)<br>26.9 (.82)<br>24.0 (.43)<br>*<br>24.6 (.75)<br>25.8 (.44)<br>Service value added (% GDP)<br>44.3 (.83)<br>44.5 (.37)<br>43.5 (.64)<br>44.7 (.40)<br>Trade (% GDP)<br>75.4 (2.29)<br>70.1 (1.26)<br>*<br>58.8 (1.6)<br>75.1 (1.3)<br>*<br>Exports (% GDP)<br>31.6 (1.15)<br>29.0 (.63)<br>*<br>26.7 (.88)<br>30.4 (.66)<br>*<br>Imports (% GDP)<br>43.1 (1.4)<br>40.9 (.77)<br>*<br>32.1 (.83)<br>44.5 (.82)<br>*<br>Real effective exchange rate (2000=100)<br>114.4 (3.05)<br>155 (10.3)<br>*<br>183.1 (21.5)<br>124.6 (3.21)<br>*<br>Terms of trade (2000=100)<br>102.5 (1.3)<br>110.9 (1.4)<br>*<br>114.4 (2.6)<br>107.3 (1.19)<br>*<br>Current account (% GDP)<br>-5.7 (.84)<br>-5.9 (.32)<br>-5.9 (.39)<br>-5.8 (.49)<br>ODA (% GDP)<br>13.6 (.79)<br>13.6 (.51)<br>11.9 (.69)<br>14.11 (.52)<br>*<br>ODA per capita (US$)<br>68.3 (4.4)<br>53.2 (2.04)<br>*<br>41.5 (1.9)<br>61.2 (2.3)<br>*<br>Consumer price index (%)<br>15.1 (1.6)<br>75.2 (32.9)<br>177 (101)<br>23.2 (5.4)<br>*<br>GDP deflator (%)<br>16.6 (1.80)<br>69.5 (30.26)<br>168.1 (98.6)<br>23.7 (5.81)<br>*<br>Public debt (% GNI)<br>112.0 (6.72)<br>95.5 (2.84)<br>*<br>115.3 (5.3)<br>95.2 (3.15)<br>*<br>Government consumption (% GDP)<br>15.9 (.47)<br>16.7 (.28)<br>15.1 (.37)<br>16.8 (.29)<br>*<br>Life expectancy (years)<br>51.3 (.80)<br>50.1 (.41)<br>48.1 (.68)<br>51.0 (.42)<br>*<br>Dependency ratio<br>.91 (.00)<br>.93 (.00)<br>*<br>.93 (.00)<br>.92 (.00)<br>*<br>Under 5 mortality (per 1,000)<br>145.8 (8.33)<br>161.9 (4.93)<br>187.1 (8.99)<br>148.8 (4.68)<br>*<br>Infant mortality (per 1,000 live births)<br>84.3 (4.58)<br>93.9 (2.92)<br>113.2 (4.96)<br>85.5 (2,71)<br>*<br>Primary completion rate (% of relevant age group)<br>52.5 (2.14)<br>49.9 (1.41)<br>41.4 (2.05)<br>52.9 (1.34)<br>*<br>Country Policy and Institutional Assessment (scale 1=low to 6=high)<br>3.19 (.04)<br>3.10 (.04)<br>2.77 (.06)<br>3.18 (.03)<br>*<br>Voice and accountability (-2.5 to +2.5, low to high)<br>-.46 (.71)<br>-.72 (.74)<br>*<br>-1.07 (.39)<br>-.56 (.75)<br>*<br>Political stability (-2.5 to +2.5)<br>-.46 (.84)<br>-.56 (.97)<br>-1.06 (.70)<br>-.46 (.92)<br>*<br>Government effectiveness (-2.5 to +2.5)<br>-.59 (.58)<br>-.71 (.59)<br>-1.01 (.38)<br>-.62 (.60)<br>*<br>Regulatory quality (-2.5 to +2.5)<br>-.46 (.65)<br>-.67 (.66)<br>*<br>-.94 (.74)<br>-.56 (.64)<br>*<br>Rule of law (-2.5 to +2.5)<br>-.66 (.62)<br>-.71 (.67)<br>-1.11 (.52)<br>-.64 (.64)<br>*<br>Control of corruption (-2.5 to +2.5)<br>-.57 (.57)<br>-.61 (.61)<br>-1.11 (.52)<br>-.64 (.65)<br>*<br>Minor conflict (frequency)<br>.08 (.01)<br>.11 (.01)<br>.15 (.02)<br>.09 (.00)<br>*<br>Major conflict (frequency)<br>.05 (.01)<br>.10 (.01)<br>*<br>.07 (.01)<br>.09 (.00)<br>|Notes:<br>(*) t-test that means are not equal significant at the 5% level.<br>Standard error in parentheses.|
|---|---|---|
||**N**<br>1,060<br>1,202<br>1,027<br>1,274<br>1,201<br>1,220<br>1,220<br>1,220<br>1,026<br>1,231<br>1,175|1,194<br>1212<br>1,213<br>1,212<br>452<br>1,046<br>1,056<br>1,212<br>1,221<br>512<br>1,213<br>248<br>267<br>451<br>401<br>307<br>307<br>307<br>307<br>305|295<br>1,243<br>1,243|
|---|---|---|---|
|**eceleration**|**p-value <.05**<br>*<br>*<br>*<br>*<br>*<br>*<br>*<br>*<br>*|*<br>*<br>*<br>*<br>*<br>*<br>*<br>*<br>*<br>*<br>*<br>*<br>*<br>*<br>*<br>*<br>*<br>*|*<br>*|
|**Growth d**|<br>**Coeff.**<br>-.177<br>-.236<br>-.166<br>-.135<br>-.058<br>.069<br>-.042<br>-.040<br>.084<br>.078<br>.089|-.084<br>-.176<br>-.083<br>-.217<br>.168<br>.082<br>.011<br>-.059<br>-.122<br>-.136<br>.053<br>.237<br>.277<br>-.178<br>-.206<br>-.209<br>-.200<br>-.203<br>-.176<br>-.227|-.182<br>.082<br>-.044|
|**cceleration**|**p-value <.05**<br>*<br>*<br>*<br>*<br>*<br>*<br>*|*<br>*<br>*<br>*<br>*<br>*<br>*<br>*|*|
|**icators**<br>**Growth a**|**Coeff.**<br>.180<br>.176<br>.125<br>.130<br>-.091<br>-.050<br>.061<br>-.044<br>-.034<br>-.028<br>.077|-.038<br>.065<br>.056<br>.054<br>-.107<br>-.102<br>.011<br>.001<br>.100<br>.062<br>-.067<br>-.108<br>-.108<br>.049<br>.065<br>.168<br>.051<br>.100<br>.129<br>.037|.025<br>-.046<br>-.070|
|**Variable**<br>**ind**|Savings (% GDP)<br>Investments (% GDP)<br>Private sector investment (% GDP)<br>Foreign direct investments net flow (% GDP)<br>Consumption (% GDP)<br>Agriculture value added (%GDP)<br>Industry value added (% GDP)<br>Service value added (% GDP)<br>Consumer price index (%)<br>GDP deflator (%)<br>Public debt (% GNI)|Government consumption (% GDP)<br>Trade (% GDP)<br>Exports (% GDP)<br>Imports (% GDP)<br>Real effective exchange rate (2000=100)<br>Terms of trade (2000=100)<br>Current account (% GDP)<br>ODA (% GDP)<br>ODA per capita (US$)<br>Life expectancy (years)<br>Dependency ratio<br>Under 5 mortality (per 1,000)<br>Infant mortality (per 1,000 live births)<br>Primary completion rate (% of relevant age group)<br>Country Policy and Institutional Assessment (scale 1<br>Voice and accountability (-2.5 to +2.5)<br>Political stability (-2.5 to +2.5)<br>Government effectiveness (-2.5 to +2.5)<br>Regulatory quality (-2.5 to +2.5)<br>Rule of law (-2.5 to +2.5)<br>to 6)|Control of corruption (-2.5 to +2.5)<br>Minor conflict (frequency)<br>Major conflict (frequency)|
The real effective exchange rate is more competitive during growth accelerations, and highly appreciated during decelerations. Correlations suggest that exchange rate appreciation is associated with growth deceleration, whereas depreciation is associated with acceleration. There is no evidence that current accounts change during growth acceleration and deceleration.

Trade is substantially lower during decelerations. Exports and especially imports drop sharply. Correlations indicate that countries that trade less are more exposed to growth decelerations. Somewhat surprisingly, the terms of trade are lower during growth accelerations. This result may indicate that while high commodity prices trigger growth, they may not be the main factor behind medium term growth spells in Africa.

ODA as percentage of GDP is similar in both good and normal times but falls during growth decelerations. Per capita ODA, however, is higher during growth accelerations and lower during decelerations. The correlation analysis suggests that a higher share of ODA in GDP is associated with fewer growth collapses, and that countries with higher ODA per capita experience more growth accelerations have, and fewer collapses. These results indicate that ODA has been pro-cyclical, reinforcing arguments for greater predictability of ODA to underpin sustained growth.

Growth variability also impacts a number of important human development indicators. Life expectancy is substantially lower in countries experiencing growth decelerations than in normal times. The correlation coefficient is negative, suggesting that more collapses are associated with lower life expectancy. The dependency ratio is slightly lower during growth accelerations, and the correlation coefficient is negative as expected. Under 5 mortality and infant mortality are substantially higher during growth decelerations than in normal times, but they do not improve during growth accelerations. Correlation coefficients suggest that and growth collapses are associated with increases in mortality. The primary completion rate is substantially lower in countries experiencing growth decelerations and is negatively correlated with growth collapses.

Policies and institutions are also closely associated with both good and – especially – bad times. The World Bank’s CPIA score, a broad measure of policy and institutional performance, is lower during decelerations, but not significantly different 17
between accelerations and normal times. The correlation coefficients suggest that countries with lower CPIA scores tend to experience more economic collapses.

All of the governance indicators – political stability, government effectiveness, rule of law, and control of corruption – are lower for growth decelerations than for the region as a whole.<sup>11</sup> Correlation coefficients are negative, suggesting that a deterioration of governance is accompanied by more frequent growth decelerations. Voice and accountability scores are higher during growth accelerations. The correlations also suggest that countries that experience more growth accelerations have more voice and accountability and better regulatory quality.

Minor conflicts are more frequent during growth deceleration episodes than during normal times. Major conflicts are less frequent during acceleration and deceleration episodes than for the region as a whole. The correlation coefficients suggest that minor conflicts are associated with collapses, and that major conflicts hamper chances of a growth acceleration.

These results reinforce the findings of other empirical studies (Ndulu et al., 2007, Dufrénot et al., 2006) of the close relationship between institutions and governance and economic performance in Africa. However they also reveal that governance appears to be more relevant to understanding how to avoid a growth deceleration than how to promote an acceleration.

Table 5 shows the conditional probabilities of a country experiencing a growth acceleration and deceleration at the aggregate level. Models 1-4 refer to growth accelerations and Models 5-12 to growth decelerations. These regressions represent a further search for stylized facts about acceleration and deceleration episodes. No causality is inferred from the relationships and no attempt has been made to control for endogeneity of some of the right hand side variables.

Model 1 shows that a one percent change in investment is on average associated with a higher probability of a growth acceleration of about 0.1%. So, an increase of, say 10%, in investment is associated with an increase of 1% in the probability of a growth acceleration. Voice and resource-endowment (Models 2 and 3) are also positively

> 11 Governance indicators are available for the following years: 1996, 1998, 2000, 2003, 2003, 2004, and 2005.

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associated with a growth acceleration. However, all coefficients are significant at only the 10% level and the R<sup>2</sup> are low. Model 4 shows a regression with all these correlates together. Only voice and resource-endowment remain significant at the 10% level.

All estimated coefficients of Models 5-11 have the expected sign and are significant at the 5% level. They show that more investment, higher ODA per capita, increased imports, and better governance indicators are associated with fewer growth decelerations. Model 12 shows the coefficients for all of the non-governance indicators together. In this case, only investment remains significant. The governance indicators were not regressed together because of their very high collinearity.

Only investment is significantly associated with the conditional probability of both acceleration and deceleration episodes. This suggests that it is likely to be an important factor for predicting spells of growth and collapse at the aggregate level, but we cannot assert with confidence that it is a leading indicator, due to its probable endogeneity. Better governance indicators reduce the likelihood of growth decelerations, but they are not closely associated with more frequent accelerations.

Table 6 shows fixed effect logistical models that predict the presence of a growth acceleration or deceleration at the country level. Increases in savings, foreign direct investments, and consumption increase the odds of a growth acceleration, while government consumption and major conflicts reduce the odds. In the deceleration regression, increases in savings, investment, foreign direct investment and trade reduce the odds of a growth deceleration, while inflation and minor conflicts increase the odds of collapse.<sup>12</sup> Only savings, foreign direct investment, and conflict appear in both regressions. These results suggest that policies aimed at attaining sustained growth and preventing growth collapses need to focus on ways to increase savings, attract foreign investments, and reduce conflicts.

> 12 We ran random effect models including dummies for oil-producer countries, and/or landlocked and/or resource-rich, but they returned statistically insignificant results. Hausman tests suggest fixed effect estimates are preferable to random effect.

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|12<br>Model|1.97)<br>-.233 (-|||.84)<br>-.036 (-|.020 (.21)|||||<br>.21|4<br>4||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Model 11<br>**country**|||||||||-.132 (-2.68)|<br>.15|44||
|Model 10<br>**leration per**||||||||-.115 (-2.60)||.14|44||
|Model 9<br>**e level**<br>**growth dece**|||||||-.127 (-2.63)|||.14|44||
|Model 8<br>**the aggregat**<br>**frequency of**||||||-.068 (-2.08)||||.09|44||
|Model 7<br>**celeration**<br>**ent variabl**<br>**at**<br>**e:**|||||-.145 (-2.40)|||||.12|4<br>5||
|Model 6<br>**ation and de**<br>**Depend**|-.253 (-3.23)|||||||||.19|45||
|Model 5<br>**owth acceler**||||-.078 (-2.17)||||||.10|45||
|Model 4<br>**bability of gr**<br>**f growth**<br>|.047 (.72)|.061 (1.98)|.082 (1.73)|||||||.16|44||
|Model 3<br>**ditional pro**<br>**frequency o**<br>**per country**|||.082 (1.77)|||||||.06|45||
|Model 2<br>**Table 5: Con**<br>**ent variable:**<br>**acceleration**||.056 (1.87)||||||||.08|44||
|Model 1<br>**Depend**|.110 (1.70)|||||||||.06|45||
|**Variable**|Ln investment|Voice (governance indicator)|Resource rich country|Ln ODA per capita|Ln imports|Political stability (governance<br>indicator)|Government effectivenness<br>(governance indicator)|Rule of law (governance<br>indicator)|Control of corruption<br>(governance indicator)|<br>R2|N|Note: t-test in parentheses.|
**-** **<u>Table 6: Predicting growth acceleration and deceleration panel data</u>**

||<br>**Dep. variable: d**<br>**accele**|**ummy of growth**<br>**ration**|<br>**Dep. variable: d**<br>**decele**|**ummy of growth**<br>**ration**|
|---|---|---|---|---|
|**Variable**|**Odds ratio**|**p-value**|**Odds ratio**|**p-value**|
|Savings|1.152|.000|.929|.000|
|Investment in fixed capital|||.956|.062|
|Foreign direct investments net flow|1.146|.000|.811|.000|
|GDP deflator|||1.010|.016|
|Consumption|1.051|.004|||
|Government consumption|.904|.000|||
|Trade|||.980|.008|
|Minor conflict|||1.744|.045|
|Major conflict|.435|.064|||
|LR (chi2)|127.6|.000|97.4|.000|
|N|825||647||

Note: fixed effect logit regression.

Taken together our results suggest that growth decelerations are more predictable than growth accelerations. In the African context it may be easier to understand what policy makers should avoid to prevent growth collapse rather than what they should do to achieve sustained growth.<sup>13</sup>

# **6.  Conclusions**

This paper investigated growth acceleration and deceleration in Africa, seeking to uncover stylized facts and identify growth challenges not captured by standard long term growth analyses. Contrary to the common wisdom, African countries have experienced numerous growth acceleration episodes in the last 30 years, but have also gone through a comparable number of growth collapses that cancelled out most benefits of growth. We find that during growth accelerations the representative African country managed to grow by 3.6%, while during decelerations it grew by -2.7%. Ceteris paribus, had Africa avoided its growth decelerations, it would have grown in per capita terms at 1.7% a year instead of 0.7%, and GDP per capita would have been more than 30% higher in 2005.

We find that it is easier to predict a growth deceleration than an acceleration. One explanation is that there may be few commonalities among success stories. Opportunities for growth vary with sound policies and good governance, but also with timing, initial conditions, inherited institutions, geography, availability of natural resources. Leadership, and other less observable factors may also have a role in achieving

> 13 HPR using a much larger set of countries also find that growth accelerations tend to be highly unpredictable, even after controlling for the standard determinants of growth, including political changes and economic reforms.

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sustainable growth. Doing the wrong things – poor macroeconomic management, poor structural policies and institutions, and poor governance – appears to be a relatively broadly based predictor of a descent into bad times, heterogeneity among countries not withstanding.

We find strong evidence that economic, social, governance, and institutional variables are significantly different during acceleration and deceleration episodes. Thus, despite the apparent lack of a significant statistical relationship between growth volatility and the long run growth rate, volatility is indeed important for economic and social progress.

We also find an important asymmetry between how growth accelerations and decelerations affect human development outcomes. While growth accelerations result in relatively small improvements in human development, decelerations have important negative impacts on education and health outcomes. These results suggest that preventing growth collapses is essential should Africa want to attain the Millennium Development Goals.

Finally, we conclude that preventing growth collapses must be a central element of any long run growth and poverty reduction strategy for Africa. Had Africa’s economies avoided growth decelerations the continent would have grown one percent faster in per capita terms over the past 30 years. But policies to accelerate growth are also important. Per capita growth of 1.7 percent per year – the simulated average if collapses could have been completely avoided – is still not sufficient to eradicate poverty in the medium term. The keys to better long economic performance are both more growth _and_ fewer collapses.

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