metadata: authors: 0: author: Cust,James Frederick
1: author: Rivera Ballesteros,Alexis
2: author: Zeufack,Albert G.
count: Africa
docna: 0: docna: The Dog that Didn’t Bark : The Missed
            Opportunity of Africa’s Resource Boom
docty: Policy Research Working Paper
abstracts: cdata!: The commodity price boom from
            2004&#x2013;2014 was a huge economic opportunity for
            African countries abundant in oil, gas and minerals. During
            this period their government revenues from resources grew by
            an average of 1.1 billion US$ per year, and economic growth
            in those same resource-rich countries surged. GDP growth in
            resource-rich countries accelerated from 4.6% to 5.4% as
            countries entered a decade long period of sustained high
            commodity prices. Nonetheless, the paper traces a
            significant missed opportunity for resource-rich countries
            in Africa, with little to show for it in the post-boom
            period, which saw growth collapse far below pre-boom levels,
            to 2.7% per annum. This paper considers the record of
            performance during the boom (2004&#x2013;2014) and
            subsequent bust from 2015 onwards. The paper describes four
            main outcomes of the boom: 1) measures of resource
            dependency rose in Sub-Saharan Africa during the boom, 2)
            the growth record was strong during the boom but collapsed
            once commodity prices fell, 3) poverty and inequality rose
            during the boom despite strong GDP growth, 4) resource-rich
            countries failed to diversify both their exports and their
            asset base, leaving them poorly prepared for the end of the
            boom and a period of lower commodity prices and subsequent
            COVID-19 pandemic. The conclusions are stark. During this
            golden decade of sustained high commodity prices and booming
            revenues, there was limited re-investment of those revenues
            into building sustainable assets for the future. In other
            words, countries consumed the boom, rather than successfully
            transformed their economies. The conclusion is that many
            resource-rich countries in the region squandered their
            &#x201c;once in a generation&#x201d; opportunity for
            economic transformation, offering policy lessons that may
            prove valuable as we enter a new period of elevated
            commodity prices.
display_title: The Dog that Didn’t Bark : The
            Missed Opportunity of Africa’s Resource Boom
listing_relative_url: /research/2022/07/33862696/dog-didn’t-bark-missed-opportunity-africa’s-resource-boom
topicv3: Growth Monitoring and Promotion m1326325 1444,Natural Resources m1326446 1979,Commodities and Resources m1327637 1079
publishtoextweb_dt: 2022-07-06T00:00:00Z
dois: 10.1596/1813-9450-10120
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1501634
1: author_key: 1539932
2: author_key: 142139
model_extractions: Policy Research Working Paper 10120 The Dog that Didn’t Bark The Missed Opportunity of Africa’s Resource Boom _James Cust Alexis Rivera Ballesteros Albert Zeufack_ Africa Region Office of the Chief Economist July 2022
Policy Research Working Paper 10120

# **Abstract**

The commodity price boom from 2004–2014 was a huge economic opportunity for African countries abundant in oil, gas and minerals. During this period their government revenues from resources grew by an average of 1.1 billion US$ per year, and economic growth in those same resource-rich countries surged. GDP growth in resource-rich countries accelerated from 4.6% to 5.4% as countries entered a decade long period of sustained high commodity prices. Nonetheless, the paper traces a significant missed opportunity for resource-rich countries in Africa, with little to show for it in the post-boom period, which saw growth collapse far below pre-boom levels, to 2.7% per annum. This paper considers the record of performance during the boom (2004–2014) and subsequent bust from 2015 onwards. The paper describes four main outcomes of the boom: 1) measures of resource dependency rose in Sub-Saharan Africa during the boom, 2) the growth record was strong during the boom but collapsed once commodity prices fell, 3) poverty and inequality rose during the boom despite strong GDP growth, 4) resource-rich countries failed to diversify both their exports and their asset base, leaving them poorly prepared for the end of the boom and a period of lower commodity prices and subsequent COVID-19 pandemic. The conclusions are stark. During this golden decade of sustained high commodity prices and booming revenues, there was limited re-investment of those revenues into building sustainable assets for the future. In other words, countries consumed the boom, rather than successfully transformed their economies. The conclusion is that many resource-rich countries in the region squandered their “once in a generation” opportunity for economic transformation, offering policy lessons that may prove valuable as we enter a new period of elevated commodity prices.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jcust@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
# **The Dog that Didn’t Bark: The Missed Opportunity of Africa’s Resource Boom**

James Cust<sup>1</sup> , Alexis Rivera Ballesteros<sup>1</sup> and Albert Zeufack<sup>2</sup>

**Keywords** : Africa, natural resources, growth, resource curse, commodities, poverty, growth.

**JEL:** O4, O13, Q3

> 1 World Bank. Office of the Chief Economist Africa Region. jcust@worldbank.org and alexis.rivera.b@gmail.com

> 2 Chief Economist, World Bank Africa Regions. azeufack@worldbank.org
# **Introduction**

In this paper we examine the effects of the commodity price boom, running from around 2004 to 2014, and what it meant for the economies of Sub-Saharan Africa. We consider how this period marked a change from the pre-boom years, and also the legacy of the boom once commodity prices fell post-2015. We examine the record of resource-rich (RR) countries in harnessing this opportunity and consider how their economic performance compares to their non-resource-rich (non-RR) neighbors in the region. We investigate how these groups fared in terms of rising resource dependence, overall record of economic growth, the impacts on poverty and inequality and diversification of economic activity.

Our work follows the recent economic literature in considering the overall performance economic of resource-rich countries compares to non-resource-rich counterparts. Here the hypothesis taken by studies in this area is to consider whether resource abundance undermines economic performance or enhances it and treats non-resource rich countries as a useful benchmark or control group to consider possible economic counter-factual experiences.

Resources generate export revenue, government revenues and can secure access to foreign currency. As such developing countries often celebrate major resource discoveries as the first step on a path to middle income status and sustainable prosperity. However, there is no guarantee resources will deliver on this promise, and instead economic performance in resource-rich countries has often been much worse than their neighbors.

Previous studies have discussed the potentially positive effects of price booms in developing resource-rich countries while balancing this against the historical record of weaker economic performance in resource rich developing countries. For example, Warner (2015) traces more recent economic performance in resource rich countries around the world and considers how poorly the non-resource economy fared during resource booms. Venables (2016) finds that it has proven challenging in recent decades for resource-rich developing countries to translate their relative abundance of subsoil assets into aboveground prosperity and asset accumulation.

The historical record of resource-rich countries globally is mixed, with many countries that have struggled to harness booms and that have recorded lower average growth rates than their non-resource-rich counterparts. This phenomenon has become known as the ‘resource curse’, for which there exists an extensive empirical literature, summarized in surveys such as by (Van der Ploeg 2011) and (Ross 2015). Earlier work analyzed the relationship between resource dependence and economic growth starting with the seminal studies by Sachs and Warner (1997) to compare growth between economies with and without substantial natural resources over the 1970-1990 period. More recent work such as van der Ploeg and Poelhekke (2009) explore the effect of resource dependence on growth during the last decades of the 20th century and find similarly disappointing growth 2
results in resource rich countries. They point to the challenges associated with volatility as a chief culprit.

Resource booms and busts provide us with an interesting time period over which to analyze the economic performance of resource rich countries as they face both the blessing of increased revenues and investments, while also riding the tiger of commodity price volatility. By contrasting their experience over such periods with resource-poor neighbors, we can build a picture of comparative performance, benchmarked using peers who are similar in many other respects. This paper builds on other comparative studies that came before. For example, Bravo-Ortega and Gregorio (2005) considers the comparative experiences within Scandinavia and Latin America. Olter (2007) and Bates et al. (2008) review the experiences of various African countries. More recent works like the IMF (2012) policy paper examines the growth in developing resource-rich countries during the rising commodity prices period of the 2000s, and Ross (2012) discusses the growth performance of oil-rich economies during a similar period.

Indeed, Warner (2015) finds little evidence to suggest that the resource boom led to sustained increases in non-resource GDP. Looking beyond Africa, he notes that some Gulf states were able to finance migration-facilitated economic expansion, however African countries fared worse. Warner examines 18 boom countries, including five from SubSaharan Africa: Chad, Equatorial Guinea, Mauritania, Mozambique and Zambia. In all cases, he finds no statistically significant difference in growth during the boom period compared to each country’s counter-factual non-boom time period.

This study contributes to this literature with a comparative analysis of resource-rich SubSaharan Africa compared to less resource endowed countries in the region to investigate the comparative economic performance and draw lessons for policymakers as they enter a new period of rising commodity prices.

Our conclusions are stark. While economic performance among resource-rich countries was strong during the commodity price boom period – as one might expect – growth rates and other economic indicators sharply declined once resource prices fell. Furthermore, during this decade of booming revenue we find limited evidence of re-investment of those revenues into building sustainable assets for the future. In other words, countries consumed the boom, rather than used it to transform their economies. This supports our conclusion that many resource-rich countries in the region squandered their “once in a generation” opportunity for economic transformation. Our results are consistent with other studies of the same commodity price boom and the economic performance of resource-rich countries in Africa and beyond (Venables 2016; Warner 2015).

We offer some reflections and policy insights based on these findings. Consistent with earlier work we conclude that resource-richness doesn’t not condemn countries to a fate of weak economic performance. Instead, we find that policy choices matter. As such the experiences of resource-rich countries can serve as useful guides to the policy missteps and successes made by those who came before. We build on previous work such as by van der Ploeg and Venables (2011) who discuss the earlier challenges resource abundance and 3
resource booms may present for developing countries, and what, if anything, macroeconomic management can do to overcome them.

We find that the “missed opportunity” was not universal. In fact, several resource-rich countries have been able to sustain relatively higher economic growth beyond the resource boom. Those who performed best were also those who saw relatively higher savings and investments rates out of resource revenues – for example those with higher levels of Adjusted Net Savings. While resource extraction implies a process of asset depletion, the high performing countries were typically those who accumulated assets in other categories of national wealth, including physical capital, human capital and even other forms of natural capital, such as forests, fisheries and natural ecosystems.

# **Key Findings**

Sub-Saharan Africa has seen rising degrees of resource dependence since 2004. First, the number of resource-rich countries rose considerably during the boom years, from 18 to 26 according to the IMF (2012) definition using 20% of exports or 20% of government revenues.<sup>3</sup> Overall natural resource exports increased their share of total merchandise exports; rising from an average of 23 percent between 1998 and 2003 to 28 percent on average during the boom years of 2004-2014.

The boom period also saw a sharp increase in the levels of resource revenues flowing to the government. During this period resource-rich African countries’ resource government revenues grew by an average of 1.1 billion US$ each year, from an average of 8.5 percent of GDP during the pre-boom years to 8.6 percent of GDP. This represents about 61% of total government revenues. At the same time, average resource revenues share in GDP in resource-rich countries were almost four times greater than in non-resource-rich SubSaharan Africa countries (2.1%) but around 7 percentage points lower than other nonresource-rich countries elsewhere in the world, standing at around 15.5% (Figure 1). Some countries stand out in particular. Natural resource revenues flows were considerably higher in Angola, the Republic of Congo, and Equatorial Guinea, where the measures of resource dependency in resource revenue terms rose to more than 25 percent of GDP (Figure 2).

> 3 For the remainder of this paper, this group of 26 resource-rich countries who exceed this ‘resource-richness’ threshold will be compared to the other 22 counties in the region who do not throughout this analysis. Second, when viewing aggregates for the entire region we see a rise in resource dependency for the region as a whole.

4
Figure 1. Average annual resource revenues share in GDP <!-- Start of picture text --> 15.5<br>16<br>14<br>12<br>10<br>8.6<br>8<br>6 4.8<br>4<br>2.1<br>2<br>0<br>Rest of the World Sub-Saharan Africa<br>non-RR RR<br>% GDP<br><!-- End of picture text --> Notes: Natural resource revenues share of GDP and in constant 2015 US$, 2004-2014 annual average. SubSaharan Africa RR includes 21 countries listed in IMF (2012) as resource rich and Sub-Saharan Africa non-RR includes 9 countries non listed in IMF (2012) with non-missing data. Source: authors’ calculations with data from UNU-WIDER Government Revenue Dataset 2021.

Figure 2: Average annual natural resource revenues, by country, between 2004-2014 <!-- Start of picture text --> 30 50<br>45<br>25<br>40<br>35<br>20<br>30<br>15 25<br>20<br>10<br>15<br>10<br>5<br>5<br>0 0<br>% of GDP (blue bars)<br>Angola Gabon Nigeria Chad Sudan Guinea Mali Niger Namibia Zambia Liberia Ghana Uganda Constant 2015 US$, billions (red bars)<br>Congo, Rep Equatorial Guinea Botswana South Sudan Sao Tome and Principe Mauritania Cameroon Congo, Dem Rep Cote d'Ivoire Zimbabwe Sierra Leone<br><!-- End of picture text --> _Notes: Natural resource revenues share of GDP (blue bars) and in constant 2015 US$ (red bars) for 2004-2014 annual average by resource-rich Sub-Saharan African country with natural resource revenues greater than zero. The following six countries from the resource-rich group are not included due to missing data: Central African Republic, Madagascar, Mozambique, Tanzania, and Togo. Source: authors’ calculations with data from UNUWIDER Government Revenue Dataset 2021._ 5
Headline economic performance during the boom was strong but proved unsustainable. Resource-rich countries saw economic growth rise during the boom years driven by resource exports and increased government revenues. However, economic growth once we subtract the expanding resource sector, was not significantly higher than the rest of Africa. This reflects the overall dominance of the resource sector as a driver of growth, but also implies a failure to translate the boom into broader based economic prosperity. The starkest evidence for an unsustainable and narrow economic boom was seen in the slump in growth rates following the fall in commodity prices. In the post-boom period, annual GDP per capita growth in resource-rich countries was on average 2.5 percentage points lower than during the boom, and 1.5 percentage points lower than non-resource rich countries in the region.

Overall, resource-rich countries suffered a ‘missed opportunity’ to translate significant revenues and growth acceleration into sustainable and broad-based prosperity. Even during the boom years, the lack of inclusive growth was seen in other economic measures such as the rising poverty headcount and growing inequality – both of which deteriorated in resource-rich African countries during this period on average.

The legacy of the boom was characterized by countries being poorly prepared for the drop in commodity prices. Many had failed to save and invest a sufficient proportion of resource revenues to grow national wealth via the accumulation offsetting assets during the boom. As a consequence, the ‘bust’ period undermined the economic gains made during the boom. One extreme of this saw several resource-rich countries enter debt crises post-2014. This post-boom (or ‘bust) period (2015-2018) has also seen a more general pattern of resourcerich countries experiencing slumps in GDP growth and non-resource sector GDP growth, with rates of both falling below those of the rest of Africa.

The legacy of the boom is therefore one of missed opportunity, overhanging macroeconomic vulnerability, and increasing concentrations of the world’s poor in resource-rich Sub-Saharan Africa.

Most alarmingly we find that by 2030, on current projections even after adjusting all countries for the impacts of COVID-19, 80 percent of the world’s poor will reside in SubSaharan Africa, while 75 percent of the world’s poor will be found in resource-rich countries. Taken together, 62 percent of the extreme poor could be concentrated in resource-rich Sub-Saharan Africa. This is up from only 13 percent in 2000.

The findings of this study are in line with those documented in the literature on the resource curse. Recent surveys such as Van der Ploeg (2011) and Ross (2015) point towards the challenges resource-rich developing countries have faced translating price booms into sustained prosperity. While the cross-country empirical evidence does not support the hypothesis that this is a problem restricted to African countries, studies do suggest that weaker political and economic institutions may exacerbate the challenges countries face harnessing resource wealth for economic development (Mehlum, Moene, and Torvik 2006).

The rest of the study is organized as follows. The next section 2 examines the four main outcomes of the boom relating to i) resource dependency, ii) economic growth iii) poverty 6
and inequality, and iv) economic transformation. In section 3 we consider the legacy of the boom and prospects for the future. Section 4 concludes.

# **Section 1: The commodity price boom**

The boom period 2004-2014 marked a critical decade in Africa’s post-independence economic history.<sup>4</sup> This decade spawned much optimism, centered on the ‘Africa Rising’ narrative<sup>5</sup> ; associated with increased rates of economic growth, higher levels of inward investment, and large, sustained revenues from natural resource exports and taxation. This period also constituted the longest sustained period of high commodity prices on record.

Overall, this gave governments large increases in their fiscal space, derived in part from the receipts from resource taxation alongside debt relief for some countries post-2000. For example, at least 8 Sub-Saharan African countries obtained revenues from natural resources representing on average more than 10% of their GDP during the boom – a significant fraction by both regional and global standards (see Figure [fig:resrev_countries]). Many chose to use this time to undertake significant expansions of public service provision, such as educational enrollment, as well as increased infrastructure investment. For example, according to IMF’s Investment and Capital Stock Dataset, public investment in physical assets, including economic infrastructure – like roads, and airports – and social infrastructure – such as hospitals and schools – rose from roughly an average of US$200 per capita during the pre-boom period (1998-2003), to an average of over US$435 per capita during the boom, and US$270 after the boom period in resource-rich countries in Africa. Similarly annual spending on health services rose from 4.6% of GDP to 4.9% on average, and expenditure on education rose from an average of 2.8% of GDP before the boom, to 3.5% of GDP after the boom.

Policymakers sought to capitalize on the boom to translate investment and revenues into broader economic transformation. This included the opportunity to boost growth of manufacturing exports and other non-resource sectors of the economy. This economic diversification, emphasizing export diversification, was a popular economic objective during this period. Despite this, there was little change in the overall sectoral composition of exports of resource-rich economies beyond a boost in the resource sector and a squeezing of agricultural exports (see Figure 3).

> 4 Previous booms had occurred between 1949 and 1952, 1972 and 1975, and 1992 and 1995 (Varangis, Takamasa, and Mitchell 1996; Radetzki 2006) However, as noted by Deaton (1999) (p. 28), while price cyclicality was present, there was little evidence of a long run increase in commodity prices.

> 5 Africa Rising, The Economist, December 2011, 3rd edition, https://www.economist.com/printedition/2011-12-03

7
Figure 3. Share of Sub-Saharan African exports by sector _Notes: Average exports share of total merchandise exports value by sector in Sub-Saharan African RR countries, pre-boom (1998-2003), boom (2004-2014) and post-boom (2015-2018) years averages. Source: authors calculations using World Bank staff estimates through the WITS platform from the Comtrade database maintained by the United Nations Statistics Division._ The boom created a set of major public policy challenges. First, high prices meant significant revenues would accrue to government, which in turn implied ‘big government’ in some form was hard to avoid. Government, as the constitutionally designated resource owners had to attempt to capture the full value of resources on behalf of citizens, while also efficiently managing state-owned resource companies and production shares. Second, they had to maximize these revenues from the sector in a way that was also consistent with inward investment into the sector, for example to take advantage of the geological potential and new discoveries being made. Third, they had to ensure those revenues were put to good use – fulfilling urgent development needs and longer-term investment goals for sustaining prosperity. Finally, they needed to ensure the economy was protected from the harmful effects of resource dependence – ranging across hazards such as Dutch disease (Corden and Neary, 1982), price and revenue volatility, corruption, conflict, and profligacy.<sup>6</sup>

> 6 These various challenges faced by government are described in documents such as the Natural Resource Charter, which highlights the various public policy choices that must be made along the decision chain, from exploration for resources, through to how to invest revenues from resources for sustainable and diversified development (Cust and Manley 2014).

8
# **The economic outcomes of the boom**

Figure 4. Evolution of global fossil fuel and metals and minerals indices.

<!-- Start of picture text --> 200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Fossil fuels Metals and minerals<br>Price index (2010=100)<br>1990 1991 1992 1993 1995 1996 1997 1998 2000 2001 2002 2003 2005 2006 2007 2008 2010 2011 2012 2013 2015 2016 2017 2018 2020 2021<br><!-- End of picture text --> _Notes: Monthly indices based on nominal US dollars, 2010=100. Fossil fuels include oil, natural gas and coal. Metals and minerals index includes aluminum, copper, lead, nickel, tin, and zinc. Source: authors’ elaboration using World Bank staff estimates._ As illustrated in Figure 4, the boom saw prices for hydrocarbons, minerals and metals hit record levels and these high prices were sustained for over a decade. Even the financial crises of 2007-08 saw only a temporary dip in the cycle, with a rapid recovery of oil prices in 2009-10. To estimate the impacts of the boom compared to the counterfactual this study takes three time periods for analysis – the ’pre-boom’ period of 1998-2003, the boom of 2004-2014, and the post-boom 2015 to 2018. We then compare resource-rich countries to non-resource-rich counterparts.

We trace four major macroeconomic outcomes of the commodity price boom. We conclude from these stylized facts that in aggregate the commodity boom was a huge opportunity that did not deliver on its promise: a dog that didn’t bark in the nighttime.<sup>7</sup> First, outcome 1, we find that Sub-Saharan Africa became a more resource-dependent region during the boom, and compared to the period prior to 2004. This rising resource dependency can be traced in terms of the number of countries – a majority of countries

> 7 The title of the paper is drawn from Sherlock Holmes’ The Adventure of Silver Blaze, Arthur Conan Doyle, 1892 and the curious incident of the dog in the nighttime.

9
during the boom were defined as resource-rich – 26 of 48, up from 18 before 2004. It can also be found in rising resource exports as a share of total merchandise exports for the entire region, 28% on average during the boom, up from 23% before 2004.<sup>8</sup> Second, outcome 2, we find a mixed growth impact of natural resources in Sub-Saharan Africa, with resource-rich countries barely outperforming their non-resource-rich counterparts during the boom. We find average growth rates during the boom of 5.6% for resource-rich countries and 4.2% for non-resource-rich countries. In the post-boom period, we find the growth slumped to an average of 2.8% in the resource-rich countries, but remained at a healthier 3.5% for the non-resource-rich countries.

Third, outcome 3, we trace a record of rising poverty headcounts and inequality deterioration in resource-rich Africa. The increased economic growth, in both GDP and non-resource GDP, failed to be pro-poor on average and drove worsening Gini coefficients across the region. Overall, the poverty headcount in resource-rich African countries rose from about 280 to 294 million people from 2004 to 2014.

Finally, outcome 4, we document a widespread failure to diversify the economy, and a failure overall to transform natural resource revenues into productive forms of above ground capital. We measure lower adjusted net savings in resource-rich Africa, and rising export concentration in these same countries. The post-boom crash in some countries underscored the failure to diversify away from resource sector dependence and vulnerability to shocks.

# **Outcome 1: Measures of resource-dependency increased across SubSaharan Africa during the boom**

During the resource boom there was a marked increase in the number of resource-rich countries, the levels of resource dependence in these countries, and the overall degree of resource dependence across the region.

First, we consider the number of resource-rich countries. Here there is a simple threshold criterion defined by the IMF as a country being resource rich if it derives either 20% of exports from resources or 20% of government revenues from resources.

By this definition, we found that the number of resource-rich countries rose from 18 out of 48 before the boom, to 26 out of 48 countries listed by the IMF (2012) as resource-rich using the same criteria and averages of first five years of the boom. This constituted a majority of the Africa region’s countries.<sup>9</sup> This trend was caused by a combination of new discoveries, new production, and rising resources prices pushing up levels of resource

> 8 While the number of countries reaching the classification threshold for ’resource-richness’ rose compared to before the boom, throughout this study we refer to the 26 resource-rich countries as our sample of resource-rich countries for other comparisons.

> 9 In this paper we focus on the WB Africa region, which is predominantly Sub-Saharan Africa, comprising 48 countries in total (see appendix 1): https://data.worldbank.org/region/sub-saharan-africa. We refer to this group of countries as Africa throughout. We follow the IMF definition of resource-richness to distinguish our groups of countries for analysis (IMF 2012) and to chart changing patterns of resource dependency. The IMF considers a country resource-rich primarily if resource share of exports or government revenues exceeds 20%.

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Figure 8. Cumulative government resource revenues for selected resource-rich African countries, 2004-2014 <!-- Start of picture text --> 500<br>450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Billion US$<br>Nigeria Angola Equatorial Guinea Sudan Congo, Rep Gabon Ghana Congo, Dem Rep Zambia Chad Cameroon Cote d'Ivoire Mali Guinea Botswana<br><!-- End of picture text --> Note: sum of government revenues from resources of selected resource-rich countries ordered by cumulative production value of oil, gas and minerals (Figure 7) with available resource revenue data. Source: authors’ calculations based on UNU-WIDER (2021).

There exists a widespread perception that governments have systematically failed to capture their fair share of revenues– thus allowing companies to make larger than necessary profits (Laporte and Quatrebarbes 2015; Mansour 2014). An economic shorthand for this fair share is the rents. Conceptually, rents are the fraction of the resource value that is left as excess and therefore should accrue to the resource owners – invariably government – once reasonable costs of extraction and a reasonable rate of return on capital have been deducted.

Figure 9 presents a comparison between government revenues per capita according to our best measures, against the World Bank’s calculation of rents per person for each country during this same period. In most cases, the value of natural resource rents exceeds the value of revenues, except in Botswana, Nigeria, Sudan, and Uganda. This implies that there 16
may have been some amount of untapped revenues for government during the boom, proxied by gap between these estimated rents values and collected revenues.<sup>10</sup> Figure 9. Cumulative natural resource rents and revenues per capita <!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Resource rents Resource revenues<br>Thousand US$<br>12,787.9 9,924.0<br>7,090.6<br>42,852.5 12,851.2 3,150.9 1,396.2 838.8 796.8 676.6 656.1 200.2 189.0 101.3 67.2 41.8 39.9 28.3 27.2 17.3<br>Equatorial Guinea Gabon Angola Botswana Congo, Rep Nigeria Sudan Chad Sao Tome and Principe Cameroon Mauritania Zambia Guinea Cote d'Ivoire Niger Congo, Dem Rep Sierra Leone Liberia Mali Uganda<br><!-- End of picture text --> Notes: Sum of natural resource rents (orange bars) and natural resource revenues (blue bars) per person between 2004 and 2014 by country in thousand US dollars. Natural resource rents include rents from oil, natural gas, coal, and principal mineral commodities (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, and zinc), but exclude rents from other important commodities – such as diamonds and other valuable minerals – which could underestimate the real value of total natural resource rents. No resource rents data for Sao Tome and Principe. Source: World Bank staff estimates based on UNU-WIDER (2021) and The Changing Wealth of Nations 2021.

10 Two important caveats are necessary to interpret this finding. First, the measure of rents is likely subject to sizeable uncertainty and measurement error. It relies on measures of both total revenues – which typically uses on world prices rather than the actual price paid for a unit of the commodity – and an estimated measure of costs – which are held confidentially by the industry, and vary significant by project, by country, by geology, and even by the level of prices for the commodity (Toews and Naumov 2015). Second, the relationship between what the government ‘ought’ to collect, i.e. the rents value once a reasonable cost and return on capital is deducted, and what it actually collects is complicated by numerous factors. Taxes and contracts might be poorly configured or badly negotiated, either due to bad luck, significant information asymmetries, lack of relevant expertise, or deliberate (and potentially corrupt) actions by decision makers. Alternatively, government may be unable to recover more of the rents without deterring investors due to additional risk to capital, like political risks, associated with operating in particular context. This is also very hard to evaluate on a general basis.

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As you see in Figure 9, there was wide variation in the extent of resource revenues when expressed in terms of a country’s population. The highly resource-rich but populous Cameroon saw only a modest $677 per person cumulative revenues over the boom decade, meanwhile the tiny oil rich countries of Equatorial Guinea recorded cumulative per capita revenues of roughly $43,000 cumulative over the decade. In the middle of the range, the large oil-rich state of Nigeria recorded $3,000 per head over the same period (an average of $300 per person per year). Zambia, meanwhile, despite its $3.5 billion in cumulative government natural resource revenues, had only around $200 per head to allocate (an average of $20/person/year).

These revenues created unprecedented levels of fiscal space for many governments, while also reflecting the scale of the boom in the resource sector. A critical question to consider is how much of this boom translated into economic growth. Furthermore, while the boom drove expansion in GDP in many countries, once we subtract the resource sector the picture may not be as rosy. We consider the extent to which non-resource sector GDP grew as well. This allows us to subtract the portion of growth that arises directly from the booming sector itself (a largely price driven effect).

# **Outcome 2: Mixed growth impact of natural resources**

During the boom years resource-rich economies saw impressive GDP growth. Countries such as Angola and Ghana saw average growth rates increase respectively from 5.1% and 4.4% in the pre-boom period to an average rate of 8.2% and 7% during the boom. Such jumps were driven by a combination of factors including a boost in the value of hydrocarbon exports, new oil discoveries in Ghana and a boom in the price of gold. However, both countries saw growth slump following the sharp decline in oil prices in 2014 - where we mark the end of the boom - to -1% and 5% respectively.

For the region, average growth rates pre-boom were 4.6% for resource-rich Sub-Saharan Africa and 2.9% for non-resource-rich Africa. During the boom this jumped in both cases, to 5.4% and 4.2% respectively (Figure 10). While resource-rich countries saw higher growth rates during the boom on average, the increase from the pre-boom period was modest, and less of a jump compared to non-resource-rich countries. This is particularly disappointing given the scale of commodity price increases during this period.

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Figure 10. Average annual GDP growth by country group <!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>RR Non-RR Rest of the World RR<br>Pre boom Boom Post boom<br>%<br><!-- End of picture text --> Note: RR= Sub-Saharan African resource-rich countries listed in IMF (2012), nonRR= rest of Sub-Saharan African countries, RR RoW= rest of the world resource-rich countries listed in IMF (2012). Source: author’s calculations based on World Bank data.

Following the boom, the group averages fell to 2.7% and 3.8%, respectively. Here the drop was biggest in the resource-rich group, and fell to below the pre-boom average growth rates. This indicates the average effects saw a bigger boom for non-resource-rich Africa, as compared to the resource exporting countries in the region. While the average effects mark a bigger bust for the resource exporters. However, if we account for population growth in these countries, GDP growth rates look less favorable. After the boom, resource-rich Africa’s GDP growth did not keep up with population growth and resulted in negative GDP growth per capita (Figure 11).

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Figure 11. Average annual GDP growth per capita by country group <!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>RR Non-RR Rest of the World RR<br>-1<br>Pre boom Boom Post boom<br>%<br><!-- End of picture text --> Note: RR= Sub-Saharan African resource-rich countries listed in IMF (2012), nonRR= rest of Sub-Saharan African countries, RR RoW= rest of the world resource-rich countries listed in IMF (2012) Source: author’s calculations based on World Bank data.

This result is striking. While the boom period represented a prosperous period of growth for resource-rich Africa, the subsequent slump in growth rates underscore how resourcedependent this was and how little economic transformation had occurred, to develop other sources of growth during the boom. The same period saw a slowing of growth across the entire region, reflecting the overall higher level of regional resource-dependency compared to before the boom.

# **What about the rest of the economy?**

It is important to consider not just total GDP growth performance but also how nonresource GDP has fared. This is because the resource sector is such a large driver of growth in most of the region, and particularly during the boom, that a focus on GDP growth numbers is largely reflecting the price-driven increase in resource sector activity in this period. To appreciate the broader growth experience, and particularly the sustainability of economic growth we need to look beyond the resource sector.

Figure 12 separates the average GDP growth rate (left panel) and compares it to the nonresource GDP boom – using sectoral GDP data that subtracts GDP attributable to resource extraction. It shows that resource-rich Africa saw a boom in both GDP and non-resource GDP during the period of higher prices - lying above the non-resource-rich countries for much of this period. During the boom, by the end of 2003 and until 2014, 13.3% of the 20
<!-- Start of picture text --> co Average GDP growth rate<br>©<br>- ‘A<br>ex o4, /r\“ esPe\; ~ SES{oy\ 7 \<br>a ‘2 1 ¥ 2 \ 7 “Ss i<br>| aa OH AN<br>Ia Vy er V ar \ 5 :<br>N nn, Vv Sof Ni<br>o<br>BRSsSSZESSESLSESSRRERERRE5 Vr ser eer e<br>----- RRSSA ---~- non-RRSSA<br><!-- End of picture text -->

<!-- Start of picture text --> o Average non-resource GDP growth rate<br>' es’ F iy<br>2 38 / ‘ 1 / \<br>es HiPSs \ Z 4 Aa <keSe al RSay<br>Pia ee: 4 \ 7 y<br>* | Se 4 \<br>laa Pa a om \ i SSO<br>n = “ese nat / ; sy S Cy<br>o<br>BRZ5SS2RLLSRRLREESSRREREAR5 Sr gereerse<br>----- RRSSA ----- non-RRSSA<br><!-- End of picture text -->
<!-- Start of picture text --> GDP per capita growth, period averages<br>vt<br>o<br>Ry<br>o<br>RR Non-RR: Non SSA, RR countries<br><!-- End of picture text --> <!-- Start of picture text --> Nonresource GDP per capita growth, period averages<br>Tv<br>oO<br>Rey<br>o<br>RR Non-RR Non SSA, RR countries<br><!-- End of picture text -->
Table 1. GDP and non-resource GDP growth of resource-rich, non-resource-rich, and selected Sub-Saharan African countries.

|**Country/**<br>**group**|**Res.**|**Pre**<br>**boom**<br>**(A)**|**GDPg**<br>**Boom**<br>**(B)**|**rowth r**<br>**Post**<br>**boom**<br>**(C)**|**ate**<br>**B-A**|**C-B**|**Non-**<br>**Pre**<br>**boom**<br>**(A)**|**resourc**<br>**Boom**<br>**(B)**|**e GDPg**<br>**Post**<br>**boom**<br>**(C)**|**rowth**<br>**B-A**|**rate**<br>**C-B**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|SSA RR||4.6|5.4|2.7|**0.8**|**-2.7**|4.5|5.8|2.5|**1.3**|**-3.3**|
|SSA non-<br>RR||2.9|4.2|3.8|**1.3**|**-0.4**|3.2|3.5|3.2|**0.3**|**-0.3**|
|Angola|Oil|5.1|8.2|-0.9|**3.1**|**-9.1**|3.8|8.8|0.1|**5**|**-8.7**|
|Botswana|Diam.|3.8|5.2|2.5|**1.4**|**-2.7**|2.7|8.2|4.4|**5.5**|**-3.8**|
|Cameroon|Oil|4.2|4.2|4.5|**0**|**0.3**|6.1|4.5|4.6|**-1.6**|**0.1**|
|Ghana|Gold,<br>Oil|4.4|7.1|5|**2.7**|**-2.1**|4.7|6.3|4.1|**1.6**|**-2.2**|
|Nigeria|Oil|6.1|6.7|0.9|**0.6**|**-5.8**|6.6|8.3|1.4|**1.7**|**-6.9**|
|Tanzania|Gold|5.5|6.5|6.3|**1**|**-0.2**|5.4|6.5|7.1|**1.1**|**0.6**|

Notes: Comparison of average growth rates pre and during the boom, SSA, RR countries only. Source: World Bank national accounts data, and OECD National Accounts data files (https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG) and UN Statistics Division, National Accounts Main Aggregates Database (https://unstats.un.org/UNSD/snaama/formulas.asp)

# **Outcome 3: Rising poverty and inequality in resource-rich countries, putting Africa on a perilous trajectory**

The commodity price boom saw large inflows to African economies via resource companies exports and government revenues from production shares, royalties, and other resource sector taxes. However, despite the economic boom, the impact on poverty alleviation and inequality was disappointing.

The overall trend in resource-rich African countries was an increasing poverty headcount during the boom – indicated by the solid red line in Figure 14 – from about 280 million in 2003 to more than 310 million living in extreme poverty by 2019.<sup>11</sup> Meanwhile nonresource-rich Africa saw more modest increases – from 110 million to over 113 million over the same period. In part this was driven by a higher average (mean) poverty rate in resource-rich Sub-Saharan African countries that failed to converge with the poverty rates

> 11 Poverty projection estimates included in this paper are based on the poverty projection methodology designed and published by Lakner et al (2021) that incorporate the impact of COVID-19 on global poverty. These projections are based on the growth forecasts from the June 2021 version of Global Economic Prospects (GEP) and take the Sustainable Development Goals (SDGs) definition of extreme poverty which groups people living below the poverty line of $1.90 a day, measured in 2011 Purchasing Power Parity prices.

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<!-- Start of picture text --> 400<br>c -<br>2 - ==<br>== 350 r —_ -_<br>ait250 ee<br>200<br>150<br>50<br>0<br>in 5 = a = Fal at ra<br>Fl a ri a mi i a<br>eeeSA RR mee SSA monRR<br><!-- End of picture text -->
<!-- Start of picture text --> 70<br>60 -=-<br>-<br>Pal<br>50 “<br>-<br>40<br>oF<br>30<br>20 — = = a<br>0<br>1955 2000 2005 2010 2015 2020 2025 2030<br>eee SSA RR ee oA MONRR<br><!-- End of picture text -->
<!-- Start of picture text --> 100,000,000Conn rd NGA<br>a<br>a<br>ZMB “Paes 1M a<br>10,000,000 civ (Mr UGA<br>ba!= Teo peed<br>a= & SLE Gr @aieson<br>=a @F cog GHA<br>=<br>a<br>E 1,000,000 @ o isz<br>= Gg<br>gS rl “e%<br>2 e- MRT BWA<br>100,000 é en<br>Pd<br>10,000 o<br>10,000: 100,000 1,000,000 10,000,000 100,000,000<br>Poverty headcount in 2003<br>@Non-RR @ AR<br><!-- End of picture text -->
<!-- Start of picture text --> 65<br>@ ZAF<br>60 | © WM<br>@ CAF<br>= 55 @ B<br>° @® MOZ<br>fo}<br>ne} @® SWZ<br>Co) @ GNB<br>s 50 |<br>6<br>© CMRe @ CPV<br>3<br>@ 45 | e Mw @ RWA @ com<br>a)§ e@ einen, Q© MAE e aco<br>© 40 | @ sen © UGA<br>uo9 oA @ BDI<br>=a oe | @ BFA<br>30 | @ STP<br>25 30 35 40 45 50 55 60 65<br>Gini coefficient (pre-boom)<br><!-- End of picture text -->

@non-RR @RR
Figure 18. Gini index change between 2004-2014, resource rich and non-resource-rich SubSaharan African countries.

<!-- Start of picture text --> 15<br>10<br>5<br>0<br>-5<br>-10<br>Resource-rich Non-resource-rich<br>-15<br>Gini index change (2004-2014)<br>Mozambique Cameroon Chad Ghana Cote d'Ivoire Zambia Tanzania Congo, Dem Rep Sao Tome and Principe Botswana Uganda Madagascar Central African Republic Sierra Leone Mali Niger Mauritania Angola Guinea Guinea-Bissau Malawi South Africa Benin Ethiopia Kenya Senegal Eswatini Namibia Rwanda Burundi Gambia, The Cabo Verde Burkina Faso Comoros<br><!-- End of picture text --> Notes: Gini index of 2014 or the closest year of reported value minus the Gini index in 2004 or closest year with reported value. Blue bars correspond to fossil fuel-rich countries and orange bars correspond to mineral-rich countries according to IMF (2012). A decrease in the Gini coefficient indicates an improvement in the income equality of a country’s population. Source: authors’ calculations using World Bank PovcalNet data.

# **Outcome 4: Failure to transform and diversify**

## **Resource-rich countries in Africa recorded lower adjusted net savings rates and increased export concentration compared to before the boom**

The transformation of resource endowments into sustained prosperity is a multi-stage economic and political problem (Venables 2016). It requires governments to navigate a series of decisions - counties must secure investment, typically from abroad to explore for and develop resources. They must secure a fair deal that maximizes value for the country without deterring investors, even in conditions of weaker governance. Then they must 28
capture those revenues, manage them prudently, and ultimately invest them judiciously to promote sustained long-term prosperity for the country.

This chain of decision is made all the harder for impediments at each stage - investors may be deterred by weak governance and business climate (Cust and Harding 2013), or from time-inconsistency challenges leading to hold-up. The negotiating of fair deals is hampered by severe informational asymmetries and uncertainty regarding the future value of resources and cost of extraction. Capturing revenues requires effective tax administrations who must carefully monitor complex operations in the face of sophisticated tax minimization techniques by multinational corporations. The management of revenues is challenged by price volatility, lumpiness and difficult distributions considerations. Finally, long term sustainable development can be elusive where countries struggle to diversify, where Dutch disease crowds out other sectors and where governments and citizens alike become addicted to the additional consumption short-term resource windfalls can sustain.

To judge how successfully resource-rich Africa diversified its economic activity away from resource dependence we consider three key criteria: 1. Economic concentration: measured in resource-sector GDP versus non-resource sector GDP; 2. Concentration of exports: measured using indexes of product and value diversification in export categories; 3. Concentration of assets: measured using Hartwick’s Rule, adjusted net savings and the diversity of asset classes across natural capital, human capital and productive assets.

# **Economic concentration**

We examine the impact of the boom on the economic concentration in resource-related GDP between the pre-boom and post-boom periods. Figure 19 shows how each country fared – those lying to the left of the 45-degree line saw a higher resource to non-resource GDP ratio after the boom, meaning economic concentration in resources increased by the end of the boom.

Figure 19 shows that 11 of 23 countries of resource-rich Africa with sectoral GDP data saw increasing concentration. Countries such as Chad, Zambia, and DRC saw a higher resource to non-resource GDP ratio by the end of the boom. In contrast Nigeria, Botswana and 10 other resource-rich countries managed to reduce economic concentration.

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Figure 19. Resource GDP to non-resource GDP ratio in Sub-Saharan African countries, 1998-2003 vs 2014-2018 averages.

_Notes: Average resource GDP to non-resource GDP ratio in 1998-2003 compared to the average in 2014-2018. Resource GDP corresponds to the GDP coming from mining activities including mining of metals and minerals, and extraction of crude petroleum and gas. Countries above the 45-degree line increased more their resource GDP over their non-resource GDP after the boom. Chad, Gabon, and Equatorial Guinea not shown, but their ratios in 1998-2003 were -0.1, 3.3, and 1.1, respectively. Their ratios in 2014-2018 were 0.2, 0.6, and 0.4. Source: authors’ calculations using UN Statistics Division data._

**Export concentration: The boom drove increased export concentration (little progress on diversification, reversal in some cases)**

A key tenet of best practice advice to resource-rich countries is to use the opportunity created by a boom to promote a more diversified economy. This can reduce exposure to price volatility and price risk, while also moving the economy up the value chain to higher value added and shift the economy into labor-intensive export manufacturing and other non-resource sectors. Diversification is a protection against depletion of the resource base, while in the case of hydrocarbons, it may be insurance against a future global shift from fossil-fuel consumption driven by alternative energy technologies and taxation of carbon emissions (Cust, Manley, and Cecchinato 2017).

While many countries have actively pursued diversification strategies during their respective resource booms, the record of success is rather poor. For many oil-rich countries the boom was associated with increases in export concentration, including in Africa. However, some regions saw some success - notably Latin American and Southeast 30
<!-- Start of picture text --> _s<br>fo)<br>><br>©<br>oO A°<br>~~ O °<br>a Sen ° 10 cn<br>Ce) GNB 2.<br>S<br>a Te) NER Se<br>x< fo} 2 far Oo<br>zg ZMB 20 Son" "eB<br>£t fore} ° RWAl agaD<br>= ° ETH 2 Bel<br>2 ° DG<br>=| ° a GMB<br>P=a oO Ee)E ° se wos<br>© oo TM usa<br>= KEN TZA<br>© N 2.<br>8 © Non resource-rich [p-value: 0.15 | B1: 0.96]<br>x<br>Wu © Resource-rich. [p-value: 0.87 | B1: 1.00]<br>1 2 3 4 5 6 7<br>Export diversification index (1998-2003 avg)<br>Regression: edi_2010 = 0 + B1*edi_1998 +e<br><!-- End of picture text -->
# **Asset concentration: What happened to the wealth of nations?**

The process of resource extraction has a first-order effect of reducing a countries overall wealth, by depleting the stock of natural capital given subsoil assets’ finite non-renewable nature. However, countries may use the proceeds from this depletion to accumulate other forms of capital – such as building physical infrastructure, or investing in human capital, via improvements in education and healthcare of citizens. Meanwhile, the stock of natural capital can also increase due to rising prices applied to remaining resource base, or new discoveries – some occurring as part of the discovery-by-extracting process.

Although the boom offered a unique opportunity for resource-rich African countries to increase of their wealth, its total wealth per capita still grew at a similar average annual rate than its non-resource-rich counterparts and around two thirds what other resourcerich elsewhere in the world achieved. The lack of investment in other assets different from the resource sector during the boom, left resource-rich African countries even less prepared for the end of the commodity boom, when their total wealth, mainly deposited in nonrenewable natural resources, resulted in negative growth numbers (see Figure 21).

Figure 21. Average annual total wealth per capita growth by period <!-- Start of picture text --> 4 3.7<br>3<br>2.3 2.2<br>1.8<br>2<br>1.1<br>1 0.7<br>0<br>-0.1<br>-0.3 -0.3<br>-1<br>Pre-boom Boom Post-boom<br>RR nonRR RR RoW<br>%<br><!-- End of picture text --> Note: RR= Sub-Saharan African resource-rich countries listed in IMF (2012), nonRR= rest of Sub-Saharan African countries, RR RoW= rest of the world resource-rich countries listed in IMF (2012). Source: Changing Wealth of Nations 2021.

This poor growth of per capita wealth in resource-rich Africa has widened the divide the amount of wealth between them, the rest of Africa and other resource-rich countries in the world. Other lower income resource-rich countries listed in IMF (2012) – such as – Mongolia managed to increase their total per capita wealth to almost double their value before the boom (Figure 22).

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Figure 22. Average annual total wealth per capita by period <!-- Start of picture text --> 90<br>78<br> 80<br>67<br> 70<br> 60<br>50<br> 50<br> 40<br> 30 26 27<br>21 21 22<br>18<br> 20<br> 10<br> -<br>Pre-boom Boom Post-boom<br>RR nonRR RR RoW<br>Thousand US$<br><!-- End of picture text --> Note: RR= Sub-Saharan African resource-rich countries listed in IMF (2012), nonRR= rest of Sub-Saharan African countries, RR RoW= rest of the world resource-rich countries listed in IMF (2012). Values expressed in thousand constant 2018 US$. Source: Changing Wealth of Nations 2021.

We find that during the boom decade the total wealth stocks in resource-rich countries in Africa generally increased. This means, including price effects, the total value of a nations stock of human wealth, productive capital, and natural assets, rose. Figure 23 plots this effect for African countries expressed in per capita terms. Countries like Namibia and Botswana added over $20,000 per person to their total national wealth during this period.

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<!-- Start of picture text --> 30<br>wa<br>au<br>£25a<br>a<br>oa<br>£= 20<br>uwwi5S 15<br>oo<br>d<br>& 10<br>aa<br>tS<br>Cc<br>g 0 LLL BRR RRR eee eee<br>5<br>-10<br>se save 2s o 7 o @ ogg vn =tRs kur. omy w<br>5a 28 SHEA ELaES eT ess RU a RB eEp wet Re RS GeVS SB<br>BEESPRSSRET= fc EPSPS2 SRST SS =IPSS PRES PPE PEE EB ESEBE=<br>BREtERPREEYVERE BYSs pag 522 S® sg RSR- 5 5E8 5<br>aeee3 Fe NSE= 0 rT] maeo OSS "8G@ & sES wv =)2 5o 5=c ffa g- Gas =ie)<br>wi Fe] ai = 3 S a =f<br>= z 5<br>e =au =<br>“ =]a oua<br>Ee<br>a<br>a<br><!-- End of picture text -->
Figure 24. Resource rents vs adjusted net savings, 2004-2014 average <!-- Start of picture text --> 40<br>30<br>BWA<br>CPV<br>20<br>LSOTZANAM MRT<br>SDN ZMB<br>10<br>MUSETHSWZSENNER CIV NGA GAB<br>0 GNBBEN COM KENUGAMWIMDG RWA SLE BFA MOZ MLI GHAZAF CMRTGOCOD AGO<br>-10 COG<br>GIN SSD<br>GMB<br>-20<br>ZWE<br>-30<br>BDI<br>-40<br>-50<br>-60 LBR<br>-70<br>0 5 10 15 20 25 30 35 40 45 50<br>Average natural resource rents % GDP (2004-2014)<br>Fossil fuel rich Non-RR Mineral rich Resource-rich<br>Average adjusted net savings % GNI (2004-2014)<br><!-- End of picture text --> _Notes: Average adjusted net savings as percent of GNI and average natural resource rents as percent of GDP over the boom period in Sub-Sahara African countries. Source: authors’ calculations using the Changing Wealth of Nations 2021 and World Bank data._ In Africa many fossil fuel-rich countries with the most depletion (proxied by rents) – such as Angola and Republic of Congo – have seen low and negative rates of net saving, meaning they are running down the overall stock of assets in the country, and instead consuming a large share of this value. Such a pattern means countries are bringing forwards consumption as the expense of future GDP, effectively reducing the available assets for future generations. Venables (2016) documents this phenomenon. Meanwhile, the ANS of some mineral-rich countries have been less impacted reaching 10% of GNI – such as Zambia and Mauritania.

According to the Changing Wealth of Nations 2021 report, in 2004, 10 countries in SubSaharan Africa had the most of their wealth held in natural assets, versus human or productive assets or net foreign assets. Surprisingly, by 2014, this same number of countries still had natural capital as the most abundant asset. Only Ghana reduced its 35
natural capital share of total wealth below its human capital share, while this share increased in Niger. A large part of this was due to countries replacing their depleting reserves with new discoveries, plus if prices rise enough the value of those depleting reserves also rises even as their stock might decrease, reflected in increased natural capital wealth. The boom did not translate in a significant improvement of the asset portfolio in resource-rich Africa. Despite resource-rich countries benefited from additional income from their abundant natural capital, they did not translate into significant higher investment in produced or human capital compared to non-resource-rich African countries. Therefore, the concentration of wealth in resource-rich Africa remain as twice as much as in non-resource-rich African countries (Figure 25).

Figure 25. Sub-Saharan Africa wealth composition, 2004 vs 2014.

<!-- Start of picture text --> 16 100%<br>14 90%<br>80%<br>12<br>70%<br>10<br>60%<br>8 50%<br>6 40%<br>30%<br>4<br>20%<br>2<br>10%<br>0 0%<br>2004 2014 2004 2014 2004 2014 2004 2014<br>nonrr nonrr rr rr nonrr nonrr rr rr<br>Natural capital Produced capital Human capital Natural capital Produced capital Human capital<br>Thousand constant 2018 US$<br><!-- End of picture text --> _Note: the left panel shows the average composition of wealth per capita of resource-rich and non-resource rich Sub-Saharan Africa in thousand US dollars. The right panel shows the component of wealth share of total wealth, excluding net foreign assets. Source: World Bank staff estimates based on the Changing Wealth of Nations 2021._ When we limit this to just nonrenewable natural assets, seven countries had the largest share (more than 50%) of total wealth in assets including oil, natural gas, coal or metals and minerals. By 2014, five of these countries still had most of its total wealth concentrated in nonrenewable natural resources. These countries were: Central African Republic, Democratic Republic of Congo, Gabon, Guinea, Liberia, Mali and Chad.

According to the Growth Commission: “Strong enduring growth requires high rates of investment. If the sustained, high-growth cases are any guide, it appears overall investment rates of 25 percent GDP or above are needed, counting both public and private investment.

We can examine this in terms of investment in physical capital (gross fixed capital formation), human capital (such as educational expenditures). First, we examine the investment rate over the period, defining investment as the sum of physical capital investment (the traditional definition of the investment rate) and the investments made into human capital (i.e. health and education spending).

From the 22 resource-rich countries with non-missing data, only 10 exceeded 25% during the boom. However, this measure excludes health and education as it does in national accounts. The Growth Commission report notes than health and education spending should 36
constitute at least an extra 7-8 percent GDP, taking the total target to around 33 percent. From these 22, only 9 exceeded this target during the boom (Figure 26).

Figure 26. Average annual investment rates, percent of GDP between 2004-2014 <!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>Gross fixed capital formation Expenditure on education Expenditure on health<br>% GDP<br>Botswana Congo, Rep Mauritania Tanzania Zambia Sudan Angola Niger Chad Gabon Uganda Madagascar Sierra Leone Mozambique Ghana Togo Guinea Mali Cote d'Ivoire Cameroon Congo, Dem Rep Central African Republic Vietnam Indonesia Uzbekistan Guyana Syrian Arab Republic<br><!-- End of picture text --> _Notes: Average annual investment rates calculated as the gross fixed capital formation, investment on education and health share of GDP over the 2004-2014 period in resource-rich Sub-Saharan African countries. Missing expenditure on education data for Nigeria and Equatorial Guinea, and missing gross fixed capital formation for Sao Tome and Principe and Liberia, therefore these were not included in the figure. Source: authors’ calculations using IMF data for the public-sector gross fixed capital formation, World Bank data for investment on health and UNESCO for investment on education._ Despite the limited number of countries exceeding the Growth Commission target, the gains from the boom boosted the resource-rich Africa group’s average annual investment rates to surpass the rates of the non-resource rich Africa group (Figure 27). This growth has been supported by an increased investment in gross fixed capital, but at the expense of a more limited growth of investment in education and health.

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Figure 27. Average annual investment rates, percent in GDP between 2004-2014 <!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Pre- Boom Post- Pre- Boom Post- Pre- Boom Post-<br>boom boom boom boom boom boom<br>RR nonRR RR RoW<br>Gross fixed capital formation Expenditure on education Expenditure on health<br>% GDP<br><!-- End of picture text --> Note: Average annual investment rates calculated as the group average of gross fixed capital formation, investment on education and health share in GDP over the 2004-2014 period. RR = resource-rich Africa, nonRR = non-resource rich Africa, RR RoW = resource-rich excluding Africa. Source: authors’ calculations using IMF data for the public-sector gross fixed capital formation, World Bank data for investment on health and UNESCO for investment on education.

African countries abundant in fossil fuels were the largest contributors to resource-rich Africa’s gross fixed capital formation. In Equatorial Guinea, Gabon, Angola, Republic of Congo and in the mineral-rich Botswana, cumulative public and private investment per capita exceeded 10 thousand US dollars between 2004 and 2014, while the remaining resource-rich African countries accumulated less than 5 thousand US dollars per person (Figure 28). In the non-resource-rich group, excluding the smaller insular countries, only South Africa, Namibia, had comparable accumulation of public and private fixed capital derived from their abundant mineral production.

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Figure 28. Cumulative public and private investment per capita, 2004-2014 <!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Non-resource-rich<br>Resource-rich<br>GFCF (public) GFCF (private)<br>Constant 2015 thousand US$<br>Equatorial Guinea Gabon Botswana Angola Congo, Rep Tanzania Cameroon Uganda Ghana Chad Mali Madagascar Niger Togo Guinea Sierra Leone Congo, Dem Rep Seychelles Mauritius South Africa Cabo Verde Namibia Eswatini Senegal Comoros Benin Zimbabwe Eritrea Ethiopia Guinea-Bissau Burundi<br><!-- End of picture text --> _Notes: Government and private cumulative gross fixed capital formation during the boom divided by population in constant million 2015 US$. Source: authors’ calculations using World Bank national accounts._ Similarly, oil-rich countries show a higher accumulation of capital stock, including economic infrastructure, such as roads and airports, and social infrastructure, as in hospitals and schools. Oil-rich Equatorial Guinea and Gabon lead the list while the rest of resource-rich countries, mainly mineral-rich, - such as Guinea and the DRC - had just a small fraction of capital stock per person, as in most non-resource-rich African countries (Figure 29).

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Figure 29. Cumulative government and private capital stock (2004-2014) <!-- Start of picture text --> 600<br>500<br>400<br>300<br>200<br>100<br>0<br>Government capital stock Private capital stock<br>Constant 2017 thousand international US$ Equatorial Guinea Gabon Botswana Angola São Tomé and Príncipe Mauritania Congo, Republic of Nigeria Sudan Ghana Cameroon Central African Republic Zambia Côte d'Ivoire Togo Liberia Tanzania Niger Madagascar Mali Uganda Guinea Chad Mozambique Sierra Leone Mauritius South Africa Eswatini Cabo Verde Namibia Comoros Lesotho Eritrea Kenya Senegal Benin Gambia, The Burkina Faso Guinea-Bissau Zimbabwe Rwanda Malawi Ethiopia Burundi<br>Congo, Democratic Republic of the<br><!-- End of picture text --> _Note: Sum of annual general government capital stock per capita and private capital stock in constant 2017 international dollars, between 2004 and 2014. Countries to the left correspond to resource-rich countries and countries to the right correspond to non-resource-rich countries. Source: authors’ calculations using IMF's Investment and Capital Stock Dataset, 2021._

# **The legacy of the boom**

The legacy of the boom for Africa’s resource-rich exporters has proven very mixed. As commodity prices fell in 2014 onwards a challenging period was entered where revenues and exports slumped. Some have weathered the end of the boom well, including managing to sustain impressive rates of growth. Tanzania is an example of this – recording 6.5% during the boom, and 6.3% after the boom. By contrast, a group of countries, and especially those rich in oil and gas, have faced growth slumps induced by the price crash and weak price recovery. Here examples include Ghana, Nigeria and Angola, with boom and postboom growth measured as 7.1% to 5.0%, 6.7% to 1.0%, and 8.2% to -1.0% respectively.

Overall, the growth rates in Sub-Saharan Africa fell following the 2014 price falls. For the region, average growth rates during the boom were around 5.7% and 3.4% for resourcerich and non-resource-rich Sub-Saharan Africa respectively. Following the boom however, the group averages fell to 2.8% and 3.1%. Here the drop was biggest in the resource-rich group, and fell to below the pre-boom average growth rates.

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<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>:0 |<br>Resource Poor Minerals & Metals Oil Rich Oil Rich excl.<br>Rich NGA<br>@2007 92013 2018<br><!-- End of picture text -->
<!-- Start of picture text --> 200% 192.3%<br>175%<br>150%<br>125%<br>100%<br>75%<br>50%<br>25% ae% 15.7% 15.2% 13:0% 12.9% 11.7%<br>fe)<br>2017 2006 2008 2010 2013 2015 2015<br>Guinea Rep. of DRC Sao Tomé & Niger Angola Rep. of<br>Chalco, CP! Congo Eximbank Principe = Eximbank CDB Congo<br>& Henan — Eximbank Gov. Nigeria Trafigura<br><!-- End of picture text -->
On average, Sub-Saharan African oil and gas rich countries enjoyed a surplus of their fiscal balances (excluding grants) compared to the rest of Sub-Saharan African countries during the boom. However, with the boom years coming to an end, the average overall fiscal balance of these oil and gas rich countries reached the deficit levels of their mineral-rich and non-resource-rich counterparts (see Figure 32).

Figure 32. Average overall fiscal balance (% of GDP) during and after the boom period.

_Notes: Oil and gas rich countries considered as oil and gas rich are Chad, Nigeria, Cameroon, Equatorial Guinea, Gabon, Republic of Congo and Angola; while mineral-rich countries are the remaining resource-rich countries, as defined by Lundgren, Thomas, and York (2013). Source: authors’ calculations using World Bank and IMF Government Finance Statistics Yearbook data._

# **Governance and corruption**

Improvements in governance can reduce collusion of some stakeholders and prevent capture by interest groups leading to the leakage of resource rents that should otherwise accrue to governments and citizens. Restuccia and Rogerson (2008) and Hsieh and Klenow (2009) describe how transparency can improve resource allocation and Stiglitz (1999), Bellver and Kaufmann (2005) argue that transparency makes governments more accountable, weakening the power of special interests leading to better policies and institutions (Kubota and Zeufack 2020). Sub-Saharan Africa has much to do in this area, since the region has the lowest scores of transparency in the world according to the World Bank Statistical Capacity Index and the IMF’s Special Data Dissemination Standards 43
<!-- Start of picture text --> 90<br>80<br>= 70<br>a5<br>=3 60<br>z<br>> 50<br>2 40<br>S 30<br>a2fg 20<br>=<br>3 10<br>%B 0<br>B Sub-Saharan Africa Non-SSA Developing East Asia South Asia<br>(SSA) Countries<br>1995-2008 mTM2009-2018 m 2014-2018<br><!-- End of picture text --> <!-- Start of picture text --> 07<br>0.6<br>=<br>= 05<br>=.<br>2 04<br>e .<br>03<br>.<br>3£i 0.2<br>0.1<br>0 Oo<br>Sub-Saharan Africa Non-SSA Developing East Asia South Asia<br>(SSA) Countries<br>@ 1995-2008 TM2009-2018 mTMm2014-2018<br><!-- End of picture text --> <!-- Start of picture text --> 6<br>5<br>4<br>3<br>22<br>£<br>°<br>21<br>A]<br>wo<br>£0 _<br>-1<br>-2<br>-3<br>-4<br>&ae é&x &eS &> eece? ESeo s< eo)p S& &> SxO we1g eS@<br>SS oy ¢s< ¥ S¥< ¥ S 2 $ & *@8 Oy‘2SS<br>S ©) Sy<br><!-- End of picture text -->
<!-- Start of picture text --> Oil Revenue Gap between World Bank's Adjusted Savings<br>6000 Project and SNH in millions of current USD 1000<br>900<br>5000<br>left scale: cumulative 800<br>4000 right scale: annual values 700<br>600<br>3000 500<br>2000 400<br>300<br>1000 " A 200<br>3 “\ e) \ Me) “\ iS) S > \e) ‘\ S) \S we) % 0<br>-1000N SV DY DW BD BD MM YPd RK JPSRK AL”S OASLY & -100<br>—— oil revenue gap (cumulative) —— oil revenue gap (annual)<br><!-- End of picture text -->
countries like Kenya, slightly increased its adjusted net savings during the boom but its polity score increased more than 6 points.

Figure 36. Adjusted net savings share in GNI vs democracy score in Sub-Saharan African countries, 2004-2014 average.

_Notes: Average adjusted net savings % of GNI vs Polity IV score in Sub-Saharan Africa change during the boom. It excludes Eswatini, Burundi and Liberia considered as outliers. Eswatini scored an average of -9 in polity IV, and Burundi and Liberia reached ANS of -32 % and -61 % of GNI, respectively. Source: authors’ calculations using World Bank data and the Center for Systemic Peace (CSP), Polity IV dataset version 2015._ During the boom, we see that higher adjusted net savings are related to higher Country Policy and Institutional Assessment (CPIA) ratings for non-resource-rich countries and for fossil fuel-rich countries significant at 95% confidence level. However, this relationship does not hold for mineral rich countries as shown in Figure 37. Although, while several mineral-rich countries that improved their control of corruption during the boom period, saw an increase of their adjusted net savings share in GDP, many oil and gas rich countries actually worsened their control of corruption with an increase of their adjusted net savings, as shown in Figure 38.

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Figure 37. Adjusted net savings share in GNI and transparency score in Sub-Saharan African countries, 2004-2014 average.

_Notes: Adjusted net savings as % of GNI vs Country Policy and Institutional Assessment (CPIA) transparency, accountability, and corruption in the public sector rating 2004-2014 average. The CPIA rating captures the level of performance of a country in a given year rated on a scale of 1 (low) to 6 (high). Oil and gas rich countries (in blue) are Chad, Nigeria, Cameroon, Equatorial Guinea, Gabon, Republic of Congo and Angola; while mineral-rich countries as defined by Lundgren, Thomas, and York (2013) are identified with color red. It excludes Burundi and Liberia which have ANS of -32 % and -61 % of GNI, respectively. Source: authors’ calculations using World Bank data and the CPIA database._ 47
Figure 38. Adjusted net savings share in GNI vs control of corruption score in Sub-Saharan African countries, 2004-2014 average _Notes: Adjusted net savings as % of GNI vs control of corruption estimates measured as units of standard normal distribution changes between 2004 and 2014. The control of corruption variable captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, or influence of the elites and private interests. According to this, Liberia is the country that has better improved the perception of control of corruption. Oil and gas rich countries are Chad, Nigeria, Cameroon, Equatorial Guinea, Gabon, Republic of Congo and Angola; while mineral-rich countries are the remaining resource-rich countries, as defined by Lundgren, Thomas, and York (2013). It excludes Burundi and Liberia which have ANS of -32 % and - 61 % of GNI, respectively. Source: authors’ calculation using World Bank data and the Worldwide Governance Indicators database._

# **Investment efficiency**

Finally, we examine the effects of the end of the super-cycle and prospects for resource-rich countries- including the vulnerability to the price crash, the future threat of depletion and the prospect that prices may not hit similar highs again. While resource-rich countries saw strong economic performance during the boom, the vulnerability and lack of fiscal buffers became apparent after prices fell, particularly in petroleum rich countries following 2014. Low rates of adjusted savings and poor public investment management during the boom in resource-rich African countries contributed to this vulnerability. For example, although resource-rich countries like the Republic of Congo and Mauritania were some of the largest recipients of natural resource rents during the boom, their adjusted savings rates and public investment management index are poorer than other non-resource-rich Africa countries, such as Ethiopia or Benin, which did not enjoy the same windfall from commodities (Figure 39).

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<!-- Start of picture text --> 70<br>g 5060 ee 554 RR<br>3<br>=]oa 40ar<br>uw<br>=<br>5<br>= 30<br>_<br>ao<br>yu<br>ie<br>wm 20 SSA nonRR<br>10 nonssA RR<br>nonss54 nonRR<br>2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br>ee ARR A NONRR = eONSSA RR ee ONSSA MONRR<br><!-- End of picture text -->
The disappointing economic outcomes from the resource boom left resource-rich Africa more exposed to the COVID-19 crisis and global economic slowdown. The need has never been greater for policy choices that learn the lessons of the past and of peers to harness resource wealth effectively and to diversify away from resource dependency.

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