Policy Research Working Paper 8153

Natural Resources, Institutions, and Economic Growth

The Case of Nigeria

Anna K. Raggl

Macroeconomics and Fiscal Management Global Practice GroupJuly 2017

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Produced by the Research Support Team

Abstract

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.

Policy Research Working Paper 8153

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group and is a background paper for the Nigeria Growth and Competitiveness Report, entitled Towards Sustainable Growth in Nigeria: Empirical Analysis and Policy Options. Vols. 1 and 2. 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://econ.worldbank.org. The author may be contacted at anna.raggl@wu.ac.at.

Using growth regressions with panel data, this study iden-tifies the determinants of economic growth, highlighting in particular the role of natural resources and institutional quality. The overarching aim of this exercise is to learn about the drivers of growth in Nigeria, and to predict growth rates of gross domestic product per capita for the country under different scenarios. This study finds that a

growth-enhancing effect of natural resources is tied to a sound institutional environment and low levels of corrup-tion. Accumulation of human as well as physical capital, but also the quality of institutions and natural resource rents are estimated to be particular important ingredi-ents for a prosperous economic development in Nigeria.

Natural Resources, Institutions, and Economic Growth: The

Case of Nigeria

Anna K. Raggl∗

Keywords: Economic growth, natural resources, institutions, Nigeria.

JEL Classification Codes: O13, O47, Q32, P48, O55.

∗Foreign Research Division, Oesterreichische Nationalbank (OeNB). Email: anna.raggl@oenb.at. The opinionsare strictly those of the author and in no way commit the OeNB. This study was written in the course of a consultancyto the World Bank while the author held a position at the University of Salzburg. The author is grateful for valuablecomments from Dilek Aykut, Santiago Herrera, Carolina Lennon, and an anonymous referee.

1 Introduction

After the discovery of oil in Nigeria in 1956, the country started oil production in 1958, and soon

after became the main oil exporting nation on the African continent. In spite of the oil boom in the

1970s, the expected prosperity was failing to appear. GDP per capita (constant) was stagnating.

It did not improve significantly and sustainably until the mid 2000s, and only caught up with

the Sub-Saharan African average in 2010. Other indicators of economic development have been

following a similar pattern—the share of people living below $1 per day increased from 36% in

1970 to a staggering 70% in 2000, and at the same time the share of extremely wealthy individuals

grew, such that the income distribution widened considerably1.

An additional characteristic of Nigeria’s development is the volatility of its growth rates. While

its average GDP per capita growth rate was just over 1% between 1980 and 2014, the standard

deviation was close to 7.5, which is higher than in other Sub-Saharan African countries, and in

other oil-producing nations (see Table A.1 in the Appendix).

Ever since the important contribution by Sachs and Warner (1995), in which a negative influ-

ence of natural resource wealth on economic development was shown empirically, Nigeria’s lack

of development was attributed to its oil-abundance, and accompanying Dutch disease effects. van

der Ploeg (2011), however, remarks that ”[i]t is hard to maintain that the standard Dutch disease

story of worsening competitiveness of the non-oil-export sector fully explains [Nigeria’s] miserable

economic performance”. Empirical research by Sala-i-Martin and Subramanian (2003, 2013) backs

up that presumption: natural resources have a deteriorating impact on the quality of institutions,

and through that channel natural resources harm economic development, even in the absence of

Dutch-disease effects.

This data-based analysis aims at re-investigating the causes for Nigeria’s growth performance.

Using panel data of close to 150 countries during 1970 and 2014, we assess the determinants of

GDP per capita growth, highlighting in particular the role of natural resources and institutional

quality. Long-term, cross-sectional analyses are performed in addition, in order to carefully take

into account that institutional quality measures are prone to endogeneity and measurement errors

by using Two-Stage-Least-Squares estimators. The ultimate aim of the analysis is to learn about

the drivers of growth in Nigeria, and to assess the country’s future growth potential. Therefore,

various interaction terms in the panel setting allow a deviating impact of several factors in Nigeria

as compared to the rest of the sample. These estimations are used to project GDP per capita

growth rates for Nigeria under different scenarios.

The remainder of this paper is organized as follows. Section 2 summarizes existing literature

on the resource-growth nexus, and the relevance of the institutional environment of countries in

this context. Before the estimation results are presented in Section 5, Sections 3 and 4 outline the

1Data on poverty rates are taken from van der Ploeg (2011), and developments in income inequality from Sala-i-Martin and Subramanian (2003).

2

estimation strategies and describe the data that are used in the analysis. Drawing on the results of

the growth regressions, Nigeria’s growth rates are predicted assuming different scenarios in Section

6. Section 7 concludes.

2 Natural Resources, Institutional Quality, and Growth

Starting with the observation that resource-abundant countries are often performing worse than

resource-poor countries, Sachs and Warner (1995) study empirically the impact of natural resources

on economic growth using worldwide cross country data. In their seminal contribution, they find

evidence for a detrimental effect of natural resources on growth, and in subsequent studies they

further confirm the robustness of this result2—a controversial finding that triggered an ongoing

scholarly discussion about the curse and the blessing of natural resources.

In response, various analyses were dedicated to deepen the understanding of this result, and

in particular, to identify the mechanisms through which natural resources result in low economic

growth rates. One of the most common arguments is that high shares of natural resources can lead

to overvaluations of the real exchange rates. A consequential contraction of the tradable sector can

weaken economies’ development prospects, especially if this sector exerts economies of scale, by

learning-by-doing, for example (Torvik, 2001; Atkinson and Hamilton, 2003). Sachs and Warner

(1995, 1997, 2001) mainly attribute their empirical findings to these so-called Dutch-disease effects.

Another strand of the literature argues that natural resources may lead to a ”crowding-out”

of investment in human capital. Strong primary sectors lower the incentives to dedicate sufficient

resources to other, more education-intensive sectors. Gylfason, Herbertsson, and Zoega (1999)

highlight that school enrollment rates are lower in countries with a high share of the labor force

engaged in the primary sector. Low human capital accumulation can translate into poor growth

rates—directly via the channel of productivity, or through indirect effects on political stability,

health, or democracy. Empirical evidence suggests, that a considerable part of the negative impact

of natural resources on growth can be attributed to lower educational attainment in resource-

abundant countries (Gylfason, 2001).

Rampant rent-seeking behavior is a further often-investigated transmission mechanism. The

rents generated by natural resources cause an increased number of agents engaged in rent-seeking,

as opposed to pursuing productive activities, and this voracity effect destroys the rents generated

by natural resources. Lane and Tornell (1996), for example, develop a model that shows that in

countries with powerful groups and low institutional quality, growth rates reduce due to natural

resource windfalls, as higher productivity increases the demand for transfers, and these redistribu-

tional effects may outweigh the growth-enhancing effects of resource endowments. Hodler (2006)

provides theoretical as well as empirical evidence that increased rent-seeking behavior weakens

2See Sachs and Warner (1997) and Sachs and Warner (2001).

3

property rights, and that in turn further reduces the attractiveness of productive activities. The

more rivaling groups, i.e. the higher the fractionalization in the countries, the more likely it is,

that natural resources are a curse, as opposed to a blessing.3

While the rent-seeking and the Dutch disease hypotheses claim an unconditional negative influ-

ence of natural resources on growth (Mehlum, Moene, and Torvik, 2006), there is a large literature

that tied the negative relationship to certain conditions, most importantly the institutional environ-

ment and corruption in the countries. The contribution by Bulte, Damania, and Deacon (2005) is

among the first studies investigating the inter-relationship between natural resources, institutions,

and economic and human development. While they find only limited evidence for a direct effect

of natural resources on human development, evidence for an indirect link via institutional quality

is presented. Similarly, Mehlum, Moene, and Torvik (2006) confirm a negative direct effect of

natural resources on growth, but conclude that the combination of ”grabber friendly” institutions

and natural resources harms growth, whereas ”producer friendly” institutions help materializing

the full benefits of resources.4

A final important literature, related to the discussed influence of institutional quality, and the

rent-seeking hypothesis, is the role played by corruption. Leite and Weidmann (1999), and more

recently Badinger and Nindl (2014), show, that natural resources facilitate corruption. Again,

the strength of this link has been tied to the countries’ institutional environments. Resource

rents appear to mainly increase corruption levels, if countries have a comparably low polity-score,

a measure of the degree of democracy in a country (Bhattacharyya and Hodler, 2010). Stable

democracies—Norway, Australia, and Canada are named as examples—do not suffer from these

adverse effects, as their institutions prohibit rent-seeking behavior to a large degree.

AGO

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0 20 40 60 80Oil rents, share in GDP

Figure 1 – Correlation between natural resource shares in GDP (left) and oil shares in GDP (right, only ifoil rents positive) and GDP per capita growth

3See also Baland and Francois (2000); Torvik (2002) for more rent-seeking models.4Brunnschweiler (2008) offers similar findings, using an alternative measure of resource-abundance.

4

AGO

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Figure 2 – Correlation between natural resource abundance and institutional indicators: rule of law (left)and corruption (right)

Figure 1 provides a graphical representation of long-term GDP per capita growth rates and

natural resource rents as well as oil rents (both measured as shares in GDP). It is apparent, that no

correlation between long-term growth and natural resources can be detected. There are high-growth

countries—such as the Asian tigers—that are poorly endowed with natural resources, whereas

countries rich in resources—Nigeria, Liberia, the Republic of Congo, or Republica Bolivariana

de Venezuela are examples—lack long-term economic progress. Recent research stresses the role

of institutions in the growth-resource-nexus. The graphs in Figure 2 attempt to find descriptive

evidence for a correlation between resource endowments and institutional quality. The correlation

is remarkable: Resource-rich countries, among them Nigeria, are associated with lower average

institutional quality and higher levels of corruption (based on long-term averages of the variables),

supporting the presumption that resources can deteriorate institutional quality and promote rent-

seeking and corruption.

Based on the current state of the literature, and on the important work by Sala-i-Martin

and Subramanian (2003, 2013), this study addresses the determinants of GDP per capita growth,

focusing in particular on the role played by natural resources, institutions, and corruption, and

highlights the growth prospects for Nigeria in a global setting.

3 Empirical Setting

Panel fixed effects estimations The main results of this analysis and the ingredients for

the out-of-sample predictions of Nigeria’s growth rates are based on cross country panel growth

regressions. The real per capita growth rate in country i and period t, git, is regressed on a variable

expressing natural resource abundance, institutional quality (Iit), and a set of covariates X, and

the basic specification can be characterized as

5

git = α+ β log

(GDPi,t−1

POPi,t−1

)+ γf

(NATitGDPit

)+ δIit +Xη + µi + νt + εit (1)

where α is a constant, µi and νt are country and period fixed effects, and εit is the remaining

error term. In particular the country fixed effects that control for time-invariant characteristics

inherent to the countries are important components in this setting, because they limit potential

unobserved heterogeneity biases.5 In order to limit the chance of business cycles and short term

GDP fluctuations distorting the results, t corresponds to five-year periods and the variables enter

as five-year averages or initial values of the respective period.6 Important control variables that are

included in the matrix X are human capital, investment, government consumption, the openness

of the countries, as well as a measure for the undervaluation of the currency. In all specifications,

a measure of natural resources is included. In a simple setting, we control for the share of total

natural resource rents in GDP. In order to allow for heterogeneous effects, we decompose that

variable into rents from oil and non-oil natural resources. As the effects of oil rents on GDP per

capita growth may be non-linear, the oil rents variable is split into four quartiles in a third setting.

The underlying hypothesis is that the degree of oil-dependence might affect the contribution of oil

rents to GDP growth rates.

As the major aim of this study is to carve out the main determinants of growth in Nigeria,

several variables are interacted with a Nigeria dummy in order to learn about possible deviating

effects.

Endogeneity The coefficient estimates of the institutional measures (incl. the corruption index)

might be biased in an estimation framework that does not account for endogeneity. Endogeneity

problems could come from various sources. First, both the institutional characteristics and the

growth rates of countries could respond simultaneously to omitted factors. Such factors could

be cultural dispositions, legal frameworks or historical conventions (see for example Mendez and

Sepulveda, 2006). As a panel setting allows to control for country-specific fixed effects, country-

inherent factors that are constant over time are controlled for, and persistent country characteristics

will not cause biased estimates. Second, the estimates could suffer from reversed causality—a

problem that arises when not only corruption influences GDP growth, but also the reverse is true.

Third, the rule of law as well as the corruption indicator are indices, and not precise measurements.

Measurement errors cause biased estimates, when they are correlated with the observed (and

potentially) mis-measured values.

Biased estimates that should not be interpreted causally are the consequences of all described

sources of endogeneity. Several attempts to instrument the institutional variable in a panel setting

were not fruitful due to a lack of credible instruments that vary over time. Instrumental variables

5See for instance Mendez and Sepulveda (2006).6See Table A.2 in the Appendix for detailed description of the variables and the form they enter the estimations.

6

that have proved helpful are constant within countries (see below for a discussion of those instru-

ments), and therefore they cannot be used in a panel framework that controls for country fixed

effects. As these country fixed effects help overcoming another important (heterogeneity) bias, the

cost of omitting them is too high. Inspired by recent literature (Werker, Ahmed, and Cohen, 2009;

Nunn and Qian, 2014; Dreher and Langlotz, 2015) on the causal link between aid and growth,

attempts were made to interact presumably excludable, but constant instruments with a variable

that varies over time. The special feature of this strategy is that the second variable that the

excludable instrument is interacted with must not necessarily be exogenous. Although promising

at first, further considerations revealed that the instruments for natural resources proposed in the

literature are not appropriate for such an instrumentation strategy. For this reason, a bias reduc-

tion with two-stage least squares methods seems not feasible in the panel setting and we rely on a

cross-sectional analysis for an attempt to establish causality.

Instrumentation of institutional quality in a non-panel, cross country setting Very

much in the line with the literature on institutional quality and economic growth, the possible

endogeneity of the institutional variable is instrumented in a cross-country, long term growth

setting. The time dimension exploited in the main results, that are based on the panel setting

outlined above, needs to be neglected, and long term averages and initial values of the variables are

used. In this setting, one can draw on the literature for successful instrumentation strategies. Hall

and Jones (1999), for instance, use the fraction of the population speaking English or another major

European language as an instrument for institutional quality, arguing that the language shares

are approximating the exposure to Europe. Using similar arguments, the seminal contribution by

Acemoglu, Johnson, and Robinson (2001) exploits the variation in mortality rates of early European

settlers in the colonial countries to approximate the foundations of current institutional quality

that have been established in the past by European settlers. Together with the presumption of high

persistence of institutional quality, their instrumentation rests on the assumption that bad living

conditions increased the likelihood of ”‘extractive”’ institutions, whereas a favorable environment

caused settlers to build ”Neo-Europes”. Easterly and Levine (2003) instrument the institutional

variable with ”endowments”, and use settler mortality, latitude, crops/minerals dummies, and a

landlocked dummy.

Using settler mortality as an instrument drastically reduces the number of observations, and

for that reason we are following Hall and Jones (1999) and Sala-i-Martin and Subramanian (2003,

2013) and use English and other European language shares as instrumental variables for the two

indicators for the quality of institutions—the rule of law and a corruption index.

Formally, the growth equations that are estimated have the following form

gi = α+ β log

(GDPi

POPi

)+ γf

(NATiGDPi

)+ δIi +Xη + εi (2)

7

Institutional quality and the level of corruption is instrumented with the fraction of people speaking

English and another European language using Two-Stage-Least-Squares (2SLS) estimation. The

corresponding first stage is

Ii = θ0 + θ1Engi + θ2Euri + θ3

(GDPi

POPi

)+ θ4f

(NATiGDPi

)+Xκ+ ui (3)

where Engi and Euri denote the proportion of people in country i, that speak English or another

European language, respectively.

4 Data

Panel analysis The sample used for the panel regressions contains 1,000 observations that rep-

resent 150 countries during 1970 (or later) and 2014 7 The time dimension is reduced to five-year

averages (for log GDP per capita, the initial value is used), in order to net out business cycles and

high short term volatility. One observation indexed (i, t) thus corresponds to a country i and the

average during years t, t+ 4 or the initial value in t. Table A.4 in the Appendix lists the countries

and periods included in detail.

The sources and precise definitions of the variables used are summarized in Table A.2 in the

Appendix. A measure of the real undervaluation of currencies is constructed by the price level

of an economy adjusted by the Balassa-Samuelson effect8 following Rodrik (2008). More specif-

ically, the real effective exchange rate, calculated as the exchange rate over the PPP conversion

factor, is regressed on (the logarithm of) per capita GDP and a set of time dummies. The differ-

ence of the real exchange rate and the predictions from this regression is used as a proxy for real

undervaluation. The resulting index is centered around zero. A positive value indicates under-

valuation, and a negative value indicates overvaluation. Figure 3 shows the natural logarithm of

the undervaluation index for Nigeria, as compared to other Sub-Saharan African oil-exporting and

non-oil-exporting countries. This comparison shows descriptively, that Nigeria’s exchange rates

have been overvalued—considerably so compared to other Sub-Saharan African countries—during

the 1980s, 1990s, and early 2000s. Only in the mid-2000s, the degree of undervaluation returns to

the Sub-Saharan African average.

In order to measure institutional quality and the level of corruption in the countries, we rely

on the recently established Varieties of Democracy Data Base (Coppedge, Gerring, Lindberg,

Skaaning, Teorell, Tzelgov, Wang, Altman, Bernhard, Fish, Glynn, Hicken, Knutsen, McMann,

Staton, and Zimmermann, 2015), that provides a wide array of indicators related to various aspects

7All figures presented here are lower bounds, and refer to the sample of 1,000 observations in columns (3) to (5)in Table 1 and columns (3) to (5) in Table 2.

8The adjustment accounts for the finding, that increases in income levels lead to a relative price increase ofnon-tradeable goods due to productivity improvements.

8

-10

12

3Lo

g(un

derv

alua

tion)

1970 1980 1990 2000 2010Year

Sub-Sahara Africa, oil Nigeria

-10

12

3Lo

g(un

derv

alua

tion)

1970 1980 1990 2000 2010Year

Sub-Sahara Africa, no oil Nigeria

Figure 3 – Log(undervaluation) in Nigeria and other Sub-Saharan-African countries that produce oil (left)and that do not produce oil (right). A positive value indicates an undervaluation, a negative valuean overvaluation of the currency.

of democracy. The indicators chosen for this analysis are a rule of law index (v2xcl rol) and a

corruption index (v2x corr). The former measures the equality before the law and individual

liberty, the transparency and the enforceability of laws, and to what extend citizen have access to

justice, secure property rights, freedom from forced labor, freedom of movement, physical integrity

rights as well as freedom of religion. The corruption index includes measures of distinct types of

corruption, thereby distinguishing between bribery and embezzlement, as well as between the levels

at which corruption takes place—the highest levels as opposed to the public sector at large. It is

calculated as a weighted average of public sector, executive, legislative, judicial corruption indices.9

Both indices in their original forms are normalized between 0 and 1, but had been rescaled for

the analysis to run from 0 to 100 for interpretation purposes. In the panel data application, the

indices enter the regressions as deviations from country-specific means (as country fixed effects are

included, the standardization does not alter the coefficient estimates in any way).

Cross-sectional analysis In order to account for a possible endogeneity of the institutional

variable and the corruption indicator that is not controlled for by country fixed effects in the panel

setting, we estimate long-run growth regressions using only long-term averages and/or initial values

of the variables. The estimation sample consists of 113 countries. The country coverage is lower in

this setting as compared to the panel regressions, because for some countries the time dimension

of the data is not long enough to calculate long-term growth rates. While in the panel setting it is

possible to control for period effects, this cannot be done in this framework, and the data need to

be comparable in order to obtain reliable results. Table A.3 in the Appendix provides an overview

9Please refer to the code book of that data set for a more detailed description (Coppedge, Gerring, Lindberg,Skaaning, Teorell, Tzelgov, Wang, Altman, Bernhard, Fish, Glynn, Hicken, Knutsen, McMann, Staton, and Zim-mermann, 2015).

9

over of data used, how they are measured, and which source they are from.

5 Growth Regressions

5.1 Panel Data Analysis

Table 1 presents the elementary specification of the growth regressions using panel data and various

extensions, all including country as well as period fixed effects. The first column allows human and

physical capital, government expenditures, inflation, as well as openness to trade and institutional

quality, measured by the rule of law, to influence GDP per capita growth rates. The inclusion of

initial GDP per capita controls for convergence within countries, i.e. for convergence to a country-

specific long-term equilibrium growth rate. The negative coefficient of initial GDP per capita

confirms that classical convergence hypothesis, as increases in income per capita are followed by

lower growth rates of GDP. Higher human capital, investment shares and trade openness, as well

as good macroeconomic management (low inflation) and low levels of government expenditures are

increasing medium-term growth rates of GDP per capita. Although the coefficient of institutional

quality cannot be interpreted causally due to a potential endogeneity bias, the estimates suggest a

positive relationship between an improved institutional environment and economic development.

The estimated coefficients are not only statistically significant, but also their magnitude shows

non-negligible impacts on economic growth. An increase in the share of upper secondary and

tertiary educated in the labor force by 10%-points is estimated to increase GDP per capita growth

by roughly 0.7%-points. Similarly, based on the results, an increase in the share of investment in

GDP by 10%-points has the potential to raise growth by 1.6%-points.

In columns (2) and (3), natural resource rents are added to the equation. Neither in the

aggregate form, nor when disaggregated into oil and non-oil resource rents a significant impact

can be detected.10 It is likely, that the impact of natural resources is too heterogeneous and/or

depends on additional factors, and only a further refinement can shed light on the role played by

natural resources. As an extension of column (2), Figure 4 presents the estimates of the impact of

natural resources on economic growth when interacted with period dummies. The variation over

time is considerable and might offer an explanation for the insignificance of the natural resource

variable in the previous columns. There is evidence for a heterogeneous effect over time, and it

appears that during the last decade, natural resource rents had a significant (at the 10% level) and

positive impact on economic growth.11

10When splitting the natural resource variable further into oil, natural gas, mineral and forest rents (not displayedin the table), no further insights are gained. While all coefficients but the one for forest rents are positive, none ofthem can be distinguished from zero at a statistically significant level of confidence.

11The exceptionally large effect in the first half of the 1970s could be due to the vast increase in oil prices, butalso to a smaller country-coverage in this early period of the sample.

10

Table 1 – Determinants of growth per capita: Panel fixed effects estimations

(1) (2) (3) (4) (5)

Log(GDP per capita) -4.558∗∗∗ -3.407∗∗∗ -3.442∗∗∗ -3.571∗∗∗ -3.538∗∗∗

(0.000) (0.000) (0.000) (0.000) (0.000)

Education 0.0683∗∗ 0.0510∗∗∗ 0.0451 0.0577∗∗ 0.0492∗

(0.029) (0.009) (0.115) (0.046) (0.087)

Governm. cons. -0.102∗∗ -0.143∗∗∗ -0.0991∗∗∗ -0.0947∗∗∗ -0.0923∗∗

(0.018) (0.000) (0.009) (0.010) (0.015)

Investment 0.162∗∗∗ 0.144∗∗∗ 0.150∗∗∗ 0.153∗∗∗ 0.153∗∗∗

(0.000) (0.000) (0.000) (0.000) (0.000)

Openness 0.0147∗∗ 0.0176∗∗∗ 0.0134∗∗ 0.0107∗ 0.0133∗∗

(0.037) (0.005) (0.029) (0.082) (0.033)

Inflation -0.00271∗∗∗ -0.00185∗∗∗ -0.00159∗∗∗ -0.00154∗∗∗ -0.00154∗∗∗

(0.008) (0.000) (0.000) (0.000) (0.000)

Rule of law 0.0159∗∗ 0.0193∗∗∗ 0.0168∗∗ 0.00479 0.0145∗∗

(0.020) (0.007) (0.014) (0.571) (0.033)

Natural res. 0.0272(0.316)

Oil rents 0.0627 0.106∗∗

(0.144) (0.038)

Non-oil rents 0.0267 -0.121∗∗ 0.0189(0.437) (0.022) (0.544)

Rule of law x oil rents -0.000843(0.325)

Rule of law x non-oil rents 0.00236∗∗∗

(0.000)

Oil rents, 1st qu. 6.753∗∗∗

(0.000)

Oil rents, 2nd qu. 0.757∗∗∗

(0.002)

Oil rents, 3rd qu. 0.151∗∗

(0.012)

Oil rents, 4th qu. 0.0766∗

(0.075)Observations 1031 1004 1000 1000 1000Countries 150 150 150 150 150R2 0.384 0.210 0.293 0.304 0.308

Each specification includes country and period fixed effects, as well as a constant. p-values in parentheses. ∗ p < 0.1, ∗∗

p < 0.05, ∗∗∗ p < 0.01.

Another reason for the lack of a clear relationship for natural resources and growth could be

a missing link to institutional quality. For natural resources to improve growth rates, a stable

institutional environment might be a prerequisite that guarantees a fair distribution of revenues

associated with the rents. When allowing for such an interaction in column (4), it appears that on

11

-.20

.2.4

70 75 80 85 90 95 00 05 10

99% CI 95% CI 90% CI

Figure 4 – Impact of natural resources on per capita growth rates, by period

average, oil rents improve growth rates when institutional quality is held constant, whereas rents

from other natural resources require solid institutions for them to be beneficial. It is important

to note again, that as the endogeneity of the institutional variable is not accounted for, causal

interpretations cannot be made.12

In the final column of Table 1, heterogeneities with respect to the countries’ dependence on oil

are allowed for (see also Raggl, 2014, for a similar setting). Therefore, oil rents are interacted with

dummies indicating the quartile of oil rents in GDP that are calculated using the full sample for

each period. The results in column (5) show, that on all levels of oil dependence, oil rents increase

GDP per capita growth rates on average. However, the magnitude of a 1%-point increase in oil

differs considerably across the four levels of oil dependence, and is highest for countries with low

oil dependence. Nigeria is highly dependent on oil, and is allocated to the fourth quartile in all but

12Several attempts to instrument the institution indicators in the panel setting were not fruitful. As commonin the literature, 2SLS estimations are carried out on the cross-sectional, long-term level, that abstracts from thevariation over time in the data (Hall and Jones, 1999; Acemoglu, Johnson, and Robinson, 2001; Sala-i-Martin andSubramanian, 2003). However, possible endogeneity in the panel framework will not negatively affect the quality ofthe out-of-sample predictions in Section 6, if it is safe to assume that the patterns of endogeneity prevail during thecourse of the prediction period.

12

one period, and belongs to the group of countries that exhibit the lowest benefits on average. A

10%-point increase in the share of oil rents in GDP—for Nigeria this would imply an incline from

approximately 16% in GDP in 2010-14 to 26%—is estimated to increase GDP per capita growth

by roughly 0.8-1.5%-points in the third and fourth quartile, respectively. The following paragraph

reviews and discusses results particular for Nigeria in more detail.

A Focus on Nigeria Table 2 displays panel fixed effects estimations that highlight the role of

natural resources and institutional quality in Nigeria. The first column suggests that the impact

of natural resources on growth are significantly lower in Nigeria than in the rest of the sample.

In fact, increases in natural resource rents appear detrimental to growth in the country. In the

second column, an index for the rule of law as a measure of institutional quality is added, and as

a result, the negative impact of natural resources declines in magnitude for Nigeria (from -0.042

to -0.029). These findings are in line with Sala-i-Martin and Subramanian (2003), and provide

evidence for a negative relationship between natural resources and institutional quality. When

omitting institutional quality, and natural resources weaken institutions, then the impact of lower

institutional quality is wrongly attributed to natural resources. Controlling for institutions in

column (2) thus increases the coefficient of resources for Nigeria.

This interdependence between resources and institutions is further confirmed in column (3),

where the addition of another measure of institutional quality, political corruption, leads to a

further (small) increase in the impact of natural resources. The positive impact of corruption in

the full sample has previously been found in the literature, it is summarized as the ”‘greasing

the wheels”’-effect of corruption (Egger and Winner, 2005; Vial and Hanoteau, 2010; Campos,

Dimova, and Saleh, 2010). As in this setting no measures have been undertaken to limit a possible

endogeneity bias, causal interpretations are not justified, however. In columns (4) and (5) the

corruption variable is interacted with a Sub-Saharan Africa dummy and with a Nigeria dummy,

respectively. Apart from the finding that corruption is associated with lower growth in Sub-Saharan

Africa, and even more so in Nigeria13, the effect of natural resources on growth in Nigeria further

increases (column 4) and becomes positive in column (5). This latter finding suggests, that in

particular in Nigeria, there is a strong connection between natural resources and corruption, and

when controlling for this link, resource rents do no longer harm, but foster economic growth. The

negative impact of natural resources in Nigeria found in the previous specifications appears to be

driven by the high levels of corruption associated with the country’s resource wealth, and in order

to enable natural resources to be beneficial for growth a stable institutional environment is crucial.

Distinguishing between the oil and non-oil rents in GDP in column (6) reveals, that both oil

and non-oil resource rents are stimulating economic growth in Nigeria. Although Nigeria is an

13The coefficient for Nigeria appears particularly large. However, when interpreting the effect in terms of standarddeviations, the effect is still considerable, but less pronounced. A one standard deviation increase in the corruptionindicator reduces growth by 0.8 standard deviations.

13

Table 2 – Determinants of growth per capita: Panel fixed effects estimations, focus on Nigeria

(1) (2) (3) (4) (5) (6) (7)

Log(GDP per capita) -3.157∗∗∗ -3.376∗∗∗ -3.313∗∗∗ -3.416∗∗∗ -3.235∗∗∗ -3.312∗∗∗ -3.403∗∗∗

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Education 0.0518∗ 0.0431 0.0521∗ 0.0454∗ 0.0502∗ 0.0540∗ 0.0584∗∗

(0.067) (0.122) (0.054) (0.078) (0.060) (0.051) (0.036)

Governm. cons. -0.139∗∗ -0.0969∗∗ -0.0898∗∗ -0.0821∗∗ -0.0849∗∗ -0.0848∗∗ -0.0785∗∗

(0.023) (0.011) (0.025) (0.031) (0.033) (0.031) (0.048)

Investment 0.191∗∗∗ 0.145∗∗∗ 0.147∗∗∗ 0.150∗∗∗ 0.151∗∗∗ 0.158∗∗∗ 0.161∗∗∗

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Openness 0.0162∗∗∗ 0.0141∗∗ 0.0137∗∗ 0.0127∗∗ 0.0127∗∗ 0.0119∗ 0.0117∗

(0.009) (0.021) (0.029) (0.045) (0.039) (0.057) (0.064)

Inflation -0.0015∗∗∗ -0.0016∗∗∗ -0.0016∗∗∗ -0.0016∗∗∗ -0.0016∗∗∗ -0.0016∗∗∗ -0.0016∗∗∗

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Rule of law 0.0164∗∗ 0.0204∗∗∗ 0.0224∗∗∗ 0.0205∗∗∗ 0.0212∗∗∗ 0.0190∗∗∗

(0.017) (0.002) (0.002) (0.002) (0.002) (0.004)

Rule of law x NGA 0.0406∗∗∗ 0.0731∗∗∗ 0.0932∗∗∗

(0.003) (0.000) (0.000)

Corruption 0.0284∗∗ 0.0517∗∗∗ 0.0295∗∗ 0.0334∗∗ 0.0338∗∗

(0.039) (0.001) (0.032) (0.015) (0.011)

Corruption x SSA -0.0659∗∗

(0.044)

Corruption x NGA -4.578∗∗∗ -3.593∗∗∗ -3.203∗∗∗

(0.000) (0.000) (0.000)

Natural res. 0.0721∗ 0.0439 0.0413 0.0449∗ 0.0425(0.082) (0.108) (0.123) (0.083) (0.112)

Nat. res. x NGA -0.114∗∗∗ -0.0730∗∗ -0.0675∗∗ -0.0665∗∗ 0.0967∗∗∗

(0.007) (0.020) (0.029) (0.026) (0.001)

Oil rents 0.0758∗

(0.087)

Oil rents x NGA -0.0504 -0.0400(0.323) (0.444)

Non-oil rents 0.0193 0.0114(0.584) (0.720)

Non-oil rents x NGA 0.675∗∗∗ 0.780∗∗∗

(0.000) (0.001)

Oil rents, 1st qu. 6.815∗∗∗

(0.000)

Oil rents, 2nd qu. 0.737∗∗∗

(0.002)

Oil rents, 3rd qu. 0.161∗∗∗

(0.007)

Oil rents, 4th qu. 0.0880∗∗

(0.048)

Observations 1088 1004 1004 1004 1004 1000 1000Countries 166 150 150 150 150 150 150R2 0.352 0.292 0.299 0.307 0.310 0.314 0.328

Each specification includes country and period fixed effects, as well as a constant. p-values in parentheses. ∗ p < 0.1, ∗∗

p < 0.05, ∗∗∗ p < 0.01.

14

economy highly dependent on oil, after controlling for both institutional quality indicators, oil

rents are contributing to growth in the country.

The impact of the rule of law indicator does not change when interactions between resources

and Nigeria dummies are included, nor when the corruption variable is added. Although no causal

interpretation can be made in this setting, improvements of institutional quality within countries

are associated with higher GDP per capita growth rates. Columns (5) to (7) of Table 2 allow

for a deviating coefficient of the rule of law measures in Nigeria as compared to the rest of the

sample. The results suggest, that the connection between the rule of law and GDP growth is

particularly relevant in the country, even when an additional measure of institutional quality,

political corruption, is controlled for. Therefore, both dimensions of institutional quality should

thus be addressed in order to smooth the way for sustainable growth, and a growth-enhancing

effect of Nigeria’s natural resources.

Further results Table A.5 in the Appendix provides additional results related to the growth

determinants in Nigeria. First, the impact of human capital on economic development is stronger

when the level of GDP per capita is comparably small. In other words, the growth-enhancing

effect of educational expansions is higher in developing countries. This result is underlined by

the findings in column (2), where the effect of education is allowed to differ between Nigeria and

the rest of the sample. It appears, that human capital accumulation is of particular importance

for growth in the country. The variable is measured as the share of people with upper secondary

education or more in the age group 20 to 64. An expansion of secondary and tertiary education is

estimated to be a fruitful strategy to enhance economic progress in the long run.

Second, a measure of the undervaluation of a currency, as suggested by Rodrik (2008) and

discussed in more detail in the Data section, is included in the specifications (3) to (5) of Table A.5.

Rodrik (2008) finds a significant and positive relationship between the degree of undervaluation of

a currency and economic growth, and he argues and shows empirically that the impact is especially

high in countries with low incomes per capita.

When interacting the measure of undervaluation with a Nigeria dummy variable, the effect for

the country is positive and significant. If, to a certain extent, the impact of natural resources on

growth is channeled through overvaluations of the real exchange rates (Dutch disease effects), then

an inclusion of a measure of undervaluation should improve the growth impact of natural resources.

In other worlds, if the degree of undervaluation of a currency is held constant, the impact of natural

resources should be more positive (less negative), as the negative effect via the exchange rate is

controlled for. This can be observed for Nigeria. The impact of natural resources on growth turns

positive, once controlled for undervaluation. For oil rents, the impact is still negative (columns

4 and 5), but considerably closer to zero compared to estimations without the undervaluation

measure. This implies that there is evidence for Dutch disease effects in Nigeria, and policies that

15

are directed towards the prevention of strong currency overvaluation could improve the growth

effects of natural resources. The magnitude of the effect of undervaluation on growth is non-

negligible in Nigeria: an increase in the undervaluation index by 1% is estimated to raise growth

rates by 0.06%-points (all relevant columns 3–5 in Table A.5), that could correspond to an increase

from a 4% to a 4.06% growth rate. Similarly, an increase in the undervaluation index by 10%—not

an unrealistic change given the large fluctuations—could improve GDP per capita growth rates by

0.6%-points. This finding is robust across different specifications, that include various alternating

measures of natural resources.

In order to highlight the potential of a less overvalued currency, Figure A.1 in the Appendix

presents the estimated growth impact of a steady improvement of the undervaluation index towards

the values estimated for the mid-1960s between 2010-14 and 2040-44. Nigeria had long periods of

a severely overvalued currency from the 1970s until the early 2000s. Currently, the undervaluation

is no longer negative, the index suggests even a slight undervaluation of the currency. Assuming

that the undervaluation index continuously improves in the future, until by the period 2040-44

it reaches the level of 1965-69 (0.38), GDP per capita growth rates could improve by more than

1%-point on average in the long run.

5.2 Cross-sectional Analysis

The results of the long-term growth analysis that instrument the institutional variable and the

corruption indicator are presented in Table 3. The top panel displays the second stage of the

2SLS estimations, in which the rule of law indicator is instrumented, whereas the bottom panel

displays the results for the corruption variable14. The first two columns refer to OLS results,

and columns (3) to (7) to 2SLS results. In the first specification (column 1), the institutional

variables are omitted, and natural resource rents are not significant in the growth regressions.

Controlling for the rule of law and the corruption index, respectively, results in a positive and

significant coefficient of the resource variable. This finding is in line with the results of the panel

analysis, and suggests a link between natural resources and institutional quality. Natural resources

are associated with lower institutional quality, and that negatively affects growth rates. As soon

as institutions are held constant, the impact of natural resources turns positive and significant.

Instrumenting the rule of law and corruption does not change the significance nor the sign of

the variables. If anything, their impact becomes more pronounced. This result holds for various

model specifications, the inclusion of variables related to macroeconomic management, a currency

undervaluation index, life expectancy and geographical characteristics. Further confirming the

panel data results, the coefficient of the oil rents for highly oil-dependent countries (oil rents >

median) is lower as compared to the coefficient for low oil-dependence.

14Due to the high collinearity and the increased standard errors resulting from the 2SLS estimation, both indicatorsare treated in two separate regressions.

16

Table 3 – Determinants of long-term growth: 2SLS estimations using cross-sectional data

(1) (2) (3) (4) (5) (6) (7)OLS OLS 2SLS 2SLS 2SLS 2SLS 2SLS

Rule of law 0.0217∗∗ 0.0857∗∗∗ 0.0871∗∗ 0.0717∗∗ 0.0850∗∗ 0.0844∗∗

(2.40) (2.65) (2.53) (2.15) (2.21) (2.21)

Log(GDP per capita) -0.684∗∗∗ -0.800∗∗∗ -1.142∗∗∗ -1.100∗∗∗ -1.348∗∗∗ -1.596∗∗∗ -1.593∗∗∗

(-4.18) (-4.91) (-3.83) (-3.54) (-4.37) (-3.60) (-3.61)

Natural res. 0.0132 0.0210∗∗ 0.0443∗∗∗ 0.0419∗∗∗ 0.0418∗∗∗

(1.28) (2.23) (2.89) (2.61) (2.85)

Oil rents 0.0586∗∗

(2.43)

Oil rents < median 0.145(0.64)

Oil rents > median 0.0586∗∗

(2.44)

Non-oil rents 0.0180 0.0179(1.22) (1.21)

Observations 113 113 113 113 113 113 113Hansen J 1.879 1.932 1.802 1.248 1.274p-value 0.170 0.165 0.180 0.264 0.259Kleibergen-Paap 8.592 7.157 7.404 6.758 6.865p-value 0.014 0.028 0.025 0.034 0.032

Corruption -0.0280∗∗∗ -0.0496∗∗∗ -0.0469∗∗∗ -0.0380∗∗∗ -0.0369∗∗∗ -0.0363∗∗∗

(-4.17) (-2.80) (-3.81) (-3.19) (-2.91) (-2.93)

Log(GDP per capita) -0.684∗∗∗ -0.822∗∗∗ -0.929∗∗∗ -0.852∗∗∗ -1.156∗∗∗ -1.231∗∗∗ -1.228∗∗∗

(-4.18) (-5.14) (-5.06) (-5.09) (-5.92) (-5.63) (-5.63)

Natural res. 0.0132 0.0185∗ 0.0227∗∗ 0.0197∗∗ 0.0234∗∗∗

(1.28) (1.83) (2.28) (2.00) (2.96)

Oil rents 0.0286∗∗∗

(2.77)

Oil rents < median 0.114(1.00)

Oil rents > median 0.0286∗∗∗

(2.77)

Non-oil rents 0.0109 0.0109(0.87) (0.87)

Observations 113 113 113 113 113 113 113Hansen J 2.243 4.016 5.465 5.719 5.855p-value 0.134 0.260 0.141 0.126 0.119Kleibergen-Paap 10.112 11.076 11.084 11.185 11.494p-value 0.006 0.026 0.026 0.025 0.022

p-values in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. All specifications include education, the logarithm of totalpopulation, an openness measure, investment, and a measure of ethnic fractionalization. In Column (4), inflation and itsstandard deviation, as well as a measure of undervaluation, in Column (5) life expectancy and latitude are added.

Overall, the results of the long-term cross-sectional analysis confirm the results drawn from the

panel setting, and most importantly, even after instrumentation, the rule of law measure and the

17

corruption index are significantly related to GDP per capita growth.

6 Predictions

In-sample fit The in-sample predictions of the growth rates of Nigeria based on the different

specifications in Tables 2 and A.5 are graphically represented in Figures A.2 and A.3 in the Ap-

pendix. Relying on this graphical analysis, specifications (5) to (7) in Table 2 provide the best

in-sample fit of Nigeria’s growth rates. This seems to be mainly driven by the prediction of the

spike in growth in the period 2000-200415. Specification (7) is chosen to serve as the base for

the out-of-sample growth predictions, because it contains the finest decomposition of the natural

resource variable.

General assumptions GDP per capita growth rates are predicted for six five-year periods

starting in 2015-19, such that the last prediction period is 2040-44. The high degree of uncertainty

about the future development of covariates such as investment, trade or government expenditures,

is accommodated by the definition of different scenarios. Common to all scenarios is the assumption

that the fixed effect of Nigeria is moderately improving over time.16 Figure A.4 in the Appendix

shows the magnitude of the fixed effects of all countries in the sample. Fixed effects can be

understood as country-specific deviations from the overall constant of the regression. Nigeria’s

fixed effect is strikingly low, and among all the countries in the sample, only three countries have

lower fixed effects, among them Liberia as another Sub-Saharan African country. Assumptions

concerning changes of fixed effects over time reflect beliefs about the development of countries

relative to each other. In other words, if income convergence is assumed, fixed effects are modeled

to converge to each other. We assume, that Nigeria’s fixed effect will increase to -3 until 2050,

which is a level that is close to Burkina Faso (-3.7), the Senegal (-3.2), Indonesia (-2.9) or Cote

d’Ivoire (-2.4) in the current estimation.

In addition, in each scenario GDP per capita is updated, i.e. the GDP per capita growth rate

in (t− 1) is used in combination with the level of GDP per capita in (t− 1) to calculate GDP per

capita in period t. The future development of the remaining covariates differs across scenarios,

and the underlying assumptions are explained in detail below.

Scenario 0: This baseline scenario is rather pessimistic, and assumes that oil rents in GDP

remain at the low level that is predicted for 2015-19 until the end of the prediction period, 2040-

44. Expected oil rents for 2015-19 are based on the World Bank’s Commodity Price Forecasts for

15The high average growth rate in this period comes from a growth rate of close to 30% in 2004, as reported inthe World Bank’s World Development Indicators, but also by the United Nations (official data) and by the NationalBureau of Statistics in Nigeria.

16See for example IIASA (2015).

18

-50

510

1980 2000 2020 2040Year

Actual PredictedScenario 0 Scenario 1Scenario 2 Scenario 3

Figure 5 – Predicted growth rates of per capita GDP for Nigeria: Scenarios 0–3

19

this period. All other covariates remain at the levels of the (last observed) period 2010-14 and are

summarized in Table 4.

Table 4 – Values of covariates in Scenario 0

Variable Value assumed for all periodsbetween 2015-2044

Share of upper secondary and tertiary educated in the labour force 34.0%Government consumption as share of GDP 9.0%Investment as share of GDP 15%Openness indicator -12.8Inflation 26.6Oil rents as share of GDP 8.2%Non-oil natural resource rents as share of GDP 2.8%Rule of law 17.4Corruption -0.3

The resulting forecasts of GDP per capita growth rates are graphically displayed in Figure 5.17

The dotted line corresponds to Scenario 0. Under this scenario, growth rates are projected to rise

only moderately as compared to the current levels. Low oil prices, which are assumed to remain

at the low level over the next decades, and stagnating human capital stocks, investment, and

institutional quality keep growth prospects below 3% until 2040-44. The top left graph in Figure

A.7 in the Appendix shows the contributions of the main explanatory variables to the growth

predictions in Scenario 0, and to what extent they compensate for the significantly negative fixed

effect of Nigeria. Inherent to the underlying assumptions is that the contributions remain constant

over time, and the variation in the expected growth rate results from income convergence alone.

The factors that contribute most to Nigeria’s growth rates are human and physical capital, oil and

non-oil natural resource rents, institutional quality, and corruption.18

Scenario 1: That scenario differs from the baseline scenario with respect to the underlying

expected oil rents in GDP. The future development of oil rents is tied to the oil price forecasts

of the World Bank’s Commodity Price Forecasts (see Figure A.5 in the Appendix). All other

covariates remain at their 2010-14 level as displayed in Table 4, including the non-oil natural

resource rents.

17Growth rates used in the regressions as well as for the predictions are growth rates of per capita GDP, thatcorrespond to yearly averages over five-year periods. With an annual population growth of roughly 2.7%, GDPgrowth rates are considerably higher.

18It might seem puzzling that the institutional environment in Nigeria positively contributes to growth. Thisfinding is explained by the construction of the underlying indices: they are measured as deviations from country-specific means. As Nigeria’s institutional quality as well as corruption levels improved since the 1980s and 1990s,the current deviation from the mean is positive (negative) for the rule of law (corruption) index. If this improvementdid not happen, growth rates were predicted to be considerably lower.

20

-10

12

34

Cha

nge

in c

ontri

butio

n

2015 2020 2025 2030 2035 2040

-10

12

34

Cha

nge

in c

ontri

butio

n

2015 2020 2025 2030 2035 2040

-10

12

34

Cha

nge

in c

ontri

butio

n

2015 2020 2025 2030 2035 2040

Education Gov. cons.Investment OpennessOil rents Non-oil rentsRule of law Corruption

Figure 6 – Change in the contributions of covariates to predicted GDP per capita growth rates: Scenario 0vs. 1 (top left), Scenario 1 vs. 2 (top right), and Scenario 2 vs. 3 (bottom left)

Projected growth rates of GDP per capita based on this scenario are moderately higher than in

Scenario 0, and by 2040-44, they are expected to be close to 4%. The better part of the difference

between Scenarios 0 and 1 is materialized not before the end of the prediction period, however.

The top left graph in Figure 6 shows that inclining oil rents in GDP drive the difference to the

baseline projections. The expected recovery of the oil prices gradually raises the growth prospects

of the country, assuming that other factors remain constant. Most importantly, this improvement

can only be materialized, if institutional quality and corruption are not worsening simultaneously.

This scenario certainly suggests that recovering oil prices alone are not sufficient for noteworthy

and sustainable improvements of GDP per capita growth rates.

Scenario 2: Growth predictions based on Scenario 2 are more optimistic, and the underlying

assumptions can be found in Table 5. The share of upper secondary and tertiary educated indi-

viduals in the working age population follows the medium scenario of the IIASA/VID Education

21

projections (Lutz and Butz, 2014). Government consumption is assumed to fall by 5.6% per period,

as during 2000-14, investment is assumed to increase by 10% per period such that it reaches a share

of 26.7% by 2040-44. The openness index is expected to gradually improve back to the level of

1990-2000, and inflation is assumed to remain at a comparable high level of 26.6. The projections

of oil rents in GDP follow the oil price forecasts of the World Bank’s Commodity Price Forecasts

as in Scenario 1, but in this scenario also non-oil resource rents develop in line with the respective

price forecasts. This implies a further decline in 2015-19, and a gradual increase to present values

until 2040-44, and altogether this is less favorable for the country than a continuation of the current

trend, but at the same time a more realistic assumption. The rule of law and corruption indicators

remain at the 2010-14 level.

Table 5 – Values of covariates in Scenario 2

Year Educ. Gov.cons.

Invest-ment

Openness Inflation Oil, 4thquart.

Non-oilrents

Rule oflaw

Corruption

2015 40 7.53 16.61 -12.10 26.63 8.18 1.56 17.41 -0.292020 46 7.10 18.27 -10.91 26.63 10.31 1.67 17.41 -0.292025 52 6.70 20.10 -8.88 26.63 11.79 1.80 17.41 -0.292030 58 6.32 22.11 -5.43 26.63 13.48 1.95 17.41 -0.292035 63 5.96 24.32 0.44 26.63 15.41 2.11 17.41 -0.292040 69 5.62 26.75 10.43 26.63 17.61 2.29 17.41 -0.29

Growth forecasts under these assumptions are significantly revised upwards as compared to

Scenarios 0 and 1, and are projected to cross the 5% threshold in 2030. Particular to this scenario

is that as opposed to the first two scenarios, the contributing factors can—at least to a certain

degree—be influenced by policies. The top right graph in Figure 6 displays how the contribution

of various factors to GDP per capita growth differs between Scenarios 1 and 2. In particular the

influence of human and physical capital accumulation is striking. Also the assumed slight reduction

in government consumption and the gradual improvement of the openness measure contribute

positively to growth. Merely the tying of non-oil natural resource rents to commodity price forecasts

causes a lower contribution of that factor to growth. This latter assumption, however, is important,

and more realistic than the presumption of constant non-oil natural resource rents in the upcoming

decades.

Scenario 3: This scenario differs from Scenario 2 with respect to the adopted institutional char-

acteristics. While in Scenario 2, the rule of law and corruption were assumed to remain constant

at the current levels, Scenario 3 shows predicted growth rates that could be materialized given

moderate improvements of the institutional quality and the corruption indicators (for a graphical

representation of the expected development, please see Figure A.6 in the Appendix).

22

Table 6 – Predicted GDP per capita growth rates for different scenarios

Period Scenario 0 Scenario 1 Scenario 2 Scenario 3

2010-14 2.94 2.94 2.94 2.942015-19 1.60 1.60 1.67 1.862020-24 2.15 2.41 3.22 3.592025-29 2.54 2.94 4.44 4.992030-34 2.80 3.35 5.51 6.222035-39 2.95 3.67 6.42 7.302040-44 3.03 3.92 7.34 8.38

Corresponding growth projections further improve, and highlight the potential inherent to

enhancements of the quality of institutions and a reduction in corruption.

Summary For the projections of Nigeria’s GDP per capita growth rates, we rely on the panel

growth regression results presented in column (7) Table 2, as this specification exhibits the best

in-sample fit and at the same time contains decomposed natural resource variables. Predicted

growth rates are yearly growth rates of per capita GDP, and correspond to a time window of five

years. In order to accommodate the high degree of uncertainty concerning the future developments

of the explanatory variables, four different scenarios are defined. Table 6 summarizes the predicted

growth rates between the 2015-19 and 2040-44 for the four scenarios, and below the key implications

are highlighted.

1. Scenario 0 effectively simulates an extrapolation of the status-quo in combination with

continuously low oil prices. Predicted GDP per capita growth rates increase moderately over

time due to income convergence, but reach merely 3% by 2040-44.

2. In Scenario 1, expected oil rents in GDP follow the path of oil price projections, which

suggest a moderate recovery after the sharp drop between 2015 and 2019. Growth rates are

predicted to improve as compared to the baseline scenario, but a rise above 4% appears not

to be feasible until 2040-44. Based on the forecasts, a recovery of oil prices alone is not

sufficient for obtaining sustainable growth rates of per capita GDP above 4%, even if they

are not accompanied by a reduction in the quality of institutions.

3. Growth predictions based on Scenario 2 exceed 5% from the period 2030-34 onwards. The

underlying assumptions are moderate improvements of human and physical capital accumu-

lation, an increasing openness of the economy, a reduction of government consumption, as

well as non-oil natural resource rents that follow commodity price forecasts. The largest

23

contribution to the predicted growth rates come from education and investment, both areas

that can be tackled by economic policy.

4. An additional improvement of the institutional quality, measured by indices of the rule of law

and corruption, leads to a further rise in growth prospects by up to 1%-point in Scenario

3. Addressing present shortcomings in the transparency and enforceability of law and the

access of civilians to justice and secure property rights, as well as delimiting corruption on

all levels, appear to be fruitful strategies for enhancing Nigeria’s growth prospects.

7 Conclusions

In this empirical assessment, the determinants of GDP per capita growth are studied using data of

approximately 150 developing and developed countries during 1970 and 2014, focusing in particular

on the role of natural resources, their interactions with institutional quality, and specific impacts

in the country of Nigeria. Blessed by an enormous wealth of natural resources but at the same

time afflicted by stagnating GDP growth rates, Nigeria is a prominent example of an economy that

lacks economic development in spite of its resource-abundance. Based on the econometric results,

Nigeria’s growth prospects are assessed under the assumption of different scenarios.

Relying on the global sample, the empirical findings suggest that a sound institutional environ-

ment, measured by an index of the rule of law, is associated with higher GDP per capita growth

rates. In addition, the impact of natural resources on GDP per capita growth turns positive,

once natural resources are interacted with the rule of law. The effect of natural resources thus

depends on the quality of institutions, and resources can be a blessing in countries with transpar-

ent and enforceable law, secure property rights, as well equality before the law of all citizens, and

their freedom of movement and religion. The estimates further suggest that the growth impact of

natural resources differs by the level of resource-dependence of the countries. Countries that are

highly dependent on resources obtain lower growth-returns than countries that have comparably

low shares of resource rents in GDP.

Shifting the focus to Nigeria reveals that improvements of institutional quality—measured by

the rule of law as well as by a corruption index—have a particular beneficial effect on growth rates

of the country. In particular a reduction of corruption not only has a direct influence on economic

growth, but also an indirect one through the improvement of the growth-enhancing potential of

natural resources. If the level of corruption is held constant, higher natural resource rents are

estimated to increase Nigeria’s growth rates. When omitting the corruption variable, the impact

of natural resources appears to be detrimental to the growth of the economy. This suggests a

strong link between resource endowments and the quality of institutions in the country, and a

stabilization of the institutional environment at an improved level should be of high priority.

24

An additional important result is found with respect to the over-/undervaluation of Nigeria’s

currency. Undervaluation is positively linked to GDP growth, and the long periods of overvaluation

significantly reduced the country’s growth rates. Similar to institutional quality, there is an addi-

tional indirect effect of that factor: when holding the degree of overvaluation constant, the impact

of natural resources on growth increases. Such findings are in line with Dutch-disease effects, and

managing the country’s real exchange rate can not only positively contribute to growth directly,

but also indirectly by improving the growth-effect of resources.

As especially the institutional variables are prone to endogeneity biases, similar growth regres-

sions have been estimated using instrumental variable estimators at the cross-sectional level. The

positive and negative growth impacts of the rule of law and corruption, respectively, are confirmed

when using instrumental variables suggested in the literature.

The results of the panel regressions are then used to assess Nigeria’s future growth potential

based on different scenarios of the covariates. The stabilization of oil prices at a higher level

than currently observed seems not sufficient for sustainable growth rates in the country. A steady

accumulation of human as well as physical capital are the main ingredients for reaching an estimated

GDP per capita growth rate of 5%, and a slight improvement of the institutional quality indicators

is estimated to add another percentage point.

25

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28

A Appendix

Table A.1 – Long-run GDP per capita growth, its standard deviation, natural resource and oil rents for NGAand various country-aggregates

GDP percapita growth

St.D. GDP percapita growth

Naturalresource rents

Oil rents N

NGA 1.06 7.47 43.44 41.10 1Sub-Saharan Africa 0.87 5.23 13.70 3.91 41Oil rents > 0 1.79 4.07 16.23 12.86 59No oil rents 1.52 4.36 9.11 0.00 60All 1.65 4.22 12.64 6.37 119

0.5

11.

52

2.5

Gro

wth

impa

ct (%

-poi

nts)

2010 2020 2030 2040year

Column 3 Column 4Column 5

Figure A.1 – Contribution to GDP per capita growth: improvement of the undervaluation index back to 0.38(level 1965-69) until 2040-44

29

Table A.2 – Variable description: Panel setting

Variable Description Source

Dependent variable Growth in per capita GDP, 5-year averages WDI

Explanatory variables

Log GDP per capita Log of GDP per capita (constant 2005$), measured at start of each 5-yrperiod

WDI

Education Share of upper secondary and tertiary educated among the 15 to 64 yearolds, 5-year average

IIASA

Governm. cons. General government final consumption expenditure (% of GDP), 5-year av-erage

WDI

Investment Gross fixed capital formation (% of GDP), 5-year average WDI

Openness Imports plus exports of goods and services (% of GDP) filtered for its rela-tion to log(area) and log(population), 5-year average

WDI

Inflation Inflation, GDP deflator (annual %), 5-year average WDI

Rule of law Equality before the law and civil liberties index (0, 100), deviations fromcountry-mean, 5-year average

V-Dem

Corruption Index of political corruption, runs from less corrupt to more corrupt, (0,100), deviations from country-mean, 5-year average

V-Dem

Natural resource rents Natural resources rents (% of GDP), 5-year averages WDI

Oil rents Oil rents (% of GDP), 5-year averages WDI

Mineral rents Mineral rents (% of GDP), 5-year averages WDI

Forest rents Forest rents (% of GDP), 5-year averages WDI

Non-oil rents Natural resource rents excluding oil (% of GDP), 5-year averages WDI

Oil rents, 1st quartile Oil rents if oil rents belong to the lowest quartile in corresponding period,0 otherwise (i.e. interaction of the oil rents variable with a dummy variableindicating the first quartile)

Oil rents, 2nd quartile Oil rents if oil rents belong to the 2nd quartile in corresponding period, 0otherwise

Oil rents, 3rd quartile Oil rents if oil rents belong to the 3rd quartile in corresponding period, 0otherwise

Oil rents, 4th quartile Oil rents if oil rents belong to the highest quartile in corresponding period,0 otherwiseNote: The Variables oil rents 1st, 2nd, 3rd and 4th quartile add up to thevariable oil rents

Log(undervaluation) Measure of currency undervaluation based on Rodrik (2008) using the pricelevel of GDP and GDP per capita,

PWT

WDI: World Development Indicators (2015), The World Bank; IIASA: IIASA-VID dateset on educational attainment (Lutzand Butz, 2014); V-Dem: Varieties of Democracy dataset (Coppedge, Gerring, Lindberg, Skaaning, Teorell, Tzelgov, Wang,Altman, Bernhard, Fish, Glynn, Hicken, Knutsen, McMann, Staton, and Zimmermann, 2015); PWT: Penn World TablesVersion 8.1 (Feenstra, Inklaar, and Timmer, 2015)

30

Table A.3 – Variable description: Cross-sectional setting

Variable Description Source

Dependent variable Growth in per capita GDP, average 1980-2014 WDI

Explanatory variables

Log GDP per capita Log of GDP per capita (constant 2005$), initial value (1980) WDI

Education Share of upper secondary and tertiary educated among the 15 to 64 yearolds, initial value (1980)

IIASA

Investment Gross fixed capital formation (% of GDP), 5-year average WDI

Openness Imports plus exports of goods and services (% of GDP) filtered for itsrelation to log(area) and log(population), 5-year average

WDI

Inflation Inflation, GDP deflator (annual %), 5-year average WDI

Rule of law Equality before the law and civil liberties index, (0, 100), average 1980-2014

V-Dem

Corruption Index of political corruption, runs from less corrupt to more corrupt,(0,100), average 1980-2014

V-Dem

Natural resource rents Natural resources rents (% of GDP), 5-year averages WDI

Oil rents Oil rents (% of GDP), 5-year averages WDI

Mineral rents Mineral rents (% of GDP), 5-year averages WDI

Forest rents Forest rents (% of GDP), 5-year averages WDI

Non-oil rents Natural resource rents excluding oil (% of GDP), 5-year averages WDI

Oil rents, below median Oil rents if oil rents belong to the lowest quartile in corresponding period,0 otherwise (i.e. interaction of the oil rents variable with a dummy variableindicating the first quartile)

Oil rents, above median Oil rents if oil rents belong to the 2nd quartile in corresponding period, 0otherwise

Log(undervaluation) Measure of currency undervaluation based on Rodrik (2008) using the pricelevel of GDP and GDP per capita,

PWT

InstrumentsEurFrac Fraction of the population speaking one of the major Western European

languages (English, Spanish, French, Portuguese, German) as a mothertongue

HJ

EngFrac Fraction of the population speaking English as a mother tongue HJ

WDI: World Development Indicators (2015), The World Bank; IIASA: IIASA-VID dateset on educational attainment (Lutzand Butz, 2014); V-Dem: Varieties of Democracy dataset (Coppedge, Gerring, Lindberg, Skaaning, Teorell, Tzelgov, Wang,Altman, Bernhard, Fish, Glynn, Hicken, Knutsen, McMann, Staton, and Zimmermann, 2015); HJ: Dataset used by Halland Jones (1999); PWT: Penn World Tables Version 8.1 (Feenstra, Inklaar, and Timmer, 2015)

31

Table A.4 – Country and time coverage in the panel estimations (the years indicate the first year of the 5-yearwindows)

Country MIN MAX t Conutry MIN MAX t Country MIN MAX t

East Asia & Pacific Spain 1970 2010 9 South AsiaAustralia 1970 2010 9 Sweden 1970 2010 9 Afghanistan 2010 2010 1Cambodia 1990 2010 5 Switzerland 1980 2010 7 Bangladesh 1980 2010 7China 1970 2010 9 Tajikistan 1995 2010 4 Bhutan 1980 2010 7Indonesia 1975 2010 8 Turkey 1970 2010 9 India 1970 2010 9Japan 1970 2010 9 Turkmenistan 1995 2010 3 Maldives 2000 2005 2Korea, Rep. 1970 2010 9 Ukraine 1995 2010 4 Nepal 1975 2010 8Lao PDR 1985 2010 4 United Kingd. 1970 2010 9 Pakistan 1970 2010 9Malaysia 1970 2010 9 Uzbekistan 2010 2010 1 Sri Lanka 2010 2010 1Mongolia 1980 2010 7New Zealand 1975 2010 8 Latin America & CaribbeanPhilippines 1970 2010 9 Argentina 1970 2010 8 Sub-Saharan AfricaThailand 1970 2010 9 Barbados 2010 2010 1 Benin 1980 2010 7Timor-Leste 2000 2000 1 Bolivia 1970 2010 9 Botswana 2010 2010 1Vanuatu 1980 2010 7 Brazil 1970 2010 9 Burkina Faso 1975 2010 8Vietnam 1985 2010 6 Chile 1970 2010 9 Burundi 1970 2010 9

Colombia 1970 2010 9 Cabo Verde 2005 2010 2Europe & Central Asia Costa Rica 1970 2010 9 Cameroon 1975 2010 8Albania 1980 2010 7 Cuba 1970 2010 9 Cen. Afr. Rep. 1975 2010 8Armenia 1995 2010 4 Dominican R. 1970 2010 9 Chad 2000 2010 3Austria 1970 2010 9 Ecuador 1970 2010 9 Comoros 1980 2010 7Azerbaijan 1995 2010 4 El Salvador 1970 2010 9 Congo, D. Rep. 1970 2010 9Belarus 1990 2010 5 Guatemala 1970 2010 9 Congo, Rep. 1970 2010 9Belgium 2000 2010 3 Guyana 1970 2010 9 Cote d’Ivoire 1970 2010 9Bosnia & Herz. 2000 2010 3 Honduras 1970 2010 9 Eritrea 2010 2010 1Bulgaria 1980 2010 7 Jamaica 1970 2010 9 Ethiopia 2010 2010 1Croatia 1995 2010 4 Mexico 1970 2010 9 Gabon 1970 2010 9Cyprus 1975 2010 8 Nicaragua 1970 2010 7 Gambia, The 1980 2010 7Czech Republic 1990 2010 5 Panama 1980 2010 7 Ghana 1970 2010 9Denmark 1970 2010 9 Paraguay 1990 2010 5 Guinea 1985 2010 6Estonia 1995 2010 4 Peru 1970 2010 9 Guinea-Bissau 1975 2010 8Finland 1970 2010 9 Suriname 1975 2005 7 Kenya 1970 2010 9France 1970 2010 9 Trinidad & T. 1970 2010 9 Lesotho 1970 2010 9Georgia 1995 2010 4 Uruguay 1970 2010 9 Liberia 2000 2010 3Germany 1970 2010 9 Venezuela, RB 1970 2010 9 Madagascar 1970 2010 9Greece 1970 2010 9 Malawi 1970 2010 9Hungary 1990 2010 5 Middle East & North Africa Mali 1970 2010 8Iceland 1995 2010 4 Algeria 1970 2010 9 Mauritania 2010 2010 1Ireland 1970 2010 9 Egypt, Arab Rep. 1970 2010 9 Mauritius 1975 2010 8Italy 1970 2010 9 Iran, Islamic Rep. 1970 2010 9 Mozambique 1980 2010 7Kazakhstan 1990 2010 5 Iraq 2000 2010 3 Namibia 1980 2010 7Kyrgyz Rep. 1995 2010 4 Israel 2010 2010 1 Niger 1980 2010 7Latvia 1995 2010 4 Jordan 1975 2010 8 Nigeria 1980 2010 7Lithuania 2000 2010 3 Lebanon 1990 2010 5 Rwanda 1970 2010 9Macedonia 1990 2010 5 Morocco 1970 2010 9 Senegal 1970 2010 9Moldova 1995 2010 4 Qatar 2000 2010 3 Sierra Leone 1980 2010 7Montenegro 2005 2010 2 Saudi Arabia 1970 2010 9 South Africa 1970 2010 9Netherlands 1970 2010 9 Syrian Arab Rep. 1970 2005 8 Sudan 2010 2010 1Norway 1970 2010 9 Tunisia 1970 2010 9 Swaziland 1970 2010 9Poland 1990 2010 5 Tanzania 1990 2010 5Portugal 1970 2010 9 North America Togo 2010 2010 1Romania 1990 2010 5 Canada 1970 2010 9 Uganda 1980 2010 7Russian Fed.n 1990 2010 5 United States 1970 2010 9 Zambia 1970 2010 6Serbia 2005 2010 2 Zimbabwe 1975 2010 8Slovak Rep. 1990 2010 5Slovenia 1995 2010 4

32

Table A.5 – Determinants of growth per capita: Panel fixed effects estimations, additional results

(1) (2) (3) (4) (5)

Log(GDP per capita) -2.980∗∗∗ -3.373∗∗∗ -3.155∗∗∗ -3.238∗∗∗ -3.321∗∗∗

(0.000) (0.000) (0.000) (0.000) (0.000)

Education 0.199∗∗ 0.0428 0.0434∗ 0.0357 0.0394(0.013) (0.131) (0.097) (0.194) (0.152)

Education x GDP per capita -0.0158∗∗

(0.036)

Education x NGA 0.321∗∗∗

(0.000)

Log(undervaluation) -0.223 0.0392 0.0450(0.613) (0.925) (0.914)

Log(underval) x NGA 6.157∗∗∗ 5.552∗∗∗ 5.517∗∗∗

(0.000) (0.000) (0.000)

Governm. cons. -0.0828∗∗ -0.0955∗∗ -0.102∗∗ -0.110∗∗∗ -0.104∗∗

(0.025) (0.011) (0.018) (0.007) (0.011)

Investment 0.148∗∗∗ 0.154∗∗∗ 0.150∗∗∗ 0.159∗∗∗ 0.161∗∗∗

(0.000) (0.000) (0.000) (0.000) (0.000)

Openness 0.0126∗∗ 0.0125∗∗ 0.0154∗∗ 0.0139∗∗ 0.0139∗∗

(0.047) (0.038) (0.025) (0.035) (0.035)

Inflation -0.00159∗∗∗ -0.00160∗∗∗ -0.00179∗∗∗ -0.00177∗∗∗ -0.00171∗∗∗

(0.000) (0.000) (0.001) (0.001) (0.002)

Rule of law 0.0130 0.0170∗∗ 0.0198∗∗∗ 0.0172∗∗ 0.0154∗∗

(0.106) (0.014) (0.003) (0.016) (0.028)

Natural res. 0.0432(0.125)

Nat. res. x NGA 0.0828∗∗∗

(0.009)

Oil rents 0.0737 0.0711 0.0693(0.107) (0.113) (0.137)

Oil rents x NGA -0.394∗∗∗ -0.0480 -0.285∗∗∗ -0.255∗∗∗

(0.000) (0.323) (0.000) (0.000)

Non-oil rents 0.0239 0.0277 0.0304 0.0229(0.491) (0.427) (0.396) (0.491)

Non-oil rents x NGA 1.480∗∗∗ 0.637∗∗∗ 1.715∗∗∗ 1.686∗∗∗

(0.000) (0.000) (0.000) (0.000)

Oil rents, 1st qu. 5.517∗∗∗

(0.000)

Oil rents, 2nd qu. 0.616∗∗∗

(0.003)

Oil rents, 3rd qu. 0.144∗∗

(0.017)

Oil rents, 4th qu. 0.0785∗

(0.096)

Corruption 0.0307∗∗

(0.034)Observations 1000 1000 960 956 956R2 0.307 0.303 0.305 0.305 0.315N g 150 150 142 142 142

Each specification includes country and period fixed effects, as well as a constant. p-values in parentheses. ∗ p < 0.1, ∗∗

p < 0.05, ∗∗∗ p < 0.01.

33

-50

510

GD

P p

er c

apita

gro

wth

1980 1990 2000 2010 2020Year

Actual In-sample fit

-50

510

GD

P p

er c

apita

gro

wth

1980 1990 2000 2010 2020Year

Actual In-sample fit

-50

510

GD

P p

er c

apita

gro

wth

1980 1990 2000 2010 2020Year

Actual In-sample fit

-50

510

GD

P p

er c

apita

gro

wth

1980 1990 2000 2010 2020Year

Actual In-sample fit

-50

510

GD

P p

er c

apita

gro

wth

1980 1990 2000 2010 2020Year

Actual In-sample fit

-50

510

GD

P p

er c

apita

gro

wth

1980 1990 2000 2010 2020Year

Actual In-sample fit

-50

510

GD

P p

er c

apita

gro

wth

1980 1990 2000 2010 2020Year

Actual In-sample fit

Figure A.2 – Actual GDP per capital growth rates in Nigeria vs. in-sample prediction based on columns (1)to (7) in Table 2

34

-50

510

GD

P p

er c

apita

gro

wth

1980 1990 2000 2010 2020Year

Actual In-sample fit

-50

510

GD

P p

er c

apita

gro

wth

1980 1990 2000 2010 2020Year

Actual In-sample fit

-50

510

GD

P p

er c

apita

gro

wth

1980 1990 2000 2010 2020Year

Actual In-sample fit

-50

510

GD

P p

er c

apita

gro

wth

1980 1990 2000 2010 2020Year

Actual In-sample fit

-50

510

GD

P p

er c

apita

gro

wth

1980 1990 2000 2010 2020Year

Actual In-sample fit

Figure A.3 – Actual GDP per capital growth rates in Nigeria vs. in-sample prediction based on columns (1)to (5) in Table A.5

35

LBRTJK

KGZ

NERGINMDGZAR

BDIMNG

MWIZWENPLTCDCAF

COGUKRGNBGHALSOMDAPHLGUYGMB

TZAUGAVNMRUSINDSLEKENKAZBGDMLI

MOZIRQ

RWABENCMRBFAIRNEGYPAKAZESEN

BLRCOM

IDNBOL

ALBDZALAOCIVSYRGEO

HNDKHM

BTNPRY

PERROMARMVENCHNBGR

JORMKD

THAVUT

NGA

ECUMYSGTMGABMAR

BIHSWZTUNCOLCZEJAM

SLVSURTMP

NAMZAFDOMARG

PANHUNCHL

SAUCUBPOLCRIHRVBRASVKESTMEX

TTOTURLTU

LVASVNMUS

URYAUSLBNCANNZL

GRCUSAQATMDVJPNKORAUTDEUBEL

CHENORCYPNLDPRT

ESPSWEFINDNK

FRAITA

IRLGBR

ISL

-10 -5 0 5 10Fixed Effects

Figure A.4 – Country fixed effects based on specification (7) in Table 2

36

1020

3040

50

1980 2000 2020 2040year

Figure A.5 – Oil rents as shares in GDP: historic and expectations for 2015-2044, Scenarios 1–3

-10

010

2030

1980 2000 2020 2040year

-.50

.51

1.5

2

1980 2000 2020 2040year

Figure A.6 – Institutional indicators: historic and expectations for 2015-2044 in Scenario 3: Rule of law (left)and corruption (right)

37

02

46

810

Con

tribu

tion

to g

row

th

2015 2020 2025 2030 2035 2040

Education Gov. cons.Investment OpennessInflation Oil rentsNon-oil rents Rule of lawCorruption

05

10C

ontri

butio

n to

gro

wth

2015 2020 2025 2030 2035 2040

Education Gov. cons.Investment OpennessInflation Oil rentsNon-oil rents Rule of lawCorruption

05

1015

Con

tribu

tion

to g

row

th

2015 2020 2025 2030 2035 2040

Education Gov. cons.Investment OpennessInflation Oil rentsNon-oil rents Rule of lawCorruption

05

1015

20C

ontri

butio

n to

gro

wth

2015 2020 2025 2030 2035 2040

Education Gov. cons.Investment OpennessInflation Oil rentsNon-oil rents Rule of lawCorruption

Figure A.7 – Contributions of covariates to predicted GDP per capita growth rates in Scenario 0 (top left),1 (top right), 2 (bottom left) and 3 (bottom right)

38