Economic Growth in Ghana Determinants and Prospect

Public Disclosure Authorized
Public Disclosure Authorized

WPS6750
Policy Research Working Paper

Economic Growth in Ghana
Determinants and Prospect

Public Disclosure Authorized

Anna K. Raggl

Public Disclosure Authorized

6750

The World Bank
Africa Region
Poverty Reduction and Economic Management Department
January 2014


Policy Research Working Paper 6750

Abstract
This paper employs a simple cross-country panel
framework to assess the determinants of growth in
Ghana’s gross domestic product over the past four
decades. A set of standard covariates is used to explain
growth rates. Natural resource variables are included

because the effects of natural resource rents in gross
domestic products are of particular interest for Ghana.
Using the preferred specification, Ghana’s growth
potential is predicted for the upcoming decades under
different scenarios. The results indicate that under the
most pessimistic scenario of no improvements in the

determinants of growth compared with the period 200509, Ghana’s gross domestic product per capita growth
rates will stagnate at approximately 4.5 percent during
the next decade and decrease thereafter. If the policy
measures and country characteristics improve in the way
they did in the past three decades, average per capita
growth rates of roughly 5.5 percent could be reached
during 2015–34. Taking into account the expected oil
production until 2034 adds 0.6 percentage points to
projected gross domestic product growth rates on average.

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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 .
The author may be contacted at

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its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Produced by the Research Support Team

Economic Growth in Ghana: Determinants and Prospects∗
Anna K. Raggl†

Keywords: Economic growth, natural resources, oil production, panel growth models, Ghana,
Sub-Saharan Africa.
JEL Classification Codes: O11, O13, O55, Q43
Sector Board Code: EPOL
∗ This report serves as a background study for the World Bank’s Policy Note on Long Run Growth in Ghana
(2014)
† WiC - Wittgenstein Centre for Demography and Global Human Capital, WU - Vienna University of Economics
and Business. Address: Welthandelsplatz 1, 1020 Vienna, Austria. Email: The author
gratefully acknowledges valuable contributions from Jes´
us Crespo Cuaresma, Leonardo Garrido, Santiago Herrera
and Mathias Moser.


1

Introduction

Ghana’s GDP per capita growth rates increased steadily in the past two decades and varied around
5-7% between 2007 and 2010 (see Figure 1). Over the same period, poverty rates decreased and
other, non-monetary, welfare indicators improved. In order to assess the sustainability of these
positive developments, their determinants need to be detected and quantified. This investigation
of historic growth rates can further be used for predicting future GDP growth rates under different
scenarios.
This piece of research uses panel growth regressions to address the question about the drivers
of Ghana’s historic growth rates, emphasizing in particular the role of natural resources. Cocoa,
timber, minerals and gold account for a large share of total exports of the country (see Lej´arraga

(2010), for example), and the recent discovery of oil raises the question about the expected impact
of natural resources on growth. The estimates obtained from the cross-country panel analysis are

-.05

5-year-average GDP per capita growth rate
0
.05

.1

used to predict the growth potential for the country under different scenarios.

1970

1980

1990
Year

2000

2010

Figure 1 – Growth rate of per capita GDP in Ghana, averages over 5 years: Data from 1970-2010 are used.
Values are 5-year averages for the years 1970-2006, and averages of 1-4 years for values that
correspond to the years 2007 and after.

The choice of variables that are used in the empirical analysis is based to a large extent on the
findings in Barro (2003), but additionally considers the aim of assessing the growth performance

of the country of Ghana by estimating various specifications that tackle the impact of natural
resources.

2


The dataset covers the period 1970-2009 and contains economic, political and institutional
characteristics of 151 countries, thereby extending the analysis in Barro (2003) by roughly 15 years
and 80 countries. We estimate different specifications of growth regressions controlling for fixed
country and period effects, trying to explain a large share of within-country variations in growth
rates.
The findings suggest that education significantly increases GDP per capita growth rates, an
effect that is larger in countries with a relatively low initial GDP per capita. Furthermore, investment, openness to trade as well as natural resources are found to be robust determinants of income
growth, all related positively to growth. Government consumption seems to decrease GDP growth
rates in the long run. Relating institutional characteristics of countries to the estimated countryspecific fixed effects shows that there exists a strong positive correlation between them. Although
no causal interpretations can be made in this context, the finding supports the supposition that
fixed effects capture the persistent part of the institutional environment and an improvement of
the quality of institutions is favorable for a country and translates into higher GDP per capita
growth.
Based on the performed growth regressions, we predict Ghana’s future growth potential for
different scenarios. In a baseline scenario, where investment, natural resources, government expenditures and other covariates are assumed to remain at the level of 2005-09, we find that average
growth rates are slightly above 4% in the upcoming two decades. In a more favorable scenario,
where it is assumed that the covariates behave as they did since the 1980s, predicted growth rates
increase by about 1%-point as compared to the baseline scenario. Finally, when taking into account
the expected oil production in Ghana, GDP growth is likely to reach 6.2-6.5% in the next 15-20
years.
The paper is organized as follows. Before the results are discussed in Section 5, the model
specification is described in Section 2 and the econometric methodology and the data are described
in Sections 3 and 4. Section 6 applies the results to predict GDP per capita growth rates for Ghana
until 2030 and Section 7 summarizes and concludes.

2

Model Specification

The specifications of the growth regressions are based on the findings in Barro (2003), and augmented by variables that are of particular interest for Ghana.
The dependent variable is the arithmetic average of the real GDP per capita growth rate of a
country over each 5-year period during 1970-2009. The basic specification includes country fixed

3


effects and time dummy variables and a set of variables that is explained in the following. In order
to control for (conditional) convergence effects, the (natural logarithm of the) level of GDP per
capita at the beginning of each 5-year period is included in the regression. The parameter estimate
associated with this variable is expected to be negative, as relatively low levels of GDP per capita
increase growth rates (holding all other factors constant). Its magnitude can be interpreted as the
speed of (conditional) convergence to a country-specific equilibrium growth rate. Human capital
enters as the share of the (female and male) working age population with tertiary education. In
order to alleviate a potential endogeneity bias due to simultaneity, the value at the start of each
period is used.
A further set of basic covariates are policy related variables and national characteristics. A variable for international openness, measured as imports plus exports as shares of GDP, is included
to assess the effects of trade and globalization. As trade shares are highly correlated with country
sizes, Barro (2003) suggests adjusting the openness measure for this relation. More specifically, the
openness variable is regressed on the logarithms of the population and area (in square-kilometers)
of the countries, and only the remaining part of the measure is used in the growth regression. The
adjusted openness to trade of the countries enters each regression as an average over the 5-year
periods for each country. Additionally, 5-year averages of inflation, government consumption1 and
investment, the latter two as shares in total GDP, are added to the basic regression model. Finally,
as a proxy for institutional quality, a democracy index is included.

The oil production expected in Ghana during the upcoming decades and the country’s dependency on cocoa, timber, gold and minerals raise the question about the growth effects of natural
resources in general, and oil production in particular. We address this issue by including different
variations of natural resource variables in the panel growth regressions. First, the 5-year averages of
natural resource rents as shares of GDP are used as a regressor. This variable combines rents from
oil, natural gas, coal, minerals and forest. As this variable does not allow to isolate an effect of oil
production per se, the measure is split into rents obtained from oil production and non-oil natural
resource rents in a subsequent specification. The coefficient of the variable that measures oil rents
as shares in GDP might still hide some heterogeneities. For that reason we further decompose the
variable by interacting the oil rents with dummy variables indicating the relative importance of oil
production as compared to other economies. This allows the effects of oil rents on GDP growth to
differ across countries with varying oil intensities. The results of this final specification are used
to perform in- and out-of-sample predictions of Ghana’s growth rates.
1 Barro (2003) suggests subtracting expenditures for defense and education from government consumption, as
these should be considered as investments. Data limitations do not allow us to perform a similar adjustment.

4


The data sources of the variables described above can be found in Section 4, and Table 1 briefly
summarizes the variables and their respective sources.
In the context of economic growth and especially in interaction with the role of natural resources,
it would be of particular interest to study the impact of the quality of institutions of the countries.
Most institutional indicators, such as those proxying the rule of law, government effectiveness or
political stability, are not available for a sufficient number of countries and years to include them
in the growth regressions. Additionally, institutional characteristics are very persistent, which
complicates the identification of the parameters in a fixed effects framework. To the extent that
institutional variables are constant over the period 1970-2009, they are captured by the fixed
country effects and only significant yearly deviations from the long-run country means would allow
including them in the estimation. The advantage of the persistent nature of institutions is that fixed
country effects control for general differences in the quality of institutions across countries, even

when the indicators are not available for most of the years considered. This approach, however,
does not allow us to isolate the effect of institutions on GDP growth, but the risk for a potential
omitted variable bias is reduced. In an extension of the analysis below, we assess the correlation of
the estimated fixed effects and a number of institutional quality indicators (measured in a recent
year, to maximize the sample for the analysis) in an attempt to explain parts of the variation of
fixed effects across countries.

3

Econometric Methodology

We estimate the specifications discussed above controlling for fixed country effects and including
period dummy variables. The period dummies control for effects that are specific for a certain
period and have an impact on all countries in a given period. The country specific fixed effects
allow controlling for unobserved heterogeneity as they capture effects that are characteristic for a
country and that do not vary over time, such as most geographical factors, colonial linkages, or
cultural factors inherent to a country. The conclusions we can draw from this analysis are withincountry effects, and do not reflect between country effects.
In particular, we estimate a model of the following form
K

yit = α +

βk xkit + µi + δt +

it

(1)

k=1

where yit is the GDP per capita growth rate of country i in year t, α is a constant, xit are
the K explanatory variables corresponding to country i and period t, µi are country-specific fixed
effects, δt are time fixed effects and

it

is an error term.

5


The explanatory variables vary by specification, but time and country fixed effects are included in every specification, so are the log of the initial GDP, education, investment, government
consumption, inflation, democracy, trade openness and some measure of natural resource rents.

4

Data
Table 1 – Description of variables

Variable

Description

Dependent variable

Growth in per capita GDP, 5-year averages

PWT

Log of initial GDP

Log of initial GDP per capita measured at start of each period

PWT

Education

Share of tertiary educated in working age population

Log initial GDP * Education

Interaction between education variable and initial GDP

Openness
Investment

Share of exports plus imports in GDP, filtered for its relation to
log(area) and log(population), 5-year average
Share of investment in GDP, 5-year average

PWT

Government consumption

Government consumption as share of GDP, 5-year average

PWT

Democracy

Democracy index (0 least, 10 most democratic), lagged by 5 years

Inflation

Inflation rates, 5-year average

WDI

Natural resource rents

Natural resource rents as share of GDP, 5-year averages

WDI

Oil rents

Oil rents as share of GDP, 5-year averages

WDI

Mineral rents

Mineral rents as share of GDP, 5-year averages

WDI

Forest rents

Forest rents as share of GDP, 5-year averages

WDI

Non-oil rents

Natural resource rents excluding oil as share 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 variable indicating the first quartile)
Oil rents if oil rents belong to the 2nd quartile in corresponding period,
0 otherwise
Oil rents if oil rents belong to the 3rd quartile in corresponding period,
0 otherwise
Oil rents if oil rents belong to the highest quartile in corresponding
period, 0 otherwise
Note: The Variables oilrent 0025, oilrent 2550, oilrent 5075 and
oilrent 7500 add up to the variable oilrent

Oil rents, 2nd quartile
Oil rents, 3rd quartile
Oil rents, 4th quartile

Source

IIASA

PWT

Polity IV

PWT corresponds to the Penn World Tables (Heston, Summers, and Aten, 2012)
IIASA corresponds to the IIASA-VID dateset on educational attainment (Lutz, Goujon, K.C., and Sanderson, 2007)
Polity IV corresponds to the Polity IV dataset obtained from Teorell, Charron, Samanni, Holmberg, and Rothstein (2011)
WDI corresponds to the World Development Indicators (2013)

6


We use a dataset of 117-151 countries spanning the period 1970-2009. The four decades are
split into 8 time periods, each covering a 5-year window. Data availability does not allow to observe
each country in each period, the resulting database has the structure of an unbalanced panel and
the numbers of countries considered depends of the empirical specification. Most of the variables
enter as averages over the 5 years or initial values measured at the start of each period are used. Table A.11 in the Appendix provides an overview of the countries and periods included in the analyses.
The dataset combines data from various sources. Per capita GDP (PPP converted, at constant
2005 prices) is taken from the Penn World Table (PWT) Version 7.1 (Heston, Summers, and Aten,
2012). Growth rates of per capita GDP are computed as log-differences of that variable. The value
of the logarithm of GDP per capita at the start of each period is used as a right hand side variable.
Government consumption, investment as well as the measure for openness and inflation are obtained from the same source. Data on educational attainment are taken from an updated version of
the IIASA/VID Dataset on Educational Attainment (Lutz, Goujon, K.C., and Sanderson, 2007).
To control for the level of democracy of a country, an index from the Quality of Government Standard Data collection (Teorell, Charron, Samanni, Holmberg, and Rothstein, 2011) is used, and in
order to extend the time coverage we additionally use data on democracy provided by Bollen (1990).
A summary and brief description of the variables used can be found in Table 1.

5

Results

Column 1 of Table 2 shows the first baseline regression including the initial GDP per capita, human capital as well as an index for democracy, the openness ratio, government consumption and
investment as shares in GDP, inflation and 7 period dummies, where the first period (1970-74)
constitutes the reference category. The negative and significant coefficient of the lagged GDP
per capita variable indicates that countries with a relatively low initial income grow faster towards
their country specific equilibrium. The institutional variable, an index of democracy, does not show
significant effects in this specification. Likewise, education is not identified as a significant driver of
growth rates. This is a common problem in cross country growth regressions and often attributed
to differences in the quality of education across countries and over time (Hanushek and W¨ossmann,
2008), the lack of including the demographic structure of educational attainment (Lutz, Cuaresma,
and Sanderson, 2008), omitting (in)equality measures of schooling within a country (Sauer and Zagler, 2012) or heterogeneous effects across countries or over time. The specification in Column 1
assumes a linear relationship between education and growth and it does not allow the impact of
human capital to differ across countries. In the following, an interaction of the initial GDP per

7


capita and the education measure is included to address this issue.
Increases in government consumption lower growth rates (in long run perspective of this analysis), so do increases in inflation. Investment and the openness ratio of a country foster growth
significantly. These results are in line with Barro (2003).
As an addition to the set of variables suggested by Barro (2003), an aggregate measure of natural
resource rents is included in specification 1 and shows a positive impact of GDP growth rates of
the countries included. A disaggregation of this measure will shed further light on the impact of
natural resources on growth rates.
Additionally to the variables in Column 1, Column 2 of Table 2 includes an interaction of
education and the initial level of GDP. The findings show that the effect of human capital on
economic growth depends on the level of development of the country. The lower the initial income
level of a country, the higher the gains from a better educated working age population. For the
case of Ghana, the effects of the historic increases in education on per capita GDP growth rates are
plotted in Figure 22 . The steady increase in the tertiary education of the working age population

between 1970 and 2009 contributes by roughly 0.5-0.9%-points to yearly GDP per capita growth
rates.
In the specifications 3-5 we are focusing on a decomposition of the natural capital variable.
The variable combines rents obtained from oil, natural gas, minerals, coal and forests. Splitting
the variable into rents from oil production and other natural capital rents (column 3 of Table 2)
suggests that while non-oil natural capital rents increase GDP growth, oil rents show no significant
impact on output growth. This however could be due to the heterogeneous nature of oil exporting
countries. While many countries obtain oil rents worth less than 1% of their GDP, some countries’
economy is more dependent on oil production and these might react to changes in oil rents in a
different way, an issue that is further addressed below.
Splitting the non-oil natural resource variable further into rents from minerals and forests (as
these two resources are of particular interest for Ghana), we find that minerals, as opposed to rents
from forests, increase GDP growth rates (Column 4). It should be noted, that the coefficients of
the other covariates do not change considerably when altering the natural resource variable.
Column 5 allows the impact of oil rents on GDP growth to depend on the relative size of the
oil rents in GDP. In order to implement that, the 25th, 50th and 75th percentile of the oil rent
2 As

specification 2 in Table 2 is not the preferred specification, the estimates shown in the figure are based on
the final specification in Column 5.

8


Table 2 – Fixed effects estimations

Log of initial GDP
Education

(1)

(2)

(3)

(4)

(5)

-0.0410***
[0.000]

-0.0386***
[0.000]

-0.0415***
[0.000]

-0.0442***
[0.000]

-0.0444***
[0.000]

-0.004
[0.931]

0.562**
[0.041]

0.535*
[0.065]

0.554*
[0.059]

0.482*
[0.094]

-0.0526**
[0.037]

-0.0507*
[0.054]

-0.0517*
[0.055]

-0.0451*
[0.084]

Log initial GDP * Education
Openness

0.0414***
[0.000]

0.0398***
[0.000]

0.0405***
[0.000]

0.0387***
[0.000]

0.0385***
[0.000]

Government consumption

-0.188***
[0.000]

-0.195***
[0.000]

-0.293***
[0.000]

-0.294***
[0.000]

-0.303***
[0.000]

Investment

0.0806***
[0.000]

0.0824***
[0.000]

0.0916***
[0.000]

0.0725***
[0.002]

0.0967***
[0.000]

Inflation

-0.000***
[0.004]

-0.000***
[0.004]

-0.000***
[0.006]

-0.000***
[0.004]

-0.000***
[0.007]

-0.0002
[0.772]

-0.0003
[0.674]

-0.0003
[0.727]

-0.0006
[0.457]

-0.0001
[0.88]

0.0673***
[0.001]

0.0639***
[0.002]
0.0233
[0.376]

0.0265
[0.318]

Democracy
Natural resource rents
Oil rents
Non-oil rents

0.166***
[0.001]

0.191***
[0.000]

Oil rents, 1st quartile

3.878
[0.301]

Oil rent, 2nd quartile

1.248***
[0.003]

Oil rents, 3rd quartile

-0.0657
[0.245]

Oil rents, 4th quartile

0.037
[0.16]

Mineral rents

0.281**

[0.010]

Forest rents
Constant
Observations
Countries
R-squared

-0.096
[0.592]
0.370***
[0.000]

0.347***
[0.000]

0.382***
[0.000]

0.412***
[0.000]

0.403***
[0.000]

906
151
0.28

906

151
0.284

724
119
0.307

712
117
0.295

724
119
0.326

Each specification includes country fixed effects and period dummy variables

9


.03

.04
.05
.06
Share tertiary education in WAP

.07

Contribution of education to growth

.01
.006
.007
.008
.009
1970

1980

1990
Year

2000

2010

Contribution of education to growth
Share tertiary education in WAP

Figure 2 – Share of tertiary education since 1970 and its contribution to GDP per capita growth rates in
Ghana (based on Specification (5) in Table 2)

variable for each period is determined. In each period, the countries are then assigned to one of
the following four groups: oil production below the 25th percentile, between the 25th and the 50th
percentile, between the 50th and the 75th percentile and above the 75th percentile. The countries’
allocation to a group must not be constant across periods, i.e. a country can belong for example
to the top quarter in one period, and to the 3rd in another. Including these four oil rent variables,
each one containing the rents of the relevant quartile, we allow the impact of oil rents to change
with the level of oil production. The results show that the positive impact seems to decrease with
an increase in the share of oil rents in GDP.3 Countries whose oil production is relatively low

compared to other oil producing countries tend to gain more from it than those whose oil rents in
GDP are comparably high, keeping everything else constant. An explanation of this result cannot
be assessed in this regression analysis, but a possible reason could be that the negative effects
often associated with natural resources (see for instance Sachs and Warner 1995, 2001) are less
accentuated when the natural resources account only for a small part of total GDP. Crowdingout effects, strong price-dependence and low competitiveness in other sectors that suppress exports
3 In another specification that is not included in the table, the oil rents variables is interacted with initial income
levels. Interactions of the oil variables with variables that capture the quality of the institutions of a country would
be preferable, but we lack sufficient data on that. The results indicate that countries with low initial income levels
tend to benefit more from oil production. However, the effects are insignificant, except for the 4th quartile.

10


.1
.2
.3
Non-oil rents in GDP

3

Growth rate (residual part)
-.1-.05 0 .05 .1 .15

.005 .01 .015 .02 .025
Oil rents, 25th-50th quanitle

1
2
Openness

.4

Growth rate (residual part)
-.05 0 .05 .1 .15 .2

0

0

0

0
.05
.1
.15
.2
Oil rents, below 50th-75th quanitle

Growth rate (residual part)
1.5
0
1
.5

.2
.4
.6
Government consumption

Growth rate (residual part)

-.1 -.05 0 .05 .1

0

-1

Growth rate (residual part)
-.1 -.05 0 .05 .1

11

Growth rate (residual part)
-.2 -.1
0
.1

7
8
9
10
Log of initial GDP

Growth rate (residual part)
-.05 0 .05 .1 .15

6

Growth rate (residual part)
-.15 -.1 -.05 0 .05

Growth rate (residual part)
-.35-.3-.25-.2-.15-.1

are less likely to happen when oil production is relatively low and not a central part of an economy.

0

.2

.4
.6
Investment

0
.001 .002 .003 .004
Oil rents, below 25th quanitle

0
.2
.4
.6
.8
1
Oil rents, above 75th quanitle

Figure 3 – Partial relationships between selected covariates and GDP growth rates; bold observations correspond the Ghana

To summarize the findings, Figure 3 plots each of the variables against the residual growth
rates, i.e. in relation to the parts of the growth rates that remain unexplained when controlling for
all covariates except the one under consideration (Barro, 2003). The residual growth rates contain

the effect of the omitted variable and the remaining error term. The magnitudes of the slopes of
the lines in the plots correspond to the explanatory power of the variable.
A last implication of the regression analysis can be obtained when looking at the estimated
country fixed effects. Loosely speaking, these effects capture the general long term economic environment of a country. All covariates of GDP growth that are constant and therefore not included
11

.8


in the regression, such as geographic factors, (constant) institutional characteristics, colonial linkages, climate, cultural conditions and similar, are summarized by the fixed effects. Technically,
the fixed effects would equal the growth rates in the case when all covariates, the constant and the
time fixed effects are zero. Factors that are common to all countries and common to all years are
summarized in the constant and the fixed effects show the country-specific deviations from that
constant. Thus, the mean of the fixed effects over all observations is zero.

Table 3 – Estimated fixed effects

Rank
1
2
3
4
5
6
7
8
9
10
...

Country
QAT
NOR
ISL
ISR
GBR
USA
FRA
SWE
AUT
JPN
...

FE

Rank

0.130
0.081
0.081
0.078
0.075
0.074
0.070
0.070
0.069
0.068
...

91

92
93
94
95
96
97
98
99
100
101

Country
VNM
IND
CMR
MAR
KHM
GHA
TJK
ZWE
SEN
MDA
BEN

FE

Rank

-0.045
-0.048

-0.053
-0.055
-0.056
-0.061
-0.061
-0.063
-0.068
-0.068
-0.069

...
106
107
108
109
110
111
112
113
114
115

Country
...
BGD
CIV
NPL
KEN
MNG
TGO

NGA
MOZ
COG
ETH

FE
...
-0.079
-0.079
-0.081
-0.084
-0.084
-0.096
-0.100
-0.101
-0.102
-0.104

Estimated fixed effects (FE) for selected countries, ranked by magnitude of estimates. Based on specification 5 in Table 2

Table 3 lists the countries exhibiting the 10 highest fixed effects (left part), the fixed effect for
Ghana and 10 countries with a similar fixed effect (central part) and the 10 countries with the
lowest fixed effects (right part). Figure A.7 in the Appendix plots the fixed effects of all countries included in the preferred specification. Although one should be very careful in interpreting
the fixed effects, as they constitute a summary measure of constant, omitted characteristics, a
ranking of the countries according to their fixed effects can give some information of the basic
economic environment of those countries. Ghana’s fixed effect of -0.061 is significantly negative
and ranks well below the average of the countries in the sample. Considering African countries,
Ghana ranks highest among all Western African countries included in the analysis. In Eastern
Africa, only Tanzania shows a higher fixed effect, while the southern and northern countries of
the continent appear to have a better economic environment to start from. Non-African countries

that exhibit fixed effects similar to Ghana are Cambodia, India, Tajikistan, Jordan or Moldova.
Many country-specific, constant factors that are captured in the fixed effects, such as geographical
characteristics, the climate or colonial linkages, are not of particular interest for policy makers,
as they are a given and cannot be changed. It is important, however, to control for them in the
regression, in order to avoid biased coefficients due to omitted variables. Other factors, such as
institutional quality, though highly persistent, could be a target for improvement. To which ex12


tent the fixed effects reflect the institutional environment of a country cannot be assessed exactly,
but simple estimates of correlation coefficients show that fixed effects tend to be high whenever
institutional quality is relatively good, and vice versa. Table 4 displays the correlation between
the estimated country fixed effects and five selected indicators measuring the institutional quality.
All indicators are created in a way that an improvement of institutional quality materializes itself
in a higher index. For a detailed description of the indicators, please refer to Table A.10 in the
Appendix. All off-diagonal elements in Columns 3-6 in Table 4 show the correlation between two
different indicators of institutional quality for the countries included in the regressions.

Table 4 – Fixed effects estimations

Fixed Effects
Rule of Law
Governm. Eff.
Polit. Stabil.
Corruption
Voice & Acc.

Fixed
Effects

Rule of

Law

Governm.
Effect.

Polit.
Stabil.

Corruption

Voice &
Account.

1
0.71
0.72
0.65
0.72
0.64

1
0.97
0.86
0.97
0.87

1
0.84
0.97
0.88

1
0.83
0.79

1
0.84

1

Correlation coefficients between estimated fixed effects and indicators of institutional quality (measured in 2000) as well
as among institution indicators; 119 countries included.

The correlation coefficients vary between 0.79 (voice and accountability and government effectiveness) and 0.97 (rule of law and government effectiveness), showing the strong positive relationship between those indicators. More importantly, as shown in column 2 (Fixed Effects) in
Table 4, all institutional indicators are highly positively correlated with the country-fixed effects.
The graphs in Figure 4 further confirm this finding. A high value of the rule of law indicator is
associated with high country-specific fixed effects. A similar correlation is found for government
effectiveness, and for the indicators for political stability and corruption, where the latter two indicators are set in relation to the fixed effects in Figure A.8 in the Appendix. The strong positive
relationship between the institutional quality indicators and the fixed effects thus strengthens the
supposition that a considerable part of the country effects is determined by the quality of institutions.
It is apparent in both graphs that Ghana lies below the regression line that represents the average relationship between the institutional indicators and the fixed effects for the countries under
consideration. Based solely on the rule of law index, for example, one could expect a fixed effect for
Ghana that is similar to the ones estimated for Egypt, Tunisia or Argentina (based on the values
corresponding to the year 2000). This can be a hint for other negative country characteristics that
13


partially offset the positive impact of institutions. The same finding can be observed when looking
at the government effectiveness indicator (Figure 4, right part), corruption and political stability
(Figure A.8 in the Appendix). It also appears that Ghana is not the only country in Sub-Saharan

Africa that is positioned below the regression line. In fact, most of the Sub-Saharan African countries exhibit lower fixed effects than one would expect looking at the average correlation between

.15
.1

.1

.15

institutional quality and fixed effects.

GNQ

Fixed Effects
0
.05

Fixed Effects
0
.05

GNQ
TCD

TCD

GAB

GAB

-.1

CIV
KEN
COG NGA

-2

ZMB

-.05

GHA
BENSEN

TGO
MOZ
ETH

-1

0
Rule of Law

-.1

-.05

TZA
CMR

ZWE

1

2

COG

-2

TZA
CMR
GHA
ZWE
BENSEN
ZMB
CIV
KEN
TGO
NGA
MOZ
ETH

-1

0
Government Effectiveness

1

2

Figure 4 – Correlation between fixed effects and institutional characteristics

It is of crucial importance, however, not to oversimplify the relationship between institutional
characteristics and the fixed country effects and not to over-interpret the findings above. The
numbers in Table 4, as well as Figures 4 and A.8, show simple correlations, and must not be
interpreted as causal effects.

6

Implications for Ghana

Based on the specification shown in Column 5, in Table 2 we assess Ghana’s growth potential for
the upcoming two decades. The high degree of uncertainty we accommodate by assuming different
scenarios which will be discussed in detail below. Figure 5 shows how the model predicts Ghana’s
past GDP per capita growth rates. Comparing 5-year averages of the actual growth rates with the
ones predicted by the model (denoted as fitted growth rates in the figure) shows that the preferred
specification predicts the large decline in growth in the 1970s and early 1980s, as well as the rise
in the 1990s and 2000s. As we assess long term growth rates, we use 5 year averages in order
to average out short term fluctuations, and for that reason our sample ends in 2009. The last
period we use actual data is thus 2005-09, the growth rates corresponding to the period 2010-14
are already projections.

14


.06
.04
.02

0
-.02
-.04
1960

1980

2000
year

2020

2040

GDP per capita growth
Fitted GDP per capita growth
Scenario 1
Scenario 2
Scenario 3

Figure 5 – Actual and fitted GDP per capita growth rates (1970-2009) and predictions under different scenarios (2010-2034)

For the projections we assume that the fixed effect specific to Ghana is converging towards
the world mean of fixed effects until 2050, with our projections ending in 2030. This assumption reflects a slow equalization of fixed effects across countries over time. If Ghana follows this
trend, it reaches a fixed effect of -0.015 by 2030, which would correspond to the fixed effect estimated for Ecuador, South Africa, Egypt or Azerbaijan in the estimation for the period 1970-2009.

Scenario 1:

In a first scenario, we assume that all covariates remain at the level of the last

observation, which corresponds to 2005-09, and only the initial income level is updated (and with
it the interaction term of education and initial GDP). This implies that the values of investment,
government consumption, openness, education, inflation, democracy and natural resource rents do
not improve as compared to the period 2005-09 and the impact of oil production is not considered.
In particular, the covariates remain at the values displayed in Table 5.
Figure 5 shows how growth rates could develop under these assumptions (Scenario 1). As the
initial income level increases, growth rates are predicted to slow down, but for the upcoming decade
growth rates above 4% are predicted.

15


Table 5 – Values of covariates in Scenario 1

Variable

Values in 2005-09

Education: share of tertiary education in working age population
Openness: imports plus exports as share of GDP (net of country size and population)
Government consumption as share of GDP
Investment as share of GDP
Inflation
Democracy index
Oil rents as share of GDP
Non-oil natural resource rents as share of GDP

6.3%
5.5%
6.7%

22.4%
13.9
9.2
0%
6.9%

Without further improvements in investment, trade, education and government consumption,
Ghana’s GDP per capita growth rates are expected to stagnate at roughly 4.5% for the upcoming
decade and begin to decline after that.
Scenario 2: In a less pessimistic scenario, education, openness, investment, government consumption and the non-oil natural resource rents are assumed to grow as they did during 1980-2009,
while inflation and democracy stay constant at 2005-09 values. It is assumed that there is no oil
production. More precisely, we use period growth rates of the explanatory variables as listed in
Table 6.
Table 6 – Values of covariates in Scenario 2

Variable

Growth per 5-year
period

Education: share of tertiary education in working age population
Openness: imports plus exports as share of GDP (net of country size and population)
Government consumption as share of GDP
Investment as share of GDP
Inflation

7.2%
39.9%
-0.04%
0.075%

0% (value of 2005-09:
13.9)
0% (value of 2005-09:
9.2)
0%
16.8%

Democracy index
Oil rents as share of GDP
Non-oil natural resource rents as share of GDP

Under this scenario, GDP per capita growth rates are predicted to steadily increase until 2025,
with an average value of approximately 5.5% GDP growth until 2030. It should be noted, that the
average growth rate of the investment share in GDP since 1980 was as low as 0.075%, nevertheless,
a continuation of this increase accounts for more than 2%-points of the predicted GDP growth rates.

16


.015
Expected oil rents in GDP
.005
.01
0
2010

2015

2020
Year

2025

2030

Period

Av. expected oil
rents

2010-14
2015-19
2020-24
2025-29

0.0086
0.0096
0.0072
0.0032

Table 7 – Expected oil rents in
GDP, 5-year averages

Figure 6 – Expected oil rents in GDP

If education, openness, investment, government consumption and the non-oil resource rents
behave as they did in the period 1980-2009 and inflation and the democracy index remain at their
2005-09 level, average growth rates of approximately 5.5% can be reached in the upcoming two
decades.
Scenario 3:

This scenario predicts per capita output growth for Ghana under the assumption

of positive oil production and otherwise the same assumptions as in Scenario 2. Projections of
the expected oil rents in GDP are obtained by comparing GDP projections for the case of zero
oil production to GDP projections that account for oil production4 . Figure 6 and the table to
the right of it show that oil rents are assumed to reach around 0.9% of GDP for the first periods,
2010-20, and fall to 0.7% and 0.3% in the periods after.
The model predicts that oil production can improve Ghana’s per capita GDP growth rates
by around 0.9%-points until 2020, which implies growth rates of approximately 6-6.5%. The long
term difference in growth rates between Scenario 2 and 3 is 0.6%-points, reflecting a) the expected
decline in oil production and b) a stronger convergence effect due to higher GDP levels in Scenario 3.
Assuming oil rents in GDP of 0.9%, 1%, 0.7% and 0.3% for the periods 2010-14, 2015-19, 202024 and 2025-2029, the model predicts an increase in long term average growth rates of 0.6%-points
as compared to the scenario without oil production.
4A

brief explanation of the methodology used can be found in the Appendix.

17


Table 8 – Predicted GDP per capita growth rates for Ghana under different scenarios

2010-14

2015-19

2020-24

2025-29

2030-34

Average

0.045
0.049
0.060

0.047
0.055
0.065

0.045
0.057
0.062

0.041
0.057
0.064

0.032
0.054
0.053

0.042
0.055
0.061

Scenario 1

Scenario 2
Scenario 3

7

Summary

Table 8 summarizes the predicted per capita GDP growth rates for the three scenarios by period.
Under the most pessimistic scenario where it is assumed that the covariates remain at the
level corresponding to the period 2005-09, growth rates of 4.5% could be expected for the next two
decades. As initial income increases, the convergence effect causes growth rates to slow down in the
following years. Scenario 2 assumes that the covariates grow as they did between 1980 and 2010.
Openness, investment, non-oil natural resource rents and education increase, while government
consumption decreases. Comparing the expected growth rates to Scenario 1, the difference for
the first period (2010-14) is relatively small, but it is increasing in the upcoming years. GDP per
capita growth is projected to reach 5.5-5.7% until 2025. In Scenario 3 the expected oil production
is taken into account. Comparing this scenario to Scenario 1, GDP growth rates are projected to
exceed the baseline projection by 2%-points on average. The impact of the expected oil rents per
se is 1%-point in the upcoming period, and declines thereafter.
Table 9 – Contribution of explanatory variables to predicted GDP growth rates (Scenario 3)

Predicted growth rate
Education
Openness
Investment
Government consumption
Inflation
Democracy
Oil rents
Non-oil rents

2010-14

2015-19

2020-24

2025-29

2030-34

0.060

0.065

0.062

0.064

0.053

0.009
0.003
0.022
-0.019
0.000
-0.001
0.011
0.015

0.009
0.004
0.022
-0.019
0.000
-0.001
0.012
0.018

0.009
0.006
0.022
-0.018
0.000
-0.001
0.009
0.021

0.008
0.008
0.022
-0.017
0.000
-0.001
0.013
0.025

0.007
0.011
0.022

-0.016
0.000
-0.001
0.007
0.029

Table 9 tabulates the contribution of different explanatory variables to predicted GDP growth
rates under Scenario 3. The numbers show the %-point contribution to GDP growth rates for the
given specification. If investment increases by the rate it did on average since the 1980s (see Table
6), it contributes to GDP growth by 2%-points. The numbers have to be interpreted with care,
18


however, as due to the dynamic nature of the model they do not imply that growth rates decline by
2%-points if investment fell to zero. A similarly high positive impact on growth rates is attributed
to the non-oil natural resource rents, while government consumption appears to be the variable
that reduces GDP growth rates by the largest extent. It should be noted, however, that the results
should be interpreted as long run effects and no predictions can be done for a short run perspective.
The democracy measure does have a very small and even negative impact on GDP growth and
it does not appear as a significant driver of growth. This fact can partially be explained by the
inclusion of fixed effects: To the extent that they are constant over time, institutional characteristics, political stability and business environment are comprised in the fixed effects. Only the
part that changes over time can be addressed in the estimation. The persistent nature of institutional characteristics usually leads to a low variation in the variables and therefore identification
is difficult.

19


References
Barro, R. J. (2003): “Determinants of Economic Growth in a Panel of Countries,” Annals of
Economics and Finance, 4(2), 231–274.

Bollen, K. (1990): “Political Democracy: Conceptual and Measurement Traps,” Studies In
Comparative International Development, 25, 7–24.
¨ ssmann (2008): “The Role of Congnitive Skills in Economic DevelHanushek, E., and L. Wo
opment,” Journal of Economic Literature, 8(1), 1–14.
Heston, A., R. Summers, and B. Aten (2012): Penn World Table Version 7.1 Center for
International Comparisons of Production, Income and Prices at the University of Pennsylvania.
´ rraga, I. (2010): “Roaring Tiger of Purring Pussycat: A Growth Diagnostics Study of
Leja
Ghana,” Development Research Department Chief Economist Complex, African Development
Bank.
Lutz, W., J. C. Cuaresma, and W. Sanderson (2008): “The demography of educational
attainment and economic growth,” Science, 319, 1047–1048.
Lutz, W., A. Goujon, S. K.C., and W. Sanderson (2007): “Reconstruction of population by
age, sex and level of educational attainment of 120 countries for 1970-2000,” Vienna Yearbook
of Population Research, 2007, 193–235.
Sauer, P., and M. Zagler (2012): “(In)equality in Education and Economic Development,”
Paper Prepared for the 32nd General Conference of The International Association for Research
of Income and Wealth Session 2D, [ />Teorell, J., N. Charron, M. Samanni, S. Holmberg, and B. Rothstein (2011): The
Quality of Government Dataset Version, 6Apr11,University of Gothenburg: The Quality of
Government Institute [].
World Bank (2013): Energizing Economic Growth in Ghana: Making the Power and Petroleum
Sectors Rise to the Challenge,Energy Group, Africa Region, The World Bank.

20


A

Appendix
Table A.10 – Description of variables

Variable

Description

Source

Rule of Law

A composition of various indicators measuring the extent to which agents
have confidence in and abide by the rules of society; it includes perceptions
of the incidence of crime, the effectiveness and predictability of judiciary,
and the enforcement of contracts.

WGI

Government Effectiveness

A composition of various indicators measuring the quality of public service
positions, the quality of bureaucracy, the competence of civil servants, the
independence of the civil service from political pressures, and the credibility
of the governments commitment to policies.

WGI

Political Stability

A composition of various indicators measuring the likelihood that the
government in power will be destabilized or overthrown by unconstitutional
or violent means, including domestic violence of terrorism.

WGI

Corruption

A composition of various indicators measuring the perception of corruption,
defined as the exercise of public power for private gain.

WGI

Indicators used to assess the institutional quality.
WGI is short for the World Bank Worldwide Governance Indicators
For a more detailed description see Teorell, Charron, Samanni, Holmberg, and Rothstein (2011)

21


TGO
NGA
MOZ
COG
ETH

-.1

SVK
URY
ARG
HUN
LTU

BLR
SLV
MEX
BRA
JAM
TUR
CHN
HRV
ARM
DOM
SAU
BGR
PAN
GTM
IRN
EST

COL
SGP
MKD
SDN
ALB
RUS
GAB
EGY
ZAF
ECU
AZE
KAZ
IRQ

SYR
HND
DZA
PER
TUN
MYS
THA
BHR
NAM
BOL
IDN
GEO
HTI
NIC
LKA
UKR
TZA
PAK
PRY
VNM
IND
CMR
MAR
KHM
GHA
TJK
ZWE
SEN
MDA
BEN

JOR
ZMB
KGZ
PHL
BGD
CIV
NPL
KEN
MNG

-.05

QAT

NOR
ISL
ISR
GBR
USA
FRA
SWE
AUT
JPN
CHE
ITA
GRC
DEU
DNK
NLD
ESP

FIN
GNQ
NZL
AUS
CAN
CRI
KOR
IRL
CYP
MLT
TTO
LUX
PRT
TCD
BEL
CZE
SVN
LVA
POL
SUR

0

.05

.1

Estimated Fixed Effects

Figure A.7 – Fixed effects based on specification (5) in Table 2

22

.15


Table A.11 – Inclusion of countries and periods in the different specifications of Table 2

Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahamas
Bahrain
Bangladesh
Belarus
Belgium
Belize
Benin
Bhutan
Bolivia
Bosnia and Herz
Brazil
Bulgaria
Burkina Faso
Burundi

Cambodia
Cameroon
Canada
Cape Verde
Centr Afric Rep
Chad
Chile
China
Colombia
Comoros
Congo, Rep.
Costa Rica
Cote dIvoire
Croatia
Cyprus
Czech Republic
Denmark
Djibouti
Dominican Rep
Ecuador
Egypt, Arab Rep.
El Salvador
Equat Guinea
Estonia
Ethiopia
Finland
France
Gabon
Gambia
Georgia

Germany
Ghana
Greece
Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hungary
Iceland
India
Indonesia
Iran, IslamRep
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Rep
Kyrgyz Rep

(1),(2)

(3),(5)

(4)

90-09
70-09
70-09
95-09
70-09
70-09
95-09
70-09
80-09
85-09
95-09
00-09
80-09
90-09
80-09
70-09
05-09
80-09
85-09
70-09
70-09
95-09
70-09
70-09
85-09
80-09
80-09
05-09

85-09
70-09
00-09
85-09
70-09
70-09
90-09
75-09
90-09
70-09
85-09
70-09
70-09
70-09
70-09
85-09
90-09
10-09
70-09
70-09
70-09
70-09
95-09
90-09
70-09
70-09
70-09
05-09
85-09
95-09

90-09
70-09
70-09
70-09
70-09
70-09
70-09
70-79,95-09
70-09
70-09
70-09
70-09
70-09
70-09
95-09
70-09
70-09
95-09

90-09
70-09
70-09
95-09
70-09
70-09
95-09

90-09
70-09
70-09

95-09
70-09
70-09
95-09

80-09
85-09
95-09
00-09

80-09
85-09
95-09
00-09

90-09

90-09

70-09
05-09
80-09
85-09

70-09
05-09
80-09
85-09

95-09

70-09
70-09

95-09
70-09
70-09

05-09
85-09
70-09

05-09
85-09
70-09

85-09
70-09
70-09
90-09
75-09
90-09
70-09

85-09
70-09
70-09
90-09
75-09
90-09
70-09

70-09
70-09
70-09
70-09

70-09
70-09
70-09
70-09

90-09
10-09
70-09
70-09
70-09

90-09
10-09
70-09
70-09
70-09

95-09
90-09
70-09
70-09
70-09

95-09

90-09
70-09
70-09
70-09

90-09
70-09
70-09
70-09
70-09
70-09
70-09
70-79,95-09
70-09
70-09
70-09
70-09
70-09
70-09
95-09
70-09
70-09
95-09

90-09
70-09
70-09
70-09
70-09
70-09

70-09
70-79,95-09
70-09
70-09
70-09
70-09
70-09
70-09
95-09
70-09
70-09
95-09

Country
Lao PDR
Latvia
Lebanon
Lesotho
Liberia
Lithuania
Luxembourg
Macedonia
Madagascar
Malawi
Malaysia
Maldives
Mali
Malta
Mauritania
Mauritius

Mexico
Moldova
Mongolia
Morocco
Mozambique
Namibia
Nepal
Netherlands
New Zealand
Nicaragua
Niger
Nigeria
Norway
Pakistan
Panama
Paraguay
Peru
Philippines
Poland
Portugal
Qatar
Russian Federa
Rwanda
Sao Tome a Princ
Saudi Arabia
Senegal
Sierra Leone
Singapore
Slovak Republic
Slovenia

South Africa
Spain
Sri Lanka
St. Lucia
Vincent a t Grena
Sudan
Suriname
Swaziland
Sweden
Switzerland
Syrian Arab Rep
Tajikistan
Tanzania
Thailand
Togo
Tonga
Trinidad and Tob
Tunisia
Turkey
Uganda
Ukraine
United Kingdom
United States
Uruguay
Vanuatu
Venezuela, RB
Vietnam
Zambia
Zimbabwe

(1),(2)
85-09
95-09
05-09
70-09
00-09
95-09
00-05
90-09
70-09
80-09
70-09
05-09
85-09
70-09
85-09
80-09
70-09
95-09
90-09
70-09
85-09
00-09
70-09
70-09
70-09
00-09
70-09
70-09
70-09

70-09
70-09
70-09
70-09
70-09
85-09
70-09
90-09
90-09
70-09
00-09
90-09
70-09
05-09
70-09
90-09
90-09
70-09
70-09
70-09
75-09
75-09
70-09
75-09
70-09
70-09
70-75,80-09
70-09
00-09
85-09

70-09
70-09
75-09
70-09
80-09
70-09
80-09
95-09
85-09
70-09
70-09
80-09
05-09
95-09
85-09
70-09

(3),(5)

(4)

95-09
05-09

95-09
05-09

95-09
00-05
90-09

95-09
00-05
90-09

70-09

70-09

70-09

70-09
95-09
90-09
70-09
85-09
00-09
70-09
70-09
70-09
00-09

70-09
95-09
90-09
70-09
85-09
00-09
70-09
70-09

70-09
00-09

70-09
70-09
70-09
70-09
70-09
70-09
70-09
85-09
70-09
90-09
90-09

70-09
70-09
70-09
70-09
70-09
70-09
70-09
85-09
70-09

90-09
70-09

90-09
70-09

70-09
90-09
90-09
70-09
70-09
70-09

70-09
90-09
90-09
70-09
70-09
70-09

70-09

70-09

70-09
70-75,80-09
70-09
00-09
85-09
70-09
70-09

70-09
70-75,80-09
70-09

00-09
85-09
70-09
70-09

70-09
80-09
70-09

70-09
80-09
70-09

95-09
85-09
70-09
70-09

95-09
85-09
70-09
70-09

05-09
95-09
85-09
70-09

05-09
95-09

85-09
70-09

90-09

The difference between the specifications (1), (2) and (3), and (5) arises from the impossibility of splitting the natural resource variable
into oil and non-oil components for some countries. Likewise, specification (4) includes only countries for which the data allow a further
decomposition of non-oil natural resources.

23