metadata: authors: 0: author: Raggl,Anna Katharina
count: Ghana
docna: 0: docna: Economic growth in Ghana : determinants and prospect
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Judicial System Reform,Industrial Economics,Educational Sciences,Coastal and Marine Resources,Economic Growth,Energy and Natural Resources,Global Environment
teratopic: Poverty Reduction,Social Protections and Labor,Macroeconomics and Economic Growth,Environment
abstracts: cdata!: 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 2005-09,
            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.
geo_reg_key: 1927320,517191
repnme: repnme: Economic growth in Ghana : determinants and prospect
display_title: Economic growth in Ghana :
            determinants and prospect
listing_relative_url: /research/2014/01/18832579/economic-growth-ghana-determinants-prospect
topicv3: Justice m1326106 840,Economic Growth m1326241 471,Natural Resources m1326446 1979,Education m1327048 470,Environment, Natural Resources and Blue
            Economy m1327391 566
publishtoextweb_dt: 2014-01-01T00:00:00Z
historic_topic: Poverty Reduction,Social Protections and Labor,Macroeconomics and Economic Growth,Environment
no_of_pages: 26
author_keys: 0: author_key: 1151438
model_extractions: WPS6750 Policy Research Working Paper 6750 Economic Growth in Ghana Determinants and Prospect _Anna K. Raggl_ 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.

This paper is a product of the Poverty Reduction and Economic Management Department, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Anna.Raggl@wu.ac.at.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Economic Growth in Ghana: Determinants and Prospects<sup>_∗_</sup> Anna K. Raggl<sup>_†_</sup>

**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: anna.raggl@wu.ac.at. 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 used to predict the growth potential for the country under different scenarios.

<!-- Start of picture text --> 1970 1980 1990 2000 2010<br>Year<br>.1<br>.05<br>0<br>5-year-average GDP per capita growth rate<br>-.05<br><!-- End of picture text -->

**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 consumption<sup>1</sup> 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.

> 1Barro (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 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 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 _εit_ 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**|**Source**|
|---|---|---|
|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|IIASA|
|Log initial GDP * Education|Interaction between education variable and initial GDP||
|Openness|Share of exports plus imports in GDP, fltered for its relation to<br>log(area) and log(population), 5-year average|PWT|
|Investment|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|Polity IV|
|Infation|Infation 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<br>period, 0 otherwise (i.e. interaction of the oil rents variable with a<br>dummy variable indicating the frst quartile)||
|Oil rents, 2nd quartile|Oil rents if oil rents belong to the 2nd quartile in corresponding period,<br>0 otherwise||
|Oil rents, 3rd quartile|Oil rents if oil rents belong to the 3rd quartile in corresponding period,<br>0 otherwise||
|Oil rents, 4th quartile|Oil rents if oil rents belong to the highest quartile in corresponding<br>period, 0 otherwise<br>Note: The Variables oilrent<br>~~0~~025, oilrent<br>~~2~~550, oilrent<br>5075 and<br>oilrent<br>~~7~~500 add up to the variable oilrent||

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 2<sup>2</sup> . 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

> 2As 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 estimations

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
|Log of initial GDP|-0.0410***|-0.0386***|-0.0415***|-0.0442***|-0.0444***|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
|Education|-0.004|0.562**|0.535*|0.554*|0.482*|
||[0.931]|[0.041]|[0.065]|[0.059]|[0.094]|
|Log initial GDP * Education||-0.0526**|-0.0507*|-0.0517*|-0.0451*|
|||[0.037]|[0.054]|[0.055]|[0.084]|
|Openness|0.0414***|0.0398***|0.0405***|0.0387***|0.0385***|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
|Government consumption|-0.188***|-0.195***|-0.293***|-0.294***|-0.303***|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
|Investment|0.0806***|0.0824***|0.0916***|0.0725***|0.0967***|
||[0.000]|[0.000]|[0.000]|[0.002]|[0.000]|
|Infation|-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|
||[0.004]|[0.004]|[0.006]|[0.004]|[0.007]|
|Democracy|-0.0002|-0.0003|-0.0003|-0.0006|-0.0001|
||[0.772]|[0.674]|[0.727]|[0.457]|[0.88]|
|Natural resource rents|0.0673***|0.0639***||||
||[0.001]|[0.002]||||
|Oil rents|||0.0233|0.0265||
||||[0.376]|[0.318]||
|Non-oil rents|||0.166***||0.191***|
||||[0.001]||[0.000]|
|Oil rents, 1st quartile|||||3.878<br>[0.301]|
|Oil rent, 2nd quartile|||||1.248***<br>[0.003]|
|Oil rents, 3rd quartile|||||-0.0657|
||||||[0.245]|
|Oil rents, 4th quartile|||||0.037<br>[0.16]|
|Mineral rents||||0.281**<br>[0.010]||
|Forest rents||||-0.096<br>[0.592]||
|Constant|0.370***|0.347***|0.382***|0.412***|0.403***|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
|Observations|906|906|724|712|724|
|Countries|151|151|119|117|119|
|R-squared|0.28|0.284|0.307|0.295|0.326|

Each specification includes country fixed effects and period dummy variables 9
<!-- Start of picture text --> 1970 1980 1990 2000 2010<br>Year<br>Contribution of education to growth<br>Share tertiary education in WAP<br>.01 .07<br>.009 .06<br>.008<br>.05<br>.007<br>.04<br>Share tertiary education in WAP<br>Contribution of education to growth<br>.006<br>.03<br><!-- End of picture text -->

**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.<sup>3</sup> 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

> 3In 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
are less likely to happen when oil production is relatively low and not a central part of an economy.

<!-- Start of picture text --> 6 7 8 9 10 11 -1 0 1 2 3 0 .2 .4 .6 .8<br>Log of initial GDP Openness Investment<br>0 .2 .4 .6 0 .1 .2 .3 .4 0 .001 .002 .003 .004<br>Government consumption Non-oil rents in GDP Oil rents, below 25th quanitle<br>0 .005 .01 .015 .02 .025 0 .05 .1 .15 .2 0 .2 .4 .6 .8 1<br>Oil rents, 25th-50th quanitle Oil rents, below 50th-75th quanitle Oil rents, above 75th quanitle<br>-.1 .1 .15<br>-.15 .05 .1<br>-.2 0 .05<br>-.25 0<br>-.05<br>-.3 -.05<br>-.35 -.1 -.1<br>Growth rate (residual part) Growth rate (residual part) Growth rate (residual part)<br>.1 .1 .2<br>.15<br>0 .05 .1<br>0 .05<br>-.1<br>-.05 0<br>-.2 -.1 -.05<br>Growth rate (residual part) Growth rate (residual part) Growth rate (residual part)<br>.15 .05 1.5<br>.1 0<br>1<br>.05 -.05<br>.5<br>0 -.1<br>0<br>-.05 -.15<br>Growth rate (residual part) Growth rate (residual part) Growth rate (residual part)<br><!-- End of picture text -->

**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
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

|**Rank**|**Country**|**FE**|**Rank**|**Country**|**FE**|**Rank**|**Country**|**FE**|
|---|---|---|---|---|---|---|---|---|
|1|QAT|0.130|91|VNM|-0.045|. . .|. . .|. . .|
|2|NOR|0.081|92|IND|-0.048|106|BGD|-0.079|
|3|ISL|0.081|93|CMR|-0.053|107|CIV|-0.079|
|4|ISR|0.078|94|MAR|-0.055|108|NPL|-0.081|
|5|GBR|0.075|95|KHM|-0.056|109|KEN|-0.084|
|6|USA|0.074|**96**|**GHA**|**-0.061**|110|MNG|-0.084|
|7|FRA|0.070|97|TJK|-0.061|111|TGO|-0.096|
|8|SWE|0.070|98|ZWE|-0.063|112|NGA|-0.100|
|9|AUT|0.069|99|SEN|-0.068|113|MOZ|-0.101|
|10|JPN|0.068|100|MDA|-0.068|114|COG|-0.102|
|. . .|. . .|. . .|101|BEN|-0.069|115|ETH|-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 estimations

||**Fixed**<br>**Efects**|**Rule of**<br>**Law**|**Governm.**<br>**Efect.**|**Polit.**<br>**Stabil.**|**Corruption**|**Voice &**<br>**Account.**|
|---|---|---|---|---|---|---|
|Fixed Efects|1||||||
|Rule of Law|0.71|1|||||
|Governm. Ef.|0.72|0.97|1||||
|Polit. Stabil.|0.65|0.86|0.84|1|||
|Corruption|0.72|0.97|0.97|0.83|1||
|Voice & Acc.|0.64|0.87|0.88|0.79|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 institutional quality and fixed effects.

<!-- Start of picture text --> GNQ GNQ<br>TCD TCD<br>GAB GAB<br>TZA TZA<br>CMR CMR<br>ZWE CIVKEN ZMB BENSEN GHA ZMBZWECIVKEN BEN GHA SEN<br>COG NGA ETH MOZ TGO COG TGO NGA ETH MOZ<br>-2 -1 0 1 2 -2 -1 0 1 2<br>Rule of Law Government Effectiveness<br>.15 .15<br>.1 .1<br>.05 .05<br>Fixed Effects 0 Fixed Effects 0<br>-.05 -.05<br>-.1 -.1<br><!-- End of picture text -->

**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.

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<!-- Start of picture text --> 1960 1980 2000 2020 2040<br>year<br>GDP per capita growth<br>Fitted GDP per capita growth<br>Scenario 1<br>Scenario 2<br>Scenario 3<br>.06<br>.04<br>.02<br>0<br>-.02<br>-.04<br><!-- End of picture text -->

**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.

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**Table 5** – Values of covariates in Scenario 1

|**Variable**|**Values in 2005-09**|
|---|---|
|Education: share of tertiary education in working age population|6.3%|
|Openness: imports plus exports as share of GDP (net of country size and population)|5.5%|
|Government consumption as share of GDP|6.7%|
|Investment as share of GDP|22.4%|
|Infation|13.9|
|Democracy index|9.2|
|Oil rents as share of GDP|0%|
|Non-oil natural resource rents as share of GDP|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**<br>**period**|
|---|---|
|Education: share of tertiary education in working age population|7.2%|
|Openness: imports plus exports as share of GDP (net of country size and population)|39.9%|
|Government consumption as share of GDP|-0.04%|
|Investment as share of GDP|0.075%|
|Infation|0% (value of 2005-09:|
||13.9)|
|Democracy index|0% (value of 2005-09:|
||9.2)|
|Oil rents as share of GDP|0%|
|Non-oil natural resource rents as share of GDP|16.8%|

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.

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<!-- Start of picture text --> 2010 2015 2020 2025 2030<br>Year<br>.015<br>.01<br>.005<br>Expected oil rents in GDP<br>0<br><!-- End of picture text -->

|**Period**<br>**Av. **|**expected oil**|
|---|---|
||**rents**|
|2010-14|0.0086|
|2015-19|0.0096|
|2020-24|0.0072|
|2025-29|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 production<sup>4</sup> . 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.

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

||**2010-14**|**2015-19**|**2020-24**|**2025-29**|**2030-34**|**Average**|
|---|---|---|---|---|---|---|
|Scenario 1|0.045|0.047|0.045|0.041|0.032|0.042|
|Scenario 2|0.049|0.055|0.057|0.057|0.054|0.055|
|Scenario 3|0.060|0.065|0.062|0.064|0.053|0.061|

# **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)

||**2010-14**|**2015-19**|**2020-24**|**2025-29**|**2030-34**|
|---|---|---|---|---|---|
|Predicted growth rate|0.060|0.065|0.062|0.064|0.053|
|Education|0.009|0.009|0.009|0.008|0.007|
|Openness|0.003|0.004|0.006|0.008|0.011|
|Investment|0.022|0.022|0.022|0.022|0.022|
|Government consumption|-0.019|-0.019|-0.018|-0.017|-0.016|
|Infation|0.000|0.000|0.000|0.000|0.000|
|Democracy|-0.001|-0.001|-0.001|-0.001|-0.001|
|Oil rents|0.011|0.012|0.009|0.013|0.007|
|Non-oil rents|0.015|0.018|0.021|0.025|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 19