Institutions and Growth Drag: Evidence from Major Crude Oil Producing African `
Economies.
Solomon A. OLAKOJO* and Abiodun O. FOLAWEWO
Department of Economics
University of Ibadan
Nigeria
Submitted to
Nigerian Economic Society (NES) for presentation at the 54th Annual Conference
scheduled for 17 – 19 September, 2013, Abuja, Nigeria
* All correspondence to Solomon Abayomi Olakojo, PhD Student, Department of Economics, University of Ibadan,
Ibadan, Nigeria, Tel: + 234 7033072945; Email: [email protected]
1
Abstract
The neoclassical growth models attribute growth differences across countries to differences in
the level of technology, accumulation of physical and human capital and research and
development neglecting the role of institutions and states. However, some of the empirical
studies in this regard conclude that African growth cannot be explained mainly by the identified
neoclassical growth variables. Therefore, this study attempts to investigate the responsiveness of
output to institutional and other variables among crude oil producing African economies using a
simple extended Solow growth framework. The model is estimated using a panel estimation
technique with annual data 1996 to 2011. The results show that what really explains output
growth among the selected major oil producing African economies is the amount of human
capital they possess. However, regulatory quality, among the five selected regulatory quality
factors, was found significantly influencing output.
The study suggests the need for the governments of these selected crude oil producing
African economies to design policies that will improve regulatory quality and enhance
employment. One way of going around this is to permit and promote private sector development
and give attention to labour intensive sectors rather than laying emphases on oil sector which is
not labour intensive. Also, conducive environment that will attract private investment will go a
long way to improve the level of employment, thus enhancing growth.
Key words:
Neoclassical growth theories, Institutions, growth drag, oil producing African
countries, panel data.
Jel. O10, O43.
2
1. Introduction
Many African Economies have enormous potential for growth and development with their vast
resources notable among them are oil and gas resources, vast agricultural land, solid minerals,
and abundant human resources. Despite these factors, growth can not be said to be satisfactory
and successive governments through several institutions have not done enough to put African
nations’ resources to effective productive use. Therefore, it can be said that the economies are
performing below their potentials. This is because rising unemployment, inflation, poverty,
misery, crime, and insecurity is synonymous with some of these African states. This has caused
many resource abundant African economies to be among the league of the world’s poor nations.
Given the aforementioned, the roles of states and institutions in economic performance
are no longer argumentative especially in the context of African countries. These roles do not
only include management of resources but also involve enacting sound policies that are key to
economic success. Besides, strategic factors of social development such as accumulation of
knowledge and skills, the level and quality of educational attainment, the physical condition and
cultural characteristics of the population are influenced to a large extent by the state (UNDP,
2003). Going by these institutions responsibilities, it could be inferred that institutions serve as
augmenting variable in the economic performance.
2. The Background and the Problem Statement.
Natural resource abundant countries constitute both growth losers and growth winners, and the
main difference between the success cases and the cases of failure lays in the quality of
institutions (Mehlum, Moene and Torvik, 2005). Given this, the poor performance of many
African countries, especially the oil rich African economies, could be undeniably traced to
ineffective institutions in managing the resources and enacting appropriate policies that are key
to sustained economic growth. For instance, in the recent time World Bank in its Human
Development Report (2002) classified thirty eight countries as Very High Human Development
(VHHD) out of which no single African country fall in this category, not even the oil rich among
them. Ironically, the CIA world fact book (2012) categorized some of the crude oil producing
African countries (for instance, Nigeria, Algeria, Angola and Libya) among the first twenty
highest crude oil producers in the world but the African countries in this category are no where
3
to be found on human development front unlike their counterparts in the same oil producing
category. Therefore, most resources abundant African countries with bad institutions suffer a
double resource curse. That is, the deterioration of institutions which strengthens the negative
effect of more natural resources.
The fundamental question of the study is what are the impact of and links between
institutions and growth outcomes among major crude oil producing African economies.
Therefore, the objective of the study is to quantify the effect of institutions on growth drag
among these economies. In terms of scope, the countries that will be considered for the study are
Nigeria, Algeria, Angola, Libya, Egypt, Sudan, Equatorial Guinea, Republic of Congo, Gabon
and South Africa. These are the top 10 highest crude oil producers in Africa. The data for the
study is sourced from World Development Indicators (WDI, 2012) and World Governance
Indicators (WGI, 2012) between the period of 1996 and 2011. This scope is based on data
availability on institutional variables.
3. Literature Review
3.1. Some Theoretical Insights
The neoclassical growth models such as Solow growth model, Infinite-Horizon and OverlappingGenerations Models and New Growth theories attribute growth differences across countries to
differences in the level of technology, accumulation of physical and human capital and research
and development respectively neglecting the role of institutions and states. However, some of the
empirical studies in this regard conclude that African growth cannot be explained mainly by the
identified neoclassical growth variables. The questions coming to mind are; are these economies
cursed by resources or institution inefficiencies? Do abundant resources tend to impede
economic growth? In line with this, several theories have been developed, mainly Dutch disease
models (Sachs and Warner, 1995) and institutional explanations. However, while Dutch disease
explanation can be true of some countries, it mat not be true for others. This is because most
African countries are price takers in the world market. Therefore, the explanation for growth
disaster can be better offered within the context of institutional effectiveness.
Sachs and Warner (1995) offered some explanation by arguing that natural resources
were never a guarantee of economic success. For instance, resource-poor Netherlands grew at a
4
higher rate than Spain, which was flooded with gold and silver from the Spanish colonies;
Switzerland and Japan out-performed resource-rich Russia in the nineteenth and twentieth
centuries. Also, South Korea, Taiwan, Hong Kong, and Singapore are some of the best
performers of the past few decades and yet all of them are nations poor in natural resources
(Sachs and Warner, 1995). In addition, some of present world’s richest nations, such as Japan,
Luxemburg, and Switzerland, are resource-poor nations (Gylfason and Zoega, 2006 cited in
Karabegović, 2009)
Having come to acceptable conclusion that institutions matters in explaining economic
growth, there are two major approaches in institutional economics: the old institutional
economics rejects the hypothesis of a rational economic player in favor of one that places
economic behavior in its cultural context. For instance, a director in a ministry employing an
individual only on the ground that he comes from his cultural affiliation. However, for new
institutionalists mankind is still a rational chooser, but more focus is given to the role of
institutions. The new institutional economics works with a set of more neoclassical scissors,
which has been pointed out by William Dugger (1990 cited in Redek and Sušjan, 2005 ).
Irrespective of the approach used, what is important is that institutions are coming to the
forefront of economic analysis. That is, many economists have realized that economic transition
is a process of institutional change and institutional effectiveness.
Basically, there are two major channels through which institutions affects growth. These
channels are economic and political channel. The economic channels emphasized on Dutch
disease. That is, a natural resource boom, caused often by discoveries of new natural resources,
results first in a “spending effect” (Auty, 2001), in which the expansion of the export-oriented
tradable resource increases demand for both tradable and non-tradable goods and services.
Eventually there will be a shift of both labor and capital from the manufacturing sector to the
resource sector and the non-tradable sector (which also expands as a result of the resource
boom.). Therefore, the Dutch Disease entails a “resource movement effect” (Auty, 2001). This is
one of the fundamental roles of institutional framework. The other channel is political channel.
According to Ross (2001), nations that obtain significant revenue from natural resources may tax
their population less heavily; their population in turn will be less likely to demand greater
accountability and representation, thus leading to breeding more grabbers which will undermine
the utilization of the resources for growth purposes.
5
3.2. Review of Some Previous Studies.
There have been several empirical studies on the role of institutions on economic outcome. For
instance, Rodrik (2000) emphasized that every well-functioning market economy is a mix of
state and market, intervention and laissez-faire. He used institutional variables reflecting roles of
property right, regulatory institutions, and institution in macroeconomic stabilization and further
analyzed the role of political regimes in determining economic performance. The results suggest
that there is little reason to believe democracy is conducive to lower growth. Further, Bai and
Wei (2000) studied the impact of bureaucratic quality in terms of level of corruption on
macroeconomic policies. Specifically, they analyzed the effects of capital controls and financial
repression. They found that corruptive bureaucratic situation translates in to reducing the
government tax revenue. Therefore, bureaucracy tends to lesson the efficiency of the market and
overall efficiency and hence lowers economic growth.
Sarwar, Siddiqi and Butt (2013) examine the relationship among the institutional
variables and economic growth in Asian countries. Estimating panel fixed effect model and
covering the time period from 1995-2010, their results show that financial and legal institutions
are more effective in increasing the economic growth as compared to other formal and informal
institutions. Also, Moers (2002) examined the performance of 25 transition economies in the
period between 1990 and 1995. His results suggest that institutions are significant for growth.
This is because institution affects FDI which in-turn influence growth. He also found that the
relationship between FDI and institutions is more likely to be direct causation. He recommends
that macro economic stabilization and peace closely followed by institution building should be
the main policy priorities.
Finally, Glaeser et al (2004) investigates whether political institution causes growth, or
whether, alternatively, growth and human capital accumulations lead to institutional
improvement. The results suggest that human capital is more basic source of growth than are
institutions. Their conclusion is that most institutional quality used to establish that institutions
cause growth are constructed to be conceptually unsuitable for that purpose.
6
4. Theoretical Framework
The framework for this study will be a variant of augmented Solow model. In this framework
institution serves as augmenting variable for resources just as knowledge is augmenting labour in
the production function. It is also assumed that capital stock is significantly privately owned.
Therefore, good institutions augmenting resources endowment is effective resources utilization
just as knowledge augmenting labour is effective labour in the original Solow model.
Let the production function be:
  0,   0,     1
Y (t )  ( K (t ))  ( I (t ) R(t ))  ( A(t ) L(t ))1 
(1)
Where
K (t ) is total capital stock in the economy
I (t ) R(t ) is institution augmenting resources
A(t ) L(t ) is knowledge augmenting labour
Labour and capital are not augmented with institution based on the assumption that grabbers are
more interested in non-human resources and capital is privately owned. Therefore, the effective
used of resources depends on the institutional framework to either permit grabbers or nongrabbers: The presence of grabbers makes the resources less available for growth purposes and
vise versa.
4.1 Dynamics of inputs

K (t );
K (t )  s(Y (t ))   ( K (t ))

I (t );
(2)

I (t )
I (t )  aI (t );
 a
I (t )
(3)
Equation (3) represents a situation in which an economy institution is a bad one that breeds
grabbing.

R(t );

R(t )
R(t )  bR(t );
 b
R(t )
(4)
Equation (4) represents a situation when the endowed resources are depletable. This may be true
of non-renewable fossil fuel resources.
7

A(t );
A(t )  gA(t );

L(t );
L(t )  nL(t );

A(t )
g
A(t )
(5)

L(t )
n
L(t )
(6)
4.2 Dynamics of the economy
Taking the natural logarithm (1) yields;
In(Y (t ))  In( K (t ))   ( In( I (t ))  In( R(t ))  (1     )( In( A(t )  In( L(t ))
(7)
Differentiating (7) with respect to time gives:




 

 

Y (t )
K (t )
 I (t ) R(t ) 
 A(t ) L(t ) 

 


(
1




)

 A(t ) L(t ) 
Y (t )
K (t )
 I (t ) R(t ) 




(8)
Assuming that the economy is characterized with good institution that discourages grabbing and
the resources is assumed to be non-depletable, equation (8) can be written as;
g Y (t )  g K (t )   (a  b)  (1     )( g  n)
(9)
Assuming (as in Solow Model) that the growth rate of Y (t ) equals that of K (t ) , equation (9) can
be written as;
g Y (t )  g K (t )  (1     )( g  n)   (a  b)
(10)
Therefore, growth rate of output on the Balance Growth Path (BGP) equals;
g YBGP
(t ) 
(1     )( g  n)   (a  b)
1
(11)
Equation (11) implies that growth rate of output is decreasing in bad institution, decreasing in
depletable resources and increasing in knowledge and labour growth. Apart from the fact that
labour in an important input in the production process, for knowledge to grow labour making
used of the knowledge must have increase.
On the other hand, assuming it is possible that the economy is such in which new
population or labour force entrants learn the norms of the society in terms of grabbing or non
grabbing reflecting either the presence of bad or good institution respectively. Therefore, with
good institution, non-grabbing increases with population. The same holds with bad institution.
Finally, assuming also that these resources is the mainstay of the economy such that the survival
8
of population depends on its continuous existence. This assumption implies that the resources
must grow at the same rate with population. Incorporating this, equation (11) can be written as:
*
g YBGP
(t ) 
(1     )( g  n)   (n  n) (1     ) g  (1     )n

1
1
(12)
Equation (12) shows that growth rate of output is increasing in growth of knowledge and
increasing in labour growth.
The growth drag expression can therefore be specified by subtracting (11) from (12):
*
BGP
g YBGP
( t )  g Y ( t )  GD 
( 2n  a  b) 
1
(13)
The implication of equation (13) is that growth drag is increasing in population or labour force
growth in the absence of knowledge. That is, labour increases without a corresponding increase
in investment in human capita that will enhance their effectiveness will lead to growth drag.
Also, growth drag is increasing in resources depletion and presence of bad institution. Thus, a
variant of equation (13) becomes the estimable model.
5. Model Specification, Methodology, Estimation and Discussion of Results
5.1 Model Specification
The estimable panel model specification is:
InRGDPit     i I it  InHC it  InIHC it  InORit   it
(14)
Where  i are the institutional parameters reflecting:
1) Voice and Accountability (VA); That is, perceptions of the extent to which a country's
citizens are able to participate in selecting their government, as well as freedom of
expression, freedom of association, and a free media.
2) Government Effectiveness (GE); That is, perceptions of the quality of public services,
the quality of the civil service and the degree of its independence from political pressures,
the quality of policy formulation and implementation, and the credibility of the
government's commitment to such policies.
3) Regulatory Quality (RQ); That is, perceptions of the ability of the government to
formulate and implement sound policies and regulations that permit and promote private
sector development.
9
4) Rule of Law (RL); That is, perceptions of the extent to which agents have confidence in
and abide by the rules of society, and in particular the quality of contract enforcement,
property rights, the police, and the courts, as well as the likelihood of crime and violence.
5) Control of Corruption (CC); that is, perceptions of the extent to which public power is
exercised for private gain, including both petty and grand forms of corruption, as well as
"capture" of the state by elites and private interests.
6) Institutional cluster; That is, the average of the previously listed institutional indicators.
Note: The above estimates range from approximately -2.5 (weak) to 2.5 (strong)
performance (Word Governance Indicator, WGI, 2012)

is the parameter of human capital (HC). This comprises people ages 15 and older who
meet
the International Labour Organization (ILO) definition of the economically active
population: all people who supply labor for the production of goods and services during a
specified period. However, it includes both the employed
and the unemployed. Given
that employment data are rare and even if available it is basically that of the formal
sector, however, informal employment dominates developing countries.

is the parameter of investment in human capital (IHC). This is proxy with Technical
cooperation grants which include free-standing technical cooperation grants, which are
intended to finance the transfer of technical and managerial skills or of technology for the
purpose of building up general national capacity without reference to any specific
investment projects; and investment-related technical cooperation grants, which are
provided to strengthen the capacity to execute specific investment projects (WDI, 2012).
The reason why each country’s education investment (that is, educational expenditure) is
not used is that it does not really explain implementation, a peculiar problem with
developing countries. However, for the donors of the grants there must be some results to
show for what has been previously given. Otherwise, such grants will not be
sustainable. Also, these grants are meant for enhancing labour skills.

is the parameter of oil rent (OR). That is, the ratio of oil rent to GDP. Oil rent is the
difference between the value of crude oil production at world prices and total costs of
production. This is assumed to be the profit from crude oil which decreases when the
resources is either depleting or the cost of production has risen.
10
 it is the residual term and
i=(1,2,…,10) and t=(1,2,…,16) representing some of the first ten major African crude oil
producers and time respectively.
Note: All non-index variables are measure in US Dollars.
5.2 Estimation and Discussion of Results
The growth equation is estimated in a panel form. First, a descriptive analysis of the data is
carried out and of interest is the institutional variable. The results of the descriptive analysis as
shown in Table 1 (in the appendix) indicates that the maximum rating of the selected countries in
average institutional performance is approximately 0.57, while the minimum is -2.07 on the scale
of -2.5 (weak) to 2.5 (strong). In addition, the average performance of these countries is
approximately -0.93 indicating that these countries performed below average on the institutional
quality scale. Besides, a careful look at the institutional variables indicates that the worse
performance is in term of voice and accountability (the mean performance of the selected
countries is approximately -1.11). This is followed by role of law, control of corruption,
regulatory quality, and government effectiveness in that order. In essence, the major selected
crude oil producing African countries perform below average on the institutional rating.
The interest of the study is to evaluate the responsiveness of real output to institutional
variables and other variables using panel data. In doing this, the aggregated model was first
estimated then institutional variables were disaggregated to appreciate the impact of each of the
institutional measure on output. In terms of sequencing, first, the pooled least squares of the
aggregated model were estimated for both models. However, at 5% level of significance, the
specification of between effect models account for heterogeneity across individual countries.
That is, at 5% level null hypothesis of no heterogeneity is rejected in both estimated models.
This is confirmed using the F-statistics test. Thus, the coefficient of determination of pooled least
squares is significantly different from that of the fixed effect or random effect model in both
models. The implication of the test result is that the model parameters are not homogenous
across the selected countries. That is, the selected countries exhibits total dissimilar output
behaviour. Therefore, the next step is to determine whether the observed heterogeneity fixed or
random. This is carried out using Hausman test (See Table 5 and 10 in the appendix). The
11
Random effects model is rejected in favour of fixed effects in both cases. This is because the
probability of chi-square value of 0.000 is less than 0.05 level of significance.
Table 1: Fixed Effects Model of Log of RGDP.
Coefficients
Variables
Model 1
Model 2
I
0.037 (0.835)
VA
0.031 (0.825)
GE
0.017 (0.870)
RQ
0.350 (0.002) **
RL
-0.271 (0.195)
CC
-0.228 (0.120)
***
LOG(HC)
2.492 (0.000)
2.372 (0.000) ***
LOG(IHC)
0.014 (0.783)
0.016 (0.748)
LOG(OR)
-0.0322 (0.410)
-0.025 (0.508)
***
CONSTANT -14.491 (0.000)
-12.841 (0.000) ***
F-Statistics
44.6 (0.000) ***
R-Square
0.73
Obs
112
Source: Authors Computation.
17.01 (0.004) **
0.83
112
Note: Model 1 averages the institutional variables, while Model 2 is disaggregated.
***
and
**
represents 1% and 5% level of significance respectively.
The results given in Table 1 indicate that, in model 1, the responsiveness of gross output
to institutional variables is positive but not significant at any reasonable level. The same with
other variables. Only human explains output responsiveness among the selected countries. The
results shows that a 1% increase in human capital will generate approximately 2.5% increase in
RGDP among the crude oil producing economies. This means that the production in most of
these economies is still significantly labour intensive. Therefore, given that the entire labour
force was used, comprising both the employed and unemployed, more employment of the
unemployed among them will have an higher impact on growth. Often time, what is often
noticed among these countries is the overemphasis on crude oil, however, looking at these results
12
oil revenue do not have any growth impact. Also, the oil sectors do not employ as much labour
that can contribute to growth as the other sectors of their economies.
An explanation that can be offered for the insignificance of average institutional variables
in output responsiveness is that performance of oil producing African economies goes beyond
institutions and other variables, as estimated in the first model, but rest significantly on
employment of available human capital. However, this does not mean that all institutional
variables do not have influence on growth. A cursory look at the disaggregated model 2 implies
that regulatory quality have positive significant impact on output responsiveness. The outcome
shows that a unit stronger in regulatory framework will increase output by approximately 0.4%.
Therefore, concluding that institution does not matter for economic growth will be flatly
contradicted by great deal of previous evidence, including this study. Hence, the outcome of this
study is not only in line with Glaeser et al (2004) who found that institutions do not influence
economic growth but also in line with Sarwar, Siddiqi and Butt (2013) who found that financial
and legal institutions are more effective in increasing the economic growth as compared to other
formal and informal institutions. In this study likewise, regulatory quality is more effective in
increasing economic growth compared to other institutional variables.
One of the preoccupations of the study to test which model predict well using linear
prediction. To carry the prediction of the model, that is, how well the models fits in the long run,
a correlation matrix combining the three models (pooled, fixed and random effect) and log of
RGDP. The results are presented in Table 6 and 11 (in the appendix). The results reveal that all
the three models predict log of output significantly in both cases. However, fixed effects model
is preferable in both estimated model based on the outcome of Hausman Tests.
Concluding Remarks
In this study, an attempt has been made to empirically investigate responsiveness of output to
institutional variables and other variables among crude oil producing African economies using a
simple growth framework. The model is estimated using a panel estimation technique with
annual data between 1996 and 2011. The results revealed that what really explains output growth
among these economies is the amount of human capital they possess reflecting their labour
intensive ways of production. On the other hand, the study shows that output do not respond
13
significantly to other variables, institutional variables inclusive except regulatory quality.
Therefore, the current high level of unemployment in most of these countries could be an
hindrance to their growth.
Based on the findings of the study, some policy recommendations are provided. The
results of the empirical analysis of the paper suggest the need for the governments of these
selected crude oil producing African economies to design policies that will enhance not only
regulatory quality but also employment. One way of going around this is to give attention to
labour intensive sectors instead of laying emphases on oil sector which is not labour intensive.
Also, a conducive environment may be created for private sectors that can serve as a good source
of employment.
14
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Economic Development (Oxford University Press): 1–16.
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Policies. NBER Working Paper no 7766.
CIA World Factbook (2012). Oil production ranking in the world. Retrieved from
www.cia.gov/library/publications
Glaeser E.L, Porta R.L, Lopez-de- Silanes and Shleifer A. (2004). “Do Institutions Causes
growth”? Journal of Economic Growth, 9, Pp 271-303.
Karabegović Amela (2009).Institutions, Economic Growth, and the “Curse” of Natural
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them. NBER working Paper no 7540.
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15
Appendix
Table 1: Descriptive Statistics
. xtsum rgdp va ge rq rl cc i hc or ihc
Variable
Mean
Std. Dev.
5.22e+10
5.21e+10
1.35e+10
Min
Max
Observations
3.74e+08
3.30e+09
1.16e+10
1.93e+11
1.54e+11
9.34e+10
N =
n =
T =
155
10
15.5
rgdp
overall
between
within
4.81e+10
va
overall
between
within
-1.105708
.7181955 -1.960072 .8524643
.7295933 -1.787052 .6596337
.1808399 -1.930086 -.6714245
N =
n =
T =
120
10
12
ge
overall
between
within
-.8590942
.6521044 -1.974043
.6399646 -1.550528
.2313946 -1.282609
.8594194
.6158949
.9787229
N =
n =
T =
120
10
12
rq
overall
between
within
-.8905679
.6887207 -2.410037 .7661821
.6838943
-1.6339 .5303766
.2232903 -1.666704 -.3397146
N =
n =
T =
120
10
12
rl
overall
between
within
-.919252
.6124682 -2.145969 .2387966
.6253865 -1.726798 .0996857
.1419923 -1.338423 -.6008453
N =
n =
T =
120
10
12
cc
overall
between
within
-.898789
.5751312 -2.061645 .7591954
.5721676 -1.479534 .4005955
.1834713 -1.482784 -.4839911
N =
n =
T =
120
10
12
i
overall
between
within
-.9346821
.6062209 -2.065885 .5697089
.6235826 -1.594808 .4612373
.1208017 -1.405759 -.6668186
N =
n =
T =
120
10
12
hc
overall
between
within
1.09e+07
1.27e+07
1.32e+07
1847334
230092
294057.7
4345036
5.03e+07
4.23e+07
1.89e+07
N =
n =
T =
150
10
15
or
overall
between
within
32.76998
23.56523
23.04891
8.443746
0
.0529917
8.556431
79.51369
64.73795
55.29768
N =
n =
T =
149
10
14.9
ihc
overall
between
within
1.19e+08
1.50e+08
2090000
1.32e+08
8317333
8.04e+07 -2.03e+08
7.81e+08
4.42e+08
4.59e+08
N =
n =
T =
150
10
15
16
Table 2: Between Regression Aggregate Estimate
. xtreg logrgdp i loghc logihc logor, be
Between regression (regression on group means) Number of obs
=
Group variable: id
Number of groups =
R-sq: within = 0.0346
between = 0.9315
overall = 0.8296
Obs per group: min =
avg =
max =
sd(u_i + avg(e_i.))= .5032831
F(4,5)
Prob > F
logrgdp
i
loghc
logihc
logor
_cons
Coef. Std. Err.
1.792943
.9606534
-.6743799
.162449
22.4556
.5639639
.1942876
.3045701
.1700376
3.833928
t
3.18
4.94
-2.21
0.96
5.86
P>|t|
0.025
0.004
0.078
0.383
0.002
=
=
112
10
7
11.2
12
17.01
0.0041
[95% Conf. Interval]
.3432282
.4612212
-1.457302
-.2746465
12.60017
3.242659
1.460086
.1085425
.5995445
32.31102
17
Table 3: Fixed Effect Regression Aggregate Estimate
. xtreg logrgdp i loghc logihc logor, fe
Fixed-effects (within) regression
Group variable: id
Number of obs
Number of groups
=
=
112
10
R-sq: within = 0.6455
between = 0.7259
overall = 0.7138
Obs per group: min =
avg =
max =
7
11.2
12
corr(u_i, Xb) = -0.9704
F(4,98)
Prob > F
logrgdp
Coef.
i
loghc
logihc
logor
_cons
.037049
2.492219
.0142941
-.0322309
-14.49124
.177451
.1998533
.0518329
.0389818
3.030355
sigma_u
sigma_e
rho
3.0540406
.22479626
.99461133
(fraction of variance due to u_i)
F test that all u_i=0:
Std. Err.
F(9, 98) =
t
0.21
12.47
0.28
-0.83
-4.78
58.66
P>|t|
=
=
0.835
0.000
0.783
0.410
0.000
44.60
0.0000
[95% Conf. Interval]
-.3150968
2.095616
-.0885666
-.1095889
-20.50488
.3891947
2.888821
.1171549
.0451271
-8.477603
Prob > F = 0.0000
18
Table 4: Random Effect Regression Aggregate Estimate
. xtreg logrgdp i loghc logihc logor, re
Random-effects GLS regression
Group variable: id
Number of obs
Number of groups
=
=
112
10
R-sq: within = 0.5712
between = 0.7274
overall = 0.7208
Obs per group: min =
avg =
max =
7
11.2
12
corr(u_i, X)
Wald chi2(4)
Prob > chi2
= 0 (assumed)
logrgdp
Coef.
i
loghc
logihc
logor
_cons
.1893245
1.106502
.0058118
.0655747
6.770776
sigma_u
sigma_e
rho
.49863972
.22479626
.83109094
Std. Err.
z
P>|z|
.1990373
.1243407
.0659634
.0433675
1.952085
0.95
8.90
0.09
1.51
3.47
0.342
0.000
0.930
0.131
0.001
=
=
99.75
0.0000
[95% Conf. Interval]
-.2007815
.8627987
-.1234741
-.019424
2.944759
.5794305
1.350205
.1350976
.1505733
10.59679
(fraction of variance due to u_i)
19
Table 5: Hausman Test
. hausman fe
Coefficients
(b)
(B)
fe
re
i
loghc
logihc
logor
.037049
2.492219
.0142941
-.0322309
.1893245
1.106502
.0058118
.0655747
(b-B)
Difference
sqrt(diag(V_b-V_B))
S.E.
-.1522755
1.385717
.0084824
-.0978055
.
.1564632
.
.
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=
30.55
Prob>chi2 =
0.0000
(V_b-V_B is not positive definite)
Table 6: Prediction
. cor logrgdp be fe re
(obs=112)
logrgdp
logrgdp
be
fe
re
be
fe
re
1.0000
0.9118 1.0000
0.8346 0.8130 1.0000
0.8715 0.8330 0.9872 1.0000
20
Table 7: Between Effect Model (Disaggregating institutional Variables).
. xtreg logrgdp va ge rq rl cc loghc logihc logor, be
Between regression (regression on group means) Number of obs
Group variable: id
Number of groups
=
=
112
10
R-sq: within = 0.0321
between = 0.9911
overall = 0.8314
Obs per group: min =
avg =
max =
7
11.2
12
sd(u_i + avg(e_i.))= .4053249
F(8,1)
Prob > F
logrgdp
Coef.
va
ge
rq
rl
cc
loghc
logihc
logor
_cons
-.2371878
.719416
-.9349901
.5443661
1.875925
.72041
-.227796
.2301796
17.95569
Std. Err.
1.613454
5.590726
2.398785
1.915186
3.211289
.2673525
.4090415
.2532187
4.636644
t
-0.15
0.13
-0.39
0.28
0.58
2.69
-0.56
0.91
3.87
P>|t|
0.907
0.919
0.763
0.824
0.663
0.226
0.677
0.530
0.161
=
=
13.95
0.2043
[95% Conf. Interval]
-20.73806
-70.31749
-31.41445
-23.79038
-38.92737
-2.676626
-5.425162
-2.98727
-40.95845
20.26368
71.75633
29.54447
24.87911
42.67921
4.117446
4.96957
3.447629
76.86983
21
Table 8: Fixed Effect Model (Disaggregating institutional Variables).
. xtreg logrgdp va ge rq rl cc loghc logihc logor, fe
Fixed-effects (within) regression
Group variable: id
Number of obs
Number of groups
=
=
112
10
R-sq: within = 0.6922
between = 0.7036
overall = 0.6966
Obs per group: min =
avg =
max =
7
11.2
12
corr(u_i, Xb) = -0.9638
F(8,94)
Prob > F
logrgdp
Coef.
va
ge
rq
rl
cc
loghc
logihc
logor
_cons
.0301047
.0167785
.349656
-.2709442
-.2278783
2.371623
.0162162
-.0251511
-12.84064
.1360141
.1019643
.1115501
.2077612
.1452161
.2270995
.0503707
.0378436
3.357389
sigma_u
sigma_e
rho
2.8750549
.21384666
.99449805
(fraction of variance due to u_i)
F test that all u_i=0:
Std. Err.
F(9, 94) =
t
0.22
0.16
3.13
-1.30
-1.57
10.44
0.32
-0.66
-3.82
40.90
P>|t|
=
=
0.825
0.870
0.002
0.195
0.120
0.000
0.748
0.508
0.000
26.43
0.0000
[95% Conf. Interval]
-.2399545
-.1856739
.1281707
-.6834589
-.5162083
1.920712
-.0837959
-.1002905
-19.50682
.3001638
.219231
.5711413
.1415705
.0604518
2.822535
.1162284
.0499883
-6.174466
Prob > F = 0.0000
22
Table 9: Random Effect Model Disaggregating institutional Variables.
. xtreg logrgdp va ge rq rl cc loghc logihc logor, re
Random-effects GLS regression
Group variable: id
Number of obs
=
Number of groups =
112
10
R-sq: within = 0.5594
between = 0.7538
overall = 0.7594
Obs per group: min =
avg =
max =
7
11.2
12
corr(u_i, X) = 0 (assumed)
Wald chi2(8)
Prob > chi2
logrgdp
va
ge
rq
rl
cc
loghc
logihc
logor
_cons
sigma_u
sigma_e
rho
Coef. Std. Err.
-.1073435
-.1906913
.4753817
.3965552
-.4224905
.9238259
.0411263
.0217828
9.002648
.1634474
.1183314
.1403096
.2371636
.1792432
.1076467
.0631553
.0426629
1.681514
z
-0.66
-1.61
3.39
1.67
-2.36
8.58
0.65
0.51
5.35
=
=
145.53
0.0000
P>|z|
[95% Conf. Interval]
0.511
0.107
0.001
0.095
0.018
0.000
0.515
0.610
0.000
-.4276946 .2130076
-.4226166 .0412341
.2003798 .7503835
-.0682769 .8613872
-.7738007 -.0711802
.7128423 1.134809
-.0826557 .1649083
-.061835 .1054007
5.706941 12.29835
.40009771
.21384666
.7778013 (fraction of variance due to u_i)
23
Table 10. Hausman Test ( Disaggregated Model)
. estimate store re
. hausman fe
Coefficients
(b)
(B)
fe
re
va
ge
rq
rl
cc
loghc
logihc
logor
.0301047
.0167785
.349656
-.2709442
-.2278783
2.371623
.0162162
-.0251511
-.1073435
-.1906913
.4753817
.3965552
-.4224905
.9238259
.0411263
.0217828
(b-B)
Difference
sqrt(diag(V_b-V_B))
S.E.
.1374482
.2074698
-.1257257
-.6674994
.1946122
1.447798
-.0249101
-.046934
.
.
.
.
.
.1999659
.
.
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=
580.06
Prob>chi2 =
0.0000
(V_b-V_B is not positive definite)
Table 11: Prediction (Disaggregated Model)
. cor logrgdp be fe re
(obs=112)
logrgdp
logrgdp
be
fe
re
be
fe
re
1.0000
0.9118 1.0000
0.8346 0.8130 1.0000
0.8715 0.8330 0.9872 1.0000
24
Table 12. List of Countries covered Based on their oil production Capacity
NIGERIA
ALGERIA
ANGOLA
LIBYA
EGYPT
SUDAN
EQUATORIALGUINEA
CONGO, DEM. REP.
GABON
SOUTH AFRICA
25