Edith Nkiruka Mazeli1
, Prof.Patrick Kanayo Adigwe
2
, Prof. Celestine Sunday Okaro3
,and
AmalachukwuChijindu Ananwude4*
1, 2, 3, 4Department of Banking and Finance, NnamdiAzikiwe University,
Anambra State, PMB 5025, Awka, Nigeria.
Email: amalision4ltd@yahoo.com (AmalachukwuChijinduAnanwude)
Corresponding author
ABSTRACT
This study presents an analysis of the effect of deposit money banks loans and advances
to services on service sector in Nigeria. Model estimation was in line with the technique
of Autoregressive Distributive Lag (ARDL) model/bound test for a long and short-run
relationship. How the service sector of the real economy have been affected by deposit
money banks’ loans and advances was evaluated following the approach of the granger
causality test and using data that spanned from 1986 to 2021. From the result of the
analysis, deposit money banks loans and advances to services has negative but
insignificant relationship with the sector’s contribution to real gross domestic product and
has not affected the growth of the service sector.The study concluded that deposit money
banks’ loans and advances has not significantly affected the service sector of the real
economy in Nigeria. This study is suggesting that government should look into domestic
regulations such as sector-targeted promotion, policies development of human capital,
strong institutions and provision of critical infrastructure to promote the service sector.
This will engender the profitable production and export of services that will contribute to
the growth of the real sector. The services industry is massive with a lot of potentials that
could be harnessed to diversify and create unprecedented employment in the country and
that if the enabling environment is created the service export alone would surpass
revenue generated from oil and gas.
Keywords: Deposit money banks’ loans and advances; Service sector.
INTRODUCTION
Deposit Money Banks (DMBs) play an important role in economic development
of developing countries by providing loanable funds to the real sector of the economy.
The banks collect savings from the people and mobilize savings for investment in
industrial projects. The investors borrow from banks to finance the projects.
Nevertheless, economic growth and development have been a major objective of
successive Nigerian governments. During the colonial period for instance, the focus was
on the provision of physical infrastructure in the belief, in line with the prevailing
economic ideas, that the facilities would induce the private investments that would
produce the desired growth. After independence in 1960, the government became more
directly involved in promoting economic growth. The thinking this time was to nurture
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17
private entrepreneurs and mobilize needed domestic resources for investment in some
preferred sectors. This brought banks and their intermediation function into prominence
in the economic history of Nigeria (Uzomba, Chukwu, Jumbo &Nwankwo, 2014).
According to Uzomba, Chukwu, Jumbo and Nwankwo (2014), deposit money
banks which are also known as commercial banks are financial institutions that provide
services, such as accepting deposits, giving business loans and auto loans, mortgage
lending, and basic investment products like savings accounts and certificates of activities
deposit. The mainstream theory asserts that commercial banks act as financial
intermediaries to channel savers’ money to firms and individuals who seek funding for
their acts (Uzomba, Chukwu, Jumbo &Nwankwo, 2014). Their importance as a catalyst
to economic growth/development is widely recognized by both monetary and
development economists. DMBs are involved in the process of increasing the wealth of
the economy, particularly the capital goods needed for raising productivity. The
developed economies need the service of the banking system to enable the economy
attain economic growth, while the developing economies need the service of banking
system for sectorial development. Furthermore, Special funds are provided to the
investors for the completion of projects. The bank provides a guarantee for industrial loan
from international agencies. The foreign capital, flows to developing countries for
investment in projects (Onoh as cited in Ikubor, 2020).
Studies on deposit money banks loans and advances have been centred on the real
sector as a whole. Even when there is disaggregation, the emphases have been on
agriculture, industrial, and wholesale and retail trade (see Okosodo, 2016;
Ayeomoni&Aladejana, 2016; Udoka, Mbat,& Duke, 2016; Makinde, 2016; Nteegah,
2017; Ogunmuyiwa, Okuneye,&Amaefule, 2017; Olowofeso, Adeboye, Adejo, Bassey,&
Abraham, 2017; Ubesie, Echekoba, Chris-Ejiogu, &Ananwude, 2019). The service sector
has been ignored based on studies in the context of the Nigeria environment thus this
study seeks to examine the effect of deposit money banks loans and advances on services
on service sector contribution to real sector growth in Nigeria from 1986 to 2021. The
remainder of this study is structured as follows: section two reviews relevant literature;
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18
section three discloses the method of analysis; section four discusses the result and the
data analysis whereas section five concluded the study.
- LITERATURE REVIEW
Money is an essential element for any business, because it fulfils the short term
and long term requirement of funds. It is not possible for the owner to bring all the money
himself, so he/she take recourse to loans and advances. Loans refer to a debt provided by
a financial institution for a particular period while advancesare the funds provided by the
banks to the business to fulfil working capital requirement which are to be payable within
one year. The loan amount is required to be repaid along with the interest, either in lump
sum or in suitable instalments. It can be a term loan (payable after 3 years) or demand
loan (payable within 3 years). In the same way, advances also requirement repayment
along with the interest within one year. Credit is the system by which goods and services
are provided in return for differed rather than immediate payment; it may be provided by
the seller, or by a bank or finance company (Nwaru&Okorontah, 2014). Bank credit is
the credit made available to the economy by the deposit money banks. Atseye, Edim and
Ezeaku (2015) assert that discussion in theoretical background regarding the relevant of
bank credits and their role in economic development have received considerable attention
in the literature of finance. Effective and efficient financial intermediation depends
mostly on the development in the banking sector, especially in a developing economy
like Nigeria. This is on the argument that deposit money banks are very important agent
of economic growth and development on the bases of the capability to mobilizing savings
from surplus units of the economy and distributing same to deficit units in the economy
for production. With bank credit, the lacuna between the borrower and the lender is filled
owing to the attribute of bank credit as a blood stream of an economy (Ogunmuyiwa,
Okuneye&Amaefule, 2017).
Aligning with Mazeli, Adigwe and Ananwude (2020), this study is anchored on
the Finance-Led Growth Hypothesis. The theory views the importance of the
development of the financial system as a catalyst for the growth and development of an
economy. The theory assumes that the efficient and effective intermediation function of
the financial system is a stimulant to the growth of the real sector (Mazeli, Adigwe,
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19
&Ananwude, 2020). This proposition is one that we wish to prove or rebut in this
research, to know whether deposit money banks contribute to growth and development.
There are death of empirical studies on the service sector and deposit money
banks loans and advances nexus in Nigeria. Consequently, this study reviewed available
empirical studies based on internet searches.Adesola and Ewa (2020) investigated the
impact of deposit money banks services on the growth of the Nigerian economy. The
study was specifically meant to examine the impact of aggregate banks credits, aggregate
banks deposits and effect of interest rates spread on the growth of the Nigerian economy.
To achieve these objectives, Time series data were collected from the CBN statistical
Bulletin using the desk survey method from 1984 to 2017. The data were analysed using
various econometrics techniques such as the descriptive statistics test, the augmented
Dickey-Fuller (ADF) unit root test, correlation matrix, and Autoregressive Distributive
Lag (ARDL) Model. Findings from the analysis showed that, there is an insignificant
short and long run effects of aggregate banks credits on the growth of the Nigerian
economy. It also revealed insignificant short and long-run effects of aggregate banks
deposits on the growth of the Nigerian economy and furthermore, insignificant short and
long-run effects of interest rates spread on the growth of the Nigerian economy.
Alzyadat (2021) studied the impact of sectoral bank credit facilities provided by
commercial banks on the non-oil economic growth in Saudi Arabia. Bank credit facilities
are given for nine economic sectors: agriculture, manufacturing, mining, electricity and
water, health services, construction, wholesale and retail trade, transportation and
communications, services, and finance sector. The study employs annual data from 1970
to 2019. The study employs the Autoregressive Distributed Lag (ARDL) approach to
identify the long-run and short-run dynamics relationships among the variables. The main
results reveal that the overall impact of total bank credit has a significant and positive
effect on non-oil economic growth in KSA. The results revealed that the effect of bank
credit on the non-oil GDP growth in the short and long run was uneven. The study finds
that all sectors have a positive and significant impact in the long run, except for the
agricultural and mining sectors. Likewise, all sectors have a positive and significant
impact in the short run, except for construction, finance, services, and transportation &
communications.
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Abina and Obi (2020) investigated the nexus between sectorial output growth and
commercial bank credit in Nigeria. Sectorial output growth was broken down into three
different proxies which include production sector, Commercial sector and Service sector
contribution to gross domestic product while bank credit to production sector, general
commerce and service sector served as the independent variables which are used to proxy
commercial bank credit. The study made use of secondary data obtained from Central
Bank of Nigeria Statistical bulletin for the period 1981 to 2019. It employed Descriptive
Statistics, Phillips-Perron (PP) Unit Root Test, Simple Regression Analysis and Granger
Causality Test. The Granger Causality result showed that bi-directional causality is
identified in the three models while there were positive and significant relationship
between bank credit to production sector, general commerce, service sector and
production sector contribution to gross domestic product, general commerce sector
contribution to gross domestic product and service sector contribution to gross domestic
product respectively. - METHODOLOGY
Model estimation was in line with the technique of Autoregressive Distributive
Lag (ARDL) model/bound test for a long and short-run relationship. How service sector
of the real economy have grown owing to the loans and advances they received from the
deposit money banks moderated by the cost of fund/interest rate were evaluated using
following the approachof the granger causality test. The data spanned from 1986 to 2021
and sourced from the Central Bank of Nigeria statistical bulletin.The study adapted the
model of Bada (2017) and stated thus;
GDPM&A = β0+β1BCTPS+β2INTR+ β3PLR+ β4MS + β5EXR +β6ACGSF
+μ……………………………….. (i)
Where:
GDPA&M = Manufacturing Sector Output and Agricultural Sector Output
BCTPS= Bank Credit to Private Sector
INT = Interest Rate
PLR = Prime lending rate
MS= Broad Money supply
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21
EXR=Exchange rate
ACGSF=Agricultural Credit Scheme Guarantee Fund
β0 = intercept
μ = Error term or the residual
Having modified the model of Bada (2017), the functional model for this study are stated
as follows:
𝑆𝑆𝐶𝑅𝐺𝐷𝑃
= 𝑓(𝐷𝑀𝐵𝐿𝑆, 𝑃𝐿𝑅) … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … . (ii)
The log-linear function are stated as:
𝐿𝑜𝑔𝑆𝑆𝐶𝑅𝐺𝐷𝑃𝑡
= 𝑎0 + 𝑎1𝑙𝑜𝑔𝐷𝑀𝐵𝐿𝑆𝑡 + 𝑎2𝑙𝑜𝑔𝑃𝐿𝑅𝑡
- 𝑢𝑡 … … … … … … . … … … … … … … … … … … … . . (iii)
Where:
𝑆𝑆𝐶𝑅𝐺𝐷𝑃 = Service sector contribution to real GDP;
𝐷𝐷𝑀𝐵𝐿𝑆 = Deposit money banks’ loans and advances to services;
𝑃𝐿𝑅 = the prime lending;
𝑎0 = constant coefficient;
𝜇 = a random error term; and
𝑡 = the time trend; normally included in standard time-series specifications to account for
the omitted variables in the model.
- RESULTS OF ANALYSIS AND DISCUSSION
The analysis started with the presentation of the descriptive attributes of the data
as shown in Table 1. The mean, median, maximum, standard deviation, skewness,
kurtosis, Jarque-Bera, p-value and number of observations of the data were captured in an
attempt to analyse the descriptive statistics of the data. From Table 1, the mean of the
variables are 13482.86 for SSCRGDP, 1752.921 for DMBLS, and 18.33611 for PLR.
The median for the data were shown to be 8983.255, 1.995000, and 17.77000
respectively for SSCRGDP, DMBLS, and PLR. The maximum and minimum values are
38771.49 and 3884.640 for SSCRGDP, 8952.960 and 0.000000 for DMBLS, and
29.80000 and 10.50000 for PLR. The standard deviation for the data are 9620.151,
2743.449, and 3.921631 accordingly for SSCRGDP, DMBLS, and PLR. The data were
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22
positively skewed to normality as evidenced by the positive coefficient of the skewness
for all the data, while the kurtosis for all the variables were positive. The Jarque-Bera
statistic p-values for all the variables discloses that SSCRGDP is not normally
distributed. Consequently, this study applied another econometric test of normality –
Shapiro-Wilk which according to Thode as cited by Yap and Sim (2011), is the best
choice and recommended by researchers as the best choice for testing the normality of
data. Owing to the weakness in Jarque-Bera power to identifying the normality of the
data, the Shapiro-Wilk normality test was presented in Table 2 for the concerned
variables. The result of the Shapiro-Wilk normality test (at 5% significance level) entails
that the data were normally distributed and inference from model estimations are reliable
in statistical term of reference.
Table 1: Descriptive Properties of the Data
Mean Median Max. Min.
Std.
Dev.
Skewnes
s
Kurtosi
s
JarqueBera
Pvalue
Obs
SSCRGDP
13482.
86
8983.2
55
38771.
49
3884.64
0
9620.1
51
0.83141
1
2.6214
20
4.362453 0.1129
0
36
DMBLS
1752.9
21
1.9950
00
8952.9
60
0.00000
0
2743.4
49
1.30460
8
3.1896
13
10.26595 0.0059
0
36
PLR
18.336
11
17.770
00
29.800
00
10.5000
0
3.9216
31
0.78233
4
4.3290
78
6.321950 0.0423
8
36
Source: Output Data from E-views 10.0
Table 2: Shapiro-Wilk Test of Normality
Variables Shapiro-WilkTest Statistic P-value
SSCRGDP 0.860234 0.0003
DMBLS 0.679641 0.0013
PLR 0.927875 0.0215
Source: Output Data from Gretl
The unit root test is utilized to ascertain stationarity in a time series. A time series
has stationarity if a shift in time does not cause a change in the shape of the distribution;
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23
unit root are one cause for non-stationarity in time series data. The assessment of the
stationarity of the data were carried with Augmented Dickey-Fuller (ADF) and Phillips
Perron (PP). The unit root test was performed at level and first difference. The nonstationarity of the data at level necessitated the first difference estimation. ADF results
are presented in Table 3 and 4, while PP tests in Table 5 and 6. The ADF and PP unit root
test results indicated that all the variable were not stationary at level but all became
stationary at first difference of estimation via none, intercept, and trend and intercept. In
overall, the data were stationary thus free from any stationarity defect that most time
series data possess.
Table 3: Result of ADF Test at Level
Variables Intercept Trend & Intercept None Inference
SSCRGDP
-3.033236
(0.04)**
-3.468068 (0.06)
-0.819033
(0.35)
Stationary
DMBLS
1.022748
(0.99) -0.892140 (0.94)
1.813040
(0.98)
Not Stationary
PLR
-4.063464
(0.00)*
-3.506646
(0.05)**
-0.626219
(0.43)
Stationary
Source: Data output via E-views 10.0
Note: The optimal lag for ADF test is selected based on the Akaike Info Criteria (AIC), pvalues are in parentheses where () & () denote significance at 1% and 5% respectively. Table 4:Result of ADF Test at First Difference Variables Intercept Trend & Intercept None Inference SSCRGDP -1.218947 (0.65) -0.225491 (0.98) -4.433353 (0.01)
Stationary
DMBLS
-5.092086
(0.00)*
-6.186576
(0.00)*
-4.746795
(0.00)*
Stationary
PLR -5.677280 -5.873450 -5.761470 Stationary
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(0.00)*
(0.00)*
(0.00)*
Source: Data output via E-views 10.0
Note: The optimal lag for ADF test is selected based on the Akaike Info Criteria (AIC), pvalues are in parentheses where () & () denote significance at 1% and 5% respectively. Table 5: Result of PP Test at Level Variables Intercept Trend & Intercept None Inference SSCRGDP 1.688682 (0.99) -2.197949 (0.47) 4.434355 (1.00) Not Stationary DMBLS 2.173384 (0.99) -0.230249 (0.98) 2.993060 (0.99) Not Stationary PLR -4.310681 (0.00)
-5.658215
(0.00)*
-0.449349
(0.52)
Stationary
Source: Output Data via E-views 10.0
Note: Spectral estimation methods are Bartlett kernel and Newey-West method for
Bandwidth, p-values are in parentheses where () & () denotes significance at 1% and 5% respectively. Table 6: Result of PP Test at First Difference Variables Intercept Trend & Intercept None Inference SSCRGDP -7.580401 (0.00)
-8.466295
(0.00)*
-6.769192
(0.00)*
Stationary
DMBLS
-5.032708
(0.00)*
-10.55723
(0.00)*
-4.746828
(0.00)*
Stationary
PLR
-10.16496
(0.00)*
-10.38978
(0.00)*
-10.30771
(0.00)*
Stationary
Source: Output Data via E-views 10.0
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25
Note: Spectral estimation methods are Bartlett kernel and Newey-West method for
Bandwidth, p-values are in parentheses where (*) & () denotes significance at 1% and
5% respectively.
The confirmation of the stationarity of the data made way for the testing of the
long run relationship between deposit money banks’ loan and advances, and the real
economy. The Autoregressive Distributive Lag (ARDL) was selected because it takes
into consideration the different order of integration of variables. The biases that may be
associated with stationarity at level or first difference estimation is completely eliminated
with the application of the ARDL co-integration methodology. From the ARDL result in
Table 7, it was observed that deposit money bank loans and advances to servicesis not
related with service sector contribution to real gross domestic product in the long run. To
back it up, the f-statistic of 3.34 is lower than the upper bound test of 3.87.
Table 7: SSCRGDP → DMBLS + PLR
T-Test 5% Critical Value Bound Remark
F-Statistic Lower Bound
Upper
Bound
3.341371 3.1 3.87 Null Hypothesis Accepted
Source: Data output via E-views 10.0
The nature of relationship between loans and advances of deposit money banks in
Nigeria and service sector contribution to real gross domestic product was assessed using
the ARDL regression approach as against the conventional OLS methodology. The
choice of ARDL is predicated on the fact that the variables have different order of
integration and ARDL is perfectly suited to handle such. The global utility of Adjusted
R-square, f-statistic, Durbin Watson and the relative statistic of the individual variables
were the statistical yardstick for interpretation of the ARDL short run relationship
analysis. Based on global utility criteria, Table 8 shows that 88.88% variation in service
sector contribution to gross domestic product was attributed to deposit money banks
loans and advances to service sector and prime lending rate which is significant by
looking at the p-value (0.00) and f-statistic (25.78). The Durbin Watson value of 1.61
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26
falls within the acceptable range that is devoid of autocorrelation problem. From the
individual variables’ coefficient analysis, deposit money banks loans and advances to
service sector has insignificant negative relationship, while prime lending rate has
insignificant negative relationship with service sector contribution to gross domestic
product. Service sector contribution to gross domestic product would be up by a tune of
N2,873.47 billion when deposit money banks loans and advances to service sector and
prime lending rate are kept constant. Rising deposit money banks loans and advances to
service sector by a percentage result in 1.13 factor appreciation in service sector
contribution to gross domestic product, while an equivalent increase in prime lending rate
would lead to a 216.09 depreciation in service sector contribution to gross domestic
product.
To determine the effect of deposit money banks loans and advances on service
sector, the granger causality analysis was performed. The choice of granger causality
analysis was based on the fact that it is structured to detect a variable that can predict or
cause change in another which is obviously not the case in the OLS technique which only
ascertains the nature of relationship between variables. Two variables may correlated but
may not have any effect or cause changes on the other. On the service sector linkage with
deposit money bank loans advances to service sector, Table 9 reveals that there is a
unidirectional relationship between service sector contribution to real gross domestic
product and deposit money bank loans advances to service sectorat significance level of
5%. The result implies that service sector contribution to real gross domestic product has
significant effect on deposit money bank loans advances to service sector. Similarly,
service sector contribution to real gross domestic product has significant effect on deposit
money bank loans advances to service sectorat significance level of 5%.
Table 8: ARDL RegressionSSCRGDP → DMBLS + PLR
Variable Coefficient Std. Error t-Statistic Prob.
SSCRGDP(-1) -0.020244 0.344656 -0.058738 0.9537
SSCRGDP(-2) 0.845848 0.398563 2.122242 0.0459
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SSCRGDP(-3) -0.640611 0.486740 -1.316125 0.2023
SSCRGDP(-4) 1.349008 0.520577 2.591370 0.0170
DMBLS 1.133125 1.216136 0.931742 0.3621
DMBLS(-1) -4.356223 2.083633 -2.090686 0.0489
DMBLS(-2) 4.170488 2.037390 2.046976 0.0534
DMBLS(-3) -5.277855 1.972372 -2.675892 0.0141
DMBLS(-4) 2.829917 1.037594 2.727383 0.0126
PLR -216.0999 199.8539 -1.081290 0.2918
C 2873.473 4842.068 0.593439 0.5592
R-squared 0.924682 Mean dependent var 14662.07
Adjusted Rsquared 0.888817 S.D. dependent var 9569.096
S.E. of regression 3190.732 Akaike info criterion 19.24017
Sum squared resid 2.14E+08 Schwarz criterion 19.74402
Log likelihood -296.8428 Hannan-Quinn criter. 19.40719
F-statistic 25.78193 Durbin-Watson stat 1.619948
Prob (F-statistic) 0.000000
Source: Data output via E-views 10.0
Table 9: Granger Causality Result for SSCRGDP → DMBLS + PLR
Null Hypothesis: Ob
s
FStatistic
Prob. Remarks
DMBLS does not Granger Cause SSCRGDP
SSCRGDP does not Granger Cause DMBLS
35 2.99504
22.1629
0.093
2
0.000
0
No Causality
Causality
PLR does not Granger Cause SSCRGDP
SSCRGDP does not Granger Cause PLR
35 1.05523
8.90786
0.312
0
0.005
4
No Causality
Causality
Source: Data output via E-views 10.0
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Table 8 reveals a non-significant positive relationship between deposit money
banks loans and advances to the services on service sector contribution to real gross
domestic product. Similarly, in Table 9, the significant effect of deposit money banks
loans and advances to the services on service sector contribution to real gross domestic
product was not supported. Despite the fact that this is not statistically significant, the
outcome may point to the developing nature of the Nigeria service sector which
dominantly are hinged to other developing countries in Africa for patronage. This is not
in agreement with Alzyadat (2021) and Abina and Obi (2020) on the positive effect of
deposit money banks loans and advances on service sector performance. The prolonged
strike by the Academic Staff Union of Universities (ASUU) has shown exist of some
lecturers and medical personnel, especially from Lagos to Europe, North America and
South America continents. Nonetheless, if the service sectors were working even up to
70%, the resignation of these lecturers and medical personnel would have been at the
barest minimum compared to what was reported within the period of ASUU strike. - CONCLUSION AND POLICY IMPLICATIONS
This study presents an analysis of the effect of deposit money banks loans and
advances to services on service sector in Nigeria. Model estimation was in line with the
technique of Autoregressive Distributive Lag (ARDL) model/bound test for a long and
short-run relationship. How the service sector of the real economy have been affected by
deposit money banks’ loans and advances were evaluated using following the approach
of the granger causality test, and using data that spanned from 1986 to 2021. From the
result of the analysis, the study concluded that deposit money banks’ loans and advances
has not significantly affected the service sector of the real economy in Nigeria.Deposit
money banks loans and advances to services has negative but insignificant relationship
with the sector’s contribution to real gross domestic product.
This study is suggesting that government should look into domestic regulations
such as sector-targeted promotion, policies development of human capital, strong
institutions and provision of critical infrastructure to promote the service sector. This will
engender the profitable production and export of services that will contribute to the
growth of the real sector. The services industry is massive with a lot of potentials that
Logos: African Journal of Philosophy and Studies. Vol. 5, 2022
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29
could be harnessed to diversify and create unprecedented employment in the country and
that if the enabling environment is created the service export alone would surpass
revenue generated from oil and gas.
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