JOURNAL OF RESEARCH IN NATIONAL DEVELOPMENT VOLUME 8 NO 2, DECEMBER, 2010
TRANSPORTATION AND ECONOMIC GROWTH IN NIGERIA
Ngozi
Mary
Nwakeze
Department
of
Economics,University of Lagos
and
Mulikat
Ajibola Yusuff
Department
of
General Studies, Federal Polytechnic, Ilaro
Email: ngnwakeze@yahoo.com
Abstract
This paper attempts
to provide empirical evidence on the contribution
of transport investment, congestion
and
traffic related accidents to economic growth in
Nigeria.
In this paper,
transport investment is
proxied by
physical stock of road infrastructure
while
congestion
is
proxied
by automobile density.
Using the
extended Cobb Douglas Production function model
and relying on cointegration/error correction
technique, it is
found that while transport investments positively
contribute to economic growth, traffic accidents
contribute negatively. Each
impact is strong and statistically significant.
An important implication of the results is
that if
Nigeria is to reverse the effect of economic
meltdown and experience rapid economic growth
by the year 2020, it is
imperative that adequate steps
should
be
taken to improve on the quantity and quality of
road network.
There
is need to
reduce pressure on existing road networks by
opening up
the
waterways.
Keywords:
Growth,
transportation,
infrastructure, automobile density, accidents
Introduction
It is universally recognized that transportation
is a crucial factor for sustained economic growth
and modernization of a nation. The adequacy of
this vital infrastructure is an important
determinant of the success of a nation’s effort in
diversifying its production base, expanding trade
and linking together resources and markets into an
integrated economy. Olebune (2006), define
transportation infrastructure as the arteries for
the flow of people, goods and information
which are necessary in a manufacturing and export
economy.
For Nigeria to be able to reverse the consequences
of economic meltdown, it is necessary to improve
on its Gross Domestic Product (GDP). However, the
achievement of higher GDP (measure of economic
growth) is threatened by inadequate and
diminishing connections to national and global
markets by air, sea, rail and road. As population
grows geometrically, the transportation
infrastructure has not been developed to the
extent that it can effectively address the
problems of accessibility and mobility needs for
the movement of people and goods. The objective of
this paper therefore, is to evaluate the
contribution of transportation to economic growth
in Nigeria. The rest of the paper is structured as
follows: section 2 presents a review of
literature. In section 3 the theoretical model is
presented, while section 4 analyses the empirical
results. Section 5 concludes the paper.
Literature review
The move to measure quantitative relationship
between growth in infrastructure and total
economic growth using macroeconomic model began
with Mera (1973), Ratner (1983) and Biehi (1986).
Though the early analytical explorations of the
contributions of public infrastructure to economic
productivity started with the trio mentioned
above, it was the study carried out by Aschauer
(1989) on the economic contribution of public
investment, of which transport capital forms part
for the G7 countries using panel data for the
period 19661985 that drew the attention of
mainstream economics and policy personnel.
Following
the seminal work of
Aschauer, interest in the relationship
between economic growth and infrastructure had
been rekindled and, as a consequence, a large body
of mainly empirical studies to support the
conclusion that infrastructure is important to
economy emerged.
Many of these studies, based on the production
function approach assume public capital as one of
the direct input factors. Pereira
(2000, cited in
Zou et
al 2008), apply sophisticated production function
on time series data of the US in 19701983. His
finding is that among core infrastructure, the
investment return on electricity and transport is
the highest, 16.1% and 9.7% respectively; both are
higher than that of education and medicare.
In another study, Canning and Bennathan (2000,
cited
Boopen 2006) estimated CobbDouglas production
function for a panel set of 89 countries; using
annual cross country data for the period of
19601990 and reported a positive rates of return
for the case of paved roads(0.0480.083).
In his contribution to empirical analysis of
transport  economy linkage, Zhu (2009), applied
production function approach on panel data
covering the period between 1992 and 2004 to
compare transporteconomy linkage of developed
countries and developing countries. His
results indicate that physical units of transport
infrastructure are positively and significantly
related to economic growth and the output
elasticity with respect to physical units for
developed countries is higher than developing
countries.
Boopen (2006), analyzed the contribution of
transport capital to growth for a sample of Sub
Saharan African (SSA) and a sample of Small Island
Developing States (SIDS), using both cross
sectional and panel data analysis. In both
cases, the analysis concluded that transport
capital has been a contributor to the economic
progress of these countries. Analysis further
revealed that in SSA case, the productivity of
transport capital stock is superior as compared to
that of over all capital while such is not the
case for the SIDS where transport capital is seen
to have the average productivity level of over all
capital stock.
For research of transport investment and growth in
developing economies, Demurger, 2001 cited Zou,
2008) examines data of 24 provinces of China in
19851998 and points out that the inequality of
transport infrastructure is one of the main
factors leading to growth inequality across
provinces.
Using a time series analysis for the
investment into road infrastructure and economic
growth in South Africa, Fedderke et al (2006 cited
Moctezuma 2008) find that road infrastructure does
indeed lead to economic growth in South Africa
both by boosting GDP directly and by raising the
marginal products of other production factors.
In Nigeria, Imobighe and Awogbemi (2006) regressed
private capital stock, nonmilitary, net
investment, time to capture the effects of the
technical changes in economic growth, one year lag
GDP and electricity supplied against Gross
Domestic Product to assess the impact of capital
stock in Nigeria’s economic growth from 19801998.
They found gross domestic product to be positively
related to private capital stock by one year lag GDP_{t1}
and electricity supply was negatively related to
recurrent and capital expenditure, except
expenditure on defense and technical change. They
further found that while lagged value of gross
domestic product significantly increases output in
Nigeria, other explanatory variables were,
individually insignificant in explaining output in
Nigeria. Loto (2006) also found that
infrastructure, when measured in physical sense,
impacts positively on economic growth
Some researchers explored the impact of public
capital on the growth rate of output. Canning
et al (2004 cited Zhu, 2009) used physical
measures like kilometers of paved road to
investigate “the long run consequences of
infrastructure provision on per capital income in
a panel of countries” covering the period between
1950 and 1992.
His estimate results suggested that for
paved road the sign of the impact of an increase
in provision on GDP per capital varies across
countries.
Some studies also show that public capital
can lead to economic growth by raising total
factor productivity of all inputs.
Aschauer, (1989 cited Rosik 2006) find evidence
that a one percent increase in public capital
stock lead to a 0.39 percent increase of total
factor productivity.
Yamaguchi, (2008) conducted a regression analysis
on panel data of five nations between 1992 and
2004 to identified road investment impact on
macroeconomic multi factor productivity growth
(MFP) and reported that physical improvement of
the road capital stock has positive effect. He
obtained a coefficient of 0.1782.
Other studies reported that the importance of
infrastructure on economic development has been
over emphasized. For instance,
Neuser (1993) using public data from Ford
and Poret (1991) for the GT countries over the
period 19701987, applied total factors
productivity growth and cointegration techniques
to the sample. He reported insignificant and
unstable results.
Tatom (1991) also confirmed that public sector
capital investment has no significant effect on
output of the private sector and investment.
Duranton and Turner (2008) estimated the effects
on major cities of major roads and public transit
on the growth of major cities in the US between
1980 and 2000 and found that a 10 percent increase
in city’s stock of roads causes about a 2 percent
increase in its population and employment and a
small decrease in its share of poor households. Zou, et
al (2008) in their own study of transport
infrastructure, growth, and poverty alleviation in
East and central China with panel data of 1994 to
2002 and a time series data of 19782002 reported
a higher growth level from better transportation.
Since increase in road safety is related to
increasing socioeconomic development, Garg and
Hyder (2006) studied the trends in injury and
death rates in India and analyzed these trends in
relation to economic and population growth. Using
linear regression models to test ‘a priori’
hypothesis of a positive relationship between net
domestic product (NDP) and death rates from road
crashes, they reported an inverted Ushaped
relationship between injury, death rates and NDP
authenticating Kuznets phenomenon for
withincountry level comparisons. He
therefore recommended a state investment in road
safety in addition to any overall national
efforts.
It is observed that most studies particularly in
Nigeria dealt with the estimation of the output
effect from public capital in general. The
novelty of this study is that it attempts to
analyze the contribution of one component of
public capital (which is transport capital) to
economic growth.
Methodology
Model
specification
An analytical framework in the form of extended
CobbDouglas production function incorporating
some variables of road transport was used for the
study. This formulation was adopted by Aschauer
(1989), Boopen (2006), Yamaguchi (2008) and Zhu
(2009).
The Extended CobbDouglas production function used
for the study is specified as
Y_{t}
=
A_{t}K_{t}^{ α1}L_{t}^{α2}G_{t}^{α3
}_{ }………………………..(1)
where:
Y_{(t) }
=
Output
A_{(t)
}=
Level of technology
K_{(t) }= Total physical capital of the country (N billion); _{ }
_{
}L_{(t)
}=
Labour Force (In millions);
G_{(t)
}=
Transportation component ;
α_{1}, α_{2}, α_{3 }
=
Elasticities with respect to capital labour and
transport
output.
Taking the natural logarithm of both sides of the
equation produces a linear equation in levels of
the form
LnY_{(t)} =
LnA_{(t)} +
α_{1}LnK_{(t) }
+
α_{2}LnL_{(t)} +
α_{3}LnG_{(t)
}…………….(2)
In this study, the transport component (G) is
broken down into total road network (LnTRN),
automobile density (LnAUD),
traffic accidents (LnTRAF)
Thus the empirical model to be estimated in this
study is given as:
LnGRRGDP =
α_{0} + α_{1}LnRGCF
+ α_{2}LnLAB +
α_{3}LnAUD +
α_{4}LnTRN
+ α_{5}LnTRAF + α_{6}ECM_{tl }+
µ_{t
}….(3)
Where:
RGDP =
Real Gross
Domestic Product
RGCF =
Real Gross
Capital Formation
LAB
=
Labour Force
ECM
=
Error Correction Parameter
AUD, TRN and TRAF are as defined above
µ_{t
}represents the
Stochastic Error Term
α_{0,} α_{1}, α_{2,}
α_{3,} α_{4}, α_{5,}
and α_{6}
are coefficients to be estimated.
The a priori expectation are:
A_{RGCF}, A_{LAB}, A_{TRN }
> 0
while
A_{AUD, }
and A_{TRAF} < 0
The a priori signs indicate that total road
network physical capital and labour force are
positively related to RGDP. However, traffic
congestion and accidents indicates loss of
valuable production hours and manpower and leads
to a decrease in economic growth. As such we
expect an inverse relationship between automobile
density, traffic accidents and economic growth.
Sources of data
The data was sourced from various issues of Annual
Reports and statement of Accounts and Statistical
Bulleting Central Bank of Nigeria (CBN); various
issues of Annual Abstract of statistics, National
Bureau of Statistics (NBS), Federal Ministry of
Transport, Federal Road Safety Corps and various
issues of CIA WORLD Fact book
Analyses of results
Analyses of results are discussed in five
subsections: (4.1) descriptive statistics, (4.2)
causality test analysis, (4.3) correlation
analysis, unit roots test analysis, (4.4)
cointegration test analysis and (4.5) regression
analysis.
Descriptive statistics
The descriptive analysis
starts with the sample statistics (table 1) on the
impact of transportation infrastructure on
economic growth over the period of 32 years
(19752006). The results in table1 show that the
real gross domestic product averages N338748.8 and
varies from N211720 to N595821.6 annually. The
real gross capital formation averages N12377.19
and it ranges from N5233.97 to N31826.35 and with
a standard deviation of N8410.17. Labour force
ranges from 29.8m to 75.1m with a mean of 40.15m
per year, and a standard deviation of 12.48. As
regards the variables of transportation used,
total road network has a mean of 126684.5 km and
it ranges from 99606.0 km to 194394.0km.
Automobile density ranges from 3.516 vehicles/km
to 6.695 v/km with a mean of 4.999594 veh/km,
while traffic accident with a mean of 22945.22
ranges from 8962.0 to 40881.0 annually.
Causality test
We proceed further by examining the Pairwise
Grangercausality between RGDP (dependent
variable) and the relevant independent variables
used in the study. This is shown in table 2. The
table reveals that there exists a causal
relationship between Real GDP and the explanatory
variables of traffic accidents, road investment
and labour. This is as a result of rejection of
the null hypothesis at probabilities of less than
0.05 for LAB and TRN, and probability of less than
0.1 for TRAF. The result also shows the existence
of bilateral relationship between traffic
accidents and economic growth.
Correlation test
We use the correlation matrix table 3 to test the
strength of relationship that exists among the
explanatory variables and the dependent variables.
The results show that Labour Force has the highest
degree of correlation with Real GDP, followed by
traffic accidents (TRAF) and road networks (TRN)
with the values of 0.868, 0.867 and 0.848
respectively. Both LAB and TRN have positive
relationship with RGDP while TRAF has a negative
relationship. The high degree of correlation
between TRAF, TRN and Real GDP emphasizes the fact
that traffic accidents does reduce GDP, and also
corroborates the idea that road network is crucial
to economic growth.
The result also shows that physical capital (RGCF)
has a positive relationship with Real GDP, however
the degree of correlation (0.566) is not as high
as others. Automobile density with a value of
0.117 was found to have a positive relationship
with RGDP. The very low figure suggests that this
relationship is weakly correlated.
The overall findings of the results, except for
AUD, are line with ‘a priori’ expectation.
Unit roots test
Prior to the estimation of equation (3) the
characteristics of the data was examine to
determine whether the data is stationary (i.e
whether it has unit roots) and the order of
integration. In this regard, the Augmented
DickeyFuller (ADF) was used. The result of the
stationarity test with intercept term is presented
in Table 4. It is clear from the table that all
the variables are stationary in their first
differences. Note that the ADFtest statistic of
each is greater in absolute value than the 95
percent critical value. Thus, these variables can
affect the longrun determination of Nigeria’s
real GDP and hence, economic growth.
Cointegration test
Given that all the variables are nonstationary,
we then decided to find out whether these
variables are cointegrated. In doing this we
adopted the Johansen procedure. The result of the
test is presented in table 5.
The result of the cointegration test shows that
there is at least one cointegrating equation. This
means that equation (3) has to be estimated using
first difference of the variables.
Regression
results
Equation (3) is estimated using the Real Gross
Domestic Product (RGDP) as the dependent variable.
The essence is to examine the relative importance
of each variable in terms of contribution to
economic growth. The main results of interest are
the coefficients of error correction variable and
the transportation variables. The results of over
parameterized and parsimonious models are reported
in table 6. The parameter estimate along with the
standard errors, tvalues and the corresponding
critical values are given in the tables. As can be
seen from the regression table above, it is found
that physical capital (proxied by real gross
capital formation) exerts a positive and
statistically very significant impact on Nigeria’s
economic growth. Its coefficient is statistically
different from zero at 1 percent significant
level.
The result also indicates that labour force has a
positive and statistically significant impact on
economic growth. As regards road transportation
variable, the result reveals that total road
network has a positive and statistically
significant relationship with economic growth. It
is significant at 2 percent level i.e. One percent
increase in road network will on the average lead
to about 4.08 percent increase in economic growth.
Automobile density has a positive and
statistically significant effect on economic
growth i.e. One percent increase in automobile
density will on the average lead to about 4.56
increase in economic growth.
In the case of traffic accident, the result shows
that it is negatively related with economic growth
and its coefficient is statistically significant
at 10 percent i.e. One percent increase in traffic
accidents will on the average lead to a less than
one percent (0.19%) decrease in economic growth.
Total road network and automobile density appear
to have the most significant having coefficients
(of 4.08 and 4.56) which are significantly
different from zero at 2 percent and 1 percent
level.
The result also shows that the error correction
variable is statistically significant at 1 percent
level and had the appropriate signs (i.e.
negative). This shows that over 99 percent
disequilibrium in economic growth in the previous
year is corrected in the current year.
Finally, if the constant term (intercept) is
regarded as a measure of economic growth, at 0.12
and 5% level of significance, this indicates that
at zero level of investment in the economy,
economic growth is impossibility i.e. negative.
Judging by the negative intercept.
Generally, the model is found to be good based on
the various diagnostic statistics. At least 64
percent variation in real gross domestic product
is explained by the explanatory variables. This is
indicated by the R^{2} value of 0.64.
The F–statistics illustrates that the parameters
are jointly significant at one percent (1%) and
that the explanatory variables are capable of
explaining the variation in economic growth i.e.
the dependent variable. Also, the Durbin Watson
statistics indicates that there is no serial
correlation of disturbance terms. This is because
the DW value of 2.04 is greater than the upper
region (d_{U}) of 1.82 at 5% significant
level i.e. d*> d_{U}.
With this result, we conclude that the parameter
estimates from our model are stable and efficient,
thus our estimates can be used for policy forecast
and predictions.
Summary, conclusion and policy recommendation
This paper has provided an empirical explanation
for the contribution of transportation to economic
growth in Nigeria from 19752006.The estimated
model is error correction mechanism with the real
Gross Domestic Product as the dependent variable.
The explanatory variables include physical
capital, labour force, total road network,
automobile density and traffic related accidents.
The paper finds that the total road network has a
positive and statistically significant
relationship with economic growth. This implies
that increasing road network would increase
economic growth. Traffic accident was found to
have a negative and statistically significant
relationship with economic growth. The implication
of this is that a rise in traffic accidents would
decrease economic growth.
It is
clear from the result that investment in road
infrastructure is a very important policy issue in
the attainment of economic growth, however, the
negative consequences that arrive from road
infrastructure should be addressed to maximize the
benefits that accrue, to achieve the overall goal
of economic growth. We therefore, recommend that
budget allocation to transport infrastructure
should be increased. This increase should be
balanced by other efforts, like transportation
regulations, strict monitoring of implementation
of the allocation, improving the quality of human
resources and the involvement of the private
sector. Provision of adequate transportation
facilities in terms of road signs, traffic lights,
street lights, medians, drainages, and functional
mass transit vehicles by government and private
individuals is necessary. This will go a long way
to minimize traffic congestion and accidents.
There is need to increase the number of quality
road networks as well as introducing high
occupancy vehicle lanes. Proper maintenance of
existing road networks should be enforced. There
is also need for construction
of
flyovers at crossroads to lighten up notorious
congestion areas. In addition, the various
institutions set up to carry out maintenance
should be strengthened with adequate financial
support.
There should be increased investment in
research on other modes of transportation such as
opening up water ways, revitalizing the railway
system so as to reduce congestion and pressure on
the existing roads.
The implementation
of the above policy suggestions will go a long way
to improve the contribution of transportation to
economic growth in Nigeria.
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Appendix 1
Table 1.
sample statistics (19752006)
Variable 
Mean 
Minimum 
Maximum 
Standard deviation 
RGDP 
338748.8 
211720.0 
595821.6 
109363.2 
RGCF 
12377.19 
5233.971 
31826.35 
8410.174 
LAB 
40.150 
29.80 
75.10 
12.48411 
TRN 
126684.5 
99606.0 
194394.0 
37620.33 
AUD 
4.999594 
3.516 
6.695 
0.861750 
TRAF 
22945.22 
8962.0 
40881.0 
9178.923 
Appendix ii
table 2.
test results for granger
causality


Null
Hypothesis: 
Obs 
FStatistic 
Probability 
RGCF
does not Granger Cause RGDP 
31 
1.65241 
0.20916 
RGDP
does not Granger Cause RGCF 
0.01618 
0.89968 

LAB
does not Granger Cause RGDP 
31 
4.38665 
0.04539 
RGDP
does not Granger Cause LAB 
1.58984 
0.21775 

TRN
does not Granger Cause RGDP 
31 
5.68506 
0.02412 
RGDP
does not Granger Cause TRN 
0.78379 
0.38353 

AUD
does not Granger Cause RGDP 
31 
0.07698 
0.78347 
RGDP
does not Granger Cause AUD 
0.83517 
0.36858 

TRAF
does not Granger Cause RGDP 
31 
3.37527 
0.07682 
RGDP
does not Granger Cause TRAF 
2.71432 
0.11063 
Appendix iii
table 3.
correlation matrix

RGDP 
RGCF 
LAB 
TRN 
AUD 
TRAF 
RGDP 
1.000000 





RGCF 
0.566470 
1.000000 




LAB 
0.867900 
0.412485 
1.000000 



TRN 
0.848125 
0.425851 
0.855076 
1.000000 


AUD 
0.116526 
0.602461 
0.188453 
0.283672 
1.000000 

TRAF 
0.867151 
0.772722 
0.639921 
0.752874 
0.341895 
1.000000 
Appendix 1v
table
4.
test results for unit root of the
variables
Variable 
Level 
1^{st }
Difference 
5% Critical
Value 
Order of Integration 
RGDP 
0.102563 
7.830714 
2.9627 
I(1) 
RGCF 
1.369231 
4.387635 
2.9627 
I(1) 
LAB 
2.031833 
3.649538 
2.9627 
I(1) 
TRN 
1.329628 
4.458033 
3.5670 
I(1) 
AUD 
1.786939 
4.698557 
2.9627 
I(1) 
TRAF 
0.788130 
9.822038 
2.9627 
I(1) 



Appendix v
table 5. cointegration
test




Likelihood 
5 Percent 
1 Percent 
Hypothesized 



Eigen value 
Ratio 
Critical Value 
Critical Value 
No. of CE(s) 



0.752511 
99.20840 
94.15 
103.18 
None * 

0.626191 
57.31672 
68.52 
76.07 
At most 1 

0.376213 
27.79643 
47.21 
54.46 
At most 2 

0.250375 
13.63803 
29.68 
35.65 
At most 3 

0.152278 
4.992578 
15.41 
20.04 
At most 4 

0.001216 
0.036505 
3.76 
6.65 
At most 5 

Appendix vi
table 6: regression
results 





Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 


C 
0.116217 
0.053293 
2.180714 
0.0397 


D(LOG(RGCF)) 
0.272811 
0.073805 
3.696375 
0.0012 


D(LOG(LAB)) 
0.912123 
0.278535 
3.274718 
0.0033 


D(LOG(TRN)) 
4.083966 
1.508804 
2.706758 
0.0126 


D(LOG(AUD)) 
4.555829 
1.486446 
3.064914 
0.0055 


D(LOG(TRAF)) 
0.185531 
0.094005 
1.973632 
0.0606 


GECM(1) 
1.81E06 
4.31E07 
4.208412 
0.0003 


Rsquared 
0.642203 
Mean dependent var 
0.033543 


Adjusted Rsquared 
0.522778 
S.D. dependent var 
0.114848 


S.E. of regression 
0.087256 
Akaike info criterion 
1.838986 


Sum squared resid 
0.175112 
Schwarz criterion 
1.512039 


Log likelihood 
34.58478 
Fstatistic 
4.540100 


DurbinWatson stat 
2.037280 
Prob(Fstatistic) 
0.003552 




