Ngozi Mary Nwakeze

Department of Economics,University of Lagos


 Mulikat Ajibola Yusuff

 Department of General Studies, Federal Polytechnic, Ilaro



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



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 1966-1985 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 1970-1983.  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 Cobb-Douglas production function for a panel set of 89 countries; using annual cross country data for the period of 1960-1990 and reported a positive rates of return for the case of paved roads(0.048-0.083).


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 transport-economy 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 1985-1998 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, non-military, 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 1980-1998. They found gross domestic product to be positively related to private capital stock by one year lag  GDPt-1 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 macro-economic 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 1970-1987, applied total factors productivity growth and co-integration 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 1978-2002 reported a higher growth level from better transportation. Since increase in road safety is related to increasing socio-economic 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 U-shaped relationship between injury, death rates and NDP authenticating Kuznets phenomenon for within-country 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.



Model specification

An analytical framework in the form of extended Cobb-Douglas 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 Cobb-Douglas production function used for the study is specified as

Yt         =          AtKt α1Ltα2Gtα3        ………………………..(1)


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 ;

α1, α2, α3 = 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)   +   α1LnK(t)   +   α2LnL(t)   +   α3LnG(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 = α0 + α1LnRGCF + α2LnLAB + α3LnAUD + α4LnTRN

+ α5LnTRAF + α6ECMt-l + µt                                    ….(3)


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

µt  represents the Stochastic Error Term

α0, α1, α2, α3, α4, α5, and α6  are coefficients to be estimated.

The a priori expectation are:    ARGCF, ALAB, ATRN  > 0     while     AAUD,  and ATRAF  < 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 sub-sections: (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 (1975-2006). 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 Granger-causality 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 Dickey-Fuller (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 ADF-test statistic of each is greater in absolute value than the 95 percent critical value. Thus, these variables can affect the long-run determination of Nigeria’s real GDP and hence, economic growth.



Cointegration test

Given that all the variables are non-stationary, 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, t-values 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 R2 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 (dU) of 1.82 at 5% significant level i.e. d*> dU.


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 1975-2006.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 (1975-2006)





Standard deviation


































Appendix ii


table 2.     test results for granger causality


  Null Hypothesis:




  RGCF does not Granger Cause RGDP




  RGDP does not Granger Cause RGCF



  LAB does not Granger Cause RGDP




  RGDP does not Granger Cause LAB



  TRN does not Granger Cause RGDP




  RGDP does not Granger Cause TRN



  AUD does not Granger Cause RGDP




  RGDP does not Granger Cause AUD



  TRAF does not Granger Cause RGDP




  RGDP does not Granger Cause TRAF





Appendix iii


table 3.       correlation matrix 






















































Appendix 1v 


table  4.      test results for unit root of the variables








5% Critical


Order of Integration


































Appendix v


table 5. co-integration test





5 Percent

1 Percent




Eigen value


Critical Value

Critical Value

No. of CE(s)







      None *





   At most 1





   At most 2





   At most 3





   At most 4





   At most 5


Appendix vi


table 6: regression results






Std. Error
















































    Mean dependent var



Adjusted R-squared


    S.D. dependent var



S.E. of regression


    Akaike info criterion



Sum squared resid


    Schwarz criterion



Log likelihood





Durbin-Watson stat