Sources of inequality in the cost of transport mobility in the city of Yaoundé, Cameroon

Abstract

This paper examines the sources of inequality in the cost of transport mobility in the city of Yaoundé, Cameroon. The variables that measure employment, the average distance covered by the respondent, the average time to the destination, the cost of fuel, and whether the respondent resides in the central business district are positively related to the cost of transport mobility. Two variables, travelling by motorcycle and the cost of fuel, largely account for inequality in the cost of transport mobility. The policy implications of the analysis include the need for adequately managing emerging means of transportation, such as motorcycles, in cities like Yaoundé.

Keywords:

• cost of transportation
• mobility
• Cameroon

JEL codes:

• R41
• R49

1. Introduction

Movement is at the core of people's existence, since it is a key factor enabling and influencing their activities, such as work, leisure and education, and even their emotional lives. Mobility enhances personal and collective development and, hence, progress and well-being (Gay et al., 2011). The rapid increase in particularly the urban population has led to a growing policy focus on transportation and mobility (UNSD, 2013).

The current challenges in urban transport in Cameroon are in part due to past policy decisions. In the 1980s, severe economic recessions in sub-Saharan Africa led to the adoption of structural adjustment programmes, which entailed, among other aspects, a reduction in state spending and a larger role for the private sector in development. These two factors had a direct impact on transportation because, in the absence of substantial state support, private investment in transport was insufficient to cope with urban population growth and transport demands (Becker et al., 1995).

During this time, Yaoundé was expanding rapidly. The lack of investment in transport infrastructure during the city's expansion was one factor in the development of the peripheral areas and the dissociation of the suburbs from the central business district (CBD). This contributed to the formation of slums and exposed various weaknesses in the transport system. The subsequent closure of the Cameroon Urban Transport Authority (SOTUC) caused the already vulnerable public transport system of Yaoundé to collapse. Over the next decade, the gap was filled by informal minibuses, which were often unreliable (Ongolo Zogo, 2002a, 2002b). Only in 2006 did the government allow the creation of a public transport enterprise in Yaoundé, financed by public and private-sector entities.

In attempting to accommodate the rapid expansion of the city, the government focused on improving road networks and reducing traffic congestion and accidents (YUC, 2010). That these efforts were largely unsuccessful is evident from the growth in the number of informal minibuses that link the peripheries to the CBD. The government's failure was due to a combination of urban growth and the constraints imposed by the structural adjustment programme mechanisms, which promoted the devolution of responsibility to local communities and the outsourcing of transport provision.

The objective of this paper is to determine the sources of inequality in the cost of transport mobility in the metropolitan region of Yaoundé and to provide policy recommendations based on these findings. The rest of the paper is structured as follows. Section 2 reviews the pattern of mobility in Yaoundé. Section 3 provides a brief overview of the literature, while Section 4 assesses the methodology of the study. Section 5 presents and comments on the results, and Section 6 concludes.

2. Pattern of mobility in Yaoundé

Yaoundé is the political capital of Cameroon and the second largest city in the country. The city comprises seven ‘councils', with an interconnected road network. In 2010 the Yaoundé Urban Council's geographic information system estimated the road network at 2536 km, of which 754 km were urban roads. The latter consisted of 61 km of national roads, 159 km of primary roads, mainly in the city centre, 57 km of secondary roads, linking the city centre to the peripheries, and 478 km of tertiary roads between and within the peripheries.

The characteristics of Yaoundé's transport infrastructure are as follows: the density of the road network is 753 km/254 km² or 0.00037 km per person (given an estimated population of 1.8 million in 2008); the average time to access a tarred road is about 11 minutes (Institut National de la Statistique, 2002); the percentage of road maintenance is 53.6%; the average speed on tarred roads is 40 km/h (YUC, 2010); and large portions of tertiary roads are earth roads, which are generally degraded and insufficient, fuelling traffic congestion and delays (Ngabmen, 2002).

On the whole, the urban transport system in Yaoundé is poor and degraded. This has encouraged the development of the informal transport sector, which has outpaced the formal sector in offering transport especially on the peripheries. Much transport activity via the informal sector is illegal and risky. Furthermore, the cost of transport mobility is unequal, with rich households located in the CBD and poor households on the peripheries, within a monocentric structure. The next section briefly reviews the literature on transport mobility.

3. Overview of the literature

The number of studies on urban transport issues, especially in developing countries, is quite limited. Barwell (1996) and Starkey et al. (2002) investigate how transport can be designed to meet the personal mobility and accessibility needs of low-income households. Garcia-López (2012) investigates issues of transportation and transport infrastructure in explaining urban social and spatial structures. Baum-Snow (2007a, 2007b) shows that transportation improvements affect spatial patterns of urbanisation and the consumption of resources (Kahn, 2000).

Levy (2011) notes that issues of public space and mobility have created strong incentives for communities and individuals to use public transportation. Crozet (2011) shows that enhanced mobility saves time, reduces the problems related to distance, and enables people to explore areas that are further away in relatively little time, thereby increasing potential opportunities for growth. Other studies investigate aspects of mobility such as the average travel time in developed countries like France and the USA (Schafer et al., 2009), the relationship between a higher potential speed of transportation and telecommunication (Kesselring, 2011), and factors that affect how different groups of people travel around in Latin American cities such as Santiago, Chile (Ducci, 2011). No such studies appear to exist for Cameroon.

4. Methodology

To compute the sources of inequality in the cost of transportation in the metropolitan region of Yaoundé, we apply a regression-based decomposition framework (Morduch & Sicular, 2002; Epo & Baye, 2013). These sources of inequality (or factors) are generated from the transportation function that reflects the determinants of the cost of transportation in the area. The variables used are reported in Table 1. Modelling determinants of the cost of transport mobility, the regression function is expressed as:(1)

Table 1: Descriptive statistics obtained from the Urban Displacement Plan

Variable	Mean	Standard deviation	Minimum	Maximum
Cost of mobility for respondent (log)	10.131	0.7967	6.7720	12.481
Travels to work using a mode of transportation (1 = yes and 0 = otherwise)	0.4091	0.4916	0	1
Average distance covered by respondent (km)	0.1488	0.1132	0.035	2
Average time to reach destination (hours)	0.0918	0.0302	0.055	0.1428
Average cost of fuel (FCFA)	133.27	36.427	113.67	511.06
Average transport fare (FCFA)	147.3	228.8	0	7000
Uses a motorcycle to travel (1 = yes and 0 = otherwise)	0.0288	0.1672	0	1
Uses a taxi to travel (1 = yes and 0 = otherwise)	0.5575	0.4966	0	1
Uses a minibus or bus to travel (1 = yes and 0 = otherwise)	0.0636	0.2441	0	1
Urban city centre (1 = yes and 0 = otherwise)	0.5324	0.4989	0	1

Source: Computed by the authors from the 2010 Urban Displacement Plan and complementary statistics using Stata 10.

where Lny_i is the log of the cost of transportation for an individual, β_i represents parameters to be estimated, X_i represents explanatory variables, α ₀ is the constant term and ϵ₁ is the error term.

The regression-based decomposition approach yields estimates of the transport cost function attributed to variables that affect the cost of transportation (see also Morduch & Sicular, 2002; Wan, 2004). We can therefore obtain the sources of inequality in the cost of transport mobility. Equation 1 gives the total cost of transportation for an individual as the sum of the estimated sources of transport cost function (plus the error term) expressed as:(2)

where is the estimate of the constant term, is the sum of the M estimated explanatory variables (j = 1, … , M) representing the different sources of mobility, and is the estimated residual of the cost function. Generating the estimated source, k, for a given individual we obtain:(3)

where .Adopting the framework by Wan (2004), using I(y_i ) as an inequality measure, overall inequality in the cost of transport mobility can be decomposed into the contribution of the constant term I(C), where , the contribution of the estimated sources of the cost of transportation and the contribution of the predicted residual I(u), where as follows:(4)

In this study we adopt the Shapley value. This approach is based on a set of axioms (Shorrocks, 1999) and has the merit of computing the weighted marginal contribution of an estimated source of mobility in various combinations of such sources. These weighted contributions exactly sum to the inequality measure being considered.

4.1. Data description

Data used in this study were obtained from the 2010 Urban Displacement Plan study of the Yaoundé Urban Council. The plan aimed to gather information, review measures taken, define the structure of traffic in Yaoundé, evaluate factors that affect circulation, and improve the understanding of why people travel. About 18 000 people were interviewed. Data were collected between May and June 2010 and comprised estimates of commuting in Yaoundé, reasons for such travel, the volume of circulation, the origins and destinations of commuters, the volume of traffic, and ways of commuting.

To evaluate the structure of traffic flows and movements from the point of origin towards a destination, 34 roundabouts were designated counting points. To assess travel patterns, Yaoundé was divided into 89 internal areas and nine external areas, using the geographic information system of the Yaoundé Urban Council. These areas were defined as parts of an agglomeration that either attract or displace commuting individuals.

Table 1 provides descriptive statistics for variables used in the regression. The cost of mobility is calculated in Central African CFA francs (FCFA).

About 40% of respondents indicated that they travel to work using one of the modes of transportation being studied. Of these, 3% use motorcycles, 55% use taxis, and 6% use buses or minibuses. The average commuting distance per respondent is 0.15 km, and the average travel time is 5.5 minutes. The average fuel price per respondent is FCFA 133, while the average transport fare is FCFA 147. About 53% of respondents travel towards the city centre.

5. Empirical results

This section reviews the determinants of the cost of transport mobility and then computes the shares of the different sources of inequality in the cost of transport.

5.1. Determinants of cost of transport mobility

Regression results indicate that the model was globally relevant, with the Fisher test being significant at a percentage point and an adjusted R-squared value of 0.41 (see Table 2). The following variables were positively related with the cost of transportation: working, average distance covered by respondent, average time to reach destination, cost of fuel, and residing in the CBD.

Table 2: Weighted determinants of the cost of mobility in Yaoundé

Variable	Estimate (t statistic)
Travels to work using a mode of transportation (1 = yes and 0 = otherwise)	0.0178*
	(1.69)
Average distance covered by respondent (km)	0.3323***
	(7.77)
Average time to reach destination (hours)	1.4176***
	(8.37)
Average cost of fuel (FCFA)	0.0057***
	(43.78)
Average transport fare (FCFA)	–0.0003***
	(–13.40)
Uses a motorcycle to travel (1 = yes and 0 = otherwise)	–2.3963***
	(–80.28)
Uses a taxi to travel (1 = yes and 0 = otherwise)	0.1971***
	(18.41)
Uses a minibus or bus to travel (1 = yes and 0 = otherwise)	0.6283***
	(28.29)
Urban city centre (1 = yes and 0 = otherwise)	0.0540***
	(5.37)
R-squared/adjusted R-squared	0.4154/0.4151
Fisher test [p value]	1 278.8 [0.000]

Notes: ***, ** and * respectively represent the 1%, 5% and 10% levels of significance. Variables in parentheses are the Student's t distribution.

Source: Computed by authors from the 2010 Urban Displacement Plan and complementary statistics using Stata 10.

The variable going to work is positively related with the cost of transport mobility. Most employed people in Yaoundé are still poor. They generally work in the CBD but live on the peripheries, where they can afford the cheap housing. They require various forms of transportation to work.

Regarding the variable distance, growth in the peripheries means that workers need to travel further to reach their places of employment. Roads linking these peripheries to the CBD were insufficient, because of the nature of the urban sprawl in Yaoundé. This meant that workers who live on the peripheries travelled longer distances and faced higher transport costs.

The variable time spent to reach destination is positively related to the cost of transport mobility. Poor and insufficient road networks between the peripheries and the CBD have contributed to traffic congestion, increasing both travelling time and fuel consumption. This has resulted in higher fares, as transport operators shift any increase in the cost or use of fuel to the commuters, thereby increasing their cost of mobility.

Regarding the different modes of transportation, using a taxi, minibus or bus was positively related to the cost of transport mobility, while using a motorcycle was negatively related to it. Note that taxis, minibuses or buses are generally expensive and used for longer distances, while motorcycles are less expensive and used largely for short distances and inaccessible areas on the peripheries. As individuals get wealthier, they tend to prefer other forms of transportation to motorcycles.

Lastly, the average prices of transport fares were negatively related to the cost of transport mobility, since higher fares discourage people from travelling.

5.2. Regression-based inequality decomposition results

To decompose measured inequality in the cost of transport mobility by regressed income sources, we compute the contributions of the various factors using an approach based on the Shapley value (see Table 3).

Table 3: Decomposition of inequality in the cost of transport mobility by sources

Source of mobility	Gini value
Travels to work using a mode of transportation (1 = yes and 0 = otherwise)	0.001 (0.001)
Average distance covered by respondent (km)	0.005 (0.010)
Average time to reach destination (hours)	0.005 (0.010)
Average cost of fuel (FCFA)	0.045 (0.089)
Average transport fare (FCFA)	0.004 (0.008)
Uses a motorcycle to travel (1 = yes and 0 = otherwise)	0.179 (0.353)
Uses a taxi to travel (1 = yes and 0 = otherwise)	0.017 (0.033)
Uses a minibus or bus to travel (1 = yes and 0 = otherwise)	0.011 (0.023)
Urban city centre (1 = yes and 0 = otherwise)	0.002 (0.004)
Residual	0.237 (0.468)
Total inequality	0.507 (1.000)

Note: Values in parentheses are relative contributions.

Source: Computed by authors from the 2010 Urban Displacement Plan and complementary statistics using DASP 2.1 and Stata 10.

Two factors, the use of a motorcycle and the cost of fuel, largely explain inequality in the cost of transport mobility (about 44%). The relative contributions of the other regressed sources sum to 10% and the residual is 46%.

Motorcycle usage accounted for about 35% of total inequality, reflecting the growing use of this mode of transportation. Respondents indicate that they opt for motorcycle transport because of lower fares. In addition, motorcycles are largely used on the peripheries, where the road network is poor and taxis or buses are not available. They are preferred for very short distances and are used mainly by poor households. The other modes of transportation (taxis and buses) account for 5.6% of total inequality in the cost of transport mobility. The modal choice (motorcycles, taxis, minibuses or buses) explains about 41% of the total inequality in the cost of transport mobility, highlighting the important role of transportation vectors in this regard.

Given the importance of fuel prices, the cost of fuel accounted for about 9% of total inequality in the cost of transport mobility. Fuel prices directly affect vehicle owners because higher prices mean more money spent on fuel. They also affect individuals indirectly through higher transport fares charged by taxi, minibus or motorcycle owners. This has a significant effect on commuters from outlying areas, underlining the importance of spatial considerations.

Other factors that contribute to inequality are distance, time, price and motives (travelling to work). The relatively minor contribution of these standard factors is probably due to the small size of the urbanised area in Yaoundé, short travelling times (except during peak hours), and relatively short distances.

The residual term accounted for 46% of inequality. Policy-makers could therefore have 54% confidence levels in the policies suggested by this study. More investigation is needed to increase the margin of confidence in addressing inequality in the cost of transport mobility in Yaoundé.

The marginal contributions of the different sources of inequality are presented in Table 4, as generated by the Distributive Analysis Stata Package (DASP 2.1) software (Araar & Duclos, 2009). The level of entry indicates the position in which a regressed source of inequality is introduced into a set of existing sources of inequality. The introduction of a particular factor into a combination of factors can be regarded as a policy mix.

Table 4: Marginal contributions of the various estimated sources of the cost of transport mobility

Estimated sources of the cost of mobility	Level 1	Level 2	Level 3	Level 4	Level 5	Level 6	Level 7	Level 8	Level 9	Level 10
Travels to work using a mode of transportation (1 = yes and 0 = otherwise)	0.0004	0.0002	0.0001	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
Average distance covered by respondent (km)	0.0015	0.0009	0.0006	0.0004	0.0003	0.0003	0.0002	0.0002	0.0001	0.0001
Average time to reach destination (hours)	0.0022	0.0013	0.0008	0.0005	0.0003	0.0002	0.0001	0.0001	0.0000	–0.0000
Average cost of fuel (FCFA)	0.0085	0.0069	0.0057	0.0049	0.0043	0.0038	0.0033	0.0029	0.0026	0.0023
Average transport fare (FCFA)	0.0021	0.0010	0.0006	0.0003	0.0001	0.0000	–0.0000	–0.0000	–0.0000	–0.0000
Uses a motorcycle to travel (1 = yes and 0 = otherwise)	0.0220	0.0208	0.0198	0.0189	0.0181	0.0173	0.0166	0.0158	0.0153	0.0145
Uses a taxi to travel (1 = yes and 0 = otherwise)	0.0048	0.0034	0.0025	0.0019	0.0014	0.0010	0.0007	0.0004	0.0003	0.0001
Uses a minibus or bus to travel (1 = yes and 0 = otherwise)	0.0022	0.0019	0.0016	0.0013	0.0011	0.0010	0.0008	0.0007	0.0006	0.0005
Urban city centre (1 = yes and 0 = otherwise)	0.0013	0.0007	0.0003	0.0001	0.0000	–0.0001	–0.0001	–0.0001	–0.0001	–0.0001
Residual	0.0066	0.0048	0.0036	0.0028	0.0022	0.0017	0.0013	0.0011	0.0008	0.0007

Notes: Levels indicate the point of entry of an estimated source into a combination of sources. Results are reported to four decimal places.

Source: Computed by authors using DASP 2.1 distributive software in Stata 10.

Regarding motorcycle usage, of the 0.179 of total inequality in the cost of transport mobility, about 0.0220 is realised at Level 1; that is, in the absence of other sources. Including other factors for a potential policy mix scenario (Levels 2 to 13), the sum of the remaining weighted marginal contributions was 0.157. The progressive decrease in its marginal contribution indicates that reducing the effects of this factor on inequality would improve the effectiveness of a policy intervention. Therefore, combining policies that improve motorcycle usage with policies that address the other factors would reduce total inequality.

The effects of the cost of fuel are similar. Its marginal contribution at the first level of entry was 18.8%. From Levels 2 to 13, its shares summed to 81.2%. This indicates that by combining policies which target the cost of fuel with those that address the other factors, the government could significantly reduce inequality in the cost of transport mobility.

Blending policies that target the two main sources of inequality as well as the other contributing factors would therefore reduce both the individual effects of each factor and their combined effects. Given the need for policies that encourage decentralisation (where local authorities share responsibility with state authorities), an adequate framework for coordination should be established.

6. Conclusion and policy recommendations

This study attempted to compute sources of inequality in the cost of transport mobility in the metropolitan region of Yaoundé, using data from the Urban Displacement Plan. It found that the variables working, average distance covered by respondent, average time to reach destination, cost of fuel and residing in the CBD are positively related to the cost of transport mobility. The variables prices (transport fares) and using a motorcycle for travel are negatively related to the cost of transport mobility. Sources of inequality in transport mobility identify two variables, using a motorcycle to travel and the cost of fuel, as accounting for most inequality in the cost of transport mobility in the metropolitan region of Yaoundé.

The regression-based decomposition framework also identifies the added value of defining policy-mix scenarios as well as situations where various transport authorities of different grades and competence work together. Given the different roles and impact of the various factors contributing to unequal transport mobility, an integrated and inter-related approach is needed, which combines the functions of the different policy actors. This could be through a decentralised framework that defines the roles of the different policy actors as per their competencies.

The results point to two policy approaches. First, the different modes of transport explain about 41% of inequality in the cost of transport mobility. This, coupled with an inadequate road infrastructure in the peripheries and the degradation of roads, contributed to chaos in the urban transport sector. Motorcycle and informal minibus drivers are often poorly trained and, when accidents occur, victims are not covered by insurance. A mass transportation system could be developed within a decentralised framework in which the roles are shared between decentralised local councils and the central authority. In this framework, tertiary roads might be constructed and managed by the urban council authorities and primary and secondary roads by the central authority or transport governing body. Road maintenance in the suburbs could be governed by the local councils through a participative approach, in which local inhabitants operate and manage funds for the maintenance of tertiary roads and control revenue collected from such roads through toll fees.

Second, motorcycles account for about 35% of inequality in the cost of mobility. The lack of regulation and the uncontrolled and informal nature of this sector pose a serious risk. Decision-making on the regulation of motorcycles appears to be problematic because of structural deficiencies and overlapping jurisdictions between the different authorities. Given the growing importance of this sector in the transport map of Yaoundé, a legal framework should be drawn up, showing the number of passengers, the type of commodities transported and the security equipment used by individual operators. Within a decentralised framework, the sector can be regulated through a structure in which local authorities are given the funds to organise training and raise awareness among motorcycle drivers. Since motorcycles are more active on the peripheries, which are controlled by local councils, the ministry in charge of issuing driving licences could establish special partnerships with local authorities regarding the issuance of licences to this sector.

Acknowledgements

This paper is extracted from a study financially supported by the Global Development Network. Funds for the study were provided by the French Ministry of Foreign and European Affairs. The views expressed in this paper are solely those of the authors.