Researchers from the University of Lyon analyzed data from a household travel survey in Douala, Cameroon that included detailed collection of individual and household income information. They found that simplified collection of income data underestimates inequalities in mobility, particularly for disaggregated indicators like motorized travel, and for the poorest individuals and households. Accurate income data is important for analyzing links between daily travel, poverty, and inequality, and for developing equitable transport policies, though it increases the cost and complexity of surveys.

1. RGS-IBG, Annual International Conference 2014
SESSION: MOBILITIES AND LIVELIHOODS IN DEVELOPING URBAN CONTEXTS
ASSESSMENT OF INCOME AND MOBILITY
INEQUALITIES IN DOUALA: METHODOLOGICAL ISSUES
Lourdes DIAZ OLVERA, Didier PLAT, Pascal POCHET
University of Lyon
lourdes.diaz-olvera@entpe.fr didier.plat@entpe.fr pascal.poche@entpe.fr
Laboratoire d’Economie
des Transports
UMR du CNRS n° 5593

2. Lourdes Diaz Olvera, Didier Plat and Pascal Pochet are researchers at the
Laboratory of Transport Economics (Laboratoire d’Economie des Transports –
LET) of the University of Lyon, France. Their research is mainly devoted to
urban transport issues in sub-Saharan Africa, such as the determinants of
daily mobility, the links between mobility, poverty and social inequalities, and
the organisation and operating conditions of public transport supply.
Selection of bibliographical references in English:
o Behrens, R., Diaz Olvera L., Plat D., Pochet P. (2006). "Collection of passenger travel data in Sub-Saharan African cities: Towards
improving survey instruments and procedures." Transport Policy 13(1): 85-96.
o Diaz Olvera L., Plat D., Pochet P. (2003). "Transportation conditions and access to services in a context of urban sprawl and
deregulation. The case of Dar es Salaam." Transport Policy. Transport and Social Exclusion 10: 287-298.
o Diaz Olvera L., Plat D., Pochet P. (2008). Household transport expenditure in Sub-Saharan African cities: Measurement and
Analysis. Journal of Transport Geography 16: 1-13.
o Diaz Olvera L., Plat D., Pochet P. (2010). Towards a two-tired city? In Calas B., From Dar es Salaam to Bongoland. Urban
mutations in Tanzania. Dar es Salaam-Nairobi, Mkuki na Nyota Publishers Ltd - IFRA: 261-278.
o Diaz Olvera L., Plat D., Pochet P. (2010). Urban transport: following the course of free enterprise. In Calas B., From Dar es
Salaam to Bongoland. Urban mutations in Tanzania. Dar es Salaam-Nairobi, Mkuki na Nyota Publishers Ltd - IFRA: 243-260.
o Diaz Olvera L., Plat D., Pochet P., Sahabana M. (2012). "Motorbike taxis in the "transport crisis" of West and Central African
cities." EchoGéo 20, 15 p. http://echogeo.revues.org/13080
o Diaz Olvera L., Plat D., Pochet P. (2013). "The puzzle of mobility and access to the city in Sub-Saharan Africa." Journal of
Transport Geography 32: 56-64.
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3. Abstract
Socio-spatial inequalities with regard to daily mobility and access to urban amenities
are significant in sub-Saharan Africa cities. They are partly related to the low rate of
motorised daily travel, the scarcity of transport supply and the cost for travelling. The
accurate assessment of the monetary resources of city-dwellers contributes to the
appropriate formulation of equitable and pro-poor transport policies. However,
collection of income data encounters a number of methodological difficulties which
are reinforced by the characteristics of jobs in the informal sector.
This paper sets out to show how the way income data is collected affects measured
mobility inequalities. We have done this by referring to the PMU (Pauvreté et Mobilité
Urbaine) household travel survey carried out in Douala in 2003. The results show that
detailed collection of income data from each individual according to its source
markedly improves the accuracy of the total income assessment. Also, the impacts of
accurate data are not randomly distributed amongst the population. The most
significant impacts concern the poorest populations (individuals and households) and
disaggregated indicators (number of motorised trips, motorised travel time budget,
individual level indicators). These results show evidence that the shortcomings in the
statistical apparatus make it more difficult to investigate the links between daily travel,
poverty and social exclusion and blur our perception of mobility inequalities.
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4. Presentation Outline
1. Context
2. Objective and methodology
3. Results
Mobility indicators concentration index
Transport expenditure concentration curve
Individuals, households
Share of hh income in transp. concentration index
4. Conclusion
4

5. Contexte
5
1. The monetary measure of poverty and inequality
What is the mean number of motorised trips/day for the poorest / wealthiest?
What is the share of income spent on transport by the poorest / wealthiest?
Is public transport affordable for the poor?
What are the effects of transport and planning policies on poverty and inequality
reduction?
Insufficient knowledge on levels of income and income distribution
2. Data on income is difficult to obtain
Share of the informal sector in the labour market
Self-employment, domestic/labour, micro-activities, turnover/profit
Several income-generating activities
Income from other sources than labour: rents, pensions, allowances, gifts
⇒ Variable amount/frequency of personal income
Household income: variable composition of households

6. Objective and Methodology (1/2)
6
1. Objective
What are the benefits of reliable income data with regard to analysis of mobility
inequalities?
2. Methodology
• Data: Douala PMU survey (Poverty & Urban Mobility), detailed collection of
income data
Identification of labour activities for each individual (aged >10 y)
Data on income for each labour activity (amount, periodicity)
Data on income from other sources, by item (allowance, pension, gifts, etc.;
amount, periodicity)
=> Estimation of monthly personal income
• Assessment of the relevancy of accuracy of income data

7. Objective and Methodology (2/2)
7
Income data collection
PMU survey: disaggregated Cavie survey: aggregated
Comparison of distribution of individuals according to main income source
(Individuals with no income: 25% in PMU, 50% in Cavie)
Impacts on estimates of inequalities if income data is less accurate in the PMU sample (as in Cavie)?
Simulations: “downgrading” the PMU income data
Mobility inequalities (concentration curve, concentration index):
comparison of initial (observed) PMU income data & simulations
Mobility indicators (indiv.) Monetary indicators (indiv., hh)

8. Results (1/3): Mobility
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0.0 0.1 0.2 0.3 0.4 0.5
Travel time budget
(motorised modes)
Number of
motorised trips
Travel time budget
Number of trips
Concentration index
Observed
Simulated
Mobility indicators
Simplified collection of income data underestimates motorised travel inequalities

9. Results (2/3): Transport expenditure
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0.0 0.2 0.4 0.6 0.8 1.0
1.0
0.8
0.6
0.4
0.2
0.0
Cumulative proportion of individuals, ranked by income
Cumulative proportion of transport expenditure
Individuals
Observed
Simulated
Cumulative proportion of households, ranked by income Cumulative proportion of transport expenditure
0.0 0.2 0.4 0.6 0.8 1.0
1.0
0.8
0.6
0.4
0.2
0.0
Households
Observed
Simulated
Simplified collection of income data: more impacts on disaggregated indicators

10. Results (3/3): Share of household income on transport
Q4
Q3
Q2
Transport expenditure and P.T. accessibility of hh location
Remote
Accessible
Simplified collection of income data: improbable estimations for the poorest households
10
0 20 40 60 80 100
Q1
% of income dedicated to urban transport
Observed
Simulated
­¯
¯

11. Conclusion
1. Impacts of accurate collection of income data on measurement
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of mobility inequalities
Less significant for:
Aggregated indicators (number of trips/TTB for all modes; hh level)
The most affluent individuals and households
Significant for:
Disaggregated indicators (motorised number of trips/TTB; indiv. level)
Monetary indicators
The poorest populations (individuals, hhs)
2. Implications for survey design and administration
Survey questionnaire
Respondent burden
Training of survey staff
=> Cost of data collection
3. Implications for public policies (definition, implementation,
assessment) to reduce poverty and inequalities

12. Assessment of income and mobility inequalities in Douala: methodological issues
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Thanks for your attention!
Lourdes Diaz Olvera Didier Plat Pascal Pochet
lourdes.diaz-olvera@entpe.fr didier.plat@entpe.fr pascal.poche@entpe.fr
Douala I
Douala II Douala III
Douala IV
Douala, PMU Survey, 2003
Douala V
Wouri
Wouri
North 0 2 4 km
Housing area
Main road
Railway
Hydrology
'Commune'
Survey area (access.)
Survey area (remote)