Assessment of mobility inequalities and income data collection. Methodological issues and a case study (Douala,Cameroon) |
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| Affiliation: | 1. Blackland Research and Extension Center, Texas A&M Agrilife Research, 720 East Blackland Road, Temple, TX 76502, USA;2. Department of Hydraulic and Ocean Engineering, National Cheng Kung University, Tainan City 70101, Taiwan;3. Department of Water Resources Engineering and Conservation, Feng Chia University, Taichung City 40724, Taiwan;4. Water Resources Planning Institute,Water Resources Agency, Ministry of Economic Affairs, Taiwan;5. Aqua-Aerobic Systems, Inc., 6306 N Alpine Rd, Loves Park, IL 61111, USA;6. Grassland, Soil & Water Research Laboratory, USDA-ARS, 808 East Blackland Road, Temple, TX 76502, USA;1. School of Civil Engineering, Richview Newstead, Belfield, Dublin 4, Ireland;2. Whitaker Institute, J.E. Cairnes Building, National University of Ireland, Galway, Ireland;3. Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin 2, Ireland |
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| Abstract: | In order to understand the scale and nature of inequalities in intra-urban mobility we need reliable income data, but this is generally difficult to collect in household surveys. The methodological approaches that are employed to overcome difficulties in income data collection may affect the relative position of individuals and households within the income distribution and our estimates of mobility inequalities. In the context of a case study of Douala, this paper evaluates how the way income data is collected affects its accuracy and therefore the measurement of daily mobility inequalities. Simplified data collection tends to minimise the scale of inequalities as it misrepresents the income distribution. The error is greater in remote zones. Shortcomings in the statistical apparatus with regard to income data thus blur our perception of mobility inequalities and impede investigation of the links between daily travel, poverty and social exclusion. |
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| Keywords: | Household travel survey Income data collection Daily mobility Transport expenditure Mobility inequality Sub-Saharan Africa |
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