Research
The applicability of measures of socioeconomic position to different ethnic groups within the UK
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* Corresponding author: Margaret Kelaher mkelaher@unimelb.edu.au
1 Centre for Health Policy, Programs and Economics, School of Population Health, University of Melbourne, 207 Bouverie St, Carlton Vic 3010, Australia
2 UCL Centre for International Health and Development, Institute of Child Health, 30 Guilford St, London, WC1N 1EH, UK
3 Department of Social Medicine, University of Bristol, Canynge Hall, Whiteladies Road, Bristol, BS8 2PR, UK
4 Social Policy Research Centre, Middlesex University, Trent Park, Bramley Road, London, N14 4YZ, UK
International Journal for Equity in Health 2009, 8:4 doi:10.1186/1475-9276-8-4
Published: 27 February 2009Abstract
Background
In this paper we seek to tease out differences in socioeconomic position between ethnic groups. There are 3 main reasons why conventional socioeconomic indicators and asset based measures may not be equally applicable to all ethnic groups:
1) Differences in response rate to conventional socioeconomic indicators
2) Cultural and social differences in economic priorities/opportunities
3) Differences in housing quality, assets and debt within socioeconomic strata
Methods
The sample consisted of White (n = 227), African-Caribbean (n = 213) and Indian and Pakistani (n = 233) adults aged between 18 and 59 years living in Leeds as measured in a stratified population survey. Measures included income, education, employment, car ownership, home ownership, housing quality, household assets, investments, debt, perceived ability to obtain various sums and perceived level of financial support given and received.
Results
Response rates to education and income questions were similar for the different ethnic groups. Overall response rates for income were much lower than those for education and biased towards wealthier people. There were differences between ethnic groups in economic priorities/opportunities particularly in relation to car ownership, home ownership, investment and debt. Differences in living conditions, household assets and debt between ethnic groups were dependent on differences in education; however differences in car ownership, home ownership, ability to obtain £10 000, and loaning money to family/friends and income from employment/self employment persisted after adjustment for education.
Conclusion
In the UK, education appears to be an effective variable for measuring variation in SEP across ethnic groups but the ability to account for SEP differences may be improved by the addition of car and home ownership, ability to obtain £10 000, loaning money to family/friends and income from employment/self employment. Further research is required to establish the degree to which results of this study are generalisable.