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Analysing Household and Intra-urban Variants in the Consumption of Financial Services: Uncovering “Exclusion” in an English City

Journal of Consumer Policy volume 39, pages 199–221 (2016)Cite this article

Abstract

This study provides an empirical assessment of the socioeconomic factors that determine household exclusion from consumer financial services. A unique microeconomic data set, of interview data, collected from a representative cross-sectional sample of 1005 households is analysed using logistic regression techniques. In investigating exclusion from consumer financial services, both financial self-exclusion and institutional-led financial exclusion are examined. Indicators of financial self-exclusion include the absence of a savings account or home contents insurance, whilst indicators of institutional-led financial exclusion include the use of “doorstep lenders.” Findings show that both measures of financial self-exclusion are determined by income, education, age, housing tenure, and social participation, whilst financial exclusion is generally associated with socioeconomic characteristics such as age, gender, housing tenure, working status, income, disability, and the presence of young people in household but not with respondents’ residential area, education level, internet use, and social participation. These results offer useful insights to policy makers and financial services providers in terms of the range and mix of policies and instruments that local and central Government can deploy to address exclusion.

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Notes

  1. For example, home collected loans attract APR’s in excess of 1500%. The website lenderscompared.org.uk quote an APR of 1834.8% on a £100 loan over 15 weeks. Quote obtained on 24 September 2012.

  2. Rubin (1977) proposes using the method of multiple imputations to calculate missing income observations. “This method produces a subjective probability interval for the statistic that would have been calculated if all non-respondents had responded. Background information which is recorded for both respondents and non-respondents plays an important role in sharpening the subjective interval…The general idea can be applied to any problem with non-respondents or missing data” (Rubin 1977, p.538). This method is considered the most reputable method to deal with missing data (Tabachnick and Fidell 2001).

  3. Using age categories reported in Table 2.

  4. For the purpose of the estimation, the variable income has been re-grouped into three main categories (0–200, 200–500, and 500+).

  5. 2007.

  6. http://www.financialinclusioncommission.org.uk/history.This was accessed on 8 September 2015.

  7. To Your Credit. http://www.toyourcredit.org.uk/#toyourcredit.

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Authors and Affiliations

  1. Department of Accounting, Finance and Economics, Ulster University, Jordanstown campus, Shore Road, Newtownabbey, Co Antrim, BT37 0QB, UK

    Sabrina Bunyan

  2. Economics and Finance, Portsmouth Business School, University of Portsmouth, Richmond Building, Portland Street, Portsmouth, PO1 3DE, UK

    Alan Collins & Gianpiero Torrisi

Authors
  1. Sabrina Bunyan
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  2. Alan Collins
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  3. Gianpiero Torrisi
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Corresponding author

Correspondence to Sabrina Bunyan.

Appendix

Appendix

Table 5 Variance inflation factor (VIF)
Full size table
Table 6 Estimation with and without district clustering
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Table 7 Logistic results to identify the interaction between income and selected variables
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Table 8 Smith and Blundell (1986) test of exogeneity of tenure and income
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Bunyan, S., Collins, A. & Torrisi, G. Analysing Household and Intra-urban Variants in the Consumption of Financial Services: Uncovering “Exclusion” in an English City. J Consum Policy 39, 199–221 (2016). https://doi.org/10.1007/s10603-016-9319-7

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  • DOI: https://doi.org/10.1007/s10603-016-9319-7

Keywords

  • Financial exclusion
  • Self-exclusion
  • Household data