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


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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  1. For example, home collected loans attract APR’s in excess of 1500%. The website 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. was accessed on 8 September 2015.

  7. To Your Credit.


  • Arnold, G. (2008). Corporate Financial Management (4th ed.). Harlow: Financial Times Prentice Hall.

    Google Scholar 

  • Beck, T., & De La Torre, A. (2007). The basic analytics of access to financial services. Financial Markets, Institutions Instruments, 16, 79–117.

    Article  Google Scholar 

  • Bucy, E. P. (2000). Social access to the internet. The Harvard International Journal of Press Politics, 5, 50–61.

    Article  Google Scholar 

  • Bunyan, S., & Collins, A. (2013). Digital exclusion despite digital accessibility: Empirical evidence from an English city. Tijdschrift voor Economische en Sociale Geografie, 104, 588–603.

  • Byrne, N., McCarthy, O., & Ward, M. (2007). Money-lending and financial exclusion. Public Money and Management, 27, 45–52.

    Article  Google Scholar 

  • Carbo, S., Gardener, E. P. M., & Molyneux, P. (2007). Financial exclusion in Europe. Public Money and Management, 27, 21–27.

    Article  Google Scholar 

  • Collard, S., Kempson, E., & Whyley, C. (2001). Tackling financial exclusion. Policy. The Policy Press and the Joseph Rowantree Foundation.

  • Department for Communities and Local Government. (2015). English housing survey: Households 2013-2014. London: Department for Communities and Local Government.

  • Devlin, J. F. (2005). A detailed study of financial exclusion in the UK. Journal of Consumer Policy, 28, 75–108.

    Article  Google Scholar 

  • Devlin, J. F. (2009). An analysis of influences on total financial exclusion. The Service Industries Journal, 29, 1021–1036.

    Article  Google Scholar 

  • Dutton, W.H., Helsper, E.J., & Gerber, M.M. (2009). The internet in Britain: 2009. Oxford Internet Institute: University of Oxford.

  • Ergungor, O. E. (2010). Bank branch presence and access to credit in low‐ to moderate‐income neighbourhoods. Journal of Money, Credit, and Banking, 42, 1321–1349.

    Article  Google Scholar 

  • EC. (2008). Financial services provision and prevention of financial exclusion. Brussels: European Commission.

  • FDIC. (2014). 2013 FDIC National Survey of Unbanked and Underbanked Households. Federal Deposit Insurance Corporation.

  • Financial Inclusion Commission. (2015). Financial Inclusion. Improving the health of the nation (March 2015). UK: Financial Inclusion Commission.

    Google Scholar 

  • Financial Inclusion Taskforce. (2010). Banking services and poorer households. London: HM Treasury.

    Google Scholar 

  • Fuller, D. (1998). Credit union development: Financial inclusion and exclusion. Geoforum, 29, 145–157.

  • Honohan, P. (2008). Cross-country variation in household access to financial services. Journal of Banking & Finance, 32, 2493–2500.

    Article  Google Scholar 

  • Huston, S. J. (2010). Measuring financial literacy. Journal of Consumer Affairs, 44, 296–316.

    Article  Google Scholar 

  • Kellstedt, P. M., Zahran, S., & Vedlitz, A. (2008). Personal efficacy, the information environment, and attitudes toward global warming and climate change in the United States. Risk Analysis, 28, 113–126.

    Article  Google Scholar 

  • Kempson, E., & Whyley, C. (1999). Kept in or opted out? Understanding and combating financial exclusion. Bristol: Policy Press.

    Google Scholar 

  • Kempson, E., Whyley, C., Caskey, J., & Collard, S. (2000). In or out? Financial exclusion: A literature and research review, Consumer Research Paper 3. London: Financial Services Authority.

    Google Scholar 

  • Leyshon, A., & Thrift, N. (1993). The restructuring of the UK financial services industry in the 1990s: A reversal of fortune? Journal of Rural Studies, 9, 223–241.

  • Leyshon, A., & Thrift, N. (1995). Geographies of financial exclusion: Financial abandonment in Britain and the United States. Transactions of the Institute of British Geographers, 20, 312–341.

  • Leyshon, A., Signoretta, P., Knights, D., Alferoff, C., & Burton, D. (2006). Walking with moneylenders: The ecology of the UK home-collected credit industry. Urban Studies, 43, 161–186.

  • Leyshon, A., French, S., & Signoretta, P. (2008). Financial exclusion and the geography of bank and building society branch closure in Britain. Transactions of the Institute of British Geographers, 33, 447–465.

    Article  Google Scholar 

  • Marshall, J. N. (2004). Financial institutions in disadvantaged areas: A comparative analysis of policies encouraging financial inclusion in Britain and the United States. Environment and Planning A, 36, 241–262.

  • McKillop, D., & Wilson, J. (2007). Financial exclusion. Public Money and Management, 27, 9–12.

    Article  Google Scholar 

  • McQuaid, R., & Edgell, V. (2010). Financial Capability: Evidence Review. Edinburgh: Edinburgh Napier University.

    Google Scholar 

  • Mitton, L. (2008). Financial inclusion in the UK: Review of policy and practice. York: Joseph Rowntree Foundation.

    Google Scholar 

  • O'Connor, R. E., Bord, R. J., & Fisher, A. (1999). Risk perceptions, general environmental beliefs, and willingness to address climate change. Risk Analysis, 19, 461–471.

    Google Scholar 

  • O'Donnell, N., & Keeney, M. (2010). Financial capability in Ireland and a comparison with the UK. Public Money & Management, 30, 355–362.

    Article  Google Scholar 

  • Ofcom. (2015). The Communications Market Report (August 2015). UK: Ofcom.

    Google Scholar 

  • ONS. (2012). Regional profiles: Key statistics – south east, august 2012. London: ONS.

  • Office for National Statistics. (2014). Trends in the United Kingdom housing market, 2014. London: Office for National Statistics.

  • OFT. (1999). Vulnerable consumers and financial services (p. OFT255). London: Office of Fair Trading.

  • Portsmouth City Council. (2010). Portsmouth Population Profile: A Profile of Portsmouth’s Population using Output Area Classification. Portsmouth: Portsmouth City Council.

    Google Scholar 

  • Portsmouth City Council. (2012). The Portsmouth Plan: Portsmouth Core Strategy. Portsmouth: Portsmouth City Council.

    Google Scholar 

  • Rowlingson, K., & McKay, S. (2014). Financial Inclusion: Annual monitoring report 2014. Birmingham: University of Birmingham.

    Google Scholar 

  • Rubin, D. B. (1977). Formalizing subjective notions about the effect of nonrespondents in sample surveys. Journal of the American Statistical Association, 72, 538–543.

    Article  Google Scholar 

  • Smith, R. J., & Blundell, W. (1986). An exogeneity test for a simultaneous equation Tobit model with an application to labor supply. Econometrica, 54, 679–685.

    Article  Google Scholar 

  • Social Exclusion Unit. (2001). National strategy for neighbourhood renewal policy action team audit. London: Cabinet Office.

  • Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (5th ed.). Boston, MA: Pearson Education.

    Google Scholar 

  • Treasury, H. M. (2007a). Financial Inclusion: The way forward. London: H.M. Treasury.

  • Treasury, H. M. (2007b). Financial Inclusion: An action plan for 2008-11. London: H.M. Treasury.

  • Vickers, D., & Rees, D. (2007). Creating the UK National Statistics 2001 output area classification. Journal of the Royal Statistical Society, Series A, 170, 379–403.

    Article  Google Scholar 

  • Wallace, A., & Quilgars, D. (2005). Homelessness and Financial Exclusion: A Literature Review. University of York: Centre for Housing Policy.

  • Winchester, N. (2009). Social housing and digital exclusion. London: National Housing Federation.

    Google Scholar 

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Correspondence to Sabrina Bunyan.



Table 5 Variance inflation factor (VIF)
Table 6 Estimation with and without district clustering
Table 7 Logistic results to identify the interaction between income and selected variables
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).

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  • Financial exclusion
  • Self-exclusion
  • Household data