Abstract
The aim of this chapter is to analyse financial exclusion far beyond the classical approach of branch disappearance by including a new dimension: use difficulties. Our main hypothesis states that branch closures have been inconsistent and depended on the vulnerability of particular communities. We empirically analyse the main drivers of branch abandonment (physical access) and branch saturation (difficulties of use) in the city of Madrid and the surrounding municipalities and we find that the main socio-economic determinants of an area’s vulnerability appear statistically significant. We apply quantile regressions to better capture the more extreme cases of financial exclusion and we test our hypothesis at municipality and district levels which adds new evidence to previous studies. We conclude by discussing the main challenges of financial exclusion.
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Notes
- 1.
Data of access to account with payment facilities, in Anderloni and Carluccio (2007).
- 2.
That is especially relevant in a competitive context (with new competitors in the banking supply chain) of very low interest rates and with new capitalization requirements for banks in Europe.
- 3.
Access difficulties due to physical distance to the nearest branch have exacerbated other psychological barriers for people living in deprived areas (Carbó et al. 2005).
- 4.
- 5.
In order to include the factor of ethnic diversity, relevant in other studies, we considered the most numerous national groups of immigrants living in Madrid and coming from different continents: Romania, Ecuador, China and Morocco. The economic and cultural background of foreign nationalities is associated with their residential segregation in the metropolitan area of Madrid (Echazarra 2010).
- 6.
Single-parent households have a higher risk of facing economic precariousness and social exclusion.
- 7.
- 8.
Alama and Tortosa-Ausina (2012) show evidence in empirical studies that demonstrates that the tails of the distribution of the response variable can differ markedly.
- 9.
When median regression and mean regression coefficients are not equal, the distribution is asymmetric.
- 10.
Other authors propose splitting the sample into quantiles and carrying out OLS estimations separately. However, this procedure suffers from selection bias as demonstrated by Hallock, Madalozzo and Reck (2003).
- 11.
We should bear in mind that quantile θ = 0.5 refers to the median while OLS regression reports regression estimates based on the mean.
- 12.
People above 65 years of age have suffered the effects of social exclusion to a lesser extent during the last crises. This collective shows higher levels of total integration (Fundacion FOESSA 2014).
- 13.
Only for the upper quantile = 0.95.
- 14.
Even though we have tried different and simplified model specifications (including fewer predictors), results do not improve in terms of goodness-of-fit or variable significance. This is consistent with theories that define financial exclusion as a problem with multidimensional causes.
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de la Cuesta González, M., Paz-Curbera, C.R., Olit, B.F. (2016). Banking System and Financial Exclusion: Towards a More Comprehensive Approach. In: Carbó Valverde, S., Cuadros Solas, P., Rodríguez Fernández, F. (eds) Liquidity Risk, Efficiency and New Bank Business Models . Palgrave Macmillan Studies in Banking and Financial Institutions. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-30819-7_6
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