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Banks and Financial Discrimination: What Can Be Learnt from the Spanish Experience?

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Abstract

This paper analyses the phenomenon of financial discrimination that has been identified in many developed countries in the aftermath of the financial crisis. We would consider the process of worsening quality in the provision of banking products and services as part of the increasing problem of financial exclusion, which should consider not only physical access to branches but also the difficulties of using banking services and products. Our primary concern is focused on the collective of vulnerable customers, so we have carried out an analysis at a micro-scale (urban districts and municipalities) to identify the main determinants of financial discrimination of territories according to their socioeconomic profile. This study constitutes a first attempt to analyse financial discrimination in the provision of banking products and services at an urban micro-scale. We have considered the cases of Madrid and Barcelona in Spain as good references, as these large urban territories have a high level of social inequality. The methodology applied is quantile regressions, a useful technique for analysing the “extreme” nature of the phenomenon of financial discrimination. Our results confirm that the more overloaded branches are located in districts characterized by a lower socioeconomic profile, indicating a banking industry trend towards “low-cost” retail banking to serve the group of less profitable, more vulnerable customers. Some recommendations are outlined for policymakers in line with the aims and scope of the Payment Accounts Directive of the European Union.

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Notes

  1. That is particularly relevant in a competitive context characterized by the entrance of new competitors, very low interest rates, and new capitalisation requirements for banks in Europe.

  2. The territories comprised in the sample are: in the Madrid metropolitan area (i) districts (Centro, Arganzuela, Retiro, Salamanca, Chamartín, Tetuán, Chamberí, Fuencarral-El Pardo, Moncloa, Latina, Carabanchel, Usera, Puente De Vallecas, Moratalaz, Ciudad Lineal, Hortaleza, Villaverde, Villa De Vallecas, Vicálvaro, San Blas-Canillejas, Barajas); (ii) municipalities (Alcalá de Henares, Alcobendas, Alcorcón, Boadilla del Monte, Brunete, Colmenar Viejo, Coslada, Fuenlabrada, Getafe, Leganés, Majadahonda, Mejorada del Campo, Móstoles, Paracuellos de Jarama, Parla, Pinto, Pozuelo de Alarcón, Rivas-Vaciamadrid, Rozas de Madrid, San Fernando de Henares, San Sebastián de los Reyes, Torrejón de Ardoz, Velilla de San Antonio, Villanueva de la Cañada, Villanueva del Pardillo, Villaviciosa de Odón, Tres Cantos. In the Barcelona metropolitan area (i) districts (Ciutat Vella, Eixample, Sants-Montjuic, Les Corts, Sarrià-Sant Gervasi, Gràcia, Horta-Guinardó, Nou Barris, Sant Andreu, Sant Martí); (ii) municipalities (Badalona, Badía del Vallés, Barberá del Vallés, Begues, Castellbisbal, Castelldefels, Cerdanyola del Vallés, Cervelló, Corbera de Llobregat, Cornellá de Llobregat, Esplugues de Llobregat, Gavá, L’Hospitalet de Llobregat, Molins de Rei, Montcada i Reixac, Montgat, Pallejá, La Palma de Cervelló, El Papiol, El Prat de Llobregat, Ripollet, Sant Adriá de Besós, Sant Andreu de la Barca, Sant Boi de Llobregat, Sant Climent de Llobregat, Sant Cugat del Vallés, Sant Feliú de Llobregat, Sant Joan Despí, Sant Just Desvern, Sant Vicenç dels Horts, Santa Coloma de Cervelló, Santa Coloma de Gramanet, Sitges, Tiana, Torrelles de Llobregat, Viladecans.

  3. See for example the variable “Commercial bank branches per 100,000 adults” in countries and regional reports https://data.worldbank.org/.

  4. www.maestre-ediban.com.

  5. To include the factor of ethnic diversity, relevant in other studies, we considered the most numerous national groups of immigrants living in Madrid and Barcelona from different continents: Romania, Ecuador, China, Pakistan, and Morocco. The economic and cultural background of foreign nationalities is associated with their residential segregation (Echazarra 2010).

  6. Details are summarised in Table 4.

  7. 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 et al. (2003).

  8. This argument agrees with behavioural economics: Individual economic action is not guided by rationality, and education has little effect.

  9. Markets in Financial Instruments Directive.

  10. British Bankers’ Association (BBA) and Council of Mortgage Lenders. https://www.cml.org.uk/industry-data/about-postcode-lending.

  11. For example, the IDA (Individual Development Accounts) developed in the USA.

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Acknowledgements

We would like to convey special thanks to Pr. Christophe Muller, who provided us with the code for the test of endogeneity in conditional quantiles. We also thank the two anonymous referees for their helpful comments and suggestions on the work.

Funding

This work was supported by the Fundación de las Cajas de Ahorro (FUNCAS) (no grant number).

We acknowledge the support of PhD. Marien Aguilera, main researcher in the National R + D Project “The Court of Justice of the European Union: its impact on the normative configuration of the Spanish civil process and on the protection of fundamental rights” (Ref. DER 2016-75567-R), funded by the Ministry of Economy and Competitiveness.

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Authors and Affiliations

  1. Department of Economy, Accounting, and Finance, EAE Business School, Príncipe de Vergara 156, 28002, Madrid, Spain

    B. Fernández-Olit

  2. Applied Economics Department of UNED, Economic and Business Faculty, UNED, Senda del Rey n° 11, 28040, Madrid, Spain

    C. Ruza & M. de la Cuesta-González

  3. Department of Applied Economics and Statistics of UNED, Economic and Business Faculty, UNED, Senda del Rey n° 11, 28040, Madrid, Spain

    M. Matilla-Garcia

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  1. B. Fernández-Olit
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  2. C. Ruza
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Correspondence to B. Fernández-Olit.

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Appendix

Appendix

Kim and Muller endogeneity test

Quantile

Endogeneity statistic

P value

0.1

49.864

0.8355

0.25

65.225

0.6867

0.5

0.8741

0.9997

0.75

22.904

0.986

0.9

0.7975

0.9998

Mean model

0.63

0.9999

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Fernández-Olit, B., Ruza, C., de la Cuesta-González, M. et al. Banks and Financial Discrimination: What Can Be Learnt from the Spanish Experience?. J Consum Policy 42, 303–323 (2019). https://doi.org/10.1007/s10603-019-09412-5

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