Abstract
The present paper examines the fundamental relationship between the country-level infrastructure of the retail payment market and overall bank performance. Using data from across 27 European markets over the period 2000–07, the results confirm that the performance of banks in countries with more developed retail payment service markets is better. This relationship is stronger in countries with a relatively high adoption of retail payment transaction technologies. Retail payment transaction technology itself can also improve bank performance, and evidence shows that heterogeneity in retail payment instruments is associated with enhanced bank performance. Similarly, higher usage of electronic retail payment instruments seems to stimulate banking business. We also show that retail payment services have a more significant impact on savings and cooperative bank performance, although they have a positive influence on the performance of commercial banks as well. Additionally, the findings reveal that the impact of retail services on bank performance is more pronounced through fee income, although their impact through interest income is also positive. Finally, an effective payment service market is found to be associated with higher bank stability. Our findings are robust to different regression specifications.
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Notes
For instance, the European banking industry joined forces to achieve a fully integrated market for retail payment services in the euro area: the Single Euro Payments Area (SEPA). The goal is to turn the individual national retail payment markets into one pan-European market. In SEPA, the three most important payment instruments will be harmonised across the euro area. In addition, the handling of cash will be harmonised too. In January 2008 the SEPA credit transfers and the European cards framework were introduced. In November 2009 the SEPA direct debit, a new payment service at the European level, was launched. Overall, SEPA will enable customers to make and receive cashless euro payments throughout the area from/on a single bank account, using a single set of payment instruments.
Radecki (1999) shows that payment-related activities contribute to between one-third and two-fifths of the operating revenue of the top 25 banks in the US. Rice and Stranton (2003) show that payment-related activities contribute up to 16% of the operating revenue of the top 40 bank holding companies in the US.
The other literature includes Heffernan (1993), who uses British data to examine the competitive behaviour of retail banks.
Scholnick et al. (2007) provide a survey of the literature on credit cards, debit cards and ATMs.
The results reported in this paper are based on retail payment services and transaction technology variables scaled by population. The results using variables scaled by GDP are qualitatively the same and available upon request from the authors.
Given a relatively high pair-wise correlation between the number of ATMs and the number of POS terminals, we only control, in our regression, for the number of ATMs. There is no qualitative change in the results when the number of POS terminals is used instead. The latter results are available upon request.
The total value of retail payment transactions is inflation-adjusted to the base year 2000.
Given a relatively high pair-wise correlation between the number of retail payments and the value of retail payments, we only control for the number of retail payments in our regressions. The results do not qualitatively change when the value of retail payments is used instead. The latter results are available upon request.
Our performance measures are mostly based on consolidated data for multinational banks. Unconsolidated data and information on the allocation of banks’ profits across different countries of operation are not available in the Bankscope database.
Z-score = (100 + average ROE) / standard deviation of ROE, where ROE and standard deviation of ROE are expressed in percentages.
A detailed description of the use of SFA to estimate bank efficiency and the estimates of the cost and profit function coefficients are available upon request from the authors.
The loans include loans to municipalities/government, mortgages, HP/leases, other loans, loans to group companies/associates, loans to other corporates, loans to banks and customer loans.
The revenues include interest revenue, commission revenue, fee revenue, trading revenue and other operating revenue.
We report only the results where ROA standard deviations are used as a proxy for risk. Results are equally robust if the variable is replaced by the standard deviation of ROE.
He finds no systematic relationship between branch network size and overall institutional profitability.
Because a higher percentage of paper-based retail payment instruments means more adoption of cheques and is positively correlated with the Herfindahl index of payment instruments, we do not control for the Herfindahl index of payment instruments in the estimation to avoid potential multicollinearity problems.
This finding is also supported by earlier studies. For example, Bolt et al. (2008) provide evidence that given the large resource cost of a country’s payments system, shifting from paper to electronic payments can entail substantial cost savings and social benefits. Similarly, Humphrey et al. (2006) and Humphrey and Vale (2004) report that banks’ average cost has been affected by the ongoing shift from expensive paper-based payment instruments to lower-cost electronic payment substitutes.
The results are not reported in this paper and are available upon request from the authors.
Čihák and Hesse (2010) investigate Islamic banks’ financial stability. Berger et al. (2009) examine the effects of bank competition and financial stability. Behr et al. (2010) test how capital regulation affects the risk-taking behaviour of commercial banks. Sinkey and Greenawalt (1991) investigate how loan-loss experience influences risk-taking behaviour at large commercial banks. Leonard and Biswas (1998) examine how regulatory changes affect the risk-taking behaviour of state-chartered savings banks.
Z-score = (100 + average ROE)/standard deviation of ROE, where ROE and standard deviation of ROE are expressed in percentages.
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The authors thank the editor Haluk Ünal and two anonymous referees. Song thanks the European Central Bank for support as part of this paper was written when he was a visiting scholar at the ECB. Hasan thanks Rensselaer Polytechnic Institute as the paper was written when he was a professor at Rensselaer. The views expressed in this paper do not necessarily reflect those of the Bank of Finland and the European Central Bank.
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Hasan, I., Schmiedel, H. & Song, L. Returns to Retail Banking and Payments. J Financ Serv Res 41, 163–195 (2012). https://doi.org/10.1007/s10693-011-0114-y
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DOI: https://doi.org/10.1007/s10693-011-0114-y