Bank competition, regulation, and efficiency: evidence from the Asia-Pacific region

ABSTRACT

This study investigates the impact of bank competition and regulation on bank efficiency in the Asia-Pacific region during 2001–2016. The result reveals that market power is positively related to bank efficiency. We also find that stringent activity restrictions, strong official supervisory power, and low capital requirements are associated with high bank efficiency. Furthermore, market power has a stronger efficiency-increasing effect in a banking system characterized by the activity restrictions, supervisory power, and capital requirements described above. Foreign banks operating under increased activity restrictions in a host country with strong official supervisory power have relatively high efficiency.

KEYWORDS:

• Bank competition
• regulation
• profit and cost efficiency
• Asia-Pacific region

Disclosure statement

No potential conflict of interest.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1. Previous studies examine the national banking systems in the Asia-Pacific region from different perspectives. Soedarmono, Machrouh, and Tarazi (2011) and Liu, Molyneux, and Nguyen (2012) analyze the competition and financial stability relationship based on a sample of 12 Asian and 4 South East Asian countries. Fu, Lin, and Molyneux (2014) consider the tradeoff between competition and financial stability based on 14 Asia-Pacific countries, whereas Noman, Gee, and Isa (2018) examine the impact of bank regulation on the relationship between bank competition and financial stability based on five South East Asian countries.

2. See Barth, Caprio, and Levine (2004) for the definitions and measurement of bank regulation variables.

3. See the detailed explanation of the SFA model in Battese and Coelli (1995).

4. Based on earlier studies, we define the output price $P_{it}$ as the ratio of total revenues to total assets. See also Koetter, Kolari, and Spierdijk (2012) and Fu, Lin, and Molyneux (2014).

5. The Z-score is calculated as follows: Z-score = ($RO{A}_{it}+ET{A}_{it}$)/$sd{\left(ROA\right)}_{it}$, where $RO{A}_{it}$ is defined as return on assets for bank i at year t, $ET{A}_{it}$ is the ratio of equity to assets, and $sd\left(\mathrm{R}\mathrm{O}\mathrm{A}\right)$ indicates the standard deviation of $\mathrm{R}\mathrm{O}\mathrm{A}$. We employ a three-year rolling window to calculate $sd\left(\mathrm{R}\mathrm{O}\mathrm{A}\right)$ instead of the whole sample period, allowing for the time variation of $sd\left(\mathrm{R}\mathrm{O}\mathrm{A}\right)$. See Demirgüç-Kunt and Huizinga (2010) for a detailed discussion.

6. The Financial openness index is constructed according to information on four categories: (1) the presence of multiple exchange rates, (2) restrictions on current account transactions, (3) restrictions on capital account transactions, and (4) the requirement to surrender export proceeds. Information on each category is provided in the Annual Report on Exchange Arrangements and Exchange Restrictions, published by the International Monetary Fund (IMF); a category is assigned 1 if the restriction does not exist and 0 otherwise.

7. For detailed information about the index of economic freedom, defined by the Heritage Foundation, see https://www.heritage.org/index/about.

8. As the information of financial openness for 2016 is not available, we use interpolation and extrapolation techniques, following Goetz, Laeven, and Levine (2016), to fill in the missing observation of financial openness of 2016 for each country.

9. Information on bank regulation is provided by Barth,  Caprio, and  Levine (2013), but the surveys were conducted in 1999, 2002, 2006, and 2011. Following Anginer, Demirguc-Kunt, and Zhu (2014), we employ the previous survey data until the new survey data become available for matching the bank regulation and supervision variables with bank-specific variables and country control variables. Surveys were conducted in 1999 for the years 1998 to 2001, in 2002 for the years 2002 to 2005, in 2006 for the years 2006 to 2010, and in 2011 for the years 2011 to 2016.

10. The positive coefficient of Bank concentration is not contradictory to the main conclusion that increased market power (measured by Lerner index) is associated with higher bank efficiency, as the degree of concentration is not necessarily related to the degree of competition, and competition and concentration can coexist (Mamatzakis, Staikouras, and Koutsomanoli-Fillipaki 2005). See Claessens and Laeven (2004), Beck, Demirgüç-Kunt, and Levine (2006), and Olivero, Li, and Jeon (2011) for the detail discussion.

11. We also include the dummy variable Foreign dummy in the regression, but it is dropped from the model owing to collinearity.

12. The instrumental variables are statistically significant in the first-stage analysis. In the first-stage regression, the Lerner index is negatively related to Restructuring power and Financial conglomerate restrictiveness at 1%. In addition, we employ different tests for weak identification and under-identification, such as the Kleibergen–Paap rk LM and Wald F statistic (Kleibergen and Paap 2006) and the Cragg–Donald Wald F statistic (Hall and Peixe 2000), and confirm that the instruments are valid.

13. The results of cost inefficiency under the country fixed effects estimation and IV regression are consistent with our main findings and are available upon request.

14. The results of cost inefficiency for commercial banks and subsample of countries with more than 200 observations have the similar findings and are available upon request.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [71801040; 61773199; 71732002]; Key Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province [2019SJZDA024]; Fundamental Research Funds for the Central Universities [2242021S30013]