Does foreign bank penetration affect the risk of domestic banks? Evidence from emerging economies

Accepted Manuscript Title: Does foreign bank penetration affect the risk of domestic banks? Evidence from emerging economies Authors: Ji Wu, Minghua Chen, Bang Nam Jeon, Rui Wang PII: DOI: Reference:

S1572-3089(17)30420-5 http://dx.doi.org/doi:10.1016/j.jfs.2017.06.004 JFS 554

To appear in:

Journal of Financial Stability

Received date: Revised date: Accepted date:

28-2-2017 13-6-2017 13-6-2017

Please cite this article as: Wu, Ji, Chen, Minghua, Jeon, Bang Nam, Wang, Rui, Does foreign bank penetration affect the risk of domestic banks? Evidence from emerging economies.Journal of Financial Stability http://dx.doi.org/10.1016/j.jfs.2017.06.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Does foreign bank penetration affect the risk of domestic banks? Evidence from emerging economies Ji Wu Research Institute of Economics and Management Collaborative Innovation Center of Financial Security Southwestern University of Finance and Economics Chengdu, China

Minghua Chen Research Institute of Economics and Management Southwestern University of Finance and Economics Chengdu, China

Bang Nam Jeon School of Economics, LeBow College of Business Drexel University, PA, USA

Rui Wang Research Institute of Economics and Management Southwestern University of Finance and Economics Chengdu, China



Corresponding author: School of Economics, Bennett S. LeBow College of Business, Drexel University, 33 rd and Market Streets, Philadelphia, PA 19104, USA. Email addresses: [email protected] (J. Wu), [email protected] (M. Chen), [email protected] (B. N. Jeon), [email protected] (R. Wang). 1

Highlights (revised)  We examine the impact of foreign bank penetration on the risk of domestic banks.  Domestic banks’ risk increases amid foreign bank presence in emerging markets.  The impact is stronger for banks with inefficiency and more diversified income

.

 Foreign banks exert stronger impacts when they enter via M&A with internal support.

Abstract We investigate whether foreign bank penetration affects the risk of domestic banks in emerging economies. By using bank-level data from 35 markets during the period of 2000-2014, we find significant evidence that the risk of domestic banks increases with the presence of foreign banks in the host economy, and this finding is shown to be consistent in a series of robustness tests. We also find that the incidence of such effects is more pronounced for domestic banks which are less efficient and less based on traditional activities. Foreign banks exert more pronounced impacts on domestic banks’ risk when they enter the host market via M&A, as opposed to greenfield investments, and when they belong to foreign conglomerates which provide strong internal support. Keywords: Foreign bank penetration, bank risk, emerging economies JEL classification: G21; G15; F36; E44 This version: June 2017 1. Introduction With the process of financial deregulation and liberalization, many emerging and developing countries witnessed a significant restructuring of their banking sector since the 1990s, which was characterized by a considerably higher presence of foreign banks. Claessens and Van Horen (2014) document that the number of foreign banks increased by 74% and their market share approximately doubled in emerging countries during the period of 1995-2009. Considering the economic importance and size of the banking industry in host countries, foreign bank penetration is notably more salient in regions such as Central and Eastern Europe, Latin America and Asia.1 Appendix A and B provide a summary on the extent to which foreign banks are present in selected emerging markets in the above three regions, respectively, measured by the assets (number) of foreign banks as a share of the total bank assets (number) in the host banking sector.2 1

Foreign banks are also prominent in the banking markets of Central Asia and Sub-Saharan Africa (Claessens and Van Horen, 2014). 2 There are significant heterogeneities across the three regions in terms of foreign bank penetration. Figures 1 and 2 depict the average levels of foreign bank penetration in all selected emerging economies, and those in Central and Eastern Europe, Latin America and Asia, respectively. Central and 2

Extant research is yet to reach a consensus regarding the role played by foreign banks. The entry of foreign banks may benefit host markets by stabilizing credit quantity during domestic financial turbulences, spurring sophistication of domestic financial regulations and fostering the overall efficiency of financial intermediaries (De Haas and Van Lelyveld, 2006; Goldberg, 2007; Lehner and Schnitzer, 2008; Kouretas and Tsoumas, 2016). The cautionary view, however, warns that foreign banks can be instead a new source of instability by transmitting external shocks, weakening the potency of domestic monetary policy, and curtailing their credit more greatly when there is an adverse shock in their home country (Goldberg, 2001; Clarke et al., 2003; Arena et al., 2006; Wu et al., 2011; De Haas and Van Horen, 2012). Despite a rich wealth of literature studying the economic impacts of foreign bank penetration on host markets, the question of whether and how foreign bank presence affects domestic banks’ risk, in particular, has been explored much less, and is still ambiguous theoretically and empirically. In this paper, we investigate this issue, and present consistent and robust empirical evidence that the risk of domestic banks increases with the presence of foreign banks. Moreover, we explore conditional factors affecting the heterogeneity of this nexus. We examine first what types of domestic banks are affected more by foreign bank penetration, and find that the impact across banks varies with domestic banks’ size, efficiency and business diversification. We next investigate what patterns of foreign bank presence exert more pronounced effects, i.e., whether different entry modes of foreign banks and the strength of intragroup internal capital markets matter for the impact of foreign bank penetration on the risk of domestic banks. Our paper differs from existing work in several ways. First, many papers discuss the effect of foreign bank penetration on host financial stability from the perspective of credit quantity, but not credit quality. Foreign banks are usually suggested as a stabilizing (destabilizing) force when the amount of their lending is less (more) volatile than that of domestic banks (Martinez Peria et al., 2002; De Haas and Van Lelyveld, 2006; De Haas and Van Horen, 2012). However, we examine the stability role played by foreign banks as their impact on credit quality and risk. We find that, after having controlled for other potential determinants, domestic banks’ risk increases when foreign bank penetration increases,

Eastern Europe is characterized by the highest foreign bank presence on average. More than 70% of total bank assets are possessed by foreign banks and nearly 60% of banks are foreign owned as of 2014 in the region. The average foreign penetration ratio in Latin American countries lies at 36%-47% (47%-53%) in terms of bank assets (bank number). In comparison, foreign banks have only a modest presence in Asian markets, where the average penetration level is 24% (40%) in terms of total bank assets (bank number) at the maximum. The level of foreign presence also varies over years. Consistent with the observation of Claessens and Van Horen (2014), the average market share of foreign banks rose steadily during the period of 2000-2008, but declined in the wake of global financial crisis, driven by the contraction of foreign banks in Central and Eastern Europe and Latin America. 3

implying a deterioration of the stability of the domestic bank sector. Our result suggests a potential tradeoff between the credit quantity of foreign banks and the credit quality of domestic banks, which can be particularly important for host financial authorities to fully predict the outcomes of banking sector openness. Second, when addressing the impact of foreign penetration on the domestic banking sector, a conventional practice in earlier work is to study the performance of foreign banks relative to domestic ones by distinguishing these two groups of banks, usually using a foreign/domestic dummy (for example, Claessens and Van Horen (2012)). The behaviors of these two groups of banks are implicitly assumed to be independent. In our paper, under a different presumption, we focus on examining the variation of domestic banks’ risk and identifying the impact attributable to the penetration of foreign banks. We believe this analysis has a complementary value to the extant literature by helping explore whether there is a connection between the market participation of foreign banks and the risk of domestic banks. Meanwhile, some conditional factors that may affect the heterogeneity of the “foreign bank penetration-domestic bank risk” association, which are under-studied so far in prior literature, are also investigated in this research. Third, our paper also contributes to a growing strand of research on the impact of financial liberalization on the risk of financial institutions. A number of works have warned of some negative effects of financial reforms and capital account openness, from the perspective of either bank risk or profit efficiency (Demirgüç-Kunt and Detragiache, 1998; Cubillas and González, 2014; Luo et al., 2016). As a part of financial liberalization, allowing the entry of multinational banks into host markets is also found to be associated with some undesired effects in this paper. We discuss useful policy implications in this regard from the major findings of our paper. The rest of the paper is organized as follows. Section 2 reviews related literature. Section 3 introduces the data and the construction of the main variables, followed by the description of our econometric model in Section 4. Section 5 reports the estimation results of the baseline model and discusses various robustness tests. In Section 6, we examine the differential effects of foreign bank penetration on the risk of domestic banks due to different characteristics of domestic banks. Section 7 reports our findings on the heterogeneous impacts associated with the different patterns of foreign bank penetration. Section 8 concludes. 2. Literature review How foreign bank presence affects domestic banks’ performance remains a question that is only partially answered. Extant work mostly centers on aspects such as banks’ profit, net interest spreads, operational costs, efficiency and credit growth (Claessens et al., 2001; Unite and Sullivan, 2003; Martinez Peria and Mody, 2004; Gormley, 2010 among others). For 4

example, Claessens et al. (2001) find that an increased foreign bank presence is associated with lower profitability, non-interest income, and overhead expenses of domestic banks. Gormley (2010) documents a market-wide reduction on the loan volume of domestic banks after the entry of foreign banks. Nevertheless, whether and how foreign bank presence affects domestic banks’ risk has not been deeply explored yet. Some research suggests that the entry of foreign banks may reduce domestic banks’ risk by generating a spillover of know-how and expertise from foreign banks to domestic ones, diversifying domestic banks’ product portfolio, or stimulating domestic banks to increase investment on modern technology and human capital and thus improve their efficiency in the long-run (Levine, 2001; Lensink and Hermes, 2004; Goldberg, 2007). Were these favorable impacts predominant, the soundness of domestic banks is expected to be bolstered. However, in the literature, several competing forces have been identified that likely offset the above beneficial impacts of foreign penetration and cause the risk of domestic banks to increase. First, domestic banks may be adversely affected by the shift of customers after the entry of foreign banks. On one side, foreign banks may focus their credit and other financial services on informationally transparent clients and crowd their domestic counterparts out of this market niche (“cherry-picking”), leaving only opaque firms to the latter (Dell'Ariccia and Marquez, 2004; Sengupta, 2007). If opaque firms are characterized by lower creditworthiness, and domestic banks’ advantage on “soft information” cannot sufficiently shield themselves from borrowers’ defaults, the asset quality of domestic banks would consequently deteriorate.3 On the other side, depositors may transfer their savings out of domestic banks and into foreign banks because of the latter’s superior service and international reputation (“flight-to-quality”), causing domestic banks to incur higher costs to either attract more deposits or substitute deposits with other sources of funding. Corresponding to higher costs of liabilities, domestic banks increase their lending rates, which may trigger the problem of adverse selection (Mian, 2003). Due to pressure from both sides, domestic banks’ fragility may increase with the expansion of foreign banks. This effect is probably more profound in less developed countries, because of the limited flexibility of domestic banks to adjust their portfolio and then diversify risk, compared to the banks in developed countries (Hermes and Lensink, 2001). Second, competition may increase as foreign banks establish their business in host markets (Claessens and Laeven, 2004; Jeon et al., 2011). Traditional theory posits that higher franchise value would limit banks’ incentive to take excessive risk (Keeley, 1990; Demsetz et al., 1996). However, if foreign bank entry is associated with higher market competition, it can 3

Beck et al. (2014) document that foreign banks are never limited to transactions lending to transparent clients, but develop their own relationship lending to opaque firms, which may squeeze the market for domestic banks, reduce their earnings and increase their risk. 5

reduce banks’ franchise value due to lower profitability, and thus its constraint on banks’ risky bets tends to be weakened (Claessens and Lee, 2003; Jiménez et al., 2013).4 As suggested by the “competition-fragility” hypothesis (Beck et al., 2006; Berger et al., 2009), more intensive competition leads to lower net interest margin, eroding the major source of bank profit and thus inducing more risky behaviors to “search for yield”. Dell'Ariccia and Marquez (2006) argue that increased competition from foreign bank entry induces existing domestic banks to relax screening of loan applications to retain their market share and thereby worsens the quality of their asset portfolio.5 Third, to secure their market shares, domestic banks may follow their foreign competitors in providing new services (Xu, 2011), which may increase their operational costs and lead to higher risk if the foreign banks own comparative advantage on these services. Analogously, foreign bank entry can compel domestic banks to increase investment into cutting-edge technology and employee training. However, the increased outlay is immediately translated into higher costs but the gains may take some time to emerge. Subsequent losses are, therefore, likely at least in the short-run, even though the supply of new services, together with the investment on modern technology and employee training, may increase domestic banks’ efficiency and strengthen their stability in the long-run. Existing literature provides very limited empirical results on the impact of foreign bank penetration on domestic banks’ risk. Unite and Sullivan (2003) find that the expansion of foreign banks leads to an increase of loan loss provision by domestic banks, while this association is found insignificant by Claessens et al. (2001). Using non-performing loan ratio as the indicator of loan quality, Barajas et al. (2000) and Degryse et al. (2012) find evidence that foreign bank entry undermines the soundness of domestic banks. However, Angkinand and Wihlborg (2010) and Agoraki et al. (2011) document only mixed results that a higher foreign bank presence is associated with either a higher or lower risk of the entire banking sector. Amid competing theories and limited empirical evidence, we aim to fill the gap in the literature by investigating the impact of foreign bank presence on the risk of domestic banks in emerging economies, in particular. 3. Data and variables We build an unbalanced bank-level panel with annual data from 35 emerging 4

Some works, for example Martynova et al. (2014), suggest that the relationship between franchise value and bank risk-taking needs to be further explored. High franchise value allows banks to borrow more, thus higher leverage may offset the lower incentives of risk-taking. The “competition-stability” view argues that competition may strengthen banks’ stability since more intense competition would reduce the market interest rates and thus lower borrowers’ probability to default (Boyd and De Nicoló, 2005). If this effect outweighs the “competition-fragility” impact, ceteris paribus, domestic banks are expected to be associated with lower risk when there is an increase in foreign bank participation. 5

6

economies located in Central and Eastern Europe, Latin America and Asia during the period of 2000-2014.6 Only commercial banks are included in our sample to minimize any possible bias due to the different nature and business scope among banks that have different objectives and conduct businesses in different specializations. In order to avoid selection bias, we include not only existing banks, but also those that have ceased business operations. We collect the data used to measure banks’ risk and individual banks’ characteristics from Bureau van Dijk’s Bankscope database.7 3.1. Bank risk Three Z-score based indicators are employed to proxy the risk of domestic banks. Using multiple measures not only exploits more dimensions of banks’ financial soundness, but also strengthens the robustness of our finding. Meanwhile, using the Z-score based indicators also differentiates our paper from some earlier ones that adopt the ratio of loan loss provisions or the non-performing loan ratio as the measure of bank risk. As commonly used in extant literature (for example, Laeven and Levine (2009), Demirgüç-Kunt and Huizinga (2010) and many others), our primary indicator of the risk of domestic banks is the time-varying Z-score, which is formally expressed as: Z ijt 

R O A ijt  E A ijt

 ( R O A )ijt

(1)

where ROAijt denotes the return on assets of bank i in country j in year t, EAijt is the ratio of equity over total assets, and σ(ROA)ijt is the standard deviation of return on assets. We follow Schaeck and Cihák (2010) by using a three-consecutive-year rolling window to calculate σ(ROA)ijt, rather than the full sample period. 8 Interpreted as the number of standard deviations by which returns must decrease to wipe out all equity owned by the bank (Roy, 1952), the Z-score can be viewed as the inverse of the probability of bank failure. A higher value of the Z-score suggests a higher financial stability of the bank, or put differently, a

6

Specifically, the selected emerging markets include: Albania, Belarus, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Moldova, Macedonia, Poland, Romania, Slovakia, Slovenia, Ukraine (Central and Eastern Europe); Argentina, Bolivia, Brazil, Chile, Colombia, Mexico, Paraguay, Peru, Uruguay, Venezuela (Latin America); China, Hong Kong SAR, India, Indonesia, Korea, Malaysia, Pakistan, Philippines, Singapore and Vietnam (Asia). However, using an extended sample with more emerging economies outside the above three regions does not change our main findings qualitatively. The results with more sampled countries are available upon request. 7

When constructing our dataset, we first select unconsolidated data of commercial banks and only use consolidated data when the former data are not available. As a result, consolidated data make up only a minority (to be specific, 13.3%) of our entire data. Meanwhile, we also experiment by dropping the domestic banks that have subsidiaries abroad, and find our main findings are not affected by the exclusion of these banks from our sample. The result will be available upon request. 8 We also experiment using a five-year rolling window to calculate Z-scores and find that our main results do not change and remain statistically significant. However, using a five-year rolling time will cause a considerable reduction in the number of our observations. 7

lower exposure to insolvency risk.9 Since this Z-score is only calculated based on banks’ own return on assets and equity-assets ratio, we view it as a measure of the “absolute” risk of banks. However, a simple comparison between the values of Z-scores across different countries may lead to biased conclusions, since it can be argued that banks’ Z-scores in some countries may be in general higher or lower than those in other countries. Thus, a larger Z-score for Bank A in country 1 than that for Bank B in country 2 may not necessarily mean that Bank A is placed at a position relatively less risky than Bank B in their own country. In order to overcome this problem, we normalize Z-scores for each country respectively as follows: Z _ ni jt 

Z i j t  m in ( Z m ax( Z

jt

jt

)

)  m in ( Z

for country j=1, 2 … jt

(2)

)

where min(Zjt) and max(Zjt) respectively denote the minimum and maximum value of Z-scores for all banks, including both domestic and foreign ones, in country j over the sample period. The results thus lie in the rage of [0, 1], suggesting the relative level of risk that banks are exposed to in their markets. As a higher value in Z_n suggests the bank has a relatively lower risk/greater stability in comparison to its counterparts across countries, we interpret this indicator as reflecting banks’ “relative” risk. Nevertheless, it can be argued that the banks’ current stability may be deviated from the potential maximum stability that they can achieve, given the different asset portfolios that banks choose to “produce”. Thus, we borrow the concept of “X-efficiency of stability” from Fang et al. (2014) and Tabak et al. (2012), who assume Z-scores as the outcome of banks’ production choice under the trade-off between return and risk, and suggest that the same Z-scores may be associated with banks’ different deviation from their implicit highest financial stability. We estimate the X-efficiency of banks’ financial stability by applying the stochastic frontier approach (SFA) to the following production function: ln (

Z w3

)ijt  c 

1

3

 2

h

ln ( y h )ijt 

h 1



1

3

1

hm

ln ( y h )ijt ln (

h 1 m 1

  1T   2 T

2

3

2 hk

ln ( y h )ijt ln ( y k )ijt 

h 1 k 1

2

 2

3

 2 wm w3

)ijt   ln E Q ijt 

 m 1

1

 m ln (

wm w3

)ijt 

3

 2 h 1

i

ln E Q ijt ln ( y h )i jt 

2

1

2

 2

m 1 n 1

1

 m n ln (

wm w3

2

 2

m 1

 m ln E Q ijt ln (

)ijt ln ( wm w3

wn w3

)ijt

)ijt

  ijt

(3) 9

Because the Z-score is highly skewed, we apply the natural logarithm to (1+ Z-score) to smooth higher values (Beck et al., 2013). Using 1+ Z-score instead of using simply Z-scores is to avoid the truncation of the Z-score at zero. We denote ln(1+ Z-score) as the Z-score in the latter part of the paper for brevity. Prior to our calculation of the Z-score, we removed the outliers of ROAijt and EAijt above the 99th percentile and below the 1st percentile of the sample distribution to rule out abnormality or probable measurement errors. 8

 ijt  u ijt   ijt

(4)

where yi represents three bank outputs, namely, loans (y1), securities (y2) and other non-interest related operations (y3), wi denotes three input prices, i.e., price of funds (w1), price of fixed capital (w2), and price of labor (w3), and EQ is banks’ equity, which is included as a netput. T denotes the time trend.10 The error term, εijt, is distinguished into two parts. The first part, uijt, denotes the random noise, assumed normally distributed ( u ijt

~ N ( 0 ,  u ) ), 2

and

represents the measurement errors and other uncontrollable factors. The second part, νijt, assumed to be half-normally distributed ( ijt



~ N ( 0 ,   ) ), 2

captures the banks’ inefficiency to

conduct production that can render an optimal financial stability. Estimating a single frontier for all banks across countries allows the X-efficiency item, νijt, to be compared against the same baseline (Tabak et al., 2012). We use the method of Battese and Coelli (1995) to estimate equation (3) and then adopt the Battese and Coelli (1988) estimator to convert νit into Z_νijt = E(exp(−νijt|ε), a term with a similar pattern to Z and Z_n, where a higher value in the range (0, 1) denotes a closer distance to the implicit greatest stability. Given banks’ different asset portfolio and input prices, a high value in Z may or may not be associated with a high Z_ν. We interpret Z_ν as the “excessive” risk of banks.11 3.2. Foreign bank penetration In order to measure the degree by which foreign banks are present in host markets, we first need to distinguish foreign banks from their domestic counterparts. In line with the common practice of related works, we define a bank as foreign-owned if more than 50% of its capital is held by foreign banks, firms, individuals or organizations. We track the year-by-year domestic/foreign ownership status for each bank in our sample by taking the following steps: First, we check the brief overview of banks documented in Bankscope, which records the ownership information for some banks in the most recent year. Second, we visit banks’ website to review their historical profile, where important events, such as the establishment and the change of controlling shareholders, are usually documented. Third, we obtain banks’ mergers and acquisitions (M&A) information from another comprehensive database, the SDC Platinum, which provides relevant information on cross-border banking M&A, including the time and the identity of acquirers. Finally, we resort to various other information sources,

10

We assume a standard production function by following earlier literature. w1 is proxied by the ratio of interest expenses to total deposits and other funds, w2 is measured by the ratio of (overhead cost – personnel expenses) to fixed assets, and w3 is calculated by the ratio of personnel expenses over total assets. The normalization by w3 ensures price homogeneity. 11

We lose many observations when estimating Z_ν because of the limited data for some variables. We also experiment estimating Z_ν in each country separately but unfortunately it fails to be implemented in many countries due to the deficiency of observations. 9

such as banks’ annual reports, central banks’ publications and news reports from the Internet to identify the ownership status for remaining banks.12 We measure the level of foreign bank penetration by using two proxies as the standard practice in the literature (Claessens et al., 2001).13 The first measure is the assets owned by foreign banks as a share of the banking sector total assets, denoted as Pene_assets. The second measure is the number of foreign banks as a proportion of the number of domestic and foreign banks in the host market, represented as Pene_number.

14

Although highly correlated,

there are different implicit assumptions behind the uses of these two measures. Pene_assets can be a proper measure of foreign penetration if foreign banks exert pressure on domestic banks only if foreign banks are sizable in host markets. For example, the hypothesis that foreign banks may introduce greater competition would be legitimate when they possess a significant share of the market and compete directly with their domestic peers. The use of Pene_number assumes that domestic banks can be affected by the mere presence of foreign counterparts. Foreign banks may probably “cherry-pick” the scarce best clients or cause a “flight-to-quality” without having to take over a large segment of the financial market. It is also possible that some domestic banks, such as those in areas remote to where foreign banks are concentrated, may change their behavior amid a potential expansion of foreign banks, even though there is no head-to-head competition yet. However, it is important to note that we do not argue that Pene_assets (Pene_number) captures only the direct competition effect (the “cherry-pick”/ “flight-to-quality”/ the threat of potential entry) of foreign banks. The channels via which foreign banks affect domestic ones may be de facto intertwined with each other, i.e., the “cherry-pick” associated with foreign entry may also encourage more fierce market competition. Unfortunately, it is empirically difficult to distinguish the clear-cut channels and measure each of them by using a particular indicator. Therefore, both Pene_assets and Pene_number may be associated with multiple effects of foreign bank penetration. 3.3. Bank characteristics, macroeconomic conditions and financial regulation Our other control variables are based on a careful review of extant literature on the 12

At the end we identify 935 domestic banks and 755 foreign banks. We compared our own data on bank ownership in emerging economies with Claessens and Van Horen’s (2015) dataset, and find that they very closely match each other. 14 It is acknowledged that our measures of foreign bank penetration, like those in many earlier works, are subject to some drawbacks due to the limitation of data. First, virtually only foreign subsidiaries are captured by our dataset, since only banks that are incorporated as separate corporate entities publish their own annual reports. As a matter of fact, many multinational banks choose to enter a market abroad by establishing branches without independent status. Second, the foreign shares in domestically controlled banks are not counted. For example, a bank with 51% of capital possessed by domestic owners and 49% by foreigners is still defined as a domestic bank. Unfortunately, it is almost practically impossible to track the specific shareholding structure for such a large pool of banks over more than a decade. Both of these two problems may cause the foreign presence level in emerging economies to be understated. 13

10

potential determinants of bank risk. We firstly control for a series of bank characteristics denoted respectively as Size, Liquidity, Capitalization, Income diversification and Funding diversification. Size is a bank’s assets as a share of the banking sector total assets. It reflects banks’ relative scale in their banking markets.15 Large banks, while owning more advanced risk management skills, may behave in the fashion of moral hazard if they presume they are “too big to fail” (Afonso, et al., 2014). Liquidity represents the ratio of liquid assets over total assets for individual banks. The abundance of liquid assets may shelter banks from unexpected monetary shocks and deposit runs, yet it is also likely that banks store more liquid assets when they foresee a higher volatility on returns (Alger and Alger, 1999). We also control for banks’ equity as a share of their total assets, denoted by Capitalization, since more capitalized banks likely incur lower probability of insolvency (Demirgüç-Kunt et al., 2013). Income diversification and Funding diversification, included as Demirgüç-Kunt and Huizinga (2010), are respectively measured by non-interest income as a share of total operating income, and non-deposit short-term funding as a share of the total short-term funding. Conventional wisdom postulates that a higher extent of diversification will translate into lower bank risk and stabilized returns, but many empirical works find conflicting evidence (for example, Stiroh, 2004). We secondly include a group of variables for macroeconomic conditions in our regressors, namely, GDP growth rate, Monetary policy and Crisis. The first is the growth rate of GDP adjusted by using the GDP deflator, which captures the impact of business cycles on financial stability (Marcucci and Quagliariello, 2009). We control for Monetary policy that is proxied using the short-term interest rates in each country, where a higher/lower value is interpreted as a relatively tightened/eased monetary supply. As suggested by a growing body of works on the “bank risk-taking channel of monetary policy”, expansionary monetary policy may increase banks’ tolerance to risk and/or encourage more behaviors of “search for yield”, thus increasing the risk of banks (Borio and Zhu, 2012). The data of the above variables are drawn from IMF’s International Financial Statistics Database. Since banks would usually incur higher risk during crisis periods, we include in our estimations a binary dummy variable, Crisis, for the episodes of banking crisis, currency exchange rate crisis, and sovereign debt crisis in emerging economies.16 Data for crisis periods are selected from Laeven and Valencia (2013).17

15

We also tried using the absolute size of banks, measured by the logarithm of bank assets in millions of constant US dollars. Our results are qualitatively unchanged. 16 We tried using individual dummies to capture banking crises, currency exchange rate crises and debt crises separately. We find the adverse effect on the risk of domestic banks is most conspicuous in the periods of banking crisis, but no significant evidence on any impact that may be caused by currency crises and debt crises. The results are available upon request. 17 We assume that financial sectors in all countries are affected by the global financial crisis and let this 11

As confirmed by rich evidence presented in as Barth et al. (2004, 2008) and Laeven and Levine (2009), financial regulatory rules are an important factor to affect the risk of the banking sector. We therefore control for the regulatory strength from four aspects, specifically, the strictness of regulations on capital adequacy (Capital), the restriction on banks’ activity mix (Activity), the authorities owned by supervisory agencies to intervene banks’ structure and operation (Supervisory power) and the extent to which banks are exposed to private monitoring and public supervision (Market discipline). Using the survey data provided by Barth et al. (2004, 2008, 2013) and following the methodology suggested by Barth et al. (2004), we build country-level time-series indices for each of the above four regulation aspects for each emerging economy in our sample. A higher score in these indices denotes more stringent regulations.18 3.4. Other control variables There are mixed results in extant literature regarding the impact of market structure on bank soundness (Boyd and De Nicoló, 2005; Beck et al., 2006). We use the Herfindahl-Hirschman Index (HHI), measured as the sum of the squares of individual bank’s market share in total banking assets, to proxy the concentration level of host markets. A higher value of HHI indicates that the banking market approaches higher consolidation. We also control for Financial depth, measured by the ratio of aggregate deposits over GDP, as a potential determinant of the risk levels of banks. On one side, a higher financial depth could imply a higher sophistication of the banking sector, while on the other side it may also reflect the credit constraints faced by bank clients. Accordingly, the degree of financial depth is expected to impose ambiguous impacts on the risk of domestic banks. 3.5. Descriptive statistics The definition of variables and the sources of data are presented in Table 1. We also report the main descriptive statistics of these variables.19 The Z-score of domestic banks is distributed with the mean value of 3.452 and the standard deviation of 1.145. Although not reported due to limited space, Z-scores are ranged between the minimum -2.345 and the maximum 9.440. The fairly high standard deviation and the wide range of Z-scores highlight

crisis dummy be equal to one for all countries in 2008-2009. We also extend the database in Laeven and Valencia (2013) since it only covers the crises up to 2011. 18 For instance, the index of capital regulations is based on the answers to 9 survey questions such as: whether the minimum capital-asset ratio requirement is risk-weighted in line with the Basel guidelines, whether the minimum ratio varies as a function of market risk, whether the sources of funds to be used as capital are verified by the regulatory authorities, and others. Summing up the answers (1 for “yes” and 0 for “no”) yields a value that denotes the strictness of regulations on the capital requirement. 19 For the Z-score and the bank specific characteristics, we delete the observations that lie below the 1 st percentile and above the 99th percentile of the sample distribution in order to rule out the possible impact of outliers. 12

a substantial variation on the level of risk across banks. As expected, the mean value of the other two risk indicators, Z_n and Z_ν, is approximately 0.5 since the range of these two indicators is between 0 and 1. This observation seemingly suggests that domestic banks and foreign banks have comparable risk levels on the whole. With regard to foreign bank penetration in terms of bank assets, the mean value of Pene_assets is 0.272 and its standard deviation is 0.266, indicating a considerable heterogeneity in the presence of foreign banks across emerging markets. In comparison, the foreign bank presence in terms of number, Pene_number, is relatively less varied, with the mean of 0.343 and the standard deviation of 0.186. The mean value of the foreign bank penetration level in our sample is largely affected by some countries that own a large number of banks but relatively modest presence of foreign entrants, for example, China and Brazil.20 We also report the pairwise correlations between key variables in Appendix C. The correlations between the Z-score of domestic banks and both foreign penetration measures are negative and statistically significant. This fact indicates higher risk by domestic banks in markets with more prominent foreign bank participation. The bank characteristic variables, and the financial regulatory variables, are found not highly correlated with each other, implying that a joint inclusion of these variables will not cause serious multicollinearity problems. The correlation between the presence of foreign banks and real GDP growth rate is found to be negative.21 This is consistent with the observation that foreign banks often consider host economic difficulties as an opportunity to seize more market share, either through new acquisitions or extending outstanding credit lines (Crystal et al., 2002). Furthermore, foreign banks are seemingly more inclined to enter a market with laxer financial supervision and lower market competition and financial depth. This fact justifies the necessity to control for these variables to better distinguish the impact of foreign bank penetration. [Table 1] 4. Model Our baseline econometric model is described as follows: R is k ijt  c    P e n e tr a tio n jt    C h a rijt    M a c r o jt    R e g u

jt

   O th e r  f i   ijt

(5) where the dependent variable, Riskijt, is the risk of domestic bank i, in country j, in year t. 20

Ruling out the countries with the largest amount of observations, i.e. China and Brazil, does not change our results. The estimated effect of foreign bank penetration and its statistical significance are found to increase without these two countries. 21 Another possible reason is that foreign bank presence is still modest in Asia, a region of rapid economic growth in recent decades. However, even as we experimentally exclude the observations from Asia, the correlation between foreign bank penetration and economic growth is still negative. 13

Our indicators of the bank risk include Z, Z_n, and Z_ν, which are defined in Sec. 3.1. Penetrationjt reflects the degree of foreign bank penetration in each host country over years, in terms of the assets (number) of foreign banks as a share of the banking sector total assets (number). As the impacts of foreign penetration on domestic banks’ risk more likely emerge after a time lag, we use one-year lagged values of foreign penetration in our estimation.22 Charijt, Macrojt and Regujt represent the series of bank characteristics, macroeconomic conditions and the proxies for bank regulatory rules, respectively. Other is the vector of the variables for market concentration, financial depth and year dummies. fi is the time-invariant bank-specific effect, and εijt is the idiosyncratic error. , , ,  and  are the coefficients to be estimated. To mitigate the problem of endogeneity, we use the one-year lag of each of the bank characteristic variables. The benchmark model is estimated by using the bank-specific fixed-effects estimator, which is chosen not only because it is commonly adopted in extant research but also because of some of its merits. First, as we are using bank-level panel data, fixed effects model allows unobservable bank-level individual effects, which may be heterogeneous across banks and constant over time. Second, fixed effects model allows the bank-level time-invariant effects to be correlated with explanatory variables, which is supported by the result of Hausman test.23 We use heteroskedasticity and within-panel serial correlation robust standard errors clustered at the host country level.24 To check the robustness of our main results, we also employ an alternative econometric methodology later. 22

We explored possible lag effects by including the contemporaneous and lagged levels of foreign bank penetration as regressors, i.e., Penetrationt, Penetrationt-1, Penetrationt-2, …, and find that, only the one-year lagged foreign penetration is statistically significant in almost all regressions, while the contemporaneous and the other lagged levels of foreign penetration are mostly statistically insignificant. This result is interpreted as that, when lagged impacts are captured, the variation of foreign penetration less likely affects the risk of domestic banks concurrently. However, as the contemporaneous and the lagged penetration levels are highly correlated with each other, we are very cautious to include more than one penetration index in our estimation because of the problem of multicollinearity. Thus, we use only the one-year lagged level of foreign penetration in our estimations, rather than including more lags. Using one-year-lagged regressor (to mitigate an endogenous concern) in estimating the bank risk measured by the Z-score has been adopted in the related literature (e.g., Demirgüç-Kunt and Huizinga (2010)). Kouretas and Tsoumas (2016) use foreign bank presence with different lags, separately, and no contemporaneous term in their study of the impact of foreign bank presence on a host country’s business regulatory environment. Analogously, we also experimented by replacing the one-year lagged foreign bank penetration with two-, three- and four-year lagged foreign penetration indicators separately in our estimation. We find that the coefficients on the lagged foreign bank penetration indicators are all consistently negative and statistically significant in most of regressions, in particular when using two- and three-year lags. The results of the above experiments are available upon request. We thank an anonymous referee for this point. 23 We also estimate our model by controlling for country-specific fixed effect. We firstly tried country-specific fixed-effects estimator. Secondly we used bank-specific fixed-effects estimator but control for country-year fixed effects by using binary dummies for each country in each year. Both approaches provide similar results as the baseline finding. The results are available upon request. 24 Alternatively, we use the number of observations for each bank as the weight of our data and find that our results are not changed qualitatively and their statistical significance remains. The results are available upon request. 14

5. Results 5.1. Baseline results We report the estimation results for our baseline model in Table 2. The columns (1)-(6) differ with each other by using different dependent variables, i.e. Z, Z_n and Z_ν, respectively, and different foreign bank penetration measures, namely, Pene_assets and Penn_number. [Table 2] First, we find that foreign bank penetration is inversely related to the Z-score based indicators. The coefficients on both Pene_assets and Pene_number are negative and statistically significant in all regressions. Since a higher Z-score suggests lower bank risk, this result is interpreted as that in general the risk of domestic banks increases amid a higher presence of foreign banks. The higher risk profile associated with domestic banks is also evidenced by the decline of their relative stability position vis-à-vis foreign banks, when using Z_n as the dependent variable. The results of Z_ν seemingly imply that domestic banks would allocate their resources less optimally, whereby their current stability is further deviated from the implicit maximum stability. Our findings are in line with the hypothesis that, for domestic banks, the risk-increasing effect attributable to foreign participation outweighs its potential beneficial impact, presumably due to a shift of customers, more intensive competition or disadvantages on innovative financial services. Quantitatively, the impact of foreign penetration is also salient. Using the result in column (1) as an example, the “absolute” risk of domestic banks tends to increase by 1.036 percent for each percentage that foreign banks increase their market share. Alternatively speaking, if foreign bank presence increases by one standard deviation (0.266, or put differently, 26.6 percent), the average risk of domestic banks would increase by nearly 27.5 percent in response.25 Second, we also find some interesting results regarding the risk impact of other control variables. Banks’ capitalization is positively related to the risk of domestic banks in all estimations, and the result is statistically significant when using Z and Z_n as the dependent variables. It is consistent with prior literature which reports that better capitalized banks incur lower insolvency risk. Nevertheless, our results provide no clear evidence on the impact of income diversification on banks’ risk since its coefficient is negative when Z and Z_n are the dependent variables while it becomes positive in other regressions. We find evidence in favor of the pro-cyclicality of financial stability. The coefficient on real GDP growth rate is positive and highly significant in all regressions, implying a lower risk incurred by banks when the economy is booming. Nevertheless, in line with the growing 25

Although not reported, we also experimented by including the (average/median) risk of foreign banks as an additional regressor in our estimations, and find that our main result is not qualitatively affected. We thank the anonymous referee for this point. 15

literature on the “risk-taking channel of monetary policy”, banks undertake more risk when central banks adopt expansionary monetary policy, as the positive coefficient on the monetary policy indicator suggests (Borio and Zhu, 2012; Delis and Kouretas, 2011). Regulatory rules are found to matter for bank risk but in seemingly different directions. Banks in countries with stricter regulations on capital adequacy and stronger market discipline are characterized by a lower risk profile than their peers in other regions, whereas a more stringent limitation on banks’ activity mix and a greater authority owned by supervisory officials only create undesirably higher risk to banks. These findings are consistent with works such as Barth et al. (2004) and Laeven and Levine (2009). 5.2. Robustness tests In this section, we conduct a series of robustness tests to check if our baseline results hold when using alternative risk indicators, different econometric methodology and different ranges of foreign bank penetration ratios. First of all, following some other practices commonly adopted in extant literature (for example, Claessens et al., 2001; Altunbas et al., 2007; Laeven and Levine, 2009), we use the ratio of non-performing loans over gross loans (NPL), the ratio of loan loss reserves over gross loans (LLR) and the standard deviation of return on equity (σ(ROE)), respectively, to replace our original dependent variables.26 Reported in Part 1 of Table 3, the estimated coefficients on foreign bank penetration, measured by Pene_assets and Pene_number respectively (Panel A and B), are found positive and statistically significant in the majority of regressions. This is interpreted as complementary evidence for our benchmark result that the degree of foreign bank penetration is positively associated with the vulnerability of domestic banks, which witness a rise of non-performing loans, hold more loan loss reserves and receive more volatile returns. [Table 3] Second, we apply a different econometric methodology to examine the nexus between foreign bank penetration and domestic banks’ risk. It can be argued that, foreign banks may be more inclined to enter markets where domestic banks are more fragile, since they incur lower costs for mergers and acquisitions, thus this reverse causality would lead to biased results. We employ the 2SLS instrumental variable estimator to address this problem, using 26

Other than using accounting information, we experiment by building market-based risk indicators. Adopting similar method as Bhagat et al. (2015), we compute the probability of bank default based on the “naïve distance-to-default (DD)” indicator suggested by Bharath and Shumway (2008) and use it as an alternative measure of bank risk. However, as the data of market price are only available in a small number of listed banks, our sample size is reduced substantially. Using this market-based risk indicator as the dependent variable, we find no statistically significant evidence for any impact of foreign penetration, in terms of both bank assets and number, on domestic (listed) banks’ risk. The result is available upon request. We thank the anonymous referee for the suggestion to use market-based risk indicators. 16

the penetration of foreign banks in other markets located in the same region (Central and Eastern Europe/Latin America/Asia) as the instrumental variable for the level of foreign bank penetration.27 Foreign banks may establish their operation more likely in areas where their peers cluster. Because the common driving factors of foreign bank penetration, such as home-host cultural and institutional closeness and the expected profitability based on host countries’ economic development, could be similar in near emerging countries, foreign banks may assemble in a region and thereby the level of foreign penetration in one country can be correlated with that in other near countries. Our data also confirm that the degrees of foreign bank penetration in emerging economies are roughly similar within regions, but are quite different across regions. Nevertheless, it is less likely that the risk of domestic banks in one country would be affected directly by the foreign penetration in other countries, suggesting that it can be a proper instrumental variable for foreign bank penetration. The result, presented in Part 2 of Table 3 (Panel C and D), is qualitatively consistent with our baseline findings, and the diagnostic statistics support the validity of the selected instrumental variable.28

29

Third, we test if our finding that foreign banks affect the risk of domestic banks is driven by only a specific range of foreign bank penetration. Put differently, we are interested in examining if the beneficial impact of foreign bank presence may dominate its risk-increasing impact and cause the stability of domestic banks to be buttressed when the degree of foreign bank presence lies in a certain range. We replace our foreign penetration measures by three alternatives, respectively, equal to the penetration level when it is between 0 and 0.33, 0.33 and 0.66 and 0.66 and 1, and equal to 0 otherwise. For instance, the alternative denoted as Pene_assets (0-0.33) is constructed to be identical to Pene_assets when the penetration level in terms of assets is between 0 and 0.33, and equal to 0 for the other observations in this series. Including these three alternatives together in regressions allows us 27

To be more specific, we calculate Pene_assets (other countries)/Pene_number (other countries), respectively, as the ratio of the assets/number of foreign banks in all other countries in the same region over the total bank assets/number of those countries, and use it as the instrumental variable of Pene_assets/Pene_number. 28 The sign on the estimated coefficient of the instrumental variable in the first-stage regression is positive, as expected, and statistically significant. We also report two diagnostic statistics at Table 3 Part 2. Firstly, the p-value of the Hausman test for endogeneity suggests that the potential endogeneity of the foreign bank penetration cannot be ruled out. Second, we report the first-stage F statistic based on the Stock and Yogo (2005) test for weak instruments. The F-statistic is found larger than the critical value (using the 10% significance level) constructed by Stock and Yogo (2005), which is interpreted as favorable evidence for the strength of our instrumental variable in the first-stage regression. 29 We also experimented by adding other instrumental variables, namely, the real GDP growth rate of the former colonizing country of host economies, the penetration level of the banks from host countries’ former colonizer and the (log) population of host markets (see, for example, Detragiache et al. (2008)). However, these variables are found associated with some drawbacks, which cast doubt on whether they can be used as valid instrumental variables. More detailed results regarding the use of these alternative instrumental variables can be available upon request. The authors thank the anonymous referee for helpful suggestions for the choice of instrumental variables. 17

to investigate if domestic banks’ risk tends to be affected by all the three categories of foreign bank penetration. The result is reported in Part 3 of Table 3 (Panel E and F). We find that the coefficients on all three categorical penetration measures are negative, providing no evidence that domestic banks’ risk tends to decrease amid any specific category of foreign bank penetration. Moreover, our results suggest a likely “threshold” for foreign banks to effectively impact the soundness of domestic banks. We find statistically significant results only when the foreign bank penetration level is more than one third in most cases. A potential explanation for this “threshold” effect is that, with a limited presence, foreign banks may not be viewed as a critical threat, and domestic banks do not change their risk-taking behavior. 6. What types of domestic banks are affected more by foreign bank penetration? After having found that domestic banks’ risk increases with the increased presence of foreign banks, we next explore some relevant factors for the heterogeneity of this nexus. We focus on two aspects, that is, what types of domestic banks are affected more by foreign bank penetration, and what patterns of foreign penetration exert more pronounced impact. In this section, we examine whether the impact of foreign banks on domestic banks’ risk would vary across banks’ specific features, including size, efficiency, and income diversification.30 6.1. Bank size We first divide our sample of domestic banks according to their size. The impact of foreign banks on domestic banks’ risk might be different across large and small domestic banks since the former may have more advanced risk management skills against the pressure exerted by foreign banks. Banks whose assets, as a share of the banking sector total assets, are located beyond the median of the distribution are defined as large banks and the others are defined as small banks.31 We construct a binary dummy variable, Dummy (Large banks), which is equal to 1 (0) if a bank is classified as a large (small) bank. Including the interaction of this dummy with the indicators of foreign bank penetration, we repeat our estimations and report the results in Panel A of Table 4. [Table 4] We find that the coefficient on the stand-alone penetration indicator is negative and statistically significant in all regressions, interpreted as evidence that the risk of small domestic banks increases with the entry of foreign banks. It is interesting that the coefficient

30

We have checked the correlation of the selected three bank characteristics and find that they are only mildly correlated. The highest correlation is found between efficiency and income diversification but it is lower than 0.25. Thus, it is not very likely that our separated bank groups largely overlap each other. 31

We use all sample of (domestic and foreign) banks to examine the distribution of bank size. 18

on the interactive term, Penetration * Dummy (Large banks), is negative (positive) when the extent of foreign bank penetration is gauged by Pene_assets (Pene_number). This finding is interpreted as twofold: First, neither large nor small domestic banks are found to be immune to the adverse risk effect with increased presence of foreign banks.

32

Second, the increase of

foreign banks’ market share and their mere number seemingly exert differential impact on large and small domestic banks. Large banks’ risk is more significantly sensitive to Pene_assets, while the variation of small banks’ risk is more closely associated with Pene_number. An explanatory conjecture is that large domestic banks’ market power would be considerably undermined only when foreign banks effectively take over a notable size of the market. Since before the entry of foreign banks, large domestic banks could earn a higher profit due to their monopolistic status, their profit would decrease by a larger rate relative to smaller banks when foreign entrants increase market competition effectively. For smaller domestic banks that owned only a modest segment in the market prior to the opening up of the banking industry, they might be insensitive to the variation on large (domestic or foreign) banks’ market power. However, the sheer increase in the number of foreign competitors, without having to change the market structure by considerably increasing their asset share, might still cause a significant “cherry-picking” and/or “flight-to-quality” effect on the clients of small domestic banks, or expose them to a threat of expansion by foreign banks, thereby inducing small domestic banks to “search for yield” by taking more risky bets.33 6.2. Bank efficiency Second, we examine if the impact of foreign banks on domestic banks’ risk would be heterogeneous across banks’ efficiency. Following a common practice in the literature, we measure bank efficiency by the ratio of overhead cost to total operational income, where a higher (lower) reading in this indicator is interpreted as a lower (higher) efficiency for the bank. We split domestic banks into two groups as above vs. below the median of the distribution of efficiency, and then build a binary dummy, Dummy (Banks with higher efficiency), which equals 1 (0) if the bank is categorized as efficient (less efficient).34 We re-conduct estimations by adding the interaction of the above dummy with the indicators of 32

When using Pene_number as the measure of foreign penetration, its overall impact on large banks, albeit smaller than that on small banks, is still statistically significant. Take Table 4, Panel A, column (4) as an example. The overall effect of Pene_number on large banks is -.902 (-1.405+ .503 = -.902) and is statistically significant at the 5% significance level. 33 This result is found analogous when employing alternative ways of division of large/small banks. We experiment by grouping banks by using the 4th quartile as the criterion, i.e., the banks whose size is beyond (below) the 4th quartile are classified as large (small) banks. The results are found qualitatively consistent, but weaker in statistical significance. 34 Since we use the sample of all banks, including both domestic and foreign ones, when examining the distribution of efficiency, the domestic banks that are classified as efficient own comparable efficiency with their foreign counterparts. 19

foreign penetration, and present the result in Panel B of Table 4. We find that the risk of less efficient banks deteriorates saliently as more foreign banks enter the host markets, as the coefficient on the stand-alone foreign penetration indicator is consistently negative and highly significant. The estimate of the coefficient on the interactive term, Penetration*Dummy (Banks with higher efficiency), is positive and significant in all cases, suggesting a lower adverse impact of foreign participation on the risk of more efficient domestic banks. Our results, which are consistent when we use either the assets or the number of foreign banks to proxy the degree of their penetration, suggest that less efficient banks are more vulnerable to the entry of foreign banks. When the business environment turns unfavorable, due to either the shift of customers, intensified competition or increased demand to upgrade banking technology and human capital, the banks that find it more difficult to secure profit by better managing costs would end up with a more disadvantageous status in the banking market. The results seemingly also imply that only foreign banks owning an advantage on operational efficiency can impose more effective pressure on domestic banks. When their advantage is less apparent compared to the domestic banks with high efficiency, the increased risk due to foreign entry is milder. This heterogeneity in addition suggests that the impact of foreign bank penetration might evolve over time. If domestic banks can improve their efficiency in the long-run, either because of the knowledge/skill spill-overs from foreign banks, or because domestic banks are compelled to leave behind their “quiet life”, the risk that results from foreign bank entry would decline.35 6.3. Income diversification Next, we assess whether the diversification of domestic banks’ business operations can ameliorate the risk introduced by foreign banks. We use non-interest income as a share of total operational income as the indicator of banks’ business diversification. If a bank concentrates itself on traditional commercial banking business, such as deposits and loans, it will document a lower non-interest income in its aggregate income. Alternatively, if a bank is more involved in non-lending activities, it is expected to receive more non-interest income like fees and commissions. Similar to our earlier practice, we distinguish domestic banks as those with high/low business diversification by using the median in the distribution of the non-interest income/total income ratio as the threshold. We construct a dummy variable, 35

Additionally, we experiment by separating the highly efficient domestic banks by using the lowest quartile of the distribution of the overhead cost/operational income ratio as the threshold. We find that, in line with our expectation, the coefficient on the interactive term increases impressively in comparison to our prior estimation, suggesting a further milder effect of foreign bank entry on those most efficient domestic banks. The overall impact of foreign bank penetration on these domestic banks, when measured by using Pene_number, even turns to be statistically insignificant. This finding seemingly suggests that, if the efficiency of domestic banks can approach that of their foreign competitors, the adverse effect from foreign penetration would be significantly buffered. The above result is available upon request. 20

Dummy (Banks with higher non-interest income), which is equal to 1 (0) for domestic banks with higher (lower) income diversification. This dummy is then interacted with the foreign penetration indicator and included in our estimation. Should higher business diversification tend to neutralize the risk attributed to the entry of foreign banks, the coefficient on this interactive term is expected to be positive and statistically significant. The results are reported in Panel C of Table 4. Our results show that the coefficient on the stand-alone penetration indicator is negative and statistically significant or only marginally not in many regressions, suggesting a deteriorated risk status in less income-diversified domestic banks amid higher foreign bank presence. However, we find no supportive evidence that a higher income diversification would reduce the risk associated with foreign penetration. The coefficient on the interactive term, Penetration* Dummy (Banks with higher non-interest income), is negative and significant in almost all regressions, implying a likely higher, instead of lower, risk effect on more business diversified banks. A possible reason could be that the comparative advantage of foreign banks does not lie on the traditional interest-bearing business since it requires a well-established retail network and rich information on local markets. Instead, foreign banks may possess more experience and skills with innovative financial products and fee-and-commission earning activities, so they may choose these areas as their business priority. In addition, foreign banks, cautioned as less committed to host markets by some studies (for example, Galindo et al. (2005)), may prefer non-lending business also because it allows them to quickly reduce their local exposure in times of economic trouble.

36

The

domestic banks with a higher non-interest income probably have a larger business area overlapping with that of foreign banks, and hence are more adversely affected by the presence of foreign competitors. This finding implies that the traditionally purported benefit from business diversification may be partially mitigated in the scenario of high foreign bank penetration. 7. What patterns of foreign penetration affect domestic banks more? After analyzing the heterogeneous impacts of foreign bank presence on banks with different characteristics, we next investigate whether different patterns of foreign bank penetration would also cause heterogeneous effects on the risk of domestic banks. 7.1. Entry modes of foreign banks We first examine if different entry modes of foreign banks, i.e. a greenfield entry or an 36

A comparison on the business model between foreign and domestic banks indeed indicates that foreign banks have a higher non-interest income/total income than their domestic counterparts. The mean value of the non-interest income as a share of total income of foreign banks is 34.8%, while it is 30.2% for domestic banks. 21

entry through mergers and acquisitions (M&A), would entail different impacts on domestic banks’ risk. In prior literature, there has been a body of research on the effect of entry modes on banks’ performance, such as credit growth, profitability and interest rates (De Haas and Van Lelyveld, 2006; Claeys and Hainz, 2014), but whether entry modes would affect the impact of foreign penetration on domestic banks’ risk is still left uninvestigated. We replace our original penetration indicator, Pene_assets, with two alternative indicators: the assets of foreign banks that were established from scratch as a share of the total banking sector assets (Pene_greefield), and the asset share of foreign banks that entered via M&A (Pene_M&A). For our original penetration indicator in terms of bank number, Pene_number, we replace it with two analogous indicators, respectively, the number of greenfield foreign banks and the number of M&A foreign banks as a proportion of the total bank number. Then we re-estimate our model by including both Pene_greenfield and Pene_M&A, and report the estimation results in Panel A of Table 5. [Table 5] We find some evidence that both entry modes of foreign banks are likely associated with higher risk among domestic banks, but the foreign penetration via M&A is shown to have more statistically pronounced impact. The coefficients on Pene_M&A, no matter the penetration of M&A foreign banks is proxied in terms of bank assets or bank number, are negative and statistically significant in all regressions. For the penetration level of greenfield foreign banks (Pene_greenfield), we find that, although it seemingly tends to deteriorate the risk of domestic banks, the effect lacks statistical significance in all regressions. This finding might be attributed to some likely reasons: First, M&A enables new foreign entrants to inherit the proprietary information on local markets that was owned by the acquired domestic banks, thereby at least partially overcoming their informational disadvantage and strengthening their competitiveness against domestic banks.37 Second, the competition pressure fueled by M&A entrance may be greater since foreign acquirers take over the branch network of domestic predecessors, hence exposing existing domestic banks to more face-to-face competition with foreign banks. Third, if foreign banks would implement restructuring after the acquisition, it may probably undermine the job security of the managers at incumbent domestic banks when they are targeted, and thus encourage more risk-taking by domestic bank managers to seek higher returns. 7.2. Internal capital markets 37

However, some works argue that the local market knowledge may be lost after a domestic bank is acquired by foreign entrants. As the parent bank imposes formal accountability on local managers, the bank-firm relationship, which was built on personal contacts and soft information, would be disrupted (for example, Bonaccorsi Di Patti and Gobbi (2007)). 22

Multinational banks allocate funding and other resources across their affiliates on a global scope, establishing internal capital markets. The impact of internal capital markets on the performance of foreign banks, global banking and international financial shock transmission has been studied (De Haas and Van Lelyveld, 2006, 2010, Jeon and Wu, 2014 and some others). However, how foreign banks with different strength of internal capital markets may lead to different impacts on the risk of domestic banks has not been examined yet in the literature. We now examine if the strength of internal capital markets of multinational banks could play any role in affecting the risk of local banks. We measure the internal support that foreign banks could receive from their parent banks and their banking groups by using the assets of foreign banks as a share of the aggregate conglomerate assets, multiplied by the net income of the conglomerate. The reasoning underlying this measure is that: First, the relative intragroup size of foreign banks indicates their internal hierarchy that may affect the priority of parent banks to allocate resources. The affiliates with larger within-group influence may receive more favorable support from their conglomerate (Cremers et al., 2011). Second, the net income of conglomerates reflects the potential availability of financial support from the conglomerates (Campello, 2002; Jeon et al., 2013). Parent banks would have a deeper pocket to assist their subsidiaries when they have higher net income, whereas they may withdraw their resources when severely hit by adverse shocks at home (Cetorelli and Goldberg, 2012). Therefore, our indicator combines these two relevant factors that affect the internal assistance to which foreign banks may resort. We then classify foreign banks according to the potential strength of their within-group assistance. The banks that are associated with the internal support above (below) the median of the distribution are grouped as foreign banks with higher (lower) internal support. The assets of foreign banks with higher (lower) internal support as a share of the banking sector total assets are denoted as Pene_higher internal support (Pene_lower internal support). When measuring foreign penetration in terms of the number of banks, we conduct similar practices. We report the estimation results, substituting the original foreign penetration levels with Pene_higher internal support and Pene_lower internal support, in Panel B of Table 5. We find that the variation of domestic banks’ risk is more closely associated with the presence of foreign banks with stronger internal support. With the penetration levels measured in both assets and bank number, the coefficient on Pene_higher internal support is negative and highly statistically significant in all cases, whereas the coefficient on Pene_lower internal support is statistically significant in only one regression. This is probably because that, with an easier access to international investment opportunities and a lower cost of intragroup funds, implemented by the conduction of internal market transactions, foreign banks with richer intragroup resources may own a larger competitive advantage in host markets, which thus 23

exerts greater pressure on domestic banks and compels them to undertake more risky projects. Additionally, more abundant internal capital may also make foreign banks more attractive, particularly to the clients who take parent banks’ financial health into account, and thus accelerate the shift of high quality customers from domestic to foreign banks. 8. Conclusion We examine the impact of foreign bank penetration on the risk of domestic banks in emerging economies, using bank-level data from 35 markets during the period of 2000-2014. We find consistent evidence that the risk of domestic banks increases with the presence of foreign banks, and the results remain the same in a series of robustness tests. We also find that the risk effect, which is introduced by the presence of foreign banks, is more conspicuous for domestic banks that are less efficient and have a higher non-interest income. Furthermore, foreign banks exert more pronounced impacts on the risk of domestic banks when foreign banks enter the host market via the M&A mode and are supported by the operation of internal capital markets within their conglomerates. Our results carry several important policy implications. First, there are both bright and dark sides for the presence of foreign banks in emerging economies. Despite serving as a steady source of credit in host markets, as documented in a vast literature of prior research, foreign banks may lead to an increase of domestic banks’ risk, suggesting a likely trade-off between the stability of credit quantity from foreign banks and the vulnerability of credit quality in domestic banks. Thus, when designing optimal financial liberalization policies, the decision makers need to be aware of the possible detrimental impact of foreign bank prominence on the stability of the banking sector in the host economies. Second, our findings on the heterogeneity of the impact of foreign banks across different types of domestic banks shed some light on the ways to offset the adverse outcomes in the wake of foreign bank entry. For example, higher levels of bank efficiency and lesser income diversification are found to be helpful for domestic banks to reduce their risk due to the entry of foreign banks. Regulatory authorities can encourage efforts for efficiency enhancement by domestic banks before or in parallel with the process of international openness of the host banking sector. However, financial supervisors should also be aware that business diversification of domestic banks may increase their risks, thus affecting the stability of domestic banking sector unfavorably. Finally, the impact of foreign banks on the risk of domestic banks is found to be related to different patterns of foreign bank penetration, suggesting that host countries may reduce the associated risk of the domestic banking sector by encouraging more greenfield entries of foreign banks than their M&A entries. Meanwhile, as found in this paper, the effect of foreign bank penetration on domestic banks is conditional on the strength of intragroup resource allocation by multinational banking conglomerates, which could be beyond the oversight of 24

domestic financial regulators. Focused international cooperation and coordinated banking regulations will be essential to reduce the risk of the banking sector in the host emerging economies. Acknowledgement: We thank the editor, Iftekhar Hasan and two anonymous referees for prompt feedback and detailed useful comments and suggestions. We also thank discussants and participants at the 13th Annual International Conference of the Western Economic Association International (WEAI), held in Santiago, Chile, January 2017 for very useful comments and discussion. We are solely responsible for any errors if remained.

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28

Table 1. Variable definition This table summarizes the definition of the main variables and the source of data. Detailed definition of each variable can be found in Section 3. This table also reports the mean, standard deviation and median of the variables. Variable Bank risk Z

Description

Data source

Mean

Std. dev

Median

Natural logarithm of Z-scores, i.e., ln[1+(ROAit+EAit)/(ROA)it]. ROA represents return on assets, EA the equity-to-assets ratio, and (ROA) the standard deviation of return on assets. A higher score suggests a lower probability of bank insolvency, or alternatively speaking, a higher degree of financial stability.

Bankscope and authors’ own calculation

3.452

1.145

3.462

Z_n

Normalized Z-scores by using [Zijt – min(Zjt)]/[max(Zjt) – min(Zjt)], where min and max represent respectively the minimum and the maximum of Z-scores in each market across sample periods. A higher score denotes a higher stability/lower risk of the bank relative to its counterparts in the resident market.

Bankscope and authors’ own calculation

.556

.154

.559

Z_ν

The X-efficiency of the natural logarithm of Z-scores. Following Fang et al. (2014), we adopt a stochastic frontier approach (SFA) to fit an upper envelop of Z-scores. The difference of the actual Z-score from the implicit optimal value represents the deviation of a bank’s stability from its potential highest stability. A higher score suggests a closer distance between the actual Z-score to its potential highest value, that is, a higher stability/lower risk of the bank.

Bankscope and authors’ own calculation

.511

.159

.538

The assets of foreign banks as a share of the total banking sector assets, ranged at [0, 1].

Author’s own collection Author’s own collection

.272 .343

.266 .186

.217 .339

Bankscope and authors’ own calculation Bankscope and authors’ own calculation Bankscope and authors’ own calculation Bankscope and authors’ own calculation Bankscope and authors’ own calculation

3.385

6.066

.941

24.838

16.532

20.752

12.634

11.051

9.210

30.525

20.436

27.671

10.763

13.765

6.082

Foreign penetration Pene_assets Pene_number

The number of foreign banks as a share of the total number of banks in the banking sector, ranged at [0, 1].

Bank characteristics Size

Bank assets as a share of the total assets of the banking sector (%).

Liquidity

The ratio of liquid assets to total assets (%).

Capitalization

The ratio of equity to total assets (%)

Income diversification

The ratio of non-interest income to total operating income (%).

Funding diversification

Non-deposit short-term funding as a share of the total short-term funding (%).

29

Macroeconomic variables GDP growth rate

Real GDP growth rate (%).

International Financial Statistics and authors’ own calculation

5.043

4.304

5.333

Monetary policy

Short-term interest rates (%). A higher (lower) figure proxies a relatively contractionary (expansionary) monetary policy stance.

8.627

6.449

7.149

Crisis

A dummy equal to 1 for the periods of banking crisis, exchange rate crisis or sovereign debt default in a country and the global financial crisis in 2008-09 for all countries, 0 for other periods.

International Financial Statistics and authors’ own calculation Laeven and Valencia (2013)

.191

.393

0

Barth et al. (2004, 2008, 2013) and authors’ own calculation Barth et al. (2004, 2008, 2013) and authors’ own calculation Barth et al. (2004, 2008, 2013) and authors’ own calculation Barth et al. (2004, 2008, 2013) and authors’ own calculation

7.583

2.208

8

5.914

1.805

6

11.478

1.777

11

6.552

1.034

7

11.281

5.454

9.827

71.255

58.131

47.790

Financial regulation Capital

Index of capital regulatory stringency. A higher score suggests more stringent regulations on banks’ overall and initial capital.

Activity

Index of activity regulatory stringency. A higher score suggests more stringent regulations on the scope of banks’ business operation.

Supervisory power

Index of supervisory power. The score in this index is higher when supervisory agencies are authorized more oversight power.

Market discipline

Index of the private monitor strength. A higher value denotes a higher private monitoring force.

Others HHI Financial depth

Herfindahl-Hirschman Index, defined as the sum of the squared shares of bank assets to total banking sector assets within a given country in a year. Aggregate deposits as a share of GDP (%).

30

Bankscope and authors’ own calculation International Financial Statistics and authors’ own calculation

Table 2. The impact of foreign penetration on the risk of domestic banks This table reports the baseline results regarding the impact of foreign bank penetration on the risk of domestic banks. The dependent variables are Z, Z_n and Z_ν, respectively defined in Section 3.1. Pene_assets is the assets of foreign banks as a share of the banking sector total assets. Pene_number is the number of foreign banks as a share of total bank number. Foreign penetration variables use one-year lags. Size is the ratio of bank assets over the banking sector total assets. Liquidity is the ratio of bank liquid assets to total assets. Capitalization is the ratio of equity to total assets. Income diversification is the ratio of bank non-interest income to total operating income. Funding diversification is the ratio of non-deposit short-term funding as a share of the total short-term funding. Bank characteristics are one-year lag variables. GDP growth rate is the growth rate of real GDP. Monetary policy is the proxy of monetary policy by using short-term interest rates. Crisis is a dummy variable equal to 1 in the periods of banking, exchange rate or sovereign debt crisis in sample countries, including the 2008-9 global financial turmoil for all countries. Among the regulatory variables, Capital proxies the capital regulatory stringency, Activity is the restriction on bank activity mix, Supervisory power reflects the official supervisory authority, and Market discipline measures the private monitor strength. For other control variables, HHI is the Herfindahl-Hirschman Index, defined as the sum of the squared shares of bank assets to total banking sector assets within a given country in a year. Financial depth is the aggregate deposits as a share of GDP. Detailed definitions for each variable can be found in Section 3. We estimate all regressions by using the fixed-effects estimator with heteroskedasticity and within-panel serial correlation robust standard errors that are clustered at the host country level. p-values are in parentheses. *** indicates the 1% significance level; ** 5% significance level; * 10% significance level. Dependent variable: (1) Z Foreign penetration Pene_assets

Liquidity Capitalization Income diversification Funding diversification Macroeconomic conditions GDP growth Monetary policy Crisis Financial regulations Capital Activity Supervisory power Market discipline Others HHI Financial depth Year dummies Observations (Number of banks) R2 Hausman (p-value) F-statistic (p-value)

(3) Z_n

-1.036*** (.003)

Pene_number Bank characteristics Size

(2) Z

(4) Z_n

-.131*** (.002) -1.075** (.015)

(5) Z_ν

(6) Z_ν

-.185*** (.001) -.137*** (.007)

-.156*** (.007)

.017 (.195) .002 (.309) .018*** (.000) -.004* (.052) .003 (.321)

.018 (.169) .003 (.277) .019*** (.000) -.004** (.043) .003 (.355)

.002 (.187) .000 (.423) .002*** (.000) -.000* (.070) .000 (.333)

.002 (.161) .000 (.383) .002*** (.000) -.000* (.059) .000 (.367)

.001 (.771) .000 (.595) .000 (.546) .000 (.284) -.001 (.251)

.001 (.602) .000 (.354) .001 (.500) .000 (.307) -.001 (.288)

.019** (.034) .016* (.053) -.236* (.051)

.022** (.023) .015* (.069) -.225* (.060)

.003** (.035) .002** (.031) -.026* (.071)

.003** (.025) .002** (.042) -.025* (.078)

.003*** (.003) .001 (.533) -.033* (.059)

.004*** (.003) .000 (.788) -.030* (.080)

.046* (.069) -.060** (.018) -.073** (.023) .114* (.092)

.054** (.034) -.053** (.026) -.064** (.044) .093 (.171)

.005 (.112) -.007** (.031) -.007* (.057) .013 (.147)

.006* (.059) -.006** (.044) -.006 (.101) .011 (.252)

.007* (.092) -.010** (.037) -.011*** (.008) .013* (.064)

.007 (.121) -.008* (.063) -.009** (.035) .013* (.086)

-.015 (.378) .006 (.264) Yes 5557 (775) .070 .000 .000

-.009 (.631) .006 (.232) Yes 5557 (775) .069 .000 .000

-.001 (.464) .001 (.155) Yes 5557 (775) .068 .000 .000

-.001 (.706) .001 (.142) Yes 5557 (775) .067 .000 .000

-.002 (.387) .000 (.553) Yes 3889 (629) .070 .001 .000

-.002 (.608) .001 (.407) Yes 3889 (629) .066 .008 .000

31

Table 3. The impact of foreign bank penetration on domestic banks’ risk: Robustness tests This table reports the results of various robustness examinations. In Part 1, the dependent variable is replaced respectively by the ratio of non-performing loans to gross loans (NPL), the loan loss reserve as a share of gross loans (LLR) and the standard deviation of return on equity (σ(ROE)). In Part 2, we adopt 2SLS instrumental variable estimator as an alternative econometric methodology, assuming that the penetration of foreign banks is endogenous. We report the results of the second-stage regression at first and then the first-stage regression results. The penetration level in other countries within the same region is used as the instrumental variable of foreign penetration in host countries. We report two diagnostic statistics, i.e., the p-value of the Hausman test for endogeneity and the first-stage F-statistic of the Stock and Yogo test for the weakness of the instrument variable. In Part 3, we replace the original foreign bank penetration indicators by three separate penetration indicators, which are identical to the foreign bank penetration level when they are ranged at 0-0.33/ 0.33-0.66/ 0.66-1 respectively, and otherwise equal to 0. In Panel A, C and E, foreign penetration is measured in terms of bank assets, while in Panel B, D and F it is measured in terms of bank number. We only report the coefficients on foreign penetration for brevity although we use a full set of independent variables in all regressions. p-values are in parentheses. *** indicates the 1% significance level; ** 5% significance level; * 10% significance level. Part 1: Alternative measures of bank risk NPL

LLR

σ(ROE)

9.040** (.016) 4264 (676) .107

3.426** (.022) 5155 (731) .169

5.864** (.011) 5507 (778) .064

2.485 (.552) 4264 (676) .112

1.152 (.624) 5155 (731) .160

6.200** (.032) 5507 (778) .063

Z_n

Z_ν

-4.396** (.037) 5463 (681) .017

-.592** (.047) 5463 (681) .005

-.436*** (.008) 3801 (541) .053

.503*** (.010) .021 53.285

.503*** (.010) .022 53.285

.620*** (.000) .011 97.014

Observations (Number of banks) R2

-3.506** (.013) 5463 (681) .047

-.481** (.014) 5463 (681) .040

-.580** (.017) 3801 (541) .027

First-stage result Penn_number (other countries) Hausman test (p-value) Stock and Yogo (F-statistic)

.579*** (.000) .068 102.555

.579*** (.000) .066 102.555

.495*** (.001) .048 68.172

Z

Z_n

Z_ν

-.565 (.435) -1.334*** (.002)

-.127 (.137) -.178*** (.001)

-.126 (.197) -.233*** (.000)

Dependent variable Panel A Pene_assets Observations (Number of banks) R2 Panel B Pene_number Observations (Number of banks) R2

Part 2: Alternative econometric methodology (2SLS) Dependent variable Z Panel C Second-stage result Pene_assets Observations (Number of banks) R2 First stage result Penn_assets (other countries) Hausman test (p-value) Stock and Yogo (F-statistic) Panel D Second-stage result Pene_number

Part 3: Separate categories of foreign bank penetration Dependent variable Panel E Pene_assets (0 -0.33) Pene_assets (0.33- 0.66)

32

Pene_assets (0.66 – 1) Observations (Number of banks) R2

-.843*** (.001) 5557 (775) .073

-.115*** (.000) 5557 (775) .071

-.162*** (.000) 3889 (629) .075

-.154 (.854) -.747 (.147) -.928* (.071) 5557 (775) .071

-.058 (.531) -.104* (.074) -.137** (.030) 5557 (775) .068

-.110 (.398) -.138* (.094) -.145** (.049) 3889 (629) .067

Panel F Pene_ number (0 -0.33) Pene_ number (0.33- 0.66) Pene_ number (0.66 – 1) Observations (Number of banks) R2

Table 4. What types of domestic banks are more affected by foreign penetration? This table reports the heterogeneous effect of foreign bank penetration on different types of domestic banks. We divide our sample of domestic banks by using various dummies. In Panel A, domestic banks are classified as large and small banks, dependent on whether their size is beyond the median of the sample distribution. Dummy (Large banks) is equal to 1 (0) when a bank is classified as a large (small) bank. In Panel B, we separate domestic banks according to their efficiency, which is reflected by the overhead cost as a share of total operational income. Banks are grouped as those with higher/lower efficiency if their efficiency indicator is lower/higher than the median in the distribution. Dummy (Banks with higher efficiency) is equal to 1 (0) when a bank is more (less) efficient. In Panel C, we divide domestic banks by their income diversification, measured by the non-interest income as a share of total operational income. Banks are grouped as those with higher/lower business diversification if their non-interest income/total income ratio is higher/lower than the median of the distribution. Dummy (Banks with higher non-interest income) is equal to 1 (0) when a bank is more (less) income diversified. We only report the coefficients on foreign penetration and its interactions for brevity although we use a full set of independent variables in all regressions. p-values are in parentheses. *** indicates the 1% significance level; ** 5% significance level; * 10% significance level. Panel A: Large banks vs. small banks

Dependent variable:

Foreign bank penetration measured in assets (Pene_assets) (1) (2) (3) Z Z_n Z_ν

Foreign bank penetration measured in number (Pene_number) (4) (5) (6) Z Z_n Z_ν

Penetration

-.731* (.052)

-.089** (.050)

-.182*** (.009)

-1.405*** (.002)

-.178*** (.001)

-.214*** (.001)

Penetration * Dummy (Large banks)

-.433** (.035)

-.060** (.031)

.003 (.935)

.503** (.042)

.062** (.040)

.081* (.072)

Observations (Number of banks)

5557 (775)

5557 (775)

3889 (629)

5557 (775)

5557 (775)

3889 (629)

R2

.076

.075

.071

.078

.076

.075

Panel B: Banks with higher efficiency vs. Banks with lower efficiency

Dependent variable: Penetration Penetration * Dummy (Banks with higher efficiency) Observations (Number of banks) R2

Foreign bank penetration measured in assets (Pene_assets) (1) (2) (3) Z Z_n Z_ν

Foreign bank penetration measured in number (Pene_number) (4) (5) (6) Z Z_n Z_ν

-1.114*** (.002)

-.142*** (.001)

-.199*** (.001)

-1.184*** (.005)

-.152*** (.002)

-.178*** (.002)

.251* (.094)

.034* (.059)

.043** (.049)

.314* (.051)

.044** (.025)

.053** (.026)

5557 (775)

5557 (775)

3889 (629)

5557 (775)

5557 (775)

3889 (629)

.071

.070

.072

.071

.070

.070

33

Panel C:

Banks with high non-interest income vs. banks with low non-interest income

Dependent variable: Penetration

Foreign bank penetration measured in assets (Pene_assets) (1) (2) (3) Z Z_n Z_ν

Foreign bank penetration measured in number (Pene_number) (4) (5) (6) Z Z_n Z_ν

-.852** (.011)

-.105*** (.007)

-.137*** (.008)

-.702 (.131)

-.091 (.106)

-.092 (.185)

Penetration * Dummy (Banks with higher non-interest income)

-.243 (.161)

-.035* (.096)

-.060** (.038)

-.483** (.030)

-.059** (.045)

-.075* (.057)

Observations (Number of banks)

5557 (775)

5557 (775)

3889 (629)

5557 (775)

5557 (775)

3889 (629)

R2

.070

.069

.082

.071

.069

.078

Table 5. What penetration patterns exert more pronounced impact? This table reports the heterogeneous impacts of foreign bank penetration on domestic banks’ risk conditional on the penetration patterns. In Panel A, we replace our original penetration indicator by two alternative ones, respectively indicating the presence of foreign banks that entered the host markets via mergers and acquisitions (Pene_M&A) and via greenfield establishment (Pene_greenfield). These two sorts of penetration are also separately proxied in terms of bank assets and bank number. In Panel B, we distinguish foreign banks according to the strength of their within-group internal capital market. The presence of foreign banks with a higher /lower internal support are respectively calculated and denoted as Pene_higher internal support and Pene_lower internal support. We only report the coefficients on foreign penetration for brevity although we use a full set of independent variables in all regressions. p-values are in parentheses. *** indicates the 1% significance level; ** 5% significance level; * 10% significance level. Panel A: Entry modes Foreign bank penetration measured in assets (1) (2) (3) Z Z_n Z_ν

Foreign bank penetration measured in numbers (4) (5) (6) Z Z_n Z_ν

-1.099*** (.002)

-.136*** (.002)

-.226*** (.000)

-1.760*** (.006)

-.240*** (.001)

-.258*** (.001)

Pene_greenfield

-.756 (.396)

-.111 (.283)

-.035 (.678)

-.745 (.233)

-.090 (.236)

-.109 (.105)

Observations (Number of banks)

5557 (775)

5557 (775)

3889 (629)

5557 (775)

5557 (775)

3889 (629)

R2

.070

.068

.072

.071

.071

.070

Dependent variable Pene_M&A

Panel B: The potential strength of internal capital market for foreign banks Foreign bank penetration measured in assets

Foreign bank penetration measured in numbers

(1) Z

(2) Z_n

(3) Z_ν

(4) Z

(5) Z_n

(6) Z_ν

Pene_higher internal support

-1.761*** (.001)

-.245*** (.001)

-.270*** (.000)

-1.597*** (.010)

-.192** (.012)

-.202*** (.009)

Pene_lower internal support

-.090 (.819)

-.026 (.593)

-.125** (.013)

.108 (.861)

.030 (.717)

-.025 (.664)

Observations (Number of banks)

5557 (775)

5557 (775)

3889 (629)

5557 (775)

5557 (775)

3889 (629)

R2

.076

.077

.070

.074

.073

.068

Dependent variable

34

Appendix A. The assets of foreign banks as a share of the total assets of banking sector This table presents the assets of foreign banks as a share of the total banking sector assets in host countries, ranged at [0, 1]. Only commercial banks are included. 2000

2002

2004

2006

2008

2010

2012

2014

Central and Eastern Europe Albania

.710

.899

.862

.857

.831

Belarus

.041

.096

.357

.274

.376

.440

.642

.628

Bulgaria

.814

.821

.781

.819

.825

.809

.720

.736

Croatia

.423

.848

.859

.885

.877

.902

.899

.902

Czech

.759

.947

.969

.974

.965

.968

.944

.929

Estonia

.979

.981

.984

.992

.986

.987

.969

.967

Hungary

.743

.987

.987

.979

.978

.968

.955

.931

Latvia

.424

.396

.459

.643

.652

.661

.600

.523

Macedonia

.543

.457

.488

.412

.687

.668

.658

.660

0

.089

.108

.222

.268

.230

.230

.147

Poland

.909

.974

.945

.916

.912

.879

.838

.839

Romania

.419

.502

.545

.920

.926

.910

.872

.852

Slovakia

.465

.958

1

1

1

1

1

1

Slovenia

.040

.232

.249

.277

.290

.276

.293

.293

Ukraine

.074

.139

.146

.375

.597

.562

Argentina

.582

.412

.314

.291

.316

.302

.297

.274

Bolivia

.398

.318

.307

.187

.201

.207

.207

.185

Brazil

.257

.283

.248

.255

.232

.209

.191

.217

Chile

.339

.454

.401

.386

.360

.331

.304

.304

Colombia

.246

.196

.178

.212

.198

.175

.208

.249

Mexico

.223

.806

.816

.812

.752

.736

.695

.691

Paraguay

.773

.767

.721

.656

.588

.459

.420

.419

Peru

.546

.558

.534

.524

.524

.509

.502

.511

Uruguay

.513

.467

.501

.544

.558

.569

.540

.549

Venezuela

.477

.396

.323

.296

.258

.152

.153

.174

China

.001

.001

.001

.005

.019

.018

.016

.015

Hong Kong, SAR

.907

.912

.922

.917

.930

.930

.929

.936

India

.027

.036

.052

.062

.073

.057

.050

.039

Indonesia

.052

.064

.200

.237

.267

.257

.264

.266

Korea

.044

.045

.182

.196

.199

.188

.108

.103

Malaysia

.230

.198

.217

.206

.232

.211

.207

.198

Pakistan

.048

.065

.394

.411

.512

.488

.491

.485

Philippines

.026

.020

.017

.017

.011

.018

.012

Singapore

.049

.056

.032

.048

.056

.086

.076

.070

Vietnam

.021

.021

.019

.017

.018

.047

.050

.050

Moldova

Latin America

Asia

Appendix B. The number of foreign banks as a share of the total number of banks 35

This table presents the number of foreign banks as a share of the total number of banks in host countries, ranged at [0, 1]. Only commercial banks are included. 2000

2002

2004

2006

2008

2010

2012

2014

.571

.875

.833

.833

.818

Central and Eastern Europe Albania Belarus

.181

.307

.500

.461

.666

.777

.750

.785

Bulgaria

.590

.695

.681

.739

.684

.700

.650

.529

Croatia

.277

.382

.285

.428

.419

.433

.500

.538

Czech

.782

.809

.809

.850

.882

.875

.823

.812

Estonia

.600

.500

.500

.571

.666

.666

.714

.714

Hungary

.909

.954

.954

.923

.875

.727

.727

.777

Latvia

.250

.285

.363

.523

.600

.571

.550

.444

Macedonia

.363

.250

.384

.416

.714

.666

.666

.666

0

.181

.200

.312

.312

.400

.357

.285

Poland

.743

.846

.790

.783

.780

.780

.717

.709

Romania

.541

.695

.791

.857

.904

.875

.857

.904

Slovakia

.769

.846

1

1

1

1

1

1

Slovenia

.117

.384

.357

.400

.400

.400

.357

.307

Ukraine

.153

.250

.263

.347

.571

.571

Argentina

.478

.388

.354

.322

.333

.339

.294

.275

Bolivia

.454

.454

.454

.400

.454

.363

.363

.363

Brazil

.456

.432

.364

.383

.407

.393

.457

.457

Chile

.586

.538

.480

.500

.541

.521

.500

.500

Colombia

.384

.321

.285

.312

.388

.368

.380

.388

Mexico

.545

.633

.551

.500

.432

.435

.431

.390

Paraguay

.666

.647

.615

.615

.500

.428

.461

.461

Peru

.562

.692

.642

.615

.642

.666

.687

.733

Uruguay

.771

.833

.846

.916

.882

.894

.888

.875

Venezuela

.333

.285

.242

.233

.222

.217

.208

.227

China

.071

.117

.085

.102

.208

.211

.214

.209

Hong Kong, SAR

.756

.783

.750

.750

.777

.793

.821

.840

India

.048

.067

.098

.103

.125

.120

.131

.107

Indonesia

.357

.319

.395

.396

.471

.440

.430

.415

Korea

.058

.062

.294

.312

.266

.266

.200

.214

Malaysia

.464

.448

.500

.500

.560

.535

.600

.620

Pakistan

.157

.157

.291

.346

.400

.391

.391

.409

Philippines

.200

.148

.120

.160

.136

.173

.136

Singapore

.500

.636

.444

.583

.583

.500

.583

.500

Vietnam

.200

.160

.148

.156

.138

.232

.225

.242

Moldova

Latin America

Asia

36

Appendix C. Pairwise correlation matrix

Z Pene_assets

-.139

1

Pene_number

-.109

.786

1

Size

.204

-.022

.120

1

Liquidity

-.074

.206

.222

-.051

1

Capitalization

-.023

.213

.216

-.131

.135

1

Income diversification

-.180

.231

.145

.111

.106

.069

1

Funding diversification

-.028

.056

.123

.012

-.020

.219

.019

1

GDP growth rate

.117

-.414

-.397

-.033

.011

-.167

-.143

-.156

1

Monetary policy

-.103

-.110

-.063

-.160

.031

.158

.014

.110

-.286

1

Crisis

-.096

.028

.064

-.013

-.039

.032

.028

.020

-.304

.119

Capital

.050

-.075

-.057

.022

-.156

-.202

.013

-.076

-.187

.090

.039

1

Activity

.070

-.346

-.336

.008

-.130

-.061

-.124

-.191

.339

.038

-.029

.023

1

Supervisory power

-.087

.086

.157

.031

.129

.078

-.077

.111

.035

-.043

-.042

-.029

-.091

1

Market discipline

.118

-.144

-.115

-.091

-.005

-.148

-.208

-.010

.264

-.174

-.042

-.138

.197

.108

1

HHI

-.030

.309

.337

.205

.182

.181

.061

.119

-.013

-.065

-.022

-.267

-.186

.075

-.058

1

Financial depth

.238

-.234

-.207

.084

-.058

-.228

-.310

-.175

.331

-.317

-.044

-.175

.206

-.176

.376

.054

Financial depth

HHI

Market discipline

Supervisory power

Activity

Capital

Crisis

Monetary policy

GDP growth rate

Funding diversification

Income diversification

Capitalization

Liquidity

Size

Pene_number

Pene_assets

Z

This table reports the pairwise correlation coefficients of main variables. The figures in bold form denote the correlation coefficients with the significance level lower than 10%.

1

37

1

1

Figure 1. The average level of foreign bank penetration in emerging economies, in terms of bank assets. This figure depicts the average level of foreign bank penetration, in terms of bank assets, across all sampled emerging economies and those in Central and Eastern Europe, Latin America and Asia, respectively, during the period of 2000-2014.

0.8 All countries Eastern and Central Europe Latin America Asia

0.7 0.6 0.5 0.4 0.3 0.2 0.1 2000

2002

2004

2006

2008

2010

2012

2014

Figure 2. The average level of foreign bank penetration in emerging economies, in terms of the number of banks. This figure depicts the average level of foreign bank penetration, in terms of bank number, across all sampled emerging economies and those in Central and Eastern Europe, Latin America and Asia, respectively, during the period of 2000-2014.

0.8

All countries Eastern and Central Europe Latin America Asia

0.7 0.6 0.5 0.4 0.3 0.2 2000

2002

2004

2006

38

2008

2010

2012

2014