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Changes in ownership structure and bank efficiency in Asian developing countries: The role of financial freedom
Changes in ownership structure and bank efficiency in Asian developing countries: The role of financial freedom Kun-Li Lin, Anh Tuan Doan, Shuh-Chyi Doong PII: DOI: Reference:
S1059-0560(15)00179-3 doi: 10.1016/j.iref.2015.10.029 REVECO 1164
To appear in:
International Review of Economics and Finance
Please cite this article as: Lin, K.-L., Doan, A.T. & Doong, S.-C., Changes in ownership structure and bank efficiency in Asian developing countries: The role of financial freedom, International Review of Economics and Finance (2015), doi: 10.1016/j.iref.2015.10.029
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ACCEPTED MANUSCRIPT CHANGES IN OWNERSHIP STRUCTURE AND BANK EFFICIENCY IN ASIAN
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Kun-Li Lin
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DEVELOPING COUNTRIES: THE ROLE OF FINANCIAL FREEDOM
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Department of Finance, Feng Chia University
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100 Wenhwa Road, Seatwen, Taichung, Taiwan 40724
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[email protected]
Anh Tuan Doan
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PhD Program in Finance, Feng Chia University
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100 Wenhwa Road, Seatwen, Taichung, Taiwan 40724
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[email protected]
Shuh-Chyi Doonga
Department of Finance, National Chung Hsing University 250 Kuo Kuang Road, Taichung 402, Taiwan [email protected]
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Correspondence: Shuh-Chyi Doong, Department of Finance, National Chung Hsing University, Taichung, Taiwan. E-mail: [email protected].
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ABSTRACT
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This paper investigates the effect of the changes in bank ownership on cost efficiency across
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twelve Asian developing economies. We also evaluate how financial freedom shapes the effect
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of the changes in bank ownership on cost efficiency. Using stochastic frontier approach to estimate bank efficiency scores during the period 2003-2012, we find that foreign presence
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improves bank efficiency, primarily in countries with high financial freedom. In addition, our results also show that increased government (domestic) ownership of bank appears to improve
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(impede) bank efficiency in countries with more financial freedom after financial crisis.
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Keywords: Ownership changes, bank efficiency, financial freedom
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1. Introduction
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In recent years, financial markets have become increasingly integrated as governments have
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liberalized domestic financial sectors and capital accounts. One facet of the larger process of
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financial globalization is the increased participation of private financial institutions in local banking sectors, especially in Asian developing economies after the 1997 financial crisis. The
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growth in bank privatizations in Asian developing countries has fostered an increased interest in this worldwide phenomenon. Recent studies focus on the causes and consequences between
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ownership structure and performance. For example, Micco et al. (2007) and Cornett et al. (2010)
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show that state-owned banks operating in developing countries tend to have lower profitability
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than the private banks, and foreign ownership banks tend to be represented by higher profitability than other counterparts. Similarly, focusing on the China banking sectors, Berger et
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al. (2009), Ferri (2009) and Lin and Zhang (2009) showed a significantly higher performance by private and foreign-owned banks relative to state-owned commercial banks. With this growing literature on ownership structure in Asian developing economies, some studies further to compare the bank efficiency in different ownership structure but are inconclusive (see Laeven, 1999; Williams and Nguyen, 2005). For example, Laeven (1999) shows that state‐owned and foreign‐owned banks as well as Korean and Malaysian banks took little risk relative to other banks, while family‐owned and company‐owned banks and Indonesian and Philippine banks were among the highest risk‐takers. Unite and Sullivan (2003) show that foreign entry in the banking market in the Philippines corresponds to an improvement in operating efficiencies, but with deterioration in the quality of loan portfolios. Williams and Nguyen (2005) find that, as a result of bank privatization programs, banks selected for domestic 2
ACCEPTED MANUSCRIPT mergers and acquisitions (M&As) exhibited relatively low rank order profit efficiency before the governance change, which improved in the short‐term but deteriorated in the long‐term implying
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a temporary efficiency gain using five East Asian countries (Indonesia, South Korea, Malaysia,
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Philippines, and Thailand) between 1990 and 2003. Although the impact of ownership structure
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has received a lot of attention recently, the consequences of the resulting changes in bank ownership structure on efficiency have yet to be adequately explored. We expect that using a
efficiency changes as ownership changes.
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real ownership change instead of dummy variable can provide a better insight into how bank
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Moreover, the recurrent episodes of the 2008 Global Financial Crisis associated with financial liberalization motivated a number of researchers to investigate between deregulations
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and bank performance. One approach points to the deregulation of financial services and institutions as a fundamental reason that led to the crisis (Keeley, 1990), while other approaches
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suggest that the seeds of the crisis were sown by a particular set of capital regulations because the cap rate tends to have positive association with the amount of risky assets, and then increase in the bank’s default probability (Tsai and Hung, 2013). So, an emerging question in the midst of this debate is if and how economic and financial liberalization may affect the performance of financial institutions (e.g. Claessens and Laeven, 2004; Goddard et al., 2011). Although research is increasingly using the indexes of economic freedom to proxy liberal effect as explanatory variables in their regressions (e.g. Powell, 2003; Altman, 2008; Heckelman and Knack, 2009), these studies have just assessed the impact of economic freedom on economic growth. Nevertheless, in the banking literature the indexes of economic freedom have been used only as control variables and/or have been inaccurately interpreted as regulation indexes. Recently, some studies try to include indicators that examine the degree of financial liberalization in bank
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ACCEPTED MANUSCRIPT efficiency. For example, Fries and Taci (2005) consider the role of banking sector reform and liberalization in the transition countries to capture the effect on bank cost efficiency. The key
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explanatory variable of interest is an index of banking sector reform published by the European Bank for Reconstruction and Development (EBRD) Transition Reports. Their results show that
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progress in banking reform is significantly associated with a decrease in banks’ costs. Hence,
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financial liberalization must take account of the interactions between bank ownership and
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efficiency.
The purpose of this paper is to investigate the relationship between ownership changes and
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bank efficiency. Specifically, we question whether the financial freedom strengthens or weakens the relationship between the changes in bank ownership and efficiency before and after Global
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Financial Crisis. We regress the efficiency estimates on the interaction between the changes in ownership and financial freedom indexes from the Foundation (2010), which aim at capturing
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the “greater independence in financial and banking markets from government control”. We employ parametric stochastic frontier approach (SFA) to measure the bank efficiency. We then use these measures to examine whether bank ownership and financial freedom enhance or impede bank efficiency.
The paper makes a number of contributions to the literature. First, our paper is related in spirit to recent studies that provide international evidence on the impact of ownership on banks’ performance (e.g. Barth et al., 2002; Demirguc-Kunt et al., 2004). In contrast to these studies, which mainly use financial ratios as indicators of performance, we measure bank efficiency using an efficient frontier technique. Berger and Humphrey (1997) emphasize that efficient frontier approaches are superior when compared to traditional measures of performance (e.g. return on assets, cost/revenue), since they account simultaneously for relevant inputs and outputs
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ACCEPTED MANUSCRIPT of a bank, as well as for differences in the input prices. Second, our paper extends on previous studies by using a real percentage to capture the changes of block shareholders' ownership, and
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provide evidence for the impact of changes in ownership structure on bank efficiency. For
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example, Iannotta et al. (2007), Berger et al. (2009) and Shen and Lin (2012) have only used a dummy variable with a twenty or fifty percent ownership threshold to classify as government or
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private domestic banks. Indeed, a dummy variable does not measure degrees of large
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shareholders above the specific threshold as well as the dynamic effect that bank ownership over time may have on bank efficiency.
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Third, we explicitly analyze the influence of financial liberalization using the economic freedom indexes and we distinguish between the concepts of financial freedom and regulation.
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This definition of financial freedom indexes we used is closely related to the broad concept of deregulation that is, the removal of artificial barriers that prevent entry and/or competition
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between products, markets and institutions. Thus one may expect that the financial freedom counterparts of the economic freedom indexes can inversely correlate with the degree of regulatory tightness in banking. 1 However, Chortareas et al. (2013) argue that regulatory tightness and measures of financial freedom can be closely associated but they do not identify with each other, either in scope or in terms of measurement. Because financial freedom indicates limited government influence/control in financial and banking markets and, in addition to the regulatory framework, it takes into account the extent of state intervention in banks and in the allocation of credit, as well as the possible obstacles in opening and operating financial services firms (for both domestic and foreign individuals). Finally, unlike most previous studies that consider the effects of economic freedom on bank performance typically treat the freedom index 1
For example, the freedom indexes inversely relate with the measures of activity restrictions and official supervisory power provided by Barth et al. (2006).
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ACCEPTED MANUSCRIPT as one of the control variables (e.g. Claessens and Laeven, 2004; Goddard et al., 2011), and include other aspects of bank performance than efficiency such as the interest rate margins
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(Demirguc-Kunt et al., 2004). Despite the extensive literature on bank efficiency used European
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bank sample (Chortareas et al., 2013), a comprehensive study on whether the changes in bank ownership and financial freedom enhance or impede efficiency in Asian developing countries not
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yet exist.
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We examine the effects of financial freedom on the relation between the changes in ownership structure and bank efficiency before and after 2008 Global Financial Crisis, and use
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data for more than 2113 bank-year observations in 12 Asian developing countries over the period 2003–2012. We obtain the following main findings. First, we find that increases in large
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government and foreign shareholders are positively associated with bank efficiency in countries with high financial freedom, while an increase in domestic ownership shows a negative impact
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on bank efficiency. Second, the changes of bank ownership do not have any insignificant effect on bank efficiency before 2008 Global Financial Crisis, whereas increased government (domestic) ownership of bank appears to improve (impede) bank efficiency in countries with more financial freedom after Global Financial Crisis. The rest of this paper proceeds as follows. Section 2 reviews literature on bank ownership, efficiency and financial freedom. Section 3 presents the data and methodology in measuring bank ownership change, efficiency and financial freedom. Section 4 analyzes how financial freedom affects the relation between bank ownership structure and efficiency. Section 5 analyzes empirical results and discusses their implication. Finally, section 6 concludes the paper with a discussion the policy implications.
2. Literature Review 6
ACCEPTED MANUSCRIPT 2.1 Evidence on Bank Efficiency in Asian Developing Countries There have been some empirical studies that have attempted to shed light on the
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determinants of this efficiency in Asian developing countries. Gardener et al. (2011) found a
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negative influence of post-1997 crisis restructuring on bank efficiency in South East Asia, while
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Sun and Chang (2011) reported a significant impact of measure risks on both the level and variability of bank efficiency in emerging Asian countries. Recently, many single-country
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studies have provided evidence that the bank efficiency by itself matters for the changes in stock prices (Sufian et al., 2007), and faces a difficulty to achieve the efficient resource allocation
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during the early 2000s (see Kalluru and Bhat, 2009; Vu and Turnell, 2010; Manlagñit, 2011). Similarly, there exists other important studies, which discovered different specific factors that
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affected the performance or efficiency of banks within a country, such as Hong Kong (Lim and Randhawa, 2005), Korea (Park and Weber, 2006), Taiwan (Kao and Liu, 2009), Thailand
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(Chansarn, 2008), Malaysia (Sufian, 2009), and Indonesia (Margono et al., 2010). Among the studies that compared bank efficiency between state-owned banks and foreign-owned banks in Greater China such as Hu et al. (2004), Ferri (2009), Lin and Zhang (2009), and Berger et al. (2009) use the sample from 1994 to 2003 but failed to describe the current problems facing the foreign bank’s competition, especially after the 2008 Global Financial Crisis. In general, their results indicated that risk-taking, corporate governance and bank reforming play an important role in the efficient performance of banks, and might demonstrate the discrepancies in effectiveness among banking systems in different countries.
2.2 Evidence on Financial Freedom in Asian Developing Countries While banking literature considering the impact of the economic freedom on productive efficiency has been extensive, the theoretical models analyzing explicitly the role of economic 7
ACCEPTED MANUSCRIPT freedom indexes on ownership changes in developing countries have not been developed. Focusing on the link between financial freedom and bank efficiency, Chortareas et al. (2013)
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point out that the more the level of a country’s financial freedom, the higher the benefits for
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banks. Using the indexes of economic freedom as control variables, Claessens and Laeven (2004), Saurav Roychoudhury and Lawson (2010) and Goddard et al. (2011) documented that
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more openness in the banking markets improvises the efficiency and reduces borrowing cost for
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banks. Furthermore, there have been other recent studies that examine the international impact of economic freedom on various aspects of economic growth (Gwartney, 2009), bank stability
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(Linh Nguyen et al., 2012), income convergence (Xu and Li, 2008), entrepreneurship (Nystrom, 2008) and global recession (Giannone et al., 2011). Nevertheless, most of these studies have
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concentrated on the effect of financial freedom in the developed countries, such as the United States, Europe and other developed countries banking sectors.
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The empirical evidence on impact of financial freedom effect in the Asian emerging and developing countries is relatively scarce. By examining different components of economic freedom, Sufian and Habibullah (2011) find that higher monetary policy increases banks’ efficiency, while the corruption in the business environment is negatively related to bank efficiency levels of the China banking sector. Other analyses of economic freedom have been considered in various contexts, but few contributions focused explicitly on the effects of the economic freedom within which foreign direct investment attraction (e.g. Quazi, 2007; Hanh, 2010). The evidence suggested that financial openness indicators are positively related to FDI inflow. Similarly, Tiwari (2011) also shows that an increase in the fiscal freedom and financial freedom are significant factors positively associated with economic growth. The impact of financial liberalization on economic outcomes, however, depends on the liberalization intensity
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ACCEPTED MANUSCRIPT of each financial sector in which full liberalization tends to be harmful for emerging Asian growth outcomes (Ben Gamra, 2009).
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2.3 Evidence on Bank Ownership Structure in Asian Developing Countries
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In emerging and developing economies, although the separation of ownership from control
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is common to all three ownership forms, there are still different arguments to define the relative efficiency of these three ownership forms of financial institutions. Several studies have found
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that bank’s government ownership is associated with inefficiency and poor performance. For example, Micco et al. (2007) and Cornett et al. (2010) show that state banks operating
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developing countries tend to have lower profitability than the private banks, and foreign ownership banks tend to be represented by higher profitability than other counterparts. Similarly,
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Sanyal and Shankar (2011) find that Indian private and foreign banks tend to have a much higher productive efficiency when compared to the public banks. Focusing on the China banking sectors,
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Berger et al. (2009), Ferri (2009) and Lin and Zhang (2009) indicate that a significantly higher performance by private and foreign-owned banks relative to state-owned commercial banks. One reason is that the bank privatization in the region has improved revenue inflow and bank performance in the short-run or long-run, particularly for banking institutions with foreignowned banks (Chunxia Jiang et al., 2013). According to this development view, however, some argued that higher level of governmental ownership might benefit from bank investment through their safer projects at lower interest rates. Indeed, state bank may enhance savings mobilization and improve the allocation of these funds to projects to the economy (Hossain et al., 2013). In this regard, the analysis of Andrianova et al. (2012) documented that government banks typically play a development role and run the bank effectively. Also, Taboada (2011) found a negative effect on
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ACCEPTED MANUSCRIPT capital allocation when there is a decline in ownership of government block shareholder and an increase in ownership of domestic block shareholders.
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3. Data and Methodology
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3.1 Data Selection
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The dataset used in this study is composed of individual bank data soured from unconsolidated statements of banks operating in the 12 Asian developing countries, as made
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available through the BankScope database of Bureau van Dijk. To avoid a domination of any
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country out of twelve, we start collecting bank financial statement data with a maximum of 50 largest banks by total assets in each country.2 As our paper focuses on commercial banks, we
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start by dropping central banks, investment banks, securities houses, multilateral government
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banks, non-banking credit institutions, and specialized government financial institutions, which
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reduces our sample from 9890 observations to 3980 observations. Next, we delete 1756 observations with less than five subsequent years of time series observation. In order to minimize the effects of measurement errors we have excluded all the outliers (111 observations) by eliminating the extreme bank-year observations (2.5% lowest values and 2.5% highest values) for each considered. As a result, we end up with an unbalanced data set consisting of 219 banks from 12 countries, for a total of 2113 bank-year observations for which we have efficiency, ownership and accounting data. To analyze the role of bank ownership change on bank efficiency before financial crisis (2003-2007) and after financial crisis (2008-2012), we use a database on the ownership structure of BankScope in 12 Asian countries as of 2003, 2007, 2008 and 2012. When BankScope’s shareholder database does not have enough information for us to determine the percentage of 2
There are three countries in our sample which have more than 50 active commercial banks, including Mainland China (168 banks), India (62 banks), and Indonesia (69 banks) provided by BvD BankScope database.
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ACCEPTED MANUSCRIPT ownership structure, we collect bank ownership information from the annual reports provided by individual bank websites or Bankers’ Almanac. Following La Porta et al. (2002), I use various
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alternate ownership measures that attempt to capture control of banks. These measures classify
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banks as domestic private, government, or foreign-owned when their equity ownership exceeds certain thresholds.
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On the other hand, data for the economic freedom are collected from the Foundation (2010)
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and data on the institutional and governance quality are drawn from the World Bank database by Kaufmann et al. (2010). The index is highly credible and its results are compatible in general.
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We use the Heritage Foundation’s Index of economic Freedom for practical purposes because one of its components measures the “Finance Freedom”. In particular, the Index of Economic
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Freedom focuses explicitly on the components of “financial freedom” (previously dubbed
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“banking freedom”). The financial freedom index is an overall indicator of banking security as
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well as a measure of independence from government control. This indicator provides an economy’s financial freedom by looking into the following five group categories: the extent of government regulation of financial services; the extent of government intervention in nonbanking financial institutions through direct and indirect ownership; the extent of capital and financial markets development; and the extent of government effect in the credit allocation system and the openness to foreign competition. These five groups are considered to reflect the overall level of financial freedom of a country that ensures effective and easy access to financing opportunities for individuals and entrepreneurs. The distribution of financial freedom index is based on a 100-point scale with 10-point intervals in which a score 100 indicates a negligible government influence, and a score of 0 indicates a repressive government influence (banking supervision and regulations are designed to
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ACCEPTED MANUSCRIPT prevent private financial institutions). In other words, financial freedom signifies an assessment of financial and banking freedom that the higher the values the more freedom. This index has
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been used as a proxy of the degree of openness of financial markets and banking industry by
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numerous previous studies (i.e. Demirguc-Kunt et al., 2004; Chortareas et al., 2013). Furthermore, we also use bank regulations as other country control variables via different
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resources. In detail, capital requirement, capital stringency and regulatory restrictions are
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collected from (Barth et al., 2006; Laeven and Levine, 2009)3. Finally, we rely on two other data sources, the use the measure of country-level control (e.g. GDP per capita, Customer Price Index)
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from the World Bank’s World Development Indicator database, and capture the economic environment factors from Kaufmann et al. (2010)4.
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3.2 Measurement of Changes in Bank Ownership To measure the ownership variables, we follow the same procedure used to calculate a
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bank’s proportion of state ownership by La Porta et al. (2002). By constructing all of ownership measures with data from 2003 to 2012, we first identify large domestic bolckholder ownership of banks. For example, a large foreign block shareholder is any foreign shareholder (a company, or an individual) which is classified by following the 10% threshold from La Porta et al. (1999). Then we calculate the total share of each bank owned by large foreign shareholders (FOi) as follows: J
FOi s ji s fj ; where s ji s fj 0.1
(1)
j 1
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Barth et al. (2006) employ use cross-country data to examine the relationship between political systems and bank supervisory and regulatory systems. 4 Kaufmann et al. (2010) summarize the Worldwide Governance Indicators (WGI) project that covers over 200 countries and territories with different dimensions of governance starting in 1996.
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ACCEPTED MANUSCRIPT where j = 1… J indexes banks’ shareholders, sji is the share of bank i owned by shareholder j and sfj is the share of shareholder j that is owned by a foreign shareholder. FOi is the total share of
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bank i that is owned by large foreign blockholders. We use a similar way to construct the
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domestic (DOi) and government (GOi) block shareholder variables. Furthermore, we also follow Taboada (2011) to construct an additional variable, (WIDEi). This variable captures the widely-
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held shareholders which are neither government, foreign, nor domestic blockholder-owned: (2)
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WIDEi 1 GOi DOi FOi
Table 1 provides descriptive statistic for various measures of bank ownership across
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countries as of 2003, 2007, 2008 and 2012. As of 2012, the mean of domestic, government and foreign block shareholder ownership of 213 banks in 12 countries for the full sample is 15.33
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percent, 27.55 percent, and 31.87 percent, respectively. The table also indicates that GO has been considerably declining, whereas FO has been increasing, especially during the period 2003 –
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2007. It would be not surprising that these changes are greatly influenced by the evolution of the privatization process. Trend of decrease in GO and increase in FO are expected to continue into the next five years, although at a significantly slower pace partly due to the Global Financial Crisis.
[Insert Table 1 here]
3.3 Measurement of Efficiency Scores Regarding the bank efficiency measure, the previous studies examining performance have used two different approaches available to measure bank efficiency, including the parametric stochastic frontier approach (SFA) and the non-parametric data envelopment analysis (DEA). While DEA is built under the assumptions that there is no random error, SFA approach modifies the traditional assumption of a deterministic production frontier which got its advantage (Sun
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ACCEPTED MANUSCRIPT and Chang, 2011). More recent studies find that SFA efficiency scores are generally higher compared to DEA scores.5 This may reflect the different treatment of stochastic noise and the
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ability to control for heterogeneity (Koetter et al., 2006). Following the previous literature on the
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banking industry and the advantages of SFA in comparison with DEA related to attributing statistic noise to inefficiency, this study tends to employ SFA for measuring the efficiency of
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developing Asia’s banking industry for the appropriate estimation.
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The model is first introduced in logs as ln yi ln xi vi ui , where xi denotes an input
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vector for firm i, vi represents random error added to the non-negative inefficiency term,
ui . Unlike ui , the random error, vi , accounts for measurement error and other random factors
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affecting the value of the output variable, together with the combined effects of unspecified input
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variables in the production function. This model is stochastic because the upper limit is
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determined by the stochastic variable exp xi vi . According to Battese and Coelli (1997), vi can be positive or negative and therefore the stochastic frontier outputs vary relative to the deterministic part of the frontier model. The fact that we need to assume a functional form when estimate the stochastic frontier model. Unfortunately, the banking industry has a multi-output, hence choosing for a production function is not feasible. Cost efficiency measures how well a bank is managing its resources, under the same outputs and environmental conditions, to reach the maximum amount of profit at a minimum cost.6 A recent study of Wang (2002) which made a combination between traditional and extended models supposes that TCit represents for total costs for the ith bank in year t, then 5
There are some studies available in which, SFA method has been used for efficient measurements. Specifically, Aigner et al. (1977) and Meeusen and Van den Broeck (1977) employed the stochastic frontier approach which is composed errors in the assumptions of a deterministic production frontier 6 Since behavioral assumptions such as cost minimization are appropriate for banks, it looks like the best way to use a cost frontier model for the estimation.
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ACCEPTED MANUSCRIPT Yit , Pit are the vectors of the output and the price of input, respectively, and presented the equation
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as TCit f1 Yit , Pit vit uit , vit N 0, v2 , uit N it , it2 . Here, vi is the stochastic error term with i.i.d. normal distribution represented for other uncontrollable factors, while ui
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represents technical and allocative inefficiency aspects that can be influenced by management.
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The uit error has a truncate normal distribution with an observation-specific mean function of some determinants Z it of
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it 0 zit and variance it2 which are assumed a
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its pre-truncated distribution. 7 The cost (in)efficiency scores of individual bank, INEi exp ui , calculated from the estimated frontier would take a value between one and infinity. To make our
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results comparable, we follow the same procedure used by Pasiouras et al. (2009) to calculate the
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index of cost efficiency as follows: CEi 1/ INEi . Therefore, our efficiency scores range
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between 0 and 1 with values closer to 1 indicating a higher level of bank efficiency. Based on the stochastic frontier analysis model of Battese and Coelli (1997) and the cost frontier model Wang (2002), we estimate efficiency levels by specifying the multi-product translog cost function. Because of convenience, we demonstrate only the following cost function:
ln TC j 0 r ln Yrj i ln Pij rj
ij
1 1 ik ln Yij ln Ykj iz ln Pij ln Pzj 2 i k 2 i z
i ln Yr ln Pi vi u j r
(3)
i
where TC is total operating cost, Yr , r 1, 2,..., 4, are outputs, Pi , i 1,...,3 are input prices and
0 is an intercept accounting for all other cost determinants. Since inefficiency leads to higher than optimal costs, note that the inefficiency term ui is positive. In addition, in order to reduce
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Huang et al. (2011) and Sun and Chang (2011) both described the previously analyzed Wang’s model as the best specification model among eight well-known stochastic frontier models.
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ACCEPTED MANUSCRIPT heteroskedasticity problem, we also use the price of labor to normalize total costs and input prices, which is presented by Lang and Welzel (1996).
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This study will base on the intermediation approach to classify the outputs and input prices
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of the banking industry. According to the intermediation approach, banks are considered as financial intermediaries that taking deposits from savers and making loans to economic agents
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requiring capital (Allen and Santomero, 1997). From this perspective, our study will focus on
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four outputs and three input prices. Four outputs previous adopted in studies of Bonin et al. (2005), and Berger et al. (2009) include total loans (Y1), other earning assets (Y2), total deposits
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(Y3), and liquid assets (Y4). Three input prices also defined widely in previous studies are the price of labor, capital, and funds. In detail, price of funds (P1) measured by the ratio of interest
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expenses to total deposits, price of capital (P2) is defined by the ratio of non-interest expenses to total fixed assets, and the ratio of personnel expenses to total assets used to measure the price of
expenses.
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labor (P3). The total costs (TC) of banks are defined by sum of interest expenses and non-interest
Table 2 indicates that the commercial banks, on average, have higher level of their total deposits than total loans. The average total cost is $1.842 billion. The average price of capital and price of funds are about 2.64 and 0.036, respectively. These measures are lower than the 2.82 of price of capital reported by Sun and Chang (2011) and the 0.21 of price of funds reported by Berger et al. (2009). It should be noted that the standard deviations of all output variables are very high. These results reflect the large differences in the bank sizes in our sample. [Insert Table 2 here]
4. The Impact of Changes in Ownership on Bank Efficiency
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ACCEPTED MANUSCRIPT Given the remarkable declines in government and increases in foreign ownership of banks, which caused by the privatization process in the early 2000s, the question remains as to whether
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this has any impact on how bank efficiency is changed after the Global Financial Crisis of late
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2008. We now examine the impact of changes in ownership on bank efficiency. If countries’ capitalization and liberalization programs are performed efficiently, high financial freedom
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countries should receive more advantages in improvement of bank efficiency. The following
Bank Efficiencyi , j ,t 0 1 FINFREE j
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basic equation is used in our analyses:
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2 FINFREE j Ownership Structurei , j 3 Ownership Structurei , j Bank Regulation Control j ,t
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Bank Controlsi , j ,t Macro Controls j ,t
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Year Dummies ei , j ,t
(4)
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where Bank Efficiencyi,j,t is the cost efficiency scores of bank i in country j in time t. Bank Efficiency is the cost efficiency scores estimated by SFA frontier from the previous section, which is always positive and it is equal to unity for the best practice or zero inefficient bank. To investigate how financial liberalization impacts on the ownership structure/ bank efficiency nexus, we set a variable called FINFREE, which is based on the index of financial freedom described in the section 3.1. FINFREEj is dummy variable, takes a value of one if the country j’s financial and banking freedom index is above the median value and zero otherwise. 8 Not surprisingly, an accessible and well-functioning financial market can ensure the availability of diversified savings, credit, investment, and other payment services to individuals. By expanding financial transactions and promoting entrepreneurship, an open and transparent banking 8
Foundation (2010)’s Financial freedom is a measure for banking sector independence from government interference in a financial system. This index is built on scale of 0 to 100 with the higher value illustrates less government control in the national banking system.
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ACCEPTED MANUSCRIPT environment encourages competition and facilitates access to providing efficient financial intermediation between households and banks as well as between investors and entrepreneurs. In
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additional, a government with strong intervention in financial markets may go beyond the
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assurance of transparency, and may increase the costs of financing entrepreneurial activity which leads to impede bank efficiency. Therefore, we expect that banks with more foreign ownership
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may improve their efficiency in the country with more open financial markets.
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For the other independent variables, Ownership Structure refers to the changes in bank ownership structure in the same period of the dependent variable. Bank Regulation Control
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represents for key bank regulations stressed by the Basel Committee. Following Laeven and Levine (2009), we control bank regulatory by capital regulations (CR) on bank activities. Bank
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Controls is the vector of control bank-level including NONINT, DDEP, BANKSIZE and EQUITY. NONINT is defined as non-interest income as a share of total assets and DDEP is demand of
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deposits as a share of total deposits. The reason for using these two control variables is that NONINT tends to be higher for banks that derive most of their income from commissions, and DDEP tends to be higher in retail commercial banks (Micco et al., 2007). BANKSIZE is defined as log of total assets (Berger et al., 2005; Westman, 2011) to control banking scale and EQUITY is the total equity to total asset ratio to capture financial distress and probability of bankruptcy. Macro Controls is the vector of macroeconomic variables including GDP per capita (GDP) and inflation (INFLATION). An estimation issue arises because the dependent variable (Bank Efficiency) in the regression ranges between 0 and 1, making this variable a truncated distribution. Use of Ordinary least squares (OLS) regression estimation to estimate parameters for a limited dependent variable may lead to the biased and inconsistent parameter estimates since OLS assumes a normal and
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ACCEPTED MANUSCRIPT homoskedastic distribution of the discrete dependent variable (Maddala, 1983). A common approach to handle this issue is to employ Tobit estimation. The Tobit model is used to deal with
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the characteristics of the distribution of efficiency scores for providing estimated results, which
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is employed by a number of researchers, such as Pasiouras (2008), Yeh et al. (2010), and Huang et al. (2011). Thus, we also conduct the regressions using the Tobit maximum likelihood
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procedure in our multivariate analysis.
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Table 3 provides a summary statistic of cost efficiency, ownership structure, bank-level and country-level controls. The mean value of change in government ownership in the whole sample
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is -1.9 percent. The change of foreign ownership (3.9 percent) is higher than those of domestic ownership (0.2 percent) and wide ownership (-2.2 percent). On average, the mean of the capital
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requirement is 8.6 percent with a standard deviation (0.9 percent). This suggests fairly low crosssectional variation. Moreover, the average non-interest income over total assets is composed of
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1.2 percent. The mean proportion of equity to total asset (EQUITY) and proportion of demand of deposit (DDEP) is about 9.4 percent and 82.0 percent, respectively. [Insert Table 3 here]
5. Empirical Results
5.1 Results of the Intermediation Approach Table 4 provides the estimation results for the SFA cost frontier. Panel A reports the signs of all parameters of the cost function explain a consistent and reasonable result. Most of the coefficients of outputs are positive and significant at 1% level. Also, the input prices and its quadratic terms show a significantly positive effect on total costs (except for the quadratic term of price of capital). These results indicate that the higher the bank input prices are and the more outputs that are produced, the higher the total costs are. Table 4 also presents average Bank 19
ACCEPTED MANUSCRIPT Efficiency levels by country and by year. The Bank Efficiency is representative for cost efficiency scores, which involves allocating inputs to outputs and minimizing total cost of production for a
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given output level. The Bank Efficiency in panel B for each country shows that banks in India
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and Mainland China reach the highest average cost efficiency scores, while banks in Singapore and Philippines average the lowest efficiency levels. The means of the Bank Efficiency score in
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the panel C range from 81.3 percent to 84.5 percent during the period from 2003 to 2012,
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whereas the overall mean presents at 83.6 percent. The results indicate that, on average, all banks could have produced their outputs using 81.3 percent to 84.5 percent of the inputs that they
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actually spent from year 2003 to 2012. The results in panel D reveal that the differential in average cost efficiency between prior and posterior financial crisis is the -0.5 percent, but not
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statistically significant.
[Insert Table 4 here]
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Table 5 shows the ordinary correlation between the independent variables during the period of 2003–2007. The matrix reports the correlation between FINFREE and ∆GO is positive and statistically significant, while the correlation between FINFREE and ∆DO shows an insignificant relationship. A negative association is reported for correlation between FINFREE and ∆FO. Countries with policies that provide higher financial freedom show the greater increase in government block shareholders. The relations between FINFREE and BANKSIZE and CR are positive, indicating the economies that have more freedom of finance are likely to enlarge their total assets, and to impose higher capital requirements. In addition, there are significant negative relations between ∆GO and DDEP as well as between ∆DO and EQUITY. These results suggest that increase in large government ownership will decline the demand of deposit for bank operation, and increase in domestic block shareholders will reduce the proportion of bank equity.
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ACCEPTED MANUSCRIPT [Insert Table 5 here] 5.2 Changes in Ownership Structure and Bank Efficiency
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Table 6 presents the results for the impact of ownership changes on the relation between
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financial freedom and bank efficiency. Each model of Table 6 demonstrates the results derived from alternative ownership variables while controlling for a selected set of relevant bank and
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macro-specific variables. The first three columns in Table 6 show the basic regression models
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that include financial freedom variable, changes of ownership structure and bank-pacific control variables for all sample periods (models 1-3). While models 4-6 report the estimation results that
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include the effects of financial freedom and bank ownership variables before the Global Financial Crisis of 2008, models 7-9 exam whether these effects change in the aftermath of the
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crisis.
Model 1 and model 3 in Table 6 appear significantly positive impacts of government and
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foreign ownership changes on bank efficiency, at least across the full sample of countries with high financial freedom. For economies experiencing a one percent increase in GO and FO, annual efficiency scores in countries with high financial freedom are 14.5 and 12.2 percentage points, respectively, higher than those of the lower financial freedom group. In contrast, the coefficient of the interaction term in model 2 shows a negative effect on bank efficiency, implying that greater financial freedom leads to an adverse relation between domestic private ownership and bank efficiency. Model 4 of Table 6 shows the coefficient of interaction FINFREE * ∆GO coefficient is not significant, indicating that there is no difference in effect of government ownership between high and low financial freedom countries on bank efficiency. In contrast, the coefficient of GO is found negatively associated with cost efficiency (-0.0469), suggested that an increase of
21
ACCEPTED MANUSCRIPT government ownership is associated with lower bank efficiency in the country group with low financial freedom. Similarly, the coefficient of interaction FINFREE * ∆DO in model 5 shows an
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insignificant result. This explains that change in domestic ownership does not appear to have a
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different impact between the two country groups on bank efficiency.
On the other hand, the model 6 shows the impact of increased foreign presence on bank
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efficiency is significantly higher in countries where the financial freedom is more prevalent.
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Although an increase in FO is associated with lower bank efficiency happened to low financial freedom economies, increases one percent in FO is associated with 0.23 percent higher cost
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efficiency for more financial freedom economies in Asian developing countries. For the average country, which experienced a 5.35 percent increase in FO, the annual efficiency score in
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economies with less financial freedom lagged behind the efficiency in economies with more financial freedom by 1.23 percentage points. One alternative explanation for an increased foreign
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presence, leading a decline for bank’s effective performance, could be that low financial freedom markets are characterized by high uncertainty and tend to have a higher cost of liquidation of projects (Vives, 2010)9. The foreign banks or majority foreign-owned banks face competition constraints in less financial freedom areas where domestic-owned banks are benefited by low funding cost and strong credit demand arising from government stimulus measure. Increase percentage of foreign ownership in these areas, meanwhile, would remain hampered by bank restrictions on what their businesses were allowed to do, and as a result they are suffered from poor profitability. Models 6 through 9 of Table 6 show the results after Global Financial Crisis. The interaction FINFREE * ∆FO continues to provide the significantly positive impact on bank
9
Vives (2010) investigates the relationship between competition and stability in banking in the aftermath of the systemic crisis started in 2007.
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ACCEPTED MANUSCRIPT efficiency, which explains a higher efficient score of banks from more financial freedom countries. Despite the impact of foreign ownership change becomes insignificant for lower
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financial freedom countries after the 2008 Global Financial Crisis, one percentage point increase
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in foreign large shareholders is associated with about 0.10 percentage point higher efficiency score for higher financial freedom countries.
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It is interesting to note that, the previous insignificant coefficients of the interaction
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FINFREE * ∆GO and FINFREE * ∆DO before the 2008 Global Financial Crisis become significant after the 2008 Global financial Crisis. While an increase in GO is associated with
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higher cost efficiency with higher financial freedom countries, there is a significantly negative effect from increases in DO. In detail, the annual efficiency score to ownership change in GO
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with lower financial freedom countries is negative 4.41 basis points, whereas that efficiency for the higher one is 13.47 basis points. For countries experiencing a one percent increase in GO,
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cost efficiency of banks from the high level group of financial freedom is 17.88 basis points higher than the low level group of country. The negative coefficient of ∆GO for the lower financial freedom countries is consistent with Micco et al. (2007), which indicate higher government ownership associated with lower profitability and higher cost than other ownership forms. In contrast, the positive coefficient of interaction FINFREE * ∆GO with the higher financial freedom countries is consistent with Bertay et al. (2012). Their findings show government ownership banks are able to expand their credit and maintain higher rates of loan growth relatively more over the banking crisis period, if they are located in countries with high governance effectiveness. Also, Andrianova et al. (2008) and Karas et al. (2010) argue that state-owned banks can play a useful role in stabilizing credit of a country, and an increase of government ownership may cause the related banks to have more
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ACCEPTED MANUSCRIPT effectiveness of mobilizing saving and lending at a time of financial instability. More broadly, the result is also loosely related to the finding of Andrianova et al. (2012), who show that
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government ownership of banks is associated with faster long-run growth.
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On the one hand, bank efficiency in high financial freedom countries experiencing a one percent increase in DO would be significant lower (0.11). One possible reason is that domestic
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private banks in countries with high-open financial environment, which are more limited in their
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operations, face an increased competition in business banking. This may cause a lower outputs and efficiency scores for domestic banks compared to foreign banks, especially in the aftermath
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of the financial crisis. The evidence is consistent with the view that the increase in domestic ownership will hamper capital allocation efficiency as well as bank performance (Taboada,
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2011). This may be due to increased switching costs of domestic banks after privatization leading to weak competition and inefficiency, especially in countries with high financial freedom
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(Karas et al., 2010). In addition, because of an imperfect financial liberalization process, the desired model of greater domestic bank’s efficiency through liberalization may not be realized when financial liberalization policies fail to reduce the magnitude of capital flight (Yalta and Yalta, 2012). Our result also adds support to the findings of Reinhart and Kaminsky (2001), Tornell (2001) and Zhang and Underhill (2003). They suggest that although domestic private banks in Asian countries provide main quantities of credit to domestic firms, their lending also remains heavily dependent on foreign financial inflows. As a response to the crisis, funds by the foreign investors evaporate quickly to their home countries, confronting the domestic banks with a bad performance. Table 6 also reports the estimated results of control variables, which are identified in the main model as having a specific impact on cost efficiency. For each of the reported
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ACCEPTED MANUSCRIPT specifications, the coefficients of growth in real GDP per capita is found to be positively associated with bank efficiency in Asian developing economies for both pre- and post-financial
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crisis. It is not surprising to see that faster economic growth could signify more development for
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bank. Although INFLATION is insignificant in every regression before financial crisis, its coefficient has a positive impact on bank efficiency in the post crisis. The minimum capital
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requirement regulation, CR, enters negatively and significantly in all model specifications. This
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suggests that lower minimum capital requirements tend to enhance the bank efficiency. Concerning to the bank level control variables, non-interest income (NONINT) is negatively
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correlated with cost efficiency for all models. The result shows that the banks have greater noninterest income tend to be less effective in cost management for countries. In addition, the
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coefficients of EQUITY and BANKSIZE indicate that bank efficiency of Asian developing countries is determined by both total equity and bank total assets. The total equity to total asset
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ratio enters negatively and significantly in all of the regression specifications, suggesting that lower equity capital enhance bank efficiency. This supports the negative impact of total equity to total asset ratio on bank performance found by Westman (2011). Bank size, however, shows a negative and statistically significant association with bank efficiency only for the prior financial crisis. This finding gives some more supports for Bonin et al. (2005) who suggest that smaller banks are more efficient in these transition countries. [Insert Table 6 here] 5.3. Robustness Tests In this section, we provide some robustness checks of our main results using different thresholds and endogeneity concerns. First, we consider a 20% ownership threshold to construct measures of government, domestic and foreign blockholders of banks (Laeven and Levine, 2009;
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ACCEPTED MANUSCRIPT Taboada, 2011). Using the 20% threshold, we classify a bank as being owned by a large shareholder if the shareholder owning more than 20% of total shares is a company or individual.
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The variables GO20, DO20 and FO20 attempt to capture the extent of bank control of state,
ownership threshold of 20%.
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[Insert Table 7 here]
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domestic private and foreign shares, respectively, where the control level is defined using an
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We report the results in Table 7 using ownership threshold of 20%, which overall appear to corroborate the key findings reported in Tables 6. Specifically, we continue to find that FO20
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still provides a positive association with bank efficiency in high financial freedom countries for the full sample from 2003 to 2012, while the increase in GO20 (DO20) is associated with a
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higher (lower) efficiency score to these countries only for the post period of crisis. This implies that the impacts of changes in ownership structure on the relation between financial freedom and
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bank efficiency are more pronounced even alternate measures are used. Second, we use two-step Heckman selection model to address potential endogeneity concerns. In the first step of the Heckman approach, we estimate the probability that bank with a block government shareholder is associated with measures of regulatory quality, such as country trade freedom (TRADE), rule of law (RULEOFLAW), government effectiveness (GOVEFFECT) provided by Foundation (2010) and Kaufmann et al. (2010). It implies that whenever state shareholders are indeed following ways to gain their own benefits, they would be likely to increase their contacts with banks where the rule of law is not well-established and where the level of trading freedom remains relatively low. Similarly, bank with a large domestic shareholder is determined by other factors of regulatory quality, including property rights
26
ACCEPTED MANUSCRIPT (PROPERTY), population proportion of work forces (POPULATION)10 and control of corruption (CORRUPITON). In this regard, when the corruption dimensions of a nation can be controlled at
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a low level, the improvement in business environment and law enforcement of property rights
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can help banks increase more ownership share of domestic investors. Finally, we also use fiscal freedom (FISCAL), political stability (POLSTABILITY) and regulatory quality (REGQUALITY)
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to estimate the first step of the Heckman technique for bank with the foreign block shareholders.
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Clearly, a country with stable politics and high quality of public services and implement policies of government can attract more foreign investors (Taboada, 2011). In general, it would be
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expected that when financial institutions operate in a better economic environment, they may be more likely to take part in competitive policies thus achieving more efficient operations. Yet,
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these proxy variables should not directly affect the way in which cost efficiency of banks is estimated.
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Panel A of Table 8 shows the results from the first-stage of the Heckman technique. The results show that the instrument variables work well in forecasting changes in government, domestic and foreign bank ownership. Panel B of Table 8 reports the results from the second step of the Heckman approach and confirms our prior findings. [Insert Table 8 here]
6. Conclusion This study has examined the effect of financial freedom on the relationship between changes in ownership structure and bank efficiency across Asian developing countries over period 2003-2012. Using cost efficiency scores, our main findings as follows. Firstly, banks with foreign presence improve bank efficiency, primarily in countries with high financial freedom. 10
POPULATION is a proxy for the population ages 15 – 64 as a percent of the total population provided by World Bank.
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ACCEPTED MANUSCRIPT Secondly, increased government (domestic) ownership of bank appears to improve (impede) bank efficiency in countries with more financial freedom after financial crisis.
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Assessing the effects of financial freedom and the changes in bank ownership structure for
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financial institutions’ efficiency has direct implications in the context of this debate, especially in the aftermath of the 2008 Global Financial Crisis, which increase the prominence of systemic
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risk. Our study highlights the importance of designing an appropriate bank regulatory and
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supervisory framework in the design of privatization and liberalization programs that helps
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maintain the efficiency (and hopefully stability) of banks.
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Table 1
GO(03)
GO(07)
GO(08)
GO(12)
DO(03)
DO(07)
Panel A – Ownership by type of owner as of 2003, 2007, 2008 and 2012 0.00% 55.36% 13.67% 6.17% 13.31% 4.64% 15.68% 9.70% 0.00% 10.33% 15.07% 0.00% 15.33% 28.18%
17.66% 2.32% 25.74% 31.75% 14.47% 40.87% 7.59% 39.18% 46.33% 42.93% 27.65% 52.19% 27.11% 37.08%
19.89% 3.36% 20.96% 40.01% 12.95% 44.23% 17.43% 41.39% 40.35% 49.85% 19.30% 34.73% 27.16% 36.14%
11.99% 5.17% 21.21% 42.05% 17.00% 46.80% 14.05% 40.07% 39.40% 51.09% 21.90% 33.36% 27.19% 35.99%
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0.00% 55.19% 18.60% 13.46% 13.84% 1.50% 14.29% 0.78% 0.00% 12.37% 14.29% 0.00% 15.51% 29.17%
MA
0.00% 53.15% 19.58% 13.12% 11.85% 0.00% 14.90% 0.73% 0.00% 13.65% 14.29% 0.00% 15.08% 28.79%
PT ED
0.00% 65.46% 28.18% 11.00% 24.45% 0.81% 27.74% 3.92% 0.00% 13.44% 13.33% 0.00% 19.84% 34.85%
CE
Hong Kong India Indonesia Korea Mainland China Malaysia Pakistan Philippines Singapore Taiwan Thailand Turkey Full sample mean Full sample Std. Dev.
DO(08)
SC
Country
RI
PT
Bank ownership of developing Asian countries: 2003, 2007, 2008 and 2012. Panel A shows the percentage of assets owned by the government (GO), domestic (DO), or foreign-held block shareholders (FO) which is classified by the 10% threshold from (La Porta et al., 1999). Panel B provides descriptive statistics of the changes in bank ownership across countries that whether experienced a banking crisis over the period. DO(12)
FO(03)
FO(07)
FO(08)
FO(12)
11.52% 5.71% 16.41% 68.01% 17.02% 44.39% 10.12% 38.75% 58.65% 50.54% 21.11% 29.05% 27.55% 36.58%
54.66% 1.40% 24.12% 13.64% 9.79% 47.06% 20.32% 13.91% 20.00% 3.95% 16.35% 17.01% 18.86% 33.15%
57.82% 6.64% 39.13% 16.73% 24.86% 44.44% 30.59% 21.17% 28.57% 4.17% 22.80% 42.86% 26.68% 36.04%
74.36% 8.57% 43.03% 17.93% 29.10% 44.44% 34.17% 28.14% 28.57% 6.36% 25.02% 44.00% 30.09% 38.02%
75.66% 5.55% 46.06% 14.29% 34.22% 44.44% 44.67% 25.19% 0.00% 8.42% 34.76% 47.32% 31.87% 39.07%
No. of banks
16 29 24 15 21 18 17 15 07 25 15 17 219
Panel B – Descriptive statistics of changes in ownership measures
Mean Max Min Standard deviation Total # of banks (2003) Total # of banks (2007) Total # of banks (2008) Total # of banks (2012)
∆DO 0.43% 100% -64.59% 16.45%
AC
Between 2003 and 2007 ∆GO -3.74% 31.87% -97.77% 13.60% 200 213 213 213
∆FO 5.35% 76.99% -51.00% 16.30%
∆WIDE -2.04% 72.80% -1.00% 20.16%
Between 2008 and 2012 ∆GO -0.18% 72.20% -100% 11.77%
∆DO -0.06% 100% -77.40% 20.27%
∆FO 2.77% 100% -51.02% 16.32%
∆WIDE -2.54% 43.52% -100% 16.27%
Correlation matrix 2003 – 2012 ∆GO ∆DO ∆FO ∆WIDE
∆GO 1.00 -0.41*** -0.04 -0.18***
∆DO -0.41*** 1.00 -0.53*** -0.41***
∆FO -0.04 -0.53*** 1.00 -0.34***
∆WIDE -0.18*** -0.41*** -0.34*** 1.00
Notes: The ownership measures (GO, DO, FO) are constructed following La Porta et al. (2002) and Taboada (2011). Domestic and foreign block shareholders are individuals, families, or companies (including bank holding companies) that own more than 10% of the bank’s shares. *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level
35
ACCEPTED MANUSCRIPT Table 2 Descriptive statistics of variables used in Bank Efficiency estimations SD
Median Minimum Maximum
PT
Mean
0.000
48.272
0.209
0.001
24.705
1.129
0.000
15.301
5.101
0.514
0.002
72.895
1.119
3.137
Non-interest expenses (in billion US $)
0.723
2.029
Personnel expenses (in billion US $)
0.367
Total costs (in billion US $)
1.842
0.286
0.102
30.445
98.097
6.950
0.005
1,399.722
22.160
90.780
3.816
0.002
1,334.961
49.158
174.481
10.819
0.008
2,537.282
11.011
47.142
1.724
0.006
727.811
P1 = Price of funds
0.036
0.031
0.029
0.002
0.663
P2 = Price of capital
2.64
3.67
1.60
0.15
44.17
0.010
0.007
0.008
0.001
0.141
NU
SC
RI
Interest expenses (in billion US $)
Outputs (in billion US $)
MA
Y1 = Total loans Y2 = Other earning assets
AC CE P
Input prices
TE
Y4 = Liquid assets
D
Y3 = Total deposits
P3 = Price of labor
Notes: Price of funds is the ratio of interest expenses to total deposits. Price of capital is the ratio of non-interest expenses to total fixed assets. Price of labor is the ratio of personnel expenses to total assets. Source of data: BankScope, 2003–2012.
36
ACCEPTED MANUSCRIPT
NU
SC
RI
PT
Table 3 Descriptive statistics of control variables used to estimate the impact of changes in ownership on bank efficiency. Bank Efficiency is cost efficiency scores of the individual bank obtained from equation (3) by parametric stochastic frontier approach (SFA). GO, DO, FO and WIDE are the percentage of assets owned by the government, domestic, and foreign-held block shareholders. ∆GO, ∆DO, ∆FO are changes in percentage owned by government, domestic and foreign block shareholders, respectively. FINFREE is dummy variable, takes a value of one if the country j’s financial and banking freedom index is above the median value and zero otherwise. Capital requirement (CR) is the minimum capital asset ratio requirement. NONINT represents for noninterest income as a share of total assets of banks and DDEP represents for demand deposits as a share of bank total deposits. BANKSIZE variable controls for economy of scale which calculated by log of bank total assets. EQUITY is denoted as the total equity to total asset ratio. GDP represents the growth in real GDP per capita. INFLATION is the deflated Consumer Price Index (CPI).
MA
Mean Bank Efficiency
SD
Median
Minimum Maximum
0.836
0.116
0.862
0.039
0.994
0.163
0.300
0.000
0.000
1.000
0.275
0.366
0.000
0.000
1.000
0.269
0.371
0.000
0.000
1.000
-0.019
0.127
0.000
-1.000
0.722
0.002
0.188
0.000
-0.744
1.000
0.039
0.168
0.000
-1.000
1.000
0.293
0.310
0.236
0.000
1.000
0.182
0.386
0.000
0.000
1.000
8.572
0.925
8.000
8.000
12.000
NONINT
0.012
0.015
0.009
0.000
0.284
DDEP
0.820
0.169
0.865
0.001
1.000
BANKSIZE
4.055
0.777
4.086
1.481
6.445
EQUITY
0.094
0.069
0.071
0.005
0.813
GDP
0.069
0.037
0.073
-0.039
0.169
INFLATION
1.165
0.244
1.097
0.851
2.219
GO
∆DO ∆FO WIDE FINFREE CR
TE
∆GO
AC CE P
FO
D
DO
Notes: The overall sample is an unbalanced panel which consists of 2113 bank-year observations (219 commercial banks), covering 10 years period – 2003–2012.
37
ACCEPTED MANUSCRIPT Table 4
PT
Results of SFA parameter estimates and cost efficiency scores using an intermediation approach. The total costs (TC) variable comprises interest expenses and non-interest expenses for each sample bank. The output variables encompass total loans (Y1), other earning assets (Y2), total deposits (Y3), and liquid assets (Y4). Three inputs are price of funds (P1), price of capital (P2), and price of labor (P3). Bank Efficiency is the cost efficiency scores of the individual bank obtained from equation (3) by frontier cost function techniques, which higher values indicate the more efficient banks.
AC CE P
TE
D
MA
NU
SC
RI
Dependent variable Parameter Results of Bank Efficiency Scores ln(TC/P3) Estimates Panel A – SFA cost function specification 1.160*** Full sample mean 0.836 0 *** ln(P1/P3) 0.436 Full sample median 0.862 ln(P2/P3) 0.123*** ln(Y1) 0.401*** Panel B – Means of efficiency level by country *** ln(Y2) 0.245 Hong Kong 0.860 ln(Y3) 0.198 India 0.882 ln(Y4) 0.141*** Indonesia 0.856 2 *** ln(P1/P3) 0.161 Mainland China 0.881 ln(P2/P3)2 -0.007 Malaysia 0.867 2 *** ln(Y1) 0.070 Pakistan 0.824 ln(Y2)2 0.067*** Philippines 0.732 ln(Y3)2 0.294*** Singapore 0.694 2 ** ln(Y4) -0.025 South Korea 0.824 ln(P1/P3)×ln(P2/P3) -0.036*** Taiwan 0.860 ln(P1/P3)×ln(Y1) -0.083*** Thailand 0.763 *** ln(P1/P3)×ln(Y2) -0.039 Turkey 0.810 ln(P1/P3)×ln(Y3) 0.160*** ln(P1/P3)×ln(Y4) -0.031*** Panel C – Means of efficiency level by year ln(P2/P3)×ln(Y1) -0.023** 2003 0.822 ln(P2/P3)×ln(Y2) 0.005 2004 0.813 * ln(P2/P3)×ln(Y3) 0.030 2005 0.840 ln(P2/P3)×ln(Y4) -0.018*** 2006 0.845 ln(Y1)×ln(Y2) 0.039*** 2007 0.844 *** ln(Y1)×ln(Y3) -0.125 2008 0.850 ln(Y1)×ln(Y4) 0.019 2009 0.831 *** ln(Y2)×ln(Y3) -0.143 2010 0.823 ln(Y2)×ln(Y4) 0.035*** 2011 0.842 ln(Y3)×ln(Y4) -0.027 2012 0.844 Number of obs. 2113 Log likelihood 1597.3 Panel D – Means by countries experiencing a financial crisis *** Sigma_u (σu) 0.112 Before crisis (2003 – 2007) 0.833 Sigma_square (σ2) 0.016*** After crisis (2008 – 2012) 0.838 Likelihood-ratio test of sigma_u=0 Difference -0.005 Chibar 2 570.0 t-stats (Before vs. After) -0.974 2 Probability ≥ 0.000 (t-statistics in italics are for differences in means) Notes: *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level
38
ACCEPTED MANUSCRIPT
Table 5
∆DO
∆FO
FINFREE
CR
NONINT
NU
∆GO
SC
RI
PT
This table reports the correlation coefficient matrix of main regression variables. The sample includes 219 banks from 12 Asian developing countries. The statistics based on annual data for the year 2003 –200711. ∆GO, ∆DO, ∆FO are changes in percentage owned by government, domestic and foreign block shareholders, respectively. FINFREE is dummy variable, takes a value of one if the country j’s financial and banking freedom index is above the median value and zero otherwise. Capital requirement (CR) is the minimum capital asset ratio requirement. NONINT represents for non-interest income as a share of total assets of banks and DDEP represents for demand deposits as a share of bank total deposits. BANKSIZE variable controls for economy of scale which calculated by log of bank total assets. EQUITY is denoted as the total equity to total asset ratio. GDP represents the growth in real GDP per capita. INFLATION is the deflated Consumer Price Index (CPI). DDEP
BANKSIZE
EQUITY
GDP
1.000
∆DO
-0.110***
1.000
∆FO
-0.211***
-0.329***
1.000
FINFREE
0.114***
0.030
-0.060*
1.000
CR
0.023
0.017
-0.029
0.467***
1.000
NONINT
0.011
-0.052*
0.025
-0.110***
-0.012
1.000
DDEP
-0.147***
0.020
0.012
0.048
0.072**
-0.099***
1.000
BANKSIZE
-0.096***
0.038
0.008
0.193***
0.024
-0.184***
0.046
1.000
EQUITY
0.075**
-0.125***
0.020
-0.016
0.059*
0.463***
-0.271***
-0.476***
1.000
GDP
-0.133***
-0.040
0.002
-0.067*
0.014
-0.059*
0.110***
0.229***
-0.090***
1.000
INFLATION
-0.016
-0.011
0.009
-0.045
-0.009
-0.052*
-0.007
0.092***
0.042
0.221***
AC
CE
PT ED
MA
∆GO
Notes: *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level
11
INFLATION
To conserve space, the descriptive statistic for the period 2008 – 2012 is not reported, but is available upon request.
39
1.000
ACCEPTED MANUSCRIPT Table 6
FINFREE * ∆DO
-0.0757*** (-3.83)
FINFREE * ∆FO ∆GO
0.1220*** (4.04) -0.0468*** (-3.27)
∆FO
INFLATION CR NONINT DDEP BANKSIZE EQUITY Year fixed effects Log likelihood Observations
0.3442*** (4.80) 0.0244** (2.37) -0.0235*** (-5.88) -2.1729*** (-6.82) 0.0100 (0.56) -0.0076** (-2.06) -0.4996*** (-7.93) YES 1953.22 2113
0.3644*** (5.11) 0.0259** (2.55) -0.0245*** (-6.19) -2.1688*** (-6.79) 0.0152 (0.85) -0.0077** (-2.09) -0.4951*** (-7.88) YES 1952.69 2113
-0.0372** (-2.42) 0.3518*** (5.05) 0.0284** (2.47) -0.0245*** (-7.00) -2.1638*** (-5.35) 0.0180 (0.98) -0.0066** (-2.03) -0.4965*** (-7.33) YES 1955.90 2113
AC
GDP
0.0950*** (3.00)
NU
SC
0.2314*** (3.94)
-0.0469*** (-2.61)
0.0357*** (3.10)
∆DO
After Global Financial Crisis (2008-2012) (7) (8) (9) 1.1746*** 1.1928*** 1.1613*** (21.39) (22.51) (19.80) -0.0153** -0.0123* -0.0187** (-2.04) (-1.73) (-2.22) 0.1788*** (3.83) -0.1071*** (-3.83)
MA
FINFREE * ∆GO
Dependent variables: Cost efficiency (CE) Before Global Financial Crisis (2003-2007) (4) (5) (6) 0.9922*** 0.9718*** 0.9632*** (12.37) (9.75) (9.57) 0.0453*** 0.0415*** 0.0327*** (3.86) (3.84) (2.90) -0.0343 (-0.05) 0.0215 (0.29)
PT ED
FINFREE
(1) 1.0815*** (28.21) 0.0163*** (2.95) 0.1453*** (2.75)
CE
Independent variables Intercept
Full sample (2003-2012) (2) (3) 1.0822*** 1.0722*** (27.48) (27.15) 0.0176*** 0.0112** (3.26) (2.05)
RI
PT
This table reports the differential impact of changes in bank ownership structure on the cost efficiency, which is estimated by Tobit regressions. Statistics based on annual data for the year 2003 – 2012. ∆GO, ∆DO, ∆FO are changes in percentage owned by government, domestic and foreign block shareholders, respectively. FINFREE is dummy variable, takes a value of one if the country j’s financial and banking freedom index is above the median value and zero otherwise. Capital requirement (CR) is the minimum capital asset ratio requirement. NONINT represents for non-interest income as a share of total assets of banks and DDEP represents for demand deposits as a share of bank total deposits. BANKSIZE variable controls for economy of scale which calculated by log of bank total assets. EQUITY is denoted as the total equity to total asset ratio. GDP represents the growth in real GDP per capita. INFLATION is the deflated Consumer Price Index (CPI). Models 1 through 3 report the basic regression results that include main independent variables and bank-specific control variables for all sample periods. Models 4 through 6 show estimated results during the period from 2003 to 2007, while models 7 through 9 provide regression results during the period from 2008 to 2012. The values of the t-statistics are in parentheses.
-0.0441** (-2.18) 0.0564*** (2.78)
0.0163 (0.97)
0.3488*** (3.26) 0.0749 (1.15) -0.0130** (-2.46) -1.6785*** (-3.37) 0.0236 (1.06) -0.0218*** (-4.19) -0.6629*** (-6.71) YES 913.37 1039
Notes: *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level
40
0.3796*** (3.29) 0.0824 (1.09) -0.0126** (-2.59) -1.6803*** (-3.98) 0.0283 (0.99) -0.0208*** (-3.23) -0.6558*** (-6.39) YES 911.89 1039
-0.0570*** (-3.27) 0.3562*** (3.15) 0.0923 (1.21) -0.0130*** (-2.73) -1.6587*** (-3.85) 0.0300 (1.02) -0.0197*** (-3.03) -0.6607*** (-6.43) YES 917.72 1039
0.1570* (1.73) 0.0243** (2.04) -0.0360*** (-7.29) -3.5952*** (-5.88) -0.0203 (-0.85) 0.0053 (0.79) -0.3044*** (-4.42) YES 1103.12 1074
0.1708* (1.95) 0.0248*** (2.82) -0.0377*** (-7.68) -3.5894*** (-5.09) -0.0140 (-0.69) 0.0031 (0.52) -0.3133*** (-5.51) YES 1104.21 1074
-0.0137 (-0.70) 0.1654* (1.81) 0.0261** (2.03) -0.0372*** (-7.51) -3.5807*** (-5.75) -0.0014 (-0.07) 0.0059 (0.87) -0.2911*** (-3.88) YES 1101.61 1074
ACCEPTED MANUSCRIPT Table 7
PT
This table reports the differential impact of changes in bank ownership structure on the cost efficiency, which is estimated by Tobit regressions. Statistics based on annual data for the year 2003 – 2012. ∆GO20, ∆DO20, ∆FO20 are changes in percentage owned by government, domestic and foreign block shareholders using a threshold of 20%, respectively. FINFREE is dummy variable, takes a value of one if the country j’s financial and banking freedom index is above the median value and zero otherwise. Capital requirement (CR) is the minimum capital asset ratio requirement. NONINT represents for non-interest income as a share of total assets of banks and DDEP represents for demand deposits as a share of bank total deposits. BANKSIZE variable controls for economy of scale which calculated by log of bank total assets. EQUITY is denoted as the total equity to total asset ratio. GDP represents the growth in real GDP per capita. INFLATION is the deflated Consumer Price Index (CPI). Models 1 through 3 report the basic regression results that include main
FINFREE FINFREE * ∆GO20
1.0784*** (27.19) 0.0172*** (3.13)
1.0703*** (27.04) 0.0115** (2.11)
-0.0611*** (-3.11)
FINFREE * ∆DO20
0.1073*** (3.52) -0.0398*** (-2.91) 0.0270** (2.22)
∆DO20 ∆FO20
INFLATION CR NONINT DDEP BANKSIZE EQUITY Year fixed effects Log likelihood Observations
0.3452*** (4.81) 0.0244** (2.36) -0.0235*** (-5.89) -2.1725*** (-6.82) 0.0103 (0.57) -0.0075** (-2.01) -0.4990*** (-7.92) YES 1952.41 2113
0.3652*** (5.12) 0.0262** (2.59) -0.0242*** (-6.09) -2.1718*** (-6.81) 0.0148 (0.83) -0.0074** (-2.03) -0.4949*** (-7.84) YES 1950.87 2113
-0.0226 (-1.44) 0.3495*** (4.99) 0.0277** (2.45) -0.0243*** (-6.91) -2.1656*** (-5.35) 0.0187 (1.01) -0.0065** (-2.00) -0.4943*** (-7.26) YES 1953.80 2113
AC
GDP
0.9744*** (9.86) 0.0412*** (3.75)
0.9727*** (10.59) 0.0354*** (2.67)
After Global Financial Crisis (2008-2012) (7) (8) (9) 1.1756*** (23.26) -0.0153** (-2.08) 0.1760*** (3.89)
0.2029*** (3.76)
Notes: *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level
41
1.1595*** (20.00) -0.0186** (-2.23)
0.0997*** (3.35) -0.0412* (-1.92) 0.0509*** (2.77)
0.0068 (0.39)
0.3492*** (3.24) 0.0759 (1.16) -0.0128** (-2.43) -1.6781*** (-3.37) 0.0240 (1.07) -0.0215*** (-4.14) -0.6617*** (-6.70) YES 912.85 1039
1.1882*** (22.15) -0.0125* (-1.79) -0.1004*** (-3.79)
0.0325 (0.51)
-0.0389** (-2.08)
CE
∆GO20
PT ED
FINFREE * ∆FO20
0.9886*** (12.17) 0.0447*** (3.77) -0.0407 (0.00)
SC
1.0810*** (28.22) 0.0162*** (2.92) 0.1383*** (2.66)
NU
Intercept
(1)
Dependent variables: Cost efficiency (CE) Before Global Financial Crisis (2003-2007) (4) (5) (6)
MA
Independent variables
Full sample (2003-2012) (2) (3)
RI
independent variables and bank-specific control variables for all sample periods. Models 4 through 6 show estimated results during the period from 2003 to 2007, while models 7 through 9 provide regression results during the period from 2008 to 2012. The values of the t-statistics are in parentheses.
0.3747*** (3.25) 0.0807 (1.07) -0.0126** (-2.58) -1.6789*** (-3.98) 0.0280 (0.98) -0.0208*** (-3.26) -0.6587*** (-6.43) YES 911.67 1039
-0.0267** (-2.08) 0.3527*** (3.89) 0.0871 (1.22) -0.0132*** (-2.92) -1.6659*** (-4.65) 0.0291 (1.32) -0.0203*** (-3.69) -0.6613*** (-5.98) YES 914.67 1039
0.1583* (1.85) 0.0241*** (2.77) -0.0361*** (-7.63) -3.5988*** (-5.20) -0.0200 (-0.83) 0.0053 (0.85) -0.3043*** (-5.40) YES 1102.97 1074
0.1744** (2.00) 0.0260*** (2.98) -0.0375*** (-7.62) -3.6106*** (-5.06) -0.0150 (-0.73) 0.0037 (0.61) -0.3131*** (-5.50) YES 1103.36 1074
-0.0187 (-0.97) 0.1654* (1.81) 0.0269** (2.12) -0.0372*** (-7.49) -3.5834*** (-5.78) -0.0008 (-0.04) 0.0061 (0.89) -0.2888*** (-3.84) YES 1101.68 1074
ACCEPTED MANUSCRIPT Table 8
SC
RI
PT
This table reports the impact of change in government ownership structure on the bank efficiency, which is estimated by Heckman regressions. In the first step, each dependent variables, STATE, DOMESTIC and FOREIGN equal 1 if the bank is owned by large government, domestic and foreign blockholders (owned more than 10%), respectively; zero otherwise. Statistics based on annual data for the year 2003 – 2012. The likelihood of these dependent variables are determined as the function of the national trade freedom (TRADE), rule of law (RULEOFLAW), government effectiveness (GOVEFFECT), property rights (PROPERTY), population proportion of work forces (POPULATION), control of corruption (CORRUPITON), fiscal freedom (FISCAL), political stability (POLSTABILITY), regulatory quality (REGQUALITY), lagged real GDP per capita and lagged country inflation. The dependent variable in the second step is the bank’s efficiency score. ∆GO is the change in percentage owned by government block shareholders. FINFREE is dummy variable, takes a value of one if the country j’s financial and banking freedom index is above the median value and zero otherwise. Capital requirement (CR) is the minimum capital asset ratio requirement. NONINT represents for non-interest income as a share of total assets of banks and DDEP represents for demand deposits as a share of bank total deposits. BANKSIZE variable controls for economy of scale which calculated by log of bank total assets. EQUITY is denoted as the total equity to total asset ratio. GDP represents the growth in real GDP per capita. INFLATION is the deflated Consumer Price Index (CPI). The values of the t-statistics are in parentheses.
Panel A – Heckman first stage regression results Intercept 1.8418*** 4.7199*** (5.39) -0.0312*** (-9.63) 1.0110*** (5.59) -1.3326*** (-6.48)
TRADE RULEOFLAW GOVEFFECT
(4.31)
-0.0393*** (-7.24) 0.4033*** (5.06) -2.2270** (-1.97)
FISCAL REGQUALITY
GDP(-1) INFLATION(-1)
statistic 2
2
STATE
DOMESTIC
FOREIGN
3.4563*** (2.66)
1.0256 (1.47)
4.8859*** (6.88) -0.0572*** (-7.32) 1.6641*** (5.03) -1.8757*** (-4.98)
11.3135*** (6.47)
2.9218*** (6.43)
-0.0352*** (-5.01) 0.3501*** (2.89) -0.8019 (-0.53)
-0.0667*** (-6.57) 0.7644*** (5.02) -8.4793*** (-4.68)
-5.1491*** (-3.23) 0.0040 (0.01)
-0.0397*** (-6.02) -0.4276*** (-4.91) 1.2621*** (7.77) -0.4232 (-0.29) 0.4968 (0.95)
4.1424*** (2.91) -0.7286** (-2.51)
-0.0069 (-0.01) -0.4322* (-1.96)
-0.0577*** (-10.03) -0.7074*** (-6.54) 1.8346*** (10.00) -0.1998 (-0.16) -0.2931 (-1.08)
-2.2998*** (-2.85) -0.1815 (-1.10)
-0.6011 0.0807 144.89
0.6682 0.1105 961.71
-0.2444 0.0952 213.05
-0.5477 0.0796 131.27
0.6421 0.1125 412.97
-0.3719 0.0973 289.08
-0.7996 0.0744 121.90
0.1524 0.0838 215.72
0.3311 0.0882 233.16
00.00
00.00
00.00
00.00
00.00
00.00
00.00
00.00
00.00
CORRUPTION
2.0747*** (3.49) -0.0338*** (-9.44) 0.4837** (2.41) -0.7896*** (-3.47)
FOREIGN
2.7432*** (2.73) -0.0883 (-0.56)
POLSTABILITY
Probability >
1.7624*** (6.69)
After Global Financial Crisis (2008-2012)
DOMESTIC
-0.0488*** (-10.86) -0.5125*** (-7.80) 1.5246*** (12.06) 0.0199 (0.03) 0.2377* (1.85)
PROPERTY
Wald
STATE
AC
POPULATION
FOREIGN
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DOMESTIC
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STATE
CE
Independent variables
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Dependent variables Before Global Financial Crisis (2003-2007)
Full sample (2003-2012)
0.9897 (0.72) -0.3254 (-0.60)
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ACCEPTED MANUSCRIPT Dependent variables: Cost efficiency (CE) Full sample (2003-2012)
Before Global Financial Crisis (2003-2007)
After Global Financial Crisis (2008-2012)
Panel B – Heckman second stage regression results (21.15) 0.0092 (0.83)
(12.68) 0.0387*** (3.04)
-0.0716** (-1.97)
FINFREE * ∆DO
0.1046*** (3.10) 0.0125 (0.66)
CR NONINT DDEP BANKSIZE EQUITY INV. MILLS RATIO Year fixed effects Observations
YES 1899
0.1892* (1.83) 0.0108 (0.43) -0.0279*** (-5.29) -2.6191*** (-6.05) -0.0803*** (-2.92) 0.0015 (0.35) -0.5870*** (-7.50) 0.0739** (2.28) YES 1899
YES 1899
0.1058** (2.42) 0.0438** (2.27)
0.0359 (1.58)
0.1531 (0.91) -0.0715 (-0.65) -0.0286*** (-4.88) -2.1988*** (-6.01) -0.0630** (-2.06) -0.0090 (-1.07) -0.7063*** (-6.24) 0.0722** (1.98)
YES 1038
YES 1038
YES 1038
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1.0881*** (11.26) -0.0281* (-1.73)
-0.0935*** (-3.16)
0.0084 (0.02) -0.1518 (-0.93) -0.0119 (-1.06) -1.0163 (-0.72) 0.1670*** (2.99) -0.0338*** (-4.30) -0.4132 (-1.62) -0.0436*** (-2.81)
Notes: *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level
1.2730*** (6.47) -0.0564*** (4.79)
0.0910** (2.13)
-0.0295 (-0.97) 0.3121** (2.10) 0.1152 (1.07) -0.0277*** (-3.48) -0.6779 (-1.13) -0.0556** (-2.01) -0.0353*** (-3.86) -0.5985*** (-3.64) -0.0362 (-1.26)
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INFLATION
0.0634 (0.40) -0.1041*** (-3.73) -0.0268*** (-3.46) -2.2642*** (-3.67) 0.1404*** (6.41) -0.0243*** (-4.25) -0.1572 (-1.15) -0.0485*** (-4.75)
AC
GDP
-0.0343 (-1.57) 0.5030*** (3.71) 0.0491** (2.17) -0.0268*** (-4.32) -1.4391** (-2.36) -0.0257 (-0.96) -0.0120* (-1.96) -0.3772*** (-3.21) -0.0233 (-1.52)
CE
∆FO
1.4503*** (10.15) -0.1014*** (-5.39) 0.1739*** (2.86)
-0.0704* (-1.93)
0.0442 (1.63)
0.0446*** (2.73)
∆DO
1.2029*** (6.58) 0.1081*** (4.61)
0.0935 (0.92)
FINFREE * ∆FO ∆GO
1.2589*** (10.14) 0.0679*** (3.48)
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(12.58) -0.0061 (-0.41) -0.0546 (-0.84)
1.2412*** (5.12) 0.0672*** (3.15) -0.3130 (-0.23)
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1.1025***
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FINFREE * ∆GO
1.1220***
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FINFREE
1.3264***
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Intercept
-0.0820 (-0.47) -0.1143*** (-3.47) -0.0354*** (-3.52) -6.0682*** (-6.11) 0.0452 (1.21) -0.0146** (-2.00) 0.6399*** (3.32) -0.0595*** (-4.33) YES 860
-0.0132 (-0.08) 0.0260 (1.14) -0.0333*** (-5.26) -4.5457*** (-5.11) -0.1289*** (-4.00) 0.0046 (0.67) -0.5094*** (-4.54) 0.0128 (0.50) YES 860
-0.0018 (-0.06) 0.2394 (1.08) 0.0171 (0.62) -0.0357*** (-4.03) -3.5412*** (-3.65) 0.0251 (0.73) 0.0064 (0.86) -0.0038 (-0.03) 0.0292** (2.02) YES 860
S1059-0560(15)00179-3 doi: 10.1016/j.iref.2015.10.029 REVECO 1164
To appear in:
International Review of Economics and Finance
Please cite this article as: Lin, K.-L., Doan, A.T. & Doong, S.-C., Changes in ownership structure and bank efficiency in Asian developing countries: The role of financial freedom, International Review of Economics and Finance (2015), doi: 10.1016/j.iref.2015.10.029
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ACCEPTED MANUSCRIPT CHANGES IN OWNERSHIP STRUCTURE AND BANK EFFICIENCY IN ASIAN
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Kun-Li Lin
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DEVELOPING COUNTRIES: THE ROLE OF FINANCIAL FREEDOM
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Department of Finance, Feng Chia University
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100 Wenhwa Road, Seatwen, Taichung, Taiwan 40724
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[email protected]
Anh Tuan Doan
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PhD Program in Finance, Feng Chia University
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100 Wenhwa Road, Seatwen, Taichung, Taiwan 40724
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[email protected]
Shuh-Chyi Doonga
Department of Finance, National Chung Hsing University 250 Kuo Kuang Road, Taichung 402, Taiwan [email protected]
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Correspondence: Shuh-Chyi Doong, Department of Finance, National Chung Hsing University, Taichung, Taiwan. E-mail: [email protected].
ACCEPTED MANUSCRIPT
ABSTRACT
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This paper investigates the effect of the changes in bank ownership on cost efficiency across
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twelve Asian developing economies. We also evaluate how financial freedom shapes the effect
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of the changes in bank ownership on cost efficiency. Using stochastic frontier approach to estimate bank efficiency scores during the period 2003-2012, we find that foreign presence
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improves bank efficiency, primarily in countries with high financial freedom. In addition, our results also show that increased government (domestic) ownership of bank appears to improve
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(impede) bank efficiency in countries with more financial freedom after financial crisis.
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Keywords: Ownership changes, bank efficiency, financial freedom
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1. Introduction
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In recent years, financial markets have become increasingly integrated as governments have
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liberalized domestic financial sectors and capital accounts. One facet of the larger process of
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financial globalization is the increased participation of private financial institutions in local banking sectors, especially in Asian developing economies after the 1997 financial crisis. The
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growth in bank privatizations in Asian developing countries has fostered an increased interest in this worldwide phenomenon. Recent studies focus on the causes and consequences between
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ownership structure and performance. For example, Micco et al. (2007) and Cornett et al. (2010)
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show that state-owned banks operating in developing countries tend to have lower profitability
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than the private banks, and foreign ownership banks tend to be represented by higher profitability than other counterparts. Similarly, focusing on the China banking sectors, Berger et
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al. (2009), Ferri (2009) and Lin and Zhang (2009) showed a significantly higher performance by private and foreign-owned banks relative to state-owned commercial banks. With this growing literature on ownership structure in Asian developing economies, some studies further to compare the bank efficiency in different ownership structure but are inconclusive (see Laeven, 1999; Williams and Nguyen, 2005). For example, Laeven (1999) shows that state‐owned and foreign‐owned banks as well as Korean and Malaysian banks took little risk relative to other banks, while family‐owned and company‐owned banks and Indonesian and Philippine banks were among the highest risk‐takers. Unite and Sullivan (2003) show that foreign entry in the banking market in the Philippines corresponds to an improvement in operating efficiencies, but with deterioration in the quality of loan portfolios. Williams and Nguyen (2005) find that, as a result of bank privatization programs, banks selected for domestic 2
ACCEPTED MANUSCRIPT mergers and acquisitions (M&As) exhibited relatively low rank order profit efficiency before the governance change, which improved in the short‐term but deteriorated in the long‐term implying
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a temporary efficiency gain using five East Asian countries (Indonesia, South Korea, Malaysia,
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Philippines, and Thailand) between 1990 and 2003. Although the impact of ownership structure
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has received a lot of attention recently, the consequences of the resulting changes in bank ownership structure on efficiency have yet to be adequately explored. We expect that using a
efficiency changes as ownership changes.
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real ownership change instead of dummy variable can provide a better insight into how bank
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Moreover, the recurrent episodes of the 2008 Global Financial Crisis associated with financial liberalization motivated a number of researchers to investigate between deregulations
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and bank performance. One approach points to the deregulation of financial services and institutions as a fundamental reason that led to the crisis (Keeley, 1990), while other approaches
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suggest that the seeds of the crisis were sown by a particular set of capital regulations because the cap rate tends to have positive association with the amount of risky assets, and then increase in the bank’s default probability (Tsai and Hung, 2013). So, an emerging question in the midst of this debate is if and how economic and financial liberalization may affect the performance of financial institutions (e.g. Claessens and Laeven, 2004; Goddard et al., 2011). Although research is increasingly using the indexes of economic freedom to proxy liberal effect as explanatory variables in their regressions (e.g. Powell, 2003; Altman, 2008; Heckelman and Knack, 2009), these studies have just assessed the impact of economic freedom on economic growth. Nevertheless, in the banking literature the indexes of economic freedom have been used only as control variables and/or have been inaccurately interpreted as regulation indexes. Recently, some studies try to include indicators that examine the degree of financial liberalization in bank
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ACCEPTED MANUSCRIPT efficiency. For example, Fries and Taci (2005) consider the role of banking sector reform and liberalization in the transition countries to capture the effect on bank cost efficiency. The key
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explanatory variable of interest is an index of banking sector reform published by the European Bank for Reconstruction and Development (EBRD) Transition Reports. Their results show that
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progress in banking reform is significantly associated with a decrease in banks’ costs. Hence,
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financial liberalization must take account of the interactions between bank ownership and
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efficiency.
The purpose of this paper is to investigate the relationship between ownership changes and
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bank efficiency. Specifically, we question whether the financial freedom strengthens or weakens the relationship between the changes in bank ownership and efficiency before and after Global
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Financial Crisis. We regress the efficiency estimates on the interaction between the changes in ownership and financial freedom indexes from the Foundation (2010), which aim at capturing
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the “greater independence in financial and banking markets from government control”. We employ parametric stochastic frontier approach (SFA) to measure the bank efficiency. We then use these measures to examine whether bank ownership and financial freedom enhance or impede bank efficiency.
The paper makes a number of contributions to the literature. First, our paper is related in spirit to recent studies that provide international evidence on the impact of ownership on banks’ performance (e.g. Barth et al., 2002; Demirguc-Kunt et al., 2004). In contrast to these studies, which mainly use financial ratios as indicators of performance, we measure bank efficiency using an efficient frontier technique. Berger and Humphrey (1997) emphasize that efficient frontier approaches are superior when compared to traditional measures of performance (e.g. return on assets, cost/revenue), since they account simultaneously for relevant inputs and outputs
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ACCEPTED MANUSCRIPT of a bank, as well as for differences in the input prices. Second, our paper extends on previous studies by using a real percentage to capture the changes of block shareholders' ownership, and
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provide evidence for the impact of changes in ownership structure on bank efficiency. For
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example, Iannotta et al. (2007), Berger et al. (2009) and Shen and Lin (2012) have only used a dummy variable with a twenty or fifty percent ownership threshold to classify as government or
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private domestic banks. Indeed, a dummy variable does not measure degrees of large
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shareholders above the specific threshold as well as the dynamic effect that bank ownership over time may have on bank efficiency.
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Third, we explicitly analyze the influence of financial liberalization using the economic freedom indexes and we distinguish between the concepts of financial freedom and regulation.
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This definition of financial freedom indexes we used is closely related to the broad concept of deregulation that is, the removal of artificial barriers that prevent entry and/or competition
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between products, markets and institutions. Thus one may expect that the financial freedom counterparts of the economic freedom indexes can inversely correlate with the degree of regulatory tightness in banking. 1 However, Chortareas et al. (2013) argue that regulatory tightness and measures of financial freedom can be closely associated but they do not identify with each other, either in scope or in terms of measurement. Because financial freedom indicates limited government influence/control in financial and banking markets and, in addition to the regulatory framework, it takes into account the extent of state intervention in banks and in the allocation of credit, as well as the possible obstacles in opening and operating financial services firms (for both domestic and foreign individuals). Finally, unlike most previous studies that consider the effects of economic freedom on bank performance typically treat the freedom index 1
For example, the freedom indexes inversely relate with the measures of activity restrictions and official supervisory power provided by Barth et al. (2006).
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ACCEPTED MANUSCRIPT as one of the control variables (e.g. Claessens and Laeven, 2004; Goddard et al., 2011), and include other aspects of bank performance than efficiency such as the interest rate margins
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(Demirguc-Kunt et al., 2004). Despite the extensive literature on bank efficiency used European
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bank sample (Chortareas et al., 2013), a comprehensive study on whether the changes in bank ownership and financial freedom enhance or impede efficiency in Asian developing countries not
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yet exist.
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We examine the effects of financial freedom on the relation between the changes in ownership structure and bank efficiency before and after 2008 Global Financial Crisis, and use
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data for more than 2113 bank-year observations in 12 Asian developing countries over the period 2003–2012. We obtain the following main findings. First, we find that increases in large
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government and foreign shareholders are positively associated with bank efficiency in countries with high financial freedom, while an increase in domestic ownership shows a negative impact
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on bank efficiency. Second, the changes of bank ownership do not have any insignificant effect on bank efficiency before 2008 Global Financial Crisis, whereas increased government (domestic) ownership of bank appears to improve (impede) bank efficiency in countries with more financial freedom after Global Financial Crisis. The rest of this paper proceeds as follows. Section 2 reviews literature on bank ownership, efficiency and financial freedom. Section 3 presents the data and methodology in measuring bank ownership change, efficiency and financial freedom. Section 4 analyzes how financial freedom affects the relation between bank ownership structure and efficiency. Section 5 analyzes empirical results and discusses their implication. Finally, section 6 concludes the paper with a discussion the policy implications.
2. Literature Review 6
ACCEPTED MANUSCRIPT 2.1 Evidence on Bank Efficiency in Asian Developing Countries There have been some empirical studies that have attempted to shed light on the
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determinants of this efficiency in Asian developing countries. Gardener et al. (2011) found a
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negative influence of post-1997 crisis restructuring on bank efficiency in South East Asia, while
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Sun and Chang (2011) reported a significant impact of measure risks on both the level and variability of bank efficiency in emerging Asian countries. Recently, many single-country
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studies have provided evidence that the bank efficiency by itself matters for the changes in stock prices (Sufian et al., 2007), and faces a difficulty to achieve the efficient resource allocation
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during the early 2000s (see Kalluru and Bhat, 2009; Vu and Turnell, 2010; Manlagñit, 2011). Similarly, there exists other important studies, which discovered different specific factors that
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affected the performance or efficiency of banks within a country, such as Hong Kong (Lim and Randhawa, 2005), Korea (Park and Weber, 2006), Taiwan (Kao and Liu, 2009), Thailand
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(Chansarn, 2008), Malaysia (Sufian, 2009), and Indonesia (Margono et al., 2010). Among the studies that compared bank efficiency between state-owned banks and foreign-owned banks in Greater China such as Hu et al. (2004), Ferri (2009), Lin and Zhang (2009), and Berger et al. (2009) use the sample from 1994 to 2003 but failed to describe the current problems facing the foreign bank’s competition, especially after the 2008 Global Financial Crisis. In general, their results indicated that risk-taking, corporate governance and bank reforming play an important role in the efficient performance of banks, and might demonstrate the discrepancies in effectiveness among banking systems in different countries.
2.2 Evidence on Financial Freedom in Asian Developing Countries While banking literature considering the impact of the economic freedom on productive efficiency has been extensive, the theoretical models analyzing explicitly the role of economic 7
ACCEPTED MANUSCRIPT freedom indexes on ownership changes in developing countries have not been developed. Focusing on the link between financial freedom and bank efficiency, Chortareas et al. (2013)
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point out that the more the level of a country’s financial freedom, the higher the benefits for
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banks. Using the indexes of economic freedom as control variables, Claessens and Laeven (2004), Saurav Roychoudhury and Lawson (2010) and Goddard et al. (2011) documented that
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more openness in the banking markets improvises the efficiency and reduces borrowing cost for
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banks. Furthermore, there have been other recent studies that examine the international impact of economic freedom on various aspects of economic growth (Gwartney, 2009), bank stability
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(Linh Nguyen et al., 2012), income convergence (Xu and Li, 2008), entrepreneurship (Nystrom, 2008) and global recession (Giannone et al., 2011). Nevertheless, most of these studies have
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concentrated on the effect of financial freedom in the developed countries, such as the United States, Europe and other developed countries banking sectors.
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The empirical evidence on impact of financial freedom effect in the Asian emerging and developing countries is relatively scarce. By examining different components of economic freedom, Sufian and Habibullah (2011) find that higher monetary policy increases banks’ efficiency, while the corruption in the business environment is negatively related to bank efficiency levels of the China banking sector. Other analyses of economic freedom have been considered in various contexts, but few contributions focused explicitly on the effects of the economic freedom within which foreign direct investment attraction (e.g. Quazi, 2007; Hanh, 2010). The evidence suggested that financial openness indicators are positively related to FDI inflow. Similarly, Tiwari (2011) also shows that an increase in the fiscal freedom and financial freedom are significant factors positively associated with economic growth. The impact of financial liberalization on economic outcomes, however, depends on the liberalization intensity
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ACCEPTED MANUSCRIPT of each financial sector in which full liberalization tends to be harmful for emerging Asian growth outcomes (Ben Gamra, 2009).
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2.3 Evidence on Bank Ownership Structure in Asian Developing Countries
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In emerging and developing economies, although the separation of ownership from control
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is common to all three ownership forms, there are still different arguments to define the relative efficiency of these three ownership forms of financial institutions. Several studies have found
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that bank’s government ownership is associated with inefficiency and poor performance. For example, Micco et al. (2007) and Cornett et al. (2010) show that state banks operating
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developing countries tend to have lower profitability than the private banks, and foreign ownership banks tend to be represented by higher profitability than other counterparts. Similarly,
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Sanyal and Shankar (2011) find that Indian private and foreign banks tend to have a much higher productive efficiency when compared to the public banks. Focusing on the China banking sectors,
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Berger et al. (2009), Ferri (2009) and Lin and Zhang (2009) indicate that a significantly higher performance by private and foreign-owned banks relative to state-owned commercial banks. One reason is that the bank privatization in the region has improved revenue inflow and bank performance in the short-run or long-run, particularly for banking institutions with foreignowned banks (Chunxia Jiang et al., 2013). According to this development view, however, some argued that higher level of governmental ownership might benefit from bank investment through their safer projects at lower interest rates. Indeed, state bank may enhance savings mobilization and improve the allocation of these funds to projects to the economy (Hossain et al., 2013). In this regard, the analysis of Andrianova et al. (2012) documented that government banks typically play a development role and run the bank effectively. Also, Taboada (2011) found a negative effect on
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ACCEPTED MANUSCRIPT capital allocation when there is a decline in ownership of government block shareholder and an increase in ownership of domestic block shareholders.
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3. Data and Methodology
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3.1 Data Selection
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The dataset used in this study is composed of individual bank data soured from unconsolidated statements of banks operating in the 12 Asian developing countries, as made
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available through the BankScope database of Bureau van Dijk. To avoid a domination of any
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country out of twelve, we start collecting bank financial statement data with a maximum of 50 largest banks by total assets in each country.2 As our paper focuses on commercial banks, we
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start by dropping central banks, investment banks, securities houses, multilateral government
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banks, non-banking credit institutions, and specialized government financial institutions, which
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reduces our sample from 9890 observations to 3980 observations. Next, we delete 1756 observations with less than five subsequent years of time series observation. In order to minimize the effects of measurement errors we have excluded all the outliers (111 observations) by eliminating the extreme bank-year observations (2.5% lowest values and 2.5% highest values) for each considered. As a result, we end up with an unbalanced data set consisting of 219 banks from 12 countries, for a total of 2113 bank-year observations for which we have efficiency, ownership and accounting data. To analyze the role of bank ownership change on bank efficiency before financial crisis (2003-2007) and after financial crisis (2008-2012), we use a database on the ownership structure of BankScope in 12 Asian countries as of 2003, 2007, 2008 and 2012. When BankScope’s shareholder database does not have enough information for us to determine the percentage of 2
There are three countries in our sample which have more than 50 active commercial banks, including Mainland China (168 banks), India (62 banks), and Indonesia (69 banks) provided by BvD BankScope database.
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ACCEPTED MANUSCRIPT ownership structure, we collect bank ownership information from the annual reports provided by individual bank websites or Bankers’ Almanac. Following La Porta et al. (2002), I use various
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alternate ownership measures that attempt to capture control of banks. These measures classify
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banks as domestic private, government, or foreign-owned when their equity ownership exceeds certain thresholds.
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On the other hand, data for the economic freedom are collected from the Foundation (2010)
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and data on the institutional and governance quality are drawn from the World Bank database by Kaufmann et al. (2010). The index is highly credible and its results are compatible in general.
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We use the Heritage Foundation’s Index of economic Freedom for practical purposes because one of its components measures the “Finance Freedom”. In particular, the Index of Economic
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Freedom focuses explicitly on the components of “financial freedom” (previously dubbed
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“banking freedom”). The financial freedom index is an overall indicator of banking security as
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well as a measure of independence from government control. This indicator provides an economy’s financial freedom by looking into the following five group categories: the extent of government regulation of financial services; the extent of government intervention in nonbanking financial institutions through direct and indirect ownership; the extent of capital and financial markets development; and the extent of government effect in the credit allocation system and the openness to foreign competition. These five groups are considered to reflect the overall level of financial freedom of a country that ensures effective and easy access to financing opportunities for individuals and entrepreneurs. The distribution of financial freedom index is based on a 100-point scale with 10-point intervals in which a score 100 indicates a negligible government influence, and a score of 0 indicates a repressive government influence (banking supervision and regulations are designed to
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ACCEPTED MANUSCRIPT prevent private financial institutions). In other words, financial freedom signifies an assessment of financial and banking freedom that the higher the values the more freedom. This index has
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been used as a proxy of the degree of openness of financial markets and banking industry by
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numerous previous studies (i.e. Demirguc-Kunt et al., 2004; Chortareas et al., 2013). Furthermore, we also use bank regulations as other country control variables via different
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resources. In detail, capital requirement, capital stringency and regulatory restrictions are
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collected from (Barth et al., 2006; Laeven and Levine, 2009)3. Finally, we rely on two other data sources, the use the measure of country-level control (e.g. GDP per capita, Customer Price Index)
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from the World Bank’s World Development Indicator database, and capture the economic environment factors from Kaufmann et al. (2010)4.
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3.2 Measurement of Changes in Bank Ownership To measure the ownership variables, we follow the same procedure used to calculate a
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bank’s proportion of state ownership by La Porta et al. (2002). By constructing all of ownership measures with data from 2003 to 2012, we first identify large domestic bolckholder ownership of banks. For example, a large foreign block shareholder is any foreign shareholder (a company, or an individual) which is classified by following the 10% threshold from La Porta et al. (1999). Then we calculate the total share of each bank owned by large foreign shareholders (FOi) as follows: J
FOi s ji s fj ; where s ji s fj 0.1
(1)
j 1
3
Barth et al. (2006) employ use cross-country data to examine the relationship between political systems and bank supervisory and regulatory systems. 4 Kaufmann et al. (2010) summarize the Worldwide Governance Indicators (WGI) project that covers over 200 countries and territories with different dimensions of governance starting in 1996.
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ACCEPTED MANUSCRIPT where j = 1… J indexes banks’ shareholders, sji is the share of bank i owned by shareholder j and sfj is the share of shareholder j that is owned by a foreign shareholder. FOi is the total share of
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bank i that is owned by large foreign blockholders. We use a similar way to construct the
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domestic (DOi) and government (GOi) block shareholder variables. Furthermore, we also follow Taboada (2011) to construct an additional variable, (WIDEi). This variable captures the widely-
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held shareholders which are neither government, foreign, nor domestic blockholder-owned: (2)
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WIDEi 1 GOi DOi FOi
Table 1 provides descriptive statistic for various measures of bank ownership across
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countries as of 2003, 2007, 2008 and 2012. As of 2012, the mean of domestic, government and foreign block shareholder ownership of 213 banks in 12 countries for the full sample is 15.33
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percent, 27.55 percent, and 31.87 percent, respectively. The table also indicates that GO has been considerably declining, whereas FO has been increasing, especially during the period 2003 –
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2007. It would be not surprising that these changes are greatly influenced by the evolution of the privatization process. Trend of decrease in GO and increase in FO are expected to continue into the next five years, although at a significantly slower pace partly due to the Global Financial Crisis.
[Insert Table 1 here]
3.3 Measurement of Efficiency Scores Regarding the bank efficiency measure, the previous studies examining performance have used two different approaches available to measure bank efficiency, including the parametric stochastic frontier approach (SFA) and the non-parametric data envelopment analysis (DEA). While DEA is built under the assumptions that there is no random error, SFA approach modifies the traditional assumption of a deterministic production frontier which got its advantage (Sun
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ACCEPTED MANUSCRIPT and Chang, 2011). More recent studies find that SFA efficiency scores are generally higher compared to DEA scores.5 This may reflect the different treatment of stochastic noise and the
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ability to control for heterogeneity (Koetter et al., 2006). Following the previous literature on the
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banking industry and the advantages of SFA in comparison with DEA related to attributing statistic noise to inefficiency, this study tends to employ SFA for measuring the efficiency of
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developing Asia’s banking industry for the appropriate estimation.
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The model is first introduced in logs as ln yi ln xi vi ui , where xi denotes an input
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vector for firm i, vi represents random error added to the non-negative inefficiency term,
ui . Unlike ui , the random error, vi , accounts for measurement error and other random factors
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affecting the value of the output variable, together with the combined effects of unspecified input
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variables in the production function. This model is stochastic because the upper limit is
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determined by the stochastic variable exp xi vi . According to Battese and Coelli (1997), vi can be positive or negative and therefore the stochastic frontier outputs vary relative to the deterministic part of the frontier model. The fact that we need to assume a functional form when estimate the stochastic frontier model. Unfortunately, the banking industry has a multi-output, hence choosing for a production function is not feasible. Cost efficiency measures how well a bank is managing its resources, under the same outputs and environmental conditions, to reach the maximum amount of profit at a minimum cost.6 A recent study of Wang (2002) which made a combination between traditional and extended models supposes that TCit represents for total costs for the ith bank in year t, then 5
There are some studies available in which, SFA method has been used for efficient measurements. Specifically, Aigner et al. (1977) and Meeusen and Van den Broeck (1977) employed the stochastic frontier approach which is composed errors in the assumptions of a deterministic production frontier 6 Since behavioral assumptions such as cost minimization are appropriate for banks, it looks like the best way to use a cost frontier model for the estimation.
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ACCEPTED MANUSCRIPT Yit , Pit are the vectors of the output and the price of input, respectively, and presented the equation
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as TCit f1 Yit , Pit vit uit , vit N 0, v2 , uit N it , it2 . Here, vi is the stochastic error term with i.i.d. normal distribution represented for other uncontrollable factors, while ui
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represents technical and allocative inefficiency aspects that can be influenced by management.
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The uit error has a truncate normal distribution with an observation-specific mean function of some determinants Z it of
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it 0 zit and variance it2 which are assumed a
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its pre-truncated distribution. 7 The cost (in)efficiency scores of individual bank, INEi exp ui , calculated from the estimated frontier would take a value between one and infinity. To make our
D
results comparable, we follow the same procedure used by Pasiouras et al. (2009) to calculate the
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index of cost efficiency as follows: CEi 1/ INEi . Therefore, our efficiency scores range
AC CE P
between 0 and 1 with values closer to 1 indicating a higher level of bank efficiency. Based on the stochastic frontier analysis model of Battese and Coelli (1997) and the cost frontier model Wang (2002), we estimate efficiency levels by specifying the multi-product translog cost function. Because of convenience, we demonstrate only the following cost function:
ln TC j 0 r ln Yrj i ln Pij rj
ij
1 1 ik ln Yij ln Ykj iz ln Pij ln Pzj 2 i k 2 i z
i ln Yr ln Pi vi u j r
(3)
i
where TC is total operating cost, Yr , r 1, 2,..., 4, are outputs, Pi , i 1,...,3 are input prices and
0 is an intercept accounting for all other cost determinants. Since inefficiency leads to higher than optimal costs, note that the inefficiency term ui is positive. In addition, in order to reduce
7
Huang et al. (2011) and Sun and Chang (2011) both described the previously analyzed Wang’s model as the best specification model among eight well-known stochastic frontier models.
15
ACCEPTED MANUSCRIPT heteroskedasticity problem, we also use the price of labor to normalize total costs and input prices, which is presented by Lang and Welzel (1996).
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This study will base on the intermediation approach to classify the outputs and input prices
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of the banking industry. According to the intermediation approach, banks are considered as financial intermediaries that taking deposits from savers and making loans to economic agents
SC
requiring capital (Allen and Santomero, 1997). From this perspective, our study will focus on
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four outputs and three input prices. Four outputs previous adopted in studies of Bonin et al. (2005), and Berger et al. (2009) include total loans (Y1), other earning assets (Y2), total deposits
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(Y3), and liquid assets (Y4). Three input prices also defined widely in previous studies are the price of labor, capital, and funds. In detail, price of funds (P1) measured by the ratio of interest
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D
expenses to total deposits, price of capital (P2) is defined by the ratio of non-interest expenses to total fixed assets, and the ratio of personnel expenses to total assets used to measure the price of
expenses.
AC CE P
labor (P3). The total costs (TC) of banks are defined by sum of interest expenses and non-interest
Table 2 indicates that the commercial banks, on average, have higher level of their total deposits than total loans. The average total cost is $1.842 billion. The average price of capital and price of funds are about 2.64 and 0.036, respectively. These measures are lower than the 2.82 of price of capital reported by Sun and Chang (2011) and the 0.21 of price of funds reported by Berger et al. (2009). It should be noted that the standard deviations of all output variables are very high. These results reflect the large differences in the bank sizes in our sample. [Insert Table 2 here]
4. The Impact of Changes in Ownership on Bank Efficiency
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ACCEPTED MANUSCRIPT Given the remarkable declines in government and increases in foreign ownership of banks, which caused by the privatization process in the early 2000s, the question remains as to whether
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this has any impact on how bank efficiency is changed after the Global Financial Crisis of late
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2008. We now examine the impact of changes in ownership on bank efficiency. If countries’ capitalization and liberalization programs are performed efficiently, high financial freedom
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countries should receive more advantages in improvement of bank efficiency. The following
Bank Efficiencyi , j ,t 0 1 FINFREE j
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basic equation is used in our analyses:
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2 FINFREE j Ownership Structurei , j 3 Ownership Structurei , j Bank Regulation Control j ,t
D
Bank Controlsi , j ,t Macro Controls j ,t
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Year Dummies ei , j ,t
(4)
AC CE P
where Bank Efficiencyi,j,t is the cost efficiency scores of bank i in country j in time t. Bank Efficiency is the cost efficiency scores estimated by SFA frontier from the previous section, which is always positive and it is equal to unity for the best practice or zero inefficient bank. To investigate how financial liberalization impacts on the ownership structure/ bank efficiency nexus, we set a variable called FINFREE, which is based on the index of financial freedom described in the section 3.1. FINFREEj is dummy variable, takes a value of one if the country j’s financial and banking freedom index is above the median value and zero otherwise. 8 Not surprisingly, an accessible and well-functioning financial market can ensure the availability of diversified savings, credit, investment, and other payment services to individuals. By expanding financial transactions and promoting entrepreneurship, an open and transparent banking 8
Foundation (2010)’s Financial freedom is a measure for banking sector independence from government interference in a financial system. This index is built on scale of 0 to 100 with the higher value illustrates less government control in the national banking system.
17
ACCEPTED MANUSCRIPT environment encourages competition and facilitates access to providing efficient financial intermediation between households and banks as well as between investors and entrepreneurs. In
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additional, a government with strong intervention in financial markets may go beyond the
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assurance of transparency, and may increase the costs of financing entrepreneurial activity which leads to impede bank efficiency. Therefore, we expect that banks with more foreign ownership
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may improve their efficiency in the country with more open financial markets.
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For the other independent variables, Ownership Structure refers to the changes in bank ownership structure in the same period of the dependent variable. Bank Regulation Control
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represents for key bank regulations stressed by the Basel Committee. Following Laeven and Levine (2009), we control bank regulatory by capital regulations (CR) on bank activities. Bank
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Controls is the vector of control bank-level including NONINT, DDEP, BANKSIZE and EQUITY. NONINT is defined as non-interest income as a share of total assets and DDEP is demand of
AC CE P
deposits as a share of total deposits. The reason for using these two control variables is that NONINT tends to be higher for banks that derive most of their income from commissions, and DDEP tends to be higher in retail commercial banks (Micco et al., 2007). BANKSIZE is defined as log of total assets (Berger et al., 2005; Westman, 2011) to control banking scale and EQUITY is the total equity to total asset ratio to capture financial distress and probability of bankruptcy. Macro Controls is the vector of macroeconomic variables including GDP per capita (GDP) and inflation (INFLATION). An estimation issue arises because the dependent variable (Bank Efficiency) in the regression ranges between 0 and 1, making this variable a truncated distribution. Use of Ordinary least squares (OLS) regression estimation to estimate parameters for a limited dependent variable may lead to the biased and inconsistent parameter estimates since OLS assumes a normal and
18
ACCEPTED MANUSCRIPT homoskedastic distribution of the discrete dependent variable (Maddala, 1983). A common approach to handle this issue is to employ Tobit estimation. The Tobit model is used to deal with
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the characteristics of the distribution of efficiency scores for providing estimated results, which
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is employed by a number of researchers, such as Pasiouras (2008), Yeh et al. (2010), and Huang et al. (2011). Thus, we also conduct the regressions using the Tobit maximum likelihood
SC
procedure in our multivariate analysis.
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Table 3 provides a summary statistic of cost efficiency, ownership structure, bank-level and country-level controls. The mean value of change in government ownership in the whole sample
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is -1.9 percent. The change of foreign ownership (3.9 percent) is higher than those of domestic ownership (0.2 percent) and wide ownership (-2.2 percent). On average, the mean of the capital
TE
D
requirement is 8.6 percent with a standard deviation (0.9 percent). This suggests fairly low crosssectional variation. Moreover, the average non-interest income over total assets is composed of
AC CE P
1.2 percent. The mean proportion of equity to total asset (EQUITY) and proportion of demand of deposit (DDEP) is about 9.4 percent and 82.0 percent, respectively. [Insert Table 3 here]
5. Empirical Results
5.1 Results of the Intermediation Approach Table 4 provides the estimation results for the SFA cost frontier. Panel A reports the signs of all parameters of the cost function explain a consistent and reasonable result. Most of the coefficients of outputs are positive and significant at 1% level. Also, the input prices and its quadratic terms show a significantly positive effect on total costs (except for the quadratic term of price of capital). These results indicate that the higher the bank input prices are and the more outputs that are produced, the higher the total costs are. Table 4 also presents average Bank 19
ACCEPTED MANUSCRIPT Efficiency levels by country and by year. The Bank Efficiency is representative for cost efficiency scores, which involves allocating inputs to outputs and minimizing total cost of production for a
PT
given output level. The Bank Efficiency in panel B for each country shows that banks in India
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and Mainland China reach the highest average cost efficiency scores, while banks in Singapore and Philippines average the lowest efficiency levels. The means of the Bank Efficiency score in
SC
the panel C range from 81.3 percent to 84.5 percent during the period from 2003 to 2012,
NU
whereas the overall mean presents at 83.6 percent. The results indicate that, on average, all banks could have produced their outputs using 81.3 percent to 84.5 percent of the inputs that they
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actually spent from year 2003 to 2012. The results in panel D reveal that the differential in average cost efficiency between prior and posterior financial crisis is the -0.5 percent, but not
TE
D
statistically significant.
[Insert Table 4 here]
AC CE P
Table 5 shows the ordinary correlation between the independent variables during the period of 2003–2007. The matrix reports the correlation between FINFREE and ∆GO is positive and statistically significant, while the correlation between FINFREE and ∆DO shows an insignificant relationship. A negative association is reported for correlation between FINFREE and ∆FO. Countries with policies that provide higher financial freedom show the greater increase in government block shareholders. The relations between FINFREE and BANKSIZE and CR are positive, indicating the economies that have more freedom of finance are likely to enlarge their total assets, and to impose higher capital requirements. In addition, there are significant negative relations between ∆GO and DDEP as well as between ∆DO and EQUITY. These results suggest that increase in large government ownership will decline the demand of deposit for bank operation, and increase in domestic block shareholders will reduce the proportion of bank equity.
20
ACCEPTED MANUSCRIPT [Insert Table 5 here] 5.2 Changes in Ownership Structure and Bank Efficiency
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Table 6 presents the results for the impact of ownership changes on the relation between
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financial freedom and bank efficiency. Each model of Table 6 demonstrates the results derived from alternative ownership variables while controlling for a selected set of relevant bank and
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macro-specific variables. The first three columns in Table 6 show the basic regression models
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that include financial freedom variable, changes of ownership structure and bank-pacific control variables for all sample periods (models 1-3). While models 4-6 report the estimation results that
MA
include the effects of financial freedom and bank ownership variables before the Global Financial Crisis of 2008, models 7-9 exam whether these effects change in the aftermath of the
TE
D
crisis.
Model 1 and model 3 in Table 6 appear significantly positive impacts of government and
AC CE P
foreign ownership changes on bank efficiency, at least across the full sample of countries with high financial freedom. For economies experiencing a one percent increase in GO and FO, annual efficiency scores in countries with high financial freedom are 14.5 and 12.2 percentage points, respectively, higher than those of the lower financial freedom group. In contrast, the coefficient of the interaction term in model 2 shows a negative effect on bank efficiency, implying that greater financial freedom leads to an adverse relation between domestic private ownership and bank efficiency. Model 4 of Table 6 shows the coefficient of interaction FINFREE * ∆GO coefficient is not significant, indicating that there is no difference in effect of government ownership between high and low financial freedom countries on bank efficiency. In contrast, the coefficient of GO is found negatively associated with cost efficiency (-0.0469), suggested that an increase of
21
ACCEPTED MANUSCRIPT government ownership is associated with lower bank efficiency in the country group with low financial freedom. Similarly, the coefficient of interaction FINFREE * ∆DO in model 5 shows an
PT
insignificant result. This explains that change in domestic ownership does not appear to have a
RI
different impact between the two country groups on bank efficiency.
On the other hand, the model 6 shows the impact of increased foreign presence on bank
SC
efficiency is significantly higher in countries where the financial freedom is more prevalent.
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Although an increase in FO is associated with lower bank efficiency happened to low financial freedom economies, increases one percent in FO is associated with 0.23 percent higher cost
MA
efficiency for more financial freedom economies in Asian developing countries. For the average country, which experienced a 5.35 percent increase in FO, the annual efficiency score in
TE
D
economies with less financial freedom lagged behind the efficiency in economies with more financial freedom by 1.23 percentage points. One alternative explanation for an increased foreign
AC CE P
presence, leading a decline for bank’s effective performance, could be that low financial freedom markets are characterized by high uncertainty and tend to have a higher cost of liquidation of projects (Vives, 2010)9. The foreign banks or majority foreign-owned banks face competition constraints in less financial freedom areas where domestic-owned banks are benefited by low funding cost and strong credit demand arising from government stimulus measure. Increase percentage of foreign ownership in these areas, meanwhile, would remain hampered by bank restrictions on what their businesses were allowed to do, and as a result they are suffered from poor profitability. Models 6 through 9 of Table 6 show the results after Global Financial Crisis. The interaction FINFREE * ∆FO continues to provide the significantly positive impact on bank
9
Vives (2010) investigates the relationship between competition and stability in banking in the aftermath of the systemic crisis started in 2007.
22
ACCEPTED MANUSCRIPT efficiency, which explains a higher efficient score of banks from more financial freedom countries. Despite the impact of foreign ownership change becomes insignificant for lower
PT
financial freedom countries after the 2008 Global Financial Crisis, one percentage point increase
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in foreign large shareholders is associated with about 0.10 percentage point higher efficiency score for higher financial freedom countries.
SC
It is interesting to note that, the previous insignificant coefficients of the interaction
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FINFREE * ∆GO and FINFREE * ∆DO before the 2008 Global Financial Crisis become significant after the 2008 Global financial Crisis. While an increase in GO is associated with
MA
higher cost efficiency with higher financial freedom countries, there is a significantly negative effect from increases in DO. In detail, the annual efficiency score to ownership change in GO
TE
D
with lower financial freedom countries is negative 4.41 basis points, whereas that efficiency for the higher one is 13.47 basis points. For countries experiencing a one percent increase in GO,
AC CE P
cost efficiency of banks from the high level group of financial freedom is 17.88 basis points higher than the low level group of country. The negative coefficient of ∆GO for the lower financial freedom countries is consistent with Micco et al. (2007), which indicate higher government ownership associated with lower profitability and higher cost than other ownership forms. In contrast, the positive coefficient of interaction FINFREE * ∆GO with the higher financial freedom countries is consistent with Bertay et al. (2012). Their findings show government ownership banks are able to expand their credit and maintain higher rates of loan growth relatively more over the banking crisis period, if they are located in countries with high governance effectiveness. Also, Andrianova et al. (2008) and Karas et al. (2010) argue that state-owned banks can play a useful role in stabilizing credit of a country, and an increase of government ownership may cause the related banks to have more
23
ACCEPTED MANUSCRIPT effectiveness of mobilizing saving and lending at a time of financial instability. More broadly, the result is also loosely related to the finding of Andrianova et al. (2012), who show that
PT
government ownership of banks is associated with faster long-run growth.
RI
On the one hand, bank efficiency in high financial freedom countries experiencing a one percent increase in DO would be significant lower (0.11). One possible reason is that domestic
SC
private banks in countries with high-open financial environment, which are more limited in their
NU
operations, face an increased competition in business banking. This may cause a lower outputs and efficiency scores for domestic banks compared to foreign banks, especially in the aftermath
MA
of the financial crisis. The evidence is consistent with the view that the increase in domestic ownership will hamper capital allocation efficiency as well as bank performance (Taboada,
TE
D
2011). This may be due to increased switching costs of domestic banks after privatization leading to weak competition and inefficiency, especially in countries with high financial freedom
AC CE P
(Karas et al., 2010). In addition, because of an imperfect financial liberalization process, the desired model of greater domestic bank’s efficiency through liberalization may not be realized when financial liberalization policies fail to reduce the magnitude of capital flight (Yalta and Yalta, 2012). Our result also adds support to the findings of Reinhart and Kaminsky (2001), Tornell (2001) and Zhang and Underhill (2003). They suggest that although domestic private banks in Asian countries provide main quantities of credit to domestic firms, their lending also remains heavily dependent on foreign financial inflows. As a response to the crisis, funds by the foreign investors evaporate quickly to their home countries, confronting the domestic banks with a bad performance. Table 6 also reports the estimated results of control variables, which are identified in the main model as having a specific impact on cost efficiency. For each of the reported
24
ACCEPTED MANUSCRIPT specifications, the coefficients of growth in real GDP per capita is found to be positively associated with bank efficiency in Asian developing economies for both pre- and post-financial
PT
crisis. It is not surprising to see that faster economic growth could signify more development for
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bank. Although INFLATION is insignificant in every regression before financial crisis, its coefficient has a positive impact on bank efficiency in the post crisis. The minimum capital
SC
requirement regulation, CR, enters negatively and significantly in all model specifications. This
NU
suggests that lower minimum capital requirements tend to enhance the bank efficiency. Concerning to the bank level control variables, non-interest income (NONINT) is negatively
MA
correlated with cost efficiency for all models. The result shows that the banks have greater noninterest income tend to be less effective in cost management for countries. In addition, the
TE
D
coefficients of EQUITY and BANKSIZE indicate that bank efficiency of Asian developing countries is determined by both total equity and bank total assets. The total equity to total asset
AC CE P
ratio enters negatively and significantly in all of the regression specifications, suggesting that lower equity capital enhance bank efficiency. This supports the negative impact of total equity to total asset ratio on bank performance found by Westman (2011). Bank size, however, shows a negative and statistically significant association with bank efficiency only for the prior financial crisis. This finding gives some more supports for Bonin et al. (2005) who suggest that smaller banks are more efficient in these transition countries. [Insert Table 6 here] 5.3. Robustness Tests In this section, we provide some robustness checks of our main results using different thresholds and endogeneity concerns. First, we consider a 20% ownership threshold to construct measures of government, domestic and foreign blockholders of banks (Laeven and Levine, 2009;
25
ACCEPTED MANUSCRIPT Taboada, 2011). Using the 20% threshold, we classify a bank as being owned by a large shareholder if the shareholder owning more than 20% of total shares is a company or individual.
PT
The variables GO20, DO20 and FO20 attempt to capture the extent of bank control of state,
ownership threshold of 20%.
SC
[Insert Table 7 here]
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domestic private and foreign shares, respectively, where the control level is defined using an
NU
We report the results in Table 7 using ownership threshold of 20%, which overall appear to corroborate the key findings reported in Tables 6. Specifically, we continue to find that FO20
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still provides a positive association with bank efficiency in high financial freedom countries for the full sample from 2003 to 2012, while the increase in GO20 (DO20) is associated with a
TE
D
higher (lower) efficiency score to these countries only for the post period of crisis. This implies that the impacts of changes in ownership structure on the relation between financial freedom and
AC CE P
bank efficiency are more pronounced even alternate measures are used. Second, we use two-step Heckman selection model to address potential endogeneity concerns. In the first step of the Heckman approach, we estimate the probability that bank with a block government shareholder is associated with measures of regulatory quality, such as country trade freedom (TRADE), rule of law (RULEOFLAW), government effectiveness (GOVEFFECT) provided by Foundation (2010) and Kaufmann et al. (2010). It implies that whenever state shareholders are indeed following ways to gain their own benefits, they would be likely to increase their contacts with banks where the rule of law is not well-established and where the level of trading freedom remains relatively low. Similarly, bank with a large domestic shareholder is determined by other factors of regulatory quality, including property rights
26
ACCEPTED MANUSCRIPT (PROPERTY), population proportion of work forces (POPULATION)10 and control of corruption (CORRUPITON). In this regard, when the corruption dimensions of a nation can be controlled at
PT
a low level, the improvement in business environment and law enforcement of property rights
RI
can help banks increase more ownership share of domestic investors. Finally, we also use fiscal freedom (FISCAL), political stability (POLSTABILITY) and regulatory quality (REGQUALITY)
SC
to estimate the first step of the Heckman technique for bank with the foreign block shareholders.
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Clearly, a country with stable politics and high quality of public services and implement policies of government can attract more foreign investors (Taboada, 2011). In general, it would be
MA
expected that when financial institutions operate in a better economic environment, they may be more likely to take part in competitive policies thus achieving more efficient operations. Yet,
TE
D
these proxy variables should not directly affect the way in which cost efficiency of banks is estimated.
AC CE P
Panel A of Table 8 shows the results from the first-stage of the Heckman technique. The results show that the instrument variables work well in forecasting changes in government, domestic and foreign bank ownership. Panel B of Table 8 reports the results from the second step of the Heckman approach and confirms our prior findings. [Insert Table 8 here]
6. Conclusion This study has examined the effect of financial freedom on the relationship between changes in ownership structure and bank efficiency across Asian developing countries over period 2003-2012. Using cost efficiency scores, our main findings as follows. Firstly, banks with foreign presence improve bank efficiency, primarily in countries with high financial freedom. 10
POPULATION is a proxy for the population ages 15 – 64 as a percent of the total population provided by World Bank.
27
ACCEPTED MANUSCRIPT Secondly, increased government (domestic) ownership of bank appears to improve (impede) bank efficiency in countries with more financial freedom after financial crisis.
PT
Assessing the effects of financial freedom and the changes in bank ownership structure for
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financial institutions’ efficiency has direct implications in the context of this debate, especially in the aftermath of the 2008 Global Financial Crisis, which increase the prominence of systemic
SC
risk. Our study highlights the importance of designing an appropriate bank regulatory and
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supervisory framework in the design of privatization and liberalization programs that helps
AC CE P
TE
D
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maintain the efficiency (and hopefully stability) of banks.
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ACCEPTED MANUSCRIPT
Table 1
GO(03)
GO(07)
GO(08)
GO(12)
DO(03)
DO(07)
Panel A – Ownership by type of owner as of 2003, 2007, 2008 and 2012 0.00% 55.36% 13.67% 6.17% 13.31% 4.64% 15.68% 9.70% 0.00% 10.33% 15.07% 0.00% 15.33% 28.18%
17.66% 2.32% 25.74% 31.75% 14.47% 40.87% 7.59% 39.18% 46.33% 42.93% 27.65% 52.19% 27.11% 37.08%
19.89% 3.36% 20.96% 40.01% 12.95% 44.23% 17.43% 41.39% 40.35% 49.85% 19.30% 34.73% 27.16% 36.14%
11.99% 5.17% 21.21% 42.05% 17.00% 46.80% 14.05% 40.07% 39.40% 51.09% 21.90% 33.36% 27.19% 35.99%
NU
0.00% 55.19% 18.60% 13.46% 13.84% 1.50% 14.29% 0.78% 0.00% 12.37% 14.29% 0.00% 15.51% 29.17%
MA
0.00% 53.15% 19.58% 13.12% 11.85% 0.00% 14.90% 0.73% 0.00% 13.65% 14.29% 0.00% 15.08% 28.79%
PT ED
0.00% 65.46% 28.18% 11.00% 24.45% 0.81% 27.74% 3.92% 0.00% 13.44% 13.33% 0.00% 19.84% 34.85%
CE
Hong Kong India Indonesia Korea Mainland China Malaysia Pakistan Philippines Singapore Taiwan Thailand Turkey Full sample mean Full sample Std. Dev.
DO(08)
SC
Country
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Bank ownership of developing Asian countries: 2003, 2007, 2008 and 2012. Panel A shows the percentage of assets owned by the government (GO), domestic (DO), or foreign-held block shareholders (FO) which is classified by the 10% threshold from (La Porta et al., 1999). Panel B provides descriptive statistics of the changes in bank ownership across countries that whether experienced a banking crisis over the period. DO(12)
FO(03)
FO(07)
FO(08)
FO(12)
11.52% 5.71% 16.41% 68.01% 17.02% 44.39% 10.12% 38.75% 58.65% 50.54% 21.11% 29.05% 27.55% 36.58%
54.66% 1.40% 24.12% 13.64% 9.79% 47.06% 20.32% 13.91% 20.00% 3.95% 16.35% 17.01% 18.86% 33.15%
57.82% 6.64% 39.13% 16.73% 24.86% 44.44% 30.59% 21.17% 28.57% 4.17% 22.80% 42.86% 26.68% 36.04%
74.36% 8.57% 43.03% 17.93% 29.10% 44.44% 34.17% 28.14% 28.57% 6.36% 25.02% 44.00% 30.09% 38.02%
75.66% 5.55% 46.06% 14.29% 34.22% 44.44% 44.67% 25.19% 0.00% 8.42% 34.76% 47.32% 31.87% 39.07%
No. of banks
16 29 24 15 21 18 17 15 07 25 15 17 219
Panel B – Descriptive statistics of changes in ownership measures
Mean Max Min Standard deviation Total # of banks (2003) Total # of banks (2007) Total # of banks (2008) Total # of banks (2012)
∆DO 0.43% 100% -64.59% 16.45%
AC
Between 2003 and 2007 ∆GO -3.74% 31.87% -97.77% 13.60% 200 213 213 213
∆FO 5.35% 76.99% -51.00% 16.30%
∆WIDE -2.04% 72.80% -1.00% 20.16%
Between 2008 and 2012 ∆GO -0.18% 72.20% -100% 11.77%
∆DO -0.06% 100% -77.40% 20.27%
∆FO 2.77% 100% -51.02% 16.32%
∆WIDE -2.54% 43.52% -100% 16.27%
Correlation matrix 2003 – 2012 ∆GO ∆DO ∆FO ∆WIDE
∆GO 1.00 -0.41*** -0.04 -0.18***
∆DO -0.41*** 1.00 -0.53*** -0.41***
∆FO -0.04 -0.53*** 1.00 -0.34***
∆WIDE -0.18*** -0.41*** -0.34*** 1.00
Notes: The ownership measures (GO, DO, FO) are constructed following La Porta et al. (2002) and Taboada (2011). Domestic and foreign block shareholders are individuals, families, or companies (including bank holding companies) that own more than 10% of the bank’s shares. *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level
35
ACCEPTED MANUSCRIPT Table 2 Descriptive statistics of variables used in Bank Efficiency estimations SD
Median Minimum Maximum
PT
Mean
0.000
48.272
0.209
0.001
24.705
1.129
0.000
15.301
5.101
0.514
0.002
72.895
1.119
3.137
Non-interest expenses (in billion US $)
0.723
2.029
Personnel expenses (in billion US $)
0.367
Total costs (in billion US $)
1.842
0.286
0.102
30.445
98.097
6.950
0.005
1,399.722
22.160
90.780
3.816
0.002
1,334.961
49.158
174.481
10.819
0.008
2,537.282
11.011
47.142
1.724
0.006
727.811
P1 = Price of funds
0.036
0.031
0.029
0.002
0.663
P2 = Price of capital
2.64
3.67
1.60
0.15
44.17
0.010
0.007
0.008
0.001
0.141
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Interest expenses (in billion US $)
Outputs (in billion US $)
MA
Y1 = Total loans Y2 = Other earning assets
AC CE P
Input prices
TE
Y4 = Liquid assets
D
Y3 = Total deposits
P3 = Price of labor
Notes: Price of funds is the ratio of interest expenses to total deposits. Price of capital is the ratio of non-interest expenses to total fixed assets. Price of labor is the ratio of personnel expenses to total assets. Source of data: BankScope, 2003–2012.
36
ACCEPTED MANUSCRIPT
NU
SC
RI
PT
Table 3 Descriptive statistics of control variables used to estimate the impact of changes in ownership on bank efficiency. Bank Efficiency is cost efficiency scores of the individual bank obtained from equation (3) by parametric stochastic frontier approach (SFA). GO, DO, FO and WIDE are the percentage of assets owned by the government, domestic, and foreign-held block shareholders. ∆GO, ∆DO, ∆FO are changes in percentage owned by government, domestic and foreign block shareholders, respectively. FINFREE is dummy variable, takes a value of one if the country j’s financial and banking freedom index is above the median value and zero otherwise. Capital requirement (CR) is the minimum capital asset ratio requirement. NONINT represents for noninterest income as a share of total assets of banks and DDEP represents for demand deposits as a share of bank total deposits. BANKSIZE variable controls for economy of scale which calculated by log of bank total assets. EQUITY is denoted as the total equity to total asset ratio. GDP represents the growth in real GDP per capita. INFLATION is the deflated Consumer Price Index (CPI).
MA
Mean Bank Efficiency
SD
Median
Minimum Maximum
0.836
0.116
0.862
0.039
0.994
0.163
0.300
0.000
0.000
1.000
0.275
0.366
0.000
0.000
1.000
0.269
0.371
0.000
0.000
1.000
-0.019
0.127
0.000
-1.000
0.722
0.002
0.188
0.000
-0.744
1.000
0.039
0.168
0.000
-1.000
1.000
0.293
0.310
0.236
0.000
1.000
0.182
0.386
0.000
0.000
1.000
8.572
0.925
8.000
8.000
12.000
NONINT
0.012
0.015
0.009
0.000
0.284
DDEP
0.820
0.169
0.865
0.001
1.000
BANKSIZE
4.055
0.777
4.086
1.481
6.445
EQUITY
0.094
0.069
0.071
0.005
0.813
GDP
0.069
0.037
0.073
-0.039
0.169
INFLATION
1.165
0.244
1.097
0.851
2.219
GO
∆DO ∆FO WIDE FINFREE CR
TE
∆GO
AC CE P
FO
D
DO
Notes: The overall sample is an unbalanced panel which consists of 2113 bank-year observations (219 commercial banks), covering 10 years period – 2003–2012.
37
ACCEPTED MANUSCRIPT Table 4
PT
Results of SFA parameter estimates and cost efficiency scores using an intermediation approach. The total costs (TC) variable comprises interest expenses and non-interest expenses for each sample bank. The output variables encompass total loans (Y1), other earning assets (Y2), total deposits (Y3), and liquid assets (Y4). Three inputs are price of funds (P1), price of capital (P2), and price of labor (P3). Bank Efficiency is the cost efficiency scores of the individual bank obtained from equation (3) by frontier cost function techniques, which higher values indicate the more efficient banks.
AC CE P
TE
D
MA
NU
SC
RI
Dependent variable Parameter Results of Bank Efficiency Scores ln(TC/P3) Estimates Panel A – SFA cost function specification 1.160*** Full sample mean 0.836 0 *** ln(P1/P3) 0.436 Full sample median 0.862 ln(P2/P3) 0.123*** ln(Y1) 0.401*** Panel B – Means of efficiency level by country *** ln(Y2) 0.245 Hong Kong 0.860 ln(Y3) 0.198 India 0.882 ln(Y4) 0.141*** Indonesia 0.856 2 *** ln(P1/P3) 0.161 Mainland China 0.881 ln(P2/P3)2 -0.007 Malaysia 0.867 2 *** ln(Y1) 0.070 Pakistan 0.824 ln(Y2)2 0.067*** Philippines 0.732 ln(Y3)2 0.294*** Singapore 0.694 2 ** ln(Y4) -0.025 South Korea 0.824 ln(P1/P3)×ln(P2/P3) -0.036*** Taiwan 0.860 ln(P1/P3)×ln(Y1) -0.083*** Thailand 0.763 *** ln(P1/P3)×ln(Y2) -0.039 Turkey 0.810 ln(P1/P3)×ln(Y3) 0.160*** ln(P1/P3)×ln(Y4) -0.031*** Panel C – Means of efficiency level by year ln(P2/P3)×ln(Y1) -0.023** 2003 0.822 ln(P2/P3)×ln(Y2) 0.005 2004 0.813 * ln(P2/P3)×ln(Y3) 0.030 2005 0.840 ln(P2/P3)×ln(Y4) -0.018*** 2006 0.845 ln(Y1)×ln(Y2) 0.039*** 2007 0.844 *** ln(Y1)×ln(Y3) -0.125 2008 0.850 ln(Y1)×ln(Y4) 0.019 2009 0.831 *** ln(Y2)×ln(Y3) -0.143 2010 0.823 ln(Y2)×ln(Y4) 0.035*** 2011 0.842 ln(Y3)×ln(Y4) -0.027 2012 0.844 Number of obs. 2113 Log likelihood 1597.3 Panel D – Means by countries experiencing a financial crisis *** Sigma_u (σu) 0.112 Before crisis (2003 – 2007) 0.833 Sigma_square (σ2) 0.016*** After crisis (2008 – 2012) 0.838 Likelihood-ratio test of sigma_u=0 Difference -0.005 Chibar 2 570.0 t-stats (Before vs. After) -0.974 2 Probability ≥ 0.000 (t-statistics in italics are for differences in means) Notes: *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level
38
ACCEPTED MANUSCRIPT
Table 5
∆DO
∆FO
FINFREE
CR
NONINT
NU
∆GO
SC
RI
PT
This table reports the correlation coefficient matrix of main regression variables. The sample includes 219 banks from 12 Asian developing countries. The statistics based on annual data for the year 2003 –200711. ∆GO, ∆DO, ∆FO are changes in percentage owned by government, domestic and foreign block shareholders, respectively. FINFREE is dummy variable, takes a value of one if the country j’s financial and banking freedom index is above the median value and zero otherwise. Capital requirement (CR) is the minimum capital asset ratio requirement. NONINT represents for non-interest income as a share of total assets of banks and DDEP represents for demand deposits as a share of bank total deposits. BANKSIZE variable controls for economy of scale which calculated by log of bank total assets. EQUITY is denoted as the total equity to total asset ratio. GDP represents the growth in real GDP per capita. INFLATION is the deflated Consumer Price Index (CPI). DDEP
BANKSIZE
EQUITY
GDP
1.000
∆DO
-0.110***
1.000
∆FO
-0.211***
-0.329***
1.000
FINFREE
0.114***
0.030
-0.060*
1.000
CR
0.023
0.017
-0.029
0.467***
1.000
NONINT
0.011
-0.052*
0.025
-0.110***
-0.012
1.000
DDEP
-0.147***
0.020
0.012
0.048
0.072**
-0.099***
1.000
BANKSIZE
-0.096***
0.038
0.008
0.193***
0.024
-0.184***
0.046
1.000
EQUITY
0.075**
-0.125***
0.020
-0.016
0.059*
0.463***
-0.271***
-0.476***
1.000
GDP
-0.133***
-0.040
0.002
-0.067*
0.014
-0.059*
0.110***
0.229***
-0.090***
1.000
INFLATION
-0.016
-0.011
0.009
-0.045
-0.009
-0.052*
-0.007
0.092***
0.042
0.221***
AC
CE
PT ED
MA
∆GO
Notes: *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level
11
INFLATION
To conserve space, the descriptive statistic for the period 2008 – 2012 is not reported, but is available upon request.
39
1.000
ACCEPTED MANUSCRIPT Table 6
FINFREE * ∆DO
-0.0757*** (-3.83)
FINFREE * ∆FO ∆GO
0.1220*** (4.04) -0.0468*** (-3.27)
∆FO
INFLATION CR NONINT DDEP BANKSIZE EQUITY Year fixed effects Log likelihood Observations
0.3442*** (4.80) 0.0244** (2.37) -0.0235*** (-5.88) -2.1729*** (-6.82) 0.0100 (0.56) -0.0076** (-2.06) -0.4996*** (-7.93) YES 1953.22 2113
0.3644*** (5.11) 0.0259** (2.55) -0.0245*** (-6.19) -2.1688*** (-6.79) 0.0152 (0.85) -0.0077** (-2.09) -0.4951*** (-7.88) YES 1952.69 2113
-0.0372** (-2.42) 0.3518*** (5.05) 0.0284** (2.47) -0.0245*** (-7.00) -2.1638*** (-5.35) 0.0180 (0.98) -0.0066** (-2.03) -0.4965*** (-7.33) YES 1955.90 2113
AC
GDP
0.0950*** (3.00)
NU
SC
0.2314*** (3.94)
-0.0469*** (-2.61)
0.0357*** (3.10)
∆DO
After Global Financial Crisis (2008-2012) (7) (8) (9) 1.1746*** 1.1928*** 1.1613*** (21.39) (22.51) (19.80) -0.0153** -0.0123* -0.0187** (-2.04) (-1.73) (-2.22) 0.1788*** (3.83) -0.1071*** (-3.83)
MA
FINFREE * ∆GO
Dependent variables: Cost efficiency (CE) Before Global Financial Crisis (2003-2007) (4) (5) (6) 0.9922*** 0.9718*** 0.9632*** (12.37) (9.75) (9.57) 0.0453*** 0.0415*** 0.0327*** (3.86) (3.84) (2.90) -0.0343 (-0.05) 0.0215 (0.29)
PT ED
FINFREE
(1) 1.0815*** (28.21) 0.0163*** (2.95) 0.1453*** (2.75)
CE
Independent variables Intercept
Full sample (2003-2012) (2) (3) 1.0822*** 1.0722*** (27.48) (27.15) 0.0176*** 0.0112** (3.26) (2.05)
RI
PT
This table reports the differential impact of changes in bank ownership structure on the cost efficiency, which is estimated by Tobit regressions. Statistics based on annual data for the year 2003 – 2012. ∆GO, ∆DO, ∆FO are changes in percentage owned by government, domestic and foreign block shareholders, respectively. FINFREE is dummy variable, takes a value of one if the country j’s financial and banking freedom index is above the median value and zero otherwise. Capital requirement (CR) is the minimum capital asset ratio requirement. NONINT represents for non-interest income as a share of total assets of banks and DDEP represents for demand deposits as a share of bank total deposits. BANKSIZE variable controls for economy of scale which calculated by log of bank total assets. EQUITY is denoted as the total equity to total asset ratio. GDP represents the growth in real GDP per capita. INFLATION is the deflated Consumer Price Index (CPI). Models 1 through 3 report the basic regression results that include main independent variables and bank-specific control variables for all sample periods. Models 4 through 6 show estimated results during the period from 2003 to 2007, while models 7 through 9 provide regression results during the period from 2008 to 2012. The values of the t-statistics are in parentheses.
-0.0441** (-2.18) 0.0564*** (2.78)
0.0163 (0.97)
0.3488*** (3.26) 0.0749 (1.15) -0.0130** (-2.46) -1.6785*** (-3.37) 0.0236 (1.06) -0.0218*** (-4.19) -0.6629*** (-6.71) YES 913.37 1039
Notes: *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level
40
0.3796*** (3.29) 0.0824 (1.09) -0.0126** (-2.59) -1.6803*** (-3.98) 0.0283 (0.99) -0.0208*** (-3.23) -0.6558*** (-6.39) YES 911.89 1039
-0.0570*** (-3.27) 0.3562*** (3.15) 0.0923 (1.21) -0.0130*** (-2.73) -1.6587*** (-3.85) 0.0300 (1.02) -0.0197*** (-3.03) -0.6607*** (-6.43) YES 917.72 1039
0.1570* (1.73) 0.0243** (2.04) -0.0360*** (-7.29) -3.5952*** (-5.88) -0.0203 (-0.85) 0.0053 (0.79) -0.3044*** (-4.42) YES 1103.12 1074
0.1708* (1.95) 0.0248*** (2.82) -0.0377*** (-7.68) -3.5894*** (-5.09) -0.0140 (-0.69) 0.0031 (0.52) -0.3133*** (-5.51) YES 1104.21 1074
-0.0137 (-0.70) 0.1654* (1.81) 0.0261** (2.03) -0.0372*** (-7.51) -3.5807*** (-5.75) -0.0014 (-0.07) 0.0059 (0.87) -0.2911*** (-3.88) YES 1101.61 1074
ACCEPTED MANUSCRIPT Table 7
PT
This table reports the differential impact of changes in bank ownership structure on the cost efficiency, which is estimated by Tobit regressions. Statistics based on annual data for the year 2003 – 2012. ∆GO20, ∆DO20, ∆FO20 are changes in percentage owned by government, domestic and foreign block shareholders using a threshold of 20%, respectively. FINFREE is dummy variable, takes a value of one if the country j’s financial and banking freedom index is above the median value and zero otherwise. Capital requirement (CR) is the minimum capital asset ratio requirement. NONINT represents for non-interest income as a share of total assets of banks and DDEP represents for demand deposits as a share of bank total deposits. BANKSIZE variable controls for economy of scale which calculated by log of bank total assets. EQUITY is denoted as the total equity to total asset ratio. GDP represents the growth in real GDP per capita. INFLATION is the deflated Consumer Price Index (CPI). Models 1 through 3 report the basic regression results that include main
FINFREE FINFREE * ∆GO20
1.0784*** (27.19) 0.0172*** (3.13)
1.0703*** (27.04) 0.0115** (2.11)
-0.0611*** (-3.11)
FINFREE * ∆DO20
0.1073*** (3.52) -0.0398*** (-2.91) 0.0270** (2.22)
∆DO20 ∆FO20
INFLATION CR NONINT DDEP BANKSIZE EQUITY Year fixed effects Log likelihood Observations
0.3452*** (4.81) 0.0244** (2.36) -0.0235*** (-5.89) -2.1725*** (-6.82) 0.0103 (0.57) -0.0075** (-2.01) -0.4990*** (-7.92) YES 1952.41 2113
0.3652*** (5.12) 0.0262** (2.59) -0.0242*** (-6.09) -2.1718*** (-6.81) 0.0148 (0.83) -0.0074** (-2.03) -0.4949*** (-7.84) YES 1950.87 2113
-0.0226 (-1.44) 0.3495*** (4.99) 0.0277** (2.45) -0.0243*** (-6.91) -2.1656*** (-5.35) 0.0187 (1.01) -0.0065** (-2.00) -0.4943*** (-7.26) YES 1953.80 2113
AC
GDP
0.9744*** (9.86) 0.0412*** (3.75)
0.9727*** (10.59) 0.0354*** (2.67)
After Global Financial Crisis (2008-2012) (7) (8) (9) 1.1756*** (23.26) -0.0153** (-2.08) 0.1760*** (3.89)
0.2029*** (3.76)
Notes: *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level
41
1.1595*** (20.00) -0.0186** (-2.23)
0.0997*** (3.35) -0.0412* (-1.92) 0.0509*** (2.77)
0.0068 (0.39)
0.3492*** (3.24) 0.0759 (1.16) -0.0128** (-2.43) -1.6781*** (-3.37) 0.0240 (1.07) -0.0215*** (-4.14) -0.6617*** (-6.70) YES 912.85 1039
1.1882*** (22.15) -0.0125* (-1.79) -0.1004*** (-3.79)
0.0325 (0.51)
-0.0389** (-2.08)
CE
∆GO20
PT ED
FINFREE * ∆FO20
0.9886*** (12.17) 0.0447*** (3.77) -0.0407 (0.00)
SC
1.0810*** (28.22) 0.0162*** (2.92) 0.1383*** (2.66)
NU
Intercept
(1)
Dependent variables: Cost efficiency (CE) Before Global Financial Crisis (2003-2007) (4) (5) (6)
MA
Independent variables
Full sample (2003-2012) (2) (3)
RI
independent variables and bank-specific control variables for all sample periods. Models 4 through 6 show estimated results during the period from 2003 to 2007, while models 7 through 9 provide regression results during the period from 2008 to 2012. The values of the t-statistics are in parentheses.
0.3747*** (3.25) 0.0807 (1.07) -0.0126** (-2.58) -1.6789*** (-3.98) 0.0280 (0.98) -0.0208*** (-3.26) -0.6587*** (-6.43) YES 911.67 1039
-0.0267** (-2.08) 0.3527*** (3.89) 0.0871 (1.22) -0.0132*** (-2.92) -1.6659*** (-4.65) 0.0291 (1.32) -0.0203*** (-3.69) -0.6613*** (-5.98) YES 914.67 1039
0.1583* (1.85) 0.0241*** (2.77) -0.0361*** (-7.63) -3.5988*** (-5.20) -0.0200 (-0.83) 0.0053 (0.85) -0.3043*** (-5.40) YES 1102.97 1074
0.1744** (2.00) 0.0260*** (2.98) -0.0375*** (-7.62) -3.6106*** (-5.06) -0.0150 (-0.73) 0.0037 (0.61) -0.3131*** (-5.50) YES 1103.36 1074
-0.0187 (-0.97) 0.1654* (1.81) 0.0269** (2.12) -0.0372*** (-7.49) -3.5834*** (-5.78) -0.0008 (-0.04) 0.0061 (0.89) -0.2888*** (-3.84) YES 1101.68 1074
ACCEPTED MANUSCRIPT Table 8
SC
RI
PT
This table reports the impact of change in government ownership structure on the bank efficiency, which is estimated by Heckman regressions. In the first step, each dependent variables, STATE, DOMESTIC and FOREIGN equal 1 if the bank is owned by large government, domestic and foreign blockholders (owned more than 10%), respectively; zero otherwise. Statistics based on annual data for the year 2003 – 2012. The likelihood of these dependent variables are determined as the function of the national trade freedom (TRADE), rule of law (RULEOFLAW), government effectiveness (GOVEFFECT), property rights (PROPERTY), population proportion of work forces (POPULATION), control of corruption (CORRUPITON), fiscal freedom (FISCAL), political stability (POLSTABILITY), regulatory quality (REGQUALITY), lagged real GDP per capita and lagged country inflation. The dependent variable in the second step is the bank’s efficiency score. ∆GO is the change in percentage owned by government block shareholders. FINFREE is dummy variable, takes a value of one if the country j’s financial and banking freedom index is above the median value and zero otherwise. Capital requirement (CR) is the minimum capital asset ratio requirement. NONINT represents for non-interest income as a share of total assets of banks and DDEP represents for demand deposits as a share of bank total deposits. BANKSIZE variable controls for economy of scale which calculated by log of bank total assets. EQUITY is denoted as the total equity to total asset ratio. GDP represents the growth in real GDP per capita. INFLATION is the deflated Consumer Price Index (CPI). The values of the t-statistics are in parentheses.
Panel A – Heckman first stage regression results Intercept 1.8418*** 4.7199*** (5.39) -0.0312*** (-9.63) 1.0110*** (5.59) -1.3326*** (-6.48)
TRADE RULEOFLAW GOVEFFECT
(4.31)
-0.0393*** (-7.24) 0.4033*** (5.06) -2.2270** (-1.97)
FISCAL REGQUALITY
GDP(-1) INFLATION(-1)
statistic 2
2
STATE
DOMESTIC
FOREIGN
3.4563*** (2.66)
1.0256 (1.47)
4.8859*** (6.88) -0.0572*** (-7.32) 1.6641*** (5.03) -1.8757*** (-4.98)
11.3135*** (6.47)
2.9218*** (6.43)
-0.0352*** (-5.01) 0.3501*** (2.89) -0.8019 (-0.53)
-0.0667*** (-6.57) 0.7644*** (5.02) -8.4793*** (-4.68)
-5.1491*** (-3.23) 0.0040 (0.01)
-0.0397*** (-6.02) -0.4276*** (-4.91) 1.2621*** (7.77) -0.4232 (-0.29) 0.4968 (0.95)
4.1424*** (2.91) -0.7286** (-2.51)
-0.0069 (-0.01) -0.4322* (-1.96)
-0.0577*** (-10.03) -0.7074*** (-6.54) 1.8346*** (10.00) -0.1998 (-0.16) -0.2931 (-1.08)
-2.2998*** (-2.85) -0.1815 (-1.10)
-0.6011 0.0807 144.89
0.6682 0.1105 961.71
-0.2444 0.0952 213.05
-0.5477 0.0796 131.27
0.6421 0.1125 412.97
-0.3719 0.0973 289.08
-0.7996 0.0744 121.90
0.1524 0.0838 215.72
0.3311 0.0882 233.16
00.00
00.00
00.00
00.00
00.00
00.00
00.00
00.00
00.00
CORRUPTION
2.0747*** (3.49) -0.0338*** (-9.44) 0.4837** (2.41) -0.7896*** (-3.47)
FOREIGN
2.7432*** (2.73) -0.0883 (-0.56)
POLSTABILITY
Probability >
1.7624*** (6.69)
After Global Financial Crisis (2008-2012)
DOMESTIC
-0.0488*** (-10.86) -0.5125*** (-7.80) 1.5246*** (12.06) 0.0199 (0.03) 0.2377* (1.85)
PROPERTY
Wald
STATE
AC
POPULATION
FOREIGN
MA
DOMESTIC
PT ED
STATE
CE
Independent variables
NU
Dependent variables Before Global Financial Crisis (2003-2007)
Full sample (2003-2012)
0.9897 (0.72) -0.3254 (-0.60)
42
ACCEPTED MANUSCRIPT Dependent variables: Cost efficiency (CE) Full sample (2003-2012)
Before Global Financial Crisis (2003-2007)
After Global Financial Crisis (2008-2012)
Panel B – Heckman second stage regression results (21.15) 0.0092 (0.83)
(12.68) 0.0387*** (3.04)
-0.0716** (-1.97)
FINFREE * ∆DO
0.1046*** (3.10) 0.0125 (0.66)
CR NONINT DDEP BANKSIZE EQUITY INV. MILLS RATIO Year fixed effects Observations
YES 1899
0.1892* (1.83) 0.0108 (0.43) -0.0279*** (-5.29) -2.6191*** (-6.05) -0.0803*** (-2.92) 0.0015 (0.35) -0.5870*** (-7.50) 0.0739** (2.28) YES 1899
YES 1899
0.1058** (2.42) 0.0438** (2.27)
0.0359 (1.58)
0.1531 (0.91) -0.0715 (-0.65) -0.0286*** (-4.88) -2.1988*** (-6.01) -0.0630** (-2.06) -0.0090 (-1.07) -0.7063*** (-6.24) 0.0722** (1.98)
YES 1038
YES 1038
YES 1038
43
1.0881*** (11.26) -0.0281* (-1.73)
-0.0935*** (-3.16)
0.0084 (0.02) -0.1518 (-0.93) -0.0119 (-1.06) -1.0163 (-0.72) 0.1670*** (2.99) -0.0338*** (-4.30) -0.4132 (-1.62) -0.0436*** (-2.81)
Notes: *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level
1.2730*** (6.47) -0.0564*** (4.79)
0.0910** (2.13)
-0.0295 (-0.97) 0.3121** (2.10) 0.1152 (1.07) -0.0277*** (-3.48) -0.6779 (-1.13) -0.0556** (-2.01) -0.0353*** (-3.86) -0.5985*** (-3.64) -0.0362 (-1.26)
PT ED
INFLATION
0.0634 (0.40) -0.1041*** (-3.73) -0.0268*** (-3.46) -2.2642*** (-3.67) 0.1404*** (6.41) -0.0243*** (-4.25) -0.1572 (-1.15) -0.0485*** (-4.75)
AC
GDP
-0.0343 (-1.57) 0.5030*** (3.71) 0.0491** (2.17) -0.0268*** (-4.32) -1.4391** (-2.36) -0.0257 (-0.96) -0.0120* (-1.96) -0.3772*** (-3.21) -0.0233 (-1.52)
CE
∆FO
1.4503*** (10.15) -0.1014*** (-5.39) 0.1739*** (2.86)
-0.0704* (-1.93)
0.0442 (1.63)
0.0446*** (2.73)
∆DO
1.2029*** (6.58) 0.1081*** (4.61)
0.0935 (0.92)
FINFREE * ∆FO ∆GO
1.2589*** (10.14) 0.0679*** (3.48)
PT
(12.58) -0.0061 (-0.41) -0.0546 (-0.84)
1.2412*** (5.12) 0.0672*** (3.15) -0.3130 (-0.23)
RI
1.1025***
SC
FINFREE * ∆GO
1.1220***
NU
FINFREE
1.3264***
MA
Intercept
-0.0820 (-0.47) -0.1143*** (-3.47) -0.0354*** (-3.52) -6.0682*** (-6.11) 0.0452 (1.21) -0.0146** (-2.00) 0.6399*** (3.32) -0.0595*** (-4.33) YES 860
-0.0132 (-0.08) 0.0260 (1.14) -0.0333*** (-5.26) -4.5457*** (-5.11) -0.1289*** (-4.00) 0.0046 (0.67) -0.5094*** (-4.54) 0.0128 (0.50) YES 860
-0.0018 (-0.06) 0.2394 (1.08) 0.0171 (0.62) -0.0357*** (-4.03) -3.5412*** (-3.65) 0.0251 (0.73) 0.0064 (0.86) -0.0038 (-0.03) 0.0292** (2.02) YES 860