TWO ESSAYS IN INTERNATIONAL BANKING AND FINANCE: (1) CROSS-BORDER BANK MERGERS AND ACQUISITIONS, (2) SMALL AND MEDIUM ENTERPRISE FINANCING IN TRANSITION ECONOMIES Except where reference is made to the work of others, the work described in this dissertation is my own or was done in collaboration with my advisory committee. This dissertation does not include proprietary or classified information. ____________________________ Dongyun Lin Certificate of Approval: __________________________ ___________________________ Denis Nadolnyak James R. Barth, Chair Assistant Professor Professor Agricultural Economics Finance __________________________ ___________________________ Keven Yost Asheber Abebe Associate Professor Associate Professor Finance Mathematics and Statistics __________________________ George T. Flowers Dean Graduate School TWO ESSAYS IN INTERNATIONAL BANKING AND FINANCE: (1) CROSS-BORDER BANK MERGERS AND ACQUISITIONS, (2) SMALL AND MEDIUM ENTERPRISE FINANCING IN TRANSITION ECONOMIES Dongyun Lin A Dissertation Submitted to the Graduate Faculty of Auburn University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Auburn, Alabama December 18, 2009 iii TWO ESSAYS IN INTERNATIONAL BANKING AND FINANCE: (1) CROSS-BORDER BANK MERGERS AND ACQUISITIONS, (2) SMALL AND MEDIUM ENTERPRISE FINANCING IN TRANSITION ECONOMIES Dongyun Lin Permission is granted to Auburn University to make copies of this dissertation at its discretion, upon the request of individuals or institutions and at their expense. The author reserves all publication rights. ________________________ Signature of Author ________________________ Date of Graduation iv DISSERTATION ABSTRACT TWO ESSAYS IN INTERNATIONAL BANKING AND FINANCE: (1) CROSS-BORDER BANK MERGERS AND ACQUISITIONS, (2) SMALL AND MEDIUM ENTERPRISE FINANCING IN TRANSITION ECONOMIES Dongyun Lin Doctor of Philosophy, December 18, 2009 (M.S. Auburn University, 2008) (B.S. Guangdong University of Foreign Studies, 2004) 236 Typed Pages Directed by James R. Barth This dissertation consists of two essays in international finance and banking. The first essay attempts to evaluate factors that promote or impede cross-border bank mergers and acquisitions using logistic regressions. The effects of bank specific features, from both target and acquiring banks? perspectives, are estimated. The effects of bank regulations are estimated, from both target and acquiring countries? perspectives. Three comprehensive and informative dataset are combined to become a unique dataset to study banks? cross-border merger and acquisition activities. The banking sector regulatory variables included are expected to make this study the first to empirically and comprehensively analyze the interrelationship between bank v regulations and cross-border bank mergers and acquisitions. The second essay examines small and medium enterprises? financing status in transition economies using different empirical specifications. Firstly, factors relevant to SMEs? financing obstacles are analyzed. These factors are further analyzed to see if they influence firms? financing patterns differently. Then this analysis is focused on which specific firm level features and bank regulatory practices are relevant to SMEs? access to short term versus long term bank loans as well as to what extent these factors influencing the structure of loans to small business. This study is the first to explore the impacts of specific bank regulatory practices on small business lendings in transition economies. The feature that data used in this study are distributed across three periods makes this study special compared to other studies using purely cross section data in that some differences over time can be captured. vi ACKNOWLEDGMENTS First of all, I would like to express my deep and sincere gratitude to my advisor, Dr. James R. Barth. Without his wide knowledge, detailed and constructive guidance, generous support and warm encouragement, I would not have completed my PhD study within four years and this dissertation research would have never been possible. It has been a great honor and pleasure for me to do research under Dr. Barth's supervision. His passionate attitude towards research and wonderful personality will have a remarkable influence on my entire career. I warmly thank Dr. Keven Yost, Dr. Ash Abebe and Dr. Nadolnyak for their valuable advice on my dissertation. The extensive discussions and their insightful comments have significantly helped in improving the quality of this dissertation. I also wish to express my warm and sincere thanks to Dr. Gropper for serving as the outside reader and proofreading my dissertation. I also wish to extend my deeply thanks to my dear parents, who always love me, support me and believe in me. Without their consistent support and selfless love, I could not get the achievements today. Last but not least, I owe my loving thanks to my husband Fangyang Shen, who is at least as concerned as me with the completion of this dissertation. It would have been impossible for me to complete this dissertation without his stimulation. vii Style manual: American Economic Review Software used: Microsoft Word 2003, Microsoft Excel 2003, SAS 9.2, Stata 9.0, and EndNote X viii TABLE OF CONTENTS LIST OF TABLES...................................................................................................... xii CHAPTER 1. CROSS-BORDER BANK MERGERS AND ACQUISITIONS .......................................................................................................1 1. INTRODUCTION ................................................................................................... 1 1.1 Mergers and Acquisitions Waves....................................................................1 1.2 Regulaitons on Bank Mergers and Acquisitions .............................................3 1.3 Basel II.............................................................................................................4 2. LITERATURE REVIEW ..........................................................................................6 2.1 Motivations of Banks? Mergers and Acquisitions...........................................6 2.2 Information Costs and Regulation Barriers.....................................................7 2.3 Micro Level and Macro Level Factors ............................................................8 3. TESTABLE HYPOTHESIS ......................................................................................8 3.1 Economies of Scale and Scope........................................................................9 3.2 X-efficiency Hypothesis................................................................................10 3.3 Market Power Hypothesis .............................................................................11 3.4 Deregulation Mitigates Cross-border Barriers ................................................1 ix 4. DATA SOURCE......................................................................................................13 4.1 World Bank Surveys under Project ? Bank Regulation and Supervision?....13 4.2 BankScope.....................................................................................................14 4.3 Dealogic M&A Analytics..............................................................................15 5. VARIABLE SELECTION ......................................................................................15 5.1 Dependent Variables .....................................................................................15 5.2 Bank Specific Variables ................................................................................16 5.3 Bank Regulatory Variables............................................................................17 5.4 Other Variables..............................................................................................20 6. EMPIRICAL ANALYSIS .......................................................................................21 6.1 Pull Factors....................................................................................................22 6.2 Robust Tests for Pull Factors ........................................................................30 6.3 Push Factors...................................................................................................32 6.4 Robust Tests for Push Factors .......................................................................37 7. CONCLUSION........................................................................................................38 CHAPTER 2. SMALL AND MEDIUM ENTERPRISE FINANCING IN TRANSITION ECONOMIES .....................................................................................41 1. INTRODUCTION ...................................................................................................41 1.1 SMEs Financing in Transition Economies....................................................41 1.2 Scope of the Study.........................................................................................42 2. THEORETICAL FRAMEWORK...........................................................................44 x 2.1 Financial Consolidation.................................................................................45 2.2 Financial Liberalization.................................................................................48 2.3 Institutional Development .............................................................................51 2.4 Public Interest View versus Private Interest View ........................................53 3. VARIABLES SELECTION ....................................................................................55 3.1 BEEPS ...........................................................................................................55 3.2 Dependent Variables .....................................................................................56 3.3 Firm Specific Variables.................................................................................57 3.4 Bank Regulatory Variables............................................................................58 3.5 Banking Sector Structure Variables ..............................................................59 3.6 Financial and Legal Institutional Variables...................................................59 4. EMPIRICAL MODEL.............................................................................................60 4.1 Obstacle Analysis ..........................................................................................61 4.1.1 Ordered Logit Model...............................................................................61 4.1.2 Financing Obstacles, Access and Costs...................................................62 4.2 Financing Pattern Analysis............................................................................62 4.2.1 Fractional Logit versus Multinomial Fractional Logit ............................62 4.2.2 Financing Channels for fixed Investments and Working Capitals..........64 4.3 Loan Structure Analysis ................................................................................64 5. RESULT INTERPRETATION ...............................................................................65 5.1 Financing Obstacles.......................................................................................65 xi 5.2 Financing Patterns .........................................................................................77 5.3 Bank Loan Structure......................................................................................83 6. CONCLUSION........................................................................................................93 REFERENCES ..........................................................................................................201 APPENDIX A: WORLD BANK SURVEY FOR BANK REGULATION AND SUPERVISION..........................................................................................................215 APPENDIX B: EBRD-WORLD BANK SURVEY FOR BUSINESS ENVIRONMENT AND ENTERPRISE PERFORMANCE .....................................218 NOTES.......................................................................................................................220 xii LIST OF TABLES Table 1. Definition and Source of Variables for Cross-border Bank Mergers and Acquisiton Study....................................................................................................... 96 Table 2. Bank Distribution Across Countries................................................................. 100 Table 3. Correlation Analysis for Economic Development and Banking Sector Regulatory Practices ............................................................................................... 104 Table 4. Summary Statistics of All Banks for Pull Factor Analysis............................... 105 Table 5. Pearson Correlation Matrix for Pull Regression............................................... 106 Table 6. Binomial Logit Estimation for Pull Factor ....................................................... 110 Table 7. Market Power Hypothesis Testing.................................................................... 112 Table 8. Percentile Statistics for Pull Factor Analysis .................................................... 114 Table 9. Pull Factor Analysis for Big Banks with Total Asset Larger Than One Billion US Dollars............................................................................................................... 115 Table 10. Pull Factor Analysis for Emerging Markets ................................................... 116 Table 11. Pull Factor Analysis for More Privatized Banking Systems .......................... 117 Table 12. Robust Tests for Pull Factor ........................................................................... 118 Table 13. Pearson Correlation Matrix for Push Regression ........................................... 120 Table 14. Percentile Statistics for Push Factor Analysis................................................. 124 xiii Table 15. Summary Statistics for Big Banks with Total Asset Greater Than Ten Billion US Dollars..............................................................................................125 Table 16. Push Factor Analysis for Big Banks with Total Asset Greater Than Ten Billion US Dollars..............................................................................................126 Table 17. Summary Statistics for Big Banks with Total Asset Greater Than Thirty Five Billion US Dollars ..........................................................................128 Table 18. Push Factor Analysis for Big Banks with Total Asset Greater Than Ten Billion US Dollars..............................................................................................129 Table 19. Variable Definition and Source for SMEs Financing Analysis.................131 Table 20. Financing Obstacle Analysis by Order ......................................................139 Table 21. Correlation Matrix for Financing Obstacle Analysis.................................142 Table 22. Overall Financing Obstacle Analysis without Interaction Terms..............146 Table 23. Overall Financing Obstacle Analysis with Interaction Terms...................149 Table 24. Marginal Probability for Overall Financing Obstacle Analysis ................152 Table 25. Cost of Finance Analysis with Interaction Terms .....................................155 Table 26. Marginal Probability for Cost of Finance Analysis...................................158 Table 27. Access to Finance Analysis with Interaction Terms..................................162 Table 28. Marginal Probability for Access to Finance Analysis ...............................165 Table 29. Fixed Investments Financing Pattern Analysis without Foreign Bank Ownership..........................................................................................................169 xiv Table 30. Marginal Effects for Fixed Investment Financing Analysis without Foreign Bank Ownership.................................................................................................171 Table 31. Fixed Investments Financing Pattern Analysis with Foreign Bank Ownership..........................................................................................................172 Table 32. Marginal Effects for Fixed Investment Financing Analysis with Foreign Bank Ownership.................................................................................................174 Table 33. Robust Tests: Multinomial Fractional Logit for Fixed Investment Analysis (Law)...................................................................................................175 Table 34. Robust Tests: Multinomial Fractional Logit for Fixed Investment Analysis (Ccorruption).......................................................................................177 Table 35. Fixed Investment Financing Pattern Analysis: Regulatory and Accountability....................................................................................................179 Table 36. Working Capital Financing Pattern Analysis ............................................180 Table 37. Marginal Effects for Working Capital Financing Analysis.......................182 Table 38. Robust Tests: Multinomial Fractional Logit for Working Capitals Analysis (Law)...................................................................................................183 Table 39. Robust Tests: Multinomial Fractional Logit for Working Capitals Analysis (Ccorruption).......................................................................................185 Table 40. Summary Statistics for Loan Structure Analysis by Order........................187 Table 41. Correlation Matrix for Loan Structure Analysis........................................191 Table 42. Short Term Long versus Long Term Loan Analysis by Order Logit ........195 xv Table 43. Marginal Effects for Short Term Long versus Long Term Loan Analysis: Forbank ..............................................................................................197 Table 44. Marginal Effects for Short Term Long versus Long Term Loan Analysis: Pribank...............................................................................................198 Table 45. Bank Loan Structure Analysis by Tobit ....................................................199 1 CHAPTER 1. CROSS-BORDER BANK MERGERS AND ACQUISITIONS 1. INTRODUCTION Cross-border mergers and acquisitions 1 have evolved as the major mode of banks? foreign direct investments since the 1980s (see, Gilroya and Lukas, 2005; Neto, Brandao and Cerqueira, 2008), when the second merger and acquisition wave in banking sector peaked. 1.1 Merger and Acquisition Waves As Brakman, Garretsen and Marrewijk (2005) argued, one important fact as to the development of merger and acquisition activity over time is that they come in waves. Starting with the first merger and acquisition wave back in the 1870s, there have been five large industrial merger and acquisition waves (see Andrade, Mitchell, and Stafford, 2001?Brakman, Garretsen, and Marrewijk, 2005; Gorton, Kahl, and Rosen 2005; Harford, 2005). The second wave began in the early 1920s, and was followed by the third wave in the 1960s. In the first three merger and acquisition waves, firms were mainly seeking economies of scale, in that mergers and acquisitions were implemented within borders. The fourth wave, which began in the 1970s, was different from the previous three in that the transactions are characterized by speculatively leveraged mergers and acquisitions. The fifth wave began in the 1990s and ended as society was about to step into 21st century, with accompanying collapse of the technology stock bubble. As they are generally classified, there have been 3 large merger and acquisition waves in the banking sector. The first wave initiated from 1890s and peaked in 1920s. 2 It was characterized by within-border bank mergers and acquisition activities resulting in monopolistic megabanks. The second wave started in the 1950s and peaked in the 1980s. This wave mainly occurred among banks in the US, Japan and the UK. Banks from these three countries sought to hold shares in each other. The third wave continued through the whole of the 1990s and reached its peak in 1998. This wave is recognized to be the most influential one not in terms of deal numbers or deal values but in the number of banks from emerging countries that became important participants; a surge in cross-border bank mergers and acquisitions was found. Recalling the waves occurred in history, whether those in the banking industry or in other industries, the following general conclusions can be reached. First, every surge in merger and acquisition activities emerged from steady economic growth periods, and was terminated by financial crises or economic downturns. Second, the frequency of waves is increasing, and the pause between waves is becoming shorter. The recent subprime mortgage crisis, which originated in the United States and has already spread to other countries, and which is still sweeping across financial sectors and is also hurting real economies, certainly has dampened and will continue to dampen merger and acquisition activities in the short run. However, the recovery of the economy from downturn, based on the past, is expected to facilitate a new round, the so-called fourth merger and acquisition wave in the banking sector, with the expected surge in multinational banks and the resulting intensification of banking sector consolidation inspiring studies on related subjects. 1.2 Regulation of Bank Mergers and Acquisitions Regulation has a greater impact on merger and acquisition activities in the banking sector than in other sectors, mainly as a result of more stringent regulatory restrictions on banks than on other firms (see, Focarelli and Pozzolo, 2001). 3 ?The unprecedented surge of domestic bank merger and acquisition activities over the last two decades may have occurred largely because of countries? progressive deregulation in the banking sector, which was characterized by an abolition of geographic restrictions and the demolition of the demarcation line between different types of financial services? (see Hagendorff, Collins, and Keasey, 2007). In fact, this is very likely. Every bank merger and acquisition wave in history was accompanied by some regulatory reliefs. In Japan, the Bank Mergers Act of 1896 legally allowed banks? merger and acquisition activities. In Italy, the Banking Act of 1933 permitted the formation of bank holding companies. In England, the Financial Services Act of 1986 resulted in the elimination of restrictions on banks? operating in nontraditional commercial bank business, promoting vertical mergers of banks with nonbank financial firms. In the United States, the Riegle-Neal Act of 1993 eliminated restrictions on interstate banking; the Gramm-Leach-Bliley Act of 1999 permitted commercial banks to engage in security, insurance and other financial business, which indicated the end of restrictions on banks pursuing nonbank financial activities in developed countries (see Barth, Brumbaugh, and Wilcox, 2000). Over recent decades, banks? cross-border mergers and acquisitions have also seen a sharp increase. Without question, intensification of economic cross-border consolidation, or globalization, is the foremost engine which is launching international banking. A search for additional revenues resulting from international comparative advantages has motivated companies to expand abroad. Banks thereby follow their customers into foreign markets via merging with or acquiring an existent firm, or through establishing a new firm. In combination with globalization, the intensified banking sector deregulation (see Barth, Nolle, and Rice, 2000), the progressive privatization of government owned assets, as well as the financial sector 4 restructuring, all contribute to substantial growth in banks? cross-border mergers and acquisitions. The primary concern of bank regulation is stabilizing the domestic banking system. Frequent bank merger and acquisition activities within borders cause greater concentration. If a more concentrated system is more stable as some studies have demonstrated (see Beck, Demirguc-Kunt, and Levine, 2006; Berger, Klapper, and Ariss, 2008), bank regulation should foster concentration. However, if a more concentrated banking structure enhances bank fragility (see Matutes and Vives, 2000; Boyd, De Nicolo, and Jalal, 2007), bank regulation should instead prevent concentration. It appears that bank regulators tend to advocate positive concentration and stability relationships, with respect to the deregulation progress in the banking sector. However, things are not that simple. Just as Beck, Demirguc-Kunt, and Levine (2008) stated, some regulatory practices associated with more competition and less concentration also are directly related to the banking system?s stability. Furthermore, some regulatory policies on the one hand may promote competition, but on the other also facilitate concentration. For example, eliminating restrictions on banks? engagement in innovative financial activities brings in more competition but also fosters financial conglomerates. 1.3 Basel II Regarding cross-border bank mergers and acquisitions, things become even more complicated. Through cross-border merger and acquisition activities, banks have not only expanded their operation networks all over the world, but have also raised the concentration of the international banking system to an all-time high. International banking industry consolidation induced by cross-border mergers and acquisitions enables banks to compete fairly in an international playing field. However, global 5 banking capital may gradually accumulate into multinational financial conglomerates, prohibiting new entrants and obstructing competition. This imposes new difficulties on international bank regulations. It is too complicated as well as unreasonable to regulate multinational megabanks or financial conglomerates solely by domestic regulators considering the unprecedented risk overlap and profit sharing among different banking systems. How do countries with different regulatory practices coordinate to maintain international banking system stability? Probably the most important activity for when the Basel committee proposes the Basel accords is to address this problem, or as it were, to foster more harmonized international regulatory practices. The most updated Basel accords, i.e., Basel II, launched by end of the year 2006, which seek to build a fairer and competitive international regulatory playing field among different countries and to better stabilize the international banking system, are receiving contradictory responses. One opinion is that, Basel II is an exceptionally complicated regulatory proposal which is too expensive to implement (see Barth, Caprio, and Levine, 2008). In fact, Basel II may actually create unfair regulatory practices for different countries, unlike the fairer practices as proposed, since countries of different backgrounds are forced to accept regulatory schemes that may only be applicable to several developed banking systems. Furthermore, harmonization may actually suppress regulatory innovations in different banking systems. What roles will Basel II play in the expected fourth cross-border bank merger and acquisition wave after the recent financial crisis? Will it really boost bank merger and acquisition activities cross-border as intuitively expected? Or will it cause the opposite? In this paper, indices computed using data from past regulatory practices in different countries are used as proxies for Basel II pillars, which are minimum capital 6 regulations, supervisory oversight and market discipline, to predict the impact that Basel II may impose on cross-border bank merger and acquisition activities. 2. BROAD LITERATURE OVERVIEW Extensive literature exists on mergers and acquisitions in the manufacturing industry, both theoretically and empirically. The literature on mergers and acquisitions in the banking sector is relatively narrow 2 . More stringent regulatory restrictions over the banking sector which complicate banks? merger and acquisition activities relative to other firms (see, Focarelli and Pozzolo, 2001). Studies on cross-border mergers and acquisitions in the banking sector are even rarer. Information and regulation asymmetry cross-country impedes international banking (see Buch and DeLong, 2004; Berger, 2007). Besides the surge in banks? cross-border merger and acquisition activities as noted above, the literature on the determinants of cross-border mergers and acquisitions in the banking sector and their impact on bank performance, financial development, and stability, as well as on real economic growth, will be highlighted in various sections below. 2.1 Motivations of Banks? Mergers and Acquisitions Studies of the motivations for banks? mergers and acquisitions using bank level data investigate improvements on bank performance generated from mergers and acquisitions. This literature refers to studies on the motivations of manufacturing firms? mergers and acquisitions. Improvements on bank performance are further subdivided into operating performance improvements as well as shareholder value enhancements. By contrast, this study focuses on banks? motivations for mergers and acquisitions by examining their changed operating performances. According to the 7 literature, banks? merger and acquisition activities will induce cost reductions and perhaps efficiency gains via economies of scale and scope; these cost reductions and efficiency gains will be reflected in the banks? financial ratios (see Cornett and De, 1991; Gropper, 1991; Houston and Ryngaert, 1994; Clark, 1996; DeYoung and Nolle, 1996; Peristiani, 1997; Berger, Demsetz, and Strahan, 1999; Milbourn, Boot, and Thakor, 1999; Huizinga, Nelissenan, and Vennet, 2001; Amel, Barnes, Panetta, and Salleo, 2004; Cornett, McNutt, and Tehranian 2006) 3 . Variations in banks? financial ratios will, therefore, expose banks? motivations for mergers and acquisitions. 2.2 Information Costs and Regulatory Barriers Most of the studies on cross-border mergers and acquisitions in the banking sector using country data or mixed country and bank data form altogether a tiny body of literature. These studies are more recent and much rarer than studies using bank level data and limited to examine only banks? domestic merger and acquisition activities. Country level studies mainly focus on studying those country level factors that impede bank cross-border mergers and acquisitions, such as information costs and regulatory barriers. One way is to group countries into pairs and analyze bilateral data (see Buch, 2003; Galindo, Micco, and Serra, 2003; Buch and DeLong, 2004; Blank and Buch, 2007; Horen, 2007; Claessens and Horen, 2007). This methodology tends to utilize a gravity type model that is commonly used in bilateral trade studies. The other method is to use aggregate level data measuring country characteristics (see Buch, 2000; Dopico and Wilcox, 2002; Berger, 2007; Schoenmaker and Laecke, 2007). This study uses countries? aggregate level data, especially banking sector regulatory data, combined with bank level data measuring individual banks? performances, to explore the determinants of banks? cross-border merger and acquisition activities. 8 2.3 Micro Level and Macro Level Factors There are rare studies using both country and bank data together in a single study (see, Focarelli and Pozzolo, 2001; Focarelli and Pozzolo, 2006; Lanine and Vennet, 2007; Pasiouras, Tanna and Gaganis, 2007; Kohler, 2008; Hernando, Nieto and Wall, 2009). This may be largely attributed to insufficient data availability. When analyzing banks? cross-border merger and acquisition behaviors, country data are indispensable. Bank level data alone cannot well explain why a bank would expand beyond the borders to acquire a foreign bank instead of acquiring a domestic bank or why a bank is more attractive to foreign acquirers instead of being acquired by domestic banks. Buch and DeLong (2004) suggest that studying merger decisions including both bank specific and country specific variables would be interesting. It would allow for an analysis of the relative importance of macro-specific versus bank-specific factors in international merger decisions. The present study is different from those mentioned above in that the researcher uses a unique and comprehensive banking sector regulation database to address the impact of banking sector regulations on cross-border bank mergers and acquisitions. 3. TESTABLE HYPOTHESIS The primary motivations for banks to consolidate are to increase revenue and decrease costs, and to ultimately maximize profits. In this sense, the general economic theory of consolidation has laid out a basic theoretical framework for studying the motivations for bank mergers and acquisitions. Additionally, with respect to individual bank features and specific country characteristics, the determinants will appear to be diverse. 9 3.1 Economies of Scale and Scope Economies of scale are the situations in which firms obtain average cost reductions when expanding their scale of operation in certain instances. Economies of scale are, therefore, the cost reduction and perhaps efficiency gains firms achieve when expanding their operation along a given output. This theory explains why firms would tend to merge horizontally with other firms 4 . Economies of scope occur when firms obtain cost reductions by expanding the types of products and services they offer. In this way, firms? efficiency increases by responding to the demand side changes they face. This effect well complements the effect of economies of scale. Together, these factors help explain why firms tend to merge or acquire other firms vertically and horizontally. Many studies on financial sector consolidation have found that scale and scope efficiency do exist after financial firms consolidate. Berger, Demsetz, and Strahan (1999) conducted detailed reviews of the broad literature on this topic. For example, Gropper (1991), Clark (1996), Hughes, and Mester (1998); Cummins, Tennyson, and Weiss (1999); and Milbourn, Boot, and Thakor (1999) all found evidence supporting scale and scope efficiency. More recent verification comes from the literature, for example, Hannan and Pilloff (2006); Matousek (2008); and Hernando, Nieto, and Wall (2009). Some recent studies dispute this literature. For example, Amel, Barnes, Panetta, and Salleo (2004), who based their studies on the main sectors of the financial industry in the major industrialized countries over the last twenty years, found that consolidation in the financial sector results in some efficiency as a consequence of economies of scale but found no evidence that mergers yield economies of scope or gains in managerial efficiency. Bernardo and Stefania (2006) derived scale, scope and 10 X-efficiency indicators from three different specifications of cost functions: Fourier flexible form, Translog, and Box-Cox. Using these methods, they failed to find evidence to support efficiency gains using European and US commercial bank data over the period of 1995 through 1998. Consistent with theories of economies of scale and scope, large banks engage not only in traditional banking business but also in other innovative banking activities, e.g., security, insurance and real estate, and more bank and commerce interactions, through which acquiring firms can noticeably enlarge their scales and diversify the scope of their business, are expected to be more attractive to foreign acquirers. Therefore, the first testable hypothesis generated here is: 1) large banks operating in countries with fewer regulatory restrictions in innovative bank activities and bank ownership in commerce are more attractive to foreign acquirers. 3.2 X-efficiency Hypothesis X-efficiency theory was first proposed by Leibenstein (1966). X-efficiency is achieved when a firm produces maximum output given the resources it employs. The author states that X-inefficiency exists and persists in a market condition of imperfect competition. This theory indicates that if firms of different efficiency levels merge this would create considerable returns for both banks. Bertrand and Zitouna (2008), although using French manufacturing firm-level data rather than banking data, found that mergers and acquisitions clearly raise the productivity of target firms. They suggested that firms probably redistribute efficiency gains at the upstream or downstream production stage. Furthermore, they found that efficiency gains are stronger for cross-border mergers and acquisitions. Matousek (2008) found foreign banks on average to be more efficient than domestic banks. The efficiency of even small foreign owned banks is greater than that of large domestic banks. 11 If this theory is correct, efficient firms should tend to acquire relatively inefficient firms. Thus, the second testable hypothesis is: 2) efficient banks are more likely to become cross-border acquirers; conversely, inefficient banks are more likely to become cross-border targets. 3.3 Market Power Hypothesis Financial service firms can maximize returns by increasing their market powers in setting prices on the services they provide. This is a very important motivation for banks to go abroad. The motivation for profit maximization induces banks to seek more market opportunities. When the domestic markets become saturated, banks would, if possible, enter foreign countries to seize market shares. The increase of market shares would increase the market power of banks in setting prices. The so-called market power hypothesis is based upon the same observations (see Lanine and Vennet, 2007; Kohler, 2008). Under the market power hypothesis, firms tend to target foreign banks that possess large market shares in their home countries, irrespective of their degree of efficiency. Large banks with large market shares in domestic countries also have incentives to expand abroad. They do not want to put all their eggs in one basket. Large banks with large market shares in one country are at greater risk in that when the domestic market is in a downturn, large banks suffer more profit cuts or losses than do smaller banks. Risk diversification motivates large banks to go abroad. The third hypothesis is: 3) large banks with large market shares tend to go abroad to acquire other firms in order to diversify risks; large banks with large market shares also are more likely to be acquired, through which action acquiring firms can obtain remarkable market shares. 12 3.4 Deregulation Reduces Cross-border Barriers According to Focarelli and Pozzolo (2001), cross-border mergers and acquisitions are less frequent in the banking sector than in other sectors because of more stringent regulatory restrictions in banking sector which complicate banks? cross-border mergers and acquisitions relative to other firms. Buch and DeLong (2004) also found results that regulatory barriers are important in affecting banks? merger decisions. Berger (2007) pointed out major explicit and implicit regulation barriers against international banking. Explicit barriers include regulations limiting foreign bank entry or restricting activities or expansion of foreign banks which have already entered. Implicit barriers mean differences in regulatory practices or legal systems between countries. In sum, regulation barriers are particularly notable for cross-border bank mergers and acquisitions. Direct bank restrictions on foreign bank entry, and bank activities, bank ownership raise the difficulty of cross-border bank merger and acquisition activities or, even worse, prohibit banks? cross-border merger and acquisition activities. As well, banks? cross-border merger and acquisition activities involve two different regulatory systems, one at home and one abroad. Foreign banks have cost disadvantages when complying with two different sets of regulatory practices, which impose additional costs on them and, furthermore, reduce the amount of cost overlapping. The result is a decrease in foreign banks? potential to benefit from economies of scale and scope. The fourth hypothesis is: 4) banks from countries which have already implemented more intense deregulation on the banking sector, for example, less stringent restrictions on bank activities and ownership, and that have bank regulation practices which are more harmonized with the global banking system, for example, 13 more consistent with the Basel capital accords, and which rely more on capital regulation or official supervision instead of on direct control over banks, are more attractive to foreign acquirers. With respect to acquiring banks, theory suggests ambiguous conclusions. Increased deregulation of the domestic banking sector facilitates banks in exploiting additional benefits in the domestic market according to economies of scope and scale, which reduces domestic banks? incentive to expand abroad. Less intensified deregulation is expected to lower the probability of exploiting extra benefits in the domestic market, conversely motivating banks to expand into foreign markets. However, regulatory burdens also increase the difficulty of domestic banks? expansion abroad, lowering the probability of banks? cross-border merger and acquisition activities. Which effect dominates should be empirically tested. 4. DATA SOURCES The data for this study mainly come from three data sources: 1) three surveys undertaken for the World Bank project ?Bank Regulation and Supervision?; 2) BankScope; and 3) Dealogic M&A Analytics. These three datasets are merged in this study to create a unique dataset of cross-border bank mergers and acquisitions. Since there was no unique identification for each bank across the datasets, merging them was accomplished by using bank name and when the names were not exactly unique, then bank asset and other bank specific information was used to be sure correct data was assigned to each and every bank name. 4.1 World Bank Surveys under Project ?Bank Regulation and Supervision? The country specific bank regulation variables come from three surveys under the World Bank project ?Bank Regulation and Supervision? (See, Barth, Caprio and 14 Levine, 2001; Barth, Caprio and Levine, 2004; Barth, Caprio and Levine, 2006). The first survey was initiated in the late 1990s by Barth, Caprio and Levinein order to compile and analyze a comprehensive dataset on how countries regulate and supervise their banks. The initial survey was completed between 1998 and 2000, with responses from bank regulatory and supervisory authorities in 118 countries all over the world. The dataset was updated the first time between 2001 and 2003 and increased the number of countries to 153. The second and most recent update was between 2005 and 2007 and included 143 countries. This dataset is by now the most comprehensive cross-country database on commercial bank regulation and supervision, which covers various aspects of banking activities, including entry requirements, ownership restrictions, capital requirements, activity restrictions, external auditing requirements, deposit insurance scheme characteristics, loan classification and provisioning requirements, accounting/disclosure requirements, troubled bank resolution actions, and, uniquely, the ?quality? of supervisory personnel and their actions. Most of the data from surveys I, II, and III characterize regulatory and supervisory practices for the years 1999, 2002, and 2005, respectively. For a detailed description of the data collecting process and survey questions, refer to Barth, Caprio and Levine (2001). All three datasets are now available on World Bank website. The details on the grouping of the individual questions and computing of the aggregate bank regulation and supervisory indices used in this study are provided in Barth, Caprio and Levine (2006). 4.2 BankScope ?BankScope is a database providing information on 28,000 public and private banks around the world. It combines data from many sources. Each bank report contains a detailed consolidated or unconsolidated balance sheet and income 15 statement totaling up to 200 data items and 36 existing ratios for each bank? (see BankScope online webpage). In addition to the existing ratios, new ratios can also be created according to individual search and analysis interests. The BankScope database is a unique collection of micro-level banking information for different countries. It is now used by many leading financial institutions, including central banks, for cross-country studies and policymaking (see, Demirguc-Kunt and Detragiache, 1998). According to Bhattacharya (2003), BankScope is an undoubtedly valuable database. Barring a few minor discrepancies, the values reported in the database are consistent with those reported in the primary sources. The discrepancies could be due to the maintenance of a uniform accounting convention in a cross-country database such as BankScope. 4.3 Dealogic M&A Analytics ?Dealogic M&A Analytics provides a comprehensive view of merger and acquisition activities worldwide, covering a wide array of transactions including public offers; open market purchases; stock swaps; buy-outs; privatizations; recapitalizations; share buy-backs; and acquisitions. Each transaction provides information on target and acquirer, deal value, advisers, financials, and multiples along with a detailed deal commentary. M&A Analytics provides a host of analytical tools to help analyze transactions, regions and industries efficiently.? (See Dealogic M&A Analytics online webpage) 5. VARIABLE SELECTION Two groups of variables are primarily used in the empirical analysis. One group is bank level data from BankScope, which are direct financial statement items or indirectly computed financial ratios. The other group is bank regulatory variables 16 computed from three World Bank surveys. Definitions and sources of all the variables are presented in Table 1. 5.1 Dependent Variables The dependent variable is binary, which equals 1 if a bank is a cross-border target or acquirer in an observed year and equals 0 if a bank is not a cross-border target or acquirer in an observed year. This variable is computed using information from Dealogic M&A Analytics and BankScope. Transactions were used from Dealogic that fall into the following groups: a) both acquirers and targets are banks; b) all acquirers are banks and targets are either banks or nonbanks; c) all targets are banks and acquirers are banks or nonbanks. In addition, classifications are based on both acquirers and targets being from the same country and acquirers and targets being from different countries. Since in this study, the pull and push of cross-border deals are the main concerns, which are those factors that pull firms to go abroad to acquire foreign banks and push banks to cross the border to acquire foreign firms, only data on cross-border deals in which targets are banks and acquirers are banks or nonbank firms and cross-border deals in which acquirers are banks and targets are banks or nonbank firms are used. BankScope provides financial statement information for all sample banks. Banks that were cross-border targets or acquirers during the observed period are eliminated from the control group. Table 2 reports all the sample banks? distribution across countries. 5.2 Bank-Specific Variables Berger, Demsetz and Strahan (1999) suggest that researchers should be careful to select correct ratios to measure banks? operating performance since a different selection of ratios would lead to quite different results. In order to test the X-efficiency hypothesis, CTIR is used as a measure for banks? cost utilization and 17 account for banks? profitability using ROA and NIM. CTIR is the cost to income ratio. It is the ratio of overhead to the sum of net interest revenues and other operating income. This ratio measures the costs of running the bank, and thus can be used as a proxy for a bank?s operating performance. A higher ratio indicates greater cost inefficiency. ROA is the return on assets. It is the ratio of net income to assets. This ratio is usually used to measure the profitability and operational performance of banks as it looks at the returns generated from the assets financed by the bank. The higher the ratio, the more profit efficient the bank is. NIM is the net interest margin ratio. It is the ratio of interest incomes minus interest expenses and divided by average earning assets. A higher ratio indicates that funding the bank is cheaper and the bank is more profitable. Including three ratios not only can test for X-efficiency hypothesis, but also can test which specific business scopes motivate banks to expand abroad or make banks more attractive to foreign acquirers. That is, the profitability of banks is decomposed into profitability from traditional banking activities, such as taking deposits and granting loans, as well as innovative financial activities, such as fees and trading activities. By controlling for cost and interest income, ROA actually accounts for revenues from fees and trading activities. 5 ROA only accounts for banks? profitability but does not account for banks? profitability foundation that is the capital structure of banks, from which banks generate profits. The leverage ratio is important because it indicates the sustainability of a bank?s profitability. Thus banks? equity to total asset ratio E_TA is also included. This ratio measures the permanent capital adequacy of the bank. It is usually used as a proxy for risk profile of the bank. The higher the ratio, the less risky the bank is. Variable logTA is also included, which is the logarithm of bank?s total assets. It is 18 usually used as a proxy for bank size. LogTA is used here to test for economies of scale hypothesis, which is expected to be a strong hypothesis. 5.3 Bank Regulatory Variables To test for the economies of scope hypothesis, it would be best if more detailed bank level information for a bank?s business scope and income structure is available. However, this kind of information is missing. But one of the advantages of this study is that it uses banking sector regulatory variables. It is reasonable to believe that banks? activities will be greatly influenced by the countries? regulatory restrictions as to what extent banks are allowed to engage in such activities. To account for the probability of banks? participation in innovative financial activities and other commercial activities, OVER3AR and BONF are included. OVER3AR is an aggregate index measuring the overall restriction on banks? activities in security, insurance and real estate. The higher the value, the more restricted the banks? activities are in these three areas. BONF is an indicator variable measuring to what extent banks authorized to own voting shares in nonfinancial firms. It ranges from 1 to 4. No restriction at all is indicated by 1 and 4 indicates full prohibition. To account for barriers protecting objective target countries from foreign acquiring firms, NFOB and GOVBANK are also included. NFOB accounts for extent to which foreign firms are able to acquire a domestic bank. NFOB is an indicator variable measuring the extent nonfinancial firms can own voting shares in banks. It ranges from 1 to 4. One (1) indicates no restriction and 4 indicate full prohibition. If a bank comes from a country that has a NFOB of 4, there is no possibility of a foreign firm acquiring the bank. GOVBANK is the government ownership of banks. It measures the percent of government-owned bank assets out of the country's total bank 19 assets. GOVBANK measures endogenetic barriers in the sense that government bank ownership is usually used as a proxy for countries? privatization process in the banking sector. The higher the government bank ownership, the less privatized the banking system is and the more difficult it is to acquire a domestic bank. NFOB and GOVBANK variables for the acquiring countries are also included. However, their effects on cross-border bank mergers and acquisitions are expected to be opposite to those of target countries. Restrictions on nonfinancial firms owning banks suppress reverse takeover of acquiring banks. Restrictions on forming financial conglomerates also ease competition from domestic markets against acquiring banks. Both facilitate banks? engaging in cross-border merger and acquisition activities. A less privatized domestic banking system indicates fewer market opportunities, also motivating domestic banks to expand into foreign markets. This research also includes two measures as proxy for two pillars of Basel II, which are CRINDEX and OSPWER. CRINDEX is the capital regulatory index measuring both the amount of capital and verifiable sources of capital that a bank is required to possess. It ranges from 3 to 10; a higher value indicates greater stringency. OSPOWER is the official supervisory power index. It measures the extent to which supervisory authorities have the power to take actions to correct problems. It ranges from 4 to 14; a higher value indicates greater power. These two variables are used to control for the harmonization of domestic regulatory practices in banking sector with global banking system. BCASSET is banking sector concentration in assets. It is measured as a percent of the largest five banks' assets compared to the country's total banking assets. This variable is included to broadly account for the degree of competition with the banking sector. 20 FORBANK is foreign bank ownership. It measures the percent of foreign-owned banking assets of a country's total banking assets. This variable should be used cautiously. On the one hand, this variable can be used as a proxy for the openness of a country?s banking sector. But on the other hand, it is also an outcome of banks? cross-border merger and acquisition activities. To measure if a bank is foreign-owned, one can look at whether more than 50% of the bank?s assets are possessed by one or more parties from foreign countries. A country?s banking sector can become highly foreign-owned, if the frequency of foreign firms composing more than 50% of a domestic bank via cross-border mergers or acquisitions is high. 5.4 Control Variables Variable LLAGGDPPC is included to account for countries? market potentials. LLAGGDPPC is the logarithm of real GDP per capita lagged one year. A lagged variable is used since there is evidence that using GDP per capita directly with other bank regulatory variables causes endogeneity problem, since a country?s per capita GDP is related to regulatory features included or not included in the model (see, Barth, Caprio and Levine, 2006). It is evident that countries of relatively smaller real GDP per capita are characterized by regulatory practices that are more restricted in bank activities and ownerships, have higher government bank ownership, and have a higher banking sector concentration (see Table 3). The OPEN variable measures countries? openness in the real economy. It is calculated as the sum of a county?s imports and exports divided by its GDP. Variable INF stands for inflation rate. This variable measures countries? macroeconomic stability. All three macroeconomic variables are obtained from World Development Indicator (WDI). The STMKTCAP variable measures the development of countries? security market. It is computed as stock market capitalization to GDP ratio. Market share 21 variables are also included to measure individual bank?s market power in the domestic market. MSHARE1 measures the share of a bank?s assets in a country?s aggregate banking assets; MSHARE2 measures the share of a bank?s deposits and other short term funding in a country?s financial system deposits; MSHARE3 measures the share of a bank?s loans in the country?s private credit from banks and other financial institutions. The stock market capitalization ratio and market share ratios are calculated by authors using data from IFS and BankScope. This dissertation also includes the ZSCORE variable to account for countries? banking sectors? stability. A bank?s Z-score is computed as a sum of ROA plus E_TA divided by the standard deviation of ROA (see, Beck, Demirguc-Kunt and Levine, 1999; Laeven and Levine, 2008). 6. EMPIRICAL ANALYSIS Both pull and push factors that stimulate cross-border bank mergers and acquisitions are analyzed. In particular, two concerns are addressed. First, what bank, industry or country specific factors that characterize target banks pull foreign acquirers to enter domestic countries via mergers and acquisitions? Second, what bank, industry or country specific factors that characterize acquiring banks push them to go abroad via mergers and acquisitions? Since the dependent variable is binary, which equals 1 or 0, a binary choice model is used. There are several different methodologies to estimate model parameters with binary choices. In this paper, a binomial logit model is estimated (see, McFadden, 1973), which models the probability of being chosen (Y=1) against not being chosen (Y=0). The determinants that affect probability of cross-border bank mergers and acquisitions are sketched by two sets of regression equations: 22 P(Y=1)=?+? 1 BANK_TAR + ? 2 REG_TAR + ? 3 ECON_TAR 1) P(Y=1)=?+? 1 BANK_ACQ + ? 2 REG_ACQ + ? 3 ECON_ACQ 2) Where 1=target bank in equation 1) and 1=acquiring bank in equation 2). BANK is a vector of bank characteristic variables for target bank and acquiring bank respectively. REG is a vector of bank regulatory variables for target country and acquiring country. A vector of country specific macroeconomic variables is also controlled. 6.1 Pull Factors Binomial regressions on pull factors estimate the effects of target bank specific factors and target country specific bank regulatory factors on the probability that banks are acquired by foreign firms versus the probability that banks are not acquired by foreign firms. Several specifications of equation 1) are estimated. Summary statistics of variables used in pull regressions including full samples are reported in Table 4. Bivariate correlations between variables are reported in Table 5. Table 6 presents regression results. Model 1 in Table 6 includes only bank specific features as explanatory variables. The coefficients of two bank-specific variables are statistically significant. As expected, bank size (measured by logTA) is positively correlated with the probability of a bank?s being acquired cross-border, indicating that banks are more likely to be acquired by foreign firms if they are large. This is consistent with the economies of scale hypothesis, which states that the main motivation for banks? mergers and acquisitions is to expand scale. The results are in line with the extensive body of literature on this subject, for example, the findings from Hannan and Pilloff (2006) and Hernando, Nieto, and Wall (2009) all suggest the existence of economies of scale 23 in the acquisition process. The results from Matousek (2008) also indicate economies of scale. However, they found scale effect to decrease with bank size. These findings are somewhat disputed. One dispute is from Koehler (2008), who found that large banks are less likely to be taken over by foreign credit institutions if merger control lacks transparency, because governments may block cross-border bank mergers to keep the largest institution in the domestic country. In this analysis, government bank ownership and official supervisory power are controlled, but this does not decrease the probability of large target banks? being acquired. Other studies such as Amel, Barnes, Panetta and Salleo (2004) and Bernardo and Stefania (2006) also disputed the findings. Cost inefficiency (measured by cost to income ratio) is also positively correlated with the probability of banks being taken over by foreign firms. This result is consistent with the X-efficiency hypothesis. The hypothesis indicates that firms of different efficiency level that merge enhance efficiency. Consistent with the hypothesis, inefficient firms are more likely to be acquired. The results are different from those of Berger and Humphrey (1992) and Lanine and Vennet (2007). Berger and Humphrey (1992) study 57 merger cases that occurred from 1981 to 1989 with the mergeing banks possessing assets over one billion dollars and found no cost efficiencies between large bank mergers. Lanine and Vennet (2007) showed that large Western European banks tend to target relatively large and efficient Central and Eastern European banks. In their study, they found evidence to support the market power hypothesis, against the efficiency hypothesis. However, the results are in line with the extensive body of literature, which also shows that less efficient banks are more likely to be acquired. Berger, Demsetz, and Strahan (1999) found that financial industry consolidation helps to increase profit 24 efficiency and diversify portfolio risks on average but that there is no evidence of cost efficiency improvement on average. However, if the participants are previously inefficient, both cost and profit efficiency improve after mergers and acquisitions. They conclude that consolidation does induce efficiency gains for institutions but more research should be carried out to verify these gains when fairlylarge institutions are included. Kohler (2008) also found less efficient banks are more likely to be acquired. Model 2 represents the regression results adding country-specific variables describing countries? banking sector regulatory practices. Including bank regulatory variables does not result in any changes with respect to Model 1. The only significant bank-specific variables are logTA and CTIR. Five bank regulatory variables have significant coefficients in model 2. As expected, banks allowed to interact more with commerce are more attractive to foreign acquirers. BONF, measuring restrictions on bank ownership of nonfinancial firms, has a negative coefficient. This means that more restrictions on bank ownership of nonfinancial firms lower domestic banks? probability of being targeted, which is consistent with scope economy hypothesis. Bank ownership restrictions limit domestic banks? potential profitability from engaging in commercial activities, thus hampering the possibility of a domestic bank achieving economies of scope via engaging in different business activities to create diversified profit flows. Banking sector openness (measured by foreign bank ownership shares) is positively and highly significantly related to domestic banks? probability of being acquired. A more open banking system would be more attractive to foreign entrants. More surprisingly, OVER3AR, measuring restrictions on banks? activities in security, insurance and real estate, has a positive coefficient. This is inconsistent with 25 the scope economy hypothesis, which states that banks should engage in diversified activities, not solely the deposit and loan business. Besides, literature also proves that greater restrictions on bank activities cause a more frangible and risky banking system, hampering banking system development and economy growth. These relationships are very robust even if the bank activity restriction variable interacted with other regulatory variables (see, Barth, Caprio and Levine, 2004). BCASSET is positively related to target probability. It means that banking sectors that are more concentrated and less competitive induce more foreign entry. By comparison, Kohler (2008) found that degree of banking market concentration plays a role in domestic mergers and acquisitions but not for cross-border mergers and acquisitions. In contrast, Hernando, Nieto, and Wall (2009) found that in more concentrated markets, antitrust authorities reduce the probability of international banking by monopoly rents that can be obtained in more concentrated markets. Although antitrust authority effect is not controlled for in this study, positive correlation between bank concentration ratio and target probability may be explained by the fact that more concentrated markets are more likely to provide monopoly rents to foreign acquirers. A government ownership share also has a positive effect. This is intuitively surprising since higher government ownership in banks indicates greater stringency and less privatization of the domestic banking system. These barriers may be regarded as increasing the difficulty of foreign entry into domestic banking systems. However, since foreign bank ownership is controlled for, this simply means the greater government bank ownership relative to private bank ownership, the more attractive the domestic banks to foreign acquirers. Model 3 differs from Model 2 in that FORBANK from the model is dropped. 26 All the other variables retain their signs and magnitudes except for GOVBANK, which becomes not significant. This corresponds to the hypothesis above. The significant positive effect of government bank ownership contributes to the inclusion of foreign bank ownership in the model, although the Pearson correlation matrix (Table 5) shows no serious bivariate correlation between these two variables. Dropping FORBANK from the model improves the fitness of the model significantly, since the p-value of the HL statistics changes from 0.285 to 0.547. However, the predictive power of the model becomes weaker since pseudo R-square reduces from 0.09 to 0.06. Since the objective is to estimate determinants of cross-border bank mergers and acquisitions instead of to predict, Model 3 is considered an improvement on Model 2. Some features of a bank regulatory and supervisory regime may be sufficiently correlated with other features of the bank regulatory and supervisory regimes (see Barth, Caprio, and Levine, 2006), that the impact of some bank regulatory variables on target probability may be significantly influenced by the other bank regulatory variables. For example, Boyd, Chang, and Smith (1998) model the effect of bank activity restriction on financial fragility in the presence of generous deposit insurance and found a negative effect. In order to identify the effects of certain bank regulatory features on the probability of a bank being targeted in the presence of other bank regulatory features, the bank activity restriction indicator is also interacted with the capital regulation index, official supervisory power index, and an indicator variable measuring if a country has an explicit deposit insurance scheme. All three interaction terms are insignificant 6 . These results are consistent with those from Barth, Caprio, and Levine (2004), who found that restricting bank activity impedes financial development and exacerbates financial fragility, even in the 27 presence of generous deposit insurance and weak institutional environments. However, an interaction term between the capital regulatory index and the official supervisory power index is also included to see if there is any official supervisory effect under a condition of insufficient capital regulation, as well as if there is capital regulatory effect under conditions of weak official supervisory power. A significantly negative effect for the interaction term is found. Additionally, including an interaction term makes both indices individually positive. The results are reported as Model 4 in Table 6. One interpretation is that in keeping capital regulatory index unchanged, the effect of official supervisory power on the target probability depends on the initial level of a country?s capital regulation. If the coefficient of the interaction term multiplied by the initial capital regulatory index is more negative than the negative coefficient of official supervisory index, increasing the supervisory power will decrease the probability of being acquired. However, if the former is less negative than the latter, increasing the official supervisory power will increase the bank?s probability of being acquired. When the second condition prevails, the greater official supervisory power increases target probability in the presence of insufficient capital regulation. However, when capital regulation exceeds a certain instance, greater official supervision will, conversely, decrease the bank?s probability of being acquired. Similar interpretations can be applied to explain the effect of capital regulation on target probability. Sufficient capital regulation will enhance a bank?s probability of being acquired when official supervisory power is below a certain level. Increasing capital regulation, however, will decrease probability if the initial official supervisory power is great. Referring to cross-border bank mergers and acquisitions, the proposal is that for those countries with a weak institutional environment, characterized by weak supervisory power and insufficient 28 capital regulation, reinforcement of either of the regulatory instruments can make domestic banks more attractive to foreign acquirers. Whereas, for those countries who already implement strong official supervision or sufficient capital regulation, excessive official supervision or capital regulation will reduce the probability of domestic banks being acquired. Buch and DeLong (2004) found that regulations strengthening a domestic banking system make domestic banks more attractive targets of international bank mergers by increasing transparency and enhancing supervisory power. The results differ from those of Buch and DeLong (2004) in that official supervisory power contributes positively to target probability conditioned on insufficient capital regulation. In order to test the robustness of the bank activity restriction variable, the stock market capitalization ratio in model 5 is included to account for countries? security market potential. 7 This ratio measures a country?s aggregate stock market capitalization relative to its GDP. Banks from countries with less developed security markets, i.e., security markets with greater potential, are expected to be more attractive to foreign acquirers. According to Barth, Caprio, and Levine (2004), restricting banks from engaging in security activities hampers countries? financial development. Thus, it is reasonable to hypothesize that the positive bank activity restriction effect actually reflects the negative security market development effect. After controlling for the stock market capitalization ratio, the bank activity restriction effect diminishes. However, the capital regulation index and the official supervisory power index are still significantly related to target probability. This is consistent with the expectation that banking systems relying more on capital regulation and official supervision and less on direct activity control induce more foreign entry. As expected, 29 the estimated coefficient of stock market capitalization ratio appears to be highly negative. This also verifies the results from Focarelli and Pozzolo (2006), who found a positive correlation between foreign bank presence and less efficient use of the equity capital market due to the profits foreign entrants gain when competing with less efficient banks, and also due to the large growth potential of the destination equity market. GDP per capita in model 6 is further included to account for the effect of real economy market potential on target probability. As mentioned in section 6, GDP per capita lagged one year is used to mitigate the endogeneity problem. GDP per capital is negatively correlated with target probability. This is consistent with the literature. According to Focarelli and Pozzolo (2006), lower per capita GDP, lower inflation, and less efficient use of credit markets indicate high growth potential of the destination country. Berger (2007) also found that developed old Europe nations have a lower foreign bank presence compared to the developing new Europe nations due to the comparative disadvantages foreign banks face after they enter old Europe nations. The comparative disadvantages for foreign banks predominate over the comparative advantages when destination nations are developed nations. Government bank ownership is also found to become negatively correlated with target probability. The conclusion is that, after factoring out indirect government bank ownership on target probability by GDP per capita, government bank ownership has a directly negative effect on target probability. This is consistent with the expectations. Greater entry barriers to the banking sector characterized by less privatization and more government ownership reduces the probability of domestic banks being cross-border acquired. To further test the market power hypothesis, variables are also included to 30 account for banks? market share in domestic markets. In particular, the ratios of each individual bank?s total assets in a domestic country?s total deposit money bank assets are calculated and included as a measure for banks? market share, named MSHARE1. To test for the robustness of the market share variable, three other variables measuring banks? market shares, which are named MSHARE2, MSHARE3 and MSHARE4 are also included. MSHARE2 is computed as a ratio of an individual bank?s deposits and other short term funding in a domestic country?s total financial system deposits. MSHARE3 is computed as a ratio of an individual bank?s loans in a domestic country?s total financial system credit to private sectors. MHARE4 is computed as the sum of an individual bank?s deposits and loans, divided by the sum of a domestic country?s total financial system deposits and private credit. The regression results are reported in Table 7. It is shown that, by controlling for each individual bank?s market shares, the banking sector concentration ratio becomes not significant. This is robust even when substituting four different specifications of market shares. All market share variables are individually positively significant except for MSHARE3, which has no significant correlation with target probability. After comparing the p-values of four HL statistics, MSHARE3 regression has the smallest p-value, almost close to 0.1, i.e., almost causing the goodness of fit of the model to be rejected. Lanine and Vennet (2007) found evidence to support the market power hypothesis and to dispute the efficiency hypothesis. The results from this study provide strong evidence supporting the efficiency hypothesis but also find evidence to support the market power hypothesis. The two effects are not tradeoffs of one another based on the results. 31 6.2 Robustness Tests for Pull Factors In order to check the robustness of the empirical results, several robustness tests have been specified. First, the data is subsampled based on percentile statistics of the variables. Second, extra explanatory variables suggested by the literature are included. Percentile statistics of the target banks (Table 8) show that about 60% of the sample target banks have a government bank ownership below 20%. As stated, high government bank ownership places an endogenetic barrier to foreign entry, i.e., some countries? domestic banks have a small probability of being cross-border acquired because of their high government ownership. These countries may also share other common features accounted for in the regressions, and this may affect the significance of some explanatory variables. It is reasonable to estimate the regressions based on the idea that the sample banks resemble those operating in other countries with similar government bank ownership. In addition to government bank ownership, target banks? total assets and target countries? GDP per capita are also appropriate sources to subsample for the data. Both percentile statistics of target banks (Table 8) and preliminary regression results show that big banks are attractive targets of cross-border bank mergers and acquisitions. It is reasonable to believe that size may dominate other features of target banks or target countries. Based on this justification, banks are subsampled to include only banks whose assets are above 1 billion U.S. dollars. This subsample keeps about 70% of the sample target banks. Emerging countries with high economic growth potential are primary objective markets for foreign capital inflows. Percentile statistics (Table 8) show that about 60% of the sample target banks come from emerging markets. Foreign bank presence 32 is relatively low in developed nations due to the comparative disadvantages of foreign banks after entering the markets. Specifically, foreign banks in developed nations are less efficient than domestic banks and not as profitable as domestic banks. This implicit barrier for cross-border bank mergers and acquisitions dominates if target banks are from developed nations. However, implicit and explicit barriers on foreign entry are different if target countries are developing nations. Developing nations place more foreign entry barriers and activity restrictions. Developing nations also are more likely to subsidize government owned banks, implicitly crowding out foreign as well as privately owned banks (see, Berger, 2007). Based on this reasoning, the banks are again subsampled to include only those from countries with per capita GDP between 1000 and 10000 U.S. dollars. Summary statistics for each subsample are reported in Tables 9, 10 and 11 respectively. In addition to subsampling the data, variables INF and ZSCORE are also included to account for the effects of the real economy and banking system stability. INF stands for countries? inflation rate. ZSCORE 8 is a reciprocal measure of banking sector insolvency. Results from robust tests are reported in Table 12. It shows that the bank size effect is quite robust. The cost inefficiency ratio has no effect when the subsample including only large banks with assets above $1 billion are used to run regressions. This is consistent with Berger and Humphrey (1992), who also found no cost efficiency between large bank mergers. The capital regulatory index and official supervisory index are both robust. Stock market development and real economy potential are always negatively related to target probability. Banking sector concentration is positively related to target probability in all four subsample regressions without controlling for market share variables. Coefficients of 33 government bank ownership are always negative except for regression on the subsample with only banks from more privatized banking systems. 6.3 Push Factors Binomial regressions on push factors estimate the effects of the specific factors of the acquiring bank and the specific regulatory factors of the acquiring country on the probability of cross-border bank mergers and acquisitions. Tables 13 and 14 present the variables? correlation matrix and percentile statistics for the full sample. Table 15 reports variable summary statistics for banks with assets above $10 billion. The regression models presented in Table 16 are estimated in a similar way as the models presented in Table 6. The difference is that now all the bank specific variables and country specific bank regulatory variables are measuring acquiring banks? and acquiring countries? characteristics. Note that all the banks included are large banks with total assets above 10 billion US dollars. First, only bank specific variables are included. Two variables are significant. LogTA has an expected positive effect, which is consistent with statements in the literature that large banks are more likely to be acquirers. The ROA ratio is positively related to the acquiring probability. As mentioned in section 6, a higher ROA means banks are more profitable from nontraditional bank activities after controlling for NIM and CTIR. In contrast, the NIM ratio measures banks? profits from traditional deposit and loan activities. Banks that are more profitable from innovative activities are usually more efficient. ROA is positively related to acquiring probability, verifying the hypothesis that efficient banks are more likely to be cross-border acquirers. This result can be interpreted in two ways. First, this result verifies that more efficient banks are more capable of engaging in international banking activities due to comparative advantages. This result also indicates that seeking profits from 34 traditional bank activities is the main motivation of the acquiring banks? cross-border takeovers. As Focarelli and Pozzolo (2001) mentioned, if banks expand their innovative business abroad, they do not need to be present in foreign countries via cross-border mergers and acquisitions. Innovative bank services require less face-to-face contact with customers and usually are exported cross-border directly. Thus, if banks are seeking to exploit additional profits from nontraditional bank activities in foreign markets, cross-border bank mergers and acquisitions will be reduced. As a result, it should be found that the more efficient a bank is, the less likely it will engage in foreign acquisitions. The results are consistent with those from Focarelli and 2HPozzolo (2001), who also found that banks with cross-border shareholdings are on average larger and more profitable. Next, both bank specific variables and country specific bank regulatory variables are included. All the bank level variables keep their signs. Compared with the pull models in Table 6, cost inefficiency is an important determinant for target banks, and profitability from nontraditional activities is an important determinant for acquiring banks. According to the X-efficiency hypothesis, after mergers and acquisitions, the less cost efficient firm becomes more cost efficient, even as efficient as the acquiring firm. Thus, efficient firms always tend to acquire relatively inefficient firms in order to obtain X-efficiency. In the push regression models, most bank regulatory variables are significant. BONF and OVER3AR are both negatively related to banks? acquiring probability which was not expected. These two variables both measure restrictions on banks. Higher values indicate greater restrictions. Banks headquartered in countries that are more restrictive on bank activities and ownerships have less incentive to go abroad. It 35 appears inconsistent with the economy of scope hypothesis. Bank restrictions reduce additional profit opportunities that can be exploited in domestic markets from innovative activities. As a result, banks should have more incentive to go abroad. However, higher restrictions on banks may indicate more burdens on completing deals, therefore reducing the probability of acquisition. A negative correlation between bank restrictions and acquiring probability implies a burden effect which dominates over the profit effect. NFOB is, as expected, positive. More restrictions on nonfinancial firms owning banks encourage banks to become cross-border acquirers. Government bank ownership shares are positively related to the banks? probabilities of acquiring other foreign firms. Higher government bank ownership means a less privatized banking system, i.e., greater barriers in the domestic banking sector against entry and expansion. This motivates domestic banks to go abroad to seek new opportunities. BCASSET has a positive coefficient. If the acquiring bank does not belong to the top five banks, higher banking sector concentration indicates less domestic market opportunity. Seeking new profit opportunities motivates the acquiring bank to expand abroad. If the acquiring bank is a top five bank, interpretation should be two folds. Higher concentration means a larger domestic market share for the acquiring bank, resulting in less incentive to expand abroad. However, risk diversification as a motivation causes them to expand abroad. Positive sign shows that the latter effect dominates. The results from this study are different from those of Focarelli and 3HPozzolo (2001) in that they found both degree of market concentration and share of assets controlled by state-owned banks to not be significantly related to banks? acquiring probability. This is interpreted as sample difference. The work of Focarelli and 4HPozzolo (2001) is based on OECD countries, most of whom are developed nations expected to be more privatized in the banking 36 sector. Comparatively, the observation countries in this study include both developed and developing nations. Variable OPEN is further included to test if acquiring banks follow their customers abroad. This variable is, as expected, positively related to acquiring probability. Banks from countries with a higher percentage of imports and exports relative to GDP, i.e., more globalized, are more likely to become cross-border acquiring banks. In order to verify the interrelations between different bank regulatory features, the interaction terms of different bank regulatory variables are also included. The only significant interaction found is between capital regulation and bank activity restriction. They are positively interrelated. In addition, including the interaction term makes the capital regulatory variable becomes significant and negative. It is concluded that the negative effect of bank activity restrictions depends on capital regulation. If capital regulation is sufficient, restrictions on bank activities are positively related to acquiring probability. More restrictions motivate banks to go abroad. But if capital regulation is not sufficient, restrictions on bank activities help reduce banking system instability. More restrictions, conversely, decrease the probability of cross-border mergers and acquisitions. Acquiring countries? official supervisory powers have no significant effect on cross-border acquisitions based on the results of this study. However, Buch and DeLong (2004) found that the stronger supervisory power of a domestic banking system conversely reduces banks? probability of engaging in foreign acquisitions. Including stock market capitalization ratio, per capita GDP, and inflation rate does not change the signs and magnitudes of other variables. Except for government bank ownership, it becomes negatively signed but insignificant. This corresponds to 37 what was mentioned in the pull regression section. Government bank ownership effects changes after controlling for per capita GDP effect. Focarelli and 5HPozzolo (2001) also found that the share of assets controlled by state-owned banks has no significant effect. But their result is based on data from OECD countries. The stock market capitalization ratio is negatively related to acquiring probability, which is consistent with Focarelli and 6HPozzolo (2001). Their explanation is that banks are seeking additional profit opportunities beyond those offered by traditional bank activities at home. When the domestic financial sector is sufficiently developed so that additional profit opportunities can be exploited in the home country simply by offering more innovative financial services, there is less incentive for banks to expand abroad. However, if the domestic financial sector is not sufficient to provide additional profit opportunities, banks would tend to exploit new profit opportunities by expanding abroad. Per capita GDP is negatively related to acquiring probability. Developed nations are more likely to encourage domestic institutions to combine into international champions mainly because of the greater abilities of domestic institutions in these nations to reach the scale of a champion and also, because some developing nations are going through the process of privatization, which leaves a vacuum that could be filled by foreign organizations (see Berger, 2007). If these assumptions by developed countries prove to be justified, a positive effect from per capita GDP should be expected. However, considering that most of the sample banks in this study are based on developed nations since only large banks are included, it may be interpreted that the relatively lower per capita GDP, indicating relatively higher growth potential, enhances acquiring probability. Comparing the conclusions to Focarelli and 7HPozzolo (2001), they found no effect for per capita GDP. But they found negative effect for 38 inflation rate, while inflation is not an important factor in this study. 6.4 Robustness Tests for Push Factors In order to test the robustness of the push regressions, banks are also subsampled to include only those with assets above 35 billion US dollars. Summary statistics for the subsample are reported in Table 17. Regression results are reported in Table 18. Two variables become not significantly influential. Restrictions on banks owning nonfinancial firms become not important. This may result from including only huge banks, which are expected to circumvent burden readily. As a result, burden effect has a tradeoff with profit effect. Banking sector concentration is also not significantly influential. Focarelli and 8HPozzolo (2001) found that degree of market concentration has no significant effects for OECD countries. An insignificant concentration effect for huge banks, which are probably headquartered in more developed nations, is found. When this is compared with a positive bank concentration effect for less huge banks, it is interpreted as verification of too big to fail. Huge banks have less incentive relative to less huge banks to diversify risks, likely because government won?t let them fail. 7 CONCLUSION This article aims to analyze both pull determinants and push determinants of cross-border bank mergers and acquisitions. The logistic regressions were applied to estimate two groups of models: pull regressions and push regressions. Both bank specific variables and country level bank regulatory variables were included in the regressions. The results show that both bank characteristics and country characteristics are important determinants of banks? cross-border merger and 39 acquisition activities. However, which effects dominate is different between target banks and acquiring banks. Bank size is always an important factor in explaining banks? cross-border merger and acquisition activities. Large banks are more likely to take over foreign firms. Likewise, large banks also a have higher probability of being cross-border acquired. It is also shown that cost effectiveness is an important factor when evaluating target banks, while profitability from innovative activities is an important factor when evaluating acquiring banks. Target banks with lower cost efficiency are more likely to be cross-border acquired. Acquiring banks that are more profitable from innovative activities in domestic markets have more incentive to exploit new profit opportunities from traditional business by cross-border mergers and acquisitions. In addition, capital adequacy level is shown to be not a primary concern when acquiring foreign banks; neither is it an influential determinant for acquiring banks engaging in cross-border mergers and acquisitions. As for country specific bank regulatory variables, a clear distinction between target banks and acquiring banks is found. For less huge acquiring banks, those headquartered in countries with more stringent regulatory restrictions on bank activities and ownerships, a less concentrated banking sector, and increasing banking sector privatization, these factors will lower the probability of becoming foreign acquirers. For huge acquiring banks, direct restrictions or indirect barriers on banking sectors become less influential, probably because they are capable of circumventing burdens. For huge or less huge acquiring banks, more restrictions on banks reduce their incentive to expand abroad if domestic banking systems are insufficient in capital regulations. 40 Comparatively, target banks operating in countries with more privatized and less restricted banking system, relying less on direct control over banks, relying more on moderate capital regulations and official supervisions, are more attractive targets to foreign acquirers. What can be concluded from the above can also be interpreted that as regulatory practices become more harmonized with the Basel accords, domestic banks become more attractive to foreign acquirers. 41 CHAPTER 2. SMALL AND MEDIUM ENTERPRISE FINANCING IN TRANSITION ECONOMIES 1. INTRODUCTION Small and medium enterprises? (SMEs) access to credit is a topic receiving much attention from both policy makers and economists, not only because SMEs, compared to large firms, are more credit insufficient and more vulnerable to credit crunch during financial crises but also because SMEs are the backbone of most developed and developing economies, which foster market diversification, promote innovation, and benefit consumers, and, more importantly, provide many employment opportunities, greatly reducing the countries? unemployment rates. Development of SMEs can contribute to significant economic growth. However, financing obstacles greatly impede SMEs? further development and their contributions to aggregate economic growth. 1.1 SME Financing in Transition Economies In this study, SMEs? access to credit in transition economies, which refer to those Central and Eastern European countries that are undergoing economic transition from centrally planned economies to capitalist market economies, is the main concern. One concern with SMEs in transition economies is greater obstacles to obtaining financing. Researchers are interested in transition economies because they consider the transition process underway here to be natural experiments of economic system transformation that seldom exist (De Haas and Naaborg, 2005). 42 The financial systems of transition economies has already become more complex, not solely bank dominated. Equity markets and bond markets have been becoming more important components of the financial sector. However, it is still strongly bank-based. Bank is an important financing source for SMEs in transition economies. The transformation of the banking sectors in these areas is aimed at developing sound, market-oriented banking systems compatible with market-based economies. Specifically, the channel through which credit is allocated to real sectors should be transferred from state oriented to market directed. This objective is achieved through a wide range of reforms, including financial liberalization, restructuring and privatization of state-owned banks, the entry of new banks into the market, and the development of financial laws and regulations (see, Transition Report 2006). After a decade or more of restructuring and development, the banking systems in transition economies have now become highly privatized. In most transition countries, banks are sound, appropriately regulated, and largely foreign owned. However, such a large percentage of foreign bank ownership has not contributed to relieving SMEs? financing obstacles in these economies as expected. In fact, SMEs? are finding it more difficult to access credit than prior to foreign entry (Rueda Maurer, 2008). 1.2 Scope of the Study This paper is not designed to analyze firm specific institutional and regulatory effects on firms? access to credit. Rather, country specific institutional effects are analyzed as well as banking sector regulation effects on firms? access to credit. The micro-level variables point to the demand side, while the regulation variables explain the supply side. More specifically, the impact of a country?s enforcement of law and regulation, effectiveness of governance, degree of democracy, and control of 43 corruption on the difficulty or ease a firm experiences in accessing financial credit is examined. Each country?s regulatory practices on the banking sector are also compared. Specifically, the main concerns are: the impacts of restrictions on the extent to which information about banks must be released and the quality of the information; minimum capital requirements and the stringency of these requirements on capital formation; the banking sector supervisory structure, such as consolidation of the banking sector supervisory power, i.e., single versus multiple bank regulators and independence of supervisory agencies from political influence; and restrictions on to the extent to which banks can engage in innovative financial activities or own nonfinancial enterprises, the extent to which nonbank financial firms can own banks, and the extent to which nonfinancial institutions can own banks. It has, incidentally, been argued that nonfinancial institutions holding banks, such as industry loan corporations (ILC), are important sources for SMEs? financing due to their specific business objectives to fund these firms. Thus regulatory practices imposing fewer restrictions on nonfinancial firms owning banks may facilitate SMEs? access to credit. The impact of the banking sector structure, such as market concentration, measuring consolidation within the banking sector; commercial banking assets in nonbank financial institutions measuring consolidation between the banking sector and other financial sectors; as well as banking sector ownership formation, are also analyzed. Whether these factors have impacts on firms? overall financing obstacles, access to credit and cost of credit is examined. If these factors influence firms? financing patterns, i.e., if these factors affect various financing sources of banks differently, is also needs further analysis. Furthermore, those factors that affect the structure of bank loans directed toward SMEs, i.e., cost of loans, duration of loans, and obstacles to accessing long term versus short term loans, are also explored. 44 The ratio of bad loans to total assets is also included to account for firms? average creditworthiness and probability of paying back loans. While a higher ratio of bad loans indicates more risk of default, part of the costs accumulated by lenders, this may induce banks to adopt prudent lending behavior. This will influence the supply side of credit and increase SMEs? access to finance. However, a higher ratio of bad loans may also indicate a weak mechanism for prohibiting loan defaults, e.g., inefficient external private monitoring in banking sectors may be the cause of banks? lack of incentive to control default risks resulting in banks? loosening of credit standards. Thus high levels of bad loans may be the consequence of banks? risk shifting behaviors and excessive levels of lending (Barth, Bertus, Jiang and Phumiwasana, 2008). Individual loan information including detailed information on both parties in the loan process is missing. However, firm specific variables as proxies for firm-bank relationships and information asymmetries are included. There are studies showing that the problem of adverse selection arising from information asymmetry is mitigated with increasing age and size. Privately owned enterprises are also more transparent than state owned enterprises. Number of years in operation is used as proxy for firm-bank relationship. Transparent versus opaque firms are also categorized by size, number of years in operation and ownership type (Beck, Demirguc-Kunt, Laeven, and Maksimovic, 2006; Hyytinen and Pajarinen, 2008; Jimenez, Salas, and Saurina, 2009). 2. THEORETICAL FRAMEWORK To Sum up what was established in the previous literature, three trends are relevant to the number of lending relationships maintained by small and medium 45 enterprises. These trends are financial sector consolidation, financial sector liberalization, and financial sector regulatory reforms. In addition, transition economies? institutional and infrastructural developments are also important factors influencing small business lending in this area (Dell?Ariccia and Marquez, 2004; Beck, Demirguc-Kunt, Laeven, and Maksimovic, 2006; Berger and Udell, 2006). 2.1 Financial Consolidation One factor affecting access to credit is credit providers? market power. There are two theories on predicting the effects of market power on access to credit, which give different conclusions (Beck, Demirguc-Kunt, and Maksimovic, 2004). One theory is structure-performance hypothesis, which states that greater market power will cause credit providers to reduce supply and raise prices. According to structure-performance hypothesis, more concentrated credit markets will reduce firms? access to credit and increase cost of credit. The other theory is information-based hypothesis, which says that information asymmetry between credit providers and borrowers will cause adverse selection and moral hazard and may change the relationship between market power and credit supply from negative to positive or nonlinear. In a market with information asymmetry, credit lenders with greater market power will be better informed and have more incentive to maintain long term relationships with credit borrowers, therefore making the lenders more effective in screening borrowers. However, a competitive market with many small lenders will result in information dispersion causing adverse selection and inefficient screening of borrowers. The result will be a shrinking of credit and an increase in costs (Dell'Ariccia and Marquez, 2004; Hauswald and Marquez, 2006; Presbitero and Zazzaro, 2009). According to the information-based hypothesis, higher credit market concentration will lead to more access to credit, especially when borrowers are younger and smaller firms, as these 46 are considered to be more opaque borrowers causing greater information asymmetries (Jimenez, Salas and Saurina, 2009). Other literature on relationship between lender size and the lending technologies applied usually concludes that small credit lenders have advantages in soft information-based relationships lending to opaque and small borrowers. Large credit lenders are equipped to provide hard information-based transactions lending to transparen't and large borrowers and are at a disadvantage in relationship lending. However, ?large banks also deploy a number of transaction-based technologies that are specifically targeted toward opaque small businesses?, including asset-based lending, leasing, and, in recent years, small business credit scoring. (Berger and Udell, 2006; Berger, Rosen, and Udell, 2007) Berger, Rosen, and Udell (2007) found that the likelihood of a small business obtaining credit from a bank of a given size is roughly proportional to the local market presence of banks of that size. Further, they also found that ?loan rate premiums on small business loans are significantly negatively affected by a greater market presence of large banks but are not significantly affected by the size of the lending bank when the market presence of large banks is taken into account?. They conclude that if banking sector consolidation does not substantially increase local market concentration, there may be little effect on small business credit availability and a possible reduction in loan prices in some of these markets. They suggest that future researchers give some thought to including market size structure because it can substantially affect the findings. According to Berger, Rosen, and Udell (2007), the size of a lender is not an important determinant in terms of its lending to small versus large borrowers. Rather, it is lenders? market share that matter. The optimal market structure that benefits small 47 borrowers best will be one that with lower market concentration but composed of a lot of large lenders. That is, a market structure with a great presence of large lenders. Some authors study the relationship between market structure and relationship lending versus transaction lending. Investment theory states that only lenders with larger market power have the incentive to invest resources toward acquiring expensive borrower private information and maintaining relationships with borrowers, since borrowers have limited exit options in more concentrated markets and lenders could extract a surplus in the long run. Investment theory holds that extensive bank-firm ties and small business lending are more commonly observed in more concentrated credit markets (Von Thadden, 2004; Ogura, 2007). Strategic theory considers the strategic nature of relationship lending. It states that when encountering more intense competition from out-of-market competitors, local lenders will direct their lending towards small and opaque borrowers who rely more on relationship lending, in which case they can create comparative advantages against outside lenders. According to strategic theory, greater competition in the local credit market can strengthen bank-firm ties and increase small business lending (Dell'Ariccia and Marquez, 2004; Hauswald and Marquez, 2006; Memmel, Schmieder, and Stein, 2007; Neuberger, Pedergnana, and Rathke-Doppner, 2008). There are studies which have found non-monotonic patterns between relationship lending and credit market structure. Some consider the non-monotonic pattern to be due to the local credit market?s degree of concentration (Elsas, 2005; Degryse and Ongena, 2007). In a credit market with a low degree of concentration, investment theory prevails, and an increase in market concentration will reduce relationship lending. Conversely, in a credit market with a high degree of concentration, more concentration will steer relationship lending, which is consistent with strategic theory. 48 Presbitero and Zazzaro (2009) suggest that the non-monotonic relationship may be due to the type of competitors operating in the local credit market. Market concentration promotes extensive bank-firm ties when the market is dominated by large, distant banks, which specialize in arm's length financial products. Interbank competition increases relationship lending when the fraction of local, relational-oriented banks in the market is high. 2.2 Financial Liberalization Two theories predict different impacts of foreign entry on domestic small business lending (Degryse, Havrylchyk, Jurzyk, and Kozak, 2009). According to performance hypothesis, foreign entrants are mostly more efficient compared to their domestic competitors and can overcome cross-border disadvantages such as information asymmetry, especially when the domestic country is a developing economy. If performance hypothesis prevails, foreign entrants can pass on the efficiency gains to domestic borrowers. The effects should be identical for both transparent and opaque borrowers. Therefore, a reduction in small business lending by foreign entry should be observed (Clarke, Cull, and Martinez Peria, 2006; De Haas and Van Lelyveld, 2006; De la Torre, Martinez Peria, and Schmukler, 2008; Hauswald and Bruno, 2009). Clarke, Cull, and Martinez Peria (2006) investigated the survey data of 3000 enterprises in 35 developing and transition economies and found that the managers of enterprises in countries having a higher level of foreign bank presence perceive interest rates and access to long-term loans to be less constraining on enterprise operations and growth than do their counterparts in countries with less foreign participation. They further found that the benefits of foreign bank participation do not accrue only to large enterprises. Even small and medium enterprises benefit. De Haas 49 and Van Lelyveld (2006) showed that during crisis periods domestic banks contract their credit. In contrast, greenfield foreign banks play a stabilizing role by keeping their credit base stable. De Haas and Van Lelyveld also found a significant and negative relationship between home country economic growth and host country credit by foreign bank subsidiaries. De la Torre, Martinez Peria, and Schmukler (2008) found that all types of banks are catering to SMEs and larger, multiple-service banks have in fact a comparative advantage in offering a wide range of products and services on a large scale, through the use of new technologies, business models, and risk management systems. They found that banks perceive the SME segment to be highly profitable but perceive macroeconomic instability in developing countries and competition in developed countries as their main obstacles. They also found that banks in developing countries tend to be less exposed to SMEs, provide a lower share of investment loans, and charge higher fees and interest rates. They conclude that the lending environment is more important than firm size or bank ownership type in shaping bank financing to SMEs. Hauswald and Bruno (2009) found that industries more heavily dependent on external finance performed significantly better in the presence of foreign banks. They interpret the result as evidence that foreign banks lessen the financial dependence of firms thereby allowing them to grow faster. They also found that such effects tend to be more significant in developing countries and particularly beneficial during crises. However, they reach contradictory conclusions for African countries. Foreign bank presence conversely leads to economic contraction during crises for African countries. According to the portfolio composition hypothesis, foreign credit lenders are better equipped to lend to transparent and large borrowers based on hard information. By contrast, domestic credit lenders have a comparative advantage in lending to 50 opaque and small borrowers via soft information (Dell?Ariccia and Marquez, 2004; Sengupta, 2007). Consistent with portfolio composition hypothesis, foreign lenders will have less incentive to engage in small business lending (Berger and Udell, 2006; Detragiache, Tressel, and Gupta, 2006; Rueda Maurer, 2008; Degryse, Havrylchyk, Jurzyk, and Kozak, 2008). Portfolio composition hypothesis also interprets the lower interest rates charged by foreign lenders compared to domestic lenders as a difference in portfolio composition rather than in efficiency level, since foreign lenders? credit portfolios are mostly composed of large and transparent borrowers expected to generate lower opportunity costs (Degryse, Havrylchyk, Jurzyk, and Kozak, 2008). According to Berger and Udell (2006), foreign owned institutions may have a comparative advantage in transaction lending and a disadvantage in relationship lending in part because these institutions are typically large. In developing nations, foreign institutions headquartered in developed nations may have an additional advantage in transaction lending because of access to better information technologies for collecting and assessing hard information. Foreign institutions may also face additional hurdles in relationship lending because of difficulties in processing and transmitting soft information over greater distances and through more managerial layers, and because of difficulties coping with multiple economic, cultural, language, and regulatory environments. Detragiache, Tressel, and Gupta (2006) studied how foreign bank penetration affects financial sector development in poor countries and found that while foreign banks are better at monitoring high-end customers than domestic banks, their entry hurts other customers and worsens the general welfare. They also showed that in poor countries, a stronger foreign bank presence is robustly associated with less access to credit and with slower credit growth. Rueda Maurer (2008) found that in transition countries foreign bank entry has resulted in more credit 51 constraints for the average firm. In transition economies only most transparent firms benefit from foreign bank entry. Degryse, Havrylchyk, Jurzyk, and Kozak (2008) found that banks of different types of ownership have a different borrower mix in their lending portfolios. Foreign banks are more willing to extend loans to transparent borrowers. At the same time domestic private banks specialize in loans to non-transparent borrowers. 2.3 Institutional Development The most important country characteristic explaining cross-country variation in firms? financing obstacles seems to be overall institutional development (Beck and Demirguc-Kunt, 2006). Underdevelopment of the institutional environment is the reason firms stay small and growth halts. Considering failures in firms overcoming their financing obstacles, the legal and institutional environment should be seriously taken into account, especially for transition and developing economies where information asymmetry tends to be a greater concern. The existence of information asymmetries should be considered the most significant mechanism through which institutional and legal systems affect firms? access to credit in transition economies. While ill-functioning legal and institutional systems can exacerbate the informational problem, in contrast, a better legal and institutional environment helps overcome the informational disadvantages that credit lenders encounter in credit markets. Developing countries can facilitate access to financial services by strengthening institutional infrastructure (Stijn Claessens, 2006). In low income countries, better contract enforcement and information about borrowers is associated with more private sector credit. The collateral regime can play a very important role to in mitigating the negative impact of information asymmetries. Well functioning legal systems and better protection of property rights can combine to raise the use of collaterals and can 52 help increase credit lenders? abilities to sort borrowers. Countries with bankruptcy rules that reduce the cost for liquidating collateral should witness higher levels of lending to private sectors. Bankruptcy proceedings that are less lengthy and are cheaper can mitigate foreign entrants? risks in lending to opaque firms due to information disadvantages, thus reducing the negative incidence of foreign bank entry on opaque firms? credit access. An efficient credit guarantee system has the effect of improving the transparency of a firm?s business and financial conditions (Okura, 2007; Sengupta, 2007; Haselmann, Pistor, and Vig, 2008; Rueda Maurer, 2008; Hauswald and Bruno, 2009). The level of institutional development influences SMEs? access to credit more than that of large firms. It is the SMEs that are the most adversely affected by institutional and legal underdevelopment. This can be explained by the fact that SMEs are mostly opaque and encounter more acute information asymmetry problems compared to large firms. However, SMEs also benefit the most from better institutional development. Development in the financial and legal system, such as better protection of property rights, stronger law enforcement, and reduction in corruption all help relax the financial constraints for SMEs foremost. The most effective way of improving SMEs? access to external finance appears to be through institutional reforms addressing the weaknesses in legal and financial systems (Beck, Demirguc-Kunt, and Maksimovic, 2005; Beck, Demirg-Kunt, and Maksimovic, 2008; Mercieca, Schaeck, and Wolfe, 2009) Many studies have found that the effects of foreign entry on small business lending are significantly influenced by the object country?s institutional and legal environment. Middle or high income countries that are associated with relatively higher levels of institutional development benefit from foreign bank entry. However, 53 low income countries associated with low levels of legal creditor protection, corporate governance, and law enforcement suffer from foreign bank entry. Higher levels of foreign bank penetration in low income countries seem to result in less efficient financial sectors and reduced private sector credits, while a larger presence of state-owned banks is correlated with more bank deposits and lower overhead costs, even after controlling for market size and concentration (Detragiache, Gupta, and Tressel, 2005). Foreign entrants benefit more from legal change by expanding their lending volume to a greater extent than do incumbent domestic lenders (Haselmann, Pistor, and Vig, 2008). For transition economies, where credit providers are mostly foreign, there is clear evidence for the necessity of a mature regulatory and institutional environment. Many of the expected benefits of foreign bank entry may not be achieved before legal and institutional development reaches a certain threshold (Rueda Maurer, 2008; Haiss and Kichler, 2009). Some studies have also found that market structure effect on small business lending is affected by countries? institutional environments. A high level of institutional development helps dampen the relation between concentration and financing obstacles at all levels of economic development. Financial consolidation may thus bring about an improvement in financing conditions in less developed economies. (Beck, Demirguc-Kunt, and Maksimovic, 2004; Ulrich Volz, 2004). Legal and institutions development also affect different types of external finance differently (Beck, Demirguc-Kunt, and Maksimovic, 2002) and influence the extent to which different lending technologies can be legally and profitably employed to provide credit to SMEs (Berger and Udell, 2006). 2.4 Public Interest View versus Private Interest View There are two broad but opposing views of how to regulate banks, namely the 54 public interest view versus the private interest view. The public interest view, based on the hypothesis of market failure, states that governments regulate banks to facilitate the efficient functioning of banks by mitigating market failures. Government, under the public interest view, is considered to be motivated by a desire to serve the public, benefit the broader civil society, and maximize social welfare. According to the public interest view, bank regulations tend to expand output and opportunities for the many and to minimize unnecessary risks. The private interest view, also accepting the existence of market failure, considers government to be motivated by a narrow concept of self interest and a tendency to serve various interest groups. Under the private interest view, bank regulatory practices should rely more on market discipline, information disclosure, and significant oversight of the regulatory process itself (Barth, Caprio and Levine, 2006). According to Barth, Caprio, and Levine (2006), it is information asymmetries that will cause market failures. Regardless of the reason for market failures, the public interest view takes for granted the existence of significant market failures and that government is capable of ameliorating these market failures. Since the focus of this study is small and medium enterprises that are more opaque relative to large firms and are located in transition economies where underdeveloped institutional environments further exacerbate opacity (Beck and Demirguc-Kunt, 2006; Jimenez, Salas, and Saurina, 2009), information asymmetry tends to be a greater concern in this study. Furthermore, as transition countries developed from plan-based socialist economies, government and enterprise are assumed to be closely tied in these regions. Based on these assumptions, the public interest view is expected to be dominant in this study. 55 3. VARIABLE SELECTION In this section, the variables used in the empirical analysis are presented as well as the sources from which these variables are obtained or computed. Variable definitions and data sources of this study are also reported in Table 19. 3.1 BEEPS The source for the dependent variables and firm specific explanatory variables is the EBRD-World Bank Business Environment and Enterprise Performance Surveys (BEEPS) of 1999, 2002 and 2005, where EBRD stands for 9HEuropean Bank for Reconstruction and Development. BEEPS is an initiative of the EBRD and the World Bank going back to 1999 to investigate the extent to which government policies and practices facilitate or impede business activity and investment in Eastern Europe and the former Soviet Union. The purpose of the BEEPS is to ?analyze the quality of governance and the investment climate from a firm level perspective for the countries in the region? (Fries, Lysenko and Polanec, 2003; Rueda Maurer, 2008). The first round of the survey was implemented in 1999 and covered 4,104 firms in 25 transition countries in Central and Eastern Europe. The second round of the survey was implemented in 2002, covering 6,153 firms in 26 transition countries of the region and also Turkey. The most recent survey in 2005 covered 9,655 enterprises in the same countries as the survey in 2002. In order to account for country specific bank regulation effects, only 18 out of 27 countries that are also covered by the three World Bank surveys on bank supervision and regulation (Barth, Caprio and Levine, 2006) are included in this study. The 18 countries included are Armenia, Bulgaria, Croatia, the Czech Republic, Estonia, FYROM8F 9 , Hungary, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, Poland, Romania, Russia, Slovakia, Slovenia, and Turkey. 56 ?BEEPS presents specific characteristic that makes it particularly attractive. While other databases rely on information provided by firms that submit financial data to local authorities, firms in the BEEPS sample were randomly selected from business directories and yellow pages. This feature ensures the inclusion of small and opaque firms in the sample? (see Volz, 2004; Rueda Maurer, 2008; Beck, Demirguc-Kunt and Honohan, 2009). The bank regulatory variables and banking sector structure variables are all obtained or computed from the three World Bank Surveys under the project ?Bank Regulation and Supervision9F 10 ? (See, Barth, Caprio, and Levine, 2001; Barth, Caprio, and Levine, 2004; Barth, Caprio, and Levine, 2006). Merging the BEEPS and BCL datasets makes this study special among studies of small and medium enterprise financing in transition economies in that the data in this study cover three periods. Compared to the previous studies using purely cross section data, this study accounts for more periods and can tell some differences over time that the other studies cannot. For example, at the initial periods of entry, foreign banks may have difficulty screening borrowers due to information asymmetry. However, over time this disadvantage may become less a problem since foreign banks become better informed about the domestic firms. In this sense, foreign bank entry may initially lead to credit constraints for SMEs, but this effect may disappear or reverse over time (Clarke, Cull, and Martinez Peria, 2006). 3.2 Dependent Variables In this study, four sets of empirical specifications are estimated to analyze the influencing factors on SMEs? financing status. The dependent variables can be categorized into three groups, in which the first group is variables measuring SMEs? financing obstacles, the second group is variables measuring SMEs? financing 57 patterns, and the final group is variables measuring SMEs? credits from bank loans. In the first groups, three index variables are used to measure SMEs? financing obstacles: namely, overall financing obstacles (financing), access to finance (access), and cost of finance (cost). They are all indices ranging from 1 to 4 measuring the firm?s financing obstacles in each aspect. Higher values indicate greater obstacles. In the second group, variables measuring SMEs? different financing sources for fixed investments and working capital are included respectively. Specifically, these variables measure SMEs? percentages of external financing for fixed investments from equity markets (finequ_f), foreign banks (finfor_f), domestic banks (findom_f), governments (finsta_f), and money lenders (finmon_f), as well as SMEs? percentages of external financing for working capital from equity markets (finequ_w), foreign banks (finfor_w), domestic banks (findom_w), governments (finsta_w), and money lenders (finmon_w). These variables are all fractions ranging from 0 to 1. The third group includes two variables measuring SMEs? access to short term versus long term loans and four variables measuring loan structures. Variables stloan and ltloan are indices ranging from 1 to 4, measuring SMEs? obstacles to accessing different loans. Higher values indicate fewer obstacles. Variable duration measures in months the duration of the most recent loans obtained by firms. Variable interestrate measures the annual interest rate of the most recent loans. Collateral measures percentage of loan values required in collateral values. Approvalday measures the days taken to approve a loan after its application. 3.3 Firm Level Variables Firm specific variables aimed at controlling for three aspects are included, which are performance, transparency, and their origin with foreign capital. Costeffi computed as a percentage of a firm?s sales price exceeding operating costs can be 58 used to measure the firm?s cost efficiency and profitability. Transparency is an indicator equal to one if a firm use International Accounting Standards and zero otherwise. Audit is also an indicator equal one if a firm use external auditors for its financial statements and zero otherwise. Size is an index measuring how many permanent employees a firm has, which equals one if the number of permanent employees are less than 50, two if that number is not less than 50 however less than 250, and three if greater than 250. Foreign is the percentage of firm?s total assets that are foreign owned. Manufacturing is an industry indicator that equals one if a firm is in manufacturing industry. 3.4 Bank Regulatory Variables The bank regulatory variables included in this study are mainly aimed at measuring four aspects of bank regulatory practices, i.e., direct regulatory restrictions on bank activities and ownerships, capital regulation, supervisory structure, and still market monitoring. Variable overbank is included to measure to what extent banks are permitted to engage in security, insurance, and real estate activities and to own nonfinancial firms. Nfob and nbffob measure to what extent nonfinancial firms can own banks and to what extent nonbank financial firms can own banks respectively. These variables are computed so that higher values indicate more restrictions. To account for the supervisory structure of each banking sector, two variables are included. Mulsup equals one if there is more than one regulator for banks and equals zero if there is a unique bank regulators. Singlefsa equals one if there is only one regulator for all main financial institutions and zero if there are multiple. The indpoli variable measures the degree to which the supervisory authority is independent within the government from political influence. Higher values indicate more independence. 59 Variable mcar measures the minimum capital to asset ratio required for banks, and crindex measures the stringency on capital composition requirement. Higher minimum capital ratios or capital regulatory index values indicate more restrictive capital regulatory practices. Fstrans is an aggregate index used to measure the following aspects of bank?s financial statement practices: whether the income statement includes accrued or unpaid interest or principal on performing and nonperforming loans; whether banks are required to produce consolidated financial statements; and whether off-balance items and risk management procedures are disclosed to the public. This variable measures the overall transparency of bank?s financial statement practices, with higher values indicating better transparency. 3.5 Banking Sector Structure Variables Two sets of variables are included to measure banking sector structure, which are variables to measure consolidation as well as liberalization of the banking sector. Bcdepo is the percentage of the banking sector deposits that are held by the five largest banks. It indicates the market power of the top five banks, with higher values indicating more concentration. Forbank is the percentage of banking sector assets owned by banks that are 50% or more foreign owned. Higher values indicate more liberalization and privatization. 3.6 Financial and Legal Institutional Variables Six World Bank Governance Indicators (Kaufmann, Kraay, and Mastruzzi, 2008) are included to measure different dimensions of institutional development: namely, the extent to which a country?s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and free media (accountability); perceptions of the likelihood that the government will be destabilized 60 or overthrown by unconstitutional or violent means, including political violence and terrorism (political); the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government?s commitment to such policies (egovernment); the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development (regulatory); the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, the police, and the courts, as well as the likelihood of crime and violence (law); and the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as ?capture? of the state by elites and private interests (corruption). An institution variable is also included, which is the equally weighted index of the six dimensions, to measure overall institutional development status. Polity2 is an index obtained from Polity IV Project: Political Regime Characteristics and Transitions, 1800-2007 (Marshall and Jaggers, 2007) measuring a country?s overall authority pattern, with a higher value indicating more democracy. Variables included to measure a country?s financial institution development are stock market capitalization to total GDP, private credits by deposit money banks to total GDP, liquidity liabilities to total GDP, and nonperforming loans to total banking assets. 4. EMPIRICAL MODEL Four sets of empirical specifications are presented in this part, which are financing obstacle regression; financing pattern regression; long term versus short term loan regression; and loan structure regression. 61 4.1 Obstacle Analysis To estimate the determinants of firms? growth obstacles, an ordered logit model proposed by Zavoina and McElvey (1975) is estimated, since the dependent variables are discrete with natural order. 4.1.1 Ordered Logit Model The ordered logit model begins with a latent regression (Greene, 2003) Y*=X??+?, where Y* is unoberved. Y is related to Y* as: Y=0 if Y*?0, Y=1 if 0 |r| under H0: Rho=0 GDPPC OVER3AR BONF NFOB CRINDEX OSPOWER BCASSET GOVBANK FORBANK -0.47 -0.12 0.03 0.05 -0.07 -0.25 -0.32 0.09 GDPPC 1 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) -0.47 0.44 0.31 -0.09 0.06 0.27 0.07 -0.01 OVER3AR (0.000) 1 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.215) -0.12 0.44 0.24 -0.16 0.15 0.15 0.11 -0.06 BONF (0.000) (0.000) 1 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 0.03 0.31 0.24 -0.07 -0.21 0.09 -0.12 0.08 NFOB (0.000) (0.000) (0.000) 1 (0.000) (0.000) (0.000) (0.000) (0.000) 0.05 -0.09 -0.16 -0.07 0.04 -0.03 -0.05 -0.02 CRINDEX (0.000) (0.000) (0.000) (0.000) 1 (0.000) (0.001) (0.000) (0.003) -0.07 0.06 0.15 -0.21 0.04 -0.11 -0.05 0.24 OSPOWER (0.000) (0.000) (0.000) (0.000) (0.000) 1 (0.000) (0.000) (0.000) -0.25 0.27 0.15 0.09 -0.03 -0.11 -0.20 0.01 BCASSET (0.000) (0.000) (0.000) (0.000) (0.001) (0.000) 1 (0.000) (0.446) -0.32 0.07 0.11 -0.12 -0.05 -0.05 -0.20 -0.36 GOVBANK (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 1 (0.000) 0.09 -0.01 -0.06 0.08 -0.02 0.24 0.01 -0.36 FORBANK (0.000) (0.215) (0.000) (0.000) (0.003) (0.000) (0.446) (0.000) 1 105 Table 4 Pull Regression: Summary Statistics for All Banks Target Banks Control Banks Variable N Mean Std Dev Minimum Maximum N Mean Std Dev Minimum Maximum E_TA 219 11.31 10.8 0.43 90.34 15000 14.42 19.83 -812.37 100 NIM 219 4.77 4.96 -1.07 47.29 15000 5.23 10.45 -266.67 918.31 ROAA 219 0.98 2.18 -14.97 13.29 15000 1.04 4.22 -111.13 73.17 CTIR 219 75.2 56.12 14.46 655 15000 71.45 55.53 0 982.54 TA(Million$) 219 49629.2 150174 8.5 1014920.4 15000 8158.51 53647.2 0.5 1483247.7 OVER3AR 219 6.89 1.75 3 11 15000 6.68 2.04 3 12 BONF 219 2.32 0.77 1 4 15000 2.37 0.76 1 4 NFOB 219 2.03 0.64 1 4 15000 2.01 0.7 1 4 CRINDEX 219 6.3 1.67 3 10 15000 6.45 1.58 2 10 OSPOWER 219 11.03 2.16 4 14 15000 10.94 2.24 4 14 BCASSET 219 56.87 20.14 11.8 98.9 15000 53.2 22.12 11.8 100 GOVBANK 219 20.62 21.2 0 75.27 15000 21.15 21.5 0 97.1 FORBANK 219 35.78 32.72 0 99.3 15000 28.22 28.64 0 100 GDPPC($) 219 10695.8 11941.2 325.55 53489.99 15000 13445 13874.1 100.49 53489.99 MSHARE1 212 0.15 0.33 0 2.77 14287 0.09 1.07 0 74.72 MSHARE2 211 0.13 0.29 0 2.3 14158 0.06 0.45 0 30.5 MSHARE3 211 0.08 0.2 0 1.72 14063 0.05 0.67 -0.02 54.84 MSHARE4 211 0.11 0.24 0 1.97 13988 0.05 0.5 0 34.74 STMKTCAP 215 0.46 0.4 0.01 2.12 14324 0.55 0.51 0 2.69 OPEN 215 80.78 51.02 15.86 326.6 14827 79.84 58.7 14.93 326.6 INF 219 5.26 5.77 -8.18 44.25 15000 6.89 18.96 -13.97 948.55 106 Table 5(a) Pull Regression Pearson Correlation Coefficients, Prob > |r| under Ho: Rho=0 CTIR ROAA E_TA logTA NIM OVER3AR BONF NFOB CRINDEX OSPOWER BCASSET OPEN 1.00 -0.40 0.06 -0.16 -0.04 -0.01 -0.02 -0.04 0.01 -0.06 0.00 -0.09 CTIR (0.00) (0.00) (0.00) (0.00) (0.19) (0.03) (0.00) (0.11) (0.00) (0.70) (0.00) -0.40 1.00 0.14 -0.03 0.16 0.03 -0.02 -0.02 -0.01 0.06 0.04 0.01 ROAA (0.00) (0.00) (0.00) (0.00) (0.00) (0.01) (0.06) (0.53) (0.00) (0.00) (0.48) 0.06 0.14 1.00 -0.38 0.16 -0.03 -0.09 -0.08 -0.02 0.00 0.01 -0.09 E_TA (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.06) (0.87) (0.09) (0.00) -0.16 -0.03 -0.38 1.00 -0.17 0.01 0.07 0.11 -0.01 0.01 -0.06 0.02 logTA (0.00) (0.00) (0.00) (0.00) (0.45) (0.00) (0.00) (0.52) (0.16) (0.00) (0.01) -0.04 0.16 0.16 -0.17 1.00 0.08 0.01 -0.05 0.00 0.06 0.03 -0.14 NIM (0.00) (0.00) (0.00) (0.00) (0.00) (0.11) (0.00) (0.56) (0.00) (0.00) (0.00) -0.01 0.03 -0.03 0.01 0.08 1.00 0.44 0.31 -0.09 0.06 0.27 -0.18 OVER3AR 0.19 0.00 0.00 0.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.02 -0.02 -0.09 0.07 0.01 0.44 1.00 0.24 -0.16 0.15 0.15 -0.01 BONF (0.03) (0.01) (0.00) (0.00) (0.11) (0.00) (0.00) (0.00) (0.00) (0.00) (0.28) -0.04 -0.02 -0.08 0.11 -0.05 0.31 0.24 1.00 -0.07 -0.21 0.09 0.20 NFOB (0.00) (0.06) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) 0.01 -0.01 -0.02 -0.01 0.00 -0.09 -0.16 -0.07 1.00 0.05 -0.03 -0.06 CRINDEX (0.11) (0.53) (0.06) (0.52) (0.56) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) -0.06 0.06 0.00 0.01 0.06 0.06 0.15 -0.21 0.05 1.00 -0.11 0.08 OSPOWER (0.00) (0.00) (0.87) (0.16) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) 0.00 0.04 0.01 -0.06 0.03 0.27 0.15 0.09 -0.03 -0.11 1.00 -0.06 BCASSET (0.70) (0.00) (0.09) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) 107 Table 5(b) Pull Regression Pearson Correlation Coefficients, Prob > |r| under Ho: Rho=0 GOVBANK FORBANK LLAGGDPPC STMKTCAP INF MSHARE1 MSHARE2 MSHARE3 MSHARE4 0.02 -0.03 0.01 -0.06 -0.01 -0.01 -0.02 -0.01 -0.02 CTIR (0.05) (0.00) (0.53) (0.00) (0.31) (0.09) (0.01) (0.14) (0.04) -0.02 0.02 -0.08 -0.01 0.09 0.00 0.01 0.00 0.01 ROAA (0.03) (0.02) (0.00) (0.26) (0.00) (0.58) (0.28) (0.61) (0.38) 0.03 -0.02 -0.03 -0.03 0.03 -0.02 -0.04 -0.02 -0.03 E_TA (0.00) (0.06) (0.00) (0.00) (0.00) (0.00) (0.00) (0.02) (0.00) -0.04 -0.07 0.31 0.20 -0.11 0.03 0.07 0.03 0.05 logTA (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) 0.06 -0.01 -0.18 -0.14 0.23 -0.01 -0.01 -0.01 -0.02 NIM (0.00) (0.15) (0.00) (0.00) (0.00) (0.20) (0.22) (0.12) (0.06) 0.07 -0.01 -0.51 -0.39 0.10 0.03 0.05 0.03 0.04 OVER3AR 0.00 0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 -0.06 -0.22 -0.17 0.10 0.03 0.04 0.03 0.04 BONF (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) -0.12 0.08 -0.12 0.00 0.02 0.01 0.02 0.01 0.02 NFOB (0.00) (0.00) (0.00) (0.64) (0.03) (0.07) (0.05) (0.18) (0.07) -0.05 -0.02 0.00 -0.02 -0.04 0.00 0.00 0.00 0.00 CRINDEX (0.00) (0.00) (0.88) (0.04) (0.00) (0.64) (0.64) (0.92) (0.91) -0.05 0.25 -0.13 -0.02 0.07 0.05 0.05 0.04 0.05 OSPOWER (0.00) (0.00) (0.00) (0.01) (0.00) (0.00) (0.00) (0.00) (0.00) -0.20 0.00 -0.20 -0.06 0.06 0.03 0.07 0.03 0.05 BCASSET (0.00) (0.57) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) 108 Table 5(c) Pull Regression Pearson Correlation Coefficients, Prob > |r| under Ho: Rho=0 CTIR ROAA E_TA logTA NIM OVER3AR BONF NFOB CRINDEX OSPOWER BCASSET OPEN 0.02 -0.02 0.03 -0.04 0.06 0.07 0.11 -0.12 -0.05 -0.05 -0.20 -0.33 GOVBANK (0.05) (0.03) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) -0.03 0.02 -0.02 -0.07 -0.01 -0.01 -0.06 0.08 -0.02 0.25 0.00 0.61 FORBANK (0.00) (0.02) (0.06) (0.00) (0.15) (0.16) (0.00) (0.00) (0.00) (0.00) (0.57) (0.00) 0.01 -0.08 -0.03 0.31 -0.18 -0.51 -0.22 -0.12 0.00 -0.13 -0.20 0.26 LLAGGDPPC (0.53) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.88) (0.00) (0.00) (0.00) -0.09 0.01 -0.09 0.02 -0.14 -0.18 -0.01 0.20 -0.06 0.08 -0.06 1.00 OPEN 0.00 0.48 0.00 0.01 0.00 0.00 0.28 0.00 0.00 0.00 0.00 -0.06 -0.01 -0.03 0.20 -0.14 -0.39 -0.17 0.00 -0.02 -0.02 -0.06 0.38 STMKTCAP (0.00) (0.26) (0.00) (0.00) (0.00) (0.00) (0.00) (0.64) (0.04) (0.01) (0.00) (0.00) -0.01 0.09 0.03 -0.11 0.23 0.10 0.10 0.02 -0.04 0.07 0.06 -0.04 INF (0.31) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.03) (0.00) (0.00) (0.00) (0.00) -0.01 0.00 -0.02 0.03 -0.01 0.03 0.03 0.01 0.00 0.05 0.03 0.02 MSHARE1 (0.09) (0.58) (0.00) (0.00) (0.20) (0.00) (0.00) (0.07) (0.64) (0.00) (0.00) (0.05) -0.02 0.01 -0.04 0.07 -0.01 0.05 0.04 0.02 0.00 0.05 0.07 0.02 MSHARE2 (0.01) (0.28) (0.00) (0.00) (0.22) (0.00) (0.00) (0.05) (0.64) (0.00) (0.00) (0.02) -0.01 0.00 -0.02 0.03 -0.01 0.03 0.03 0.01 0.00 0.04 0.03 0.00 MSHARE3 (0.14) (0.61) (0.02) (0.00) (0.12) (0.00) (0.00) (0.18) (0.92) (0.00) (0.00) (0.76) -0.02 0.01 -0.03 0.05 -0.02 0.04 0.04 0.02 0.00 0.05 0.05 0.01 MSHARE4 (0.04) (0.38) (0.00) (0.00) (0.06) (0.00) (0.00) (0.07) (0.91) (0.00) (0.00) (0.14) 109 Table 5(d) Pull Regression Pearson Correlation Coefficients, Prob > |r| under Ho: Rho=0 GOVBANK FORBANK LLAGGDPPC STMKTCAP INF MSHARE1 MSHARE2 MSHARE3 MSHARE4 1.00 -0.36 -0.30 -0.29 0.06 -0.03 -0.05 -0.02 -0.04 GOVBANK (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.01) (0.00) -0.36 1.00 0.03 0.15 0.00 0.02 0.03 0.01 0.02 FORBANK (0.00) (0.00) (0.00) (0.74) (0.04) (0.00) (0.23) (0.00) -0.30 0.03 1.00 0.54 -0.24 -0.06 -0.08 -0.06 -0.07 LLAGGDPPC (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) -0.33 0.61 0.26 0.38 -0.04 0.02 0.02 0.00 0.01 OPEN 0.00 0.00 0.00 0.00 0.00 0.05 0.02 0.76 0.14 -0.29 0.15 0.54 1.00 -0.10 0.04 -0.02 -0.03 -0.03 STMKTCAP (0.00) (0.00) (0.00) (0.00) (0.00) (0.01) (0.00) (0.00) 0.06 0.00 -0.24 -0.10 1.00 0.01 0.01 0.01 0.01 INF (0.00) (0.74) (0.00) (0.00) (0.28) (0.21) (0.32) (0.23) -0.03 0.02 -0.06 0.04 0.01 1.00 0.88 0.97 0.97 MSHARE1 (0.00) (0.04) (0.00) (0.00) (0.28) (0.00) (0.00) (0.00) -0.05 0.03 -0.08 -0.02 0.01 0.88 1.00 0.81 0.96 MSHARE2 (0.00) (0.00) (0.00) (0.01) (0.21) (0.00) (0.00) (0.00) -0.02 0.01 -0.06 -0.03 0.01 0.97 0.81 1.00 0.94 MSHARE3 (0.01) (0.23) (0.00) (0.00) (0.32) (0.00) (0.00) (0.00) -0.04 0.02 -0.07 -0.03 0.01 0.97 0.96 0.94 1.00 MSHARE4 (0.00) (0.00) (0.00) (0.00) (0.23) (0.00) (0.00) (0.00) 110 Table 6(a) Pull Regression: All Samples Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Parameter Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) Intercept -6.397*** -8.640*** -7.438*** -10.546*** -10.239*** -7.390*** (0.269) (0.787) (0.698) (1.580) (1.592) (1.799) CTIR 0.002*** 0.003*** 0.003*** 0.003*** 0.002** 0.002** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) ROAA 0.008 0.016 0.017 0.018 0.012 0.006 (0.018) (0.021) (0.021) (0.021) (0.022) (0.022) logTA 0.266*** 0.397*** 0.335*** 0.341*** 0.389*** 0.432*** (0.024) (0.036) (0.033) (0.033) (0.035) (0.038) E_TA 0.001 0.008 0.004 0.004 0.007 0.008 (0.005) (0.006) (0.006) (0.006) (0.006) (0.006) NIM 0.003 0.001 0.001 0.001 0.001 -0.001 (0.002) (0.003) (0.004) (0.004) (0.005) (0.008) OVER3AR 0.102** 0.084* 0.089** 0.023 -0.024 (0.042) (0.043) (0.044) (0.047) (0.048) BONF -0.261** -0.333*** -0.358*** -0.342*** -0.290*** (0.106) (0.109) (0.110) (0.111) (0.111) NFOB -0.052 -0.032 -0.045 -0.033 -0.114 (0.110) (0.112) (0.112) (0.118) (0.116) CRINDEX -0.049 -0.06 0.434* 0.546** 0.513** (0.043) (0.042) (0.226) (0.228) (0.223) OSPOWER 0.005 0.054 0.334** 0.384*** 0.338** (0.034) (0.035) (0.131) (0.132) (0.131) 111 Table 6(b) Pull Regression: All Samples Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Parameter Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) BCASSET 0.012*** 0.010*** 0.010*** 0.009*** 0.007 (0.004) (0.003) (0.003) (0.003) (0.004) GOVBANK 0.011*** 0.004 0.003 -0.003 -0.009** (0.004) (0.003) (0.003) (0.004) (0.004) FORBANK 0.016*** (0.003) CRINDEX*OSPOWER -0.044** -0.055*** -0.053*** (0.020) (0.020) (0.020) STMKTCAP -1.127*** -0.880*** (0.230) (0.228) LLAGGDPPC -0.250*** (0.076) Observations 33007 15219 15219 15219 14539 14539 Pseudo R2 0.05 0.09 0.06 0.06 0.08 0.08 HL Statistics 0.015 0.285 0.547 0.034 0.581 0.456 Model (1) through (6) are estimated using binomial logistic regressions, where the dependent variable equals one if the bank has been cross-border acquired and zero otherwise. CTIR, ROA, logTA, E_TA and NIM are bank specific variables. OVER3AR, BONF, NFOB, CRINDEX, OSPOWER, BCASSET, GOVBANK and FORBANK are country specific bank regulatory variables. Data on bank specific variables are from BankScope. Bank regulatory variables are computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. Pseudo R2 is reported as reference for prediction power of the models. Hosmer-Lemeshow (HL) statistic is reported as reference for goodness of fit of the models. 112 Table 7(a) Market Power Hypothesis Asset Deposit Loan Deposit and Loan Parameter Coeff. Coeff. Coeff. Coeff. (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) Intercept -7.365*** -7.506*** -7.500*** -7.556*** (1.821) (1.824) (1.823) (1.824) CTIR 0.002** 0.003** 0.003** 0.002** (0.001) (0.001) (0.001) (0.001) ROAA 0.007 0.009 0.005 0.005 (0.022) (0.023) (0.023) (0.023) logTA 0.420*** 0.415*** 0.427*** 0.423*** (0.040) (0.041) (0.041) (0.041) E_TA 0.009 0.01 0.011* 0.012* (0.006) (0.006) (0.006) (0.007) NIM 0 0 0.004 0.004 (0.006) (0.006) (0.014) (0.014) OVER3AR -0.017 -0.015 -0.016 -0.015 (0.050) (0.050) (0.050) (0.050) BONF -0.277** -0.297*** -0.295*** -0.293*** (0.111) (0.112) (0.112) (0.112) NFOB -0.121 -0.09 -0.1 -0.094 (0.120) (0.121) (0.121) (0.121) CRINDEX 0.510 ** 0.496** 0.492** 0.490** (0.226) (0.226) (0.225) (0.226) OSPOWER 0.329** 0.327** 0.323** 0.321** (0.132) (0.132) (0.132) (0.132) BCASSET 0.006 0.006 0.006 0.006 (0.004) (0.004) (0.004) (0.004) GOVBANK -0.008* -0.007* -0.008* -0.007* (0.004) (0.004) (0.004) (0.004) CRINDEX* OSPOWER -0.052*** -0.051** -0.051* -0.051** (0.020) (0.020) (0.020) (0.020) STMKTCAP -0.881*** -0.863*** -0.852*** -0.856*** (0.232) (0.232) (0.233) (0.233) LLAGGDPPC -0.237*** -0.221*** -0.227*** -0.219*** (0.078) (0.079) (0.079) (0.079) 113 Table 7(b) Market Power Hypothesis Asset Deposit Loan Deposit and Loan Parameter Coeff. Coeff. Coeff. Coeff. (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) MSHARE1 0.350 * (0.205) MSHARE2 0.501* (0.263) MSHARE3 0.505 (0.385) MSHARE4 0.529* (0.319) Observations 13990 13873 13766 13704 Pseudo R2 0.08 0.08 0.08 0.08 HL Statistics 0.42 0.265 0.107 0.228 Models are estimated using binomial logistic regressions, where the dependent variable equals one if the bank has been cross-border acquired and zero otherwise. CTIR, ROA, logTA, E_TA and NIM are bank specific variables. OVER3AR, BONF, NFOB, CRINDEX, OSPOWER, BCASSET, GOVBANK and FORBANK are country specific bank regulatory variables. MSHARE1 through 4 are market share variables calculated using data from Bankscope and IFS. Data on bank specific variables are from BankScope. Bank regulatory variables are computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. Pseudo R2 is reported as reference for prediction power of the models. Hosmer-Lemeshow(HL) statistic is reported as reference for goodness of fit of the models. 114 Table 8 Pull Regression: Percentile Statistics: Target Banks Percentile 100% 99% 95% 90% 75% 50% TA(Million$) 1014920.40 829540.90 422867.10 121520.90 12856.90 2148.80 GOVBABK 75.27 75.27 66.60 46.84 39.97 12.30 GDPPC($) 53489.99 51590.18 37227.27 25966.62 16027.24 5022.60 Control Banks TA(Million$) 1483247.70 155283.80 22212.93 9848.21 2159.20 477.10 GOVBABK 97.10 75.27 64.70 45.20 39.99 13.30 GDPPC($) 53489.99 49996.10 40413.01 35947.37 23560.36 5489.60 Target Banks Percentile 50% 25% 10% 5% 1% 0% TA(Million$) 2148.80 336.30 66.60 33.40 10.50 8.50 GOVBABK 12.30 0.30 0.00 0.00 0.00 0.00 GDPPC($) 5022.60 2122.32 1022.31 636.53 425.30 325.55 Control Banks TA(Million$) 477.10 131.50 46.40 24.80 7.87 0.50 GOVBABK 13.30 1.10 0.00 0.00 0.00 0.00 GDPPC($) 5489.60 2118.37 636.12 419.41 270.86 100.49 115 Table 9: Pull Regression: Total Asset above US $1 Billion Target Banks Control Banks Variable N Mean Std Dev Minimum Maximum N Mean Std Dev Minimum Maximum E_TA 137 7.54 3.67 0.43 21.12 5408 7.72 7.08 -35.16 99.22 NIM 137 3.78 2.98 0.52 14.37 5408 3.58 13.16 -17.32 918.31 ROAA 137 0.98 1.37 -7.15 6.13 5408 0.80 2.86 -111.13 73.01 CTIR 137 66.24 26.44 14.46 233.12 5408 63.30 35.94 0.16 936.91 TA(Million$) 137 79160.48 183857.76 1033.10 1014920.40 5408 22127.37 87625.94 1001.50 1483247.70 OVER3AR 137 7.00 1.73 3.00 10.00 5408 6.75 1.95 3.00 11.00 BONF 137 2.42 0.72 1.00 4.00 5408 2.44 0.72 1.00 4.00 NFOB 137 2.06 0.64 1.00 3.00 5408 2.12 0.69 1.00 4.00 CRINDEX 137 6.42 1.67 3.00 10.00 5408 6.47 1.56 2.00 10.00 OSPOWER 137 11.11 2.21 6.00 14.00 5408 10.97 2.18 4.00 14.00 BCASSET 137 56.63 20.74 11.80 98.90 5408 52.01 22.44 11.80 100.00 GOVBANK 137 21.95 22.01 0.00 75.27 5408 20.66 23.41 0.00 97.10 FORBANK 137 33.72 33.88 0.00 99.30 5408 25.99 29.83 0.00 100.00 GDPPC($) 137 12664.55 12936.24 419.41 53489.99 5408 18091.81 15256.55 325.06 53489.99 MSHARE2 136 0.19 0.34 0.00 2.30 5230 0.08 0.19 0.00 3.71 MSHARE3 136 0.12 0.24 0.00 1.72 5204 0.06 0.14 0.00 3.25 MSHARE1 136 0.22 0.40 0.00 2.77 5234 0.11 0.26 0.00 6.02 MSHARE4 136 0.15 0.28 0.00 1.97 5204 0.07 0.16 0.00 3.52 STMKTCAP 137 0.52 0.39 0.05 2.12 5374 0.66 0.46 0.00 2.69 OPEN 133 81.63 58.56 15.86 326.60 5320 80.90 70.55 14.93 326.60 INF 137 4.26 4.09 -8.18 20.53 5408 4.69 10.53 -13.97 377.78 116 Table 10: Pull Regression: Per Capita GDP between US $1000 and $10000 Target Banks Control Banks Variable N Mean Std Dev Minimum Maximum N Mean Std Dev Minimum Maximum E_TA 123 12.66 10.92 2.79 90.34 6794 16.85 23.51 -812.37 100.00 NIM 123 6.20 5.91 0.64 47.29 6794 7.34 14.03 -266.67 918.31 ROAA 123 1.03 2.61 -14.97 13.29 6794 1.23 4.89 -111.13 62.56 CTIR 123 82.08 69.46 14.46 655.00 6794 74.37 61.42 0.00 960.53 TA(Million$) 123 5610.51 12158.18 10.30 92580.80 6794 1997.21 6305.67 0.50 138665.70 OVER3AR 123 7.27 1.58 3.00 11.00 6794 7.40 1.65 3.00 12.00 BONF 123 2.35 0.82 1.00 4.00 6794 2.45 0.83 1.00 4.00 NFOB 123 1.79 0.53 1.00 3.00 6794 1.89 0.65 1.00 4.00 CRINDEX 123 6.05 1.61 3.00 10.00 6794 6.21 1.70 2.00 10.00 OSPOWER 123 11.62 2.07 4.00 14.00 6794 11.35 2.28 4.00 14.00 BCASSET 123 62.37 14.97 11.80 98.90 6794 57.25 19.18 11.80 100.00 GOVBANK 123 21.27 20.08 0.00 75.20 6794 23.68 20.01 0.00 95.78 FORBANK 123 49.45 31.90 0.00 99.30 6794 34.64 25.69 0.00 100.00 GDPPC($) 123 3930.96 1894.46 1022.31 9929.88 6794 3946.59 2048.11 1000.25 9980.81 MSHARE2 121 0.14 0.34 0.00 2.30 6553 0.06 0.14 0.00 3.06 MSHARE3 121 0.10 0.25 0.00 1.72 6524 0.04 0.11 0.00 2.01 MSHARE1 122 0.16 0.40 0.00 2.77 6621 0.06 0.17 0.00 2.91 MSHARE4 121 0.12 0.29 0.00 1.97 6487 0.05 0.12 0.00 2.33 STMKTCAP 121 0.29 0.27 0.01 2.12 6428 0.34 0.34 0.00 2.53 OPEN 123 85.50 44.65 15.86 174.40 6756 73.41 44.00 14.93 244.47 INF 123 6.98 6.47 -1.84 44.25 6794 9.73 16.60 -13.97 948.55 117 Table 11: Pull Regression: Government Bank Ownership Below 20% Target Banks Control Banks Variable N Mean Std Dev Minimum Maximum N Mean Std Dev Minimum Maximum E_TA 130 10.89 8.17 2.79 51.58 8741 13.25 21.16 -812.37 100.00 NIM 130 3.92 2.67 -1.07 18.99 8741 4.48 6.69 -266.67 139.13 ROAA 130 1.10 1.50 -4.49 7.89 8741 1.15 3.62 -83.41 73.17 CTIR 130 72.33 41.11 16.09 412.68 8741 69.02 47.58 0.00 982.54 TA(Million$) 130 39944.47 124964.36 10.50 927696.10 8741 9721.75 58021.00 0.50 1471132.30 OVER3AR 130 6.94 1.78 3.00 11.00 8741 6.74 2.14 3.00 12.00 BONF 130 2.42 0.72 1.00 4.00 8741 2.38 0.72 1.00 4.00 NFOB 130 2.21 0.57 1.00 4.00 8741 2.16 0.69 1.00 4.00 CRINDEX 130 6.39 1.62 3.00 10.00 8741 6.45 1.71 2.00 10.00 OSPOWER 130 11.14 2.20 4.00 14.00 8741 11.07 2.31 4.00 14.00 BCASSET 130 60.02 20.03 20.80 98.90 8741 56.41 21.99 13.60 100.00 GOVBANK 130 5.32 6.19 0.00 19.80 8741 5.17 5.98 0.00 20.00 FORBANK 130 46.65 36.40 0.00 99.30 8741 36.75 32.87 0.00 100.00 GDPPC($) 130 12397.94 13619.71 425.30 53489.99 8741 16659.50 15714.12 100.49 53489.99 MSHARE2 124 0.17 0.36 0.00 2.30 8120 0.08 0.58 0.00 30.50 MSHARE3 124 0.11 0.25 0.00 1.72 8047 0.07 0.89 -0.02 54.84 MSHARE1 125 0.20 0.42 0.00 2.77 8184 0.13 1.41 0.00 74.72 MSHARE4 124 0.14 0.30 0.00 1.97 8016 0.07 0.66 0.00 34.74 STMKTCAP 128 0.52 0.48 0.01 2.12 8280 0.69 0.62 0.00 2.69 OPEN 126 99.09 56.84 20.49 326.60 8605 98.13 68.27 18.97 326.60 INF 130 4.84 4.18 -8.18 21.55 8741 5.83 22.45 -13.97 948.55 118 Table 12(a) Pull Regression: Robust Tests Large Banks Emerging Markets More Privatized Stability Parameter Coeff. Coeff. Coeff. Coeff. (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) Intercept -8.530*** -6.631** -11.021*** -6.880*** (2.462) (2.908) (2.386) (1.820) CTIR 0.003 0.003** 0.003 0.003** (0.002) (0.001) (0.002) (0.001) ROAA 0.045 -0.005 0.021 0.009 (0.040) (0.022) (0.033) (0.023) logTA 0.516*** 0.440*** 0.421*** 0.432*** (0.059) (0.062) (0.050) (0.038) E_TA 0.003 0.005 0.01 0.009 (0.015) (0.008) (0.007) (0.006) NIM -0.003 0 -0.040* 0.001 (0.017) (0.008) (0.022) (0.005) OVER3AR -0.013 -0.011 -0.101* -0.031 (0.068) (0.066) (0.057) (0.048) BONF -0.202 -0.260* -0.08 -0.289*** (0.158) (0.138) (0.154) (0.110) NFOB -0.148 -0.478*** 0.035 -0.135 (0.156) (0.173) (0.148) (0.117) CRINDEX 0.665** 0.546 1.001*** 0.490** (0.299) (0.336) (0.285) (0.223) OSPOWER 0.403** 0.346* 0.607*** 0.331** (0.173) (0.193) (0.172) (0.131) BCASSET 0.009* 0.018*** 0.011** 0.008** (0.005) (0.006) (0.005) (0.004) GOVBANK -0.010* -0.012* 0.027 -0.009** (0.005) (0.006) (0.017) (0.004) CRINDEX* OSPOWER -0.062** -0.057** -0.090*** -0.051*** (0.026) (0.028) (0.025) (0.020) STMKTCAP -0.887*** -1.356*** -0.852** -0.893*** (0.290) (0.418) (0.246) (0.229) 119 Table 12(b) Pull Regression: Robust Tests Large Banks Emerging Markets Less Privatized Stability Parameter Coeff. Coeff. Coeff. Coeff. (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) LLAGGDPPC -0.355*** -0.321* -0.288*** -0.284*** (0.105) (0.182) (0.100) (0.077) INF -0.016* (0.009) ZSCORE 0.008 (0.018) Observations 5511 6549 8408 14499 Pseudo R2 0.08 0.09 0.08 0.08 HL Statistics 0.467 0.746 0.776 0.442 Models are estimated using binomial logistic regressions, where the dependent variable equals one if the bank has been cross-border acquired and zero otherwise. CTIR, ROA, logTA, E_TA and NIM are bank specific variables. OVER3AR, BONF, NFOB, CRINDEX, OSPOWER, BCASSET, GOVBANK and FORBANK are country specific bank regulatory variables. Data on bank specific variables are from BankScope. Bank regulatory variables are computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. Pseudo R2 is reported as reference for prediction power of the models. Hosmer-Lemeshow (HL) statistic is reported as reference for goodness of fit of the models. 120 Table 13(a) Push Regression Pearson Correlation Coefficients, Prob > |r| under Ho: Rho=0 CTIR ROAA E_TA logTA NIM OVER3AR BONF NFOB CRINDEX OSPOWER BCASSET OPEN 1.00 -0.39 0.08 -0.16 -0.04 -0.02 -0.02 -0.05 0.01 -0.06 0.00 -0.09 CTIR (0.00) (0.00) (0.00) (0.00) (0.06) (0.03) (0.00) (0.17) (0.00) (0.60) (0.00) -0.39 1.00 0.15 -0.03 0.15 0.03 -0.02 -0.01 -0.01 0.06 0.02 0.00 ROAA (0.00) (0.00) (0.00) (0.00) (0.00) (0.01) (0.20) (0.33) (0.00) (0.00) (0.57) 0.08 0.15 1.00 -0.45 0.15 -0.02 -0.08 -0.10 -0.01 0.01 0.02 -0.11 E_TA (0.00) (0.00) (0.00) (0.00) (0.01) (0.00) (0.00) (0.27) (0.15) (0.01) (0.00) -0.16 -0.03 -0.45 1.00 -0.17 0.00 0.07 0.13 0.01 -0.01 -0.06 0.03 logTA (0.00) (0.00) (0.00) (0.00) (0.76) (0.00) (0.00) (0.12) (0.24) (0.00) (0.00) -0.04 0.15 0.15 -0.17 1.00 0.08 0.02 -0.06 -0.01 0.08 0.01 -0.14 NIM (0.00) (0.00) (0.00) (0.00) (0.00) (0.01) (0.00) (0.22) (0.00) (0.08) (0.00) -0.02 0.03 -0.02 0.00 0.08 1.00 0.44 0.32 -0.08 0.09 0.24 -0.16 OVER3AR (0.06) (0.00) (0.01) (0.76) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) -0.02 -0.02 -0.08 0.07 0.02 0.44 1.00 0.23 -0.14 0.14 0.14 -0.04 BONF (0.03) (0.01) (0.00) (0.00) (0.01) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) -0.05 -0.01 -0.10 0.13 -0.06 0.32 0.23 1.00 -0.03 -0.19 0.08 0.21 NFOB (0.00) (0.20) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) 0.01 -0.01 -0.01 0.01 -0.01 -0.08 -0.14 -0.03 1.00 0.03 -0.03 -0.04 CRINDEX (0.17) (0.33) (0.27) (0.12) (0.22) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) -0.06 0.06 0.01 -0.01 0.08 0.09 0.14 -0.19 0.03 1.00 -0.16 0.06 OSPOWER (0.00) (0.00) (0.15) (0.24) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) 0.00 0.02 0.02 -0.06 0.01 0.24 0.14 0.08 -0.03 -0.16 1.00 -0.06 BCASSET (0.60) (0.00) (0.01) (0.00) (0.08) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) 121 Table 13(b) Pearson Correlation Coefficients, Prob > |r| under Ho: Rho=0 GOVBANK FORBANK LLAGGDPPC STMKTCAP INF MSHARE1 MSHARE2 MSHARE3 MSHARE4 0.02 -0.04 0.01 -0.06 -0.01 -0.02 -0.02 -0.02 -0.02 CTIR (0.04) (0.00) (0.53) (0.00) (0.29) (0.06) (0.01) (0.08) (0.02) -0.02 0.02 -0.08 -0.01 0.08 0.01 0.01 0.01 0.01 ROAA (0.02) (0.02) (0.00) (0.55) (0.00) (0.49) (0.24) (0.46) (0.30) 0.04 -0.03 -0.04 -0.04 0.03 -0.03 -0.05 -0.03 -0.04 E_TA (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) -0.03 -0.06 0.29 0.19 -0.10 0.05 0.09 0.05 0.07 logTA (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) 0.06 -0.02 -0.18 -0.14 0.23 -0.01 -0.01 -0.01 -0.02 NIM (0.00) (0.07) (0.00) (0.00) (0.00) (0.20) (0.21) (0.10) (0.05) 0.04 0.03 -0.53 -0.39 0.11 0.03 0.05 0.04 0.04 OVER3AR (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) 0.13 -0.08 -0.24 -0.17 0.11 0.03 0.04 0.03 0.03 BONF (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) -0.12 0.13 -0.11 0.01 0.02 0.02 0.02 0.02 0.02 NFOB (0.00) (0.00) (0.00) (0.14) (0.03) (0.02) (0.04) (0.05) (0.03) -0.05 -0.01 0.00 0.00 -0.02 0.00 -0.01 0.00 0.00 CRINDEX (0.00) (0.25) (0.80) (0.89) (0.01) (0.89) (0.42) (0.74) (0.60) -0.05 0.24 -0.17 -0.02 0.07 0.04 0.04 0.04 0.04 OSPOWER (0.00) (0.00) (0.00) (0.01) (0.00) (0.00) (0.00) (0.00) (0.00) -0.20 0.00 -0.16 -0.06 0.04 0.04 0.07 0.05 0.06 BCASSET (0.00) (0.90) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) 122 Table 13(c) Pearson Correlation Coefficients, Prob > |r| under Ho: Rho=0 CTIR ROAA E_TA logTA NIM OVER3AR BONF NFOB CRINDEX OSPOWER BCASSET OPEN 0.02 -0.02 0.04 -0.03 0.06 0.04 0.13 -0.12 -0.05 -0.05 -0.20 -0.35 GOVBANK (0.04) (0.02) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) -0.04 0.02 -0.03 -0.06 -0.02 0.03 -0.08 0.13 -0.01 0.24 0.00 0.63 FORBANK (0.00) (0.02) (0.00) (0.00) (0.07) (0.00) (0.00) (0.00) (0.25) (0.00) (0.90) (0.00) 0.01 -0.08 -0.04 0.29 -0.18 -0.53 -0.24 -0.11 0.00 -0.17 -0.16 0.29 LLAGGDPPC (0.53) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.80) (0.00) (0.00) (0.00) -0.09 0.00 -0.11 0.03 -0.14 -0.16 -0.04 0.21 -0.04 0.06 -0.06 1.00 OPEN (0.00) (0.57) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) -0.06 -0.01 -0.04 0.19 -0.14 -0.39 -0.17 0.01 0.00 -0.02 -0.06 0.39 STMKTCAP (0.00) (0.55) (0.00) (0.00) (0.00) (0.00) (0.00) (0.14) (0.89) (0.01) (0.00) (0.00) -0.01 0.08 0.03 -0.10 0.23 0.11 0.11 0.02 -0.02 0.07 0.04 -0.04 INF (0.29) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.03) (0.01) (0.00) (0.00) (0.00) -0.02 0.01 -0.03 0.05 -0.01 0.03 0.03 0.02 0.00 0.04 0.04 0.03 MSHARE1 (0.06) (0.49) (0.00) (0.00) (0.20) (0.00) (0.00) (0.02) (0.89) (0.00) (0.00) (0.00) -0.02 0.01 -0.05 0.09 -0.01 0.05 0.04 0.02 -0.01 0.04 0.07 0.03 MSHARE2 (0.01) (0.24) (0.00) (0.00) (0.21) (0.00) (0.00) (0.04) (0.42) (0.00) (0.00) (0.00) -0.02 0.01 -0.03 0.05 -0.01 0.04 0.03 0.02 0.00 0.04 0.05 0.01 MSHARE3 (0.08) (0.46) (0.00) (0.00) (0.10) (0.00) (0.00) (0.05) (0.74) (0.00) (0.00) (0.09) -0.02 0.01 -0.04 0.07 -0.02 0.04 0.03 0.02 0.00 0.04 0.06 0.03 MSHARE4 (0.02) (0.30) (0.00) (0.00) (0.05) (0.00) (0.00) (0.03) (0.60) (0.00) (0.00) (0.00) 123 Table 13(d) Pearson Correlation Coefficients, Prob > |r| under Ho: Rho=0 GOVBANK FORBANK LLAGGDPPC STMKTCAP INF MSHARE1 MSHARE2 MSHARE3 MSHARE4 1.00 -0.38 -0.30 -0.28 0.06 -0.04 -0.05 -0.03 -0.04 GOVBANK (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) -0.38 1.00 0.04 0.15 0.00 0.03 0.05 0.03 0.04 FORBANK (0.00) (0.00) (0.00) (0.79) (0.00) (0.00) (0.00) (0.00) -0.30 0.04 1.00 0.54 -0.23 -0.05 -0.07 -0.06 -0.06 LLAGGDPPC (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) -0.35 0.63 0.29 0.39 -0.04 0.03 0.03 0.01 0.03 OPEN (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.09) (0.00) -0.28 0.15 0.54 1.00 -0.09 0.04 -0.02 -0.03 -0.02 STMKTCAP (0.00) (0.00) (0.00) (0.00) (0.00) (0.03) (0.00) (0.01) 0.06 0.00 -0.23 -0.09 1.00 0.01 0.01 0.01 0.01 INF (0.00) (0.79) (0.00) (0.00) (0.35) (0.26) (0.33) (0.28) -0.04 0.03 -0.05 0.04 0.01 1.00 0.94 0.97 0.97 MSHARE1 (0.00) (0.00) (0.00) (0.00) (0.35) (0.00) (0.00) (0.00) -0.05 0.05 -0.07 -0.02 0.01 0.94 1.00 0.92 0.99 MSHARE2 (0.00) (0.00) (0.00) (0.03) (0.26) (0.00) (0.00) (0.00) -0.03 0.03 -0.06 -0.03 0.01 0.97 0.92 1.00 0.97 MSHARE3 (0.00) (0.00) (0.00) (0.00) (0.33) (0.00) (0.00) (0.00) -0.04 0.04 -0.06 -0.02 0.01 0.97 0.99 0.97 1.00 MSHARE4 (0.00) (0.00) (0.00) (0.01) (0.28) (0.00) (0.00) (0.00) 124 Table 14 Push Regression: Percentile Statistics: Acquirer Banks Percentile 100% 99% 95% 90% 75% 50% TA(Million$) 1483247.70 1179390.00 829540.90 631216.20 371134.20 90268.26 GOVBANK 75.27 74.00 45.20 42.20 31.82 0.18 GDPPC($) 51590.18 37791.50 37084.45 35959.76 25451.71 23559.57 Control Banks TA(Million$) 1379706.20 85633.40 19086.80 8725.40 2015.70 467.05 GOVBANK 97.10 80.00 66.70 46.10 39.99 13.30 GDPPC($) 53489.99 51590.18 40413.01 35828.41 23365.95 5370.25 Acquirer Banks Percentile 50% 25% 10% 5% 1% 0% TA(Million$) 90268.26 8013.84 1111.40 545.70 61.90 44.00 GOVBANK 0.18 0.00 0.00 0.00 0.00 0.00 GDPPC($) 23559.57 9854.56 3497.90 1204.53 439.50 402.27 Control Banks TA(Million$) 467.05 128.95 46.40 24.79 7.80 0.50 GOVBANK 13.30 1.10 0.00 0.00 0.00 0.00 GDPPC($) 5370.25 2049.06 588.37 403.44 263.77 100.49 125 Table 15: Push Regression: Total Asset Above US $10 Billion Acquirer Banks Control Banks Variable N Mean Std Dev Minimum Maximum N Mean Std Dev Minimum Maximum E_TA 136 5.49 2.28 2.39 11.47 1290 5.91 5.34 -16.48 91.69 NIM 136 2.30 1.96 0.62 11.40 1290 3.43 25.74 -6.00 918.31 ROAA 136 0.80 0.74 -0.99 3.37 1290 0.42 1.83 -27.90 13.87 CTIR 136 65.97 11.78 29.14 116.83 1290 60.89 23.29 7.01 404.64 TA(Million$) 136 301870.49 302345.36 10422.62 1483247.70 1290 55408.35 126136.72 10009.02 1379706.20 OVER3AR 136 6.15 1.73 3.00 10.00 1290 7.02 1.67 3.00 10.00 BONF 136 2.24 0.53 1.00 3.00 1290 2.54 0.63 1.00 4.00 NFOB 136 2.24 0.67 1.00 3.00 1290 2.24 0.67 1.00 4.00 CRINDEX 136 6.32 1.84 3.00 10.00 1290 6.46 1.47 2.00 10.00 OSPOWER 136 10.10 2.45 6.00 14.00 1290 10.89 2.13 5.00 14.00 BCASSET 136 57.83 26.29 11.80 100.00 1290 50.25 22.11 11.80 100.00 GOVBANK 136 15.96 21.64 0.00 75.27 1290 16.34 22.24 0.00 90.00 FORBANK 136 11.93 17.56 0.00 99.30 1290 23.49 30.66 0.00 99.30 GDPPC($) 136 22553.89 10524.43 468.96 51590.18 1290 23669.72 15771.88 392.92 53489.99 MSHARE2 117 0.31 0.35 0.01 2.30 1264 0.14 0.30 0.00 3.71 MSHARE3 117 0.18 0.24 0.00 1.72 1258 0.09 0.19 0.00 3.25 MSHARE1 117 0.32 0.37 0.01 2.26 1264 0.19 0.41 0.00 6.02 MSHARE4 117 0.24 0.28 0.00 1.97 1258 0.11 0.24 0.00 3.52 STMKTCAP 118 0.78 0.47 0.14 2.62 1285 0.74 0.41 0.14 2.69 OPEN 113 77.87 40.19 18.97 297.24 1263 76.45 79.49 14.93 326.60 INF 118 2.99 5.50 -8.18 49.23 1287 3.15 7.56 -4.90 54.18 126 Table 16(a): Push Regression: Total Asset Above US $10 Billion Parameter Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) Intercept -13.606*** -19.070*** -19.799*** -15.511*** -14.637*** -8.869*** -8.881*** (0.725) (2.082) (2.317) (3.104) (3.073) (3.411) (3.415) CTIR 0.005* 0.007 0.008 0.007 0.007 0.01 0.01 (0.003) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) ROAA 0.412*** 0.422*** 0.404*** 0.408*** 0.416*** 0.376*** 0.377*** (0.085) (0.104) (0.102) (0.103) (0.103) (0.111) (0.112) E_TA -0.023 0.026 0.036 0.043 0.043 0.038 0.036 (0.025) (0.041) (0.036) (0.034) (0.035) (0.040) (0.042) logTA 0.973*** 1.342*** 1.375*** 1.373*** 1.395*** 1.502*** 1.505*** (0.056) (0.115) (0.123) (0.122) (0.124) (0.134) (0.135) NIM -0.079 -0.003 0 0 0 -0.002 -0.002 (0.048) (0.030) (0.011) (0.011) (0.011) (0.020) (0.026) OVER3AR -0.206* -0.184* -0.840** -0.998*** -0.979*** -0.984*** (0.106) (0.104) (0.367) (0.376) (0.359) (0.361) BONF -0.875*** -0.889*** -0.874*** -0.850*** -0.615* -0.605* (0.314) (0.311) (0.318) (0.318) (0.316) (0.321) NFOB 0.717*** 0.671*** 0.678*** 0.718*** 0.564** 0.558** (0.238) (0.245) (0.249) (0.254) (0.256) (0.257) CRINDEX 0.054 0.054 -0.503* -0.621** -0.539* -0.543* (0.068) (0.071) (0.303) (0.305) (0.292) (0.293) OSPOWER 0.07 0.059 0.035 0.035 -0.038 -0.042 (0.076) (0.077) (0.078) (0.078) (0.079) (0.081) 127 Table 16(b): Push Regression: Total Asset Above US $10 Billion Parameter Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) BCASSET 0.024*** 0.024*** 0.021*** 0.021*** 0.016** 0.016** (0.006) (0.006) (0.006) (0.006) (0.007) (0.007) GOVBANK 0.020*** 0.023*** 0.020*** 0.016** -0.004 -0.005 (0.006) (0.007) (0.007) (0.007) (0.010) (0.010) OPEN 0.004* 0.003 0.005** 0.007*** 0.007*** (0.002) (0.002) (0.002) (0.003) (0.003) OVER3AR*CRINDEX 0.095* 0.116** 0.099** 0.099** (0.051) (0.052) (0.049) (0.050) STMKTCAP -0.526 -0.681* -0.680* (0.353) (0.347) (0.347) LLAGGDPPC -0.617*** -0.611*** (0.193) (0.195) INF 0.005 (0.021) Observations 3662 1405 1376 1376 1374 1374 1374 Pseudo R2 0.26 0.46 0.46 0.46 0.48 0.48 0.48 HL Statistics 0.453 0.151 0.386 0.836 0.443 0.371 0.358 Models are estimated using binomial logistic regressions, where the dependent variable equals one if the bank is cross-border acquirer and zero otherwise. CTIR, ROA, logTA, E_TA and NIM are bank specific variables. OVER3AR, BONF, NFOB, CRINDEX, OSPOWER, BCASSET, GOVBANK and FORBANK are country specific bank regulatory variables. Data on bank specific variables are from BankScope. Bank regulatory variables are computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. 128 Table 17: Push Regression: Total Asset Above US $35 Billion Acquirer Banks Control Banks Variable N Mean Std Dev Minimum Maximum N Mean Std Dev Minimum Maximum E_TA 111 4.98 1.97 2.39 11.11 366 5.02 2.41 -16.48 17.62 NIM 111 1.97 1.57 0.71 11.40 366 2.49 3.20 -0.30 30.81 ROAA 111 0.65 0.66 -0.99 3.07 366 0.32 1.32 -18.74 3.28 CTIR 111 67.97 10.34 34.61 116.83 366 61.51 15.04 14.05 149.45 TA(Million$) 111 364878.60 300634.73 37039.07 1483247.70 366 149458.49 209035.76 35067.70 1379706.20 OVER3AR 111 5.84 1.62 3.00 10.00 366 6.84 1.58 3.00 10.00 BONF 111 2.18 0.53 1.00 3.00 366 2.46 0.59 1.00 3.00 NFOB 111 2.22 0.68 1.00 3.00 366 2.26 0.65 1.00 3.00 CRINDEX 111 6.22 1.93 3.00 10.00 366 6.58 1.63 2.00 10.00 OSPOWER 111 9.84 2.45 6.00 14.00 366 10.63 2.20 6.00 14.00 BCASSET 111 57.93 26.07 11.80 100.00 366 50.03 23.79 11.80 100.00 GOVBANK 111 15.46 20.73 0.00 74.00 366 12.52 18.99 0.00 75.27 FORBANK 111 9.51 10.08 0.00 96.06 366 15.05 22.02 0.00 98.00 GDPPC($) 111 23902.39 8420.00 588.37 37791.50 366 26391.71 13134.27 468.96 53489.99 MSHARE2 97 0.31 0.31 0.01 1.58 364 0.16 0.24 0.00 1.67 MSHARE3 97 0.16 0.20 0.00 1.23 364 0.10 0.15 0.00 0.87 MSHARE1 97 0.31 0.35 0.01 2.14 364 0.21 0.37 0.00 2.21 MSHARE4 97 0.23 0.25 0.00 1.43 364 0.12 0.18 0.00 0.93 STMKTCAP 98 0.80 0.46 0.14 2.62 365 0.76 0.41 0.14 2.69 OPEN 93 73.86 31.59 18.97 172.77 360 59.71 64.11 18.97 326.60 INF 98 1.99 1.82 -1.73 8.96 365 2.82 8.56 -2.88 54.18 129 Table 18(a): Push Regression: Total Asset Above US $35 Billion Parameter Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) Intercept -13.562*** -19.836*** -19.715*** -13.301*** -10.985** -6.913 -5.843 (1.126) (3.191) (3.476) (4.537) (4.754) (5.459) (5.649) CTIR 0.012** 0.026* 0.022 0.017 0.017 0.021 0.021 (0.006) (0.014) (0.014) (0.014) (0.014) (0.015) (0.015) ROAA 0.713*** 1.388*** 1.046** 0.874** 1.038** 0.949** 0.907* (0.155) (0.386) (0.420) (0.422) (0.443) (0.457) (0.477) E_TA -0.062* -0.023 0.013 0.018 0 0.006 -0.002 (0.032) (0.131) (0.142) (0.144) (0.148) (0.147) (0.151) logTA 0.942*** 1.370*** 1.378*** 1.431*** 1.454*** 1.480*** 1.477*** (0.084) (0.190) (0.198) (0.203) (0.204) (0.207) (0.206) NIM -0.111 -0.154 -0.083 -0.021 -0.044 -0.078 0.015 (0.072) (0.131) (0.144) (0.148) (0.153) (0.156) (0.182) OVER3AR -0.392** -0.382** -1.396** -1.748*** -1.670*** -1.685*** (0.161) (0.158) (0.540) (0.587) (0.592) (0.593) BONF -0.531 -0.621 -0.763 -0.75 -0.571 -0.561 (0.573) (0.593) (0.594) (0.582) (0.583) (0.596) NFOB 0.810** 0.812** 0.872*** 0.891*** 0.792** 0.795** (0.320) (0.324) (0.331) (0.337) (0.340) (0.339) CRINDEX 0.019 0.032 -0.777* -1.068** -0.995** -0.976** (0.088) (0.095) (0.413) (0.450) (0.453) (0.453) OSPOWER 0.083 0.055 -0.004 -0.003 -0.024 -0.045 (0.105) (0.108) (0.112) (0.109) (0.108) (0.112) 130 Table 18(b): Push Regression: Total Asset Above US $35 Billion Parameter Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) BCASSET 0.014* 0.015** 0.01 0.01 0.008 0.008 (0.007) (0.007) (0.008) (0.008) (0.008) (0.008) GOVBANK 0.018* 0.019* 0.014 0.006 -0.006 -0.009 (0.010) (0.010) (0.010) (0.011) (0.014) (0.014) OPEN 0.002 0.002 0.003 0.005 0.007 (0.003) (0.003) (0.004) (0.004) (0.004) OVER3AR*CRINDEX 0.145** 0.195** 0.177** 0.177** (0.072) (0.079) (0.081) (0.081) STMKTCAP -0.741* -0.901** -0.857* (0.444) (0.458) (0.460) LLAGGDPPC -0.445 -0.546* (0.295) (0.319) INF -0.088 (0.102) Observations 1343 463 453 453 453 453 453 Pseudo R2 0.24 0.47 0.46 0.47 0.48 0.48 0.49 HL Statistics 0.129 0.57 0.484 0.36 0.617 0.745 0.433 Models have been estimated using binomial logistic regressions, where the dependent variable equals one if the bank is cross-border acquirer and zero otherwise. CTIR, ROA, logTA, E_TA and NIM are bank specific variables. OVER3AR, BONF, NFOB, CRINDEX, OSPOWER, BCASSET, GOVBANK and FORBANK are country specific bank regulatory variables. Data on bank specific variables are from BankScope. Bank regulatory variables are computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percentage. 131 Table 19(a) Variable Defination and Source Note: Please refer to Appendix A and B for survey questions. Variable Definition Description Source Financing Obstacle Variable Financing An index ranging from 1 to 4 measuring firm?s financing obstacle Access An index ranging from 1 to 4 measuring firm?s access to finance obstacle Cost An index ranging from 1 to 4 measuring firm?s cost of finance obstacle Can you tell me how problematic are these different factors for the operation and growth of your Business: (1) no obstacle, (2) minor obstacle, (3) moderate obstacle, (4) major obstacle ? EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS), 1999, 2002, 2005, Financing Pattern Variable Finequ_f Percentage of firm?s financing for new fixed investment over the last year from sale of stocks Findom_f Percentage of firm?s financing for new fixed investment over the last year from domestic bank loans Finfor_f Percentage of firm?s financing for new fixed investment over the last year from foreign bank loans What proportion of your firm?s and new fixed investment has been financed from each of the following sources, over the last 12 months? EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS), 1999, 2002, 2005 132 Table 19(b) Variable Defination and Source Finmon_f Percentage of firm?s financing for new fixed investment over the last year from money lenders Finsta_f Percentage of firm?s financing for new fixed investment over the last year from the government Finequ_w Percentage of firm?s financing for working capital over the last year from sale of stocks Findom_w Percentage of firm?s financing for working capital over the last year from domestic bank loans Finfor_w Percentage of firm?s financing for working capital over the last year from foreign bank loans Finsta2_w Percentage of firm?s financing for working capital over the last year from state-owned bank loans Finmon_w Percentage of firm?s financing for working capital over the last year from money lenders Fingov_w Percentage of firm?s financing for working capital over the last year from the government other than state-owned banks What proportion of your firm?s working capital has been financed from each of the following sources, over the last 12 months? EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS), 2002, 2005 133 Table 19(c) Variable Defination and Source Access to Loan Variable Collateral Percentage of loan value required in collateral value Interestrate Percentage rate of interest for the loan Duration Duration of the loan in months Approvalday Days the bank takes to agree the loan from the date of application. Thinking of the most recent loan you obtained from a financial institution: (1) what was the approximate value of the collateral required as a percentage of the loan value? (2) what is the loan?s annual cost (i.e., rate of interest)? (3) what is the duration of the loan in months? (4) how many days did it take to agree the loan with the bank from the date of application? EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS), 2002, 2005 Stloan An index ranging from 1 to 5 measuring firm?s easiness to obtain short- term working capital loan Ltloan An index ranging from 1 to 5 measuring firm?s easiness to obtain long- term new investment loan How easy would it be for your firm to obtain a short-term working capital loan on commercial terms. And how easy would it be for your firm to obtain a longer term banking loan for new investment: (1) impossible, (2) very difficult, (3) fairly difficult, (4) fairly easy, (5) very easy? EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS), 2002 Firm Characteristi c Variable Costeffi Percentage of firm?s sales price exceeding operating costs Considering your main product line or main line of services in the domestic market, by what margin does your sales price exceed your operating costs (i.e., the cost material inputs plus wage costs but not overheads and depreciation) ? EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS), 1999, 2002, 2005 134 Table 19(d) Variable Defination and Source Transparency An indicator valued 1 if firm?s accounting practices are transparent, and 0 otherwise Does your firm use international accounting standards (IAS): (1) yes, (2) no? EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS), 1999, 2002, 2005 Audit An indicator valued 1 if firm?s financial statement is checked by an external auditor, and 0 otherwise Does your firm have its annual financial statement checked and certified by an external auditor: (1) yes, (2) no? EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS), 1999, 2002, 2005 Size An indicator ranging from 1 to 3, measuring if firm?s full-time employees are less than 50, 250 or more How many full-time employees work for this company: (1) 2-49, (2) 50-249, (3) 250 or more? EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS), 2002, 2005 Foreign The percentage of firm?s total assets that are hold by private foreign company or organization What percentage of your firm is owned by: (1) private foreign company/organisation, (2) private domestic company/organisation, (3) government/State? EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS), 2002, 2005 Manufacturin g Percentage of sales from manufacturing What percentage of your sales comes from the following sectors in which your establishment operates: (1) mining and quarrying, (2) construction, (3) manufacturing, (4) transport storage and communication, (5) wholesale, retail, repairs, (6) real estate, renting and business services, (7) hotels and restaurants? EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS), 2002, 2005 135 Table 19(e) Variable Defination and Source Institutional Quality Variable Accountabil ity An index ranging from -2.5 to 2.5 measuring voice and accountability, higher value indicating better outcome The extent to which a country?s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and free media Political An index ranging from -2.5 to 2.5 measuring political stability and absence of violence, higher value indicating better outcome Perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including political violence and terrorism Egovernme nt An index ranging from -2.5 to 2.5 measuring government effectiveness, higher value indicating better outcome The quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government?s commitment to such policies Regulatory An index ranging from -2.5 to 2.5 measuring regulatory quality, higher value indicating better outcome The ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development Law An index ranging from -2.5 to 2.5 measuring rule of law, higher value indicating better outcome The extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, the police, and the courts, as well as the likelihood of crime and violence World Bank Governance Indicators, Kaufmann, Kraay and Mastruzzi (2008) 136 Table 19(f) Variable Defination and Source Ccorruption An index ranging from -2.5 to 2.5 measuring control of corruption, higher value indicating better outcome The extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as ?capture? of the state by elites and private interests Polity2 An index ranging from -10 (strongly autocratic) to 10 (strongly democratic) measuring country?s authority pattern Polity IV Project: Political Regime Characteristics and Transitions, 1800-2007, Marshall and Jaggers (2007) Financial Sector Variable Stmktcap Ratio of country?s stock market capitalization to total GDP measuring equity market development Llgdp Ratio of country?s liquidity liability to total GDP measuring financial development Pcrdbgdp Ratio fo country?s private credit by deposit money bank to GDP measuring banking sector development International Financial Statistics, 2008 Npl_ta Percentage of country? non-performing loans to total banking assets World Bank Survey I/II/III under World Bank Project ?Bank Regulation and Supervision?, Barth, Caprio and Levine (2006) 137 Table 19(g) Variable Defination and Source Bank Regulatory Variable Crindex Capital regulatory index measuring stringency of capital requirment, higher value indicating greater stringency (a) + (b) (a) Overall capital stringency: WBG3.1.1+3.2+3.3+3.9.1+3.9.2+3.9.3+(1 if 3.7<0.75); Yes=1; No=0 (b) Initial capital stringency: WBG1.5+1.6+1.7; WBG1.5: Yes=0, No=1; WBG1.6&1.7: Yes=1, No=0 (see appendix a) Mcar Minimum Capital-Asset Ratio WBG3.1: What is the minimum capital-asset ratio requirement? World Bank Survey I/II/III under World Bank Project ?Bank Regulation and Supervision?, Barth, Caprio and Levine (2006) Overbank Overall restrictiveness on bank activities and bank owning nonfinancial firms, higher values indicating more restrictiveness WBG 4.1-4.4: What is the level of regulatory restrictiveness for bank participation in securities, insurance and real estate activities, and for bank ownership of nonfinancial firms: (1) Unrestricted, (2) Permitted, (3) Restricted, (4) Prohibited? Nbffob The extent to which nonbank financial firms may own and control commercial banks, higher values indicating more restrictiveness WBG2.5: What is the level of regulatory restrictiveness for nonbank financial firms ownership of banks: (1) Unrestricted, (2) Permitted, (3) Restricted, (4) Prohibited? Nfob The extent to which nonfinancial firms may own and control commercial banks, higher values indicating more restrictiveness WBG2.3: What is the level of regulatory restrictiveness for nonfinancial firms ownership of banks: (1) Unrestricted, (2) Permitted, (3) Restricted, (4) Prohibited? World Bank Survey I/II/III under World Bank Project ?Bank Regulation and Supervision?, Barth, Caprio and Levine (2006) 138 Table 19(h) Variable Defination and Source Fstrans The transparency of bank financial statement practices, higher values indicate better transparency WBG 10.1+10.3+10.4.1+10.5+10.6+(10.1.1-1)*(-1); Yes=1, No=0 (see appendix a) Bcdepo The percentage of deposits that are held by the five largest banks, measuring banking sector concentration in deposits WBG 2.6.1: of commercial banks in your country, what fraction of deposits is held by the five largest banks? Forbank The fraction of the banking assets is in banks that are 50% or more foreign owned Govbank The fraction of the banking assets is in banks that are 50% or more government owned WBG 3.8.1-3.8.2: What fraction of the banking system?s assets is in banks that are 50% or more government owned? 50% or more foreign owned? World Bank Survey I/II/III under World Bank Project ?Bank Regulation and Supervision?, Barth, Caprio and Levine (2006) Mulsup An indicator equals 1 if there are multiple bank regulators and 0 if there is only one regulators WBG 12.1: Is there more than one supervisory body supervises banks? Singlefsa An indicator equals 1 if there is only one regulators for all main financial institutions and zero if there are multiple regulators. WBG 12.1.4: Is there a single financial supervisory agency for all of the main financial institutions (insurance companies, contractual savings institutions, savings banks)? Indpoli The degree to which supervisory authority is independent within the government from political influence WBG 12.2: To whom are the supervisory bodies responsible or accountable? If (c) then equal 1, others then equals 0. World Bank Survey I/II/III under World Bank Project ?Bank Regulation and Supervision?, Barth, Caprio and Levine (2006) 139 Table 20(a) Financing Obstacle Analysis by Order Variable Cost Access Financing N Mean Min Max N Mean Min Max N Mean Min Max transparency 1883 0.321 0.000 1.000 2639 0.315 0.000 1.000 1662 0.318 0.000 1.000 audit 1883 0.504 0.000 1.000 2639 0.519 0.000 1.000 1662 0.511 0.000 1.000 costeffi 1883 21.202 0.000 100.000 2639 20.861 0.000 400.000 1662 20.945 0.000 100.000 operationyear 1883 15.695 3.000 138.000 2639 15.429 3.000 180.000 1662 15.658 3.000 138.000 size 1883 1.442 1.000 3.000 2639 1.471 1.000 3.000 1662 1.452 1.000 3.000 foreign 1883 12.099 0.000 100.000 2639 13.114 0.000 100.000 1662 12.895 0.000 100.000 manufacturing 1883 0.292 0.000 1.000 2639 0.324 0.000 1.000 1662 0.298 0.000 1.000 npl_ta 1883 5.962 0.100 30.000 2639 6.484 0.100 30.000 1662 6.035 0.100 30.000 egovernment 1883 0.251 -0.894 1.109 2639 0.231 -0.894 1.109 1662 0.247 -0.894 1.109 overafc 1883 13.378 9.000 19.000 2639 13.410 9.000 19.000 1662 13.351 9.000 19.000 bcdepo 1883 68.169 35.500 99.400 2639 67.688 35.500 99.400 1662 68.175 35.500 99.400 forbank 1883 51.954 3.470 99.300 2639 51.437 3.470 99.300 1662 52.182 3.470 99.300 stmktcap 1883 0.202 0.004 0.534 2639 0.204 0.004 0.534 1662 0.206 0.004 0.534 llgdp 1883 0.378 0.128 0.681 2639 0.379 0.128 0.681 1662 0.377 0.128 0.681 1 pcrdbgdp 1883 0.272 0.039 0.564 1 2639 0.274 0.039 0.564 1 1662 0.272 0.039 0.564 transparency 1594 0.321 0.000 1.000 1463 0.314 0.000 1.000 1381 0.331 0.000 1.000 audit 1594 0.537 0.000 1.000 1463 0.483 0.000 1.000 1381 0.529 0.000 1.000 costeffi 1594 21.282 0.000 400.000 1463 21.280 1.000 100.000 1381 21.567 0.000 400.000 2 operationyear size 1594 1594 15.147 1.456 3.000 1.000 180.000 3.000 2 1463 1463 15.176 1.402 3.000 1.000 202.000 3.000 2 1381 1381 15.233 1.469 3.000 1.000 180.000 3.000 140 Table 20(b) Financing Obstacle Analysis by Order Variable Cost Access Financing N Mean Min Max N Mean Min Max N Mean Min Max foreign 1594 11.695 0.000 100.000 1463 10.076 0.000 100.000 1381 11.851 0.000 100.000 manufacturing 1594 0.364 0.000 1.000 1463 0.393 0.000 1.000 1381 0.360 0.000 1.000 npl_ta 1594 6.928 0.100 30.000 1463 7.292 0.100 30.000 1381 7.025 0.100 30.000 egovernment 1594 0.247 -0.894 1.109 1463 0.208 -0.894 1.109 1381 0.208 -0.894 1.109 overafc 1594 13.428 9.000 19.000 1463 13.398 9.000 19.000 1381 13.505 9.000 19.000 bcdepo 1594 67.295 35.500 99.400 1463 65.741 35.500 99.400 1381 67.171 35.500 99.400 forbank 1594 51.119 3.470 99.300 1463 49.379 3.470 99.300 1381 48.061 3.470 99.300 stmktcap 1594 0.204 0.004 0.534 1463 0.196 0.004 0.534 1381 0.199 0.004 0.534 llgdp 1594 0.391 0.128 0.681 1463 0.386 0.128 0.681 1381 0.384 0.128 0.681 pcrdbgdp 1594 0.275 0.039 0.564 1463 0.261 0.039 0.564 1381 0.271 0.039 0.564 transparency 2071 0.263 0.000 1.000 1777 0.259 0.000 1.000 2127 0.277 0.000 1.000 audit 2071 0.455 0.000 1.000 1777 0.450 0.000 1.000 2127 0.463 0.000 1.000 costeffi 2071 20.824 0.000 170.000 1777 21.538 0.000 170.000 2127 21.215 0.000 170.000 operationyear 2071 15.315 3.000 202.000 1777 14.929 3.000 202.000 2127 15.047 3.000 202.000 size 2071 1.394 1.000 3.000 1777 1.365 1.000 3.000 2127 1.390 1.000 3.000 foreign 2071 9.556 0.000 100.000 1777 8.112 0.000 100.000 2127 9.813 0.000 100.000 manufacturing 2071 0.407 0.000 1.000 1777 0.403 0.000 1.000 2127 0.394 0.000 1.000 npl_ta 2071 6.848 0.100 30.000 1777 6.508 0.100 30.000 2127 6.685 0.100 30.000 egovernment 2071 0.237 -0.894 1.109 1777 0.223 -0.894 1.109 2127 0.234 -0.894 1.109 overafc 2071 13.404 9.000 19.000 1777 13.287 9.000 19.000 2127 13.388 9.000 19.000 bcdepo 2071 65.933 35.500 99.400 1777 65.086 35.500 99.400 2127 66.285 35.500 99.400 3 forbank 2071 53.216 3.470 99.300 3 1777 52.570 3.470 99.300 3 2127 52.330 3.470 99.300 141 Table 20(c) Financing Obstacle Analysis by Order Variable Cost Access Financing N Mean Min Max N Mean Min Max N Mean Min Max stmktcap 2071 0.197 0.004 0.534 1777 0.197 0.004 0.534 2127 0.199 0.004 0.534 llgdp 2071 0.386 0.128 0.681 1777 0.382 0.128 0.681 2127 0.383 0.128 0.681 pcrdbgdp 2071 0.259 0.039 0.564 1777 0.254 0.039 0.564 2127 0.260 0.039 0.564 transparency 1881 0.211 0.000 1.000 1481 0.196 0.000 1.000 2362 0.215 0.000 1.000 audit 1881 0.407 0.000 1.000 1481 0.406 0.000 1.000 2362 0.418 0.000 1.000 costeffi 1881 20.949 1.000 200.000 1481 20.632 1.000 200.000 2362 20.694 1.000 200.000 operationyear 1881 14.859 3.000 202.000 1481 15.022 3.000 167.000 2362 15.043 3.000 202.000 size 1881 1.325 1.000 3.000 1481 1.314 1.000 3.000 2362 1.334 1.000 3.000 foreign 1881 5.982 0.000 100.000 1481 5.311 0.000 100.000 2362 6.122 0.000 100.000 manufacturing 1881 0.435 0.000 1.000 1481 0.418 0.000 1.000 2362 0.418 0.000 1.000 npl_ta 1881 7.540 0.100 30.000 1481 7.206 0.100 30.000 2362 7.253 0.100 30.000 egovernment 1881 0.218 -0.894 1.109 1481 0.262 -0.894 1.109 2362 0.238 -0.894 1.109 overafc 1881 13.128 9.000 19.000 1481 13.163 9.000 19.000 2362 13.169 9.000 19.000 bcdepo 1881 63.292 35.500 99.400 1481 64.037 35.500 99.400 2362 63.842 35.500 99.400 forbank 1881 55.761 3.470 99.300 1481 58.027 3.470 99.300 2362 56.612 3.470 99.300 stmktcap 1881 0.192 0.004 0.534 1481 0.197 0.004 0.534 2362 0.195 0.004 0.534 llgdp 1881 0.385 0.128 0.681 1481 0.394 0.128 0.681 2362 0.390 0.128 0.681 4 pcrdbgdp 1881 0.253 0.039 0.564 4 1481 0.258 0.039 0.564 4 2362 0.256 0.039 0.564 142 Table 21(a) Correlation Matrix for Financing Obstacle Analysis Pearson Correlation Coefficients Prob > |r| under H0: Rho=0 transparency audit costeffi operationyear size foreign manufacturing npl_ta overafc bcdepo 1.000 0.251 -0.041 0.073 0.211 0.210 -0.013 -0.163 -0.059 0.167 transparency (0.000) (0.001) (0.000) (0.000) (0.000) (0.257) (0.000) (0.000) (0.000) 0.251 1.000 -0.047 0.190 0.345 0.187 0.062 -0.098 -0.046 0.100 audit (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) -0.041 -0.047 1.000 -0.030 -0.045 0.021 -0.012 -0.029 0.003 -0.032 costeffi (0.001) (0.000) (0.010) (0.000) (0.074) (0.319) (0.012) (0.806) (0.005) 0.073 0.190 -0.030 1.000 0.375 -0.027 0.127 -0.045 -0.054 -0.055 operationyear (0.000) (0.000) (0.010) (0.000) (0.018) (0.000) (0.000) (0.000) (0.000) 0.211 0.345 -0.045 0.375 1.000 0.165 0.156 -0.023 0.005 0.010 size (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.049) (0.685) (0.367) 0.210 0.187 0.021 -0.027 0.165 1.000 0.041 -0.025 0.004 0.056 foreign (0.000) (0.000) (0.074) (0.018) (0.000) (0.000) (0.030) (0.731) (0.000) -0.013 0.062 -0.012 0.127 0.156 0.041 1.000 0.149 0.047 -0.052 manufacturing (0.257) (0.000) (0.319) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) -0.163 -0.098 -0.029 -0.045 -0.023 -0.025 0.149 1.000 0.214 0.190 npl_ta (0.000) (0.000) (0.012) (0.000) (0.049) (0.030) (0.000) (0.000) (0.000) -0.059 -0.046 0.003 -0.054 0.005 0.004 0.047 0.214 1.000 0.195 overafc (0.000) (0.000) (0.806) (0.000) (0.685) (0.731) (0.000) (0.000) (0.000) 143 Table 21(b) Correlation Matrix for Financing Obstacle Analysis transparency audit costeffi operationyear size foreign manufacturing npl_ta overafc bcdepo 0.167 0.100 -0.032 -0.055 0.010 0.056 -0.052 0.190 0.195 1.000 bcdepo (0.000) (0.000) (0.005) (0.000) (0.367) (0.000) (0.000) (0.000) (0.000) 0.010 0.099 0.004 0.066 -0.019 0.047 -0.030 -0.205 -0.028 0.304 forbank (0.366) (0.000) (0.714) (0.000) (0.099) (0.000) (0.009) (0.000) (0.014) (0.000) -0.122 0.036 0.066 -0.003 0.001 -0.023 -0.030 -0.184 -0.229 -0.130 stmktcap (0.000) (0.002) (0.000) (0.765) (0.905) (0.050) (0.011) (0.000) (0.000) (0.000) -0.042 0.053 0.024 0.071 -0.003 0.029 -0.080 -0.042 0.016 0.066 llgdp (0.000) (0.000) (0.037) (0.000) (0.775) (0.013) (0.000) (0.000) (0.170) (0.000) -0.074 0.078 0.049 0.074 -0.014 0.022 -0.013 0.063 0.092 0.240 pcrdbgdp (0.000) (0.000) (0.000) (0.000) (0.216) (0.056) (0.279) (0.000) (0.000) (0.000) -0.027 0.114 0.038 0.070 -0.023 0.040 -0.057 -0.202 -0.151 0.283 egovernment (0.018) (0.000) (0.001) (0.000) (0.051) (0.001) (0.000) (0.000) (0.000) (0.000) -0.006 0.121 0.034 0.072 -0.021 0.044 -0.041 -0.129 -0.094 0.312 law (0.576) (0.000) (0.004) (0.000) (0.072) (0.000) (0.000) (0.000) (0.000) (0.000) -0.006 0.124 0.043 0.088 -0.020 0.043 -0.051 -0.196 -0.133 0.293 ccorruption (0.581) (0.000) (0.000) (0.000) (0.080) (0.000) (0.000) (0.000) (0.000) (0.000) -0.026 0.045 0.013 0.099 -0.006 0.021 -0.088 -0.474 -0.388 -0.124 polity2 (0.028) (0.000) (0.275) (0.000) (0.631) (0.069) (0.000) (0.000) (0.000) (0.000) -0.021 0.115 0.031 0.072 -0.021 0.045 -0.046 -0.147 -0.085 0.324 institution (0.070) (0.000) (0.009) (0.000) (0.067) (0.000) (0.000) (0.000) (0.000) (0.000) -0.228 -0.012 0.039 -0.001 0.008 -0.030 0.014 0.043 -0.301 -0.391 lgdp (0.000) (0.300) (0.001) (0.935) (0.493) (0.010) (0.213) (0.000) (0.000) (0.000) 144 Table 21(c) Correlation Matrix for Financing Obstacle Analysis Pearson Correlation Coefficients Prob > |r| under H0: Rho=0 forbank stmktcap llgdp pcrdbgdp egovernment law ccorruption polity2 institution lgdp 0.010 -0.122 -0.042 -0.074 -0.027 -0.006 -0.006 -0.026 -0.021 -0.228 transparency (0.366) (0.000) (0.000) (0.000) (0.018) (0.576) (0.581) (0.028) (0.070) (0.000) 0.099 0.036 0.053 0.078 0.114 0.121 0.124 0.045 0.115 -0.012 audit (0.000) (0.002) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.300) 0.004 0.066 0.024 0.049 0.038 0.034 0.043 0.013 0.031 0.039 costeffi (0.714) (0.000) (0.037) (0.000) (0.001) (0.004) (0.000) (0.275) (0.009) (0.001) 0.066 -0.003 0.071 0.074 0.070 0.072 0.088 0.099 0.072 -0.001 operationyear (0.000) (0.765) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.935) -0.019 0.001 -0.003 -0.014 -0.023 -0.021 -0.020 -0.006 -0.021 0.008 size (0.099) (0.905) (0.775) (0.216) (0.051) (0.072) (0.080) (0.631) (0.067) (0.493) 0.047 -0.023 0.029 0.022 0.040 0.044 0.043 0.021 0.045 -0.030 foreign (0.000) (0.050) (0.013) (0.056) (0.001) (0.000) (0.000) (0.069) (0.000) (0.010) -0.030 -0.030 -0.080 -0.013 -0.057 -0.041 -0.051 -0.088 -0.046 0.014 manufacturing (0.009) (0.011) (0.000) (0.279) (0.000) (0.000) (0.000) (0.000) (0.000) (0.213) -0.205 -0.184 -0.042 0.063 -0.202 -0.129 -0.196 -0.474 -0.147 0.043 npl_ta (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) -0.028 -0.229 0.016 0.092 -0.151 -0.094 -0.133 -0.388 -0.085 -0.301 overafc (0.014) (0.000) (0.170) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 145 Table 21(d) Correlation Matrix for Financing Obstacle Analysis forbank stmktcap llgdp pcrdbgdp egovernment law ccorruption polity2 institution lgdp 0.304 -0.130 0.066 0.240 0.283 0.312 0.293 -0.124 0.324 -0.391 bcdepo (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 1.000 -0.095 0.536 0.441 0.724 0.721 0.739 0.560 0.781 -0.195 forbank (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) -0.095 1.000 0.219 0.328 0.173 0.069 0.139 0.288 0.075 0.640 stmktcap (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 0.536 0.219 1.000 0.708 0.732 0.710 0.718 0.563 0.694 0.250 llgdp (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 0.441 0.328 0.708 1.000 0.622 0.598 0.683 0.281 0.630 0.111 pcrdbgdp (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 0.724 0.173 0.732 0.622 1.000 0.968 0.968 0.677 0.976 0.163 egovernment (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 0.721 0.069 0.710 0.598 0.968 1.000 0.976 0.642 0.981 0.112 law (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 0.739 0.139 0.718 0.683 0.968 0.976 1.000 0.656 0.981 0.093 ccorruption (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 0.560 0.288 0.563 0.281 0.677 0.642 0.656 1.000 0.647 0.245 polity2 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 0.781 0.075 0.694 0.630 0.976 0.981 0.981 0.647 1.000 0.088 institution (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) -0.195 0.640 0.250 0.111 0.163 0.112 0.093 0.245 0.088 1.000 lgdp (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 146 Table 22 Financing Obstacle Analysis: without interaction term financing egovernment law ccorruption polity2 institution transparency -0.102* -0.076 -0.080 -0.067 -0.079 (0.05) (0.05) (0.05) (0.05) (0.05) audit -0.126** -0.144** -0.138** -0.110* -0.136** (0.05) (0.05) (0.05) (0.05) (0.05) costeffi -0.002 -0.002 -0.002 -0.002 -0.002 (0.00) (0.00) (0.00) (0.00) (0.00) operationyear -0.001 -0.001 -0.001 -0.002 -0.001 (0.00) (0.00) (0.00) (0.00) (0.00) size -0.136*** -0.128*** -0.130*** -0.136*** -0.132*** (0.04) (0.04) (0.04) (0.04) (0.04) foreign -0.005*** -0.005*** -0.005*** -0.005*** -0.005*** (0.00) (0.00) (0.00) (0.00) (0.00) manufacturing 0.336*** 0.322*** 0.326*** 0.327*** 0.328*** (0.05) (0.05) (0.05) (0.05) (0.05) npl_ta 0.027*** 0.030*** 0.031*** 0.038*** 0.029*** (0.00) (0.00) (0.00) (0.00) (0.00) overafc -0.024* -0.008 -0.008 0.015 -0.012 (0.01) (0.01) (0.01) (0.01) (0.01) bcdepo -0.026*** -0.030*** -0.029*** -0.025*** -0.029*** (0.00) (0.00) (0.00) (0.00) (0.00) forbank 0.011*** 0.009*** 0.009*** 0.007*** 0.009*** (0.00) (0.00) (0.00) (0.00) (0.00) 147 Table 22 (continued) financing egovernment law ccorruption polity2 institution stmktcap 0.170 0.263 0.229 -0.174 0.215 (0.20) (0.20) (0.20) (0.21) (0.20) llgdp 0.895*** 0.185 0.380 -0.204 0.438 (0.26) (0.26) (0.24) (0.26) (0.24) pcrdbgdp -1.913*** -2.144*** -2.323*** -1.547*** -2.169*** (0.27) (0.27) (0.28) (0.27) (0.27) egovernment -0.120 (0.07) law 0.256*** (0.06) ccorruption 0.230** (0.07) polity2 0.059*** (0.01) institution 0.030** (0.01) cut1 -3.054*** -3.494*** -3.420*** -2.546*** -3.353*** (0.21) (0.22) (0.22) (0.22) (0.22) cut2 -2.137*** -2.577*** -2.504*** -1.626*** -2.436*** (0.21) (0.22) (0.22) (0.22) (0.21) cut3 -0.888*** -1.325*** -1.253*** -0.371 -1.187*** (0.21) (0.21) (0.22) (0.22) (0.21) 148 Table 22 (continued) financing egovernment law ccorruption polity2 institution N 7532 7532 7532 7532 7532 pseudo R 2 0.028 0.029 0.029 0.031 0.029 chi2 585.823 600.112 592.824 633.433 590.032 Note: Models are estimated using ordered logit regressions, where the dependent variable equals one to four. Data on dependent variable and firm specific variables are obtained or computed from BEEPS. Bank regulatory variables are obtained or computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. 149 Table 23 Financing Obstacle Analysis: with interaction term obstacle egovernment law ccorruption Polity2 institution transparency -0.092 -0.096 -0.082 -0.017 -0.067 (0.05) (0.05) (0.05) (0.05) (0.05) audit -0.138** -0.150** -0.146** -0.131** -0.137** (0.05) (0.05) (0.05) (0.05) (0.05) costeffi -0.001 -0.002 -0.002 -0.002 -0.002 (0.00) (0.00) (0.00) (0.00) (0.00) operationyear -0.001 -0.001 -0.001 -0.001 -0.001 (0.00) (0.00) (0.00) (0.00) (0.00) size -0.143*** -0.131*** -0.132*** -0.142*** -0.136*** (0.04) (0.04) (0.04) (0.04) (0.04) foreign -0.005*** -0.005*** -0.005*** -0.005*** -0.005*** (0.00) (0.00) (0.00) (0.00) (0.00) manufacturing 0.348*** 0.323*** 0.323*** 0.324*** 0.327*** (0.05) (0.05) (0.05) (0.05) (0.05) npl_ta -0.004 0.002 0.001 0.029*** -0.000 (0.00) (0.00) (0.00) (0.00) (0.00) nfob -0.091 -0.099 -0.075 -0.035 -0.079 (0.06) (0.06) (0.06) (0.06) (0.06) nbffob -0.477*** -0.347*** -0.306*** -0.238* -0.405*** (0.09) (0.09) (0.09) (0.10) (0.09) overbnk -0.004 -0.002 -0.017 0.061*** -0.000 (0.01) (0.01) (0.01) (0.01) (0.01) 150 Table 23 (continued) obstacle egovernment law ccorruption Polity2 institution bcdepo -0.003 -0.007* -0.006 -0.017*** -0.006 (0.00) (0.00) (0.00) (0.00) (0.00) forbank 0.008*** 0.006*** 0.005*** 0.005*** 0.005*** (0.00) (0.00) (0.00) (0.00) (0.00) stmktcap -0.673* -0.117 -0.383 -0.791** -0.252 (0.26) (0.27) (0.27) (0.27) (0.27) llgdp 0.234 -0.007 0.230 -0.688* 0.252 (0.29) (0.28) (0.28) (0.30) (0.27) pcrdbgdp -0.527 -0.962** -0.907** -1.051*** -1.063*** (0.30) (0.31) (0.33) (0.29) (0.31) loggdp 0.180*** 0.212*** 0.274*** -0.103* 0.176*** (0.02) (0.03) (0.03) (0.05) (0.03) egovernment -9.363*** (0.95) loggdpegovernment 0.373*** (0.04) law -8.019*** (1.00) loggdplaw 0.327*** (0.04) ccorruption -9.158*** (1.04) loggdpccorruption 0.376*** (0.04) 151 Table 23 (continued) obstacle egovernment law ccorruption Polity2 institution polity2 -0.625*** (0.14) loggdppolity2 0.029*** (0.01) institution -1.398*** (0.15) loggdpinstitution 0.057*** (0.01) cut1 1.625* 2.270** 3.840*** -4.846*** 1.393 (0.71) (0.86) (0.92) (1.12) (0.76) cut2 2.552*** 3.194*** 4.765*** -3.922*** 2.319** (0.71) (0.86) (0.92) (1.12) (0.76) cut3 3.822*** 4.458*** 6.030*** -2.661* 3.585*** (0.71) (0.86) (0.92) (1.12) (0.76) N 7532 7532 7532 7532 7532 pseudo R 2 0.036 0.034 0.034 0.033 0.035 chi2 733.571 693.101 704.270 678.778 710.760 Note: Models are estimated using ordered logit regressions, where the dependent variable equals one to four. Data on dependent variable and firm specific variables are obtained or computed from BEEPS. Bank regulatory variables are obtained or computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. 152 Table 24(a) Marginal Effects for Financing Obstacle Analysis egovernment mfx1 mfx2 mfx3 mfx4 transparency (d) 0.015 0.007 -0.003 -0.019 audit (d) 0.022** 0.011** -0.004** -0.029** costeffi 0.000 0.000 -0.000 -0.000 operationyear 0.000 0.000 -0.000 -0.000 size 0.023*** 0.011*** -0.004*** -0.030*** foreign 0.001*** 0.000*** -0.000*** -0.001*** manufacturing (d) -0.055*** -0.027*** 0.007*** 0.075*** npl_ta 0.001 0.000 -0.000 -0.001 egovernment 1.519*** 0.714*** -0.256*** -1.977*** loggdpegovernment -0.061*** -0.028*** 0.010*** 0.079*** nfob 0.015 0.007 -0.002 -0.019 nbffob 0.077*** 0.036*** -0.013*** -0.101*** overbnk 0.001 0.000 -0.000 -0.001 bcdepo 0.000 0.000 -0.000 -0.001 forbank -0.001*** -0.001*** 0.000*** 0.002*** stmktcap 0.109* 0.051* -0.018* -0.142* llgdp -0.038 -0.018 0.006 0.049 pcrdbgdp 0.085 0.040 -0.014 -0.111 loggdp -0.029*** -0.014*** 0.005*** 0.038*** N 7532 7532 7532 7532 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. Table 24(b) Marginal Effects for Financing Obstacle Analysis law mfx1 mfx2 mfx3 mfx4 transparency (d) 0.016 0.007 -0.003 -0.020 audit (d) 0.024** 0.011** -0.004** -0.032** costeffi 0.000 0.000 -0.000 -0.000 operationyear 0.000 0.000 -0.000 -0.000 size 0.021*** 0.010*** -0.004*** -0.028*** foreign 0.001*** 0.000*** -0.000*** -0.001*** manufacturing (d) -0.051*** -0.025*** 0.007*** 0.069*** npl_ta -0.000 -0.000 0.000 0.000 law 1.306*** 0.608*** -0.219*** -1.695*** loggdplaw -0.053*** -0.025*** 0.009*** 0.069*** nfob 0.016 0.008 -0.003 -0.021 nbffob 0.057*** 0.026*** -0.009*** -0.073*** overbnk 0.000 0.000 -0.000 -0.001 bcdepo 0.001* 0.001* -0.000* -0.002* forbank -0.001*** -0.000*** 0.000*** 0.001*** 153 Table 24(b) (continued) law mfx1 mfx2 mfx3 mfx4 stmktcap 0.019 0.009 -0.003 -0.025 llgdp 0.001 0.000 -0.000 -0.001 pcrdbgdp 0.157** 0.073** -0.026** -0.203** loggdp -0.035*** -0.016*** 0.006*** 0.045*** N 7532 7532 7532 7532 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. Table 24(c) Marginal Effects for Financing Obstacle Analysis ccorruption mfx1 mfx2 mfx3 mfx4 transparency (d) 0.014 0.006 -0.002 -0.017 audit (d) 0.024** 0.011** -0.004** -0.031** costeffi 0.000 0.000 -0.000 -0.000 operationyear 0.000 0.000 -0.000 -0.000 size 0.022*** 0.010*** -0.004*** -0.028*** foreign 0.001*** 0.000*** -0.000*** -0.001*** manufacturing (d) -0.051*** -0.025*** 0.007*** 0.069*** npl_ta -0.000 -0.000 0.000 0.000 nfob 0.012 0.006 -0.002 -0.016 nbffob 0.050*** 0.023*** -0.008*** -0.065*** overbnk 0.003 0.001 -0.000 -0.004 bcdepo 0.001 0.000 -0.000 -0.001 forbank -0.001*** -0.000*** 0.000*** 0.001*** stmktcap 0.062 0.029 -0.011 -0.081 llgdp -0.037 -0.017 0.006 0.048 pcrdbgdp 0.148** 0.069** -0.025** -0.192** loggdp -0.045*** -0.021*** 0.008*** 0.058*** ccorruption 1.491*** 0.695*** -0.251*** -1.935*** loggdpccorruption -0.061*** -0.029*** 0.010*** 0.079*** N 7532 7532 7532 7532 Table 24(d) Marginal Effects for Financing Obstacle Analysis polity2 mfx1 mfx2 mfx3 mfx4 transparency (d) 0.003 0.001 -0.000 -0.004 audit (d) 0.021** 0.010** -0.004** -0.028** costeffi 0.000 0.000 -0.000 -0.000 operationyear 0.000 0.000 -0.000 -0.000 size 0.023*** 0.011*** -0.004*** -0.030*** foreign 0.001*** 0.000*** -0.000*** -0.001*** manufacturing (d) -0.052*** -0.025*** 0.007*** 0.070*** 154 Table 24(d) (continued) polity2 mfx1 mfx2 mfx3 mfx4 npl_ta -0.005*** -0.002*** 0.001*** 0.006*** polity2 0.102*** 0.047*** -0.017*** -0.132*** loggdppolity2 -0.005*** -0.002*** 0.001*** 0.006*** nfob 0.006 0.003 -0.001 -0.007 nbffob 0.039* 0.018* -0.007* -0.050* overbnk -0.010*** -0.005*** 0.002*** 0.013*** bcdepo 0.003*** 0.001*** -0.000*** -0.004*** forbank -0.001*** -0.000*** 0.000*** 0.001*** stmktcap 0.129** 0.060** -0.022** -0.167** llgdp 0.112* 0.052* -0.019* -0.145* pcrdbgdp 0.171*** 0.079*** -0.029*** -0.222*** loggdp 0.017* 0.008* -0.003* -0.022* N 7532 7532 7532 7532 Table 24(e) Marginal Effects for Financing Obstacle Analysis institution mfx1 mfx2 mfx3 mfx4 transparency (d) 0.011 0.005 -0.002 -0.014 audit (d) 0.022** 0.010** -0.004** -0.029** costeffi 0.000 0.000 -0.000 -0.000 operationyear 0.000 0.000 -0.000 -0.000 size 0.022*** 0.010*** -0.004*** -0.029*** foreign 0.001*** 0.000*** -0.000*** -0.001*** manufacturing (d) -0.052*** -0.025*** 0.007*** 0.070*** npl_ta 0.000 0.000 -0.000 -0.000 nfob 0.013 0.006 -0.002 -0.017 nbffob 0.066*** 0.031*** -0.011*** -0.086*** overbnk 0.000 0.000 -0.000 -0.000 bcdepo 0.001 0.000 -0.000 -0.001 forbank -0.001*** -0.000*** 0.000*** 0.001*** stmktcap 0.041 0.019 -0.007 -0.053 llgdp -0.041 -0.019 0.007 0.053 pcrdbgdp 0.173*** 0.081*** -0.029** -0.224*** loggdp -0.029*** -0.013*** 0.005*** 0.037*** institution 0.227*** 0.106*** -0.038*** -0.295*** loggdpinstitution -0.009*** -0.004*** 0.002*** 0.012*** N 7532 7532 7532 7532 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. 155 Table 25 Cost of Finance Analysis: with Interaction Term cost egovernment law ccorruption polity2 institution transparency -0.100 -0.111* -0.092 -0.028 -0.075 (0.05) (0.05) (0.05) (0.05) (0.05) audit -0.140** -0.153** -0.149** -0.136** -0.137** (0.05) (0.05) (0.05) (0.05) (0.05) costeffi -0.001 -0.002 -0.002 -0.002 -0.002 (0.00) (0.00) (0.00) (0.00) (0.00) operationyear -0.002 -0.002 -0.002 -0.002 -0.002 (0.00) (0.00) (0.00) (0.00) (0.00) size -0.132*** -0.117** -0.119*** -0.131*** -0.125*** (0.04) (0.04) (0.04) (0.04) (0.04) foreign -0.004*** -0.004*** -0.004*** -0.004*** -0.004*** (0.00) (0.00) (0.00) (0.00) (0.00) manufacturing 0.408*** 0.380*** 0.381*** 0.387*** 0.387*** (0.05) (0.05) (0.05) (0.05) (0.05) npl_ta 0.000 0.004 0.004 0.033*** 0.002 (0.00) (0.00) (0.00) (0.00) (0.00) nfob -0.126* -0.147* -0.114 -0.073 -0.117* (0.06) (0.06) (0.06) (0.06) (0.06) nbffob -0.538*** -0.409*** -0.356*** -0.390*** -0.477*** (0.09) (0.09) (0.09) (0.10) (0.09) overbnk -0.007 -0.009 -0.024 0.061*** -0.005 (0.01) (0.01) (0.01) (0.01) (0.01) bcdepo -0.005 -0.007* -0.006 -0.020*** -0.006* (0.00) (0.00) (0.00) (0.00) (0.00) forbank 0.007*** 0.005*** 0.003** 0.005*** 0.004** (0.00) (0.00) (0.00) (0.00) (0.00) 156 Table 25 (continued) cost egovernment law ccorruption polity2 institution stmktcap -0.311 0.319 0.004 -0.311 0.135 (0.27) (0.28) (0.27) (0.27) (0.28) llgdp 0.101 -0.119 0.153 -0.581 0.215 (0.29) (0.28) (0.28) (0.30) (0.27) pcrdbgdp -0.540 -0.889** -0.912** -1.195*** -1.057*** (0.31) (0.31) (0.33) (0.30) (0.31) lgdp 0.143*** 0.198*** 0.262*** -0.187*** 0.147*** (0.03) (0.03) (0.03) (0.05) (0.03) egovernment -10.359*** (0.95) lgdpegovernment 0.416*** (0.04) law -9.853*** (1.00) lgdplaw 0.402*** (0.04) ccorruption -10.762*** (1.05) lgdpccorruption 0.443*** (0.04) polity2 -0.762*** (0.15) lgdppolity2 0.034*** (0.01) 157 Table 25 (continued) cost egovernment law ccorruption polity2 institution institution -1.620*** (0.15) lgdpinstitution 0.066*** (0.1) cut1 0.592 1.861* 3.465*** -7.175*** 0.614 (0.73) (0.88) (0.94) (1.15) (0.77) cut2 1.612* 2.880** 4.484*** -6.160*** 1.634* (0.73) (0.88) (0.94) (1.15) (0.77) cut3 2.926*** 4.192*** 5.797*** -4.859*** 2.947*** (0.73) (0.88) (0.94) (1.15) (0.78) N 7429 7429 7429 7429 7429 pseudo R 2 0.038 0.037 0.037 0.034 0.038 chi2 781.887 760.518 767.563 703.732 770.708 Note: Models are estimated using ordered logit regressions, where the dependent variable equals one to four. Data on dependent variable and firm specific variables are obtained or computed from BEEPS. Bank regulatory variables are obtained or computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. 158 Table 26 (a) Marginal Effects for Cost of Finance Analysis cost mfx1 mfx2 mfx3 mfx4 transparency (d) 0.018 0.007 -0.007 -0.018 audit (d) 0.025** 0.010** -0.009** -0.025** costeffi 0.000 0.000 -0.000 -0.000 operationyear 0.000 0.000 -0.000 -0.000 size 0.024*** 0.009*** -0.009*** -0.024*** foreign 0.001*** 0.000*** -0.000*** -0.001*** manufacturing (d) -0.071*** -0.029*** 0.024*** 0.076*** npl_ta -0.000 -0.000 0.000 0.000 egovernment 1.864*** 0.710*** -0.687*** -1.886*** lgdpegovernment -0.075*** -0.028*** 0.028*** 0.076*** nfob 0.023* 0.009* -0.008* -0.023* nbffob 0.097*** 0.037*** -0.036*** -0.098*** overbnk 0.001 0.000 -0.000 -0.001 bcdepo 0.001 0.000 -0.000 -0.001 forbank -0.001*** -0.000*** 0.000*** 0.001*** stmktcap 0.056 0.021 -0.021 -0.057 llgdp -0.018 -0.007 0.007 0.018 pcrdbgdp 0.097 0.037 -0.036 -0.098 lgdp -0.026*** -0.010*** 0.009*** 0.026*** N 7429 7429 7429 7429 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. Table 26 (b) Marginal Effects for Cost of Finance Analysis cost mfx1f5 mfx2f5 mfx3f5 mfx4f5 transparency (d) 0.020* 0.007* -0.008* -0.020* audit (d) 0.028** 0.010** -0.010** -0.028** costeffi 0.000 0.000 -0.000 -0.000 operationyear 0.000 0.000 -0.000 -0.000 size 0.021** 0.008** -0.008** -0.021** foreign 0.001*** 0.000*** -0.000*** -0.001*** manufacturing (d) -0.067*** -0.027*** 0.023*** 0.071*** npl_ta -0.001 -0.000 0.000 0.001 law 1.776*** 0.672*** -0.653*** -1.795*** lgdplaw -0.072*** -0.027*** 0.027*** 0.073*** nfob 0.027* 0.010* -0.010* -0.027* nbffob 0.074*** 0.028*** -0.027*** -0.075*** overbnk 0.002 0.001 -0.001 -0.002 bcdepo 0.001* 0.000* -0.000* -0.001* forbank -0.001*** -0.000*** 0.000*** 0.001*** 159 Table 26 (b) (continued) cost mfx1f5 mfx2f5 mfx3f5 mfx4f5 stmktcap -0.058 -0.022 0.021 0.058 llgdp 0.021 0.008 -0.008 -0.022 pcrdbgdp 0.160** 0.061** -0.059** -0.162** lgdp -0.036*** -0.013*** 0.013*** 0.036*** N 7429 7429 7429 7429 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. Table 26(c) Marginal Effects for Cost of Finance Analysis Cost mfx1f6 mfx2f6 mfx3f6 mfx4f6 transparency (d) 0.017 0.006 -0.006 -0.017 audit (d) 0.027** 0.010** -0.010** -0.027** costeffi 0.000 0.000 -0.000 -0.000 operationyear 0.000 0.000 -0.000 -0.000 size 0.022*** 0.008*** -0.008** -0.022*** foreign 0.001*** 0.000*** -0.000*** -0.001*** manufacturing (d) -0.067*** -0.027*** 0.023*** 0.071*** npl_ta -0.001 -0.000 0.000 0.001 ccorruption 1.940*** 0.734*** -0.715*** -1.959*** lgdpccorruption -0.080*** -0.030*** 0.029*** 0.081*** nfob 0.020 0.008 -0.008 -0.021 nbffob 0.064*** 0.024*** -0.024*** -0.065*** overbnk 0.004 0.002 -0.002 -0.004 bcdepo 0.001 0.000 -0.000 -0.001 forbank -0.001** -0.000** 0.000** 0.001** stmktcap -0.001 -0.000 0.000 0.001 llgdp -0.028 -0.010 0.010 0.028 pcrdbgdp 0.164** 0.062** -0.061** -0.166** lgdp -0.047*** -0.018*** 0.017*** 0.048*** N 7429 7429 7429 7429 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. 160 Table 26 (d) Marginal Effects for Cost of Finance Analysis cost mfx1f7 mfx2f7 mfx3f7 mfx4f7 transparency (d) 0.005 0.002 -0.002 -0.005 audit (d) 0.025** 0.009** -0.009** -0.025** costeffi 0.000 0.000 -0.000 -0.000 operationyear 0.000 0.000 -0.000 -0.000 size 0.024*** 0.009*** -0.009*** -0.024*** foreign 0.001*** 0.000*** -0.000*** -0.001*** manufacturing (d) -0.068*** -0.027*** 0.023*** 0.072*** npl_ta -0.006*** -0.002*** 0.002*** 0.006*** polity2 0.138*** 0.051*** -0.050*** -0.139*** lgdppolity2 -0.006*** -0.002*** 0.002*** 0.006*** nfob 0.013 0.005 -0.005 -0.013 nbffob 0.071*** 0.026*** -0.026*** -0.071*** overbnk -0.011*** -0.004*** 0.004*** 0.011*** bcdepo 0.004*** 0.001*** -0.001*** -0.004*** forbank -0.001*** -0.000*** 0.000*** 0.001*** stmktcap 0.056 0.021 -0.021 -0.057 llgdp 0.105 0.039 -0.038 -0.106 pcrdbgdp 0.217*** 0.081*** -0.079*** -0.218*** lgdp 0.034*** 0.013*** -0.012*** -0.034*** N 7429 7429 7429 7429 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. Table 26 (e) Marginal Effects for Cost of Finance Analysis cost mfx1f8 mfx2f8 mfx3f8 mfx4f8 transparency (d) 0.014 0.005 -0.005 -0.014 audit (d) 0.025** 0.009** -0.009** -0.025** costeffi 0.000 0.000 -0.000 -0.000 operationyear 0.000 0.000 -0.000 -0.000 size 0.022*** 0.009*** -0.008*** -0.023*** foreign 0.001*** 0.000*** -0.000*** -0.001*** manufacturing (d) -0.068*** -0.028*** 0.023*** 0.072*** npl_ta -0.000 -0.000 0.000 0.000 institution 0.292*** 0.111*** -0.108*** -0.295*** lgdpinstitution -0.012*** -0.005*** 0.004*** 0.012*** nfob 0.021* 0.008* -0.008* -0.021* nbffob 0.086*** 0.033*** -0.032*** -0.087*** overbnk 0.001 0.000 -0.000 -0.001 bcdepo 0.001* 0.000* -0.000* -0.001* forbank -0.001** -0.000** 0.000** 0.001** 161 Table 26 (e) (continued) cost mfx1f8 mfx2f8 mfx3f8 mfx4f8 stmktcap -0.024 -0.009 0.009 0.025 llgdp -0.039 -0.015 0.014 0.039 pcrdbgdp 0.190*** 0.072*** -0.070*** -0.192*** lgdp -0.026*** -0.010*** 0.010*** 0.027*** N 7429 7429 7429 7429 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. 162 Table 27 Access to Finance Analysis: with Interaction Term access egovernment law ccorruption polity2 institution transparency -0.093 -0.099 -0.091 -0.034 -0.076 (0.05) (0.05) (0.05) (0.05) (0.05) audit -0.137** -0.145** -0.141** -0.128** -0.134** (0.05) (0.05) (0.05) (0.05) (0.05) costeffi -0.000 -0.000 -0.000 -0.001 -0.001 (0.00) (0.00) (0.00) (0.00) (0.00) operationyear 0.000 0.000 0.000 -0.000 0.000 (0.00) (0.00) (0.00) (0.00) (0.00) size -0.192*** -0.183*** -0.184*** -0.191*** -0.188*** (0.04) (0.04) (0.04) (0.04) (0.04) foreign -0.006*** -0.006*** -0.006*** -0.006*** -0.006*** (0.00) (0.00) (0.00) (0.00) (0.00) manufacturing 0.310*** 0.291*** 0.292*** 0.290*** 0.296*** (0.05) (0.05) (0.05) (0.05) (0.05) npl_ta -0.006 -0.003 -0.005 0.021*** -0.005 (0.00) (0.00) (0.00) (0.00) (0.00) nfob -0.327*** -0.322*** -0.303*** -0.254*** -0.312*** (0.07) (0.07) (0.06) (0.06) (0.06) nbffob -0.039 0.037 0.063 0.100 -0.016 (0.09) (0.09) (0.09) (0.10) (0.09) overbnk -0.014 -0.013 -0.027 0.045** -0.011 (0.01) (0.01) (0.01) (0.01) (0.01) bcdepo -0.003 -0.005 -0.003 -0.012*** -0.003 (0.00) (0.00) (0.00) (0.00) (0.00) forbank 0.008*** 0.007*** 0.006*** 0.005*** 0.006*** (0.00) (0.00) (0.00) (0.00) (0.00) 163 Table 27 (continued) access egovernment law ccorruption polity2 institution stmktcap -0.872** -0.429 -0.702* -0.972*** -0.579* (0.27) (0.28) (0.28) (0.28) (0.28) llgdp 0.139 0.018 0.275 -0.575 0.252 (0.29) (0.29) (0.28) (0.30) (0.28) pcrdbgdp -0.806* -1.107*** -0.921** -1.192*** -1.119*** (0.32) (0.32) (0.34) (0.30) (0.32) lgdp 0.184*** 0.216*** 0.275*** -0.071 0.192*** (0.03) (0.03) (0.03) (0.05) (0.03) egovernment -7.531*** (0.95) lgdpegovernment 0.301*** (0.04) law -6.679*** (1.01) lgdplaw 0.271*** (0.04) ccorruption -7.887*** (1.05) lgdpccorruption 0.320*** (0.04) polity2 -0.587*** (0.14) lgdppolity2 0.027*** (0.01) 164 Table 27 (continued) access egovernment law ccorruption polity2 institution institution -1.200*** (0.15) lgdpinstitution 0.049*** (0.01) cut1 2.559*** 3.324*** 4.893*** -3.148** 2.768*** (0.72) (0.88) (0.94) (1.13) (0.78) cut2 3.424*** 4.187*** 5.757*** -2.286* 3.632*** (0.72) (0.88) (0.94) (1.13) (0.78) cut3 4.637*** 5.397*** 6.968*** -1.075 4.844*** (0.73) (0.88) (0.94) (1.13) (0.78) N 7360 7360 7360 7360 7360 pseudo R 2 0.031 0.029 0.030 0.029 0.030 chi2 609.177 587.746 597.808 585.022 604.535 Note: Models are estimated using ordered logit regressions, where the dependent variable equals one to four. Data on dependent variable and firm specific variables are obtained or computed from BEEPS. Bank regulatory variables are obtained or computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. 165 Table 28(a) Marginal Effects for Access to Finance Analysis access mfx1f3 mfx2f3 mfx3f3 mfx4f3 transparency (d) 0.021 0.002 -0.009 -0.014 audit (d) 0.031** 0.003** -0.013** -0.021** costeffi 0.000 0.000 -0.000 -0.000 operationyear -0.000 -0.000 0.000 0.000 size 0.044*** 0.004*** -0.018*** -0.029*** foreign 0.001*** 0.000*** -0.001*** -0.001*** manufacturing (d) -0.070*** -0.007*** 0.028*** 0.049*** npl_ta 0.001 0.000 -0.001 -0.001 egovernment 1.713*** 0.142*** -0.701*** -1.154*** lgdpegovernment -0.068*** -0.006*** 0.028*** 0.046*** nfob 0.074*** 0.006*** -0.030*** -0.050*** nbffob 0.009 0.001 -0.004 -0.006 overbnk 0.003 0.000 -0.001 -0.002 bcdepo 0.001 0.000 -0.000 -0.000 forbank -0.002*** -0.000*** 0.001*** 0.001*** stmktcap 0.198** 0.016** -0.081** -0.134** llgdp -0.032 -0.003 0.013 0.021 pcrdbgdp 0.183* 0.015* -0.075* -0.123* lgdp -0.042*** -0.003*** 0.017*** 0.028*** N 7360 7360 7360 7360 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. Table 28(b) Marginal Effects for Access to Finance Analysis access mfx1f5 mfx2f5 mfx3f5 mfx4f5 transparency (d) 0.023 0.002* -0.009 -0.015 audit (d) 0.033** 0.003** -0.013** -0.022** costeffi 0.000 0.000 -0.000 -0.000 operationyear -0.000 -0.000 0.000 0.000 size 0.042*** 0.003*** -0.017*** -0.028*** foreign 0.001*** 0.000*** -0.001*** -0.001*** manufacturing (d) -0.065*** -0.006*** 0.026*** 0.046*** npl_ta 0.001 0.000 -0.000 -0.000 law 1.520*** 0.125*** -0.620*** -1.025*** lgdplaw -0.062*** -0.005*** 0.025*** 0.042*** nfob 0.073*** 0.006*** -0.030*** -0.049*** nbffob -0.008 -0.001 0.003 0.006 overbnk 0.003 0.000 -0.001 -0.002 bcdepo 0.001 0.000 -0.000 -0.001 forbank -0.001*** -0.000*** 0.001*** 0.001*** 166 Table 28(b) (continued) access mfx1f5 mfx2f5 mfx3f5 mfx4f5 stmktcap 0.098 0.008 -0.040 -0.066 llgdp -0.004 -0.000 0.002 0.003 pcrdbgdp 0.252*** 0.021** -0.103*** -0.170*** lgdp -0.049*** -0.004*** 0.020*** 0.033*** N 7360 7360 7360 7360 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. Table 28(c) Marginal Effects for Access to Finance Analysis access mfx1f6 mfx2f6 mfx3f6 mfx4f6 transparency (d) 0.021 0.002 -0.009 -0.014 audit (d) 0.032** 0.003** -0.013** -0.022** costeffi 0.000 0.000 -0.000 -0.000 operationyear -0.000 -0.000 0.000 0.000 size 0.042*** 0.003*** -0.017*** -0.028*** foreign 0.001*** 0.000*** -0.001*** -0.001*** manufacturing (d) -0.066*** -0.006*** 0.026*** 0.046*** npl_ta 0.001 0.000 -0.000 -0.001 ccorruption 1.795*** 0.148*** -0.734*** -1.209*** lgdpccorruption -0.073*** -0.006*** 0.030*** 0.049*** nfob 0.069*** 0.006*** -0.028*** -0.046*** nbffob -0.014 -0.001 0.006 0.010 overbnk 0.006 0.001 -0.003 -0.004 bcdepo 0.001 0.000 -0.000 -0.000 forbank -0.001*** -0.000*** 0.001*** 0.001*** stmktcap 0.160* 0.013* -0.065* -0.108* llgdp -0.063 -0.005 0.026 0.042 pcrdbgdp 0.210** 0.017* -0.086** -0.141** lgdp -0.063*** -0.005*** 0.026*** 0.042*** N 7360 7360 7360 7360 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. 167 Table 28(d) Marginal Effects for Access to Finance Analysis access mfx1f7 mfx2f7 mfx3f7 mfx4f7 transparency (d) 0.008 0.001 -0.003 -0.005 audit (d) 0.029** 0.002** -0.012** -0.020** costeffi 0.000 0.000 -0.000 -0.000 operationyear 0.000 0.000 -0.000 -0.000 size 0.043*** 0.004*** -0.018*** -0.029*** foreign 0.001*** 0.000*** -0.001*** -0.001*** manufacturing (d) -0.065*** -0.006*** 0.026*** 0.046*** npl_ta -0.005*** -0.000*** 0.002*** 0.003*** polity2 0.134*** 0.011*** -0.055*** -0.090*** lgdppolity2 -0.006*** -0.001*** 0.002*** 0.004*** nfob 0.058*** 0.005*** -0.024*** -0.039*** nbffob -0.023 -0.002 0.009 0.015 overbnk -0.010** -0.001** 0.004** 0.007** bcdepo 0.003*** 0.000*** -0.001*** -0.002*** forbank -0.001*** -0.000*** 0.000*** 0.001*** stmktcap 0.221*** 0.018** -0.090*** -0.149*** llgdp 0.131 0.011 -0.053 -0.088 pcrdbgdp 0.271*** 0.022*** -0.111*** -0.183*** lgdp 0.016 0.001 -0.007 -0.011 N 7360 7360 7360 7360 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. Table 28(e) Marginal Effects for Access to Finance Analysis access mfx1f8 mfx2f8 mfx3f8 mfx4f8 transparency (d) 0.017 0.001 -0.007 -0.012 audit (d) 0.031** 0.002** -0.013** -0.021** costeffi 0.000 0.000 -0.000 -0.000 operationyear -0.000 -0.000 0.000 0.000 size 0.043*** 0.004*** -0.018*** -0.029*** foreign 0.001*** 0.000*** -0.001*** -0.001*** manufacturing (d) -0.066*** -0.007*** 0.027*** 0.046*** npl_ta 0.001 0.000 -0.001 -0.001 institution 0.273*** 0.023*** -0.112*** -0.184*** lgdpinstitution -0.011*** -0.001*** 0.005*** 0.007*** nfob 0.071*** 0.006*** -0.029*** -0.048*** nbffob 0.004 0.000 -0.001 -0.002 overbnk 0.002 0.000 -0.001 -0.002 bcdepo 0.001 0.000 -0.000 -0.000 forbank -0.001*** -0.000*** 0.001*** 0.001*** 168 Table 28(e) (continued) access mfx1f8 mfx2f8 mfx3f8 mfx4f8 stmktcap 0.132* 0.011* -0.054* -0.089* llgdp -0.057 -0.005 0.023 0.039 pcrdbgdp 0.255*** 0.021** -0.104*** -0.171*** lgdp -0.044*** -0.004*** 0.018*** 0.029*** N 7360 7360 7360 7360 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. 169 Table 29 Fixed Investments Financing Pattern Analysis without Foreign Bank Ownership variable equity1 equity2 foreign1 foreign2 domestic1 domestic2 money1 money2 state1 state2 transparenc y -0.205 -0.273 0.604** 0.622*** 0.029 0.039 -0.043 -0.067 0.305* 0.294* (0.19) (0.19) (0.19) (0.19) (0.09) (0.09) (0.25) (0.25) (0.13) (0.13) audit 0.427** 0.447** 0.141 0.134 0.171* 0.162* -0.158 -0.155 0.159 0.165 (0.16) (0.16) (0.20) (0.20) (0.08) (0.08) (0.22) (0.22) (0.13) (0.13) costeffi 0.003 0.003 -0.003 -0.003 -0.002 -0.002 0.001 0.001 0.000 0.000 (0.00) (0.00) (0.01) (0.01) (0.00) (0.00) (0.01) (0.01) (0.00) (0.00) operationy ear -0.008 -0.009 -0.001 -0.001 -0.002 -0.002 -0.015 -0.013 0.008*** 0.008*** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.01) (0.01) (0.00) (0.00) size -0.034 -0.032 0.346** 0.349** 0.247*** 0.253*** -0.519** -0.531** 0.353*** 0.350*** (0.11) (0.11) (0.13) (0.13) (0.05) (0.05) (0.19) (0.19) (0.08) (0.08) foreign 0.000 0.001 0.012*** 0.011*** -0.004** -0.004** 0.004 0.004 -0.010*** -0.009*** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) manufactur ing 0.089 0.073 0.619*** 0.619*** 0.497*** 0.496*** -0.195 -0.191 -0.095 -0.093 (0.14) (0.14) (0.19) (0.19) (0.07) (0.07) (0.20) (0.20) (0.11) (0.11) npl_ta -0.069** -0.048* -0.006 -0.007 0.004 0.007 -0.001 -0.004 0.011 0.015 (0.02) (0.02) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) overafc 0.093* 0.134** 0.015 0.016 0.054** 0.061*** -0.038 -0.047 0.033 0.032 (0.04) (0.04) (0.04) (0.04) (0.02) (0.02) (0.06) (0.06) (0.03) (0.03) bcdepo -0.003 0.001 -0.020* -0.021** 0.002 0.001 -0.011 -0.011 -0.032*** -0.030*** (0.01) (0.01) (0.01) (0.01) (0.00) (0.00) (0.01) (0.01) (0.01) (0.00) llgdp 1.555** 1.866*** 2.346** 2.128** -0.741* -0.978** 2.090** 2.547** -0.348 -0.165 (0.54) (0.49) (0.73) (0.75) (0.33) (0.33) (0.80) (0.85) (0.63) (0.60) law 1.596*** -0.102 0.274*** -0.404* 0.614*** (0.20) (0.16) (0.08) (0.18) (0.17) ccorruption 1.434*** 0.016 0.388*** -0.634** 0.557** (0.16) (0.19) (0.08) (0.21) (0.17) 170 Table 29 (continued) variable equity1 equity2 foreign1 foreign2 domestic1 domestic2 money1 money2 state1 state2 _cons -5.404*** -6.259*** -5.221*** -5.100*** -3.530*** -3.452*** -3.368*** -3.492*** -2.276*** -2.406*** (0.82) (0.75) (0.72) (0.73) (0.33) (0.33) (0.90) (0.87) (0.55) (0.54) N 6251 6251 6251 6251 6251 6251 6251 6251 6251 6251 chi2 197.758 191.265 168.146 170.071 118.073 129.332 26.036 28.963 139.833 139.642 p 0.000 0.000 0.000 0.000 0.000 0.000 0.011 0.004 0.000 0.000 Note: Models are estimated using fractional logit regressions, where the dependent variable is fraction between 0 and 1. Data on dependent variable and firm specific variables are obtained or computed from BEEPS. Bank regulatory variables are obtained or computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. 171 Table 30(a) Marginal Effects for Fixed Investment Financing Analysis without Forbank Law mfxequ1 mfxfor1 mfxdom1 mfxmon1 mfxsta1 transparency (d) -0.003 0.008** 0.002 -0.000 0.011* audit (d) 0.007* 0.002 0.014* -0.001 0.005 costeffi 0.000 -0.000 -0.000 0.000 0.000 operationyear -0.000 -0.000 -0.000 -0.000 0.000*** size -0.001 0.004** 0.019*** -0.004** 0.011*** foreign 0.000 0.000*** -0.000** 0.000 -0.000*** manufacturing (d) 0.001 0.008** 0.041*** -0.001 -0.003 npl_ta -0.001*** -0.000 0.000 -0.000 0.000 overafc 0.002* 0.000 0.004** -0.000 0.001 bcdepo -0.000 -0.000* 0.000 -0.000 -0.001*** llgdp 0.025** 0.027** -0.058* 0.016** -0.011 law 0.026*** -0.001 0.022*** -0.003* 0.020*** N 6251 6251 6251 6251 6251 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. Table 30(b) Marginal Effects for Fixed Investment Financing Analysis without Forbank Corruption mfxequ2 mfxfor2 mfxdom2 mfxmon2 mfxsta2 transparency (d) -0.005 0.008** 0.003 -0.001 0.294* audit (d) 0.008** 0.002 0.013* -0.001 0.165 costeffi 0.000 -0.000 -0.000 0.000 0.000 operationyear -0.000 -0.000 -0.000 -0.000 0.008*** size -0.001 0.004** 0.020*** -0.004** 0.350*** foreign 0.000 0.000*** -0.000** 0.000 -0.009*** manufacturing (d) 0.001 0.008** 0.041*** -0.001 -0.093 npl_ta -0.001** -0.000 0.001 -0.000 0.015 overafc 0.002** 0.000 0.005*** -0.000 0.032 bcdepo 0.000 -0.000* 0.000 -0.000 -0.030*** llgdp 0.033*** 0.025** -0.077** 0.019** -0.165 ccorruption 0.025*** 0.000 0.030*** -0.005** 0.557** N 6251 6251 6251 6251 6251 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. 172 Table 31 Fixed Investmetn Financing Pattern Analysis: with Foreign Bank Ownership variable equity1 equity2 foreign1 foreign2 domestic1 domestic2 money1 money2 state1 state2 transparency -0.188 -0.261 0.576** 0.585** -0.006 -0.007 0.004 -0.003 0.196 0.200 (0.19) (0.19) (0.22) (0.22) (0.10) (0.10) (0.26) (0.27) (0.14) (0.14) audit 0.230 0.265 0.178 0.175 0.166 0.166 -0.204 -0.206 0.094 0.108 (0.16) (0.16) (0.23) (0.23) (0.09) (0.09) (0.24) (0.24) (0.13) (0.13) costeffi 0.005 0.005 -0.014 -0.014 0.000 0.000 0.003 0.003 -0.004 -0.004 (0.00) (0.00) (0.01) (0.01) (0.00) (0.00) (0.01) (0.01) (0.00) (0.00) operationyea r -0.009* -0.009 -0.004 -0.004 -0.003 -0.003 -0.011 -0.011 0.008** 0.008** (0.00) (0.00) (0.01) (0.01) (0.00) (0.00) (0.01) (0.01) (0.00) (0.00) size 0.009 0.001 0.438** 0.439** 0.277*** 0.277*** -0.509** -0.506** 0.290*** 0.282** (0.12) (0.11) (0.15) (0.15) (0.06) (0.06) (0.19) (0.19) (0.09) (0.09) foreign 0.000 0.000 0.011*** 0.011*** -0.005** -0.005** 0.005 0.005 -0.009** -0.009** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) manufacturi ng 0.083 0.075 0.506* 0.505* 0.446*** 0.445*** 0.010 -0.001 0.051 0.060 (0.15) (0.15) (0.21) (0.21) (0.08) (0.08) (0.22) (0.22) (0.12) (0.12) npl_ta -0.067** -0.081*** 0.011 0.014 0.007 0.007 -0.007 -0.015 -0.009 -0.006 (0.02) (0.02) (0.02) (0.02) (0.01) (0.01) (0.02) (0.02) (0.01) (0.01) overafc 0.117** 0.114** -0.065 -0.062 0.002 0.000 -0.025 -0.042 0.083* 0.088* (0.04) (0.04) (0.05) (0.05) (0.02) (0.02) (0.06) (0.06) (0.04) (0.04) bcdepo 0.016 0.034** -0.031** -0.032** -0.005 -0.005 0.005 0.003 -0.033*** -0.028*** (0.01) (0.01) (0.01) (0.01) (0.00) (0.00) (0.01) (0.01) (0.01) (0.01) forbank 0.017*** 0.018*** 0.002 0.002 0.004* 0.004* 0.008 0.009 -0.011*** -0.011*** (0.00) (0.00) (0.01) (0.00) (0.00) (0.00) (0.01) (0.01) (0.00) (0.00) stmktcap -8.289*** -8.870*** -3.600* -3.500* -0.956 -0.963 0.467 0.483 -2.580** -2.922*** (1.45) (1.41) (1.42) (1.39) (0.54) (0.52) (1.34) (1.36) (0.79) (0.77) llgdp -0.971 -0.669 1.829 1.791 -1.818*** -1.809*** 5.747*** 5.229*** -0.998 -0.632 (0.95) (0.93) (1.04) (1.05) (0.50) (0.50) (1.29) (1.23) (0.74) (0.73) pcrdbgdp 6.903*** 7.821*** 2.305* 2.035 3.493*** 3.564*** -6.703*** -5.474*** 0.875 0.223 (1.16) (1.16) (1.10) (1.22) (0.52) (0.55) (1.52) (1.56) (0.78) (0.87) 173 Table 31 (continued) variable equity1 equity2 foreign1 foreign2 domestic1 domestic2 money1 money2 state1 state2 lgdp 0.310 0.510*** -0.147 -0.161 -0.099* -0.098* 0.050 0.042 0.157* 0.199** (0.17) (0.14) (0.13) (0.12) (0.05) (0.04) (0.12) (0.12) (0.07) (0.07) law 0.690* 0.078 -0.045 -0.881* 0.922*** (0.29) (0.32) (0.12) (0.39) (0.20) ccorruption -0.054 0.198 -0.072 -1.062* 0.944*** (0.22) (0.40) (0.14) (0.50) (0.22) _cons -14.855** -21.015*** 0.198 0.668 -0.530 -0.572 -6.141 -5.864 -5.277** -6.496*** (4.71) (3.86) (3.59) (3.48) (1.25) (1.20) (3.81) (3.80) (1.91) (1.89) N 5523 5523 5523 5523 5523 5523 5523 5523 5523 5523 chi2 178.358 246.259 219.422 217.914 188.167 188.702 40.587 39.961 138.337 136.777 p 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.001 0.000 0.000 Note: Models are estimated using fractional logit regressions, where the dependent variable is fraction between 0 and 1. Data on dependent variable and firm specific variables are obtained or computed from BEEPS. Bank regulatory variables are obtained or computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. 174 Table 32(a) Marginal Effects for Fixed Investment Analysis with Forbank Law mfxequ1 mfxfor1 mfxdom1 mfxmon1 mfxsta1 transparency (d) -0.002 0.006* -0.000 0.000 0.007 audit (d) 0.003 0.002 0.013 -0.001 0.003 costeffi 0.000 -0.000 0.000 0.000 -0.000 operationyear -0.000 -0.000 -0.000 -0.000 0.000** size 0.000 0.004** 0.021*** -0.004** 0.010*** foreign 0.000 0.000*** -0.000** 0.000 -0.000** manufacturing (d) 0.001 0.005* 0.035*** 0.000 0.002 npl_ta -0.001* 0.000 0.001 -0.000 -0.000 overafc 0.002** -0.001 0.000 -0.000 0.003* bcdepo 0.000 -0.000** -0.000 0.000 -0.001*** forbank 0.000*** 0.000 0.000* 0.000 -0.000*** stmktcap -0.109*** -0.033* -0.072 0.003 -0.086** llgdp -0.013 0.017 -0.137*** 0.042*** -0.033 pcrdbgdp 0.091*** 0.021* 0.263*** -0.049*** 0.029 lgdp 0.004 -0.001 -0.007* 0.000 0.005* law 0.009** 0.001 -0.003 -0.006* 0.031*** N 5523 5523 5523 5523 5523 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. Table 32(b) Marginal Effects for Fixed Investment Analysis with Forbank ccorruption mfxequ2 mfxfor2 mfxdom2 mfxmon2 mfxsta2 transparency (d) -0.004 0.006* -0.001 -0.000 0.200 audit (d) 0.004 0.002 0.013 -0.002 0.108 costeffi 0.000 -0.000 0.000 0.000 -0.004 operationyear -0.000 -0.000 -0.000 -0.000 0.008** size 0.000 0.004** 0.021*** -0.004** 0.282** foreign 0.000 0.000*** -0.000** 0.000 -0.009** manufacturing (d) 0.001 0.005* 0.035*** -0.000 0.060 npl_ta -0.001*** 0.000 0.001 -0.000 -0.006 overafc 0.002** -0.001 0.000 -0.000 0.088* bcdepo 0.000** -0.000** -0.000 0.000 -0.028*** forbank 0.000*** 0.000 0.000* 0.000 -0.011*** stmktcap -0.127*** -0.032* -0.073 0.004 -2.922*** llgdp -0.010 0.016 -0.136*** 0.038*** -0.632 pcrdbgdp 0.112*** 0.018 0.269*** -0.040*** 0.223 lgdp 0.007*** -0.001 -0.007* 0.000 0.199** ccorruption -0.001 0.002 -0.005 -0.008* 0.944*** N 5523 5523 5523 5523 5523 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. 175 Table 33 Robust Tests: Multinomial Fractional Logit for Fixed Inverstment Analsysis (Law) law eta_finequ_f eta_findom_f eta_finfor_f eta_finmon_f eta_finsta_f transparency -0.180 0.019 0.575* -0.017 0.189 (0.20) (0.10) (0.22) (0.26) (0.14) audit 0.262 0.190* 0.184 -0.165 0.142 (0.17) (0.09) (0.23) (0.24) (0.14) costeffi 0.005 0.000 -0.012 0.003 -0.004 (0.00) (0.00) (0.01) (0.01) (0.00) operationyear -0.013* -0.002 -0.003 -0.012 0.007* (0.01) (0.00) (0.01) (0.01) (0.00) size 0.089 0.295*** 0.489** -0.445* 0.340*** (0.12) (0.06) (0.15) (0.20) (0.09) foreign -0.000 -0.005** 0.009*** 0.005 -0.009** (0.00) (0.00) (0.00) (0.00) (0.00) manufacturing 0.163 0.456*** 0.557** 0.046 0.116 (0.15) (0.08) (0.21) (0.22) (0.12) npl_ta -0.047** -0.001 -0.001 -0.008 -0.003 (0.02) (0.01) (0.01) (0.02) (0.01) overafc 0.107* 0.018 -0.034 -0.022 0.071* (0.04) (0.02) (0.05) (0.06) (0.04) bcdepo -0.006 -0.003 -0.026* 0.001 -0.037*** (0.01) (0.00) (0.01) (0.01) (0.01) forbank 0.018*** 0.005** 0.004 0.006 -0.010*** (0.00) (0.00) (0.01) (0.01) (0.00) 176 Table 33 (continued) law eta_finequ_f eta_findom_f eta_finfor_f eta_finmon_f eta_finsta_f stmktcap -6.961*** -1.938*** -5.211*** 0.455 -1.466* (1.42) (0.40) (1.02) (1.04) (0.61) llgdp -0.994 -2.074*** 1.165 5.551*** -0.757 (0.95) (0.49) (1.02) (1.26) (0.74) pcrdbgdp 6.353*** 4.128*** 3.690*** -6.135*** 0.710 (1.13) (0.50) (1.11) (1.47) (0.73) law 1.070*** -0.088 -0.095 -0.724* 0.998*** (0.25) (0.12) (0.29) (0.34) (0.20) _cons -6.060*** -3.132*** -3.850*** -4.585*** -1.282* (0.81) (0.41) (0.97) (1.04) (0.57) N 5506 chi2 731.734 p 0.000 Note: Models are estimated using multinomial fractional logit regressions, where the dependent variable is fraction between 0 and 1. Data on dependent variable and firm specific variables are obtained or computed from BEEPS. Bank regulatory variables are obtained or computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. 177 Table 34 Robust Tests: Multinomial Fractional Logit for Fixed Inverstment Analsysis (Ccorruption) ccorruption eta_finequ_f eta_findom_f eta_finfor_f eta_finmon_f eta_finsta_f transparency -0.294 0.018 0.586** -0.023 0.183 (0.20) (0.10) (0.22) (0.26) (0.14) audit 0.309 0.191* 0.179 -0.165 0.155 (0.17) (0.09) (0.23) (0.24) (0.14) costeffi 0.005 0.000 -0.012 0.003 -0.004 (0.00) (0.00) (0.01) (0.01) (0.00) operationyear -0.013* -0.002 -0.003 -0.012 0.006* (0.01) (0.00) (0.01) (0.01) (0.00) size 0.092 0.294*** 0.489** -0.444* 0.334*** (0.12) (0.06) (0.15) (0.19) (0.09) foreign 0.000 -0.005** 0.009*** 0.005 -0.009** (0.00) (0.00) (0.00) (0.00) (0.00) manufacturing 0.151 0.456*** 0.556** 0.037 0.129 (0.15) (0.08) (0.21) (0.22) (0.12) npl_ta -0.043** -0.002 -0.001 -0.015 0.002 (0.02) (0.01) (0.01) (0.02) (0.01) overafc 0.126** 0.015 -0.032 -0.037 0.069 (0.04) (0.02) (0.05) (0.06) (0.04) bcdepo 0.001 -0.003 -0.027* -0.000 -0.033*** (0.01) (0.00) (0.01) (0.01) (0.01) forbank 0.019*** 0.005** 0.003 0.007 -0.010*** (0.00) (0.00) (0.01) (0.01) (0.00) 178 Table 34 (continued) ccorruption eta_finequ_f eta_findom_f eta_finfor_f eta_finmon_f eta_finsta_f stmktcap -6.110*** -1.935*** -5.287*** 0.408 -1.471* (1.34) (0.40) (1.04) (1.07) (0.59) llgdp -0.619 -2.029*** 1.127 5.114*** -0.123 (0.96) (0.48) (1.01) (1.17) (0.72) pcrdbgdp 6.250*** 4.238*** 3.731** -5.016*** -0.214 (1.13) (0.52) (1.15) (1.47) (0.84) ccorruption 0.510* -0.139 -0.049 -0.911* 0.997*** (0.25) (0.14) (0.39) (0.43) (0.21) _cons -6.938*** -3.176*** -3.773*** -4.546*** -1.477* (0.78) (0.41) (1.00) (1.01) (0.59) N 5506 chi2 784.291 p 0.000 Note: Models are estimated using multinomial fractional logit regressions, where the dependent variable is fraction between 0 and 1. Data on dependent variable and firm specific variables are obtained or computed from BEEPS. Bank regulatory variables are obtained or computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. 179 Table 35 Fixed Investmetn Financing Pattern Analysis: Regulatory and Accountability Fractional Logit equity1 foreign1 domestic1 money1 state1 accountability 0.634** -0.177 -0.213* -0.683* 0.662*** (0.22) (0.26) (0.10) (0.31) (0.15) regulatory 0.971** -0.328 -0.138 -0.726* 0.972*** (0.32) (0.31) (0.13) (0.34) (0.26) Marginal Effects mfxequ1 mfxfor1 mfxdom1 mfxmon1 mfxsta1 accountability 0.009*** -0.002 -0.016* -0.005* 0.023*** regulatory 0.013*** -0.003 -0.010 -0.005* 0.972*** Multinomil Fraction Logit eta_finequ_f eta_findom_f eta_finfor_f eta_finmon_f eta_finsta_f accountability 0.612** -0.190 -0.217 -0.657* 0.586*** (0.21) (0.10) (0.27) (0.31) (0.15) regulatory 0.968** -0.083 -0.312 -0.647 0.929*** (0.32) (0.13) (0.31) (0.34) (0.26) Note: Models include variable Regulatory and variable Accountability are estimated seperately using fractional logit and multinomial fractional logit regressions. Marginal effects for Regulatory and Accountability variables are reported. The control variables are the same as in table 34, but are not reported. The dependent variable is fraction between 0 and 1. Data on dependent variable and firm specific variables are obtained or computed from BEEPS. Bank regulatory variables are obtained or computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. 180 Table 36 Working Capital Financing Pattern Analysis equity1 equity2 foreign1 foreign2 domestic1 domestic2 state1 state2 money1 money2 transparen cy -0.192 -0.308* 0.577** 0.564** -0.046 -0.043 0.004 0.007 -0.220 -0.227 (0.14) (0.14) (0.20) (0.20) (0.08) (0.08) (0.12) (0.12) (0.20) (0.20) audit 0.035 0.098 0.265 0.269 0.268*** 0.265*** 0.064 0.075 0.104 0.101 (0.13) (0.13) (0.20) (0.20) (0.08) (0.08) (0.11) (0.11) (0.18) (0.18) costeffi -0.001 -0.000 -0.019* -0.019* 0.002 0.002 -0.004 -0.004 0.005 0.005 (0.00) (0.00) (0.01) (0.01) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) operationy ear -0.013*** -0.014*** -0.001 -0.001 -0.004* -0.004* 0.008*** 0.008*** -0.006 -0.005 (0.00) (0.00) (0.01) (0.01) (0.00) (0.00) (0.00) (0.00) (0.01) (0.01) size 0.134 0.140 0.432** 0.432** 0.388*** 0.389*** 0.412*** 0.409*** -0.358** -0.355** (0.09) (0.09) (0.13) (0.13) (0.05) (0.05) (0.07) (0.07) (0.12) (0.12) foreign -0.001 -0.001 0.012*** 0.012*** -0.002 -0.002 -0.008*** -0.008*** 0.003 0.004 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) manufactur ing 0.140 0.121 0.276 0.277 0.481*** 0.480*** -0.008 -0.005 0.053 0.046 (0.12) (0.12) (0.18) (0.18) (0.07) (0.07) (0.11) (0.11) (0.16) (0.16) npl_ta -0.023 -0.023* 0.015 0.011 -0.005 -0.006 -0.009 -0.004 -0.008 -0.015 (0.01) (0.01) (0.01) (0.01) (0.00) (0.00) (0.01) (0.01) (0.01) (0.01) overafc 0.035 0.040 0.019 0.009 0.049** 0.047** 0.005 0.006 0.002 -0.017 (0.03) (0.03) (0.05) (0.05) (0.02) (0.02) (0.03) (0.03) (0.04) (0.04) bcdepo 0.001 0.006 -0.027** -0.026** -0.005 -0.005 -0.026*** -0.024*** 0.018* 0.017* (0.01) (0.01) (0.01) (0.01) (0.00) (0.00) (0.01) (0.01) (0.01) (0.01) forbank 0.021*** 0.023*** 0.013** 0.014** 0.007*** 0.006*** -0.011*** -0.011*** 0.006 0.007 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) 181 Table 36 (continued) equity1 equity2 foreign1 foreign2 domestic1 domestic2 state1 state2 money1 money2 stmktcap -9.125*** -8.210*** -2.433* -2.419* -1.352*** -1.351*** -2.867*** -2.835*** 0.048 0.051 (1.07) (1.06) (0.97) (0.97) (0.33) (0.33) (0.53) (0.52) (0.72) (0.73) llgdp -2.080* -1.696 1.263 1.320 -1.688*** -1.750*** 0.561 0.889 3.607*** 3.194*** (0.84) (0.89) (1.09) (1.07) (0.45) (0.44) (0.61) (0.59) (1.01) (0.96) pcrdbgdp 7.217*** 7.069*** 0.505 0.909 2.834*** 2.976*** -0.458 -1.232 -3.424** -2.304* (0.90) (0.91) (1.21) (1.23) (0.47) (0.48) (0.59) (0.66) (1.14) (1.06) law 1.146*** -0.264 -0.189 0.683*** -0.889*** (0.16) (0.26) (0.11) (0.15) (0.23) ccorruptio n 0.596*** -0.445 -0.197 0.761*** -1.103*** (0.17) (0.35) (0.13) (0.17) (0.31) _cons -5.143*** -5.721*** -5.181*** -5.317*** -4.052*** -4.026*** -1.319** -1.320** -6.270*** -6.254*** (0.56) (0.58) (0.89) (0.91) (0.36) (0.37) (0.48) (0.50) (0.87) (0.84) N 7537 7537 7537 7537 7537 7537 7537 7537 7537 7537 chi2 297.288 367.412 171.389 172.180 311.614 311.153 173.831 174.683 41.270 39.419 p 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 Note: Models are estimated using fractional logit regressions, where the dependent variable is fraction between 0 and 1. Data on dependent variable and firm specific variables are obtained or computed from BEEPS. Bank regulatory variables are obtained or computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. 182 Table 37 Marginal Effects for Working Capital Analysis mfxequ1 mfxfor1 mfxdom1 mfxsta1 mfxmon1 audit (d) 0.000 0.002 0.015*** 0.002 0.001 bcdepo 0.000 -0.000** -0.000 -0.001*** 0.000* costeffi -0.000 -0.000* 0.000 -0.000 0.000 forbank 0.000*** 0.000** 0.000*** -0.000*** 0.000 foreign -0.000 0.000*** -0.000 -0.000*** 0.000 law 0.013*** -0.002 -0.011 0.019*** -0.008*** llgdp -0.024** 0.008 -0.094*** 0.016 0.031*** manufacturing (d) 0.002 0.002 0.029*** -0.000 0.000 npl_ta -0.000 0.000 -0.000 -0.000 -0.000 operationyear -0.000** -0.000 -0.000* 0.000*** -0.000 overafc 0.000 0.000 0.003** 0.000 0.000 pcrdbgdp 0.085*** 0.003 0.158*** -0.013 -0.029** size 0.002 0.003** 0.022*** 0.012*** -0.003** stmktcap -0.107*** -0.015** -0.076*** -0.081*** 0.000 transparency (d) -0.002 0.004* -0.003 0.000 -0.002 N 7537 7537 7537 7537 7537 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. 183 Table 38 Robust Tests: Multinomial Fractional Logit for Working Capital Analsysis (Law) eta_finequ_w eta_findom_w eta_finfor_w eta_finsta_w eta_finmon_w transparency -0.221 -0.045 0.534** -0.005 -0.232 (0.14) (0.08) (0.20) (0.12) (0.20) audit 0.046 0.284*** 0.303 0.110 0.133 (0.13) (0.08) (0.20) (0.11) (0.18) costeffi -0.000 0.002 -0.018* -0.003 0.005 (0.00) (0.00) (0.01) (0.00) (0.00) operationyear -0.013** -0.004* -0.002 0.007** -0.006 (0.00) (0.00) (0.01) (0.00) (0.01) size 0.187* 0.421*** 0.500*** 0.450*** -0.287* (0.09) (0.05) (0.14) (0.07) (0.12) foreign -0.002 -0.002 0.011*** -0.008** 0.003 (0.00) (0.00) (0.00) (0.00) (0.00) manufacturing 0.180 0.496*** 0.321 0.037 0.088 (0.12) (0.07) (0.18) (0.11) (0.16) npl_ta -0.024 -0.008 0.011 -0.012 -0.010 (0.01) (0.00) (0.01) (0.01) (0.01) overafc 0.036 0.049** 0.022 0.013 0.005 (0.03) (0.02) (0.05) (0.03) (0.04) bcdepo 0.001 -0.006 -0.028** -0.027*** 0.017* (0.01) (0.00) (0.01) (0.01) (0.01) forbank 0.021*** 0.007*** 0.013** -0.010*** 0.006 (0.00) (0.00) (0.00) (0.00) (0.00) 184 Table 38 (continued) eta_finequ_w eta_findom_w eta_finfor_w eta_finsta_w eta_finmon_w stmktcap -9.342*** -1.651*** -2.897** -3.110*** -0.279 (1.07) (0.34) (1.00) (0.54) (0.73) llgdp -1.951* -1.777*** 1.250 0.455 3.518*** (0.85) (0.45) (1.10) (0.62) (1.02) pcrdbgdp 7.327*** 3.209*** 0.866 0.003 -2.998** (0.91) (0.47) (1.24) (0.59) (1.15) law 1.135*** -0.150 -0.236 0.679*** -0.870*** (0.16) (0.10) (0.27) (0.15) (0.23) _cons -5.137*** -3.900*** -5.106*** -1.345** -6.187*** (0.57) (0.36) (0.92) (0.48) (0.87) N 7521 chi2 982.226 p 0.000 Note: Models are estimated using multinomial fractional logit regressions, where the dependent variable is fraction between 0 and 1. Data on dependent variable and firm specific variables are obtained or computed from BEEPS. Bank regulatory variables are obtained or computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 percent; * indicates a significance level between 5 and 10 percent. 185 Table 39 Robust Tests: Multinomial Fractional Logit for Working Capital Analsysis (Ccorruption) eta_finequ_w eta_findom_w eta_finfor_w eta_finsta_w eta_finmon_w transparency -0.336* -0.044 0.518* -0.004 -0.241 (0.14) (0.08) (0.20) (0.12) (0.20) audit 0.109 0.283*** 0.308 0.121 0.131 (0.13) (0.08) (0.20) (0.11) (0.18) costeffi 0.000 0.002 -0.017* -0.003 0.005 (0.00) (0.00) (0.01) (0.00) (0.00) operationyear -0.014*** -0.004* -0.002 0.007** -0.006 (0.00) (0.00) (0.01) (0.00) (0.01) size 0.193* 0.421*** 0.500*** 0.447*** -0.286* (0.09) (0.05) (0.14) (0.07) (0.12) foreign -0.001 -0.002 0.011*** -0.008** 0.003 (0.00) (0.00) (0.00) (0.00) (0.00) manufacturing 0.161 0.496*** 0.322 0.042 0.082 (0.12) (0.07) (0.18) (0.11) (0.16) npl_ta -0.025* -0.009 0.008 -0.008 -0.017 (0.01) (0.00) (0.01) (0.01) (0.01) overafc 0.040 0.046** 0.011 0.012 -0.014 (0.03) (0.02) (0.05) (0.03) (0.04) bcdepo 0.006 -0.006 -0.027** -0.025*** 0.016 (0.01) (0.00) (0.01) (0.01) (0.01) forbank 0.023*** 0.007*** 0.014** -0.010*** 0.008 (0.00) (0.00) (0.00) (0.00) (0.00) 186 Table 39 (continued) eta_finequ_w eta_findom_w eta_finfor_w eta_finsta_w eta_finmon_w stmktcap -8.414*** -1.652*** -2.888** -3.081*** -0.298 (1.06) (0.34) (1.00) (0.53) (0.74) llgdp -1.539 -1.768*** 1.354 0.839 3.124** (0.89) (0.44) (1.07) (0.60) (0.97) pcrdbgdp 7.162*** 3.357*** 1.291 -0.739 -1.828 (0.91) (0.48) (1.26) (0.66) (1.07) ccorruption 0.581*** -0.190 -0.439 0.724*** -1.103*** (0.18) (0.13) (0.35) (0.17) (0.31) _cons -5.725*** -3.921*** -5.260*** -1.385** -6.185*** (0.59) (0.37) (0.93) (0.50) (0.84) N 7521 chi2 1048.742 p 0.000 Note: Models are estimated using multinomial fractional logit regressions, where the dependent variable is fraction between 0 and 1. Data on dependent variable and firm specific variables are obtained or computed from BEEPS. Bank regulatory variables are obtained or computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 percent; * indicates a significance level between 5 and 10 percent. 187 Table 40 Short Term Loan and Long Term Loan Analysis by Order stloan Label N Mean SD Min Max ltloan Label N Mean SD Min Max transparency 818 0.402 0.491 0.000 1.000 transparency 1084 0.405 0.491 0.000 1.000 audit 818 0.498 0.500 0.000 1.000 audit 1084 0.492 0.500 0.000 1.000 costeffi 818 19.216 12.998 1.000 150.000 costeffi 1084 19.137 12.684 1.000 150.000 foreign 818 9.744 26.670 0.000 100.000 foreign 1084 10.371 27.819 0.000 100.000 npl_ta 818 6.010 5.276 0.320 22.000 npl_ta 1084 5.742 5.198 0.320 22.000 indpoliall 818 1.304 0.774 0.000 2.000 indpoliall 1084 1.346 0.772 0.000 2.000 mulsup 818 0.289 0.453 0.000 1.000 mulsup 1084 0.271 0.445 0.000 1.000 singlefsa 818 0.174 0.379 0.000 1.000 singlefsa 1084 0.208 0.406 0.000 1.000 mcar 818 10.017 1.785 8.000 12.000 mcar 1084 10.098 1.745 8.000 12.000 crindex 818 6.101 1.716 4.000 10.000 crindex 1084 6.232 1.786 4.000 10.000 fstrans 818 4.977 0.815 3.000 6.000 fstrans 1084 4.922 0.828 3.000 6.000 nfob 818 2.059 0.338 1.000 3.000 nfob 1084 2.055 0.337 1.000 3.000 nbffob 818 1.954 0.470 1.000 3.000 nbffob 1084 1.957 0.462 1.000 3.000 overbnk 818 9.048 2.052 5.000 13.000 overbnk 1084 9.153 2.184 5.000 13.000 1 bcdepo 818 66.983 9.903 57.000 99.400 1 bcdepo 1084 67.104 9.991 57.000 99.400 pcrdbgdp 818 0.199 0.092 0.039 0.443 pcrdbgdp 1084 0.197 0.090 0.039 0.443 govbank 818 18.803 12.715 0.000 41.800 govbank 1084 19.227 12.922 0.000 41.800 forbank 818 48.532 32.346 3.470 98.900 forbank 1084 46.989 32.313 3.470 98.900 pribank 818 32.665 26.811 1.100 81.600 pribank 1084 33.784 26.738 1.100 81.600 188 Table 40 (continued) stloan Label N Mean SD Min Max ltloan Label N Mean SD Min Max transparency 795 0.389 0.488 0.000 1.000 transparency 872 0.400 0.490 0.000 1.000 audit 795 0.498 0.500 0.000 1.000 audit 872 0.516 0.500 0.000 1.000 costeffi 795 19.323 12.473 1.000 100.000 costeffi 872 19.577 13.241 1.000 100.000 foreign 795 11.107 28.853 0.000 100.000 foreign 872 12.487 30.127 0.000 100.000 npl_ta 795 5.932 5.526 0.320 22.000 npl_ta 872 6.000 5.506 0.320 22.000 indpoliall 795 1.270 0.802 0.000 2.000 indpoliall 872 1.287 0.809 0.000 2.000 mulsup 795 0.313 0.464 0.000 1.000 mulsup 872 0.314 0.464 0.000 1.000 singlefsa 795 0.208 0.406 0.000 1.000 singlefsa 872 0.192 0.394 0.000 1.000 mcar 795 10.060 1.749 8.000 12.000 mcar 872 10.106 1.719 8.000 12.000 crindex 795 6.170 1.742 4.000 10.000 crindex 872 6.135 1.804 4.000 10.000 fstrans 795 4.919 0.861 3.000 6.000 fstrans 872 4.911 0.843 3.000 6.000 nfob 795 2.078 0.371 1.000 3.000 nfob 872 2.076 0.360 1.000 3.000 nbffob 795 1.966 0.504 1.000 3.000 nbffob 872 1.956 0.503 1.000 3.000 overbnk 795 9.145 2.233 5.000 13.000 overbnk 872 9.117 2.258 5.000 13.000 bcdepo 795 67.367 9.903 57.000 99.400 bcdepo 872 67.918 10.219 57.000 99.400 pcrdbgdp 795 0.204 0.094 0.039 0.443 pcrdbgdp 872 0.207 0.097 0.039 0.443 govbank 795 18.573 13.198 0.000 41.800 govbank 872 18.510 13.290 0.000 41.800 forbank 795 47.030 32.645 3.470 98.900 forbank 872 48.143 32.325 3.470 98.900 2 pribank 795 34.397 27.414 1.100 81.600 2 pribank 872 33.347 27.171 1.100 81.600 189 Table 40 (continued) stloan Label N Mean SD Min Max ltloan Label N Mean SD Min Max transparency 1000 0.418 0.493 0.000 1.000 transparency 733 0.411 0.492 0.000 1.000 audit 1000 0.500 0.500 0.000 1.000 audit 733 0.492 0.500 0.000 1.000 costeffi 1000 19.395 12.661 1.000 170.000 costeffi 733 19.982 13.544 1.000 170.000 foreign 1000 12.551 30.063 0.000 100.000 foreign 733 11.955 29.130 0.000 100.000 npl_ta 1000 5.480 4.717 0.320 22.000 npl_ta 733 5.524 4.680 0.320 22.000 indpoliall 1000 1.333 0.788 0.000 2.000 indpoliall 733 1.295 0.777 0.000 2.000 mulsup 1000 0.282 0.450 0.000 1.000 mulsup 733 0.293 0.456 0.000 1.000 singlefsa 1000 0.193 0.395 0.000 1.000 singlefsa 733 0.156 0.363 0.000 1.000 mcar 1000 10.074 1.647 8.000 12.000 mcar 733 9.872 1.689 8.000 12.000 crindex 1000 6.143 1.884 4.000 10.000 crindex 733 5.944 1.791 4.000 10.000 fstrans 1000 5.001 0.874 3.000 6.000 fstrans 733 5.142 0.868 3.000 6.000 nfob 1000 2.045 0.324 1.000 3.000 nfob 733 2.034 0.314 1.000 3.000 nbffob 1000 1.922 0.473 1.000 3.000 nbffob 733 1.903 0.471 1.000 3.000 overbnk 1000 9.018 2.408 5.000 13.000 overbnk 733 8.905 2.333 5.000 13.000 bcdepo 1000 68.809 11.665 57.000 99.400 bcdepo 733 68.865 11.995 57.000 99.400 pcrdbgdp 1000 0.212 0.095 0.039 0.443 pcrdbgdp 733 0.219 0.096 0.039 0.443 govbank 1000 18.930 13.583 0.000 41.800 govbank 733 18.701 13.739 0.000 41.800 forbank 1000 47.219 33.644 3.470 98.900 forbank 733 48.842 34.637 3.470 98.900 3 pribank 1000 33.850 27.599 1.100 81.600 3 pribank 733 32.457 28.240 1.100 81.600 190 Table 40 (continued) stloan Label N Mean SD Min Max ltloan Label N Mean SD Min Max transparency 411 0.428 0.495 0.000 1.000 transparency 273 0.465 0.500 0.000 1.000 audit 411 0.506 0.501 0.000 1.000 audit 273 0.513 0.501 0.000 1.000 costeffi 411 20.377 23.369 1.000 400.000 costeffi 273 19.571 25.753 1.000 400.000 foreign 411 16.238 33.791 0.000 100.000 foreign 273 17.026 34.889 0.000 100.000 npl_ta 411 5.536 4.996 0.320 22.000 npl_ta 273 5.671 4.947 0.320 22.000 indpoliall 411 1.397 0.740 0.000 2.000 indpoliall 273 1.381 0.713 0.000 2.000 mulsup 411 0.224 0.417 0.000 1.000 mulsup 273 0.209 0.407 0.000 1.000 singlefsa 411 0.139 0.346 0.000 1.000 singlefsa 273 0.147 0.354 0.000 1.000 mcar 411 9.815 1.642 8.000 12.000 mcar 273 9.714 1.624 8.000 12.000 crindex 411 5.891 1.929 4.000 10.000 crindex 273 5.853 1.904 4.000 10.000 fstrans 411 5.095 0.825 3.000 6.000 fstrans 273 5.106 0.844 3.000 6.000 nfob 411 2.024 0.269 1.000 3.000 nfob 273 2.015 0.257 1.000 3.000 nbffob 411 1.920 0.414 1.000 3.000 nbffob 273 1.919 0.394 1.000 3.000 overbnk 411 9.002 2.266 5.000 13.000 overbnk 273 8.967 2.210 5.000 13.000 bcdepo 411 69.837 11.995 57.000 99.400 bcdepo 273 70.099 12.256 57.000 99.400 pcrdbgdp 411 0.236 0.112 0.039 0.443 pcrdbgdp 273 0.241 0.114 0.039 0.443 govbank 411 18.082 13.865 0.000 41.800 govbank 273 17.328 13.568 0.000 41.800 forbank 411 53.191 34.065 3.470 98.900 forbank 273 54.148 34.667 3.470 98.900 4 pribank 411 28.726 27.713 1.100 81.600 4 pribank 273 28.524 28.046 1.100 81.600 191 Table 41 Correlation Matrix for Short Term Loan and Long Term Loan Analysis stloan ltloan transparency audit costeffi foreign npl_ta indpoliall mulsup singlefsa 1.000 0.772 0.021 0.005 0.020 0.066 -0.042 0.038 -0.040 -0.017 stloan (0.000) (0.245) (0.793) (0.278) (0.000) (0.022) (0.037) (0.029) (0.358) 0.772 1.000 0.027 0.007 0.019 0.052 -0.014 -0.004 -0.017 -0.058 ltloan (0.000) (0.139) (0.716) (0.298) (0.005) (0.435) (0.834) (0.345) (0.002) 0.021 0.027 1.000 0.254 -0.026 0.186 -0.133 0.027 -0.200 0.134 transparency (0.245) (0.139) (0.000) (0.150) (0.000) (0.000) (0.144) (0.000) (0.000) 0.005 0.007 0.254 1.000 -0.032 0.179 -0.043 -0.070 -0.058 0.049 audit (0.793) (0.716) (0.000) (0.077) (0.000) (0.018) (0.000) (0.002) (0.007) 0.020 0.019 -0.026 -0.032 1.000 0.021 -0.029 -0.027 0.016 -0.009 costeffi (0.278) (0.298) (0.150) (0.077) (0.251) (0.109) (0.138) (0.385) (0.628) 0.066 0.052 0.186 0.179 0.021 1.000 -0.013 0.034 -0.016 0.029 foreign (0.000) (0.005) (0.000) (0.000) (0.251) (0.462) (0.064) (0.382) (0.107) -0.042 -0.014 -0.133 -0.043 -0.029 -0.013 1.000 -0.203 0.658 -0.258 npl_ta (0.022) (0.435) (0.000) (0.018) (0.109) (0.462) (0.000) (0.000) (0.000) 0.038 -0.004 0.027 -0.070 -0.027 0.034 -0.203 1.000 -0.535 0.101 indpoliall (0.037) (0.834) (0.144) (0.000) (0.138) (0.064) (0.000) (0.000) (0.000) -0.040 -0.017 -0.200 -0.058 0.016 -0.016 0.658 -0.535 1.000 -0.027 mulsup (0.029) (0.345) (0.000) (0.002) (0.385) (0.382) (0.000) (0.000) (0.139) -0.017 -0.058 0.134 0.049 -0.009 0.029 -0.258 0.101 -0.027 1.000 singlefsa (0.358) (0.002) (0.000) (0.007) (0.628) (0.107) (0.000) (0.000) (0.139) -0.021 -0.071 0.019 -0.059 -0.004 -0.002 -0.289 -0.277 0.088 0.108 mcar (0.243) (0.000) (0.304) (0.001) (0.816) (0.911) (0.000) (0.000) (0.000) (0.000) 192 Table 41 (continued) stloan ltloan transparency audit costeffi foreign npl_ta indpoliall mulsup singlefsa -0.024 -0.073 0.079 -0.060 0.005 -0.018 0.053 0.430 -0.192 0.276 crindex (0.183) (0.000) (0.000) (0.001) (0.770) (0.334) (0.003) (0.000) (0.000) (0.000) 0.043 0.098 0.050 0.102 0.020 0.022 -0.020 0.178 -0.238 -0.336 fstrans (0.018) (0.000) (0.006) (0.000) (0.282) (0.231) (0.263) (0.000) (0.000) (0.000) -0.037 -0.037 -0.053 -0.031 0.060 -0.034 -0.085 -0.505 0.213 0.058 nfob (0.043) (0.043) (0.004) (0.093) (0.001) (0.063) (0.000) (0.000) (0.000) (0.002) -0.033 -0.038 0.061 -0.024 0.043 -0.026 -0.234 0.045 -0.228 0.154 nbffob (0.073) (0.037) (0.001) (0.190) (0.018) (0.160) (0.000) (0.014) (0.000) (0.000) -0.011 -0.039 -0.001 -0.074 0.003 0.005 0.126 0.278 0.154 0.492 overbnk (0.544) (0.037) (0.941) (0.000) (0.850) (0.767) (0.000) (0.000) (0.000) (0.000) 0.092 0.088 0.113 0.084 -0.028 0.040 0.257 -0.045 0.136 0.149 bcdepo (0.000) (0.000) (0.000) (0.000) (0.128) (0.027) (0.000) (0.014) (0.000) (0.000) 0.109 0.131 -0.069 0.022 -0.003 0.060 0.477 0.138 0.368 0.063 pcrdbgdp (0.000) (0.000) (0.000) (0.224) (0.868) (0.001) (0.000) (0.000) (0.000) (0.001) -0.009 -0.034 -0.077 -0.070 0.012 -0.042 -0.413 0.092 -0.372 -0.416 govbank (0.640) (0.066) (0.000) (0.000) (0.527) (0.021) (0.000) (0.000) (0.000) (0.000) 0.027 0.051 -0.063 0.075 -0.035 0.064 0.356 0.077 0.305 0.066 forbank (0.143) (0.006) (0.001) (0.000) (0.051) (0.000) (0.000) (0.000) (0.000) (0.000) -0.028 -0.046 0.114 -0.056 0.037 -0.058 -0.230 -0.137 -0.189 0.121 pribank (0.123) (0.014) (0.000) (0.002) (0.040) (0.002) (0.000) (0.000) (0.000) (0.000) 193 Table 41 (continued) mcar crindex fstrans nfob nbffob overbnk bcdepo pcrdbgdp govbank forbank pribank -0.021 -0.024 0.043 -0.037 -0.033 -0.011 0.092 0.109 -0.009 0.027 -0.028 stloan (0.243) (0.183) (0.018) (0.043) (0.073) (0.544) (0.000) (0.000) (0.640) (0.143) (0.123) -0.071 -0.073 0.098 -0.037 -0.038 -0.039 0.088 0.131 -0.034 0.051 -0.046 ltloan (0.000) (0.000) (0.000) (0.043) (0.037) (0.037) (0.000) (0.000) (0.066) (0.006) (0.014) 0.019 0.079 0.050 -0.053 0.061 -0.001 0.113 -0.069 -0.077 -0.063 0.114 transparency (0.304) (0.000) (0.006) (0.004) (0.001) (0.941) (0.000) (0.000) (0.000) (0.001) (0.000) -0.059 -0.060 0.102 -0.031 -0.024 -0.074 0.084 0.022 -0.070 0.075 -0.056 audit (0.001) (0.001) (0.000) (0.093) (0.190) (0.000) (0.000) (0.224) (0.000) (0.000) (0.002) -0.004 0.005 0.020 0.060 0.043 0.003 -0.028 -0.003 0.012 -0.035 0.037 costeffi (0.816) (0.770) (0.282) (0.001) (0.018) (0.850) (0.128) (0.868) (0.527) (0.051) (0.040) -0.002 -0.018 0.022 -0.034 -0.026 0.005 0.040 0.060 -0.042 0.064 -0.058 foreign (0.911) (0.334) (0.231) (0.063) (0.160) (0.767) (0.027) (0.001) (0.021) (0.000) (0.002) -0.289 0.053 -0.020 -0.085 -0.234 0.126 0.257 0.477 -0.413 0.356 -0.230 npl_ta (0.000) (0.003) (0.263) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) -0.277 0.430 0.178 -0.505 0.045 0.278 -0.045 0.138 0.092 0.077 -0.137 indpoliall (0.000) (0.000) (0.000) (0.000) (0.014) (0.000) (0.014) (0.000) (0.000) (0.000) (0.000) 0.088 -0.192 -0.238 0.213 -0.228 0.154 0.136 0.368 -0.372 0.305 -0.189 mulsup (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 0.108 0.276 -0.336 0.058 0.154 0.492 0.149 0.063 -0.416 0.066 0.121 singlefsa (0.000) (0.000) (0.000) (0.002) (0.000) (0.000) (0.000) (0.001) (0.000) (0.000) (0.000) 1.000 -0.090 -0.533 0.294 -0.068 0.027 -0.035 -0.379 0.143 0.081 -0.167 mcar (0.000) (0.000) (0.000) (0.000) (0.135) (0.056) (0.000) (0.000) (0.000) (0.000) 194 Table 41 (continued) mcar crindex fstrans nfob nbffob overbnk bcdepo pcrdbgdp govbank forbank pribank -0.090 1.000 0.087 0.020 0.291 0.416 -0.179 0.086 -0.130 -0.319 0.449 crindex (0.000) (0.000) (0.283) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) -0.533 0.087 1.000 -0.439 -0.314 -0.426 -0.232 0.291 0.122 -0.060 0.014 fstrans (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.438) 0.294 0.020 -0.439 1.000 0.745 0.280 0.017 -0.267 -0.112 -0.256 0.364 nfob (0.000) (0.283) (0.000) (0.000) (0.000) (0.357) (0.000) (0.000) (0.000) (0.000) -0.068 0.291 -0.314 0.745 1.000 0.415 0.114 -0.337 -0.166 -0.327 0.476 nbffob (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 0.027 0.416 -0.426 0.280 0.415 1.000 0.131 0.093 -0.239 0.070 0.031 overbnk (0.135) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.087) -0.035 -0.179 -0.232 0.017 0.114 0.131 1.000 0.163 -0.683 0.580 -0.370 bcdepo (0.056) (0.000) (0.000) (0.357) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) -0.379 0.086 0.291 -0.267 -0.337 0.093 0.163 1.000 -0.513 0.540 -0.404 pcrdbgdp (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 0.143 -0.130 0.122 -0.112 -0.166 -0.239 -0.683 -0.513 1.000 -0.595 0.235 govbank (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 0.081 -0.319 -0.060 -0.256 -0.327 0.070 0.580 0.540 -0.595 1.000 -0.921 forbank (0.000) (0.000) (0.001) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) -0.167 0.449 0.014 0.364 0.476 0.031 -0.370 -0.404 0.235 -0.921 1.000 pribank (0.000) (0.000) (0.438) (0.000) (0.000) (0.087) (0.000) (0.000) (0.000) (0.000) 195 Table 42 Short Term Versus Long Term Loan stloan2 stloan3 ltloan2 ltloan3 transparency -0.110 -0.093 -0.052 -0.049 (0.08) (0.08) (0.08) (0.08) audit -0.006 -0.014 -0.060 -0.060 (0.07) (0.07) (0.07) (0.07) costeffi 0.003 0.003 0.002 0.002 (0.00) (0.00) (0.00) (0.00) foreign 0.003** 0.003** 0.002* 0.002* (0.00) (0.00) (0.00) (0.00) npl_ta -0.040* -0.065*** -0.046* -0.061*** (0.02) (0.02) (0.02) (0.02) indpoliall -0.008 0.038 -0.364** -0.315* (0.12) (0.12) (0.12) (0.13) mulsup -0.317* -0.459** -0.213 -0.321* (0.15) (0.15) (0.15) (0.15) singlefsa -0.520** -0.892*** -0.671*** -0.936*** (0.16) (0.15) (0.17) (0.15) mcar 0.183** 0.090 0.156** 0.106 (0.06) (0.05) (0.06) (0.06) crindex -0.118** -0.092* -0.150*** -0.146** (0.04) (0.05) (0.05) (0.05) fstrans 0.066 -0.044 0.191* 0.126 (0.08) (0.08) (0.08) (0.08) nfob -0.445 0.156 -1.310** -0.921* (0.45) (0.44) (0.46) (0.45) nbffob -0.023 -0.705* 0.645* 0.182 (0.31) (0.30) (0.32) (0.30) overbnk 0.086** 0.120*** 0.117*** 0.144*** (0.03) (0.03) (0.03) (0.03) bcdepo 0.047*** 0.038*** 0.032*** 0.027*** (0.00) (0.00) (0.00) (0.00) pcrdbgdp 7.122*** 5.661*** 6.956*** 6.075*** (0.77) (0.68) (0.78) (0.70) forbank -0.019*** -0.014*** (0.00) (0.00) pribank 0.018*** 0.014*** (0.00) (0.00) 196 Table 42 (continued) stloan2 stloan3 ltloan2 ltloan3 cut1 3.553*** 2.896** 2.711** 2.512* (1.02) (1.02) (1.04) (1.04) cut2 4.736*** 4.074*** 3.992*** 3.792*** (1.02) (1.02) (1.05) (1.05) cut3 6.550*** 5.884*** 5.693*** 5.493*** (1.03) (1.03) (1.05) (1.05) N 3024 3024 2962 2962 pseudo R 2 0.027 0.025 0.027 0.027 chi2 221.375 206.445 208.934 206.099 Note: Models are estimated using ordered logit regressions, where the dependent variable equals 1 to 4. Data on dependent variable and firm specific variables are obtained or computed from BEEPS. Bank regulatory variables are obtained or computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. 197 Table 43 Marginal Effects for Short Term Loans and Long Term Loans Analysis: Forbank mfx1f3 mfx2f3 mfx3f3 mfx4f3 mfx1f4 mfx2f4 mfx3f4 mfx4f4 transparency (d) 0.021 0.006 -0.015 -0.012 0.012 -0.000 -0.008 -0.004 audit (d) 0.001 0.000 -0.001 -0.001 0.014 -0.000 -0.009 -0.005 costeffi -0.001 -0.000 0.000 0.000 -0.000 0.000 0.000 0.000 foreign -0.001** -0.000** 0.000** 0.000** -0.001* 0.000 0.000* 0.000* npl_ta 0.008* 0.002* -0.006* -0.004* 0.011* -0.000 -0.007* -0.004* indpoliall 0.001 0.000 -0.001 -0.001 0.083** -0.002 -0.053** -0.028** mulsup (d) 0.063* 0.016* -0.045* -0.033* 0.049 -0.003 -0.031 -0.016 singlefsa (d) 0.107** 0.020*** -0.076** -0.051*** 0.160*** -0.023* -0.094*** -0.044*** mcar -0.035** -0.011** 0.026** 0.020** -0.036** 0.001 0.023* 0.012* crindex 0.023** 0.007** -0.016** -0.013** 0.034*** -0.001 -0.022*** -0.012** fstrans -0.013 -0.004 0.009 0.007 -0.044* 0.001 0.028* 0.015* nfob 0.085 0.026 -0.062 -0.049 0.300** -0.008 -0.191** -0.101** nbffob 0.004 0.001 -0.003 -0.003 -0.148* 0.004 0.094* 0.050* overbnk -0.017** -0.005** 0.012** 0.009** -0.027*** 0.001 0.017*** 0.009*** bcdepo -0.009*** -0.003*** 0.006*** 0.005*** -0.007*** 0.000 0.005*** 0.002*** pcrdbgdp -1.364*** -0.409*** 0.992*** 0.781*** -1.594*** 0.045 1.014*** 0.536*** forbank 0.004*** 0.001*** -0.003*** -0.002*** 0.003*** -0.000 -0.002*** -0.001*** N 3024 3024 3024 3024 2962 2962 2962 2962 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. 198 Table 44 Marginal Effects for Short Term Loans and Long Term Loans Analysis: Pribank mfx1f5 mfx2f5 mfx3f5 mfx4f5 mfx1f6 mfx2f6 mfx3f6 mfx4f6 transparency (d) 0.018 0.005 -0.013 -0.010 0.011 -0.000 -0.007 -0.004 audit (d) 0.003 0.001 -0.002 -0.002 0.014 -0.000 -0.009 -0.005 costeffi -0.001 -0.000 0.000 0.000 -0.000 0.000 0.000 0.000 foreign -0.001** -0.000** 0.000** 0.000** -0.001* 0.000 0.000* 0.000* npl_ta 0.012*** 0.004*** -0.009*** -0.007*** 0.014*** -0.000 -0.009*** -0.005*** indpoliall -0.007 -0.002 0.005 0.004 0.072* -0.002 -0.046* -0.024* mulsup (d) 0.092** 0.021*** -0.066** -0.047*** 0.075* -0.005 -0.046* -0.023* singlefsa (d) 0.191*** 0.020*** -0.131*** -0.080*** 0.225*** -0.041*** -0.127*** -0.057*** mcar -0.017 -0.005 0.012 0.010 -0.024 0.001 0.015 0.008 crindex 0.018* 0.005* -0.013* -0.010* 0.034** -0.001 -0.021** -0.011** fstrans 0.008 0.002 -0.006 -0.005 -0.029 0.001 0.018 0.010 nfob -0.030 -0.009 0.022 0.017 0.211* -0.006 -0.134* -0.071* nbffob 0.135* 0.040* -0.098* -0.078* -0.042 0.001 0.026 0.014 overbnk -0.023*** -0.007*** 0.017*** 0.013*** -0.033*** 0.001 0.021*** 0.011*** bcdepo -0.007*** -0.002*** 0.005*** 0.004*** -0.006*** 0.000 0.004*** 0.002*** pcrdbgdp -1.086*** -0.324*** 0.786*** 0.624*** -1.392*** 0.039 0.885*** 0.468*** pribank -0.003*** -0.001*** 0.003*** 0.002*** -0.003*** 0.000 0.002*** 0.001*** N 3024 3024 3024 3024 2962 2962 2962 2962 Note: (d) for discrete change of dummy variable from 0 to 1, * p<0.05, ** p<0.01, *** p<0.001. 199 Table 45 Bank Loan Structure Analysis duration1 duration3 approvalday1 approvalday3 interestrate1 interestrate3 collateral1 collateral3 transparency 4.487*** 4.333*** 2.325 2.843* -1.316*** -1.099** -9.356* -7.443 (1.26) (1.27) (1.39) (1.40) (0.35) (0.35) (4.02) (4.04) audit -1.118 -0.860 -0.653 -0.724 0.295 0.148 -1.872 -2.263 (1.23) (1.23) (1.36) (1.37) (0.34) (0.34) (3.92) (3.92) costeffi -0.028 -0.025 -0.032 -0.034 0.009 0.007 0.083 0.068 (0.04) (0.04) (0.05) (0.05) (0.01) (0.01) (0.13) (0.13) foreign 0.014 0.014 -0.029 -0.030 -0.002 -0.002 0.052 0.050 (0.02) (0.02) (0.02) (0.02) (0.01) (0.01) (0.07) (0.07) npl_ta -0.859*** -0.799** 0.215 0.276 -0.104 -0.122 -1.309 -1.176 (0.25) (0.25) (0.28) (0.28) (0.07) (0.07) (0.80) (0.80) indpoliall 2.189 3.033* -0.074 0.815 1.380*** 1.153*** 18.545*** 21.137*** (1.21) (1.19) (1.34) (1.31) (0.34) (0.33) (3.94) (3.86) mulsup 6.267** 6.900*** 0.586 5.438* -5.421*** -4.506*** 2.967 20.681*** (1.91) (1.97) (2.11) (2.18) (0.53) (0.54) (6.11) (6.27) singlefsa -1.543 -0.626 -8.093*** -4.194* -1.656*** -1.150* 9.796 23.410*** (1.73) (1.72) (1.93) (1.91) (0.48) (0.47) (5.62) (5.59) mcar -1.312* -1.216* -1.586** -1.805** 1.097*** 0.993*** 2.024 1.084 (0.52) (0.53) (0.58) (0.59) (0.15) (0.14) (1.65) (1.66) crindex 0.342 0.041 1.245* 1.863** 0.182 0.505** 5.850*** 8.357*** (0.54) (0.58) (0.61) (0.64) (0.15) (0.16) (1.71) (1.81) fstrans -1.058 -1.004 -5.145*** -4.716*** -4.004*** -3.943*** -16.181*** -15.166*** (1.18) (1.18) (1.31) (1.31) (0.33) (0.32) (3.77) (3.77) nfob -2.751 -2.143 2.565 3.543 1.971** 1.898** 21.627** 24.714*** (2.20) (2.19) (2.46) (2.45) (0.62) (0.61) (7.05) (7.01) 200 Table 45 (continued) duration1 duration3 approvalday1 approvalday3 interestrate1 interestrate3 collateral1 collateral3 nbffob -3.545 -4.486 -1.487 1.761 -3.191*** -1.816** -12.442 0.305 (2.31) (2.45) (2.56) (2.72) (0.65) (0.68) (7.61) (8.08) overbnk -0.738 -0.687 0.282 -0.392 0.844*** 0.632*** -6.008*** -8.732*** (0.47) (0.49) (0.52) (0.54) (0.13) (0.13) (1.46) (1.52) bcdepo 0.228** 0.282*** -0.307*** -0.225** -0.152*** -0.161*** -0.646** -0.407 (0.07) (0.07) (0.08) (0.07) (0.02) (0.02) (0.22) (0.22) forbank 0.083** 0.258*** 0.021* 0.873*** (0.03) (0.03) (0.01) (0.10) pcrdbgdp 37.192*** 44.452*** -0.225 0.823 -13.139*** -16.955*** -65.848** -67.685*** (6.06) (6.10) (6.76) (6.78) (1.69) (1.68) (20.00) (20.02) pribank -0.021 -0.302*** -0.067*** -1.091*** (0.04) (0.04) (0.01) (0.12) _cons 30.980* 29.339* 60.256*** 67.407*** 30.599*** 33.473*** 188.861*** 222.103*** (12.59) (12.73) (14.08) (14.22) (3.52) (3.52) (40.10) (40.53) N 2046 2046 2079 2079 2019 2019 1715 1715 pseudo R 2 0.015 0.015 0.009 0.008 0.070 0.072 0.011 0.011 ll -9544.154 -9547.589 -9923.311 -9925.584 -6821.067 -6803.954 -9842.071 -9841.103 chi2 294.149 287.279 171.777 167.232 1028.489 1062.715 209.438 211.375 p 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 R 2 0.134 0.131 0.079 0.077 0.399 0.409 0.115 0.116 Note: Models are estimated using tobit regressions, where the dependent variables all have lower bound above 0. Data on dependent variables and firm specific variables are obtained or computed from BEEPS. Bank regulatory variables are obtained or computed from World Bank Survey I/II/III under project ?Bank Regulation and Supervision?. Standard errors are reported below coefficients. The symbol *** indicates a significance level of 1 percent or less; ** indicates a significance level between 1 and 5 per cent; * indicates a significance level between 5 and 10 percent. 201 REFERENCES Alessandrini, P.; Calcagnini, G. and Zazzaro, A. "Asset Restructuring Strategies in Bank Acquisitions: Does Distance between Dealing Partners Matter?" Journal of Banking & Finance, 2008, 32(5), pp. 699-713. Alessandrini, P.; Presbitero, A. F. and Zazzaro, A. "Banks, Distances and Firms' Financing Constraints." Review of Finance, 2009, 13, pp. 261-307. Altman, Edward and Sabato, Gabriele. "Effects of the New Basel Capital Accord on Bank Capital Requirements for Smes." Journal of Financial Services Research, 2005, 28(1), pp. 15-24. Altunbas, Y.; Molyneux, P. and Thornton, J. "Big-Bank Mergers in Europe: An Analysis of the Cost Implications." Economica, 1997, 64(254), pp. 317-29. Amel, Dean; Barnes, Colleen; Panetta, Fabio and Salleo, Carmelo. "Consolidation and Efficiency in the Financial Sector: A Review of the International Evidence." Journal of Banking & Finance, 2004, 28(10), pp. 2493-519. Amihud, Y.; DeLong, G. L. and Saunders, A. "The Effects of Cross-Border Bank Mergers on Bank Risk and Value." Journal of International Money and Finance, 2002, 21(6), pp. 857-77. Barth, James R.; Bertus, Mark; Jiang, Hai and Phumiwasana, Triphon. "A Cross-Country Assessment of Bank Risk-Shifting Behavior." Review of Pacific Basin Financial Markets and Policies, 2008, 11(1), pp. 1-32. Barth, James R.; Brumbaugh, R. Dan and Wilcox, James A. ""The Repeal of Glass-Steagall and the Advent of Broad Banking," With R. Dan Brumbaugh Jr. And James A. Wilcox, Journal of Economic Perspectives, 14(2), Spring 2000." Journal of Economic Perspectives, 2000, 14(2). Barth, James R.; Caprio, Gerard and Levine, Ross. "Bank Regulation and Supervision: What Works Best?" Journal of Financial Intermediation, 2004, 13(2), pp. 205-48. Barth, James R.; Caprio, Gerard and Levine, Ross. "Reassessing the Rationale and Practice of Bank Regulation and Supervision around the Globe after Basel Ii." Current Developments in Monetary and Financial Law, International Monetary Fund, 2008. Barth, James R.; Caprio, Gerard and Levine, Ross. "The Regulation and Supervision of Banks around the World: A New Database." World BankPolicy Research Working Paper Series, 2001, No. 2588. 202 Barth, James R.; Caprio, Gerard and Levine, Ross. "Rethinking Bank Regulation: Till Angels Govern." 2006, Cambridge University Press. Barth, James R.; Nolle, Daniel E.; Phumiwasana, Triphon and Yago, Glenn. "A Cross Country Analysis of the Bank Supervisory Framework and Bank Performance." Financial Markets, Institutions & Instruments, 2003, 12(2), pp. 67-120. Barth, James R.; Nolle, Daniel E. and Rice, Tara N. "Commercial Banking Structure, Regulation, and Performance: An International Comparison " Modernizing Financial Systems, 2000, D. B. Papadimitriou, editor, Macmillan Press and St. Martin's Press. Barth, James R.; Phumiwasana, Triphon and Yost, Keven. "The Role of Banks in Global Mergers and Acquisitions " The Chinese Banker, 2007. Beck, Thorsten; Demirg-Kunt, Asli and Maksimovic, Vojislav. "Financing Patterns around the World: Are Small Firms Different?" Journal of Financial Economics, 2008, 89(3), pp. 467-87. Beck, Thorsten and Demirguc-Kunt, Asli. "Small and Medium-Size Enterprises: Access to Finance as a Growth Constraint." Journal of Banking & Finance, 2006, 30(11), pp. 2931-43. Beck, Thorsten; Demirguc-Kunt, Asli and Honohan, Patrick. "Access to Financial Services: Measurement, Impact, and Policies." The World Bank Research Observer, 2009, 24(1), pp. 119-45. Beck, Thorsten; Demirguc-Kunt, Asli; Laeven, Luc and Maksimovic, Vojislav. "The Determinants of Financing Obstacles." Journal of International Money and Finance, 2006, 25(6), pp. 932-52. Beck, Thorsten; Demirguc-Kunt, Asli and Levine, Ross. "Bank Concentration and Competition: Challenges for Stability." G20 Seminar on Competition in the Financial Sector in Bali, Indonesia, 2008. Beck, Thorsten; Demirguc-Kunt, Asli and Levine, Ross. "Bank Concentration, Competition, and Crises: First Results." Journal of Banking & Finance, 2006, 30(5), pp. 1581-603. Beck, Thorsten; Demirguc-Kunt, Asli and Levine, Ross. "A New Database on Financial Development and Structure " World Bank Policy Research Working Paper Series, 1999, No. 2146. Beck, Thorsten; Demirguc-Kunt, Asli and Maksimovic, Vojislav. "Bank Competition and Access to Finance: International Evidence." Journal of Money, Credit & Banking, 2004, 36(3), pp. 627-48. Beck, Thorsten; Demirguc-Kunt, Asli and Maksimovic, Vojislav. "Financial and Legal Constraints to Growth: Does Firm Size Matter?" The Journal of Finance, 2005, 60(1), pp. 137-77. 203 Beck, Thorsten; Demirguc-Kunt, Asli and Maksimovic, Vojislav. "Financing Patterns around the World: The Role of Institutions." World Bank Policy Research Working Paper No. 2905, 2002. Berger, Allen; Klapper, Leora and Turk-Ariss, Rima. "Bank Competition and Financial Stability." Journal of Financial Services Research, 2009, 35(2), pp. 99-118. Berger, A. N. "Obstacles to a Global Banking System: "Old Europe" Versus "New Europe"." Journal of Banking & Finance, 2007, 31(7), pp. 1955-73. Berger, A. N.; Clarke, G. R. G.; Cull, R.; Klapper, L. and Udell, G. F. "Corporate Governance and Bank Performance: A Joint Analysis of the Static, Selection, and Dynamic Effects of Domestic, Foreign, and State Ownership." Journal of Banking & Finance, 2005, 29(8-9), pp. 2179-221. Berger, Allen N.; Demsetz, Rebecca S. and Strahan, Philip E. " The Consolidation of the Financial Services Industry: Causes, Consequences, and Implications for the Future." Journal of Banking & Finance, 1999, 23(2-4), pp. 135-94. Berger, A. N. and Deyoung, R. "Technological Progress and the Geographic Expansion of the Banking Industry." Journal of Money Credit and Banking, 2006, 38(6), pp. 1483-513. Berger, A. N. and Frame, W. S. "Small Business Credit Scoring and Credit Availability." Journal of Small Business Management, 2007, 45(1), pp. 5-22. Berger, Allen N. and Humphrey, David B. "Megamergers in Banking and the Use of Cost Efficiency as an Antitrust Defense." Antitrust Bulletin 1992, 37, pp. 541-600. Berger, A. N.; Klapper, L. F.; Peria, M. S. M. and Zaidi, R. "Bank Ownership Type and Banking Relationships." Journal of Financial Intermediation, 2008, 17(1), pp. 37-62. Berger, A. N.; Rosen, R. J. and Udell, G. F. "Does Market Size Structure Affect Competition? The Case of Small Business Lending," 2007, 11-33. Berger, Allen N.; Saunders, Anthony; Scalise, Joseph M. and Udell, Gregory F. "The Effects of Bank Mergers and Acquisitions on Small Business Lending " Journal of Financial Economics, 1998, 50. Berger, Allen N. and Udell, Gregory F. "A More Complete Conceptual Framework for Sme Finance." Journal of Banking & Finance, 2006, 30(11), pp. 2945-66. Bernardo, Maggi and Stefania, P. S. Rossi. "Does Banking Consolidation Lead to Efficiency Gains? Evidence from Large Commercial Banks in Europe and Us." Icfai University Journal of Bank Management, 2006, 2, pp. 7-35. Bertrand, O. and Zitouna, H. "Domestic Versus Cross-Border Acquisitions: Which Impact on the Target Firms' Performance?" Applied Economics, 2008, 40(17), pp. 2221-38. 204 Bertrand, O. and Zitouna, H. "Trade Liberalization and Industrial Restructuring: The Role of Cross-Border Mergers and Acquisitions." Journal of Economics & Management Strategy, 2006, 15(2), pp. 479-515. Bertrand, O. and Zuniga, P. "R&D and M&A: Are Cross-Border M&a Different? An Investigation on Oecd Countries." International Journal of Industrial Organization, 2006, 24(2), pp. 401-23. Bhattacharya. "How Good Is the Bankscope Database? A Cross-Validation Exercise with Correction Factors for Market Concentration Measures." BIS Working Paper, 2003, NO.133. Bjorvatn, K. "Economic Integration and the Profitability of Cross-Border Mergers and Acquisitions." European Economic Review, 2004, 48(6), pp. 1211-26. Black, E. L.; Carnes, T. A.; Jandik, T. and Henderson, B. C. "The Relevance of Target Accounting Quality to the Long-Term Success of Cross-Border Mergers." Journal of Business Finance & Accounting, 2007, 34(1-2), pp. 139-68. Blank, Sven and Buch, Claudia M. "The Euro and Cross-Border Banking: Evidence from Bilateral Data." Comparative Economic Studies, 2007, 49(389-410). Bonin, John P.; Hasan, Iftekhar and Wachtel, Paul. "Bank Performance, Efficiency and Ownership in Transition Countries." Journal of Banking & Finance, 2005, 29(1), pp. 31-53. Boyd, John H.; Chang, Chun and Smith, Bruce D. "Moral Hazard under Commercial and Universal Banking." Journal of Money, Credit and Banking, 1998, 30(3.2), pp. 426-268. Boyd, John H.; Nicolo, Gianni De and Jalal, Abu M. "Bank Risk-Taking and Competition Revisited: New Theory and New Evidence," IMF Working Papers Series No. 06/297. 2007. Brakman, Steven; Garretsen, Harry and Marrewijk, Charles van. "Cross-Border Mergers and Acquisitions: On Revealed Comparative Advantage and Merger Waves." CESifo Working Paper Series, 2005, No. 1602. Buch, C. M. "Information or Regulation: What Drives the International Activities of Commercial Banks?" Journal of Money Credit and Banking, 2003, 35(6), pp. 851-69. Buch, Claudia M. "Why Do Banks Go Abroad? Evidence from German Data." Financial Markets, Institutions & Instruments, 2000, 9(1), pp. 33-67. Buch, C. M. and DeLong, G. "Cross-Border Bank Mergers: What Lures the Rare Animal?" Journal of Banking & Finance, 2004, 28(9), pp. 2077-102. Buch, C. M. and Lipponer, A. "Fdi Versus Exports: Evidence from German Banks." Journal of Banking & Finance, 2007, 31(3), pp. 805-26. Campa, J. M. and Hernando, I. "M&as Performance in the European Financial Industry." Journal of Banking & Finance, 2006, 30(12), pp. 3367-92. 205 Carbo-Valverde, S.; Rodriguez-Fernandez, F. and Udell, G. F. "Bank Market Power and Sme Financing Constraints." Review of Finance, 2009, 13(2), pp. 309-40. Carletti, E.; Hartmann, P. and Spagnolo, G. "Bank Mergers, Competition, and Liquidity." Journal of Money Credit and Banking, 2007, 39(5), pp. 1067-105. Claessens, Stijn. "Access to Financial Services: A Review of the Issues and Public Policy Objectives " The World Bank Research Observer, 2006, 21(2), pp. 207-40. Claessens, Stijn. "Benefits and Costs on Integrated Financial Services Provision in Developing Countries." Brookings-Wharton Papers on Financial Services, 2003, (85-139). Claessens, Stijn and Horen, Neeltje Van. "Location Decisions of Foreign Banks and Competitive Advantage " World Bank Policy Research Working Paper Series, 2007, No. 4113. Claeys, Sophie and Hainz, Christa. "Acquisition Versus Greenfield: The Impact of the Mode of Foreign Bank Entry on Information and Bank Lending Rates." ECB Working Paper No. 653, 2007. Clark, Jeffrey A. "Economic Cost, Scale Efficiency and Competitive Viability in Banking." Journal of Money, Credit and Banking, 1996, 28(342-64). Clarke, George R.G.; Cull, Robert and Martinez Peria, Maria Soledad. "Foreign Bank Participation and Access to Credit across Firms in Developing Countries." Journal of Comparative Economics, 2006, 34, pp. 774-95. Coluzzi, Chiara; Ferrando, Annalisa and Martinez-Carrascal, Carmen. "Financing Obstacles and Growth: An Analysis for Euro Area Non-Financial Corporations " ECB Working Paper No. 997, 2009. Cornett, M. M. and De, S. "Medium of Payment in Corporate Acquisitions - Evidence from Interstate Bank Mergers - Note." Journal of Money Credit and Banking, 1991, 23(4), pp. 767-76. Cornett, M. M.; McNutt, J. J. and Tehranian, H. "Performance Changes around Bank Mergers: Revenue Enhancements Versus Cost Reductions." Journal of Money Credit and Banking, 2006, 38(4), pp. 1013-50. Craig, Steven G. and Hardee, Pauline. "The Impact of Bank Consolidation on Small Business Credit Availability." Journal of Banking & Finance, 2007, 31(4), pp. 1237-63. Crouzille, C.; Lepetit, L. and Bautista, C. "How Did the Asian Stock Markets React to Bank Mergers after the 1997 Financial Crisis?" Pacific Economic Review, 2008, 13(2), pp. 171-82. Cummins, J. David; Tennyson, Sharon and Weiss, Mary A. "Consolidation and Efficiency in the U.S. Life Insurance Industry." Wharton School Center for Financial Institutions Working Papers Series, 1999, No. 98-08. 206 De la Torre, Augusto; Martinez Peria, Maria Soledad and Schmukler , Sergio L. "Bank Involvement with Smes : Beyond Relationship Lending." World Bank Policy Research Working Paper Series NO. 4649, 2008. Degryse, Hans; Havrylchyk, Olena; Jurzyk, Emilia and Kozak, Sylwester. "The Effect of Foreign Bank Entry on the Cost of Credit in Transition Economies. Which Borrowers Benefit the Most?" CEPII Research Center Working Papers Series NO. 2008-15, 2008. Degryse, H. and Ongena, S. "The Impact of Competition on Bank Orientation." Journal of Financial Intermediation, 2007, 16(3), pp. 399-424. Degryse, H. and Ongena, S. "The Impact of Technology and Regulation on the Geographical Scope of Banking." Oxford Review of Economic Policy, 2004, 20(4), pp. 571-90. Degryse, Hans A.; Havrylchyk, Olena; Jurzyk, Emilia Magdalena and Kozak, Sylwester J. "Foreign Bank Entry and Credit Allocation in Emerging Markets." SSRN, 2009. Delgado, J.; Salas, V. and Saurina, J. "Joint Size and Ownership Specialization in Bank Lending." Journal of Banking & Finance, 2007, 31(12), pp. 3563-83. Dell'Ariccia, Giovanni and Marquez, Robert. "Information and Bank Credit Allocation." Journal of Financial Economics, 2004, 72(1), pp. 185-214. DeLong, G. "Does Long-Term Performance of Mergers Match Market Expectations? Evidence from the Us Banking Industry." Financial Management, 2003, 32(2), pp. 5-25. DeLong, G. and DeYoung, R. "Learning by Observing: Information Spillovers in the Execution and Valuation of Commercial Bank M&As." Journal of Finance, 2007, 62(1), pp. 181-216. Demirguc-Kent, Asli and Detragiache, Enrica. "Financial Liberalization and Financial Fragility." World Bank Policy Research Working Paper Series 1998, No. 1917. Detragiache, Enrica; Gupta, Poonam and Tressel, Thierry. "Finance in Lower-Income Countries: An Empirical Exploration." IMF Working Paper No. 05/167, 2005. Detragiache, Enrica; Tressel, Thierry and Gupta, Poonam. "Foreign Banks in Poor Countries: Theory and Evidence." IMF Working Papers NO. 06/18, 2006. DeYoung, R. "Operational Efficiency in Banking: An International Perspective - Comment." Journal of Banking & Finance, 1997, 21(10), pp. 1325-29. 207 DeYoung, Robert and Nolle, Daniel E. "Foreign-Owned Banks in the United States: Earning Market Share or Buying It?" Journal of Money, Credit and Banking, 1996, 28(4), pp. 622-36. Di Giovanni, J. "What Drives Capital Flows? The Case of Cross-Border M&a Activity and Financial Deepening." Journal of International Economics, 2005, 65(1), pp. 127-49. Dopico, Luis G. and Wilcox, James A. "Openness, Profit Opportunities and Foreign Banking? Journal of International Financial Markets, Institutions and Money, 2002, 12(4-5), pp. 299-320. Dymski, G. A. "The Global Bank Merger Wave: Implications for Developing Countries." Developing Economies, 2002, 40(4), pp. 435-66. European Bank for Reconstruction and Development. "Finance in Transition," EBRD Transition Report., 2006. Elsas, Ralf. "Empirical Determinants of Relationship Lending." Journal of Financial Intermediation, 2005, 14(1), pp. 32-57. Firth, M.; Lin, C.; Liu, P. and Wong, S. M. L. "Inside the Black Box: Bank Credit Allocation in China's Private Sector." Journal of Banking & Finance, 2009, 33(6), pp. 1144-55. Focarelli, D. and Panetta, F. "Are Mergers Beneficial to Consumers? Evidence from the Market for Bank Deposits." American Economic Review, 2003, 93(4), pp. 1152-72. Focarelli, D. and Pozzolo, A. F. "Cross-Border M&As in the Financial Sector: Is Banking Different from Insurance?" Journal of Banking & Finance, 2008, 32(1), pp. 15-29. Focarelli, D. and Pozzolo, A. F. "The Patterns of Cross-Border Bank Mergers and Shareholdings in Oecd Countries." Journal of Banking & Finance, 2001, 25(12), pp. 2305-37. Focarelli, D. and Pozzolo, A. F. "Where Do Banks Expand Abroad? An Empirical Analysis." Journal of Business, 2006, 79(1). Francis, B. B.; Hasan, I. and Sun, X. "Financial Market Integration and the Value of Global Diversification: Evidence for Us Acquirers in Cross-Border Mergers and Acquisitions." Journal of Banking & Finance, 2008, 32(8), pp. 1522-40. Fries, S.; Lysenko, T. and Polanec, S. "The 2002 Business Environment and Enterprise Performance Survey: Results from a Survey of 6,100 Firms." EBRD Working Paper No. 84, 2003. Galindo, Arturo; Micco, Alejandro and Serra, C?sar Manuel. "Better the Devil That You Know: Evidence on Entry Costs Faced by Foreign Banks." Inter-American Development Bank Research Department RES Working Papers Series, 2003, No. 4313. 208 Gilroya, Bernard Michael and Lukas, Elmar. "The Choice between Greenfield Investment and Cross-Border Acquisition: A Real Option Approach " The Quarterly Review of Economics and Finance, 2005, 46(3), pp. 447-65. Gorton, Gary; Kahl, Matthias and Rosen, Richard. "Eat or Be Eaten: A Theory of Mergers and Merger Waves." National Bureau of Economic Research Working Paper Series, 2005, No. 11364. 1BGropper, D. ?An Empirical Investigation of Changes in Scale Economies for the Commercial Banking Firm, 1979-1986.??Journal of Money, Credit and Banking, 1991, 23(4), pp.718-27. De Haas, Ralph. "Multinational Banks and Credit Growth in Transition Economies." Utrecht University Ph.D. Dissertation, 2006. De Haas, Ralph and Van Lelyveld, Iman. "Foreign Banks and Credit Stability in Central and Eastern Europe. A Panel Data Analysis." Journal of Banking & Finance, 2006, 30(7), pp. 1927-52. De Haas, Ralph and Naaborg, Ilko. "Does Foreign Bank Entry Reduce Small Firms' Access to Credit? Evidence from European Transition Economies." DNB Working Paper No. 50, 2005. Hackethal, Andreas and Schmidt, Reinhard H. "Financing Patterns: Measurement Concepts and Empirical Results." Frankfurt Department of Finance Working Paper No. 125, 2004. Hagendorff, Jens; Collins, Michael and Keasey, Kevin. "Bank Deregulation and Acquisition Activity: Cases of the US, Italy and Germany " Journal of Financial Regulation and Compliance, 2007, 15(2), pp. 199-209. Haiss, Peter and Kichler, Elisabeth. "Leasing, Credit and Economic Growth : Evidence for Central and South Eastern Europe " EI Working Papers NO. 80, 2009. Hannan, Timothy H. and Pilloff, Steven J. "Acquisition Targets and Motives in the Banking Industry." Board of Governors of the Federal Reserve System, Finance and Economics Discussion Series, 2006, No. 2006-40. Hannan, Timothy H. and Pilloff, Steven J. "Will the Proposed Application of Basel Ii in the United States Encourage Increased Bank Merger Activity? Evidence from Past Merger Activity." Board of Governors of the Federal Reserve System , Finance and Economics Discussion Series, 2004, No. 2004-13. Hao, L.; Nandy, D.K. and Roberts, G.S. "How Bank Regulation, Supervision and Lender Identity Impact Loan Pricing: A Cross-Country Comparison." Federal Reserve Bank of Chicago Proceedings, 2007. Harford, Jarrad. "What Drives Merger Waves?" Journal of Financial Economics, 2005, 77(3), pp. 529-60. 209 Harrison, Ann E. and McMillan, Margaret S. "Does Direct Foreign Investment Affect Domestic Firms' Credit Constraints?" NBER Working Paper No. 8438, 2001. Haselmann, Rainer; Pistor, Katharina and Vig, Vikrant. "How Law Affects Lending." Columbia Law and Economics Working Paper No. 285, 2008. Hauswald, Robert B. H. and Bruno, Valentina. "The Real Effect of Foreign Banks." Paolo Baffi Centre Research Paper No. 2009-50, 2009. Hernando, Ignacio; Nieto, Mar?a J. and Wall, Larry D. "Determinants of Domestic and Cross-Border Bank Acquisitions in the European Union " Journal of Banking & Finance, 2009, 33(6), pp. 1022-32. Hijzen, A.; Gorg, H. and Manchin, M. "Cross-Border Mergers and Acquisitions and the Role of Trade Costs." European Economic Review, 2008, 52(5), pp. 849-66. Horen, Neeltje Van. "Foreign Banking in Developing Countries; Origin Matters." Emerging Markets Review, 2007, 8(2), pp. 81-105. Houston, J. F. and Ryngaert, M. D. "The Overall Gains from Large Bank Mergers." Journal of Banking & Finance, 1994, 18(6), pp. 1155-76. Hughes, Joseph P. and Mester, Loretta J. "Bank Capitalization and Cost: Evidence of Scale Economies in Risk Management and Signaling." Federal Reserve Bank of Philadelphia Working Papers Series, 1998, No. 96-2/R. Huizinga, H.P.; Nelissen, J.H.M. and Vennet, R. Vander. "Efficiency Effects of Bank Mergers and Acquisitions." Tinbergen Institute Discussion Papers, 2001, No. 01-088/3. Humphrey, D. B. and Vale, B. "Scale Economies, Bank Mergers, and Electronic Payments: A Spline Function Approach." Journal of Banking & Finance, 2004, 28(7), pp. 1671-96. Hyytinen, A. and Pajarinen, M. "Opacity of Young Businesses: Evidence from Rating Disagreements." Journal of Banking & Finance, 2008, 32(7), pp. 1234-41. Jiangli, Wenying; Unal, Haluk and Yom, Chiwon. "Relationship Lending, Accounting Disclosure, and Credit Availability During the Asian Financial Crisis." Journal of Money, Credit and Banking, 2008, 40(1), pp. 25-55. Jimenez, G.; Salas, V. and Saurina, J. "Organizational Distance and Use of Collateral for Business Loans." Journal of Banking & Finance, 2009, 33(2), pp. 234-43. Kiymaz, H. "Cross-Border Acquisitions of Us Financial Institutions: Impact of Macroeconomic Factors." Journal of Banking & Finance, 2004, 28(6), pp. 1413-39. Kneiding, Christoph; Al-Hussayni, Edward and Mas, Ignacio. "Multi-Country Data Sources for Access to Finance." World Bank CGAP Publication, 2009. 210 Koehler, Matthias. "Transparency of Regulation and Cross-Border Bank Mergers." Center for European Economic Research ZEW Discussion Papers Series, 2008, No. 08-009. Korkeamaki, Timo P. and Rutherford, Matthew W. "Industry Effects and Banking Relationship as Determinants of Small Firm Capital Structure Decisions." The Journal of Entrepreneurial Finance & Business Ventures, 2006, 11(1). Kumar, Anjali and Francisco, Manuela. "Enterprise Size, Financing Patterns and Credit Constraints in Brazil: Analysis of Data from the Investment Climate Assessment Survey " World Bank Working Paper No. 49, 2005. Kwast, M. L. "Bank Mergers: What Should Policymakers Do?" Journal of Banking & Finance, 1999, 23(2-4), pp. 629-36. Laeven, Luc and Levine, Ross. "Bank Governance, Regulation, and Risk Taking " NBER Working Paper Series, 2008, No. 14113. Lamoreaux, N. R. "Bank Mergers in Late 19th-Century New-England - the Contingent Nature of Structural-Change." Journal of Economic History, 1991, 51(3), pp. 537-57. Lanine, G. and Vennet, R. V. "Microeconomic Determinants of Acquisitions of Eastern European Banks by Western European Banks." Economics of Transition, 2007, 15(2), pp. 285-308. Leibenstein, Harvey. "Allocative Efficiency Vs. "X-Efficiency"." American Economic Review, 1966, 56(3), pp. 392-415. Lepetit, Laetitia; Nys, Emmanuelle; Rous, Philippe and Tarazi, Amine. "The Expansion of Services in European Banking: Implications for Loan Pricing and Interest Margins Q." Journal of Banking & Finance, 2008, 32, pp. 2325-35. Lin, P. W. "An Empirical Analysis of Bank Mergers and Cost Efficiency in Taiwan." Small Business Economics, 2005, 25(2), pp. 197-206. Liu, X. H. and Zou, H. "The Impact of Greenfield Fdi and Mergers and Acquisitions on Innovation in Chinese High-Tech Industries." Journal of World Business, 2008, 43(3), pp. 352-64. Marquez, Robert. "Competition, Adverse Selection, and Information Dispersion in the Banking Industry." The Review of Financial Studies., 2002, 15(3), pp. 901-26. Masciandaro, Donato. "Divide Et Impera: Financial Supervision Unification and Central Bank Fragmentation Effect." European Journal of Political Economy, 2007, 23(2), pp. 285-315. Masciandaro, Donato. "Politicians and Financial Supervision Unification Outside the Central Bank: Why Do They Do It?" Journal of Financial Stability, 2009, 5(2), pp. 124-46. 211 Masciandaro, Donato. "Unification in Financial Sector Supervision: The Trade-Off between Central Bank and Single Authority." Journal of Financial Regulation and Compliance, 2004, 12(2), pp. 151-69. Matousek, Roman. "Efficiency and Scale Economies in Banking in New Eu Countries " International Journal of Monetary Economics and Finance, 2008, 1(3), pp. 235-49. Matutes, Carmen and Vives, Xavier. "Imperfect Competition, Risk Taking, and Regulation in Banking." European Economic Review, 2000, 44(1), pp. 1-34. McFadden, D. "Conditional Logit Analysis of Qualitative Choice Behavior." Frontiers in Econometrics, 1973, Paul Zarembka, editor, Academic Press, pp. 105-35. Memmel, Christoph; Schmieder, Christian and Stein, Ingrid. "Relationship Lending - Empirical Evidence for Germany." European Investment Bank Economic and Financial Studies Economic and Financial Reports No. 2008/1., 2008. Mercieca, Steve; Schaeck, Klaus and Wolfe, Simon. "Bank Market Structure, Competition, and Sme Financing Relationships in European Regions." Journal of Financial Services Research, 2009. Milbourn, Todd T.; Boot, Arnoud W. A. and Thakor, Anjan V. "Megamergers and Expanded Scope: Theories of Bank Size and Activity Diversity." Journal of Banking and Finance, 1999, 23. Muravyeva, Alexander; Talaverad, Oleksandr and Schafer, Dorothea. "Entrepreneurs' Gender and Financial Constraints: Evidence from International Data " Journal of Comparative Economics, 2009, 37(2), pp. 270-86. Nadant, Anne-Laure Le and Perdreau, Frederic. "Financial Profile of Leveraged Buy-out Targets: Some French Evidence." Review of Accounting and Finance, 2006, 5 (4), pp. 370 - 92. Neary, J. P. "Cross-Border Mergers as Instruments of Comparative Advantage." Review of Economic Studies, 2007, 74(4), pp. 1229-57. Neto, Paula; Brand?o, Ant?nio and Cerqueira, Ant?nio. "The Macroeconomic Determinants of Cross Border Mergers and Acquisitions and Greenfield Investments." FEP Working Paper Series, 2008, No. 281. Neuberger, D.; Pedergnana, M. and Rathke-Doppner, S. "Concentration of Banking Relationships in Switzerland: The Result of Firm Structure or Banking Market Structure?" Journal of Financial Services Research, 2008, 33(2), pp. 101-26. Nocke, V. and Yeaple, S. "Cross-Border Mergers and Acquisitions Vs. Greenfield Foreign Direct Investment: The Role of Firm Heterogeneity." Journal of International Economics, 2007, 72(2), pp. 336-65. Norback, P. J. and Persson, L. "Investment Liberalization - Why a Restrictive Cross-Border Merger Policy Can Be Counterproductive." Journal of International Economics, 2007, 72(2), pp. 366-80. 212 Norton, J. J. and Olive, C. D. "A by-Product of the Globalization Process: The Rise of Cross-Border Bank Mergers and Acquisitions - the Us Regulatory Framework." Business Lawyer, 2001, 56(2), pp. 591-+. Ogura, Yoshiaki and Uchida, Hirofumi. "Bank Consolidation and Soft Information Acquisition in Small Business Lending." RIETI Discussion Paper No. 07-E-037, 2008. Okura, Masanori. "Financing Structure and Bank Loan Access of Smes in China Empirical Analysis," The 4th SMEs In A Global Economy Conference 2007. Park, K. and Pennacchi, G. "Harming Depositors and Helping Borrowers: The Disparate Impact of Bank Consolidation." Review of Financial Studies, 2009, 22(1), pp. 1-40. Pasiouras, Fotios; Tanna, Sailesh and Gaganis, Chrysovalantis. "What Drives Acquisitions in the Eu Banking Industry? The Role of Bank Regulation and Supervision Framework, Bank Specific and Market Specific Factors " Coventry University, Economics, Finance and Accounting Applied Research Working Paper Series, 2007, No. 2007-3 Patti, Emilia Bonaccorsi Di and Gobbi, Giorgio. "Winners or Losers? The Effects of Banking Consolidation on Corporate Borrowers." The Journal of Finance, 2007, 62(2), pp. 669-95. Peng, M. W. "Making M&a Fly in China." Harvard Business Review, 2006, 84(3), pp. 26. Peria, Maria Soledad Martinez and Mody, Ashoka. "How Foreign Participation and Market Concentration Impact Bank Spreads: Evidence from Latin America." Journal of Money, Credit and Banking, 2004, 36(3), pp. 511-37. Peristiani, Stavros. "Do Mergers Improve the X-Efficiency and Scale Efficiency of U.S. Banks? Evidence from the 1980s " J. OF MONEY, CREDIT, AND BANKING, 1997, 28(3). Presbitero, Andrea Filippo and Zazzaro, Alberto. "Competition and Relationship Lending: Friends or Foes?" MoFiR Working Paper No. 13, 2009. Rhoades, S. A. "The Efficiency Effects of Bank Mergers: An Overview of Case Studies of Nine Mergers." Journal of Banking & Finance, 1998, 22(3), pp. 273-91. Rossi, S. and Volpin, P. F. "Cross-Country Determinants of Mergers and Acquisitions." Journal of Financial Economics, 2004, 74(2), pp. 277-304. Rueda Maurer, Maria Clara "Foreign Bank Entry, Institutional Development and Credit Access: Firm-Level Evidence from 22 Transition Countries." Swiss National Bank Working Papers Series NO. 2008-4., 2008. Mishkin, Frederic S. "Financial Consolidation: Dangers and Opportunities." Journal of Banking & Finance, 1999, 23(2-4), pp. 675-91. 213 Santomero, A. M. "Bank Mergers: What's a Policymaker to Do?" Journal of Banking & Finance, 1999, 23(2-4), pp. 637-43. Sapienza, Paola. "The Effects of Banking Mergers on Loan Contracts." The Journal of Finance, 2002, 57(1), pp. 329-67. Schmieder, Christian; Marsch, Katharina and Aerssen, Katrin Forster-van. "Does Banking Consolidation Worsen Firms' Access to Credit? Evidence from the German Economy", Small Business Economics, 2009. Schoenmaker, Dirk and Laecke, Christiaan Van. "Determinants of International Banking: Evidence from the World's Largest Banks." Social Science Research Network Working Paper Series, 2007. Sengupta, Rajdeep. "Foreign Entry and Bank Competition." Journal of Financial Economics, 2007, 84(2), pp. 502-28. Shirai, Sayuri. "Banks' Lending Behavior and Firms' Corporate Financing Pattern in the People's Republic of China." ADB Institute Research Paper No. 43, 2002. Siregar, Reza Y. and James, William E. . "Designing an Integrated Financial Supervision Agency: Selected Lessons and Challenges for Indonesia " ASEAN Economic Bulletin, 2006, 23(1), pp. 98-113. Taci, Steven Fries and Anita. "Banking Reform and Development in Transition Economies." EBRD Working Paper NO. 71, 2002. Thadden, Ernst-Ludwig von. "Asymmetric Information, Bank Lending and Implicit Contracts: The Winner's Curse." Finance Research Letters, 2004, 1, pp. 11-23. Tion, Gabriele. "The Impacts of the Basel Ii Accord on the Concentration of the Entrepreneurial and Banking System " Transition Studies Review, 2008, 15(2), pp. 403-15. Tirri, Virginia. "Multiple Banking Relationships and Credit Market Competition: What Benefits the Firm?" EFA 2007 Ljubljana Meetings Paper, 2007. Uchida, H.; Udell, G. F. and Watanabe, W. "Bank Size and Lending Relationships in Japan." Journal of the Japanese and International Economies, 2008, 22(2), pp. 242-67. Vander, Vennet, R. "Cost and Profit Efficiency of Financial Conglomerates and Universal Banks in Europe." Journal of Money, Credit & Banking, 2002, 34(1), pp. 254-82. VanHoose, David. "Theories of Bank Behavior under Capital Regulation." Journal of Banking & Finance, 2007, 31(12), pp. 3680-97. Volz, Ulrich. "European Financial Integration and the Financing of Local Businesses in the New Eu Member States " EBRD Working Paper No. 89, 2004. 214 Wall, Larry D.; Reichert, Alan K. and Liang, Hsin-Yu. "The Last Frontier: The Integration of Banking and Commerce in the U.S." Federal Reserve Bank of Chicago Proceedings, 2007. Wang, Y. Z.; Liu, Z. Z. and Mang, Y. "Cross-Border Mergers and Acquisitions: Innovative Capacity and National Economic Security." Journal of Economic Policy Reform, 2007, 10(4), pp. 263-81. Wheelock, D. C. and Wilson, P. W. "Why Do Banks Disappear? The Determinants of Us Bank Failures and Acquisitions." Review of Economics and Statistics, 2000, 82(1), pp. 127-38. 215 APPENDIX A: WORLD BANK SURVEY FOR BANK REGULATION AND SUPERVISION WBG1.5: Are the sources of funds to be used as capital verified by the regulatory/supervisory authorities? WBG1.6: Can the initial disbursement or subsequent injections of capital be done with assets other than cash or government securities? WBG1.7: Can initial disbursement of capital be done with borrowed funds? WBG2.3: What is the level of regulatory restrictiveness for nonfinancial firms ownership of banks: (1) Unrestricted-A nonfinancial firm may own 100 percentage of the equity in a bank, (2) Permitted-Unrestricted with prior authorization or approval, (3) Restricted-Limits are placed on ownership, such as a maximum percentage of a bank?s capital or shares (4) Prohibited-No equity investment in a bank? WBG2.5: What is the level of regulatory restrictiveness for nonbank financial firms ownership of banks: (1) Unrestricted-A nonbank financial firm may own 100 percentage of the equity in a bank, (2) Permitted-Unrestricted with prior authorization or approval, (3) Restricted-Limits are placed on ownership, such as a maximum percentage of a bank?s capital or shares (4) Prohibited-No equity investment in a bank? WBG2.6: Of commercial banks in your country, what fraction of: WBG2.6.1: deposits is held by the five largest banks? WBG3.1: What is the minimum capital-asset ratio requirement? WBG3.1.1: Is this ratio risk weighted in line with the 1988 Basle guidelines? WBG3.2: Does the minimum ratio vary as a function of an individual bank's credit risk? WBG3.3: Does the minimum ratio vary as a function of market risk? WBG3.7: What fraction of revaluation gains is allowed as part of capital? WBG3.8: What fraction of the banking system's assets is in banks that are: WBG3.8.1: 50% or more government owned? WBG3.8.2: 50% or more foreign owned? WBG3.9: Before minimum capital adequacy is determined, which of the following are deducted from the book value of capital? WBG3.9.1: Market value of loan losses not realized in accounting books? 216 WBG3.9.2: Unrealized losses in securities portfolios WBG3.9.3: Unrealized foreign exchange losses? WBG4.1: What are the conditions under which banks can engage in securities activities: (1) Unrestricted-A full range of activities in the given category can be conducted directly in the bank, (2) Permitted-A full range of these activities are offered but all or some of these activities must be conducted in subsidiaries or in another part of a common holding company, (3) Restricted-Less than a full range of activities can be conducted in the bank or subsidiaries, (4) Prohibited-The activity cannot be conducted in either the bank or subsidiaries? WBG4.2: What are the conditions under which banks can engage in insurance activities: (1) Unrestricted-A full range of activities in the given category can be conducted directly in the bank, (2) Permitted-A full range of these activities are offered but all or some of these activities must be conducted in subsidiaries or in another part of a common holding company, (3) Restricted-Less than a full range of activities can be conducted in the bank or subsidiaries, (4) Prohibited-The activity cannot be conducted in either the bank or subsidiaries? WBG4.3: What are the conditions under which banks can engage in real estate activities: (1) Unrestricted-A full range of activities in the given category can be conducted directly in the bank, (2) Permitted-A full range of these activities are offered but all or some of these activities must be conducted in subsidiaries or in another part of a common holding company, (3) Restricted-Less than a full range of activities can be conducted in the bank or subsidiaries, (4) Prohibited-The activity cannot be conducted in either the bank or subsidiaries? WBG4.4: What is the level of regulatory restrictiveness for bank ownership of nonfinancial firms: (1) Unrestricted-A bank may own 100 percentage of the equity in any nonfinancial firm, (2) Permitted-A bank may own 100 percentage of the equity in a nonfinancial firm, but ownership is limited based on a bank?s equity capital, (3) A bank can only acquire less than 100 percentage of the equity in a nonfinancial firm (4) A bank may not acquire any equity investment in a nonfinancial firm? WBG 10.1: Does accrued, though unpaid, interest/principal enter the income statement while the loan is still performing? WBG10.1.1: Does accrued, though unpaid, interest/principal enter the income statement while the loan is still non-performing? WBG10.3: Are financial institutions required to produce consolidated accounts covering all bank and any nonk-bank financial subsidiaries (including affiliates of common holding companies)? WBG10.4.1: Are off-balance sheet items disclosed to the public? WBG10.5: Must banks disclose their risk management procedures to the public? WBG10.6: Are bank directors legally liable if information disclosed is erroneous or misleading? 217 WBG12.1: What body/agency supervises banks? WBG12.1.1:The central bank? WBG12.1.2: A Single Bank Supervisory Agency/ Superintendency? WBG12.1.3: Multiple Bank Supervisory Agencies/Superintendencies? WBG12.1.4: Is there a single financial supervisory agency for all of the main financial institutions (insurance companies, contractual savings institutions, savings banks)? WBG12.2: To whom are the supervisory bodies responsible or accountable: (a) the Prime Minister, (b) the Finance Minister or other cabinet level official, (c) a legislative body, such as Parliament or Congress, (d) other? Note: for more questions of the survey, please refer to Barth, Caprio and Levine (2006). 218 APPENDIX B: EBRD-WORLD BANK SURVEY FOR BUSINESS ENVIRONMENT AND ENTERPRISE PERFORMANCE (BEEPS) Q2: What percentage of your sales comes from the following sectors in which your establishment operates? Q2c: Manufacturing? Q14: Considering your main product line or main line of services in the domestic market, by what margin does your sales price exceed your operating costs (i.e., the cost material inputs plus wage costs but not overheads and depreciation)? Q45a: What proportion of your firm?s working capital and new fixed investment has been financed from each of the following sources, over the last 12 months? Working capital (i.e. inventories, accounts receivable, cash): Q45a2: Equity (i.e. issue new shares)? Q45a3: Borrowing from local private commercial banks? Q45a4: Borrowing from foreign banks? Q45a5: Borrowing from state-owned banks, including state development banks? Q45a6: Loans from family/friends? Q45a7: Money lenders or other informal sources (other than family/friends)? Q45a12: The government (other than state-owned banks)? New investments (i.e. new land, buildings, machinery, equipment): Q45a16: Equity (i.e. issue new shares)? Q45a17: Borrowing from local private commercial banks? Q45a18: Borrowing from foreign banks? Q45a19: Borrowing from state-owned banks, including state development banks? Q45a20: Loans from family/friends? Q45a21: Money lenders or other informal sources (other than family/friends)? Q45a26: The government (other than state-owned banks)? Q46: Thinking of the most recent loan you obtained from a financial institution Q46a: Did the financing require collateral: (1) Yes, (2) No? Q46c: What was the approximate value of the collateral required as a percentage of the loan value? Q46d: What is the loan?s annual cost (i.e., rate of interest)? Q46e: What is the duration of the loan in months? Q46h: How many days did it take to agree the loan with the bank from the date of application? Q48: Does your firm use international accounting standards (IAS) as provided by the International Accounting Standards Board or US GAAP or national accounting standards as provided by the Ministry of Finance or securities regulator? Q48a: International Accounting Standards: (1) Yes, (2) No, (3) Don?t know? 219 Q48b: US GAAP: (1) Yes, (2) No, (3) Don?t know? Q48c: National Accounting Standards: (1) Yes, (2) No, (3) Don?t know? Q49: Does your firm have its annual financial statement checked and certified by an external auditor: (1) Yes, (2) No, (3) Don?t know? Q54: Can you tell me how problematic are these different factors for the operation and growth of your business? Q54a: Access to financing (e.g., collateral required or financing not available from banks): (1) No obstacle, (2) Minor obstacle, (3) Moderate obstacle, (4) Major obstacle, (5) Don?t Know? Q54b: Cost of financing (e.g., interest rates and charges): (1) No obstacle, (2) Minor obstacle, (3) Moderate obstacle, (4) Major obstacle, (5) Don?t Know? S4: How many full-time employees work for this company today? S4b1(small size firm): 2-49? S4b2(medium size firm): 50-249? S4b3(large size firm): 250-9999? S5: What percentage of your firm is owned by: S5b: Private foreign individual(s)/ company(s)/organization(s)? 220 NOTES: 1 When a company decides to invest abroad, it can do it in two different ways: i) through the establishment of a new firm (Greenfield investment), ii) or through acquiring a pre-existent foreign firm or merging with a foreign firm. 2 According to Barth, Phumiwasana, and Yost (2007), there were 154,061 completed mergers and acquisitions with a total deal value of $24.7 trillion in 173 countries during the period of January 1995 through January 2007. Of these transactions, 27% were cross-border and they accounted for 30% of the total value of all deals. However, the role of banks in all the mergers and acquisitions during the past decade is relatively modest. Banks accounted for only 3% of all the unique acquirers and acquired less than 4% of all the firms involved in completed deals. Of these deals, 30% of the firms acquired were cross-border, with 24% of the total deal value also being cross-border. 3 Berger, Demsetz, and Strahan (1999) categorize the analyses into static analyses and dynamic analyses. The static analyses are based on analyzing the overall performance of banks after mergers and acquisitions. This category of studies tries to determine if mergers and acquisitions promote economies of scale and scope and increase banks? overall performances. The dynamic analyses try to compare banks? specific performances such as changes in organizational structures or management behaviors before and after merger activities. 4 Both targets and acquirers in horizontal mergers and acquisitions are banks whose products and services are similar. Vertical mergers and acquisitions refer to the deals that one party is bank while another party is nonbank financial firms. Conglomerate mergers and acquisitions have characteristics of both horizontal and vertical mergers and acquisitions. At least one party in conglomerate mergers and acquisitions should be financial group that operates both in traditional banking activities and innovative financial services. 221 5 ROA= (Net Income+Interest Expense)/Total Asset= (Net Interest Income+Other Net Income+Interest Expense)/Total Asset=NIM+Other Net Income/Total Asset+ Interest Expense/Total Asset; CTIR=Non-interest Expense/ (Net Interest Income+Non-Interest Income). 6 The results are not reported in the paper due to paper layout. 7 We also ran a regression deconstructing the bank activity restriction index into its three components: bank restrictions on security activities, real estate activities, and insurance activities. Only the security component is significantly positive. Restrictions on real estate as well as insurance activities have no influence on acquiring decisions. 8 Z-score is a measure of bank stability and indicates the distance from insolvency. It indicates the number of standard deviations that a bank?s return on assets has to drop below its expected value before equity is depleted and the bank is insolvent. A higher Z-score indicates the bank is more stable. Please see Laeven and Levine (2008). ZSCORE in the robustness test is computed using banking sector ROA and ROE. 9 FYROM is abbreviation of the former Yugoslav Republic of Macedonia. It is used to be called the Republic of Macedonia. It is one of the successor states of the former Yugoslavia, from which it declared independence in 1991. 10 For information on the three World Bank surveys and how to compute regulatory indices, refer to chapter 1, section 5 of this dissertation. 11 According to Clessens (2003), a dis-intermediation problem exists when firms bypass banks and raise money directly from public markets or where they obtain other types of financial products from non-banks, including from insurance companies.