(Luận văn) networks and bank financing, the study of smes in vietnam

t to ng VIETNAM-NETHERLANDS PROGRAM hi FOR MASTER OF ARTS IN DEVELOPMENT ECONOMICS ep w n lo ad ju y th yi pl n ua al Networks and Bank Financing: va n The Study of SMEs in Vietnam ll fu oi m at nh z A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of z k jm ht vb MASTER OF ARTS IN DEVELOPMENT ECONOMICS om l.c DANG NGUYEN KHANG gm By n a Lu Academic Supervisor: n va DR DINH CONG KHAI y te re th HCM, December, 2013 t to ng ABSTRACTS hi ep Small and medium-size enterprises (SMEs) play crucial roles in the economy They w n generate over 60% employment in many countries and are the key factor for the economic lo ad growth SMEs, however, face constraints to access external finance, which negatively impacts y th ju on their business performance and growth Therefore, a research on SMEs’ ability to access to yi pl external finance poses a significant important issue for academic scholars and policy makers al n ua In such emerging countries as Vietnam, bank financing tends to be the most importance n va financial resources for SMEs However, the bank employs credit rationing because of ll fu asymmetric information between banks and SMEs Networks may be the most effective oi m channels for SMEs to overcome information asymmetries, thus enabling them to gain access nh to external financial resources The objective of this study is to investigate the effects of at z supporting networks and network diversity on banking financial accessibility of SMEs in z vb Vietnam Particularly, four types of networks including networking with government officials, jm ht bank officials, business associations and network diversity will be examined in depth k gm Longitudinal data set of more than 1500 manufacturing SMEs in Vietnam from 2007 to 2011, om l.c random effect estimator and Stata program will be employed in this research n a Lu Key words: Networks, Bank financing, SMEs n va y te re th i t to ACKNOWLEDGMENT ng hi ep Foremost, I would like to gratefully and sincerely thank my supervisor, Dr Dinh Cong w Khai, for his valuable guidance, insightful comments and supports in all the time of research n lo and writing of this thesis ad y th Besides, I would also like to thank Dr Truong Dang Thuy and Dr Pham Khanh Nam, ju yi who gave me assistance and guidance through my thesis process Another special thank goes pl ua al to all the lecturers for their wonderful knowledge, and program administrator and technical n staffs at the Vietnam – Netherlands Program for their help during the time I studied in the n va ll fu program m oi Last but not least, I would like to thank my family and friend for their support and nh at encouragement not only through my thesis process but also throughout my life z z k jm ht vb om l.c gm n a Lu n va y te re th ii t to CONTENTS ng hi ep ABSTRACTS i ACKNOWLEDGMENT ii w n lo LIST OF TABLES vi ad ju y th LIST OF FIGURES viii yi ABBREVIATIONS ix pl INTRODUCTION 1.1 Problem Statements 1.2 Research objectives 1.3 Research questions 1.4 Scope of the research 1.5 Methodology 1.6 The structure of the research LITERATURES REVIEW 2.1 SMEs and Networks n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm 2.1.1 Overview of SMEs a Lu 2.1.2 Networks concepts 10 n va Theoretical review 10 n 2.2 te re 2.2.1 Credit rationing theory by Stiglitz and Weiss (1981) 11 y iii th 2.2.2 Strength of weak ties theory by Granovetter (1973) 13 t to 2.2.3 Resources Dependency Theory (RDT) by Pfeffer and Salancik (2003) 14 ng hi ep 2.3 Empirical review 15 RESEARCH METHODOLOGY 25 w n Estimating Model 25 lo 3.1 ad Variables and measurement 26 ju y th 3.2 Data description 28 3.4 Selection estimators 30 yi 3.3 pl ua al n 3.4.1 Estimating fixed effects 30 n va ll fu 3.4.2 Estimating Random effects 31 oi m 3.4.3 Pool OLS, Fixed effects or Random effects 32 nh 3.4.4 Choosing Pool OLS and Fixed effects by F-test 33 at z z 3.4.5 Choosing the Pool OLS and Random effects by LM-test 34 vb jm ht 3.4.6 Choosing Fixed effect and Random effects by Hausman test 34 EMPIRICAL RESULTS 36 4.1 Data analysis 36 k om l.c gm 4.1.1 Data description 36 a Lu 4.1.2 Correlation matrix 37 n va n 4.1.3 Network over time 38 te re 4.1.4 Networking with government officials and bank loan 40 y iv th 4.1.5 Networking with bank officials and bank loan 41 t to 4.1.6 Networking with business associations and bank loan 42 ng hi ep 4.1.7 Network diversity and bank loan 43 4.2 Selection estimator results 44 w n lo 4.2.1 F-test: Choosing between FE and Pool OLS 44 ad ju y th 4.2.2 LM-test : Choosing between RE and Pool OLS 44 yi 4.2.3 Hausman test : Choosing between FE and RE 45 pl Regression results 46 CONCLUSIONS AND POLICY IMPLICATION 51 5.1 Conclusions 51 5.2 Policy Implications 52 5.3 Limitation and Future research 53 n ua al 4.3 n va ll fu oi m at nh z z REFERENCES 54 vb jm ht APPENDIX A: CHOOSING APPROPRIABLE ESTIMATOR 59 k APPENDIX B: ESTIMATION RESULTS 61 om l.c gm n a Lu n va y te re th v t to ng LIST OF TABLES hi ep Table 2.1: Classification of SMEs in Vietnam w n Table 2.2: Share of SMEs in total Enterprises lo ad Table 2.3: The Ownership structure of SMEs y th ju Table 3.1: Variables and measurement 28 yi pl Table 3.2: Size of SMEs in the sample 29 al n ua Table 4.1: Description of the sample 37 va Table 4.2: Correlation matrix between variables of the sample 38 n fu ll Table 4.3: Network over time (mean value) 39 oi m Table 4.4: Level changed of network over time (%) 39 at nh Table 4.5: F-test results 44 z z ht vb Table 4.6: LM-test results 45 k jm Table 4.7: Hausman-test results 45 gm Table 4.8: Regression results 48 l.c Table A.1: Fixed Effects result 59 om n a Lu Table A.2: LM-test result 59 Table A.3: Hausman-test result 60 n va y te re Table B.1: The estimated result 61 th vi t to ng hi ep w n lo ad ju y th yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th vii t to ng LIST OF FIGURES hi ep Figure 2.1: Conceptual framework 24 w n Figure 3.1: Process of choosing the appropriate estimator 33 lo ad Figure 4.1: Networking with government and bank loan rate 40 y th ju Figure 4.2: Networking with bank and bank loan rate 41 yi pl Figure 4.3: Networking with business associations and bank loan rate 42 al n ua Figure 4.4: Network diversity and bank loan rate 43 n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th viii t to ABBREVIATIONS ng hi ep w n Fixed effect GDP Gross Domestic Product Pool OLS Ordinary Least Squares lo FE ad y th Resources Dependency Theory ju RDT yi Random effect pl RE al Small and medium size enterprises n ua SMEs n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th ix t to The relationship between networking with government and bank financing, the first ng hi hypothesis, is presented in the results of model As can be seen in Table 4.8, the networking ep with government official coefficient is significant at 1%, and has a negative sign Thus, the w n logarithm of bank loan rate and networking with government officers is negative correlation lo ad This implies that networking with government have a negative effect on bank financing To y th ju be more detailed, when networking with government increases one unit, the bank loan rate yi pl will reduce 5.7% Therefore, hypothesis is supported in this regression This result i s al n ua different from the result of N T Le & Nguyen (2009) when their search could not support this n va hypothesis This difference may be from the difference of data or from the estimation method ll fu The panel data of this study may provide more information than cross section data of their oi m research In other hand, this result is similar to N T Le, Venkatesh, & Nguyen (2006) in at nh expected sign; however, they are at different levels, when N T Le, Venkatesh, & Nguyen z (2006) find that when the strength of relationship with government increases one unit, the z vb bank loan rate reduces 14.6%, which are approximately three times higher when compared jm ht with this thesis’s result This can be explained that when the manager of a firm has the strong k gm relationship with government officers, this firm can access more aid, fund and supporting om l.c credit programs, thus reducing the demand for bank financing n a Lu n va y te re th 47 t to Table 4.8: Regression results ng hi Dependent variable ep Independent variables Ln (BankLoanRate) w -0.071621*** n Manager_Gender lo (0.0058923) ad 0.0094412*** y th Manager_Edu (0.0019536) ju yi -0.0190707*** Firm_Age pl (0.0003754) Firm_Size ua al 0.0031938*** n (0.0001033) va n Network_Goverment -0.05691*** ll fu (0.0010513) oi m Network_Bank 0.0898885*** (0.0020544) nh at Network_Association 0.0899634*** z (0.0062676) z vb 0.0857421*** Network_Diversity om l.c Source: Author’s calculations based on Table B.1 gm Notes: The figures in parentheses are standard errors *** 1% significance; k jm ht (0.00336) a Lu The effect of networking with bank officials on accessing bank loans, hypothesis 2, is n also presented in Table 4.8 Contrary to networking with government, the coefficient of va n networking with bank officials is positively significant at 1% This coefficient implies a firm, th 48 y more bank loans When networking with bank raises one unit, the bank loan rate of the firm te re which is more likely to possess strong networking with bank officials, is more likely to obtain t to will increase by % In brief, these results support hypothesis This network channel ng hi provides a powerful key for firms to access bank credit directly Based on bank -firm ep relationship, the banks can collect more information of firms, including formal and informal w n information Such information develops trust and reduces effects of the asymmetric lo ad information problem existing between them Therefore, the firm can access more bank loans y th ju This quantitative research result can support the argument in the qualitative research of yi pl Nguyen, Le, & Freeman (2006) Moreover, the positively significant effects of networking al n ua with bank on bank financing of SMEs are supported in many researches such as Berger & n va Udell (1995, 2002), Mizruchi and Stearns (2001), and Bharath et al (2007) fu ll The hypothesis mentions about networking with business associations and bank m oi financing correlation In results of model, networking with business associations has the nh at positive significance coefficient (Table 4.8) Based on this result, hypothesis is supported z z This proves that the strength of networking with business association is beneficial for bank vb jm ht financing accessibility of enterprises Firms can access more banks financing if they improve k networking with business associations When firms anticipate in one more business gm associations, their bank loan rate grows % Again, this result and N T Le & Nguyen l.c om (2009)’s result are different They regression cannot support this hypothesis and the reason for n a Lu this difference may be from data or estimation However, one research in China supports this hypothesis The research of Talavera et al (2012) finds that if a firm joins in relevant business n va y access business associations to collect more information of firms In addition, business te re associations, their ability of accessing bank financing increases by 15% They argue that banks th 49 t to associations help firms expand their networks These facts have positive impacts on bank ng hi financing of SMEs Therefore, these two researchers reach the same results ep w The final hypothesis, hypothesis 4, is also supported based on the regression results n lo Table 4.8 indicates that network diversity variable has a significant and positive relationship ad ju y th with the bank loan rate To put it in other words, when firms diversify their networks, the ability to access bank financing will rise This research indicates that if the firm has more than yi pl one type of networks, its bank loan rate increases 8.6 % The research of Manolova et al ua al (2006), Uzzi (1999) and Barr (1998) advocates this hypothesis They indicate that network n va diversity helps firms expand their reputation in a variety of dimensions Moreover, n fu ll entrepreneurs can access superior information on many levels through network diversity m oi Therefore, enterprises can obtain more bank financing at nh In general, this research reaches all of the questions The regression can test and z z k jm ht vb support for all four hypotheses om l.c gm n a Lu n va y te re th 50 t to CONCLUSIONS AND POLICY IMPLICATION ng hi 5.1 Conclusions ep The dominant argument through this study is that SMEs play important roles in the w n lo economy; however, they face obstacles in bank financing mobilizes, which is the most ad ju y th important formal external financing resources in emerging country like Vietnam, because of lacking information or information asymmetry between banker and borrower In the economy, yi pl which presents inefficient business information institutions, networks appear to be an ua al important channel for SMEs to spread their existence, activities, reputation to the public, and n va gain useful information, therefore reducing information asymmetry and access bank financing n fu ll This study uses panel data of more than 1,500 manufacturing SMEs in Vietnam from oi m at nh 2007 to 2011 and RE estimator to test the above argument The thesis proves that networks significantly affect bank financing However, different types of networks affect accessing z z ht vb bank credit in different ways Particular, the strength of networking with government officials k jm helps firms access the government supporting financing from aid, fund and government gm support programs, thereby reducing the demand for bank financing Conversely, networking with bank officials, business associations and network density have positive significant effects om l.c on bank financing through reducing asymmetric information between SMEs and bankers The n a Lu stronger networks they have the higher probability for firms to obtain bank loans va n This research has some added values compared with the two previous researches about y te re the effect of networks on bank financing in Vietnam of N T Le, Venkatesh, & Nguyen (2006) 51 th and N T Le & Nguyen (2009) Firstly, this study uses updated panel data and investigates the t to individual effect which cross section data cannot Secondly, in the way of networks ng hi measurement, this study measures by quantitative measurement while the two previous ep researches measure by qualitative method Compared with qualitative method of the two w n previous researches, the quantitative method applied in this study is more particular and lo ad accurate Finally, this study investigates the two significant networks, which have strongly y th ju effects on bank financing but are not considered in the two previous studies The two networks yi pl are networking with bank officials and network diversity ua al 5.2 Policy Implications n va n As mentioned above, this study investigates the effect of networks on bank financing ll fu oi m Based on this result, firms can build up an appropriate network policy to improve their at nh accessibility to external financial resources This study proves that networks play important roles in external financial accessibility of Vietnam SMEs However, different types of z z ht vb networks have different functions, which serve for different objectives of SMEs For example, k jm if firms tend to access government supporting financing like aid, fund or supporting program, gm entrepreneurs should promote networking with government officials If firms are considered om l.c about bank financing, the managers of these firms should build up strong networking with bank officials This offers a powerful channel which directly assists firms to access bank a Lu credit When firms have the strategy to enlarge their reputation, expand networks, collect n n va superior information from varying dimensions or attract investment from many resources, they y te re should improve networking with business associations and network diversity th 52 t to 5.3 Limitation and Future research ng hi ep This study indicates that the bigger size and the larger diversity of networks are useful w for SMEs to gain bank financing However, when firms attempt to build up networks and n lo diverse networks, entrepreneurs will have to bear in mind the cost which affects the efficiency ad ju y th of the networks However, this study cannot test this hypothesis because of the lack of data Therefore, future researches may focus on testing how significant size and wide diversity of yi pl network effect enterprises in overall and finding the optimum size, density and diversity of n ua al networks n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th 53 t to REFERENCES ng hi Ahlstrom, David, & Bruton, Garry D (2006) Venture capital in emerging 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