UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DETERMINANTS OF ACCESS TO FORMAL CREDIT BY SMALL AND MEDIUM ENTERPRISES IN VIETNAM By TRAN NGUYEN THUY BAO ANH MASTER OF ARTS IN DEVELOPMENT ECONOMICS Ho Chi Minh City, April, 2014 UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM – NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DETERMINANTS OF ACCESS TO FORMAL CREDIT BY SMALL AND MEDIUM ENTERPRISES IN VIETNAM A thesis submitted in partial fulfillment of the requirements for the degree of Master of Arts in Development Economics By TRAN NGUYEN THUY BAO ANH Academic supervisor Dr PHAM KHANH NAM Ho Chi Minh City, April, 2014 DECLARATION I declare that: "Determinants of access to formal credit by small and medium enterprises in Vietnam" is my own work; it has not been submitted to any degree at other universities I confirm that I have made all possible effort and applied all knowledge for finishing this thesis to the best of my ability Ho Chi Minh City, April 2014 TRAN NGUYEN THUY BAO ANH i ACKNOWLEDGEMENT This thesis would not have been accomplished without the kind assistance and enthusiastic guidance of several individuals who have in one way or another contributed toward to the formation and fulfillment of this paper First of all, I would like to express our deepest gratitude to my supervisor Dr Pham Khanh Nam for invaluable comments, guidance and engagement through the learning process of the thesis I would like to express my special thanks Dr Truong Dang Thuy for his comment and advice about thesis research design Another special thank goes to Nguyen Quang, from whom I have a lot of things to learn I am thankful for Phan Thach Truc for all your kind help during my time in class 17 I sincerely would like to thank all my loved classmates in class MDE17 and staff in the VNP office, who always give me their restless assistance when I was in trouble Last but not least, I must express my most gratitude to my family members for all the kind understanding and spiritual support ii ABSTRACT The shortage of capital and difficulties in accessing bank loans were the most challenging issues for SMEs According to a survey of SMEs Development Department - Ministry of Planning and Investment, only one-third of SMEs can access to bank funds; one-third has obstacles to reach the loans; and one-third cannot access Among businesses in VN which could not access to bank loans, the 80% does not meet loan conditions The descriptive statistic result shows that State Owned Commercial Bank (SOCB) is the most important formal source for SMEs The banks appreciate the Certificate of Land Use Right or housing which can be used as collateral for the most important formal loans The enterprises which applied for formal loans may be have problems getting loans The main reasons are difficulties in obtaining clearance from bank authorities and lack of collateral Enterprises in credit constrained group have the option of accessing to the informal credit market The proportion of credit constrained group applied for informal credit is always higher than non- credit constrained These proportions have tended to increase for both groups Asymmetric information is the main theory of the research to classify the factors determining access to credit of SMEs into three main groups: (i) a grouped factor representing for Owner’s characteristics comprises education, ethnicity, (ii) a grouped factor representing for firm’s characteristics consists of firm age, firm size, type of firm, (iii) a grouped factor representing for relationship between banks and borrowers includes previously borrowed, overdue debt Based on the data set of 1427 enterprise from “Characteristics of the Vietnamese business environment: evidence from a SME survey in 2009”, the research has applied probit model to identify determinants of access to formal credit by small and medium-sized enterprises (SMEs) in Vietnam The result shows that Education (negative), Employee, Equipment, Liabilities and Borrow (positive) which are significant on probabilities of access to credit The research finds that 50% of enterprises have probability of access to credit higher than 75.4% The paper finds that Ethnicity, Year, From, Revenue, Ap, Ar, Overdue debt not contribute to credit access of SMEs and are not significant at 10% level iii In conclusion, the formal credit market plays a very important role for capital of SMEs However, access to this source is still a challenge for SMEs The barriers, difficulties in accessing credit from formal sources have forced the SMEs to involve in the informal credit market iv CONTENT DECLARATION i ACKNOWLEDGEMENT ii ABSTRACT iii CONTENT v LIST OF FIGURES vii CHAPTER 1: INTRODUCTION 1.1.Problem statement 1.2.Research objectives 1.3.Research questions 1.4.Organization of the study CHAPTER LITERATURE REVIEW 2.1 SME definition 2.2 Theoretical literature 2.2.1 Theory of monopoly 2.2.2 Theory of asymmetric information 2.2.3 BARRIERS TO FINANCE FOR SMEs 2.3 EMPIRICAL STUDIES 2.3.1 International empirical studies 2.3.2 Vietnamese empirical studies 2.4 Conceptual framework 15 CHAPTER 3: DATA AND RESEARCH METHODOLOGY 21 3.1 Background of SME Financing in Vietnam 21 3.2 Data 28 3.3 Research methodology 28 3.3.1 Descriptive analysis 28 3.3.2 Econometric model 28 CHAPTER 4: EMPIRICAL RESULTS 31 v 4.1 Descriptive Statistics 31 4.2 Empirical results 34 CHAPTER 5: CONCLUSIONS AND POLICY IMPLICATION 44 5.1 Conclusion 44 5.2 Policy Implication 45 5.3 Limitations and directions for further studies 45 REFERENCES viii APPENDIS xi vi LIST OF FIGURES Figure 2.1: Monopoly & competitive markets Figure 2.2: Access to credit: determinants and channels of influence 16 Figure 3.1: Capital for investment of SMEs 21 Figure 3.2: The main purpose of investment of SMEs 22 Figure 3.3: Problems getting the bank loan of SMEs 24 Figure 3.4: Why don’t Enterprises apply for loans? (%) 25 Figure 3.5: Source of formal loan 26 Figure 3.6: Type of Collateral 27 vii LIST OF TABLES Table 2.1: Definition for Small and Medium Enterprises in Viet Nam Table 2.2 Summary of empirical studies 11 Table 2.3: Variable summary 19 Table 3.1 Access to Credit 23 Table 3.2: Informal Loans and Credit Constraints (%) 27 Table 4.1: The reason Why enterprises did not apply for formal loan 31 Table 4.2: Access to credit 32 Table 4.3: Summary statistics of explanatory variables 32 Table 4.4: Correlation matrix 34 Table 4.5: Regression result 35 Table 4.6: Detail of Pr(access) 39 Table 4.7: Marginal effects at means 41 Table 4.8: Average Marginal Effects 42 viii decisions of banks are not influenced by enterprises’ form Because this variable maybe dose not provide information about the ability of repay of borrowers This finding is not corresponding with a study of Gartner et al (2011), Le (2013) However, the result reflects the real situation in Vietnam Unexpectedly, Revenue is not significant at 10% level Revenue is one of the most important factors when the banks issued credit quota to enterprises Banks are usually based on estimated revenue to calculate loan demand of the enterprises Depending on the loan demand, credit rating, business plan, profit and collateral; banks issued credit quota for business Once enterprises have credit quotas, enterprises can borrow to add working capital for the business The total loans are always less or equal credit quotas Land, Building, Inventories, Ap and Ar are not significant at 10% level In real situation, those factors are used as tool to control or check manage- loan (moral hazard) Overdue debt represents credit profile of companies and relationship with banks Unexpectedly, this variable is not significant at 10% level In real situation, banks can check credit profile of borrowers as overdue debt, credit ranking… from The Credit Information Centre (CIC) Enterprises which have bad debt background will be low credit rank Those companies still borrow; however, banks will require more collateral and control hardly According to the regression result, the research calculates the probability of access to credit for each observation The research finds that 50% of enterprises have higher probability than 0.754 (table 4.6) Table 4.6: Detail of Pr(access) Pr(access) Percentiles 1% 0.389 5% 0.424 10% 0.436 25% 0.507 50% 0.754 Percentiles 75% 0.822 90% 0.898 95% 0.973 99% 0.999 Observations 1427 Smallest 0.315 0.333 0.353 0.356 Largest 1 1 39 Mean Std Dev Variance Skewness Kurtosis Source: Calculated from 0.685 0.181 0.032 -0.131 1.629 Characteristics of the Vietnamese business environment: evidence from a SME survey in 2009 We see that the mean of Pr( access) is 68.5% and that the standard deviation is 0.181 The median Pr( access) (the 50th percentile) is 75.4% The 25th percentile is 50.7%, and the 75th percentile is 82.2% When we performed summarize, we learned that the minimum and maximum were 31.5% and 100%, respectively We now see that the four smallest values in our dataset are 31.5%, 33.3%, 35.3%, and 35.6% The largest value is Skewness is a measure of the lack of symmetry of a distribution If the distribution is symmetric (a normal distribution), the coefficient of skewness is In result, the skewness of the distribution is -0.131, the median is greater than the mean (75.4%> 68.5%) That means the distribution is said to be skewed left Kurtosis is a measure of peakedness of a distribution The normal distribution has a coefficient of kurtosis of and provides a convenient benchmark The smaller the coefficient Density of kurtosis is, the flatter the distribution is In result, the kurtosis of the distribution is 1.629 .2 Pr(access) 40 In probit model, coefficients are the partial effects of each explanatory variable on – the latent variable To get the effect of a unit increase in xk on the probability that y =1, the research use marginal effect In probit model, marginal effect is not a constant It is the standard normal probability density function and depends on the particular value of the explanatory variables The research calculates marginal effects at means (MEMs) of explanatory variables MEMs are computed by setting the values of X variables at their means, and then seeing how a change in one of the Xk variables changes P(Y = 1) (detail table 4.7) Table 4.7: Marginal effects at means Marginal effects after probit y = Pr(access) (predict) = 0.78530574 Variable MEMs Owner’s characteristics Ethnicity* Coefficient Mean 0.0671 0.2123 -0.0637 -0.2219 Year 0.0009 0.0031 13.41 From* 0.0128 0.043 0.36 Revenue 0.0003 0.0012 7.46 Employee 0.0007 0.0024 * 26.88 Equipment 0.0243 0.0825 ** 1.11 Liabilities 0.0330 0.1121 *** 1.31 Ap -0.0364 -0.1235 0.36 Ar 0.0382 0.1296 1.26 0.2555 0.8492 -0.0489 -0.1576 Education * 0.96 *** 0.65 Firm’s characteristics Relationship with lenders Borrow* Overduedebt* *** 0.55 0.07 (*) dy/dx is for discrete change of dummy variable from to Source: Calculated from Characteristics of the Vietnamese business environment: evidence from a SME survey in 2009 41 The first line of the output indicates that the marginal effects were calculated after a probit estimation The second line of the output describes the form of y and the predict command that we would type to calculate y separately The third line of the output gives the value of latent variable (y= 0.785) given the values of explanatory variable, which are displayed in the last column of the table (mean value) The result shows that the probability of access to credit of group graduated from college will lower 6.37% than group non- graduated from college (holding all other variables at mean) The MEMs of Employee is 0.0007 implying that if an enterprise has one more employee, it’ probability will increase 0.07%, (assume all other variables equal their means) The MEMs of Equipment is 0.0243 implying that if an enterprise has one more billions of equipment, it’ probability will increase 2.43%, (assume all other variables equal their means) The MEMs of Liabilities is 0.0330 implying that if an enterprise has one more billions of Liabilities, it’ probability will increase 3.30%, (assume all other variables equal their means) The result shows that the probability of access to credit of group borrowed will higher 25.55% than group non- borrowed (holding all other variables at mean) MEMs are easy to explain and popular However, MEMs are criticized because (a) no real enterprises may actually have mean values on all the Xs (b) no real enterprises has a value like 0.65 on a categorical variable like Education, or 0.05 Borrow (c) effects are only calculated at one set of values, the means Therefore, the study computed Average Marginal Effects (AMEs) which a marginal effect is computed for each case, and the effects are then averaged AMEs provide a better representation of how changes in Xk affect P(Y = 1) Table 4.8: Average Marginal Effects Variable AMEs MEMs Coefficient Owner’s characteristics Ethnicity 0.0641 0.0671 0.2123 -0.0677 -0.0637 -0.2219 Year 0.0097 0.0009 0.0031 From 0.0133 0.0128 0.043 Education (***) Firm’s characteristics 42 Revenue 0.0003 0.0003 0.0012 Employee (*) 0.0007 0.0007 0.0024 Equipment (**) 0.0252 0.0243 0.0825 Liabilities (***) 0.0342 0.0330 0.1121 Ap -0.0377 -0.0364 -0.1235 Ar 0.0395 0.0382 0.1296 Borrow (***) 0.2592 0.2555 0.8492 Overduedebt -0.0481 -0.0489 -0.1576 Relationship with lenders Source: Calculated from Characteristics of the Vietnamese business environment: evidence from a SME survey in 2009 The result shows that on average, the probability access to credit of group graduated from college will lower 6.77% than group non- graduated from college (holding all other variables equal) The AMEs of Employeet is 0.0007 implying that if an enterprise has one more employee, it’ probability will increase averagely 0.07% (assume all other variables are left unchanged) The AMEs of Equipment is 0.0252 implying that if an enterprise has one more billions of equipment, it’ probability will increase averagely 2.52% (assume all other variables are left unchanged) The AMEs of Liabilities is 0.0342 implying that if an enterprise has one more billions of Liabilities, it’ probability will increase 3.42% (assume all other variables are left unchanged) The result shows that on average the probability access to credit of group borrowed will higher 25.92% than group non- borrowed (holding all other variables equal) Marginal Effects at the Means (MEMs) are computed by setting the values of X variables at their means, and then seeing how a change in one of the Xk variables changes P(Y = 1) With Average Marginal Effects (AMEs) a marginal effect is computed for each case, and the effects are then averaged Many prefer AMEs because they think they provide a better representation of how changes in Xk affect P(Y = 1) 43 CHAPTER 5: CONCLUSIONS AND POLICY IMPLICATION 5.1 Conclusion The descriptive statistic result shows that State Owned Commercial Bank (SOCB) is the most important formal source for SMEs The banks appreciate the Certificate of Land Use Right or housing which can be used as collateral for the most important formal loans The enterprises which applied for formal loans may be have problems getting loans The main reasons are difficulties in obtaining clearance from bank authorities and lack of collateral Enterprises in credit constrained group have the option of accessing to the informal credit market The proportion of credit constrained group applied for informal credit is always higher than non- credit constrained These proportions have tended to increase for both groups Asymmetric information is the main theory of the research to classify the factors determining access to credit of SMEs into three main groups: Group concerns for Owner’s characteristics comprises education, ethnicity, Group concerns for firm’s characteristics consists of firm age, firm size, type of firm; Group concerns for relationship between banks and borrowers includes previously borrowed, overdue debt Based on the data set of 1427 enterprise from “Characteristics of the Vietnamese business environment: evidence from a SME survey in 2009”, the research has applied probit model to identify the determinants of the access to formal credit by small and medium-sized enterprises (SMEs) in Vietnam The result shows that Education (negative), Employee, Equipment, Liabilities and Borrow (positive) which are significant on probabilities of access to credit The research finds that 50% of enterprises have probability of access to credit higher than 75.4% The paper finds that Ethnicity, Year, From, Revenue, Ap, Ar, Overdue debt not contribute to credit access of SMEs and are not significant at 10% level In conclusion, the formal credit market plays a very important role for capital of SMEs However, access to this source is still a challenge for SMEs The barriers, difficulties in accessing credit from formal sources have forced the SMEs to involve in the informal credit market 44 5.2 Policy Implication From the statistical results, the study has some important implications Firstly, the regression result found that Owner’s characteristics such as Ethnicity, Education not contribute much to probability of access to credit of SMEs Ethnicity has a positive sign; however it is not significant at 10% level In Vietnam, the loan applications are not required about borrower’s ethnicity Therefore, the result reflects the real situation in Vietnam Education has a negative sign That means owners who higher education will have lower probability access to credit than others Similar to ethnicity, education is not information which must be declared in loan applications Therefore, when banks consider loan applications, they maybe not appreciate the Education of borrowers Secondly, Employee, Equipment and Liabilities influence the accessibility to credit markets significantly Employee represents firm size The larger firms may be easier to access bank credit they may have higher financial capacity and probability repayment Therefore, banks can feel secure for their loans Equipment represents availability of collateral The bank use collateral to minimize the negative impact of moral hazard and control borrowers’ loan utilization Therefore, the higher availability of collateral is, the more probability of access to credit enterprises will have The enterprises have bank loans that mean the banks have information of enterprises The banks will decrease the negative impact of asymmetric information and adverse selection Therefore, Liabilities impacts positively and strongly on probability of access to credit Thirdly, the probability of access to credit of group borrowed will higher than group nonborrowed That means the bank seem prefer enterprises have the relationship with banks The banks use the history of transaction to value financial capacity of SMEs Therefore, the banks deny adverse selection 5.3 Limitations and directions for further studies The research objective is to identify determinants of access to formal credit by SMEs in Vietnam The data set of research was collected in 2009 with sample of 1427 enterprise The study finds four factors significantly impact on SMEs credit accessibility but not found in the data set such as firm age, type of firm, Revenue and length of relationship with the lender 45 REFERENCES Agency for Enterprise Development (2010), White paper on small and medium enterprises in Viet Nam 2009 Ha Noi Akoten, J E., Sawada, Y., & Otsuka, K (2006) The determinants of credit access and its impacts on micro and small enterprises: The case of garment producers in Kenya Economic development and cultural change, 54(4), 927-944 http://dx.doi.org/10.1086/503585 Ayyagari, M., T Beck, and A Demirguc-Kunt 2007 “Small and Medium Enterprises across the Globe,” Small Business Economics, 29, 415–434 Beck, T (2007) Financing constraints of SMEs in developing 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Code: Yes (1), No (2) _(Aq99) If No, then skip to question 106 100 Did your firm experience any problems getting the loan? _(Aq100) Code: Yes (1), No (2) a) If yes, why? _(Aq100a) Code: Lack of collateral (1); Did not deliver a proper description of the potential of the enterprise (2); Complicated government regulations (3) Administrative difficulties in obtaining clearance from bank authorities (4); Other (5) 101 How many formal loans (short/long term) have your firm obtained since August 2007 (last survey)? a) Number of formal short term loans _(Aq101a) b) Number of formal long term loans _(Aq101b) xi 102 Which bank/formal credit institution you primarily use? _(Aq102a) Code: State Owned Commercial Bank (SOCB) (1), Private/joint stock bank (2), Foreign bank (3), Social Policy Bank (4), DAF (Development assistance fund) (5), Targeted programs (6), Other (7) 103 w many formal loan applications (short and long term) have been denied since August 2007? a) Number of formal short term loans _(Aq103a) b) Number of formal long term loans _(Aq103b) 104 Specification of the most important (in value terms) current formal loan a) Source _(Aq104a) Codes: State Owned Commercial Bank (SOCB) (1), Private/joint stock bank (2), Foreign bank (3), Social Policy Bank (4), DAF (Development assistance fund) (5), Targeted programs (6), Other sources (7) b) Amount originally borrowed (1,000 VND) _(Aq104b) c) Which year and month did you borrow? _(Aq104c) c1) What is the duration of the loan (months) (Aq104c1) d) Current liability (1,000 VND) _(Aq104d) e) Interest rate, % month _(Aq104e) f) Did your firm have to offer assets as collateral for the loan? _(Aq104f) Code: Yes (1), No (2) fa) If Yes, what kind of collateral? _(Aq104fa) Code: Land (CLUR) (1); Housing (2); Capital equipment (3); Personal belongings (4); Other (5) g) Is there a guarantor for this loan? _(Aq104g) Code: Yes (1), No (2) ga) If yes, which relations guarantor and the firm have? _(Aq104ga) Code: Family (1); Friends (2); Trade partner or business relationship (3), Credit guarantee fund (4), Member of this guarantee organization (5); Other (6) xii 105 Do you still think that you are in need of a loan? _(Aq105) Code: Yes (1), No (2) a) If yes (1), why? _(Aq105a) Code: To pay debt/to compensate for losses (1); for recurrent spendings (2); investment (3); Other (4) b) If no (2), why? _(Aq105b) Code: Have enough own funds (1), don’t want/need to invest (2), other (3) 106 Why has the firm not applied for formal loans since August 2007 (last survey)? _(Aq106) Code: Inadequate collateral (1), Don’t want to incur debt (2), Process too difficult (3), Didn’t need one (4), Interest rate too high (5), Already heavily indebted (6), Other (7) APPENDIX 2: CORRELATION OF DEPENDENT VARIABLE AND INDEPENDENT VARIABLES corr (obs=1427) ethnic~y educat~n ethnicity education year from revenue employee land buildings equipment inventories liabilities ap ar borrow overduedebt access prob ap ar borrow overduedebt access prob 1.0000 0.0877 -0.0162 -0.0042 0.0028 0.0130 -0.0353 -0.0097 -0.0116 -0.0588 -0.0186 -0.0035 0.0065 0.1122 -0.0082 0.0558 0.1415 1.0000 -0.1970 0.4017 0.0740 0.1569 0.1521 0.1689 0.1974 0.1467 0.1199 0.1172 0.0296 0.0213 0.0774 0.0013 0.0038 ap ar 1.0000 0.0716 0.0581 0.0570 0.0838 0.2092 1.0000 -0.0243 -0.0042 0.0253 0.0640 year from 1.0000 -0.3101 -0.0519 -0.0710 0.0145 -0.0533 -0.0563 -0.0301 -0.0194 -0.0523 0.0089 0.0139 -0.0285 0.0075 0.0215 1.0000 0.0983 0.1888 0.1895 0.2001 0.2321 0.1731 0.1341 0.1304 0.0477 0.0706 0.0596 0.0848 0.2198 1.0000 0.0804 0.0758 0.0883 0.1508 0.1264 0.1583 0.2607 0.0257 -0.0017 0.0189 0.0528 0.1332 1.0000 0.1390 0.2182 0.2536 0.1993 0.1639 0.2257 0.0393 0.0788 0.0354 0.0981 0.2470 borrow overdu~t access prob 1.0000 0.0397 0.3413 0.8638 1.0000 0.4070 1.0000 1.0000 -0.0017 0.0011 revenue employee xiii land buildi~s equipm~t invent~s liabil~s 1.0000 0.3008 0.3308 0.1989 0.2260 0.1789 0.0264 0.0369 0.0219 0.0686 0.1557 1.0000 0.5531 0.3159 0.2287 0.1713 0.0422 0.0627 0.0188 0.1186 0.3004 1.0000 0.3379 0.3974 0.4146 0.0335 0.1098 0.0214 0.1481 0.3750 1.0000 0.3521 0.3083 0.0188 0.0242 0.0705 0.0921 0.2339 1.0000 0.5430 0.0495 0.0780 0.0137 0.1028 0.2486 APPENDIX 3: THE FIRST REGRESSION RESULT probit access ethnicity education year from revenue employee land buildings equipment inventories liabilities ap ar > borrow overduedebt Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: 6: 7: log log log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood = = = = = = = = -887.90952 -779.18224 -767.61173 -764.23047 -763.77634 -763.30134 -763.29079 -763.29079 Probit regression Number of obs LR chi2(15) Prob > chi2 Pseudo R2 Log likelihood = -763.29079 access Coef ethnicity education year from revenue employee land buildings equipment inventories liabilities ap ar borrow overduedebt _cons 210607 -.2229666 003127 0368943 0013585 0023848 -.0072493 0715107 072408 -.0389642 1095085 -.1168573 138291 8563727 -.1632543 -.2350616 Std Err .1747206 0849048 003736 0963353 0019704 0015184 0080765 0457734 0412491 0463487 0386417 0925894 1112542 0753907 1494691 18424 z 1.21 -2.63 0.84 0.38 0.69 1.57 -0.90 1.56 1.76 -0.84 2.83 -1.26 1.24 11.36 -1.09 -1.28 P>|z| = = = = 1427 249.24 0.0000 0.1404 [95% Conf Interval] 0.228 0.009 0.403 0.702 0.491 0.116 0.369 0.118 0.079 0.401 0.005 0.207 0.214 0.000 0.275 0.202 -.131839 -.3893769 -.0041955 -.1519196 -.0025035 -.0005911 -.023079 -.0182036 -.0084386 -.1298061 0337722 -.2983292 -.0797632 7086096 -.4562083 -.5961654 553053 -.0565562 0104495 2257081 0052205 0053608 0085803 1612249 1532547 0518776 1852448 0646146 3563451 1.004136 1296996 1260422 Note: failures and 11 successes completely determined APPENDIX 4: THE FIRST REGRESSION RESULT probit access ethnicity education year from revenue employee Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: 6: 7: log log log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood = = = = = = = = Probit regression Number of obs LR chi2(12) Prob > chi2 Pseudo R2 Log likelihood = -765.10877 access Coef ethnicity education year from revenue employee equipment liabilities ap ar borrow overduedebt _cons 2123034 -.221982 0031889 0436802 0012499 002408 0825895 1121144 -.1235 1296437 8492163 -.1576927 -.2306489 equipment liabilities ap ar borrow overduedebt -887.90952 -781.05442 -768.92121 -765.96124 -765.65681 -765.1263 -765.10878 -765.10877 Std Err .1752826 0847723 0037162 0954529 0015498 0014471 0409012 0364867 0921527 1103902 0751358 149053 1845826 z 1.21 -2.62 0.86 0.46 0.81 1.66 2.02 3.07 -1.34 1.17 11.30 -1.06 -1.25 P>|z| = = = = 1427 245.60 0.0000 0.1383 [95% Conf Interval] 0.226 0.009 0.391 0.647 0.420 0.096 0.043 0.002 0.180 0.240 0.000 0.290 0.211 -.1312442 -.3881327 -.0040947 -.143404 -.0017875 -.0004283 0024247 0406017 -.3041159 -.0867172 7019527 -.4498312 -.5924241 Note: failures and 10 successes completely determined xiv 5558509 -.0558312 0104724 2307644 0042874 0052442 1627543 1836271 0571159 3460045 9964798 1344457 1311263 APPENDIX 5: WALD TEST test land= buildings= inventories=0 ( 1) ( 2) ( 3) [access]land - [access]buildings = [access]land - [access]inventories = [access]land = chi2( 3) = Prob > chi2 = 3.32 0.3452 APPENDIX 6: LR TEST lrtest m1 m2 Likelihood-ratio test (Assumption: m2 nested in m1) LR chi2(3) = Prob > chi2 = 3.64 0.3036 APPENDIX 7: MARGINAL EFFECTS AT MEANS Marginal effects after probit y = Pr(access) (predict) = 78127272 variable dy/dx ethnic~y* educat~n* year from* revenue employee equipm~t liabil~s ap ar borrow* overdu~t* 0671654 -.0637765 0009411 0128294 0003689 0007106 0243728 0330859 -.0364458 0382589 2555275 -.0489102 Std Err .05916 02391 0011 02809 00046 00044 01213 01111 0255 02991 02845 04827 z 1.14 -2.67 0.86 0.46 0.81 1.63 2.01 2.98 -1.43 1.28 8.98 -1.01 P>|z| [ 0.256 0.008 0.391 0.648 0.419 0.103 0.044 0.003 0.153 0.201 0.000 0.311 -.048792 183123 -.110631 -.016922 -.00121 003092 -.042229 067888 -.000526 001263 -.000143 001564 000608 048137 011317 054855 -.086417 013526 -.020357 096874 199769 311286 -.143527 045706 95% C.I ] X 956552 648213 13.4107 361598 7.45919 26.8802 1.10506 1.31272 35882 1.26071 552207 070778 (*) dy/dx is for discrete change of dummy variable from to APPENDIX 8: AVERAGE MARGINAL EFFECTS margin, dydx(*) Average marginal effects Model VCE : OIM Number of obs = 1427 Expression : Pr(access), predict() dy/dx w.r.t : ethnicity education year from revenue employee equipment liabilities ap ar borrow overduedebt dy/dx ethnicity education year from revenue employee equipment liabilities ap ar borrow overduedebt 0648109 -.0677655 0009735 0133345 0003816 0007351 0252125 0342257 -.0377014 039577 2592444 -.0481396 Delta-method Std Err .0534323 0257129 0011335 0291264 0004729 0004406 0124409 0111286 0281013 0336513 0195788 0454621 z 1.21 -2.64 0.86 0.46 0.81 1.67 2.03 3.08 -1.34 1.18 13.24 -1.06 P>|z| 0.225 0.008 0.390 0.647 0.420 0.095 0.043 0.002 0.180 0.240 0.000 0.290 [95% Conf Interval] -.0399145 -.1181619 -.0012482 -.0437521 -.0005453 -.0001286 0008287 0124141 -.092779 -.0263784 2208707 -.1372436 xv 1695363 -.0173691 0031951 0704211 0013084 0015987 0495963 0560373 0173761 1055323 2976181 0409644 ... access to formal credit by small and medium- sized enterprises (SMEs) in Vietnam 1.2.Research objectives General research objective is to examine determinants of access to formal credit by SMEs in Vietnam. .. factors on credit access is different between studies According to above problems, this paper aims to indicate Determinants of access to formal credit by small and medium enterprises (SME) in Vietnam. .. are: a To investigate factors that effect of probabilities of access to formal credit by SMEs in Vietnam b To recommend policy implications in order to improve SMEs’s access to formal credit