UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DETERMINANTS AFFECT THE ACCESS TO FORMAL- INFORMAL FINANCE AND ITS IMPACT ON SALES GROWTH A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By HOANG QUYNH TRANG Academic Supervisor: DR TRUONG DANG THUY HO CHI MINH CITY, Nov 2016 DECLARATION I declare that this thesis is entitled “Determinants affect the probability of access to formal- informal finance and its impact on sales growth” is submitted by me in fulfillment of the requirements for the degree of Master of Arts in Development Economics to the Vietnam – The Netherlands Programme (VNP) This thesis is my original work and under the guidance of my supervision and acknowledgement has been made in the text to all materials used Hoang Quynh Trang ACKNOWLEDGMENTS First of all, from the bottom of my heart, I would like to say thanks to my supervisor, Dr Truong Dang Thuy, who always listens to my ideas, discusses and gives me helpful advices for my thesis He is also the first teacher who taught me how to process and analyze the data with econometric tools Although his time is quite limited, he always reminds and encourages me to this thesis harder Again, I deeply say thanks to Dr Truong Dang Thuy, without you it will take very long time to complete my dissertation I would like to express my special thanks to Professor Nguyen Trong Hoai, Dr Pham Khanh Nam and Dr Le Van Chon and all lecturers in Viet Nam- Netherland Programme where I received useful knowledge throughout two years, which will positively support for my future work Finally, I must express my profound attitude to my parents as well as to my better haft for non-stop encouraging me through the process of doing this research This accomplishment could have not been possible if I did not receive extraordinary support from these people Thank you ABBREVIATIONS CIEM Central Institute Economic Management GDP Gross Domestic Product GSO General Statistic Office MPI Ministry of Planning and Investment IV Instrumental variable OLS Ordinary Least Square SMEs Small and Medium Sized Enterprises ABSTRACT Do different sources of finance make different sales growth? In this study, I used a dataset from Small and Medium sized enterprises (SMEs) in Vietnam in 2013 to explore the determinants affect the probability of access to formal and informal finance and its impact on growth rate By employing bivariate probit model and instrumental variable method to solve endogeneity problem, this study finds that while formal finance plays significantly positive role in improving firm performance, informal finance goes on opposite direction on growth rate Besides, in term of accessibility to official finance, firm size, receiving government assistance and good connection with banks have more advantageous than other factors Social networks such as network with banks and others also are found significantly positive effect on the probability of obtaining non-official finance More interestingly, this study also points out that young entrepreneurs are more likely to select informal sector to finance their business operation than the old ones Keywords: formal, informal, access to financing, sales growth, Vietnam TABLE OF CONTENTS CHAPTER ONE: INTRODUCTION 1.1 PROBLEM STATEMENT 1.2 RESEARCH OBJECTIVE 1.3 RESEARCH QUESTIONS 1.4 SCOPE OF THE STUDY 1.5 THE STRUCTURE OF STUDY CHAPTER TWO: LITERATURE REVIEW 2.1 FIRM’S ACCESS TO FINANCE 2.1.1 Theoretical studies 2.1.2 Empirical studies 2.2 FINANCING CHOICES AND SALES GROWTH 14 2.2.1 Theoretical studies 14 2.2.2 Empirical studies 16 CHAPTER THREE: DATA AND METHODOLOGY 23 3.1 DATA 23 3.2 METHODOLOGY 23 3.2.1 Determinants affect firm’s access to finance 23 3.2.2 Financing choices and firm’s growth 26 CHAPTER FOUR: RESULTS 30 4.1 DESCRIPTIVE RESULTS 30 4.2 REGRESSION RESULTS 37 CHAPTER FIVE: CONCLUSION AND IMPLICATION 51 5.1 KEY FINDINGS 51 5.2 POLICY IMPLICATIONS 52 5.3 LIMITATIONS AND SUGGESTIONS FOR FURTHER STUDY 53 5.3.1 Limitations 53 5.3.2 Suggestions for further research 53 REFERENCES 54 APPENDICES 58 LIST OF TABLES Table 2.1 Summary of theoretical studies 16 Table 3.1 Definition of variables 24 Table 3.2 Definition of variables 26 Table 4.1 Summary statistics of the sample 30 Table 4.2 Sales growth by gender and education 31 Table 4.3 Sales growth by the type of ownership 32 Table 4.4 Sales growth by region 32 Table 4.5 Sales growth by industry 33 Table 4.6 Sales growth by financing choices 34 Table 4.7 Sales growth by government assistance 35 Table 4.8 Determinants of formal/ informal finance accessibility 37 Table 4.9 How different sources of finance affect sales growth 45 LIST OF FIGURES Figure 1.1 Framework for capital structure categorization Figure 2.1 The expected rate of return of lenders Figure 4.1 The relationship between sales growth and some continuous variables 36 CHAPTER ONE: INTRODUCTION 1.1 PROBLEM STATEMENT Small and medium scaled enterprises (SMEs) play a significant role in economic development Data collected from Ministry of Planning and Investment (MPI) showed that SMEs contributed about 40% to Gross Domestic Product (GDP), 30% to total export turnover, and 15% to government revenue’s contribution Furthermore, SMEs also play critical role in term of employment generation and make a job for over 60% employees However, according to the report of MPI in 2012, return on revenue of SMEs stayed at 2.8% in 2007 and decreased to 2.34% in 2009 In addition, based on the dataset collected from General Statistics Office (GSO) of Vietnam in 2011 showed that SMEs represented just over 43% aggregate gross income of all endeavors and just under 14% aggregate profit before duty These numbers are quite modest compared to SMEs’ contribution to economy Capital is one of the most important input factors of any type of manufacturing and business operation Furthermore, data about SMEs collected by Central Institute Economic Management (CIEM) in 2013 stated that 741 of more than 2500 enterprises accessed to financing sources, in which firms obtained formal loan accounted for roughly 72%, while this number was approximately recorded at 20.24% and 7.83% for loan from informal sector and combination of two sources, respectively In addition, out of 662 firms applied for bank loan, there was only 158 firms reported that they experienced problem when getting a loan This difficulty of SMEs mainly resulted from administrative procedures in obtaining clearance from bank officials and lack of collateral The positive effect of formal finance on firm performance is proved in many empirical studies For example, Ayyagari, Demirgỹỗ-Kunt, and Maksimovic (2010) utilized crosssection data from Chinese firms in 2003 to explore whether there exist the positive role of informal finance on firm performance These authors claimed that whereas financing from official sector supports higher firm performance, non-official finance does not Similar finding Page | can be found in the study of Saeed (2009) where the author pointed out that formal financing inserts a positive impact on firm performance while informal abates the outcome In practice, enterprises could not get money from standard mechanism at the level of 100%, sometimes they have to seek finance from another sector such as their family, partners, relatives or even in the black market to have enough money to finance their business operation Along with official source of fund, non-official finance in some empirical studies is also considered as an alternative channel to fill in the demand for finance of firms, especially in developing countries where the weakness of financial and legal system exist This can be seen clearly through Allen, Chakrabarti, De, and Qian (2012)’s study when they research for small and medium sized enterprises in India The authors claimed that non-legal governance mechanisms dominate legal mechanism in solving disputes, overcoming bureaucracies and fostering firm’s performance In another study, such as Degryse, Lu, and Ongena (2013), the author suggest that the combination of formal and informal fund is an optimal choice for small firms, especially in emerging countries, where asymmetric information is pretty severe There are many sources to finance firm’s business operation such as from retained earnings, issuing stocks, borrowing from financial institutions or even combination two-three that of sources Due to an important role of capital, many empirical studies worldwide investigate the relationship between so-called capital structure and firm performance The structure of capital describes the way that firms raised their needs to establish or expand their business activities In other words, capital structure is defined as “the relative amount of debt and equity that firms need to finance” (T D K Nguyen & Ramachandran, 2006) Figure 1.1 presents the categorization of capital structure for further information Page | Appendix 2: Determine factors affect the probability of access to formal credit by employing probit model probit formal firm_age total_assets gov_ass NW_firm NW_bank NW_official NW_others age gender edu4 edu > ow2 ow4 ow5 ow7 indus2 indus3 indus4 indus5 indus6 indus8 indus9 indus10 indus11 indus12 indus13 in > dus16 regi5 regi6 regi7 regi8 regi9 regi10, robust Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood = = = = = -1214.9775 -921.13098 -915.97953 -915.96176 -915.96176 Number of obs Wald chi2(33) Prob > chi2 Pseudo R2 Probit regression Log pseudolikelihood = -915.96176 formal Coef firm_age total_assets gov_ass NW_firm NW_bank NW_official NW_others age gender edu4 edu5 ow2 ow4 ow5 ow7 indus2 indus3 indus4 indus5 indus6 indus8 indus9 indus10 indus11 indus12 indus13 indus16 regi5 regi6 regi7 regi8 regi9 regi10 _cons -.0069978 0214619 1.016597 -.0001152 2233748 -.0409294 0009691 -.0022391 0732318 1713202 0442459 1757441 277403 5560958 7115023 369446 -.1778858 -.1035102 3044673 3441633 4115039 2291564 1123924 0648812 0098976 3230368 -.1391756 -.5321599 -.5599238 0823861 1753878 -1.030858 0360703 -1.022148 Robust Std Err .0036932 0041744 1035941 0016143 0244539 0222656 0034714 0036872 0728943 1668699 1652582 1315887 246368 1016867 1639406 1836585 2477457 3317133 1149246 1986185 2472147 1670091 1781841 1038471 2075245 1325006 1393509 1148322 1346637 1323032 1733091 140634 1639441 251819 z -1.89 5.14 9.81 -0.07 9.13 -1.84 0.28 -0.61 1.00 1.03 0.27 1.34 1.13 5.47 4.34 2.01 -0.72 -0.31 2.65 1.73 1.66 1.37 0.63 0.62 0.05 2.44 -1.00 -4.63 -4.16 0.62 1.01 -7.33 0.22 -4.06 P>|z| 0.058 0.000 0.000 0.943 0.000 0.066 0.780 0.544 0.315 0.305 0.789 0.182 0.260 0.000 0.000 0.044 0.473 0.755 0.008 0.083 0.096 0.170 0.528 0.532 0.962 0.015 0.318 0.000 0.000 0.533 0.312 0.000 0.826 0.000 = = = = 2165 399.35 0.0000 0.2461 [95% Conf Interval] -.0142363 0132803 8135568 -.0032791 1754461 -.0845692 -.0058348 -.009466 -.0696383 -.1557387 -.2796543 -.0821649 -.2054694 3567935 3901845 0094819 -.6634585 -.7536563 0792193 -.0451217 -.0730281 -.0981753 -.236842 -.1386554 -.3968431 0633403 -.4122983 -.757227 -.8238599 -.1769233 -.1642918 -1.306496 -.2852543 -1.515704 0002407 0296435 1.219638 0030488 2713035 0027103 007773 0049877 2161019 4983792 3681461 4336531 7602754 7553981 1.03282 7294101 307687 5466359 5297154 7334483 8960358 5564881 4616269 2684178 4166382 5827332 1339471 -.3070929 -.2959878 3416955 5150674 -.7552208 3573949 -.5285919 Page | 63 Appendix 3: Marginal effect for formal source mfx Marginal effects after probit y = Pr(formal) (predict) = 19706922 variable firm_age total_~s gov_ass* NW_firm NW_bank NW_off~l NW_oth~s age gender* edu4* edu5* ow2* ow4* ow5* ow7* indus2* indus3* indus4* indus5* indus6* indus8* indus9* indus10* indus11* indus12* indus13* indus16* regi5* regi6* regi7* regi8* regi9* regi10* dy/dx -.0019417 0059552 3541573 -.000032 061982 -.0113571 0002689 -.0006213 0201905 0492995 0121849 0517839 085308 1729936 2429301 1164162 -.0456959 -.0274675 0927723 1079312 1321062 0689063 0325438 0183113 0027572 0995654 -.0366995 -.137055 -.1274949 0234926 0519583 -.2187284 010145 Std Err .00103 00117 03948 00045 00702 00619 00096 00102 01997 04958 04514 04087 08276 03421 06318 06407 05859 084 03787 06881 08885 05388 0537 02979 05803 04454 03485 02702 02417 03875 05452 02126 04673 z -1.89 5.08 8.97 -0.07 8.83 -1.83 0.28 -0.61 1.01 0.99 0.27 1.27 1.03 5.06 3.85 1.82 -0.78 -0.33 2.45 1.57 1.49 1.28 0.61 0.61 0.05 2.24 -1.05 -5.07 -5.27 0.61 0.95 -10.29 0.22 P>|z| [ 0.059 0.000 0.000 0.943 0.000 0.067 0.780 0.543 0.312 0.320 0.787 0.205 0.303 0.000 0.000 0.069 0.435 0.744 0.014 0.117 0.137 0.201 0.545 0.539 0.962 0.025 0.292 0.000 0.000 0.544 0.341 0.000 0.828 -.003957 000074 003657 008254 276785 431529 -.00091 000846 048227 075737 -.023493 000779 -.001619 002157 -.002625 001383 -.018946 059327 -.047867 146466 -.07628 10065 -.028318 131886 -.076894 24751 105938 24005 119109 366752 -.009159 241992 -.160524 069132 -.192105 13717 018556 166989 -.026935 242798 -.042029 306242 -.036702 174515 -.072714 137802 -.040081 076704 -.110989 116504 012263 186868 -.105003 031604 -.190022 -.084088 -.174874 -.080116 -.052452 099437 -.054903 158819 -.260404 -.177053 -.08145 10174 95% C.I ] X 14.424 4.66321 105312 27.279 1.36813 1.55982 5.19723 44.7917 601386 241109 698845 077136 021247 218938 047575 040647 016628 009238 104388 029099 016166 057275 039261 188453 03418 076674 08545 34642 127945 10254 03649 227252 055427 (*) dy/dx is for discrete change of dummy variable from to predict formal_pb, pr (394 missing values generated) predict formal_res, de (410 missing values generated) Page | 64 Appendix 4: Determine factors affect the probability of access to informal credit by employing probit model probit informal firm_age total_assets gov_ass NW_firm NW_bank NW_official NW_others age gender edu4 e > du5 ow2 ow4 ow5 ow7 indus2 indus3 indus4 indus5 indus6 indus8 indus9 indus10 indus11 indus12 indus13 > indus16 regi5 regi6 regi7 regi8 regi9 regi10, robust Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood = = = = = -623.18169 -577.43471 -575.83824 -575.83388 -575.83388 Number of obs Wald chi2(33) Prob > chi2 Pseudo R2 Probit regression Log pseudolikelihood = -575.83388 informal Coef firm_age total_assets gov_ass NW_firm NW_bank NW_official NW_others age gender edu4 edu5 ow2 ow4 ow5 ow7 indus2 indus3 indus4 indus5 indus6 indus8 indus9 indus10 indus11 indus12 indus13 indus16 regi5 regi6 regi7 regi8 regi9 regi10 _cons -.0039499 -.0061274 0336492 -.0023444 1370841 -.0248277 0059932 -.0088884 1479384 0289783 0421412 0273453 0686517 1612129 -.3461709 3023643 -.2110362 -.3877264 0706679 1050069 0588857 -.2372623 -.1610008 1286012 4267975 2700544 0017951 2270093 -.0258923 -.2155081 5239677 1271444 394521 -1.448364 Robust Std Err .0050239 0046544 129949 0019692 0204516 0256031 0033953 0041655 0908439 2024887 1977678 1625488 3075338 1200632 2300705 19736 3588773 498818 1494249 2371333 3127615 2146815 2264376 1194182 2034609 1573982 1685443 1637676 1922459 2156265 2284985 1805704 2124824 2984159 z -0.79 -1.32 0.26 -1.19 6.70 -0.97 1.77 -2.13 1.63 0.14 0.21 0.17 0.22 1.34 -1.50 1.53 -0.59 -0.78 0.47 0.44 0.19 -1.11 -0.71 1.08 2.10 1.72 0.01 1.39 -0.13 -1.00 2.29 0.70 1.86 -4.85 P>|z| 0.432 0.188 0.796 0.234 0.000 0.332 0.078 0.033 0.103 0.886 0.831 0.866 0.823 0.179 0.132 0.126 0.557 0.437 0.636 0.658 0.851 0.269 0.477 0.282 0.036 0.086 0.992 0.166 0.893 0.318 0.022 0.481 0.063 0.000 = = = = 2174 116.31 0.0000 0.0760 [95% Conf Interval] -.0137966 -.0152498 -.2210461 -.006204 0969997 -.0750088 -.0006614 -.0170527 -.0301125 -.3678923 -.3454765 -.2912445 -.5341034 -.0741067 -.7971007 -.0844541 -.9144228 -1.365392 -.2221996 -.3597659 -.5541156 -.6580302 -.6048103 -.1054543 0280214 -.0384405 -.3285456 -.0939692 -.4026873 -.6381283 076119 -.2267671 -.0219369 -2.033248 0058969 002995 2883445 0015151 1771685 0253534 0126479 -.0007241 3259892 4258488 4297589 3459351 6714068 3965325 1047589 6891828 4923504 5899389 3635354 5697796 6718869 1835056 2828088 3626567 8255735 5785493 3321358 5479878 3509028 2071122 9718164 481056 8109788 -.8634791 Page | 65 Appendix 5: Marginal effect for informal source mfx Marginal effects after probit y = Pr(informal) (predict) = 06881388 variable firm_age total_~s gov_ass* NW_firm NW_bank NW_off~l NW_oth~s age gender* edu4* edu5* ow2* ow4* ow5* ow7* indus2* indus3* indus4* indus5* indus6* indus8* indus9* indus10* indus11* indus12* indus13* indus16* regi5* regi6* regi7* regi8* regi9* regi10* dy/dx -.0005234 -.000812 0045476 -.0003107 0181653 -.00329 0007942 -.0011778 0191963 003883 0055155 0036862 0095491 0228449 -.0362098 0489231 -.0239729 -.0384768 0097592 0149639 0081383 -.0268546 -.019088 0180827 0748577 0422459 0002381 0317547 -.0033824 -.0251263 0971917 0177394 0671076 Std Err .00067 00062 01791 00026 00283 00338 00045 00055 01154 02743 02556 02229 04484 01809 01846 03798 03444 03502 02144 0362 04502 02038 02385 01776 04491 02856 02238 02412 02476 02192 05506 02644 04438 z -0.79 -1.32 0.25 -1.19 6.41 -0.97 1.77 -2.13 1.66 0.14 0.22 0.17 0.21 1.26 -1.96 1.29 -0.70 -1.10 0.46 0.41 0.18 -1.32 -0.80 1.02 1.67 1.48 0.01 1.32 -0.14 -1.15 1.77 0.67 1.51 P>|z| [ 0.432 0.188 0.800 0.233 0.000 0.331 0.078 0.033 0.096 0.887 0.829 0.869 0.831 0.207 0.050 0.198 0.486 0.272 0.649 0.679 0.857 0.188 0.423 0.309 0.096 0.139 0.992 0.188 0.891 0.252 0.078 0.502 0.131 -.001828 000781 -.00202 000397 -.030556 039651 -.000822 0002 012611 02372 -.009924 003344 -.000088 001676 -.00226 -.000096 -.00342 041813 -.049878 057644 -.044578 055609 -.040004 047376 -.078342 097441 -.012605 058295 -.072391 -.000029 -.025509 123356 -.091474 043528 -.107109 030155 -.032269 051788 -.055995 085923 -.080099 096375 -.066799 013089 -.065826 02765 -.016734 052899 -.013173 162889 -.013729 098221 -.043633 044109 -.015511 07902 -.051902 045138 -.068084 017831 -.010723 205106 -.034089 069568 -.019875 15409 95% C.I ] X 14.4154 4.67339 105336 27.2649 1.37075 1.55474 5.18307 44.7672 600276 24103 698712 077277 021159 220331 047378 040478 016099 00966 104876 028979 016099 057958 039558 189052 034039 076357 085097 344526 128795 102576 037718 227691 055198 (*) dy/dx is for discrete change of dummy variable from to predict informal_pb, pr (394 missing values generated) predict informal_res, de (401 missing values generated) Appendix 6: Determinants of formal/ informal credit accessibility (Full version) Page | 66 VARIABLES Firm age (years) Firm size (billions) Government assistance NW- firm (number) NW- bank (number) NW- official (number) NW- others (number) Age (years) Gender (male = 1) Education (Base: Primary school) Secondary school High school BIVARIATE PROBIT MODEL FORMAL INFORMAL (1) (2) -0.004 -0.007* (-1.74) (-0.87) -0.007 0.021*** (5.35) (-1.36) 0.049 1.017*** (10.18) (0.38) -0.000 -0.002 (-0.09) (-1.20) 0.222*** 0.135*** (11.28) (5.92) -0.022 -0.042** (-2.00) (-0.92) 0.001 0.006* (0.31) (1.62) -0.002 -0.008* (-0.59) (-1.88) 0.068 0.158* (0.93) (1.73) 0.173 (1.07) 0.043 (0.27) 0.032 (0.15) 0.044 (0.22) PROBIT MODEL FORMAL (3) -0.007* (-1.89) 0.021*** (5.14) 1.017*** (9.81) -0.000 (-0.07) 0.223*** (9.13) -0.041* (-1.84) 0.001 (0.28) -0.002 (-0.61) 0.073 (1.00) dy/dx (4) -0.002* (-1.89) 0.006*** (5.08) 0.354*** (8.97) -0.000 (-0.07) 0.062*** (8.83) -0.011* (-1.83) 0.000 (0.28) -0.001 (-0.61) 0.020 (1.01) INFORMAL (5) -0.004 (-0.79) -0.006 (-1.32) 0.034 (0.26) -0.002 (-1.19) 0.137*** (6.70) -0.025 (-0.97) 0.006* (1.77) -0.009** (-2.13) 0.148* (1.63) dy/dx (6) -0.001 (-0.79) -0.001 (-1.32) 0.005 (0.25) -0.000 (-1.19) 0.018*** (6.41) -0.003 (-0.97) 0.001* (1.77) -0.001** (-2.13) 0.019* (1.66) 0.171 (1.03) 0.044 (0.27) 0.049 (0.99) 0.012 (0.27) 0.029 (0.14) 0.042 (0.21) 0.004 (0.14) 0.006 (0.22) Type of ownership (Base: Household establishment) Page | 67 Private owner Partnership/cooperative Limited liability company Joint-stock company Industry (Base: Food and beverage) Textiles Wearing apparel Tanning & dressing leather Wood & wood products Paper & paper products Chemical products Rubber and plastic products Non-metallic mineral products Fabricated metal products Other machinery and equipment 0.178 (1.28) 0.280 (1.21) 0.557*** (5.40) 0.714*** (4.53) 0.028 (0.17) 0.061 (0.20) 0.141 (1.16) -0.340 (-1.46) 0.176 (1.34) 0.277 (1.13) 0.556*** (5.47) 0.712*** (4.34) 0.052 (1.27) 0.085 (1.03) 0.173*** (5.06) 0.243*** (3.85) 0.027 (0.17) 0.069 (0.22) 0.161 (1.34) -0.346 (-1.50) 0.004 (0.17) 0.010 (0.21) 0.023 (1.26) -0.036** (-1.96) 0.373** (2.15) -0.169 (-0.54) -0.101 (-0.28) 0.310*** (2.61) 0.349* (1.68) 0.416* (1.62) 0.234 (1.49) 0.118 (0.69) 0.071 (0.68) 0.014 (0.07) 0.297 (1.52) -0.216 (-0.58) -0.354 (-0.72) 0.009 (0.06) 0.109 (0.45) 0.066 (0.20) -0.223 (-1.07) -0.150 (-0.61) 0.131 (1.06) 0.432** (2.09) 0.369** (2.01) -0.178 (-0.72) -0.104 (-0.31) 0.304*** (2.65) 0.344* (1.73) 0.412* (1.66) 0.229 (1.37) 0.112 (0.63) 0.065 (0.62) 0.010 (0.05) 0.116* (1.82) -0.046 (-0.78) -0.027 (-0.33) 0.093*** (2.45) 0.108 (1.57) 0.132 (1.49) 0.069 (1.28) 0.033 (0.61) 0.018 (0.61) 0.003 (0.05) 0.302 (1.53) -0.211 (-0.59) -0.388 (-0.78) 0.071 (0.47) 0.105 (0.44) 0.059 (0.19) -0.237 (-1.11) -0.161 (-0.71) 0.129 (1.08) 0.427** (2.10) 0.049 (1.29) -0.024 (-0.7) -0.038 (-1.1) 0.010 (0.46) 0.015 (0.41) 0.008 (0.18) -0.027 (-1.32) -0.019 (-0.8) 0.018 (1.02) 0.075* (1.67) Page | 68 Furniture manufactures Services Region (Base: North-east) Red river delta North central coast South central coast Central highlands Southeast Mekong delta _cons 0.330*** (2.54) 0.270* (1.73) 0.323** (2.44) 0.100** (2.24) 0.270* (1.72) 0.042 (1.48) -0.134 (-0.89) 0.007 (0.04) -0.139 (-1.00) -0.037 (-1.05) 0.002 (0.01) 0.000 (0.01) -0.532*** (-4.63) -0.560*** (-4.16) 0.082 (0.62) 0.175 (1.01) -1.031*** (-7.33) 0.036 (0.22) -1.022*** (-4.06) -0.137*** (-5.07) -0.127*** (-5.27) 0.023 (0.61) 0.052 (0.95) -0.219*** (-10.29) 0.010 (0.22) 0.227 (1.39) -0.026 (-0.13) -0.216 (-1.00) 0.524** (2.29) 0.127 (0.70) 0.395* (1.86) -1.448*** (-4.85) 0.032 (1.32) -0.003 (-0.14) -0.025 (-1.15) 0.097* (1.77) 0.018 (0.67) 0.067 (1.51) -0.532*** (-4.45) -0.569*** (-3.93) 0.082 (0.59) 0.175 (0.92) -1.030*** (-7.16) 0.036 (0.21) -1.022*** (-4.09) 0.228 (1.41) -0.065 (-0.33) -0.216 (-0.99) 0.536** (2.31) 0.126 (0.70) 0.388* (1.75) -1.467*** (-4.60) 0.003 0.003 /atrho21 rho21 Likelihood ratio test of rho21 = Chi2(1) = 003 Prob > Chi2 = 0.9591 ***, **, and * present statistical significance level at 1%, 5%, and 10%, respectively z-statistics are reported in parentheses Page | 69 Appendix 7: The probability of access to financing sources by mean value sum formal_pb informal_pb formal_mv informal_mv Variable Obs Mean formal_pb informal_pb formal_mv informal_mv 2181 2181 2181 2181 2457662 0830379 2455217 0826352 Std Dev .2254948 0645305 225295 0641682 Min Max 0038962 0061858 0037837 0062359 9994652 5223722 9994302 5213665 *** TESTING FOR ENDOGENEITY Page | 70 Appendix 8: Testing for endogeneity problem with growth_1 equation reg growth_1 formal informal firm_age total_assets gov_ass age gender edu4 edu5 i.industry1 i.ownersh > ip formal_res informal_res, r Linear regression Number of obs F( 27, 2085) Prob > F R-squared Root MSE Robust Std Err t P>|t| = = = = = 2113 2.35 0.0001 0.0266 24307 growth_1 Coef [95% Conf Interval] formal informal firm_age total_assets gov_ass age gender edu4 edu5 0870021 -.2153186 -.0011253 0008302 -.0044021 -.0017226 -.0056816 -.0192578 -.0250962 0524444 1480827 0006423 0007149 0205293 00061 0119844 0205442 0201337 1.66 -1.45 -1.75 1.16 -0.21 -2.82 -0.47 -0.94 -1.25 0.097 0.146 0.080 0.246 0.830 0.005 0.635 0.349 0.213 -.0158467 -.5057239 -.002385 -.0005718 -.0446621 -.0029189 -.0291842 -.0595471 -.0645804 1898509 0750867 0001344 0022322 035858 -.0005262 0178209 0210314 014388 industry1 10 11 12 13 16 -.0274864 -.0624828 -.0749329 -.0248117 -.0064363 -.0513106 -.0629439 -.02263 -.0467082 -.0375775 -.0414777 -.0761419 0385555 0422431 0654477 0169055 024402 0353479 0218533 0403818 0160144 0301976 0202286 0229978 -0.71 -1.48 -1.14 -1.47 -0.26 -1.45 -2.88 -0.56 -2.92 -1.24 -2.05 -3.31 0.476 0.139 0.252 0.142 0.792 0.147 0.004 0.575 0.004 0.213 0.040 0.001 -.1030978 -.1453258 -.2032826 -.057965 -.0542912 -.1206315 -.1058004 -.1018229 -.078114 -.096798 -.0811482 -.1212429 048125 0203602 0534169 0083416 0414186 0180103 -.0200874 0565629 -.0153023 0216431 -.0018073 -.0310408 ownership 016649 0634279 -.0070795 -.034617 0295037 0332928 01776 0351006 0.56 1.91 -0.40 -0.99 0.573 0.057 0.690 0.324 -.0412108 -.0018627 -.0419087 -.1034529 0745088 1287184 0277497 0342189 formal_res informal_res _cons -.0251521 0950583 1398051 0222318 0554464 0395353 -1.13 1.71 3.54 0.258 0.087 0.000 -.0687509 -.0136778 0622723 0184468 2037943 217338 test formal_res informal_res ( 1) ( 2) formal_res = informal_res = F( 2, 2085) = Prob > F = 1.62 0.1980 Page | 71 Appendix 9: Testing for endogeneity problem with growth equation reg growth formal informal firm_age total_assets gov_ass age gender edu4 edu5 i.industry1 i.ownership > formal_res informal_res, r Linear regression Number of obs F( 27, 2081) Prob > F R-squared Root MSE Robust Std Err t P>|t| = = = = = 2109 2.27 0.0002 0.0306 22.791 growth Coef [95% Conf Interval] formal informal firm_age total_assets gov_ass age gender edu4 edu5 8.584184 -34.10734 -.1112498 0655492 -.9889987 -.130031 4523778 -2.978206 -2.628651 4.683732 10.03717 0615224 065849 1.86332 0569344 1.089106 2.202652 2.164536 1.83 -3.40 -1.81 1.00 -0.53 -2.28 0.42 -1.35 -1.21 0.067 0.001 0.071 0.320 0.596 0.022 0.678 0.176 0.225 -.6011035 -53.79128 -.2319017 -.0635875 -4.643164 -.2416854 -1.683474 -7.297838 -6.873531 17.76947 -14.42339 0094021 194686 2.665166 -.0183766 2.588229 1.341425 1.61623 industry1 10 11 12 13 16 -4.904997 -8.874591 -5.989182 -2.200916 -1.095737 -5.289085 -6.249222 -4.747871 -5.076331 -2.849226 -5.284307 -6.593753 2.597187 4.892276 6.34255 1.837835 2.512094 3.453711 2.344084 2.721154 1.563932 2.724424 1.806594 2.3132 -1.89 -1.81 -0.94 -1.20 -0.44 -1.53 -2.67 -1.74 -3.25 -1.05 -2.93 -2.85 0.059 0.070 0.345 0.231 0.663 0.126 0.008 0.081 0.001 0.296 0.003 0.004 -9.998353 -18.46886 -18.42759 -5.805103 -6.022216 -12.06217 -10.84621 -10.08434 -8.143365 -8.192107 -8.827228 -11.13018 1883584 7196745 6.449221 1.403272 3.830742 1.484003 -1.652228 5885964 -2.009297 2.493655 -1.741387 -2.057326 ownership 3.050086 6.621815 -.16414 -4.149993 2.726697 3.962467 1.740708 2.944555 1.12 1.67 -0.09 -1.41 0.263 0.095 0.925 0.159 -2.297252 -1.148998 -3.57785 -9.924573 8.397425 14.39263 3.24957 1.624587 formal_res informal_res _cons -2.108215 14.17727 15.68726 1.973887 3.897426 3.739103 -1.07 3.64 4.20 0.286 0.000 0.000 -5.979214 6.534008 8.354488 1.762784 21.82053 23.02003 test formal_res informal_res ( 1) ( 2) formal_res = informal_res = F( 2, 2081) = Prob > F = 7.27 0.0007 Page | 72 *** REGRESSION WITH GROWTH_1 Appendix 10: Run growth_1 regression by using OLS method reg growth_1 formal informal firm_age total_assets gov_ass age gender edu4 edu5 i.industry1 i.ownersh > ip, r Linear regression Number of obs F( 25, 2362) Prob > F R-squared Root MSE Robust Std Err t P>|t| = = = = = 2388 2.72 0.0000 0.0285 2575 growth_1 Coef [95% Conf Interval] formal informal firm_age total_assets gov_ass age gender edu4 edu5 0318736 0277652 -.0010125 0014247 0246765 -.001143 -.0046808 -.0176442 -.0168552 0132967 0209631 0006266 0005902 0240228 0005625 0121266 0194906 0190412 2.40 1.32 -1.62 2.41 1.03 -2.03 -0.39 -0.91 -0.89 0.017 0.185 0.106 0.016 0.304 0.042 0.700 0.365 0.376 0057993 -.0133428 -.0022412 0002674 -.0224316 -.002246 -.0284606 -.0558646 -.0541944 0579479 0688731 0002162 002582 0717845 -.0000399 019099 0205763 0204839 industry1 10 11 12 13 16 -.0532277 -.0421673 -.0657398 -.0307893 -.0313176 0565742 -.0643613 -.034566 -.0565241 -.074711 -.0485652 -.0747802 0338821 0369413 0610208 0163616 0228268 1067141 0215717 035686 0152938 0278081 0225748 02102 -1.57 -1.14 -1.08 -1.88 -1.37 0.53 -2.98 -0.97 -3.70 -2.69 -2.15 -3.56 0.116 0.254 0.281 0.060 0.170 0.596 0.003 0.333 0.000 0.007 0.032 0.000 -.1196694 -.114608 -.1853998 -.0628739 -.0760802 -.1526888 -.1066627 -.104545 -.0865147 -.1292419 -.0928338 -.1159997 013214 0302734 0539202 0012954 013445 2658372 -.0220599 0354131 -.0265336 -.0201802 -.0042967 -.0335607 ownership 0021083 0658972 -.0101887 0381898 0259954 0355516 0177789 0523377 0.08 1.85 -0.57 0.73 0.935 0.064 0.567 0.466 -.0488678 -.0038184 -.0450526 -.0644428 0530845 1356128 0246753 1408225 _cons 0853774 0317304 2.69 0.007 023155 1475997 Page | 73 *** REGRESSION WITH GROWTH Appendix 11: Run growth regression by using OLS method reg growth formal informal firm_age total_assets gov_ass age gender edu4 edu5 i.industry1 i.ownership > , r Linear regression Number of obs F( 25, 2356) Prob > F R-squared Root MSE Robust Std Err t P>|t| = = = = = 2382 2.41 0.0001 0.0278 22.643 growth Coef [95% Conf Interval] formal informal firm_age total_assets gov_ass age gender edu4 edu5 3.71745 2.923251 -.0578089 1428741 -.5430018 -.0820643 -.3352125 -2.990541 -2.397873 1.20697 1.778569 0564631 0566231 1.427182 0513061 1.016466 2.055516 2.010979 3.08 1.64 -1.02 2.52 -0.38 -1.60 -0.33 -1.45 -1.19 0.002 0.100 0.306 0.012 0.704 0.110 0.742 0.146 0.233 1.350616 -.564473 -.1685315 0318378 -3.341665 -.182674 -2.328473 -7.021349 -6.341345 6.084284 6.410975 0529137 2539104 2.255661 0185454 1.658048 1.040267 1.545599 industry1 10 11 12 13 16 -7.591401 -5.772548 -4.658225 -2.513734 -2.927744 -4.659115 -5.41099 -5.871398 -5.943382 -6.604433 -6.725845 -5.881378 2.406162 4.307466 5.949956 1.746056 2.236846 3.142498 2.251409 2.631376 1.440992 2.520516 1.714413 2.046995 -3.15 -1.34 -0.78 -1.44 -1.31 -1.48 -2.40 -2.23 -4.12 -2.62 -3.92 -2.87 0.002 0.180 0.434 0.150 0.191 0.138 0.016 0.026 0.000 0.009 0.000 0.004 -12.30982 -14.21937 -16.32592 -5.937701 -7.314135 -10.82146 -9.825939 -11.03145 -8.769127 -11.54709 -10.08776 -9.895476 -2.872987 2.67427 7.009468 9102326 1.458648 1.503234 -.9960412 -.7113448 -3.117638 -1.661774 -3.36393 -1.867279 ownership 1.427343 7.725896 -1.223497 -1.877983 2.386853 3.88328 1.539737 2.687432 0.60 1.99 -0.79 -0.70 0.550 0.047 0.427 0.485 -3.253208 1108946 -4.242878 -7.14796 6.107893 15.3409 1.795883 3.391994 _cons 9.213433 3.043897 3.03 0.002 3.244439 15.18243 Page | 74 Appendix 12: Run growth regression by using Bivariate probit model reg growth formal_mv informal_mv firm_age total_assets gov_ass age gender edu4 edu5 i.industry1 i.own > ership, r Linear regression Number of obs F( 25, 2103) Prob > F R-squared Root MSE Robust Std Err = = = = = 2129 2.57 0.0000 0.0309 23.099 growth Coef formal_mv informal_mv firm_age total_assets gov_ass age gender edu4 edu5 11.89893 -44.71643 -.1080369 059882 -1.6344 -.1453928 4710492 -3.201462 -2.695373 4.310431 9.904608 0616151 0695469 1.976038 056776 1.100612 2.207936 2.154596 2.76 -4.51 -1.75 0.86 -0.83 -2.56 0.43 -1.45 -1.25 0.006 0.000 0.080 0.389 0.408 0.011 0.669 0.147 0.211 3.445781 -64.14029 -.2288699 -.0765059 -5.509595 -.2567359 -1.687353 -7.531429 -6.920735 20.35209 -25.29258 0127961 1962698 2.240795 -.0340498 2.629451 1.128504 1.52999 industry1 10 11 12 13 16 -4.749651 -8.682043 -7.290257 -2.300718 -1.535476 -5.941099 -6.518027 -3.444269 -5.22807 -1.825924 -5.20206 -6.436919 2.569302 4.819413 6.302402 1.875478 2.551867 3.433851 2.313753 3.096106 1.563774 2.672562 1.781765 2.294826 -1.85 -1.80 -1.16 -1.23 -0.60 -1.73 -2.82 -1.11 -3.34 -0.68 -2.92 -2.80 0.065 0.072 0.248 0.220 0.547 0.084 0.005 0.266 0.001 0.495 0.004 0.005 -9.788291 -18.13336 -19.64985 -5.978704 -6.539923 -12.6752 -11.05551 -9.51602 -8.294775 -7.067066 -8.696267 -10.93729 2889885 7692729 5.069337 1.377269 3.468972 7930012 -1.980543 2.627482 -2.161365 3.415217 -1.707853 -1.936553 ownership 2.631549 6.212766 -.3753117 -5.649623 2.739774 3.953074 1.723977 3.028836 0.96 1.57 -0.22 -1.87 0.337 0.116 0.828 0.062 -2.741402 -1.539579 -3.756191 -11.58945 8.0045 13.96511 3.005568 2902059 _cons 14.48555 3.50296 4.14 0.000 7.61592 21.35518 t P>|t| [95% Conf Interval] Page | 75 Appendix 13: Run growth regression by using probit model reg growth formal_pb informal_pb firm_age total_assets gov_ass age gender edu4 edu5 i.industry1 i.own > ership, r Linear regression Number of obs F( 25, 2103) Prob > F R-squared Root MSE Robust Std Err t P>|t| = = = = = 2129 2.41 0.0001 0.0296 23.114 growth Coef [95% Conf Interval] formal_pb informal_pb firm_age total_assets gov_ass age gender edu4 edu5 11.23125 -40.51464 -.1054131 0696353 -1.559438 -.141415 3202857 -3.172155 -2.670646 4.257388 10.00841 0616026 0694372 1.972568 057033 1.100224 2.209741 2.156882 2.64 -4.05 -1.71 1.00 -0.79 -2.48 0.29 -1.44 -1.24 0.008 0.000 0.087 0.316 0.429 0.013 0.771 0.151 0.216 2.88212 -60.14206 -.2262216 -.0665375 -5.427826 -.253262 -1.837355 -7.505661 -6.90049 19.58038 -20.88722 0153954 2058081 2.30895 -.029568 2.477927 1.16135 1.559199 industry1 10 11 12 13 16 -4.954833 -8.620216 -7.311863 -1.905722 -1.637903 -5.93442 -6.529938 -3.390295 -5.312033 -2.252738 -5.297888 -6.489991 2.570761 4.825358 6.318676 1.883265 2.550945 3.440104 2.31789 3.099534 1.559527 2.680395 1.786641 2.294672 -1.93 -1.79 -1.16 -1.01 -0.64 -1.73 -2.82 -1.09 -3.41 -0.84 -2.97 -2.83 0.054 0.074 0.247 0.312 0.521 0.085 0.005 0.274 0.001 0.401 0.003 0.005 -9.996334 -18.08319 -19.70337 -5.59898 -6.640543 -12.68078 -11.07554 -9.468769 -8.37041 -7.509242 -8.801656 -10.99006 0866668 8427581 5.079647 1.787536 3.364737 8119426 -1.98434 2.68818 -2.253657 3.003766 -1.79412 -1.989926 ownership 2.606097 6.212039 -.2970647 -5.416516 2.740203 3.950595 1.724863 3.02136 0.95 1.57 -0.17 -1.79 0.342 0.116 0.863 0.073 -2.767696 -1.535444 -3.679682 -11.34168 7.979889 13.95952 3.085552 5086503 _cons 14.09571 3.518059 4.01 0.000 7.196475 20.99496 Page | 76 Appendix 14: Testing for multi-collinearity with VIF vif Variable VIF 1/VIF formal_pb informal_pb firm_age total_assets gov_ass age gender edu4 edu5 industry1 10 11 12 13 16 ownership 3.65 2.20 1.25 1.60 1.66 1.31 1.17 3.89 4.16 0.274102 0.455090 0.796958 0.626665 0.604089 0.764031 0.851178 0.256884 0.240159 1.15 1.05 1.04 1.28 1.15 1.07 1.24 1.13 1.44 1.30 1.27 1.29 0.866227 0.952665 0.962524 0.778674 0.866915 0.931667 0.809217 0.883967 0.696611 0.767684 0.789734 0.776927 1.18 1.08 1.89 1.52 0.847832 0.921725 0.527787 0.656912 Mean VIF 1.64 Page | 77 ... likely to select informal sector to finance their business operation than the old ones Keywords: formal, informal, access to financing, sales growth, Vietnam TABLE OF CONTENTS CHAPTER ONE: INTRODUCTION... investigate the determinants affect the access to formal and informal finance and its impact on SMEs’ sales growth by using small and medium sized enterprises in Vietnam in 2013 There is a lot...DECLARATION I declare that this thesis is entitled Determinants affect the probability of access to formal- informal finance and its impact on sales growth is submitted by me in fulfillment of the