Determinants affect the access to formal informal credit and its impact on sales growth

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 .4 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 .5 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 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 0: log pseudolikelihood = -1214.9775 Iteration 1: log pseudolikelihood = -921.13098 Iteration 2: log pseudolikelihood = -915.97953 Iteration 3: log pseudolikelihood = -915.96176 Iteration 4: log pseudolikelihood = -915.96176 Probit regression Log pseudolikelihood = -915.96176 Number of obs = 2165 Wald chi2(33) = 399.35 Prob > chi2 = 0.0000 Pseudo R2 = 0.2461 Robust Std Err formal Coef firm_age total_assets -.0069978 0214619 0036932 0041744 gov_ass 1.016597 NW_firm -.0001152 NW_bank z P>|z| -1.89 5.14 0.058 0.000 1035941 9.81 0016143 -0.07 2233748 0244539 NW_official -.0409294 NW_others [95% Conf Interval] -.0142363 0132803 0002407 0296435 0.000 8135568 1.219638 0.943 -.0032791 0030488 9.13 0.000 1754461 2713035 0222656 -1.84 0.066 -.0845692 0027103 0009691 0034714 0.28 0.780 -.0058348 007773 age -.0022391 0036872 -0.61 0.544 -.009466 0049877 gender 0732318 0728943 1.00 0.315 -.0696383 2161019 edu4 1713202 1668699 1.03 0.305 -.1557387 4983792 edu5 0442459 1652582 0.27 0.789 -.2796543 3681461 ow2 1757441 1315887 1.34 0.182 -.0821649 4336531 ow4 277403 246368 1.13 0.260 -.2054694 7602754 ow5 5560958 1016867 5.47 0.000 3567935 7553981 ow7 7115023 1639406 4.34 0.000 3901845 1.03282 indus2 369446 1836585 2.01 0.044 0094819 7294101 indus3 -.1778858 2477457 -0.72 0.473 -.6634585 307687 indus4 -.1035102 3317133 -0.31 0.755 -.7536563 5466359 indus5 3044673 1149246 2.65 0.008 0792193 5297154 indus6 3441633 1986185 1.73 0.083 -.0451217 7334483 indus8 4115039 2472147 1.66 0.096 -.0730281 8960358 indus9 2291564 1670091 1.37 0.170 -.0981753 5564881 indus10 1123924 1781841 0.63 0.528 -.236842 4616269 indus11 0648812 1038471 0.62 0.532 -.1386554 2684178 indus12 0098976 2075245 0.05 0.962 -.3968431 4166382 indus13 3230368 1325006 2.44 0.015 0633403 5827332 indus16 -.1391756 1393509 -1.00 0.318 -.4122983 1339471 regi5 -.5321599 1148322 -4.63 0.000 -.757227 -.3070929 regi6 -.5599238 1346637 -4.16 0.000 -.8238599 -.2959878 regi7 0823861 1323032 0.62 0.533 -.1769233 3416955 regi8 1753878 1733091 1.01 0.312 -.1642918 5150674 regi9 -1.030858 140634 -7.33 0.000 -1.306496 -.7552208 regi10 0360703 1639441 0.22 0.826 -.2852543 3573949 _cons -1.022148 251819 -4.06 0.000 -1.515704 -.5285919 Appendix 3: Marginal effect for formal source mfx Marginal effects after probit y = Pr(formal) (predict) = 19706922 variable dy/dx P>|z| [ firm_age -.0019417 00103 total_~s 0059552 00117 -1.89 0.059 -.003957 000074 14.424 5.08 0.000 003657 008254 4.66321 gov_ass* 3541573 NW_firm -.000032 03948 8.97 0.000 276785 431529 105312 00045 -0.07 0.943 -.00091 000846 NW_bank 27.279 061982 00702 8.83 0.000 048227 075737 1.36813 NW_off~l -.0113571 00619 -1.83 0.067 -.023493 000779 1.55982 NW_oth~s 0002689 00096 0.28 0.780 -.001619 002157 5.19723 age -.0006213 00102 -0.61 0.543 -.002625 001383 44.7917 0201905 01997 1.01 0.312 -.018946 059327 601386 edu4* 0492995 04958 0.99 0.320 -.047867 146466 241109 edu5* 0121849 04514 0.27 0.787 -.07628 10065 698845 ow2* 0517839 04087 1.27 0.205 -.028318 131886 077136 ow4* 085308 08276 1.03 0.303 -.076894 24751 021247 ow5* 1729936 03421 5.06 0.000 105938 24005 218938 ow7* 2429301 06318 3.85 0.000 119109 366752 047575 indus2* 1164162 06407 1.82 0.069 -.009159 241992 040647 indus3* -.0456959 05859 -0.78 0.435 -.160524 069132 016628 indus4* -.0274675 084 -0.33 0.744 -.192105 13717 009238 indus5* 0927723 03787 2.45 0.014 018556 166989 104388 indus6* 1079312 06881 1.57 0.117 -.026935 242798 029099 indus8* 1321062 08885 1.49 0.137 -.042029 306242 016166 indus9* 0689063 05388 1.28 0.201 -.036702 174515 057275 indus10* 0325438 0537 0.61 0.545 -.072714 137802 039261 indus11* 0183113 02979 0.61 0.539 -.040081 076704 188453 indus12* 0027572 05803 0.05 0.962 -.110989 116504 03418 indus13* 0995654 04454 2.24 0.025 012263 186868 076674 indus16* -.0366995 03485 -1.05 0.292 -.105003 031604 08545 regi5* -.137055 02702 -5.07 0.000 -.190022 -.084088 34642 regi6* -.1274949 02417 -5.27 0.000 -.174874 -.080116 127945 regi7* 0234926 03875 0.61 0.544 -.052452 099437 regi8* 0519583 05452 0.95 0.341 -.054903 158819 03649 regi9* -.2187284 02126 -10.29 0.000 -.260404 -.177053 227252 regi10* 010145 04673 0.22 0.828 gender* Std Err z 95% C.I -.08145 (*) 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) ] 10174 X 10254 055427 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 Probit regression Number of obs Wald chi2(33) Prob > chi2 Pseudo R2 Log pseudolikelihood = -575.83388 informal 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 Coef -.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 Appendix 5: Marginal effect for informal source mfx Marginal effects after probit y = Pr(informal) (predict) = 06881388 variable dy/dx P>|z| [ firm_age -.0005234 00067 total_~s -.000812 00062 -0.79 0.432 -.001828 000781 14.4154 -1.32 0.188 -.00202 000397 gov_ass* 0045476 4.67339 01791 0.25 0.800 -.030556 039651 NW_firm 105336 -.0003107 00026 -1.19 0.233 -.000822 0002 27.2649 NW_bank 0181653 00283 6.41 0.000 012611 02372 1.37075 NW_off~l -.00329 00338 -0.97 0.331 -.009924 003344 1.55474 NW_oth~s 0007942 00045 1.77 0.078 -.000088 001676 5.18307 -.0011778 00055 -2.13 0.033 -.00226 -.000096 44.7672 0191963 01154 1.66 0.096 -.00342 041813 600276 edu4* 003883 02743 0.14 0.887 -.049878 057644 24103 edu5* 0055155 02556 0.22 0.829 -.044578 055609 698712 ow2* 0036862 02229 0.17 0.869 -.040004 047376 077277 ow4* 0095491 04484 0.21 0.831 -.078342 097441 021159 ow5* 0228449 01809 1.26 0.207 -.012605 058295 220331 ow7* -.0362098 01846 -1.96 0.050 -.072391 -.000029 047378 indus2* 0489231 03798 1.29 0.198 -.025509 123356 040478 indus3* -.0239729 03444 -0.70 0.486 -.091474 043528 016099 indus4* -.0384768 03502 -1.10 0.272 -.107109 030155 00966 indus5* 0097592 02144 0.46 0.649 -.032269 051788 104876 indus6* 0149639 0362 0.41 0.679 -.055995 085923 028979 indus8* 0081383 04502 0.18 0.857 -.080099 096375 016099 indus9* -.0268546 02038 -1.32 0.188 -.066799 013089 057958 indus10* -.019088 02385 -0.80 0.423 -.065826 02765 039558 indus11* 0180827 01776 1.02 0.309 -.016734 052899 189052 indus12* 0748577 04491 1.67 0.096 -.013173 162889 034039 indus13* 0422459 02856 1.48 0.139 -.013729 098221 076357 indus16* 0002381 02238 0.01 0.992 -.043633 044109 085097 regi5* 0317547 02412 1.32 0.188 -.015511 07902 344526 -.0033824 02476 -0.14 0.891 -.051902 045138 128795 -.0251263 02192 -1.15 0.252 -.068084 017831 102576 0971917 05506 1.77 0.078 -.010723 205106 037718 0177394 02644 0.67 0.502 -.034089 069568 227691 0671076 04438 1.51 0.131 -.019875 15409 055198 age gender* regi6* regi7* regi8* regi9* regi10* Std Err z 95% C.I ] X (*) 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) 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 | 75 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) 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 zstatistics are reported in parentheses Appendix 7: The probability of access to financing sources by mean value sum formal_pb informal_pb formal_mv informal_mv Variable formal_pb informal_pb formal_mv informal_mv Obs 2181 2181 2181 2181 Mean 2457662 0830379 2455217 0826352 Std Dev .2254948 0645305 225295 0641682 Min 0038962 0061858 0037837 0062359 Max 9994652 5223722 9994302 5213665 *** TESTING FOR ENDOGENEITY Page | 78 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 2113 2.35 0.0001 0.0266 24307 growth_1 Coef 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) Robust Std Err = = = = = formal_res = informal_res = F( 2, 2085) = Prob > F = 1.62 0.1980 t P>|t| [95% Conf Interval] 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 Coef 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 ( 1) formal_res = ( 2) informal_res = F( 2, 2081) = Prob > F = 7.27 0.0007 P>|t| 2109 2.27 0.0002 0.0306 22.791 growth test formal_res informal_res t = = = = = [95% Conf Interval] *** 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 Robust Std Err t P>|t| Number of obs = F( 25, 2362) = Prob > F = 2388 2.72 0.0000 R-squared = 0.0285 Root MSE = 2575 growth_1 Coef [95% Conf Interval] formal informal firm_age total_assets gov_ass age gender edu4 edu5 0318736 0132967 2.40 0.017 0057993 0579479 0277652 0209631 1.32 0.185 -.0133428 0688731 -.0010125 0006266 -1.62 0.106 -.0022412 0002162 0014247 0246765 0005902 0240228 2.41 1.03 0.016 0.304 0002674 -.0224316 002582 0717845 -.001143 -.0046808 0005625 0121266 -2.03 -0.39 0.042 0.700 -.002246 -.0284606 -.0000399 019099 -.0176442 0194906 -0.91 0.365 -.0558646 0205763 -.0168552 0190412 -0.89 0.376 -.0541944 0204839 -.0532277 0338821 -1.57 0.116 -.1196694 013214 -.0421673 0369413 -1.14 0.254 -.114608 0302734 -.0657398 0610208 -1.08 0.281 -.1853998 0539202 -.0307893 0163616 -1.88 0.060 -.0628739 0012954 -.0313176 0228268 -1.37 0.170 -.0760802 013445 0565742 -.0643613 1067141 0215717 0.53 -2.98 0.596 0.003 -.1526888 -.1066627 2658372 -.0220599 industry1 10 -.034566 035686 -0.97 0.333 -.104545 0354131 11 -.0565241 0152938 -3.70 0.000 -.0865147 -.0265336 12 -.074711 0278081 -2.69 0.007 -.1292419 -.0201802 13 -.0485652 0225748 -2.15 0.032 -.0928338 -.0042967 16 -.0747802 02102 -3.56 0.000 -.1159997 -.0335607 ownership 0021083 0259954 0.08 0.935 -.0488678 0530845 0658972 0355516 1.85 0.064 -.0038184 1356128 -.0101887 0177789 -0.57 0.567 -.0450526 0246753 0381898 0523377 0.73 0.466 -.0644428 1408225 0853774 0317304 2.69 0.007 023155 1475997 _cons *** 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 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 P>|t| 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 = = = = = [95% Conf Interval] 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 P>|t| 2129 2.41 0.0001 0.0296 23.114 growth Coef 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 t = = = = = [95% Conf Interval] 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 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 0.866227 0.952665 0.962524 0.778674 0.866915 0.931667 0.809217 0.883967 0.696611 0.767684 0.789734 16 ownership 1.29 0.776927 1.18 1.08 1.89 1.52 0.847832 0.921725 0.527787 0.656912 Mean VIF 1.64 ... from the government and the access to formal credit of this type of industry is quite low, relative to the other groups The last is the effect of region on access to formal credit Based on the. .. 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... explore the connection between internal- external finance or bank credit and credit constraint on firm performance, very few papers evaluate the impact of formal and informal finance on sales growth