(Luận văn) the impact of loans to small and medium enterprises, the case study of vietnam

t to UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS ng hi ep w VIETNAM – NETHERLANDS n lo ad PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS ju y th yi pl ua al n THE IMPACT OF LOANS TO va n SMALL AND MEDIUM ENTERPRISES: ll fu oi m THE CASE STUDY OF VIET NAM at nh z z BUI THI HONG CHINH k jm ht vb By om l.c gm an Lu MASTER OF ARTS IN DEVELOPMENT ECONOMICS n va ey t re Ho Chi Minh City, January 2018 i t to UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS ng hi ep w n VIETNAM – NETHERLANDS lo ad PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS ju y th yi pl ua al THE IMPACT OF LOANS TO n SMALL AND MEDIUM ENTERPRISES: n va ll fu THE CASE STUDY OF VIET NAM oi m at nh By z z k jm ht vb BUI THI HONG CHINH an Lu Dr NGUYEN THI THUY LINH om l.c gm Supervisor n va ey t re Ho Chi Minh City, January 2018 ii t to ng ACKNOWLEDGEMENT hi ep This process of writing a thesis is a collaborative experience involving the w support and helps from many people I want to express my gratitude to those who n lo give me the tremendous support to complete this thesis ad y th First of all, I would like to thank gratefully to my supervisor Dr Nguyen Thi ju Thuy Linh I cannot finish my thesis if not get the support and the advice from yi pl her al n ua Beside that, I want to thank to Dr Pham Khanh Nam who give me many n va useful and important advice that help me very much through the time I the thesis oi m questions when I need it ll fu Moreover, I want to thank Dr Truong Dang Thuy who helps me answer nh And I want to thank the professors and the teacher staff of Viet Nam – Neth- at erlands Program that gave many supports to me to have the knowledge, to solve the z z difficult problems in my studying process vb k jm ht Last, I want to thank my closet friends and my family om l.c gm an Lu n va ey t re iii ABBREVIATIONS t to ng hi ep w n SME Small and Medium Enterprise RD Regression Discontinuity Design PSM Propensity Score Matching Method DD Difference in difference lo ad VIF Variance inflation factor ju y th IV Heteroscedasticity pl al State Owned Enterprise n ua SOE yi HET Instrumental variable Organization for Economic Co-operation and Development DNNN State enterprises NHNN State Bank NHTM Commercial Bank n va OECD ll fu oi m at nh z z k jm ht vb om l.c gm an Lu n va ey t re iv ABSTRACT t to ng After a period of growth and affected by the crisis, Vietnam's economy has de- hi creased Hundreds of thousands of small and medium-sized enterprises went bank- ep rupt and shut down Loan is a solution for business to expand scale, increase sales w and profits, but it can create jobs, increase salaries to improve social welfare or not? n lo To verify that argument, the author uses the PSM method and combines with DD on ad the SME data set from 2009 to 2013 to more accurately assess the impact of the ju y th loans yi The results show that loans not have the effect of improving employee in- pl ua al comes, as well as creating more jobs In addition, the loans from informal sources n with low cost not help enterprises to expand their operations because of the small n va scale Loans from official sources are large scale, but the high costs overwhelm ll fu profits Moreover, the impact of formal loans also causes businesses to reduce their oi m jobs The topic also shows other factors such as export, type of ownership, scale, nh production technique, entrepreneurial qualification that affects to employment and at wage z z k jm ht vb om l.c gm an Lu n va ey t re v TABLE OF CONTENTS t to ng ACKNOWLEDGEMENT III hi ep ABBREVIATIONS .IV ABSTRACT V w n CHAPTER 1: INTRODUCTION lo ad 1.1 PROBLEM STATEMENT y th 1.2.1 Research objectives ju 1.2.2 Main research question yi pl CHAPTER 2: LITERATURE REVIEW al Definition of small and medium enterprises and types of credits va 2.1.1 n ua 2.1 REVIEW OF THEORY n 2.1.2 The impact of loans to employee from producer theory fu ll 2.1.3 Factors affecting the operation of the business m oi 2.2 REVIEW OF EMPIRICAL STUDIES 10 at nh 2.2.1 Impact of loan to SMEs in Viet Nam 10 2.2.2 Previous researches 12 z z 2.3 SUMMARY 13 vb jm ht CHAPTER 3: RESEARCH METHODOLOGY 14 k 3.1 ANALYTICAL FRAMEWORK 14 gm 3.2 ECONOMETRICS MODELS 15 l.c 3.2.1 Impact assessment methodology 15 om 3.2.2 Research proposal and select model 18 an Lu 3.2.3 Dependent variables 21 3.2.4 Independent variables 21 n va 3.3 DATA 24 4.2 DESCRIPTIVE STATISTICS 28 vi ey 4.1 OVERVIEW OF THE RESEARCH TOPIC 25 t re CHAPTER 4: RESEARCH RESULTS 25 4.3 REGRESSION RESULTS 30 t to 4.3.1 OLS regression results 30 ng 4.3.2 PSM combined with DD results 32 hi ep 4.3.3 Verification of model stability 41 4.4 DICUSSIONS 42 w CHAPTER 5: CONCLUSIONS AND POLICY IMPLICATIONS 47 n lo ad 5.1 CONCLUSIONS 47 y th 5.2 POLICY IMPLICATIONS 48 ju 5.3 LIMITS OF THE STUDY 49 yi pl REFERENCES 50 al n ua APPENDICES 55 va APPENDICES 1: DIVIDE THE SIZE OF THE BUSINESS 55 n APPENDICES INFLATION AND PRICE INDEX VND (1994=1) 56 fu ll APPENDICES INFLATION AND PRICE INDEX VND (1994=1) (CONT) 56 m oi APPENDICES IMPACT ASSESSMENT BY MATHEMATICAL METHOD 57 nh APPENDICES GROUPS WERE DEVIDED BY PSM METHOD 58 at z APPENDICES REGRESSION DISCONTINUITY DESIGN - RD 59 z APPENDICES INSTRUMENTAL VARIABLE - IV 60 vb jm ht APPENDICES DEFINITION OF SOME VARIABLES 61 APPENDICES DESCRIPTIVE STATISTICS 62 k om l.c gm APPENDICES 10 ANALYSIS OF CORRELATION BETWEEN QUANTITATIVE VARIABLES 64 an Lu n va ey t re vii LIST OF TABLE t to ng hi TABLE 3.1 DESCRIPTION AND MEASUREMENT VARIABLES 22 ep TABLE 4.1 DATA STATISTICS 25 w TABLE 4.2 STATISTICS DESCRIBE THE PARTICIPANTS AND THE CONTROL GROUPS n lo BEFORE THE LOAN 28 ad TABLE 4.3 IMPACT OF LOAN TO SMALL AND MEDIUM ENTERPRISES- BASIC MODEL 31 y th TABLE 4.4 REGRESSION MODEL OF THE LOANS TO SME 32 ju yi TABLE 4.5 TREND POINT OF GENERAL SUPPORT AREA 34 pl TABLE 4.6 IMPACT OF LOANS TO SME ON THE LABOUR COSTS 35 al n ua TABLE 4.7 IMPACT OF LOANS TO SME ON THE NUMBER OF EMPLOYEES 37 n va TABLE 4.8 IMPACT OF EACH TYPE TO SME ON THE LABOUR COSTS 38 fu TABLE 4.9 IMPACT OF EACH TYPE TO SME ON THE NUMBER OF EMPLOYEES 40 ll TABLE 4.10 INVESTMENT AND LABOUR 42 m oi TABLE 4.11 THE SCALE OF THE MOST IMPORTANT LOAN 43 at nh z z k jm ht vb om l.c gm an Lu n va ey t re viii LIST OF FIGURE t to ng hi ep Figure 2.1 Illustration of the impact of loan to SMEs………………………………5 Figure 2.2 Optimal coordination of production factors when loan increases…….…6 w n Figure 3.1 Impact of loans to SMEs when enterprises participate and not join in loans………………………………………………………………………….…….15 lo ad y th Figure 3.2 Impact assessment by DD method……………………………… … 18 ju Figure 3.3 Illustrate the general support area and the observation area discarded with PSM…………………………………………………………………… ……19 yi pl ua al Figure 4.1 The supply of formal credit………………………………………….…26 n Figure 4.2 The supply of informal credit………………………………………….27 n va Figure 4.3 The biggest difficulties prevent the development of SMEs……………44 ll fu oi m at nh z z k jm ht vb om l.c gm an Lu n va ey t re ix t to ng hi ep w n lo ad ju y th yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm an Lu n va ey t re REFERENCES t to ng Acevedo, G L., & Tan, H W (Eds.) 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opposite, it lo will get the value of ad ju y th Yi|Ti : the variable that has the result of business activities under condition T yi Average treatment effect – ATE for all business is: pl = E(Yi|Ti=1) – E(Yi|Ti=0) ua al ATE n = E(Yi|Ti=1) – E(Yi0|Ti=1) + E(Yi0|Ti=1) – E(Yi|Ti=0) va n = ATT + Selection Bias fu ll In practice it is impossible to observe the counter-facto situation Yi0|Ti=1, so it is oi m impossible to estimate the exact ATE at nh z z k jm ht vb om l.c gm an Lu n va ey t re 57 Appendices Groups were devided by PSM method t to With attnd command in nearest – neighbor matching method, we select the ng hi nearest n observations for each value range Each unit that participates in the policy, ep will be compared to a opposite unit with the nearest trend point (n = commonly used) or uses a non-participating object to collate with different participants w n lo With attr command in radius matching method, we choose to observe the ad given value range In the attnd statement there are very high trend difference points y th (the participants are not very close together) This problem makes poor quality ju yi comparisons and should be avoided by setting thresholds or tolerances above the pl maximum trend point (within range) We only compare there alternatives among al n ua trend points within a certain range However, if the number of participating n va companies is high, it will likely increase the sampling error ll fu With atts command in stratification matching method, we compare each oi m given value range It divides the support capital into several levels and calculates nh the impact of the program in each space For example, in each space, the impact of at the program would be the median difference in outcomes between the intervention z z and control observations The weighted average of the estimates which affect this vb jm ht space will show the overall program impact with the proportion of participants in each space that is weighted k gm With attk command in kernel matching method, we use an internal regres- l.c sion with non-parametric The risk is that there is a small group of non-participants om that satisfies the criteria within the overall support and results in counter-measures an Lu Without the number of parameters such as the internal kernel and linear comparison, the weighted average of all non-participants is used as a counter-argument for n ey t re with no quantitative number on the results of the comparison group near to each in- va each participant In contrast, linear regression calculates region-weighted regression tervential observation 58 Appendices Regression Discontinuity Design - RD t to This method selects program participants based on certain criteria, at which point is ng hi the cut point For example, if you want to get credit for the poor, the participants ep must first earn a poor income Then, select the above and below cut points for com- parison Subjects that are close to the cut point are often assumed to have similar w n characteristics If the policy is effective, the difference will be very clear around the lo ad cut point y th Impact assessment using RD ju yi pl n ua al Y= Labour productivity n va Control group ll fu + + + ++ + + ++ oi m nh Participating group at ++ + + + ++ + + + + z z k jm ht vb X= Number of employees l.c gm 20 Labours Source: Ruiz, Claudia; Love, Inessa (2012) Figure 7, page 27 om an Lu n va ey t re 59 Appendices Instrumental variable - IV t to This method use to overcome the endogenous state, the offense assumption, the ng hi goal is to clean up the correlation between Ti and ei in equation (3.1), meaning cov ep (T, ε) ≠ The reason is that the selection error from the unobserved characteristics will be included in the estimated surplus The remainder will contain variables that w n lo have correlations with the dummy variables This will make the normal OLS esti- ad mate to be biased (Ramu Ramanathan, 2002) y th ju This way will be done in two steps, called Two-Stage least squares - 2SLS: yi pl Step 1: Regression Ti = αZi + ui to find the probability of participating in the pro- ua al gram of the business i This variable Zi must be correlated with T (cov (Z, T) ≠ 0) n and not correlated with εi (cov (Z, ε) = 0) va n Step 2: Using Ti from step to regression Yi = βTi + εi And β will measure the ef- ll fu fect of the program m oi Choice of tool variables is a difficult problem, if poor quality variables can increase nh at the error, or higher than standard OLS calculations So, we need to prepare well be- z fore using method IV z k jm ht vb om l.c gm an Lu n va ey t re 60 Appendices Definition of some variables t to Number of regular employees: full time employees, contracted to work or work ng hi more than months, average working 20 days per month or 20 hours per week, or ep 183 days per year Regular and sufficient time reflects the size of the business Regular employees are often self employed or family members w n lo Number of employees paid in the enterprise (PaidLabour): are labor chosen from ad society This shows the ability of the enterprise to create jobs for employment, y th contributing to solving the problem of creating jobs for laborers ju yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm an Lu n va ey t re 61 Appendices Descriptive statistics t to Participating groups Number of observations = 574 Mean sd max 25,30 40,11 350 182,2 387,5 5206 989,5 2848 2,221 39148 1773 4103 3,977 42562 15,99 0,798 0,752 0,666 0,0873 0,0422 0,309 0,601 12,03 0,402 0,432 0,472 0,282 0,201 0,462 0,490 0 0 0 55 1 1 1 13,10 0,589 0,592 0,585 0,0383 0,0801 0,277 0,523 9,751 0,492 0,492 0,493 0,192 0,272 0,448 0,500 0 0 0 55 1 1 1 90 1 1 43,96 0,676 0,956 0,636 0,226 9,916 0,468 0,204 0,482 0,419 22 0 0 72 1 1 ng Control group Number of observations = 1042 mean sd max 11,58 22,43 270 71,32 181,3 1942 hi ep Labour (person) Salary (million dong) Total assets (million VND) Business features AGE8 MICRO8 HOUSEHOLD8 OWNLAND8 HAND8 EXPORT8 SOUTH8 LowTech8 Characteristics of the business owner HAge8 HGen8 Kinh8 Edu8 ProEdu8 Economic characteristics NSLD8 (million VND/person/year) ShareDept8 Source: SME 2009 w n lo ad ju y th yi pl n ua al n va ll fu oi m z z k jm ht vb 20 0 0 at 10,52 0,470 0,277 0,499 0,353 nh 46,71 0,670 0,917 0,530 0,146 0,168 2,1e+06 79,426 128,762 6125 1,7e+06 2,396 0,163 l.c 0,0689 gm 55,810 94,202 1243 0,346 6,092 om an Lu Labor: the control group has an average of 11 - 12 laborers, while the participation 62 ey that enterprises with loans have a higher number of employees t re than 13 to 14 participants in the control group and statistical meaning This shows n viation of 22 The participants were to 350 and 40, respectively, who were more va group is 25 - 26 people The control group ranged from to 270 with a standard de- Total Assets: The average asset of the participating group was 1.7 billion and 0.9 t to billion of the control group Group assets of the participating group are also higher ng from million to 42 billion The difference in mean total assets was a control group hi ep of 783 million compared to the control group in a statistically significant Salaries: Since the participants have more assets and more labor, the average salary w n for labor to participant group is more than 110 million Average attendance of the lo ad group is VND 182 million between and 5.2 billion; the control group was 71 mil- y th lion VND between and 1.9 billion VND ju yi AGE: the mean of participation group operates 13-14 years, while the control group pl is 15-16 years The control group operated more years than loan group about n ua al years va MICRO: the participants were significantly larger than the other group The control n ll fu group was over 79% of the control group, while the participants were 58% m oi HOUSEHOLD: 75% of the control group was household enterprises, while the par- at nh ticipating group had a lower rate, only 59% z HAND, EXPORT and LowTech: The participants in the loan had more points than z ht vb the control group: more exports (more than 3.8%), using hand tools (less than k jm 4.9%), low technology (less than 7.8%) college qualifications (ProEdu) was at 22% compared with 14% l.c gm Edu: The participation group graduated high school at 63% compared to 53%, and om HAge: The mean age of participating group was 43-44 years, compared with 46-47 an Lu of the control group NSLD: The participating group was 79 million VND/person/year compared with 55 ey 63 t re control group was 6% n in a statistically significant ShareDept: the participation group was 16% while the va million VND/person/year of the control group, an average of 24 million/person/year Appendices 10 Analysis of correlation between quantitative variables t to LNWAGE lnLABOR LNASSET lnNSLD AGE HAge ng hi ep 1.0000 lnLABOR 0.7383* 1.0000 0.6593* 0.7181* 1.0000 0.2926* 0.2136* 0.3289* 1.0000 -0.1948* -0.1832* -0.1213* -0.1288* 1.0000 -0.1504* -0.0659* -0.1014* 0.2664* 1.0000 0.0608* 0.0528* -0.0382* -0.0597* LNWAGE w LNASSET ShareDept n lo lnNSLD ad -0.1441* yi HAge ju y th AGE pl 0.1371* 1.0000 n ua 0.1065* al ShareDept va n * Statistically significant, meaning level 5% ll fu oi m Quantitative variables have no correlation The highest correlation coefficient was nh only 0.73 between the lnLABOR and LNWAGE variables, and the between lnLA- at BOR and LNASSET was 0.71 Less more 0.7 for the correlation of the remaining z z variables vb k so the model will have less multi-collinearity jm ht The quantitative independent variables are not strongly correlated with each other, gm The two independent variables (AGE and HAge) are negative with the dependent l.c variable (LNWAGE, lnLABOR), the remainder is the positive relationship The om LNASSET variable has the highest positive correlation for two dependent variables an Lu compared to the other independent variables n va ey t re 64