UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS IMPACT EVALUATION OF MICROCREDIT ON WELFARE OF THE VIETNAM RURAL HOUSEHOLD BY PHAM TIEN THANH MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, OCTOBER 2012 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS IMPACT EVALUATION OF MICROCREDIT ON WELFARE OF THE VIETNAM RURAL HOUSEHOLD A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By PHAM TIEN THANH Academic Supervisor: DR PHAM BAO DUONG HO CHI MINH CITY, OCTOBER 2012 DECLARATION I certify that the contents of thesis have been carried out and written by me to the best of my knowledge and with the support in preparing this paper from many different sources I certify that this thesis has not been submitted to any other programs or journals HCMC, October 15th , 2012 PHAM TIEN THANH i ACKOWLEDGEMENTS This thesis is impossible to be achieved without the support and assistance of the following people: Firstly, I would like to express my greatest gratitude to Dr Pham Bao Duong, my academic supervisor, who advised and instructed and supported me during the process of this thesis His expertise and his suggestions have provided a good basis for the improvement of my research His enthusiasm and encouraging is also a motivation for me to achieve me thesis I would like to give my special thanks to Prof.Dr Nguyen Trong Hoai, Dean of Vietnam–The Netherlands Programme and Dr Pham Khanh Nam, Academic Director of Vietnam –The Netherlands Programme Their knowledge and enthusiasm has supported me a lot during my thesis writing process This is also a good opportunity to express my appreciation to all the lecturers who equipped me with valuable knowledge during my study at Vietnam –The Netherlands Programme I would also like to appreciate Mr Nguyen Khanh Duy, Lecturer at the Faculty of Development Economics, University of Economics, Ho Chi Minh City His support with data as well as using econometrics software is a great contribution to the completion of my thesis Lastly, I am grateful to my beloved parents who gave moral support and encouraged me to finish my thesis during writing process ii ABSTRACT This research evaluates the impact of microcredit on the welfare of households living in the Vietnam rural areas, especially the poor The research is analyzed based on a data of the Vietnam household living standard survey (VHLSS) in the year 2008 The difference of the research in comparison with the previous studied about the relationship between microcredit and welfare is the employment of propensity score matching (PSM) method, thus it reflects the impact of microcredit on rural households’ living standard better and more precisely The result shows that microcredit will result in better welfare of rural households via a greater increase in the income and consumption per capita per month of the participating households However, the result about the poor rural households showed that microcredit does not result in a higher increase in income of the participants than that of the nonparticipants, but contributes to a greater rise in the consumption The research also showed the determinants on the accessibility to microcredit programs of the households living in rural regions The results found out that the probability of accessing the microcredit sources of the rural households in Vietnam is still low Moreover, the proportionate of accessibility to microcredit of the poor household is even less that of the nonpoor households, which means microcredit programs mistarget the poor households From those results, the research gives policy recommendations to improve microcredit programs in rural areas as well as to support more poor households to access to microcredit sources iii TABLE OF CONTENT DECLARATION i ACKNOWLEDMENTS ii ABSTRACT iii TABLE OF CONTENT iv LIST OF ABBRIVIATIONS vi LIST OF TABLES AND FIGURES vii CHAPTER I .1 INTRODUCTION 1.1 Problem Statement .1 1.2 Objectives of the study 1.3 Research questions .4 1.4 Organization of the research CHAPTER II .6 LITERARTURE REVIEW 2.1 Overview of Poverty 2.1.1 Definition 2.1.2 Method of defining poverty 2.2 Overview of Microcredit .8 2.2.1 Some definitions 2.2.2 Characteristics of Microcredit 2.2.3 Overview of rural credit market in Vietnam 11 2.2.4 Overview of microcredit program in Vietnam .12 2.3 Empirical Study 17 2.3.1 Impact of micro credit on welfare/ living standard of the rural households 17 2.3.2 Determinants of the accessibility to microcredit programs 24 iv CHAPTER III 30 RESEARCH METHODOLOGY AND DATA DESCRIPTION .30 3.1 Model of determinants of access to credit 30 3.2.Impact Evaluation techniques .34 3.2.1 Some Definition 34 3.2.2 Impact evaluation using PSM technique 34 3.2.3 Impact evaluation using DID technique 38 3.3 Data Description 41 3.3.1 Survey area 41 3.3.2 Data sources 41 3.3.3 Sample selection 41 CHAPTER IV 44 RESULT 44 4.1 Descriptive Statistics 44 4.2 Determinants on microcredit participation .46 4.3 Impact of microcredit on welfare of rural households using PSM .51 4.4 Impact of microcredit on welfare of the rural poor using PSM 52 4.5 Impact of microcredit on welfare of rural households using DID with fixed effect 55 4.6 Comparison between the results of PSM and DID method 56 4.7 Comparison with previous studies .57 CHAPTER V 59 CONCLUSION, POLICY RECOMMENDATION AND LIMITATION 59 5.1 Conclusion 59 5.2 Policy Recommendation 62 5.3 Limitation 64 REFERENCES 65 APPENDIX .63 v LIST OF ABBREVIATIONS GSO General Statistics Office MOLISA Ministry of Labor, Invalids and Social Affairs DOLISA Department of Labor, Invalids and Social Affairs MFIs Micro Finance Institutions VBA Vietnam Bank for Agriculture and Rural Development VBSP The Vietnam Bank for Social Policies WB World Bank UN The United Nations PSM Propensity Score Matching VHLSS Vietnam household living standard survey NN Nearest neighbor PSM Propensity Score Matching DD or DID Difference in Difference or Double Difference vi LIST OF TABLES AND FIGURES List of Tables Table 2.1: Poverty Rate in Vietnam Table 2.2: Characteristics of Microcredit Programs in Vietnam from 2005 to 2011 12 Table 2.3: Main Characteristics of the MFIs in 2011 14 Table 2.4: Characteristics of Microcredit Programs by VBSP from 2005 to 2011 16 Table 2.5: Summary of Some Main Findings about the Impact of Microcredit Programs on Welfare/ Living Standards 20 Table 3.1: Descriptions of the Determinants on Accessibility to Microcredit 31 Table 3.2: Variables in the Analysis of the Impact of Microcredit using DID 40 Table 3.3: Characteristics of Comparison Groups in 2008 43 Table 4.1: Impact of Microcredit on Income/Consumption of Rural Households using Independent Sample T-Test Methods 44 Table 4.2: Impact of Microcredit on Income/Consumption of the Rural Poor using Independent Sample T-Test Methods 44 Table 4.3: Distribution of Eligibility with respect to Treatment Households 45 Table 4.4: Credit Access with respect to Eligible Households 45 Table 4.5: Probit Estimations of Determinants on Accessibility to Microcredit 47 Table 4.6: Probit Estimation of Model with Marginal Effect 48 Table 4.7: Impact of Microcredit on Income of Rural Households using PSM 51 Table 4.8: Impact of Microcredit on Consumption of Rural Households using PSM 52 Table 4.9: Impact of Microcredit on Income of the Rural Poor using PSM 53 Table 4.10: Sector of Production and Business on Which the Loan was Spent 53 Table 4.11: Reasons of Unchanged or Worse Living Condition 54 Table 4.12: Impact of Microcredit on Consumption of the Rural Poor using PSM 54 Table 4.13: Impact of Microcredit on Welfare of Rural households using DID with fixed effect 55 Table 4.14: Result Comparison between PSM and DID Method 56 Table 4.15: Results from the Previous Studies 57 vii List of Figures Figure 2.1 : Gross Loan Portfolio of microcredit in Vietnam from 2005 to 2011 13 Figure 2.2 : Characteristics of Microcredit Programs by VBSP from 2005 to 2011 15 Figure 2.3 : Determinants on Accessibility to Microcredit and Welfare Indicators 29 Figure 3.1 : Illustration of Impact Evaluation Using DID Method 39 viii 1.2.4 Marginal Effect of Probit Model Marginal effects after probit y = Pr(credit) (predict) = 09857761 -variable | dy/dx Std Err z P>|z| [ 95% C.I ] X -+ -hgender*| -.0306262 026 -1.18 0.239 -.081593 020341 794216 age | 0091425 00357 2.56 0.010 002142 016143 50.4116 age2 | -.0001125 00003 -3.25 0.001 -.00018 -.000045 2743.42 hedu | 0044193 00199 2.22 0.026 000517 008322 6.47988 hmar*| 0118135 02473 0.48 0.633 -.036665 060292 816429 ost*| 0473607 03206 1.48 0.140 -.015483 110204 054904 ethnic*| 0243143 01759 1.38 0.167 -.010162 05879 801341 hhsize | 0051596 00443 1.17 0.244 -.003516 013835 4.1404 drate | -.0090407 01013 -0.89 0.372 -.028896 010814 777072 lpc | 1.06e-07 00000 0.14 0.885 -1.3e-06 1.5e-06 2295.05 hval | -3.54e-07 00000 -3.23 0.001 -5.7e-07 -1.4e-07 94961.5 distance | 0002262 00073 0.31 0.757 -.001209 001661 7.05692 geof1*| -.0287043 01756 -1.63 0.102 -.063121 005713 149204 geof2*| -.0659635 01816 -3.63 0.000 -.101561 -.030366 534367 geo2*| 0437096 01914 2.28 0.022 006202 081217 219195 rbs*| -.042231 01856 -2.28 0.023 -.078602 -.00586 805532 ca2*| 0020188 01932 0.10 0.917 -.035841 039878 887678 po*| -.0177212 02118 -0.84 0.403 -.059231 023789 892707 -(*) dy/dx is for discrete change of dummy variable from to 76 The impact of microcredit program on welfare of rural households using PSM 2.1 Estimation of propensity score **************************************************** Algorithm to estimate the propensity score **************************************************** The treatment is credit =0 : No ; | =1 : Yes | Freq Percent Cum + | 2,096 87.85 87.85 | 290 12.15 100.00 + Total | 2,386 100.00 Estimation of the propensity score (sum of wgt is Iteration 0: Iteration 1: Iteration 2: Iteration 3: Iteration 4: 5.3093e+06) log pseudolikelihood log pseudolikelihood log pseudolikelihood log pseudolikelihood log pseudolikelihood = = = = = -833.18428 -769.26135 -765.72159 -765.6464 -765.64634 Probit regression Number of obs Wald chi2(18) Prob > chi2 Pseudo R2 Log pseudolikelihood = -765.64634 = = = = 2386 120.10 0.0000 0.0811 -| Robust credit | Coef Std Err z P>|z| [95% Conf Interval] -+ -hgender | -.2204426 1450126 -1.52 0.128 -.504662 0637769 age | 0512422 0225119 2.28 0.023 0071198 0953646 age2 | -.0006241 0002232 -2.80 0.005 -.0010614 -.0001867 hedu | 0176923 0123278 1.44 0.151 -.0064698 0418544 hmar | 1465891 1661613 0.88 0.378 -.1790811 4722593 ost | 2457945 160846 1.53 0.126 -.0694579 5610468 ethnic | 0786153 1282612 0.61 0.540 -.172772 3300026 hhsize | 0322468 0276332 1.17 0.243 -.0219132 0864068 drate | -.0619393 0564842 -1.10 0.273 -.1726462 0487677 lpc | 3.39e-06 4.05e-06 0.84 0.402 -4.55e-06 0000113 hval | -2.04e-06 6.35e-07 -3.21 0.001 -3.28e-06 -7.93e-07 distance | 0039783 0045815 0.87 0.385 -.0050013 0129578 geof1 | -.170938 1218422 -1.40 0.161 -.4097443 0678682 geof2 | -.3183673 1026071 -3.10 0.002 -.5194736 -.1172611 geo2 | 2445213 1032658 2.37 0.018 0421241 4469186 rbs | -.1804218 0925165 -1.95 0.051 -.3617508 0009072 ca2 | 0771773 1152368 0.67 0.503 -.1486828 3030373 po | -.0082208 1123458 -0.07 0.942 -.2284145 211973 _cons | -2.002358 5618691 -3.56 0.000 -3.103601 -.9011146 Note: the common support option has been selected The region of common support is [.012981, 47304715] 77 Description of the estimated propensity score in region of common support Estimated propensity score Percentiles Smallest 1% 015509 012981 5% 0246869 0130057 10% 0362468 0130107 Obs 2284 25% 067725 0130646 Sum of Wgt 2284 50% 75% 90% 95% 99% 1029042 1694928 2392524 2805114 365969 Largest 4342763 4371411 4579433 4730472 Mean Std Dev .1240969 0793075 Variance Skewness Kurtosis 0062897 1.063566 3.974971 ****************************************************** Step 1: Identification of the optimal number of blocks Use option detail if you want more detailed output ****************************************************** The final number of blocks is This number of blocks ensures that the mean propensity score is not different for treated and controls in each block ********************************************************** Step 2: Test of balancing property of the propensity score Use option detail if you want more detailed output ********************************************************** The balancing property is satisfied This table shows the inferior bound, the number of treated and the number of controls for each block Inferior | of block | =0 : No ; =1 : Yes of pscore | | Total -+ + -.012981 | 754 43 | 797 0833333 | 547 57 | 604 125 | 242 50 | 292 1666667 | 423 127 | 550 3333333 | 28 13 | 41 -+ + -Total | 1,994 290 | 2,284 Note: the common support option has been selected ******************************************* End of the algorithm to estimate the pscore ******************************************* 78 2.2 Impact evaluation of microcredit on welfare 2.2 Impact on log of average income per capita 2.2 1.Nearest Neighbor Matching method Analytical standard errors n treat n contr ATT Std Err t 290 254 0.082 0.034 2.431 Note: the numbers of treated and controls refer to actual nearest neighbour matches Bootstrapped standard errors n treat n contr ATT Std Err t 290 254 0.082 0.029 2.785 Note: the numbers of treated and controls refer to actual nearest neighbour matches 2.2 Radius Matching method Analytical standard errors n treat n contr ATT Std Err t 170 362 0.090 0.038 2.361 Note: the numbers of treated and controls refer to actual matches within radius Bootstrapped standard errors n treat n contr ATT Std Err t 170 362 0.090 0.043 2.071 Note: the numbers of treated and controls refer to actual matches within radius 2.2 Stratification method Analytical standard errors n treat n contr ATT Std Err t 290 1994 0.092 0.028 3.325 - Bootstrapped standard errors n treat n contr ATT Std Err t 290 1994 0.092 0.028 3.343 - 79 2.2 Impact on average income per capita 2.2 1.Nearest Neighbor Matching method Analytical standard errors n treat n contr ATT Std Err t 290 254 90.331 23.554 3.835 Note: the numbers of treated and controls refer to actual nearest neighbour matches Bootstrapped standard errors n treat n contr ATT Std Err t 290 254 90.331 21.763 4.151 Note: the numbers of treated and controls refer to actual nearest neighbour matches 2.2 2 Radius Matching method Analytical standard errors n treat n contr ATT Std Err t 170 362 91.762 27.817 3.299 Note: the numbers of treated and controls refer to actual matches within radius Bootstrapped standard errors n treat n contr ATT Std Err t 170 362 91.762 38.510 2.383 Note: the numbers of treated and controls refer to actual matches within radius 2.2 Stratification method Analytical standard errors n treat n contr ATT Std Err t 290 1994 93.915 20.941 4.485 - Bootstrapped standard errors n treat n contr ATT Std Err t 290 1994 93.915 22.841 4.112 - 80 2.2 Impact on log of average consumption per capita 2.2 Stratification method Analytical standard errors n treat n contr ATT Std Err t 290 254 0.118 0.032 3.634 Note: the numbers of treated and controls refer to actual nearest neighbour matches Bootstrapped standard errors n treat n contr ATT Std Err t 290 254 0.118 0.036 3.237 Note: the numbers of treated and controls refer to actual nearest neighbour matches 2.2 Stratification method Analytical standard errors n treat n contr ATT Std Err t 170 362 0.130 0.036 3.604 Note: the numbers of treated and controls refer to actual matches within radius Bootstrapped standard errors n treat n contr ATT Std Err t 170 362 0.130 0.041 3.200 Note: the numbers of treated and controls refer to actual matches within radius 2.2 3 Stratification method ATT estimation with the Stratification method Analytical standard errors n treat n contr ATT Std Err t 290 1994 0.124 0.025 5.054 - Bootstrapped standard errors n treat n contr ATT Std Err t 290 1994 0.124 0.020 6.299 - 81 2.2 Impact on consumption per capita 2.2 1.Nearest Neighbor Matching method Analytical standard errors n treat n contr ATT Std Err t 290 254 73.284 18.190 4.029 Note: the numbers of treated and controls refer to actual nearest neighbour matches Bootstrapped standard errors n treat n contr ATT Std Err t 290 254 73.284 18.618 3.936 Note: the numbers of treated and controls refer to actual nearest neighbour matches 2.2 Radius Matching method Analytical standard errors n treat n contr ATT Std Err t 170 362 86.793 21.982 3.948 Note: the numbers of treated and controls refer to actual matches within radius Bootstrapped standard errors n treat n contr ATT Std Err t 170 362 86.793 29.912 2.902 Note: the numbers of treated and controls refer to actual matches within radius 2.2 Stratification method Analytical standard errors n treat n contr ATT Std Err t 290 1994 77.714 14.780 5.258 - Bootstrapped standard errors n treat n contr ATT Std Err t 290 1994 77.714 12.318 6.309 - 82 The impact of microcredit program on welfare of the rural poor using PSM 3.1 Estimation of propensity score **************************************************** Algorithm to estimate the propensity score **************************************************** The treatment is credit =0 : No ; | =1 : Yes | Freq Percent Cum + | 241 70.88 70.88 | 99 29.12 100.00 + Total | 340 100.00 Estimation of the propensity score (sum of wgt is Iteration 0: Iteration 1: Iteration 2: Iteration 3: Iteration 4: 7.3806e+05) log pseudolikelihood log pseudolikelihood log pseudolikelihood log pseudolikelihood log pseudolikelihood = = = = = -203.13416 -167.80027 -165.87674 -165.85165 -165.85164 Probit regression Number of obs Wald chi2(18) Prob > chi2 Pseudo R2 Log pseudolikelihood = -165.85164 = = = = 340 73.11 0.0000 0.1835 -| Robust credit | Coef Std Err z P>|z| [95% Conf Interval] -+ -hgender | 2546174 3139148 0.81 0.417 -.3606443 8698791 age | -.0114784 0398684 -0.29 0.773 -.089619 0666622 age2 | -.0000578 0003734 -0.15 0.877 -.0007896 000674 hedu | 0738638 0276461 2.67 0.008 0196785 128049 hmar | 0461368 3307367 0.14 0.889 -.6020952 6943687 ost | -.0677631 4929231 -0.14 0.891 -1.033875 8983484 ethnic | 5703301 2627605 2.17 0.030 0553289 1.085331 hhsize | 1593863 0646162 2.47 0.014 0327409 2860318 drate | -.3689113 1451254 -2.54 0.011 -.6533518 -.0844708 lpc | 7.26e-06 7.45e-06 0.98 0.329 -7.34e-06 0000219 hval | 5.46e-06 1.97e-06 2.77 0.006 1.59e-06 9.32e-06 distance | 0104119 0081855 1.27 0.203 -.0056313 0264551 geof1 | -.1127256 2612711 -0.43 0.666 -.6248075 3993562 geof2 | -.3248203 2335246 -1.39 0.164 -.7825201 1328796 geo2 | 4703378 2255054 2.09 0.037 0283552 9123203 rbs | -.1457987 204928 -0.71 0.477 -.5474502 2558528 ca2 | 2659439 2631803 1.01 0.312 -.2498801 7817678 po | 8480587 3086781 2.75 0.006 2430609 1.453057 _cons | -2.584804 1.08646 -2.38 0.017 -4.714226 -.4553815 Note: the common support option has been selected The region of common support is [.01778183, 90920033] 83 Description of the estimated propensity score in region of common support Estimated propensity score Percentiles Smallest 1% 0200174 0177818 5% 0361762 0179178 10% 0540576 0184947 Obs 330 25% 1257768 0200174 Sum of Wgt 330 50% 75% 90% 95% 99% 272886 4406013 6000481 6742904 824314 Largest 824314 8413337 8747703 9092003 Mean Std Dev .3016466 2062667 Variance Skewness Kurtosis 0425459 5873015 2.565311 ****************************************************** Step 1: Identification of the optimal number of blocks Use option detail if you want more detailed output ****************************************************** The final number of blocks is This number of blocks ensures that the mean propensity score is not different for treated and controls in each block ********************************************************** Step 2: Test of balancing property of the propensity score Use option detail if you want more detailed output ********************************************************** The balancing property is satisfied This table shows the inferior bound, the number of treated and the number of controls for each block Inferior | of block | =0 : No ; =1 : Yes of pscore | | Total -+ + -.0177818 | 52 | 55 0833333 | 24 | 25 125 | 15 11 | 26 1666667 | 67 19 | 86 3333333 | 50 28 | 78 | 19 24 | 43 6666667 | 11 | 14 8333333 | | -+ + -Total | 231 99 | 330 Note: the common support option has been selected ******************************************* End of the algorithm to estimate the pscore ******************************************* 84 3.2 Impact evaluation of microcredit on welfare using PSM 3.2 Impact on log of average income per capita 3.2 1.Nearest Neighbor Matching method Analytical standard errors n treat n contr ATT Std Err t 99 62 0.082 0.061 1.354 Note: the numbers of treated and controls refer to actualnearest neighbour matches Bootstrapped standard errors n treat n contr ATT Std Err t 99 62 0.082 0.054 1.526 Note: the numbers of treated and controls refer to actual nearest neighbour matches 3.2 Radius Matching method Analytical standard errors n treat n contr ATT Std Err t 51 76 0.136 0.064 2.113 Note: the numbers of treated and controls refer to actual matches within radius Bootstrapped standard errors n treat n contr ATT Std Err t 51 76 0.136 0.085 1.598 Note: the numbers of treated and controls refer to actual matches within radius 3.2 Stratification method ATT estimation with the Stratification method Analytical standard errors n treat n contr ATT Std Err t 99 231 0.079 - Bootstrapped standard errors n treat n contr ATT Std Err t 99 231 0.079 0.057 1.394 - 85 3.2 Impact on average income per capita 3.2 1.Nearest Neighbor Matching method Analytical standard errors n treat n contr ATT Std Err t 99 62 51.768 33.760 1.533 Note: the numbers of treated and controls refer to actual nearest neighbour matches Bootstrapped standard errors n treat n contr ATT Std Err t 99 62 51.768 31.258 1.656 Note: the numbers of treated and controls refer to actual nearest neighbour matches 3.2 2.Near Radius Matching method Analytical standard errors n treat n contr ATT Std Err t 51 76 85.586 39.481 2.168 Note: the numbers of treated and controls refer to actual matches within radius Bootstrapped standard errors n treat n contr ATT Std Err t 51 76 85.586 58.900 1.453 Note: the numbers of treated and controls refer to actual matches within radius 3.2 3.Near Radius Matching method ATT estimation with the Stratification method Analytical standard errors n treat n contr ATT Std Err t 99 231 47.168 - Bootstrapped standard errors n treat n contr ATT Std Err t 99 231 47.168 31.438 1.500 - 86 3.2 Impact on log of average consumption per capita 3.2 Stratification method Analytical standard errors n treat n contr ATT Std Err t 99 62 0.129 0.059 2.201 Note: the numbers of treated and controls refer to actual nearest neighbour matches Bootstrapped standard errors n treat n contr ATT Std Err t 99 62 0.129 0.057 2.266 Note: the numbers of treated and controls refer to actual nearest neighbour matches 3.2 Radius Matching method Analytical standard errors n treat n contr ATT Std Err t 51 76 0.199 0.068 2.949 Note: the numbers of treated and controls refer to actualmatches within radius Bootstrapped standard errors n treat n contr ATT Std Err t 51 76 0.199 0.079 2.519 Note: the numbers of treated and controls refer to actual matches within radius 3.2 Stratification method Analytical standard errors n treat n contr ATT Std Err t 99 231 0.141 - Bootstrapped standard errors n treat n contr ATT Std Err t 99 231 0.141 0.051 2.754 - 87 3.2 Impact on consumption per capita 3.2 1.Nearest Neighbor Matching method Analytical standard errors n treat n contr ATT Std Err t 99 62 65.913 28.485 2.314 Note: the numbers of treated and controls refer to actual nearest neighbour matches Bootstrapped standard errors n treat n contr ATT Std Err t 99 62 65.913 27.212 2.422 Note: the numbers of treated and controls refer to actual nearest neighbour matches 3.2.4.2 Radius Matching method Analytical standard errors n treat n contr ATT Std Err t 51 76 98.095 33.093 2.964 Note: the numbers of treated and controls refer to actual matches within radius Bootstrapped standard errors n treat n contr ATT Std Err t 51 76 98.095 39.301 2.496 Note: the numbers of treated and controls refer to actual matches within radius 3.2 Stratification method ATT estimation with the Stratification method Analytical standard errors n treat n contr ATT Std Err t 99 231 71.720 - Bootstrapped standard errors n treat n contr ATT Std Err t 99 231 71.720 20.512 3.496 - 88 Impact evaluation of microcredit on welfare using DID with Fixed Effect Impact on average income per capita Note: credit08 omitted because of collinearity (*) Fixed-effects (within) regression Group variable: hhid Number of obs Number of groups = = 358 179 R-sq: Obs per group: = avg = max = 2.0 within = 0.0780 between = 0.0070 overall = 0.0321 corr(u_i, Xb) F(2,177) Prob > F = 0.0077 = = 7.49 0.0008 -avinc | Coef Std Err t P>|t| [95% Conf Interval] -+ -year | 96.39623 41.96409 2.30 0.023 13.58189 179.2106 credit08 | (omitted) timecredit | 61.06953 65.71173 0.93 0.354 -68.60977 190.7488 _cons | 475.7151 22.83438 20.83 0.000 430.6524 520.7778 -+ -sigma_u | 296.20514 sigma_e | 305.5032 rho | 48455091 (fraction of variance due to u_i) -F test that all u_i=0: F(178, 177) = 1.88 Prob > F = 0.0000 4.2 Impact on log of average income per capita Note: credit08 omitted because of collinearity (*) Fixed-effects (within) regression Group variable: hhid Number of obs Number of groups = = 358 179 R-sq: Obs per group: = avg = max = 2.0 within = 0.3235 between = 0.0005 overall = 0.1039 corr(u_i, Xb) F(2,177) Prob > F = -0.0044 = = 42.32 0.0000 -lnavinc | Coef Std Err t P>|t| [95% Conf Interval] -+ -year | 2900307 04673 6.21 0.000 197811 3822503 credit08 | (omitted) timecredit | 092364 0731747 1.26 0.209 -.0520431 2367712 _cons | 5.977053 0254277 235.06 0.000 5.926872 6.027233 -+ -sigma_u | 42302578 sigma_e | 34019963 rho | 60725805 (fraction of variance due to u_i) -F test that all u_i=0: F(178, 177) = 3.09 Prob > F = 0.0000 89 4.3 Impact on average consumption per capita Note: credit08 omitted because of collinearity (*) Fixed-effects (within) regression Group variable: hhid Number of obs Number of groups = = 358 179 R-sq: Obs per group: = avg = max = 2.0 within = 0.2089 between = 0.0048 overall = 0.0880 F(2,177) = 23.37 corr(u_i, Xb) = -0.0162 Prob > F = 0.0000 -avexp | Coef Std Err t P>|t| [95% Conf Interval] -+ -year | 121.2292 33.72849 3.59 0.000 54.66751 187.791 credit08 | (omitted) timecredit | 115.1666 52.81557 2.18 0.031 10.93738 219.3959 _cons | 382.3017 18.35305 20.83 0.000 346.0827 418.5206 -+ -sigma_u | 230.84967 sigma_e | 245.54711 rho | 46917808 (fraction of variance due to u_i) -F test that all u_i=0: F(178, 177) = 1.76 Prob > F = 0.0001 4.4 Impact on log of average consumption per capita Note: credit08 omitted because of collinearity (*) Fixed-effects (within) regression Group variable: hhid Number of obs Number of groups = = 358 179 R-sq: Obs per group: = avg = max = 2.0 within = 0.5439 between = 0.0051 overall = 0.2015 F(2,177) = 105.52 corr(u_i, Xb) = -0.0029 Prob > F = 0.0000 -lnavexp | Coef Std Err t P>|t| [95% Conf Interval] -+ -year | 3814157 0399287 9.55 0.000 3026181 4602133 credit08 | (omitted) timecredit | 1451803 0625245 2.32 0.021 0217908 2685698 _cons | 5.7923 0217269 266.60 0.000 5.749423 5.835177 -+ -sigma_u | 39940252 sigma_e | 29068547 rho | 65372553 (fraction of variance due to u_i) -F test that all u_i=0: F(178, 177) = 3.77 Prob > F = 0.0000 (*) Note: The credit08 variable (the participation in the program) is omitted because of its perfect correlattion with the other dummy variables (the dummy variable of households generated from using fixed effect model) Therefore, when using DID with fixed-effect regression, we should only consider the pre and post program impact over time (which means the coefficient of timecredit variable) 90 ... income of the poorest households than that of the medium-income households in Vietnam rural regions, then this leads to the positive impact on poverty alleviation In a research on the case of the Vietnam. .. the determinants on the participation of microcredit programs; and the most important, the result of the impact of microcredit programs on the welfare of the household via income per capita and. .. programs, and to support the poor 1.2 Objectives of the study The general objective of this study is to evaluate the impact of the programs on the welfare of the households living in the rural areas,