Determinants on households’ partial credit rationing an analysis from VARHS 2008

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UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM-NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DETERMINANTS ON HOUSEHOLDS’ PARTIAL CREDIT RATIONING AN ANALYSIS FROM VARHS 2008 By NGUYEN VAN HOANG Academic Supervisor: Dr TRAN TIEN KHAI HO CHI MINH CITY, NOVEMBER 2013 AKNOWLEDGEMENT I am indebted to many individuals for their enthusiastic support and guidance while doing this thesis This paper would be impossible to accomplish without their unlimited support Firstly of all, I would like to express my great appreciation with Dr Tran Tien Khai, my supervisor, for the whole support to help me constructing ideas, structure, advices and comments, from the beginning to the end May thanks for Prof Dr Nguyen Trong Hoai, Dean of Vietnam – The Netherlands Programme who has provided necessary assistance and motivation for me to achieve this thesis I would like give my special thanks for Dr Pham Khanh Nam, Academic Director of Vietnam – The Netherlands Programme Without his introduction for VARHS 2008, an important data set used in this research, this paper would be impossible to complete Many thanks for Dr Truong Dang Thuy, Chair of the Department of Economics - Faculty of Development Economics, who has passionately cooperate with me to solve issues related to econometric techniques Last but not least, I must express my most gratitude to my parents and my aunt’s family for providing comfortable condition during hard time, so that I can finish this thesis List of Tables and Figures LIST OF TABLES Table - VARHS 2008 Survey Questions 27 Table - Model Specification 44 Table - Determinants of Credit Accessibility 54 Table - Determinants of Partial Credit Rationing Probability 56 Table - Determinants of Partial Credit Rationing Degree 60 LIST OF FIGURES Figure – Credit Supplier Expected Return 15 Figure - Rationing in Credit Market 17 Figure - Identify Case of being Credit Rationed 20 Figure - Survey Site Mapping for VARHS 2008 Source: IPSARD (2006-2008) 26 Figure - Sample Distribution Source: Author Calculation from VARHS 2008 29 Figure - Analytical Framework 38 Figure - Credit Access & Credit Ration Source: Author’s calculation from VARHS 2008 45 Figure - Credit Access & Household Head Age Source: Author’s calculation from VARHS 2008 46 Figure 10 - Household Head Age & Credit Ration Source: Author’s calculation from VARHS 2008 47 Figure 11 - Credit Access & Household Head Education Level Source: Author’s calculation from VARHS 2008 48 Figure 12 - Household Head Education Level & Credit Ration Source: Author’s calculation from VARHS 2008 49 Figure 13 - Credit Access & Loan Purposes Source: Author’s calculation from VARHS 2008 50 Figure 14 - Loan Purposes & Credit Ration Source: Author’s calculation from VARHS 2008 51 Figure 15 - Credit Access & Credit Institutions Source: Author’s calculation from VARHS 2008 52 Figure 16 - Partial Ration & Credit Institutions Source: Author’s calculation from VARHS 2008 53 Abstract This study aim to identify key factors affected the partial credit ration’s probability and its degree in rural area of 12 provinces in Vietnam including Ha Tay, Nghe An, Khanh Hoa, Lam Dong, Phu Tho, Quang Nam, Long An, Dac Lac, Dac Nong, Lao Cai, Dien Bien, Lai Chau period 2006-2008 Based on VARHS 2008 data set, the research has employed Heckman sample selection bias model to investigate the determinants of partial ration’s degree, and bivariate probit with sample selection model to examine the determinants of partial ration’s probability Besides that, the impact of credit accessibility’s determinants, as a supplement outcome from the two regression models, were also revealed The result showed that households who have following characteristics - Kinh ethnicity, large household size, high land value, suffering shock at household level (economic shock, illness, unemployment, etc.), holding social position (at least one member working for government, local authority unit) tend to have higher chance of credit access, while those who have high dependency ratio, and older household, tend to have negative correlation with credit accessibility Formal credit institutions appeared to have higher rate of partial credit rationed than the informal sector, and those who requested a large size of loan were likely to be partial rationed as well In contrast, households who own larger house, borrowed for investment purposes (build/buying house, land and other assets) or holding social position had a lower chance of being partial rationed The finding also uncovered the negative correlation between the degree of partial credit ration and following factors - Household head age, dependency ratio, house size and collateral value On the contrary, household size, loan size applied, loan for consumption purposes negatively affect the degree of partial credit ration The regression result also shown that unless treatments such as bivariate probit with sample selection bias or Heckman two stages regression are applied, the regression result might be bias due to inherent sample selection problem in the data set Table of Contents Chapter Introduction 1.1 Research Context 1.2 Research Problem 10 1.3 Research Objectives 11 1.4 Research Questions 11 1.5 Scope of Study 11 1.6 Thesis Structure 12 Chapter Literature Review 12 2.1 Rural credit 12 2.1.1 Definition 12 2.1.2 Characteristics of rural credit market 12 2.1.3 Types of rural credit 13 2.2 Asymmetric Information and Credit Rationing 14 2.2.1 Asymmetric information 14 2.2.2 Problems of lenders in context of asymmetric information 15 2.2.3 Screening mechanism in lending 17 2.3 Credit Rationing 18 2.3.1 Types of Credit Rationing 18 2.3.2 Identify Credit Rationing 19 2.3.3 Impact of Credit Rationing in Rural Area 21 2.4 Empirical Studies 21 2.4.1 Factors of Credit Demand 22 2.4.2 Factor of Credit Supply 23 Chapter Methodology 25 3.1 Data Source and Features 25 3.2 Issue of Data Bias (Sample Selection Problem) 28 3.3 Heckman Two-Stages Model 30 3.3.1 Sample Selection Bias vs Omitted Variables Bias 30 3.3.2 Heckman Two Stages Procedures 32 3.3.3 Application to study & Model Specification 33 3.4 Bivariate Probit with Sample Selection Model 34 3.4.1 Model Review 34 3.4.2 Application to study & Model Specification 36 3.5 Multicollinearity Test 37 3.6 Analytical Framework 38 3.7 Hypothesis 40 3.7.1 Hypothesis for the probability and degree of partial credit rationing 40 3.7.2 Hypothesis for the probability of access to credit 42 3.8 Model Specification 44 Chapter Results and Discussion 45 4.1 Characteristics of Borrowers by Credit Rationing 45 4.2 Determinants of Credit Accessibility 54 4.3 Determinants of Partial Credit Rationing Probability 56 4.4 Determinants of Partial Credit Rationing Degree 58 4.5 Multicollinearity Test 61 Chapter Conclusions and Policy Implications 62 5.1 Conclusions 62 5.1.1 Findings, answers for research questions 62 5.1.2 Conclusions on degree of solving research objectives 62 5.1.3 Limitation of the study 63 5.2 Policy implications 63 5.2.1 Policy implications 63 5.2.2 Research perspectives 65 Chapter Introduction “Credit rationing is not necessary the source of poverty trap, but it reinforce them ” (Ping, Heidhues, & Zeller, 2010) 1.1 Research Context Since 1986, Vietnam Government has initiated the economic reform that has transformed the nation from the central planning to market oriented economy Major achievements in terms of economic growth and poverty reduction have been attained as a result of the reform However, the large gap between rural and urban areas has still existed To ensure the sustainability of economic development and the stability of political environment, rural and agriculture development are therefore considered as a priory goal in the nation development strategy Providing access to finance to the poor or microfinance has been considered as a tool for economic development and poverty reduction (Morduch & Haley, 2002; Khandker, 2003) It is the interest of many policy makers and researchers in recent years Thus, in this strategy, rural credit, which aims at ensuring rural households having access to financial services, is regarded as an important component The Government has launched many credit programs supporting the development of rural area such as preferential credit for the poor, agriculture forestry and fishery encouragement through special state own banks or government agencies The credit program has some significant impact to economic development of Vietnam rural areas; however there still exist some issues such credit rationing in the program When credit rationing occurs, credit suppliers ignore to offer loan to some borrowers to avoid the risk of default, thus it limit the credit accessibility of the poor Due to it implication to the economic development in general, and to the effectiveness of Government credit for the poor program in particular, this paper will aim at examining the factors that affect to credit rationing, particularly focus on the cases of partial credit ration, with the hope of revealing some potential implications for policy makers 1.2 Research Problem Rural credit market is an importance factor that helps to foster the economic development in rural areas, thus improving the poor living standard and supporting poverty alleviation One of its function is funding household’s credit demand However, the degree to which rural credit impacts on the rural area welfare depends on how well the rural credit market operates, however the problem of credit rationing could have negative effect on the performance of rural credit market Credit rationing could be described as the cases in which credit lenders refuse to offer loan to borrowers, or offer an amount of loan that is less than borrower’s request, even though the borrowers willing to accept higher level of interest rate to help the lender to cover the default risk (Barham, Boucher, & Cater, 1996; Buchenrieder, 1996; Heidhues & Schrieder, 1998; Zeller, 1993) The higher the probability of credit rationing, the more difficulties for household to satisfy their credit demand In other word, if credit rationing is a common practice in the area, it may lead to the inefficiency of the credit market in the area as a consequence As Stiglitz, J and A Weiss (1981) pointed out, asymmetric information is an explanation for the problem of the credit rationing In rural credit market, which is characterized by numbers of poor households and the difficulties to evaluate their credit worthiness, is concealed by a fog of asymmetry information between lenders and borrowers In other word, lenders are reluctant to lend as they are uncertain about the loan repayment probability To overcome this problem, lenders require different kinds of information about their borrowers such as household’s dependency ratio, household size, land value, social position, etc., to assess their repayment ability and make a basis for lending decision As such kinds of information may affect to the probability and the degree to which a borrower be credit rationed, a question has been raised by many researchers is that what factors determined lenders’ decision of credit ration The answers are different depending on the period and location under examination of a study For this paper, the research aim to examine the determinants of credit rationing – especially for the case of partial credit ration, concentrating on 12 provinces Ha 10 Collateral is another factors affect to partial credit ration Households who own larger houses, or offer higher value of collateral would less suffer from partial credit ration - they were likely to satisfy the credit as they requested Households who had at least one member working as government authorities or who owned larger houses had lower chance of being partial credit rationed On the other hand, household who had large loan size requested or borrowed from formal credit institutions including social policy bank, bank of agriculture and rural development, other state owned bank, local authorities, private bank, farmer union, veterans union, women’s union, people credit fund and other credit association, would significantly tend to be partially credit rationed Finally, the probability of partial rationing was lower for loan of building or buying house, land and assets 5.1.3 Limitation of the study Due to issue of sample selection of VARHS 2008 arises in the scope of this research, treatments has been adopted to minimize bias on final result However, it should be better if the research was based on non – sample selection data, so that the result could be estimated directly 5.2 Policy implications 5.2.1 Policy implications From the statistical results, it appears that households who have members working as government officers or local authorities have more advantages than others in credit market, i.e they were more likely to get borrow and less likely to be credit rationed The discrimination may raise the issue of inefficient credit allocation in which sources of fund may not benefit those who actually need but not have much title in community The issue may become severe in case of government authorities can make use of their entitlement or political relationship to exploit cheap credit from social policy programs This is an issue of moral hazard in social policy implementation, thus it is important to monitor such social policy programs well 63 Formal credit sector, particularly social policy and bank of agriculture and rural development, took large share of credit market (about 59% of the market) and play an important role in rural credit market, but households applied for credit in this sector may have high probability of being constrained As the main suppliers in the market, their constraint could erode the benefit of credit on the development of rural areas The poor who cannot find support from formal sector will have to rely on informal credit sector However, loan in this sector are not appropriate for business purposes to generate income as they normally offer high interest rate but small loan size contracts Screening to identify credit worthy borrowers could be a major reason of credit constraint in this sector Collaterals such as house and livestock are still important criteria for credit lenders to make decision about the loan This screening mechanism unfavors the poor who normally not have capital endowment, makes them more difficult to find support from credit Enforcement to collect the loan repayment could be a main explanation for this issue To minimize the credit constraint issue and direct credit flow to right targets efficiently, developing micro-finance services to solve the problems of screening and enforcement could be an appropriate solution Micro finance services such as micro-credit have some unique characteristics, i.e required no collateral and group-lending scheme As collaterals are not required, the poor who not have capital endowment have better chance to get credit While, group-lending provides credit for a whole group and require them to monitor each other, allowing the control and monitoring the use of loan and repayment via peer to peer mechanism Thus, micro-finance can somehow fulfill the gap of formal credit sectors, especially state own banks, in rural areas with lower cost, but well organized than the informal sectors Recently, form of micro-finance such as women union, farmer unions and veteran unions have played a small role in the examined areas It took about 12% market share comparing to 24% of informal sector and 59% of the two state own banks - social policy and bank of agriculture and rural development (Author’s calculation from VARHS 2008) The role of micro-finance organizations in rural credit market should be expanded more than current status, and regulations 64 about micro-finance should provide an appropriate legal environment to facilitate the participation of those organizations 5.2.2 Research perspectives The study has examined the determinants of partial credit ration one of the three aspect of credit constraint The other two situations i.e completely ration and discourage could be potential research objectives to develop better understanding about credit ration Effect of credit ration on households’ welfare could be an interesting topic as well Vietnam micro-finance regulation is another topic worth to study Good legal framework can promote the development of micro-finance market, thus minimizing the issue of credit constraint Analyzing the advantages and disadvantages of recent regulation could provide a basis for developing better one 65 References Aghion, B.A.D., & Morduch, J (2004) The Economics of Microfinance, (April 2004) Aguilera, N (1990) Credit Rationing and Loan Default in Formal Rural Credit Market Presented in Partial Fullfilment of the Requirements for the Degree Doctor of Philosophy in 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287 Zeller, M (1994) Determinants of Credit Rationing: A Study of Informal Lenders and Formal Credit Groups in Madagascar World Development, Vol 22, No 12, 1895-1907 70 Appendix Appendix 1- Sample Distribution by Partial Ration Partial Ration Num Observations Non_Partial Ration 940 Partial Ration 119 Total 1,059 Percent 88.76 11.24 100 Cummulative (%) 88.76 100 Appendix - Sample Distribution by Credit Access Credit Access Credit Access No Credit Access Total Num Observations 1059 1339 2,398 Percent 44.16 55.84 100 Cummulative (%) 44.16 100 Appendix – Sample Distribution by Head Age Head Age 60 Total Num Observations 199 573 740 460 426 2,398 Percent 8.3 23.89 30.86 19.18 17.76 100 Cummulative (%) 8.3 32.19 63.05 82.24 100 Appendix - Head Age & Credit Access Head Age 60 Total Credit Access 87 255 356 220 141 1,059 No Credit Access 112 318 384 240 285 1,339 71 Total 199 573 740 460 426 2,398 Appendix - Head Age & Partial Ration Head Age 60 Total Non_Partial Ration 78 232 310 192 128 940 Partial Ration 23 46 28 13 119 Total 87 255 356 220 141 1,059 Appendix - Sample Distribution by Education Education Level Total Num Observations 2,234 64 15 58 10 17 2,398 Percent 93.16 2.67 0.63 2.42 0.42 0.71 100 Cummulative (%) 93.16 95.83 96.46 98.87 99.29 100 Appendix - Education & Credit Access Education Level Total Credit Access 985 30 27 1,059 No Credit Access 1,249 34 31 13 1,339 Total 2,234 64 15 58 10 17 2,398 Appendix - Education & Partial Ration Education Level Total Non_Partial Ration 877 27 20 940 72 Partial Ration 108 119 Total 985 30 27 1,059 Appendix - Loan Purpose & Credit Access Loan Purpose Rice Other crop production Animal husbandry Forestry Fishery Non-farm activity Repay other loan Build/buy house Buy land Buy another asset Pay for wedding/funer Education expenses Health expenses General consumption Other (please specify Total Credit Access 119 166 412 11 10 54 11 72 39 61 18 15 55 1,059 Total 119 166 412 11 10 54 11 72 39 61 18 15 55 1,059 Appendix 10 - Loan Purpose & Partial Ration Loan Purpose Non_Partial Ration Rice 113 Other crop production 141 Animal husbandry 371 Forestry 11 Fishery Non-farm activity 46 Repay other loan Build/buy house 63 Buy land Buy another asset 38 Pay for wedding/funer Education expenses 49 Health expenses 14 General consumption 13 Other (please specify 52 Total 940 73 Partial Ration 25 41 12 119 Total 119 166 412 11 10 54 11 72 39 61 18 15 55 1,059 Appendix 11 - Credit Institution & Credit Access Credit Institution Social Policy Bank Bank of Agriculture a Other State-owned com Private Bank Farmers Union Veterans Union Women's Union People's Credit Funds Other credit associat Private Trader Private Money Lender Friends/Relatives Informal credit schem Other (please specify Total Credit Access 334 289 16 41 16 71 21 91 27 121 15 1,059 Total 334 289 16 41 16 71 21 91 27 121 15 1,059 Appendix 12 - Credit Institution & Partial Ration Credit Institution Non_Partial Ration Social Policy Bank 296 Bank of Agriculture a 244 Other State-owned com 15 Private Bank Farmers Union 37 Veterans Union 11 Women's Union 61 People's Credit Funds 19 Other credit associat Private Trader 86 Private Money Lender 25 Friends/Relatives 116 Informal credit schem Other (please specify 15 Total 940 74 Partial Ration 38 45 10 2 5 0 119 Total 334 289 16 41 16 71 21 91 27 121 15 1,059 Appendix 13 - Determinants of Partial Ration Degree - Heckman two-steps regression Heckman selection model two-step estimates (regression model with sample selection) Coef Std Err Number of obs Censored obs Uncensored obs = = = 2390 1334 1056 Wald chi2(16) Prob > chi2 = = 297.61 0.0000 z P>|z| [95% Conf Interval] loansize_differ2 hhsize head_age depend_ratio head_edu head_gend1 home_size2006 total_landvalue total_livestock_value income loansize_apply loan_purpose_prod loan_purpose_consump loan_purpose_invest collateral_value_f social_position1 credit_inst_dummy 225.5947 -46.01776 -2688.717 -232.5285 -258.7641 -21.77689 001322 -.0062135 -.0085702 1538791 173.1439 1736.836 -1620.19 -.0011311 110.8083 208.656 132.582 19.83446 1247.758 340.7968 638.9062 7.296857 0009491 0131442 0066153 0098343 820.245 1022.778 986.0945 0006781 929.0784 510.3437 1.70 -2.32 -2.15 -0.68 -0.41 -2.98 1.39 -0.47 -1.30 15.65 0.21 1.70 -1.64 -1.67 0.12 0.41 0.089 0.020 0.031 0.495 0.685 0.003 0.164 0.636 0.195 0.000 0.833 0.089 0.100 0.095 0.905 0.683 -34.26122 -84.89258 -5134.279 -900.478 -1510.997 -36.07847 -.0005381 -.0319756 -.0215359 1346041 -1434.507 -267.7718 -3552.9 -.0024603 -1710.152 -791.5992 485.4506 -7.142933 -243.1557 435.4209 993.469 -7.475317 0031821 0195486 0043955 173154 1780.794 3741.443 312.5194 000198 1931.768 1208.911 credit_borrow1 ethnicity_dummy hhsize head_age num_adult depend_ratio head_edu head_gend1 home_size2006 total_landvalue total_livestock_value income household_shock1 social_position1 4418059 0773077 -.0113385 -.0274953 -.5457069 -.0477131 0019275 0001754 2.07e-07 -1.71e-06 -8.40e-07 185849 2756147 0607402 0284967 0020522 0435316 1797477 0380228 0704814 0008392 1.14e-07 1.32e-06 6.51e-07 0543501 1159383 7.27 2.71 -5.53 -0.63 -3.04 -1.25 0.03 0.21 1.81 -1.30 -1.29 3.42 2.38 0.000 0.007 0.000 0.528 0.002 0.210 0.978 0.834 0.070 0.195 0.197 0.001 0.017 3227573 0214553 -.0153607 -.1128156 -.8980059 -.1222365 -.1362136 -.0014694 -1.66e-08 -4.31e-06 -2.11e-06 0793249 0483798 5608545 1331602 -.0073162 057825 -.193408 0268102 1400686 0018201 4.30e-07 8.78e-07 4.36e-07 2923732 5028496 lambda 3137.949 1431.486 2.19 0.028 332.2878 5943.61 rho sigma 0.42755 7339.4511 mills 75 Appendix 14 - Determinants of Partial Ration Probability - Bivariate with Sample Selection Regression Probit model with sample selection Log likelihood = -1941.077 Coef Std Err Number of obs Censored obs Uncensored obs = = = 2390 1334 1056 Wald chi2(16) Prob > chi2 = = 25.82 0.0566 z P>|z| [95% Conf Interval] partial_ration hhsize head_age depend_ratio head_edu head_gend1 home_size2006 total_landvalue total_livestock_value income loansize_apply loan_purpose_prod loan_purpose_consump loan_purpose_invest collateral_value_f social_position1 credit_inst_dummy -.0154793 -.0026863 -.0920622 0218606 -.0866814 -.0025328 3.88e-08 -1.86e-06 1.62e-06 3.12e-06 -.2389276 0821521 -.3655781 -5.84e-07 -.3236198 3696375 0261824 0038546 2410132 0598485 1208897 0014999 1.83e-07 3.04e-06 1.16e-06 1.69e-06 1471759 1694673 1890317 4.46e-07 18601 1103649 -0.59 -0.70 -0.38 0.37 -0.72 -1.69 0.21 -0.61 1.40 1.85 -1.62 0.48 -1.93 -1.31 -1.74 3.35 0.554 0.486 0.702 0.715 0.473 0.091 0.832 0.539 0.162 0.064 0.105 0.628 0.053 0.190 0.082 0.001 -.066796 -.0102413 -.5644393 -.0954403 -.3236209 -.0054725 -3.21e-07 -7.81e-06 -6.50e-07 -1.86e-07 -.527387 -.2499978 -.7360734 -1.46e-06 -.6881926 1533262 0358373 0048686 3803149 1391615 1502581 000407 3.98e-07 4.09e-06 3.90e-06 6.43e-06 0495318 414302 0049172 2.89e-07 0409531 5859487 credit_borrow1 ethnicity_dummy hhsize head_age num_adult depend_ratio head_edu head_gend1 home_size2006 total_landvalue total_livestock_value income household_shock1 social_position1 412709 0802778 -.0105294 -.0332163 -.5629536 -.0450151 0068741 0002097 2.03e-07 -1.79e-06 -7.91e-07 1521098 2719706 0621666 0275004 0020448 0415389 1750563 0378799 0705937 000838 1.13e-07 1.33e-06 6.48e-07 0529814 1160187 6.64 2.92 -5.15 -0.80 -3.22 -1.19 0.10 0.25 1.80 -1.35 -1.22 2.87 2.34 0.000 0.004 0.000 0.424 0.001 0.235 0.922 0.802 0.071 0.177 0.223 0.004 0.019 2908648 026378 -.0145372 -.1146311 -.9060576 -.1192584 -.1314869 -.0014327 -1.76e-08 -4.40e-06 -2.06e-06 0482681 0445781 5345533 1341777 -.0065216 0481985 -.2198496 0292282 1452351 0018521 4.24e-07 8.11e-07 4.80e-07 2559515 4993631 /athrho -.9836932 2758664 -3.57 0.000 -1.524381 -.4430049 rho -.7546602 1187572 -.9094582 -.4161321 LR test of indep eqns (rho = 0): chi2(1) = 76 11.79 Prob > chi2 = 0.0006 Appendix 15 - Correlation Matrix Ethnicity Ethnicity Household Size Head 's Age Dependency Ratio Head Education Level Household Head Gender House Size Total Land Value Total Livestock Value Household Income Loan Size Applied Loan Purposes (Production) Loan Purposes (Consumption) Loan Purposes (Investment) Collateral Value Social Position Credit Institution (Formal) 0.0705 -0.131 0.057 -0.0662 0.0967 0.1201 -0.0798 0.1062 -0.078 -0.0778 0.0825 -0.061 -0.0541 -0.0432 0.0024 0.1017 Household Size 0.0148 0.3248 -0.0509 0.1719 0.1212 -0.0311 0.1914 0.0838 0.0002 0.1409 -0.0701 -0.0538 -0.0389 0.0836 0.0744 Head 's Age -0.3137 0.0696 -0.279 0.1261 0.0254 0.0301 0.1147 0.0311 -0.0399 0.0895 -0.0649 0.0193 0.066 -0.046 Dependency Ratio -0.1416 0.0501 -0.1151 -0.0405 0.0044 -0.12 -0.0169 0.1352 -0.1639 -0.0201 -0.0521 -0.0735 -0.0982 Head Education Level 0.0015 0.0703 0.0796 -0.0613 0.1479 0.0732 -0.1247 0.0609 0.0896 0.1521 0.151 0.0231 Household House Size Head Gender 0.0367 0.0037 0.0901 -0.002 -0.0113 0.076 -0.0455 -0.0047 -0.0592 0.0418 0.0871 0.2026 0.2333 0.2175 0.1223 0.0379 -0.0386 -0.0666 0.0758 0.0913 0.0769 Total Land Value 0.1 0.4593 0.2756 0.02 -0.0379 -0.0221 0.3161 0.0925 0.0709 77 Total Livestock Value 0.1646 0.0327 0.0657 -0.0733 -0.0029 0.0717 0.0722 0.0707 Household Income 0.4451 -0.0712 -0.037 0.0814 0.1579 0.1197 0.0266 Loan Size Applied -0.0673 -0.0849 0.1158 0.1536 0.0301 0.087 Loan Loan Loan Purposes Purposes Purposes (Production) (Consumptio (Investment) n) -0.5352 -0.5833 -0.034 0.045 0.1611 -0.1165 0.0543 -0.0238 -0.0993 -0.0016 -0.0359 -0.1408 Collateral Value 0.0094 0.076 Social Position 0.0989 Credit Institution (Formal) ... Specification 44 Table - Determinants of Credit Accessibility 54 Table - Determinants of Partial Credit Rationing Probability 56 Table - Determinants of Partial Credit Rationing. .. examine the determinants of credit rationing – especially for the case of partial credit ration, concentrating on 12 provinces Ha 10 Tay, Nghe An, Khanh Hoa, Lam Dong, Phu Tho, Quang Nam, Long An, Dac... affects the degree of partial credit rationing  Identify key determinants on the probability of partial credit rationing  Suggest policy implication to reduce partial credit rationing practice 1.4
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