Impact of Microcredit on the Income of Poor Households in the Southeast Region

Nội dung text: Impact of Microcredit on the Income of Poor Households in the Southeast Region

1. THE STATE BANK OF VIETNAM MINISTRY OF EDUCATION AND TRAINING BANKING UNIVERSITY OF HO CHI MINH CITY NGUYEN HONG THU THESIS SUMMARY IMPACT OF MICROCREDIT ON THE INCOME OF POOR HOUSEHOLDS IN THE SOUTHEAST REGION ECONOMIC DOCTORAL THESIS MAJOR: FINANCE - BANKING CODE: 9 34 02 01 HO CHI MINH CITY, 2018 i
2. RELATED STUDY WORKS I The scientific study topics 1. Nguyen Hong Thu et. al., (2016). Solutions to improve the performance of microfinance in Binh Duong. Approved on Aug 19, 2016. (Chairman of scientific research topic at grassroots levels). 2. Nguyen Hong Thu et. al., (2017). Factors affecting the income of poor households through microcredit activities in Binh Duong. Approved on Oct 31, 2017. (Chairman of scientific research topic at grassroots levels). II The scientific articles published in journals 1. Nguyen Hong Thu, Pham Cong Luan and Tran Thi Cam Van (2017). Poverty reduction in Binh Duong from the perspective of microfinance. Journal of Thu Dau Mot University. No. 02 (33), Apr 2017. 2. Nguyen Hong Thu (2017). Social capital with access to micro-credit of rural households in the Southeast region. Journal of the Asia Pacific Economy, Mar 2017. 3. Nguyen Hong Thu and Nguyen Van Diep (2017). Impact of resources on income of rural households in Binh Duong province. Journal of Economic Management. No. 81 (Mar + Apr 2017). 4. Nguyen Hong Thu (2017). Microcredit with income of the poor in rural Southeast region. Journal of the Asia Pacific Economy, Apr 2017. 5. Nguyen Hong Thu (2017). Access to microcredit of households in the lacquer village in Binh Duong. Banking Technology Magazine. No. 134, May 2017. 6. Nguyen Hong Thu (2018). The role of microcredit in livelihoods of poor households in the Southeast region. Journal of Economic Management, No. 88 (Apr + May 2018). ii
3. CHAPTER 1: INTRODUCTION OF STUDY OVERVIEW 1.1. The necessity of study topic Along with the activities of microfinance, microcredit contributes to the practical effect of poverty reduction. It is an important tool in the fight against the poverty, especially in developing countries (Humle and Mosley, 1996; Shaw, 2004). In spite of that, this conclusion is still a matter continuing to be discussed and increasingly attracted many different opinions of researchers, with each study, the authors have introduced, analyzed different aspects and fields. There are some options that impact relation of microcredit on income is statistically insignificant (Sen, 2008; Rukiye, 2012) or other options showed that they did not find the impacts of microcredit on the income of households (Diadup and Zeller, 2001); the study of Morduch (1998) found that credit from the Grameen Bank in Bangladesh reduced the vulnerability rather than poverty reduction and the study of Coleman (1999) showed that they found only a significant impact of microcredit on the welfare of households in Thailand. Up to now, the government in most countries has recognized the active role of microcredit to poverty reduction, its science research activities on microcredit are increasingly attracting attention from many experts as well as scholars in the country and abroad. In order to continue the inheritance, to understand the impact of microcredit on the income of poor households and especially in the Southeast region, there is no research conducted yet, but in this study, theoretical framework is formed on the background of theories, the analysis based on the practical characteristics of the region to clarify the objectives set out in the study. Before this context, the study proposes the key issues that need to be explored, that are (1) to check the theoretical point that microcredit affecting poverty reduction through income factor of the households is necessary, (2) to improve income for poor households, it is needed to 3
4. improve the access to microcredit to the households and (3) how to improve the access to microcredit to the poor to have additional capital to improve their income? Therefore, the study topic needed to be implemented is “Impact of Microcredit on the Income of Poor Households in the Southeast Region”. 1.2. Overview of the study topic As part of the microfinance activity, microcredit is recognized contributing to the enhancement of self-reliance and capacity to create product value, opening opportunities for the poor to develop their livelihoods to improve and enhance the quality of life. In Vietnam, the famous typical studies of authors such as Nguyen Kim Anh et al., (2011) with the study of microcredit to the poor in Vietnam - Verification and comparison, or a series of other studies on the poor such as evaluation of poverty reduction policies in Ho Chi Minh City (Phung Duc Tung et al., 2013), evaluation of poverty reduction models of foreign partners in Vietnam (Nguyen Duc Nhat et al., 2013), Most of the studies have high consensus on the effectiveness of microcredit with the poverty reduction war of the country. The studies above outlined the basic modes of operation of microcredit both in the country and abroad, evaluated and analyzed the impact of microcredit on the ability to generate income from customers. However, these studies have just mentioned overall the impact of microcredit on income. The objective of this thesis is to clarify the issues mentioned in this study and practical application in the Southeast region. 1.3. Objectives and questions 1.3.1. General objectives The objective of this thesis is to verify the hypothesis of the microcredit impact on income for poor households and to identify factors affecting the access to microcredit of the poor households in order to find solutions to increase income through increasing access to microcredit for them. 4
5. 1.3.2. Specific objectives Based on the general objectives of the research, the thesis sets out three specific objectives that need to be addressed as follows: Study the impact of microcredit on the income of poor households in the Southeast region. Understand the impact of factors on access to microcredit of the poor households. Recommend the solutions contributing to income increasing for the poor households through enhancing the access to microcredit and other non-financial activities for poor households in the region. 1.3.3. Questions for study To achieve the target set out, the thesis shall answer the following questions: Does microcredit affect the income of poor households? And how the levels of impact are? How the access to microcredit of the poor households is? What solutions should be taken to increase the access to microcredit for poor households? What policy recommendations contribute to increase the income for poor households through microcredit activities in the Southeast region? 1.4. Object and scope of study 1.4.1. Object of study Originating from the urgency of study and practice in the study area, the thesis determining the objects to be clarified in this study is the income of poor households (specifically, the income of the microcredit borrowers and the non-microcredit borrowers). 1.4.2. Scope of study 5
6. 1.4.2.1. Scope of space Provinces of the Southeast region, including Binh Duong, Binh Phuoc, Dong Nai and Tay Ninh province. 1.4.2.2. Scope of time Studying to understand and analyze the secondary data sources from relevant local reports in the period of 2011 - 2017. Time for survey implementation is 2016. 1.5. Approach, study method, study data sources 1.5.1. Approach The study uses the systematic approach. The study uses qualitative in combination with quantitative research method. 1.5.2. Analysis method of the study The study uses the statistical, analysis method of related primary and secondary data. The method interviews 15 experts. The survey method using semi-structured questionnaire with the surveyed subjects as poor households in the area. 1.5.3. Source of study data Secondary data source. Primary data source. 1.6. New points and contributions of the thesis 1.6.1. Contribution in terms of practicality Firstly, the thesis has practical application in the current period when the whole country has made a re-evaluation of the poverty reduction and is stepping into a new phase with the application of the multidimensional poverty (period 2016- 2020). 6
7. Secondly, the thesis has proved that microcredit plays an important contribution to poverty reduction strategy, especially in a Southeast region with a high economic growth rate in the Southern key economic region of Vietnam. 1.6.2. Contribution in terms of academically Firstly, the thesis summarizes the assessment comprehensively to the impact of microcredit on the poor household incomes and affirms that microcredit is an effective tool in poverty reduction strategy. Although there are still many controversial discussions about the effectiveness of microcredit, within the scope of this research, the thesis contributes to sum up relevant theoretical foundations. The summing-up is meant to help the next researchers access the credit-income theory, the theory to access microcredit, and the statistical implications with the impact of microcredit, the impact of non-financial activities affecting the poor household income. Secondly, it compares and asserts that there is a difference in income between the two groups of microcredit borrowers and non-microcredit borrowers. Particularly, the group of microcredit borrowers has the average income higher than the group of non-borrowers. Thirdly, together with the microcredit activities, non-financial activities are organized in parallel with the process of deploying financial resources to poor households, which is assessed to be an efficient complementary activity, contributing to improve the performance of microcredit with poverty reduction. This research assumes that non-financial activity influences the ability to raise income to poor households. Fourthly, the social capital (SC), which currently has many studies to analyze the influence of social capital on the participation in credit services in general, but the understanding of the impact of social security on the access to microcredit in the 7
8. previous studies have not paid much attention; the thesis confirms that social capital is an issue that is necessary to promote and expand during the performance of poverty reduction strategy. 1.7. The structure of the thesis The thesis consists of 5 chapters with the following contents: Chapter 1: Introduction to study overview Chapter 2: Overview of theoretical basis on microcredit to income of poor households Chapter 3: Research method Chapter 4: Microcredit activities in the area and test results Chapter 5: Conclusions of solutions CHAPTER 2: OVERVIEW OF THEORETICAL BASIS ON MICROCREDIT TO INCOME OF POOR HOUSEHOLDS 2.1. Micro credit 2.1.1. Concept The term of “microcredit” shall be defined as “Microcredit is the provision of small loans to poor, low-income borrowers to help them create the business, build the asset, and increase the income”. 2.1.2. The role of microcredit for poverty reduction Firstly, starting from the initiation by Prof. Muhamad Yunus from very small loans, but it has helped thousands of poor workers have the opportunity to open the door to earn the living. Although it is small loans, it comes to them when they need it most, as a “lifebuoy” to help them overcome difficulties and obstacles. In Vietnam, the small lending activity is provided by MFIs, financial institutions, NGOs or social organizations in the formal, semi-formal and informal area (Nguyen Kim Anh et al., 8
9. 2011). Up to now, it still cannot deny the absence of the role of microcredit for campaigns against poverty, especially in developing countries. 2.1.3. Overview of microcredit activities in countries around the world Operation of Grameen Bank model in Bangladesh (GB) Microcredit operation in Thailand Microcredit operations in India Microcredit operations in Indonesia Microcredit operations in Canada 2.2. Poverty 2.2.1. Concept Poverty: The World Bank (2011) considers that poverty is divided into different levels: absolute poverty, relative poverty and poverty with minimal demands as follows: + Absolute poverty: is the status that a part of the population in the poor area is unable to satisfy the minimum demands of life: eating, wearing, living, traveling, ect. + Relative poverty: is the status that a part of the population in the poor area has the living below the average living standard of the considered community and locality. + Poverty with minimum demands: is the status that a part of the population has the minimum subsistence to maintain their livelihoods, such as enough food, clothing, living and some daily activities but at a minimum level. 2.2.2. Poverty line of some countries in the world The popular standard of calories/day in some ASEAN countries is as follows: 9
10. In India, the standard is 2250 calories/person/day. In Bangladesh, the standard is 2100 calories/person/day. In Indonesia: In the early 1980s, the consumption of calories was 2100 calories/person/day as a standard to determine the boundaries between rich and poor. In China: In 1990, the consumption of calories was 2150 calories/person/day. Industrialized countries in Europe: 2570 calories/person/day. 2.2.3. Poverty line of Vietnam In the 1990s, the poverty line in Vietnam was defined as: households with income per capita in rural and mountainous areas are from 45,000 VND/person/month (540,000 VND/person/year) downwards. In rural and delta areas, the households with average income per capita was VND 70,000/person/month (840,000 VND/person/year). In urban areas, the income per capita was 100,000 VND/person/month (1,200,000 VND/person/year). In 2006, the poverty line in rural areas was 200,000 VND/person/month and in urban areas was 260,000 VND/person/month. In the period of 2011-2015, the poverty line was 500,000 VND/person/month (urban area) and 400,000 VND/person/month (rural area) 2.3. Income 2.3.1. Concept The General Statistics Office (GSO) (2011) defines that: Income is the total amount of money that a person or family earned in a day, a week or a month, or more specifically, all that a person earned when devoting the work force properly, so it is called income. The monthly income per capita is calculated by dividing the total income in year of the household by the number of household members and dividing by 12 months. 10
11. 2.3.2. Factors affecting income The income of each individual is obtained from devoting the work force, participating in labor activities. The contribution of labor force of each individual brings income to each individual and family. That contribution brings economic value to the family through daily, monthly or even annually income for the household. Therefore, to create the valuable products, it requires a combination of physical capital and human capital (Ismail and Yussof, 2010). The combination of human capital and physical capital produces the labor value expressed by the added value of income achieved. In human labor activities, these two inseparable factors as follows. - Relationship between financial capital and income Capital is considered as a “lever” for the process of economic growth and development, a stimulation of the process of expanding the scale of production, implementation of economic projects and a contribution to increase the benefits, create the momentum to the economic development process. When credit markets are limited, the producing decision of a household depends on the price of market efficiency, p, the characteristics of production and the characteristics of access to credit shall be: q q( p q h K). - Difference in income The Neoclassical economists gave the theories: Human capital theory, income and discrimination theory, production theory to explain the fundamental differences in the income of individuals or households. Thus, the income is a multivariate function depending on many different factors (Y =f (x1,x2,x3 xn). Today, to analyze the impact of factors on income, the function most commonly used in the analysis is the Cobb-Daughlas function. The Cobb-Daughlas function has the form as follows: α1 α2 αn β D+x D +λ D Y = A.X1 . X1 . Xn .e i i 1 i 2 11
12. In which, Y is the income; A is the constant; Xi (in 1, ) is the independent variable affecting the dependent variable, Y, (income). The hypothesized independent variables include credit, household characteristics, environmental factors and related policies; e is factors other than factor Xi. In addition to the Cobb-Daughlas function, there is also a semi-logarithm function: LN(Y)=β0+β1X1+β2X2+ +βnXn+ei (Mincer, 1974). Or multivariate linear function such as Y = β0 + β1X1 + β2X2 + + βnXn + ei is also widely used to estimate household income. From here, the study selects the proposes the research model that is a regression model with the form of multi- variable linear function to evaluate the impact of microcredit on income of poor households as follows: Y = β0 + β1X1 + β2X2 + + βnXn + ei 2.3.3. Microcredit for income generating activities Microcredit is needed to help poor households generate income (Krog, 2000). Microcredit activities are used in developing countries and are highly effective in poverty reduction, especially microcredit focuses on women customers in rural areas, who have no accessibility capital resources from other financial institutions by barriers on collateral and complex and cumbersome procedures, helping them to create jobs and generate income (Mohanan, 2005). 2.4. Overview of access credit theory and barriers to access to credit 2.4.1. Asymmetric information in credit transaction and credit restriction Asymmetric information occurs when the borrower understands their ability to repay their loans while the lender does not know the borrower’s limitations. At the same time, the borrower does not collect enough information about the loan or the lending institutions. As a result, the borrower desires to borrow but cannot access to the loan; and the lender does not know the customers who need the loan and bringing about the 12
13. consequences associated such as lending under the relationship, other complicated and cumbersome procedures such as collaterals, guarantors, etc. But to meet these conditions, most small and micro enterprises, poor households and low income households cannot meet this condition. 2.4.2. Social capital, measurement of social capital and accessibility to credit Until 1990, the American sociologist, James Coleman, gave the concept that social capital as the characteristics in the everyday life, social networks, norms, and social trust helps members in the society to work together effectively to achieve the common goals. Bourdieu (1986) defines that social capital is derived from a direct or indirect network, and is a durable network of interrelated relationships that are be acquainted and recognize each other. 2.4.3. Characteristics of households, environmental factors and policies with access to credit Environmental factor with the distance on geographic gap, housing location and characteristics of the living area, and the inadequacy of imperfect information make it difficult for customers to access capital but they have to deal with difficulties to access (Le Khuong Ninh, 2016, Nguyen Trong Hoai, 2005). People who want to borrow cannot borrow and people who do not need the loans are more likely to get access, thereby creating distorting motives when borrowing, to get the loans, people who want to borrow seeks the way to get loans through the relationship, leading to asymmetry in the supply and demand of credit in the financial market. 2.5. Overview of relevant research documents 13
14. Table 2.2. Summary table of relevant research results Studies Object and scope Study method Study results of study Microcredit with income Quach Manh Hao Access to credit With a cross-sectional dataset and Characteristics of household (2005) and poverty econometric model analysis through affecting the access to credit and reduction in rural the field survey data and the income of poor households Vietnam population living standard survey dataset VLSS 1992/1993 VLSS and VLSS 1997/1998 Phan Dinh Khoi Living standards of With a cross-sectional dataset for The impact of microcredit on income (2012) poor households in survey in the Mekong Delta, the study has not been demonstrated clearly in the Mekong Delta used the Propensity Score Matching this study. (PSM) technique and Instrumental Variable (IV-PE) method in the statistical analysis. Phung Duc Tung et The work of Quantitative and qualitative method Credit has impact on poverty al., (2013) poverty reduction with discontinuous regression model reduction in Ho Chi Minh in the study City in period of 2009-2013 14
15. Dinh Phi Ho & Income of poor The study uses the Difference in Credit has officially impacted on Dong Duc (2015) households in Differences (DID) method to assess household incomes and expenditures. Vietnam in the the impact of formal credit on period of 2006- incomes and expenditures of farmer 2012 households Mohanan (2005) Study the Qualitative research method Microcredit affects the income- microcredit generating capacity of the poor, activities in India empowering the women position. Islam and Ahmed Study the Quantitative method with statistical Microcredit affects the ability to do (2010) microcredit impact analysis through SEM linear model business, create jobs, and build assets on income of customers generating activities of customers Brown (2010) Study the Qualitative research method Microcredit offers borrowers the microcredit impact opportunity to increase the labor on economic changes, diversify the livelihoods for development, job poor households creation and income generation Ahmed et al., (2011) The role of The qualitative analysis method with Microcredit has no statistically microcredit in the analysis of random interview data 15
16. economic from 20 people surveyed significant impact development and poverty reduction Vitor et al., (2012) Microcredit with Quantitative research method with Microcredit helps improve the income of women logit regression model, survey data business skills and generate the in Central Ghana from 300 microcredit women income of credit women borrowers. borrowers Rykiye (2012) Study the The survey data analysis method from MICROCREDIT does not have an microcredit with 2,036 observations of poor households impact on income, microcredit income change of in Turkey borrowers as a normal need for participating capital. members Ayen (2016) Study the Quantitative analysis method with There is a difference in income differences in Propensity Score Matching (PSM) between borrowers and non- income of from the female head of household borrowers microcredit survey dataset at Jimma Zone borrowers and non- borrowers Accessibility to credit of households Nguyen Quoc Oanh Study the accessibility Quantitative method with two- The monthly income and purpose of and Pham Thi My step regression technique of borrowing affects accessibility to 16
17. Dung (2010) of formal credit Heckman (1999) through a formal credit of households. survey dataset of 116 households Nguyen Phuong Le Accessibility to formal Statistical method describes the The families with better economic and Nguyen Mau credit resources of survey data from 60 households conditions can access higher credit Dung (2011) farmer households interviewed based on the pre-set questionnaire structure Phan Dinh Khoi Formal and informal Statistical analysis based on Income, employment of households (2013) accessibility of farmers survey data of 358 observation affect accessibility of households. in the Mekong Delta samples from 919 rural households Tran Ai Kiet and Accessibility to formal Statistical analysis based on the The values of assets, income, and Huynh Trung Thoi credit of farmers in An survey results of 150 farmer purpose of borrowing affect the (2013) Giang households in the studied area accessibility to formal credit of farmer households. AFD (2008) Accessibility to Causal analysis compares the Rural households may participate microcredit of rural ability to borrow from more in microcredit loan when they households in Morocco microcredit have stable income and are not affected by seasonal factor. Ibrhim and Bauer Access to microcredit Quantitative method with probit The characteristics of households (2013) and the impact of the regression technique with members with good production microcredit approach to experience can access more 17
18. income of farmer households Masud and Islam Study the social capital Quantitative method with probit Social capital affects the access to (2014) with access to credit of regression technique through credit of households households in random survey data from 153 Bangladesh households in Bangladesh Source: Summaries of the author 18
19. 2.6. Theoretical foundations forming theoretical frameworks for study and the establishment of research hypotheses Theory of capital resource of Ismail and Yussof (2010) shown that income is formed by physical capital and human capital. Physical capital is acquired by themselves or borrowing in the form of monetary or physical (physical is shown in the form of assets, labor materials, means of production, etc.). Human capital is gained through the labor accumulation, which is the skills and knowledge accumulated during the learning and life experience. During creating value for labor product, it cannot lack one of these two elements above; they complete each other and indispensable in every human activity. Human capital is involved in the operation, creation on the basis of physical capital and vice versa. Thus, when considering the income of an individual, a family needs a general assessment of the many factors that make up the value of income. Therefore, to assess the income of poor households, it is necessary to assess overall between the income and access of microcredit of households. Speeding up income for poor households on the basis of increasing access to microcredit for poor households. With the above arguments, the thesis establishes two research models and needs to be clarified in the study and theoretical framework of the study formed as Figure 2.2. Social capital Microcredit Household Household characteristics Access Income characteristics to MC Environment and Environmental preferential credit characteristics and policies of the locality non-financial 19 activities
20. Figure 2.2. Theoretical framework for study Source: The author proposes based on theory foundations and previous studies Based on the above studies and based on the arguments of the research theory foundations, the thesis formed hypothesis on income of households affected by the following groups of hypothesis: (1) Microcredit (group of hypothesis H1); (2) Household characteristics (employment, number of labor generating jobs, number of dependents in the family - group of hypothesis H2); (3) Environmental characteristics and non-financial activities (group of hypothesis H3) Thus, it shown that, in order to promote the income increasing, it requires to increase accessibility to microcredit for them. The thesis establishes a Microcredit access model based on the foundations of the previous theory and studies. The summarized groups of hypothesis are included: (1) Social capital (group of hypothesis H4); (2) Household characteristics (income, employment - group of hypothesis H5); (3) Environment and preferential credit policies of the locality (group of hypothesis H6). 2.7. Gaps in research Through examine summarily the relevant studies, most studies affirm that microcredit offers many benefits to the poor such as increasing welfare, increasing empowerment for women, generating income and improving the living. By this argument, it shown that the gaps should continue to be inherited and clarified in the study are: (1) the study of the microcredit impact on the income of poor households and to find out whether there is the income difference between two groups of microcredit borrowers and non-borrowers? (2) the 20
21. specificity of the study area and (3) through the specificity of the area, to determine the impact of microcredit through the value of income and to increase the income and to increases the accessibility of microcredit for poor households, what limitations affects the accessibility of microcredit of poor households? From these gaps, the thesis continues to inherit previous studies and clarify gaps in this research thesis. 2.8. Conclusions of Chapter 2 Chapter 2 summed up the relevant theoretical foundations, examined summarily the studies in the country and abroad, and related studies; from that, the thesis has developed a theoretical framework for study. CHAPTER 3: STUDY METHODS 3.1. Study models As presented in the arguments of study theory in Chapter 2, the study uses a multivariate linear regression model with the hypothesis of microcredit impact on the change of poor household income and the Binary Logistic regression on the hypothesis of access factor to microcredit. 3.1.1. Microcredit model affects the income of poor households The thesis uses a multivariate linear regression model to test the study hypothesis. The analytical techniques through linear regression model, and the model has the following form: Y = β0 + β 1X1 + β 2X2+ + β 9X9 + e (3.1) e: Residuals β0: Vertical axis-cutting factor; β1 to β9: Regression coefficient (correlation) of the variable X1, X9. 21
22. Dependent variable, Y: Per capita income of the poor household, measured by the total income of the household divided by the number of household members (unit: million/year). The model should go through the system with the following 6 tests: Firstly, Test the partial correlation of the regression coefficients Secondly, interpretation level of the model Thirdly, suitability level of the model Fourthly, phenomenon of collinearity Fifthly, test the autocorrelation of residuals Sixthly, Heteroskedasticity 3.1.2. Model of factors affecting access to microcredit Logistic regression model: ∑ (3.2) In which: Y: The dependent variable has two states (0,1); X1, X2 Xi is the value of independent variables; β0 is the estimated value of Y when the variables X value 0; βk is the regression coefficients; u is residual. According to Cox, D.R (1970), the general form of the Binary Logistic regression model is as follows: = β + β X + β X + β X (3.3.) 0 1 1 2 2 n n In which, P (Y=1)=P0: Probability of households access to microcredit; and P(Y=0)=1-P: Probability of household not access to microcredit. [ ] 22
23. Take 0 = ; with 0 : Odds Coefficient 0 0 LnO0 = β0 + β1X1 + β2X2 + β7X7 (3.5) Therefore, Log of Odds coefficient is a linear function with independent variables Xi (i 1,7). Equation (3.5) has the form of a logit function. According to Agresti (2007), the model is approved the test system includes: Firstly, the Wald test. Secondly, test the suitability of the model. Thirdly, test the level of interpretation of the model. 3.1.3. Developing the basis for variable selection in study models 3.1.3.1. Microcredit model affecting income (M1) Microcredit is expressed MICROCREDIT through factors: loan scale, interest rate, loan term and (hypothesis H1) loan purpose Number of dependents HOUSEHOLD CHARACTERISTICS Labor scale (hypothesis H2) INCOME Employment ENVIRONMENTAL Environmental risks CHARACTERISTICS AND NON-FINANCIAL SUPPORT POLICIES Non-financial policies (hypothesis H3) Figure 3.1: Model of impact of microcredit on household income Source: Propose to study based on theoretical framework and previous studies 23
24. Selected variable Hypothesis Basis for variable selection [X1]. QM_VON Bateman’s Theory (2010) and Janvry’s Theory (1995) [X2]. TH_VAY Brown’s studies (2010); Islam and Ahmed (2010); [X3]. L_SUAT H1 Vitor et al., (2012); Ibrahim and Bauer (2013), Banerjee and Dulfo (2016); Alhassan and Akuduga [X4]. MD_VAY (2012). Vitor et al., (2012). [X5]. Dinh Phi Ho and Dong Duc (2015) S_PTHUOC [X6]. World Bank (2012); Ismail and Yussof’s Theory QM_LDONG (2010); Dinh Phi Ho and Dong Duc (2015) H2 World Bank (2012); Ismail and Yussof’s Theory [X7]. V_LAM (2010); Dinh Phi Ho and Dong Duc (2015) Janvry’s Theory (1995), Dinh Phi Ho and Dong Duc [X8]. R_RO (2015) H3 Manganhele (2010), Phung Duc Tung et al., (2013); Banerjee and Dulfo (2016); Boamah and Alam (2016); [X9]. CS_TPC Nguyen Duc Nhat et al., (2013); Alhassan and Akuduga (2012). Source: Summarize from theory foundations and previous studies 3.1.3.2. Microcredit access model (MH2) 24
25. Social capital (hypothesis H4): Participating in social capital, frequency of participating in social capital Household characteristics (hypothesis H5): Access to Income, employment microcre dit Environmental factors and preferential credit policies of the locality (hypothesis H6): The housing location of the households, living area and preferential policies of the locality Figure 3.2. Microcredit access model Table 3.2. Summarize the foundations for developing variables for the microcredit access model Selected variable Hypothesis Basis for variable selection [X1]. VON_XH Masud and Islam (2014); Putnam (1995); Baurm and [X2]. Ziersch (2003); Stone (2001); Kilpatrick (2002); Ajam TS_TGVXH H4 (2009); Lin et al., (20010); Okten (2004). AFD (2008); Tran Ai Ket and Huynh Trung Thoi [X3]. V_LAM (2013); Phan Dinh Khoi (2013). Brown (2010); Armed et al., (2011); Vitor et al., H5 (2012); Ibrahim and Bauer (2013) Mohannan (2005), Phan Dinh Khoi (2013). Tran Ai Ket and Huynh Trung [X4]. T_NHAP Thoi (2013). 25
26. Le Khuong Ninh (2016); Dinh Phi Ho (2012), Banerjee [X5].V_TRI and Dulfo (2016) and characteristics of the study area. World Bank (2012), Le Khuong Ninh (2016), Nguyen Trong Hoai (2005). Phan Dinh Khoi (2013) and H6 [X6]. K_VUC characteristics of the study area Phung Duc Tung et al., (2012), Boamah and Alam [X7]. CS_TC (2016); Nguyen Duc Nhat et al., (2013); Alhassan and Akuduga (2012). Source: Summarize proposals from study theory foundation and previous studies 3.2. Measurement of concepts in study models Model 1: The impact model of microcredit on income change Table 3.3: Measurement of variables in the model of microcredit impacts income Sign No. Contents Measurement expectat ion I INDEPENDENT VARIABLES: 1 [X1]. QM_VON Representing the amount of loan (Unit: mil dong) + 2 [X2]. TH_VAY Term of loan use (unit: month) + 3 [X3]. MD_VAY Loan purpose + 4 [X4]. L_SUAT Interest rate by month (unit:%/month) - The risks in the past 3 years of the household, 5 [X5]. R_RO value 1 = yes and vice versa = 0. - Number of children and adults outside the working age (children under 15 years old, older than 60 years for men and 55 years for women), 6 [X6]. S_PTHUOC (unit: person) - [X7]. Number of main employees in the household 7 QM_LDONG generating income for the family during the past 6 + 26
27. months (unit: person) Employment of the main decision maker/ householder in the family, value 1 = employment 8 [X8]. V_LAM (within 6 months) and vice versa = 0. + The support policies for non-financial activities such as creating a livelihood environment to help the poor create jobs, train to transfer of science and technology knowledge, cultivating and 9 [X9]. CS_PTC breeding knowledge, develop the business plans, + labor and occupation, value 1 = have support policy and vice versa = 0. DEPENDENT VARIABLE (Y): The income per capita is measured by the total income of the poor household divided by the number of household members (mil II dong) Model 2: Microcredit access model Table 3.4. Measurement of variables in the study of microcredit approach study model Variable name Sign Measurement expectation Dependent variable: Binary variable, value=1 if it participates in Microcredit microcredit borrowing and value=0, by contrast. Independent variables: + Households participating in activities in the locality, [X1].VON_XH value = 1 and vice versa = 0. + Number of times (frequency) participating in [X2]. TS_TGVXH activities/last 6 months (unit: times) + Householder or main decision maker in the household, value = 1 means to have employment and vice versa = [X3].V_LAM 0 27
28. [X4].T_NHAP + Income per capita/year (mil dong) - Housing location of the household compared to the [X5].V_TRI main road (inter-commune traffic) (unit: km) + Living area of household: Urban area, value = 1 and [X6].K_VUC vice versa = 0. [X7].CS_TC Preferential financial policies of the locality, value = 1 means get preferential policy and = 0, by contrast. 3.3. Study design 3.3.1. General overview of the study 3.3.1.1. Qualitative research method Qualitative research method performs to examine summarily the related theoretical documents and studies before forming the theoretical framework of the study and establishing the study hypotheses, forming study models for the thesis using the method of field observation, interviewing experts performing poverty reduction in the locality as well as the staff performing credit policy. 3.3.1.2. Quantitative research method Quantitative research method is performed based on the study hypotheses of the qualitative method and rechecks the hypotheses outlined in the study through the econometric model. 3.4. Sample size determination With the total number of poor households in the area of 33,159 households, the sample size determination following Taro (1967) suggested that the minimum sample size for the study should be obtained using the following formula: N n 1 Ne2 In which: n: Number of samples to be determined, 28
29. N: 33,159 poor households (overall), e: accuracy level for the standard error: +/-5% (0.05). Therefore, the sample size needs to be achieved at least under the Taro’s formula: n = 395 households. 3.5. Process of sample selection and study data collection 3.5.1. Sample selection To conduct the study topic, the topic searches and selects relevant sources. Conduct survey on access to microcredit of poor households in the Southeast region. The steps for conducting observation sampling are as follows: Step 1. Select the list of poor households in each locality. Step 2. Allocate the observation sample. Step 3. Conduct the sample survey (preliminary investigation) Step 4. Complete the study questionnaire Step 5. The trained staffs conduct sampling Step 6. Enter data into statistical software, encryption, clean data 3.5.2. Preliminary investigation Conduct a preliminary investigation and adjust the study questionnaire 3.6. Conclusions of Chapter 3 Chapter 3 presents arguments of the theoretical foundations forming the theoretical framework for study and the establishment of hypotheses for study, the establishment of study models, the necessary tests for study model analysis, determine sample size, construct the study variables and measure the variables included in the study model. 29
30. CHAPTER 4: MICRO-CREDIT ACTIVITIES IN THE AREA AND TEST RESULTS 4.1. Overview of the socio-economic characteristics of the Southeast region It is a dynamic economic region with high, sustainable economic growth and leading in the cause of industrialization and modernization of the country. As the leading economic development area of the country, it is the area with role as bridge to the Mekong Delta and the Central Highlands. The economy in the Southeast region has rapidly developed in terms of industry and services with most industrial enterprises and focused the most in the whole country with all kinds of industries. The rate of poor households in the region is the lowest in the country. Table 4.1. Rate of poor households in the region Surveyed Total number Total number of Poverty rate (%) provinces of households poor households Dong Nai 775,139 7,085 0.91 Binh Phuoc 237,728 14,627 6.15 Tay Ninh 291,830 6,117 2.1 Binh Duong 284343 0 0 Ba Ria - Vung Tau 260,797 4,986 1.91 Ho Chi Minh City 1,962,121 344 0.02 TOTAL 3,811,958 33,159 Source: MOLISA (2016). 30
31. Table 4.2. Summarize the poverty line standards of each locality Provinces Poverty line of Poverty line of the locality Source Compari the Central sion Period 2011-2013: (According to the - Rural: 800,000 Decision No. By 2 VND/person/month 49/2010/QD-UBND times Binh -Urban: 1,000,000 dated Dec 22, 2010) Duong VND/person/month. (According to the Period 2014-2015: Decision No. -Rural: 1,000,00 Rural: 400,000 51/2013/QD-UBND VND/person/month VND/person/mon dated Dec 27, 2013 -Urban: 1,100,000 th VND/person/month Urban: 500,000 Binh VND/person/mon No change - - Phuoc th (According to the Period 2012 – 2015 (According to the Tay Ninh Decision No. - Rural: 521,000 Decision No. By 1.3 09/2011/QD-TTg VND/person/month 54/2012/QD-UBND times dated Jan 30, - Urban: 651,000 dated Nov 27, 2012 2011 of the Prime VND/person/month Minister) Period 2010 – 2015: (According to the Ba Ria - - Rural: 700,000 Decision No. By 1.75 Vung Tau VND/person/month 12/2010/NQ-HDND times - Urban: 900,000 dated Jul 14, 2010 VND/person/month by the People’s Council of Ba Ria - Vung Tau province) 31
32. Period 2011-2015: According to the Dong Nai - Rural: 650,000 Decision No. VND/person/month 176/2010/NQ- By 1.6 - Urban: 850,000 HDND dated Jul 2, times VND/person/month 2010 Period 2014-2015: According to the - Rural: 1,000,000 Decision No. VND/person/month 126/2014/NQ- - Urban 1,200,000 HDND dated Sep VND/person/month 26, 2014. Period 2010-2014, poverty line (According to the By 3 of Ho Chi Minh City is: Decision No. times Ho Chi 12,000,000 23/2010/QD-UBND No Minh VND/person/month dated Mar 29, 2010 distinctio n City Period 2014-2015, poverty line (According to the between of Ho Chi Minh City is: Decision No. inner city 16,000,000 03/2014/QD-UBND or VND/person/month dated Jan 14, 2014 suburban Source: Summarize statistics from the Decisions of the provinces and cities on the Issuance of poverty line and near poverty line in period of 2011-2015 4.2. Overview and describe survey data in the locality 4.3. Overview of microcredit activity in Vietnam and in the region 4.4. There are difficulties and causes of difficulties and shortcomings in poverty reduction in the region 4.5. Statistics describe the variables in the study model 32
33. 4.6. Test the study results. 4.6.1 Results of the impact model of microcredit to income 4.6.1.1 Test the study model Table 4.9. The regression coefficient of the microcredit model on income Unstandardized Standardized Collinearity Coefficients Coefficients t Sig. Statistics Std. B Error Beta Tolerance VIF (Constant) 6.02 1.91 3.16 0.00 [X1] QM_VON 0.37 0.10 0.41 3.66 0.00 0.53 1.89 [X2] TH_VAY 0.03 0.14 0.02 0.19 0.85 0.46 2.18 [X3] MD_VAY 0.55 0.76 0.06 0.72 0.47 0.94 1.06 [X4] L_SUAT -0.30 0.41 -0.06 -0.72 0.47 0.89 1.12 [X5] R_RO -1.68 0.93 -0.18 -1.80 0.08* 0.69 1.45 [X6] S_PTHUOC -0.82 0.29 -0.24 -2.85 0.06* 0.93 1.07 [X7] QM_LD 1.58 0.89 0.16 1.78 0.08* 0.80 1.24 [X8] VIECLAM 1.04 0.79 0.11 1.30 0.20 0.90 1.11 [X9] CS_PTC 1.77 0.70 0.23 2.53 0.01 0.83 1.21 Durbin-Watson : 1.670 R2 Square : 0.47 Sig.: 0.00 F: 8.87 (*), )( ), ( ) with significance level are 10%, 5% and 1% respectively. Through the analysis of 6 model tests, it determined 4 variables including capital size [X1].QM_VON, number of dependents in the family [X6]. S_PTHUOC, labor size [X7].QM_LD and non-financial operating policies [X9].CS_PTC that had significant statistical effect on the Income variable of poor households in the region. 33
34. 4.6.1.2. Discuss the result of regression coefficient M1: Impact of microcredit on poor household income To discuss the study results of model 1: The model evaluates the impact of microcredit on income of poor households. From the Unstandardized Coef., evaluate the effect of an independent variable on a dependent variable. From the results of the study model summarized in Table 4.11, the significant variables include capital size, number of dependents, labor size, and policies. Table 4.11. Summary of the impact of microcredit on income Standardized Sig. Independent variables Coefficients Impact assessment Beta [X1]. QM_VON 0.41 0.00 1 [X6]. S_PTHUOC -0.24 0.01 4 [X7]. QM_LDONG 0.16 0.08 3 [X9]. CS_PTC 0,23 0.01 2 4.6.1.3. Assume the difference in income between two groups of households By T-test, the results show that microcredit borrowers have higher income than non-borrowers and the study results show that there is a similarity with the study by Dinh Phi Ho and Dong Duc (2015). The income of microcredit borrowers has the average income higher than that of non-borrowers (0.49 million dong). Table 4.12. Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means Sig. (2- Mean Std. Error F Sig. t df tailed) Difference Difference Per Equal variances 10.228 0.001 -1.729 598 0.084 -0.488 0.282 capita assumed income/ Equal variances -1.613 300.616 0.108 -0.488 0.302 year not assumed 34
35. 4.6.2. Test results of microcredit access model 4.6.2.1. Test results Table 4.13. Binary Logistic regression results 95% C.I.for EXP(B) B S.E. Wald Sig. Exp(B) Lower Upper [X1].V_XH 1.52 0.86 3.14 0.08* 4.57 0.85 24.50 [X2].TS_TGIA 1.64 0.84 3.80 0.05 5.15 0.99 26.76 [X3].VTDLY -0.87 0.46 3.52 0.06* 0.42 0.17 1.04 [X4].K_VUC 0.87 0.80 1.18 0.28 2.39 0.49 11.54 [X5].T_NHAP 0.37 0.17 4.63 0.03 1.45 1.03 2.04 [X6].VIECLAM 0.44 1.52 0.08 0.77 1.55 0.08 30.50 [X7].CS_TC 0.28 0.78 0.13 0.72 1.33 0.29 6.13 Constant -6.32 3.18 3.94 0.05 0.00 R2 – Nagelkerke: 0.484 -2 Log likelihood: 49.210 Omnibus - Model (Sig): 0.001 (*), ( ) with significance level are 10% and 5% respectively 4.6.2.2. Discuss the result of regression coefficient M2: Microcredit access model According to Agresti (2007), the regression coefficients should be discussed according to the probability of occurrence. It assumes the initial probability that the household accessing microcredit is 10% (P0=10%). Due to 1 factor Xi impacts, the probability of household accessing microcredit is P . Thus: B Pe0 P1 B (4.1) 1 Pe0 (1 ) In which: eB: impact coefficient corresponding to independent variable Xi. 35
36. Replace the corresponding eB values of the independent variables with statistically significant in the formula (4.1), we have the corresponding P1 values in Table 4.20. Specifically, the social capital element has eB = 4.57, with P0 = 0.1. From the formula (4.1), we obtain the following result: 0.1x 4.57 P 0.3365 1 1 0.1(1 4.57) Similarly, we have the probability of occurrence of other factors in Table 4.20 Table 4.14. Summarize the effects of independent variables on access to microcredit Assume initial probability with P0 = 0.1 Code of variable name eB P1 (%) [X1].VON_XH 4.57 34 [X2].TS_TGVXH 5.15 36 [X3].VTDLY 0.42 4 [X5].T_NHAP 1.45 14 Based on the results of the statistical analysis, the study summarizes the independent variables affecting the dependent variable according to the level of impact shown in Table 4.15 as follows: Table 4.15. Summarize the variables in the study model to access to microcredit Code of variable Impact name B eB P0 P1(%) location [X1].VON_XH 1.52 4.57 34 2 0.1 [X2].TS_TGVXH 1.64 5.15 36 1 [X3].VTDLY -0.87 0.42 4 4 [X5].T_NHAP 0.37 1.45 14 3 36
37. 4.7. Conclusions of Chapter 4 Chapter 4 of the thesis presents the situation of poor households in the region. It indicates the achievements and difficulties in the task of poverty reduction in the last period in the region. Chapter 4 also describes the reality of the study region and variables in the study model. CHAPTER 5: CONCLUSIONS AND SOLUTIONS 5.1. Conclusions The study thesis has presented 3 basic objectives: (1) Determine how microcredit changes the income of poor households and what is the impact level of microcredit on the income of poor households; (2) From the first objective, the thesis identifies the barriers that restrict the access of poor households to microcredit; (3) What policy implications contribute to improve the income through increasing accessibility to microcredit to poor households in the Southeast region. Based on foundation theory and the characteristics of microcredit through capital scale variables, they are the main components affecting the income of poor households. 5.2. Solutions 5.2.1. Group of solutions to improve income for poor households through the microcredit activities - Strengthen the scale of credit loans for poor households - Labor size and employment issues - Strengthen non-financial activity policies for poor households. - Strengthen the microcredit activities to push back the black credit in the localities and villages - Promote internal strengths and peculiarities of each locality in the region in parallel to promote of non-financial policies. 37
38. 5.2.2. Group of solutions to improve access to credit for poor households - Expanse social welfare for households: - Increase access to microcredit through increase incomes for poor households - The gaps of living area of the household 5.2.3. Suggest other solutions 5.3. Limitations of the study and orientation of the next studies 38