The impact evaluation of rural credit on accessibility to education, health care and clean water in rural vietnam

The results found that participation in rural credit only increase the expenditure on education while there is no evidence to conclude the relationship between rural credit program and s

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

THE IMPACT EVALUATION OF RURAL CREDIT

ON ACCESSIBILITY TO EDUCATION, HEALTH CARE

AND CLEAN WATER IN RURAL VIETNAM

BY

VO VAN TAI

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY, NOVEMBER 2013

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

THE IMPACT EVALUATION OF RURAL CREDIT

ON ACCESSIBILITY TO EDUCATION, HEALTH CARE

AND CLEAN WATER IN RURAL VIETNAM

A thesis submitted in partial fulfilment of the requirements for the degree of

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By

VO VAN TAI

Academic Supervisor:

Dr PHAM KHANH NAM

HO CHI MINH CITY, NOVEMBER 2013

CERTIFICATION

I hereby declare that the substance of this thesis is my own work and knowledge This dissertation has not been submitted for any other degree or diploma of the university or higher degree I certify that its contain has not been published or written by another person

VO VAN TAI

ACKNOWLEDGEMENTS

During the time of studying in Vietnam - Netherlands programme for M.A in Development Economics, I have learned so much useful knowledge Therefore, I want to thank to the programme and all the teachers that have taught me

I would like to express my deepest gratitude to my academic supervisor - Dr Pham Khanh Nam for his guidance and valuable comments in writing and finishing

my M.A thesis His enthusiasm and encourage has supported me during the process

of this thesis to finish it

Besides my supervisor, I want to acknowledge the tremendous support that I received from Prof Dr Nguyen Trong Hoai – Dean of the programme for his assistance and great encouragement

I also would like to thank Ms Vo Thi Phi - Director of TAN VUONG Food and Fisheries Import and Export Company Limited, where I am working, for her encourage and financial support, as well as the work supports of my colleagues, for

me to study and finish the programme

My grateful thanks to my classmates, Pham Tien Thanh and Cao Thi Tuyet Mai who helped me overcome the difficulties to finish my thesis

Last but not least, I am truly grateful to my family for their love and spiritual support in my life

Thanks for all

VO VAN TAI

ABSTRACT

The research is conducted in order to evaluate the impact of rural credit program on living standard of the rural households The estimation is based on the secondary data, namely the Vietnam household living standard survey in 2010 (VHLSS2010) The research applied propensity score matching (PSM) method with various techniques in order to estimate the impact of rural credit program on living standard The results found that participation in rural credit only increase the expenditure on education while there is no evidence to conclude the relationship between rural credit program and such living standard indicators as accessibility to health care and clean water

Moreover, the research also applied PROBIT model to investigate the factors that affect the probability of accessing to rural credit program The results showed that the probability of participating in rural credit program of the rural households are affected by such factors as age of household head, leadership status of household head, household size, house value, total land owned and managed by the household, household being poor or not, and geographic location

The research also found that the rural credit program may not serve the poor because among the participants in the credit program, the number of the poor households is less than that of the non-poor households

Finally, the research suggested policies and solution to improve the effectiveness of rural credit program in order to support the poor households

TABLE OF CONTENTS

ABSTRACT i

TABLE OF CONTENTS ii

LIST OF FIGURES iv

LIST OF TABLES iv

CHAPTER 1: INTRODUCTION 1

1.1 Problem statement 1

1.2 Objectives of the research 3

1.3 Research questions 3

1.4 Research structures 3

CHAPTER 2: LITERATURE REVIEW 4

2.1 Theory of impact evaluation methods 4

2.2 Empirical studies on impact of rural credit on living standard of the rural households .6

2.3 Empirical studies on determinants of the participation in rural credit programs 8

2.3.1 Characteristics at household head level 8

2.3.2 Characteristics at household level 8

2.3.3 Characteristics at commune level 9

CHAPTER 3: METHODOLOGY 12

3.1 Analytical framework 12

3.2 Models and estimation strategies 12

3.2.1 Determinants of demand for rural credit 13

3.2.2 Impact evaluation by PSM 15

3.3 Data description 18

CHAPTER 4: OVERVIEWS OF RURAL CREDIT AND ACCESS TO

EDUCATION, HEALTH CARE, CLEAN WATER IN VIETNAM 19

4.1 Rural credit market in Vietnam 19

4.1.1 The formal credit sector 19

4.1.2 The semi-formal credit sector 20

4.1.3 Informal credit sectors 20

4.2 Accessibility to education, health treatment and clean water in Vietnam 21

4.2.1 Accessibility to education in Vietnam 21

4.2.2 Accessibility to health treatment in Vietnam 23

4.2.3 Accessibility to clean water in Vietnam 24

CHAPTER 5: EMPIRICAL RESULTS 26

5.1 Non-parametric analysis 26

5.1.1 Descriptive Statistics 26

5.1.2 Participation in the rural credit program of the poor 28

5.1.3 Impact evaluation using two-sample t-test methods 29

5.2 Results of determinants of the participation of the rural household in rural credit programs 31

5.3 Impact evaluation using PSM methods 36

5.3.1 Balancing Test 36

5.3.2 Impact Evaluation via PSM 39

CHAPTER 6: CONCLUSION 43

6.1 Conclusion 43

6.2 Policy Implication 45

REFERENCES 47

APPENDIX 52

LIST OF FIGURES

Figure 2.1: Evaluation using a with and without comparison 4

Figure 3.1: Analytical framework on how rural credit affects the accessibility to education, health care and clean water 12

Figure 5.1: The comparison about educost, health and waterexp between participants and non-participants .30

LIST OF TABLES Table 2.1: Empirical studies about determinants of the participation in rural credit programs 10

Table 3.1: Determinants on demand for rural credit 13

Table 3.2: Indicators reflecting living standard in rural area 17

Table 4.1: Sources of rural credit 21

Table 4.2: Monthly consumption for education per capita 22

Table 4.3: Monthly consumption for health care per capita 23

Table 4.4: Percentage of households by main source of drinking water 25

Table 5.1: Descriptive statistics of all variables 26

Table 5.2: Participation in the rural credit program of the poor 28

Table 5.3: Impact of rural credit on living standard of rural households using Independent Sample T-Test Method 29

Table 5.4: Correlation Matrix of the continuous independent variables in PROBIT model 31

Table 5.5: Determinants of participating in rural credit program 32

Table 5.5a: Models of determinants of participating in rural credit program 33

Table 5.6: Impact of rural credit using NN technique 39

Table 5.7: Impact of rural credit using Stratification technique 40

Table 5.8: Impact of rural credit using Kernel Matching technique 41

Table 5.9: Summary of Impact of rural credit using PSM techniques 42

CHAPTER 1 INTRODUCTION 1.1 Problem statement

Vietnam has remarkable achievements in poverty reduction, specifically a report by World Bank and Vietnam General Statistics Office (GSO) in 2013 stated the poverty rate in Vietnam has decreased from 60 percent in 1990s to 14.2 percent

in 2010 (using official poverty lines of Vietnam Ministry of Labour Invalids and Social Affairs (MOLISA) at VND500,000/person/month for the urban area and VND400,000/person/month for the rural areas), and nearly 30 million people have escaped poverty However, a lot of people in rural Vietnam are still living in poverty with very low living standard, specifically, World Bank and GSO in 2013 reported that the poor, especially those living in rural area, have less opportunities

to access to education (as the Vietnam Population and Housing census by GSO in

2009, the illiteracy rate in rural area is 8 percent, which is 5 percent higher than that

in urban area), have limited access to formal health care (a report by GSO in 2010 showed that 7.07 percent of people in rural area cannot access to health treatment, and the rate of the poor is higher than that of the non-poor), and have less opportunities to have a good job Therefore, improving living standard of the poor

in rural area is considered as a top concern of the Vietnam Government With the objective of improving living standard of the rural poor, the Government has applied many programs such as free health care, food assistance, house assistance, education assistance, credit programs, etc Among these programs, rural credit program is considered one of the most effective programs to improve living standard of the poor (World Bank, 2012) The Government has applied a great number of the credit programs that provide loans to support the rural households Rural credit program has been applied in many countries and researched by many authors Waheed (2009) stated that credit may increase the living standard of the borrowers via improving their incomes, and then better education as well as health care To confirm the role of rural credit, Pitt and Khandker (1996) found that

credit program has a positive significant impact on such factors as education, esteem, organizational and management skills, etc Pitt et al (2003) also concluded that participation in credit program has effect on the health status of children In addition, Coleman (2006) found that rural loan has positive effect on households’ living standards including clean water accessibility As a report by CARE program (2009), with a microfinance loan, the poor can run their own business and do production, then they can generate income to pay for education of their children, pay for health treatment as well as access more to clean water

self-The main objective of this research is to investigate whether rural credit programs have positive impact on the living standard of the households living in the rural areas in Vietnam via increasing their accessibility to education, health treatment and clean water

Morduch and Haley (2002) found that when the poor are provided with credit, they can improve their living standard or at least smooth their expenditure However, due to the budget constraint, the people in the rural areas, especially the poor, have difficulty in accessing to formal and semi-formal credit sources Therefore, the government as well as financial institutions also makes efforts to support the poor to access to formal and semi-formal credit sources via providing programs that target the households in need of borrowing, especially the poor In order to target the poor effectively, many researches on the demand for credit of the rural poor have been conducted The credit providers need the information on characteristics of the households who are more likely to participate in credit programs This research also investigates the determinants of participation in rural credit programs of rural households in Vietnam

The research aims at evaluating the impact of rural credit program on accessibility to education, health care and clean water of rural household, especially the poor, as well as investigating the factors that affect the participation in credit program of the rural households In order to achieve these objectives, the research

applies: (1) PSM method with cross-section data in order to investigate this impact; (2) PROBIT model to investigate the determinants of accessing to credit program The research uses secondary data, namely the Vietnam household living standard survey in 2010 (VHLSS2010) This data set is collected by GSO

1.2 Objectives of the research

- Identify the determinants on rural credit participation of rural household

- Evaluate the impact of rural credit on living standard of the rural households via increasing accessibility to education, health care and clean water

1.3 Research questions

- Which factors affect the participation in credit program of rural household?

- Do credit programs have a significant positive impact on living standard of the rural households?

1.4 Research structures

Chapter I introduces the importance of rural credit program, methodology,

objectives, and questions of the research

Chapter II presents literature review and empirical studies about impact

evaluation methods, the impacts of rural credit programs on living standard of the rural households, and determinants of participation in rural credit programs

Chapter III presents analytical framework of the research as well as the model

and estimation strategies applied in the research

Chapter IV briefs the overviews of rural credit market in Vietnam and the

current situation of living standard indicators such as education, health care and clean water

Chapter V presents the results of the research using the model and estimation

strategies

Chapter VI presents conclusions of findings and suggests policies

CHAPTER 2 LITERATURE REVIEW

2.1 Theory of impact evaluation methods

Baker (2000) defined that “Impact evaluation is an assessment of how the intervention being evaluated affects outcomes, whether these effects are intended or unintended The proper analysis of impact requires a counterfactual of what those outcomes would have been in the absence of the intervention” Impact evaluation is conducted to compare the impact of a program intervention on the outcome of the participants in the program with that of the non-participants The results from impact evaluation will help the policy makers check whether a program is effective

or not, and implement solutions to improve the efficiency of the program

Figure 2.1: Evaluation using a with and without comparison

Source: Khandker et al (2010)

The objective of impact evaluation is to evaluate the difference in outcome of

households with program participation and without program participation In fact,

we cannot find households that both participate and do not participate in a program

At this time, we need to find out a counterfactual (non-participants, or control units) that have similar characteristics with the participants (treatment units) After that,

we calculate the difference in outcome between treatment group and control groups

to achieve the program’s impact (illustrated by Figure 2.1)

The following function presents the difference in outcomes between treatment group and control group:

D = E(Yi(1)|Ti=1) – E(Yi(0)|Ti=0)

Where: T = 1 : Participating households (treatment group)

T = 0 : Non-participating households (control group)

Yi(1)|Ti=1): the value of Yi under treatment (Participants)

Yi(0)|Ti=0): the value of Yi under control (Non-participants) However, the treated and non-treated groups may not be the same prior to the intervention Therefore, the following function is applied:

D = E(Yi(1)Ti=1) – E(Yi(0)|Ti=0) + [E(Yi(0)|Ti=1) – E(Yi(0)|Ti=1)]

Where: T = 1 : Participating households

T = 0 : Non-participating households

Yi(1)|Ti=1: the value of Yi under treatment (Participants)

Yi(0)|Ti=0: the value of Yi under control (Non-participants) E(Yi(0)|Ti=1): the expected outcome for nonparticipants if they

participated in the program Impact evaluation methods will support to find out the counterfactual There are many impact evaluation methods such as Propensity Score Matching (PSM), Difference in Difference (DID), Application of instrumental variables (IV), etc

- In DID method, we can capture the difference in outcome between treatment and control group over time (post- and pre- program) However, the application of DID is costly because it requires the panel data (information of post- and pre- program)

- In IV method, selection bias on unobserved characteristics is adjusted by finding a variable that has relationship with participation in the program but does not have relationship with unobserved characteristics affecting the outcome However, it is very difficult for all researchers to find an instrumental variable

- In PSM method, only cross-sectional data is required PSM can reduce selection bias and be considered as one of the best techniques to reduce the selection bias

From the above mentioned comparison, PSM is the most feasible to be applied Therefore, this research applies PSM method to evaluate the program impact

2.2 Empirical studies on impact of rural credit on living standard of the rural households

Living standard of rural households is reflected via such indicators as income, consumption, health care accessibility, education accessibility, clean water accessibility, etc

There are many factors that affect living standard of the rural household Among these factors, credit plays an outstanding role

Waheed (2009) stated that credit may increase the living standard of the borrowers via improving their incomes, and then better education as well as health care To reaffirm that, Thorat (2006) stated that credit enables participating households to spend more on education than non-participating households Pitt and Khandker (1996) concluded that credit program has a positive significant impact on

such factors as education, self-esteem, organizational and management skills, etc Pitt et al (2003) also found out that participation in credit program has effect on the health status of children In addition, Coleman (2006) found that rural loan has positive effect on households’ living standard including clean water accessibility

In a report of CARE program (2009), it was found that microfinance loan can help the poor to generate income via running their own business and doing production, and then they can pay for education of their children, health treatment

as well as access more to clean water

A report by the World Bank in 2004 stated that participation in rural credit programs increases the income and expenditure Khandker (2005) concluded that credit plays a significant role in helping the poor to escape poverty Chakrabarty (2003), Antwi and Antwi (2010) also found that rural credit improve the income as well as eradicate poverty of the poor in the rural regions Nader (2008), Morduch and Haley (2002) stated that credit is considered as a tool of fighting poverty Madajewicz (2003), Copestake et al (2000) also found that with a rural credit, the rural poor can run their own business or do self-production to generate income, so they can have opportunity to escape poverty

Morduch and Haley (2002) found that when the poor are provided with credit, they can improve their living standard or at least smooth their expenditure

For the case of Vietnam, Nguyen (2006) and Pham (2003) found that rural credit has a positive impact on the living standard of borrowing households Quach (2005) also concluded that rural credit has positive impact on living standard of households via improving their expenditure

This research uses education accessibility (via the expenditure on education), health care accessibility (via the number of health treatment at hospital), and clean water accessibility (via the expenditure on drinking and running water) to reflect living standard

2.3 Empirical studies on determinants of the participation in rural credit programs

2.3.1 Characteristics at household head level

Age Tang et al (2010) and Zeller (1994) found that age of households head

have positive effect on the participating in rural credit program (i.e the older the heads of family are, the more likely that household participate in credit program) Meanwhile, Mpuga (2008) proved that if the head of the family are too old, they will be less likely to demand for credit He explained that when the heads are too old, they may do production or run small business less effectively, so it is difficult for them to pay the debt

Gender As the study by Mpuga (2008), it is founded that men have more

probability of participating in the rural credit program Tang et al (2010) confirmed that men are more likely to borrow from rural credit sources than women

Education Gropp et al (1997) stated that household heads with higher

education level may be more likely to participate in credit program As a research for the case in Vietnam by Nguyen (2007), it is also found that education level has positive affect on the demand for rural credit

Marital status In his research, Mpuga (2008) found that married household

heads have more demand for credit than unmarried ones

Leadership status Li (2010) stated household heads with leadership status has

more demand for credit He explained that household heads with leadership status have more tendencies to run business and expand production so they are usually in need of borrowing from rural credit sources

2.3.2 Characteristics at household level

Household size Ho (2004) and Tang et al (2010) proved that household size

has positive effect on the probability of participating in credit program In contrast, Mpuga (2008) found that households with more members may be less likely to participate in credit program because they have less probability of paying debt

Dependency ratio Pham and Izumida (2002) proved that households with

higher dependency ratio have greater probability of accessibility to credit program

Wealth Diagne (1999), Crook (2001), Duca and Rosenthal (1993) stated that

the households with more value of assets has a positive impact on the demand for credit, amount applied for, and the amount of credit received They explained that these better-off households have tendency to expand production, so they often demand for capital To affirm this statement, the researches by Gropp et al (1997), Cox and Jappelli (1993) also found that households with more assets are more likely to borrow

Land As the study by Tang et al (2010), land is found to have positive

effects on the demand for credit For the case of Vietnam, the researches by Nguyen (2007), Pham and Izumida (2002) also concluded that households with more land have more probability of participating in rural credit program because they need capital to expand agricultural production

2.3.3 Characteristics at commune level

Geography factor Sharma and Zeller (1999) and Mpuga (2008) found that

regional differences also affect the demand for credit

Distance to bank or financial institution Mpuga (2008), Tang et al (2010), Li

(2010) found that distance to bank or financial institution has impact on the probability of participating in credit programs (i.e households living farther away from bank have less likelihood of demand for credit) They explained that these household must incur higher cost of borrowing because they have to pay some extra cost such as travelling, time, communication, etc

Facility of the commune Khandker (1998) proved that the condition or living

standard of the commune has impact on the demand for credit of households living

in that commune The condition and living standard are reflected via the facilities in that commune including post office, road to the commune, that commune being poor or not, etc

Table 2.1 summarizes empirical studies about determinants of the participation in rural credit programs On the basis of those variables, this research will investigate the factors that affect the accessibility to rural credit

Table 2.1: Empirical studies about determinants of the participation in rural credit programs

Tang et al (2010),

Zeller (1994)

Age of household heads

Age positively affects the decision to demand for credit

Mpuga (2008) Age Squared of

household heads

Age Squared is proved to have negative effect on demand for credit Mpuga (2008),

Tang et al (2010)

Gender of household heads

Men access to credit sources more than women

Gropp et al (1997),

Nguyen (2007),

Education level of household heads

Household heads with higher education level may be more likely to participate in credit program

Mpuga (2008) Marital status of

household heads

Married household heads have more demand for credit than unmarried ones

in the less likelihood of accessing to credit program

Pham and Izumida

(2002)

Dependency ratio Higher number of dependents leads

to the greater probability of accessibility to credit program

of the house, value

of assets)

The value of assets owned by the household has a positive impact on the demand for credit, amount applied for, and the amount of credit received

Tang et al (2010),

Nguyen (2007),

Pham and Izumida

(2002)

Total land area Land is found to have positive effects

on the demand for credit

Sharma and Zeller

(1999), Mpuga

(2008)

Geography factor There are regional differences in

demand for credit

Mpuga (2008),

Tang et al (2010),

Li (2010)

Distance to bank or financial institution

Households living farther away from bank have less likelihood of demand for credit

Khandker (1998) Facility of the

commune (Post office, Road, Poor commune or not)

Condition of the commune is proved

to have impact on the demand for credit of households living in that commune

CHAPTER 3 METHODOLOGY

in the future and may go deeper into debt (Dearden et al., 2010)

(2) The rural households may use the loan to do production or run small business Using this mechanism can not only increase the ability of paying debt, but also help them increase their income, then they can have more saving, and then they can have more expenditure for education, health treatment, and clean water

Figure 3.1: Analytical framework on how rural credit affects the accessibility to education, health care and clean water

3.2 Models and estimation strategies

This research applies quantitative methods to achieve the research objectives and give answers to research questions The question 1 would be answered by using PROBIT model The question 2 will be answered by Propensity Score Matching (PSM) method

Rural

Credit

Income Saving

Accessibility to education, health treatment, clean water Others

(2) (2)

3.2.1 Determinants of demand for rural credit

PROBIT model was first introduced by Bliss (1934) and then it was applied in many studies by many researchers Many authors also applied this model to examine the determinants of accessing to credit program (Aghion and Morduch (2005); Coleman (2006); Anjugam and Ramasamy (2007); Tang et al (2010)) The function of PROBIT model is as followed:

PROBIT (Y=1) = F (Hi, Fj, Cp)

Where:

Y : Participation in rural credit program (0 = Households who are not provided with rural credit; 1 = Households who are provided with rural credit);

Hi : is a vector of household head characteristics;

Fj : is a vector of household (family) characteristics;

Cp : represents vector of characteristics of commune

Based on the empirical studies, this research uses the following variables to investigate the determinants of demand for credit in rural areas (Table 3.1):

Table 3.1: Determinants on demand for rural credit

AGE Age of household head Continuous variable: in years

household head

Continuous variable: in years

GENDER Gender of household head Dummy variable:

OSTATUS

Household with members having an official status (leadership status) in the

HSIZE Household Size Continuous variable: number of

members living in the household

Continuous variable: number of nonworking members over working member

HVALUE Value of the house that

household living in

Continuous variable: in thousand dong

LAND Total land area owned or

= 0 if not living in distant regions;

= 1 if living in distant regions

DISTANCE

Distance to bank or semiformal credit-providing organization

Dummy variable:

= 0 if that commune not having road;

= 1 if that commune having road

POST Commune with/without

post office

Dummy variable:

= 0 if that commune not having post office;

= 1 if that commune having post office

On the basis of Table 3.1 and vectors of PROBIT function, characteristics of household heads include such variables as age, gender, education level, marital status, leadership status in the commune; charateristics of households include household size, dependency ratio, value of the house, total land area used or managed by households, family situation (poor or not poor), and characteristics at commune level such as location distance to banks or financial institutions, commune with/without 135 program, commune with/without road accessible to that commune and commune with/without post office

Estimation Strategy of PROBIT model includes three following steps:

Step 1: Estimate the PROBIT model

Step 2: Estimate the marginal effect

Step 3: Interpret the results

3.2.2 Impact evaluation by PSM

PSM method applied in the researches on evaluating the impact of a program (rural credit) on the outcome (income/consumption) includes two main stages: (1) Investigate the determinants on the participation in credit program of rural households, and (2) Evaluate the impact of credit program on living standard The results from PROBIT and PSM will be carried out using STATA software This research applies PSM to investigate the impact of rural credit programs

on living standard of rural households Based on the studies by Khandker et al (2010), Becker and Ichino (2002), PSM method can be divided into the following steps:

Step 1: Construct a PROBIT model of determinants on participation in the programs; then, based on the estimated regression model to calculate the probability

of participation in program This probability is also called the propensity score

Step 2: Specify the common support region of propensity scores for treatment

group (participating units) and control group (non-participating units) This step will automatically take out some observations which have so high or so low estimated propensity scores that they cannot be compared with any other observations

Step 3: Conduct the balancing tests

Step 4: Match on participant with one or more than one non-participants that have the closest propensity score by applying matching techniques such as:

Nearest-Neighbor (NN) In this technique, each treatment unit will be

compared with one control unit that has the most similar characteristics The comparison can be conducted using replacement or without replacement Comparison with replacement means that one control unit can be used to compare with many other treatment units provided that they have the similar characteristics

In this research the comparison with replacement is applied

Stratification or Interval Matching In this technique, common support region

will be divided into several intervals The impact will be calculated in each interval

In each interval, the impact is the average of difference between treatment and control unit The impact of the program is calculated using weighted average of the impact in each interval (weighted is calculated based on the number of treatment units in each interval)

Kernel and Local Linear Matching This technique applies weighted average

of all control units in the common support region to create a match for each treatment unit The control unit with more similar characteristics in comparison to treated unit will have higher weight, and vice versa The estimator includes a linear term in the weighted function in order to reap the better result

Then, based on this propensity score to compare the outcome (e.g the following table 3.2 shows the outcomes used in this research) between each participant and non-participants The difference between each pair of comparison represents “individual impact” of the program

Table 3.2: Indicators reflecting living standard in rural area

educost Expenditure on education

within 12 months

Continuous variable (thousand dong)

health Number of health treatment

at hospital within 12 months

Continuous variable

waterexp

Expenditure on drinking and running water within 12 months

Continuous variable (thousand dong)

watertap Tap water accessibility

Dummy variable (1=Households with private tap; 0=Households without private tap)

Step 5: Calculate the average value of all individual impacts that reflects the impact of program

Step 6: Apply PSM with the Bootstrap to estimate standard errors in order to have better estimated value

3.3 Data description

The research applies secondary data collected by Vietnam General Statistics Office (GSO), namely Vietnamese Household Living Standard Surveys in 2010 (VHLSS2010)

This research will select borrowers (treatment group) and non-borrowers (control group) in the rural areas in Vietnam Treated households and control households are defined as follows:

- Treated households are the households that borrow within 12 months of the survey in 2010, from formal and semi-formal sources such as Vietnamese Bank for Social Policy (VBSP), Employment Support Fund, Poverty Reduction Fund, Sociopolitical Organizations and other organizations

- Control households are the households that do not borrow from any sources

in the same time (within 12 months of the survey in 2010)

From the criteria above, the number of observations in the research is 3,615 households, in which there are 787 borrowing households (treated households) and 2,828 non-borrowing households (control households)

CHAPTER 4 OVERVIEWS OF RURAL CREDIT AND ACCESS TO EDUCATION,

HEALTH CARE, CLEAN WATER IN VIETNAM

4.1 Rural credit market in Vietnam

4.1.1 The formal credit sector

“In Vietnam, formal rural credit is defined as the credit that provided by the

Vietnam Bank for Agriculture and Rural Development (VBARD) and access to

such a formal kind of credit referred to ability to obtain a loan” (Ha, 2006) Some characteristics of VBARD are as follows:

- Loans from VBARD are mainly used for agricultural production Since

2012, VBARD has reserved 70% of the total outstanding loans for agriculture and rural areas

- Most loans are short-term and interest rates are determined by State Bank of Vietnam (SBV);

- In order to reduce default risk, VBARD set up a network from central level

to commune level, as well as get the support from social political organizations at the communes to monitor the borrowers Up to October

2012, VBARD has 2,400 branches and transaction offices with 42,000 staffs nationwide

However, VBARD is reducing its weight in the rural area, and the VBSP is nowadays considered the most major credit provider in the rural area The main characteristics of VBSP are as follows:

- Contributing to hunger and poverty reduction The customers of VBSP are mainly the poor and other policy beneficiaries Credits granted to the poor

by VBSP are mainly used for the purposes of production, business, employment and life improvement in order to implement the national target

self-program of hunger eradication and poverty reduction (Decree No.78 by the Prime minister)

- Providing loans with low interest rates and in small amounts Initially, the establishment of VBSP is to provide the poor with preferential credits; however the interest rate currently approaches the market rate (Decision

No 852/QĐ-TTg) The maximum loan size granted by VBSP household is normally about 30 million VND (Decision No 31/2007/QĐ-TTg)

4.1.2 The semi-formal credit sector

The semi-formal credit sector provides loans via sociopolitical unions in rural areas In this research, semi-formal credit providers are Employment Support Fund,

Poverty Reduction Fund, sociopolitical organizations and other organizations

4.1.3 Informal credit sectors

Informal credit sectors have been main traditional credit sources in rural areas, which reflect the underdevelopment of the rural credit market Informal credit sources may be categorized into such forms as: (1) Borrowing from friends, relatives, or neighbors; (2) Borrowing from rotating savings and credit associations promoting periodic savings among group of people from the same commune that trust each other; (3) Borrowing from moneylenders

In Vietnam, informal credit sources were main credit providers to the households in rural area in the past This sector has the following characteristics as: (1) Interest rates may vary from very high to very low, or even to none; (2) Loan size is small; (3) No collateral is required when borrowing money; (4) The loan procedure is simple

This research only focuses on the role of formal and semiformal credit sectors Table 4.1 shows the participation in each credit sector regarding to 787 rural households who borrow within the survey of 2010

Table 4.1: Sources of rural credit

Source: Calculated from VHLSS2010

4.2 Accessibility to education, health treatment and clean water in Vietnam

4.2.1 Accessibility to education in Vietnam

As a result of VHLSS2010, it is reported that the ratio of people who have no diploma or have never gone to school among the population aged 15 years and older

in the poorest group is 38.1 percent, 4.6 times higher than that in the richest group Table 4.2 shows that in 2008, the average expenditure on education is 43 thousand VND per capita per month Average expenditure on education of the richest households is 86 thousand VND per capita per month, which is about five times higher than that of the poorest quintile (17 thousand VND per capita per month); moreover, for the case of urban households, the expenditure is 75 thousand VND per capita per month, which is 2.4 times higher than that of rural households (31 thousand VND per capita per month) Similarly, in 2010, the average expenditure on education per capita per month is 68 thousand VND Average expenditure on education of the richest quintile is 149 thousand VND per capita per month, which is 5.7 times higher than that of the poorest quintile (26 thousand VND per capita per month); moreover, that of urban areas is 120 thousand VND per capita per month, which is 2.6 times higher than that of rural areas (46 thousand VND per capita per month)

Table 4.2: Monthly consumption for education per capita

Source: Result of VHLSS2010

From table 4.2, it can be indicated that from 2008 to 2010 the expenditure on education has increased by 58 percent from 43 to 68 thousand VND per capita per month, but the increasing rates of the rural households and the poorest households are respectively smaller than the rates of the urban households and the richest households For the case of rural households, the increasing rate is about 48 percent while the rate of the urban households is about 60 percent Similarly, the increasing rate of the households with the highest income is 73 percent, while the rate of household with the lowest income is 53 percent

It can be concluded that the rural households, especially the poor households have less opportunity to access the education

4.2.2 Accessibility to health treatment in Vietnam

The results of VHLSS2010 by GSO found that consumption per capita per month for medical examination and treatment in 2010 is about 62 thousand VND, which is about 1.4 times higher than in 2008

Table 4.3: Monthly consumption for health care per capita

62 thousand VND per capita per month The expenditure per capita per month of the richest quintile is 110 thousand VND, which is 3.5 times higher than that of the poorest quintile (31 thousand VND); meanwhile, that of the urban households is 79 thousand VND, which is 1.4 times higher than that of the rural households (55 thousand VND)

For the case of whole country, the increasing rate of expenditure on healthcare over time is about 38 percent (from 45 thousand dong per capita per month in 2008

to 62 thousand dong per capita per month in 2010) From 2008 to 2010, the increasing rate of the households in rural area is about 45 percent (from 38 to 55 thousand VND per capita per month), while the rate of the urban households is 23% (from 64 to 79 thousand VND per capita per month) Similarly, table 4.3 shows that, from 2008 to 2010, the increasing rate of the households with lowest income is about 29 percent (from 24 to 31 thousand VND per capita per month), while the rate

of the households with highest income is about 43 percent (from 77 to 110 thousand VND per capita per month)

It may be indicated that the rural households, especially the poor households have less opportunity to access the healthcare

4.2.3 Accessibility to clean water in Vietnam

As the result of VHLSS2010 by GSO, the ratio of households using safe drinking water sources reached 90.5%, of which this ratio in rural areas is 87.4% Table 4.4 shows that, in both 2008 and 2010, the urban households have higher proportion of using private tap water than the rural households, and the households with higher income have higher share of using private tap water than the households with lower income

Table 4.4 shows that in 2008, the share of households using private tap water was 23.3%, of which it was 60.7% in urban areas and 8.8% in rural areas Simultaneously, in 2008, the proportion of the households with highest income using tap water is 48.2%, while that of the poorest households is only 6.6%

Similarly, table 4.4 shows that in 2010, the share of households using tap water was 26.7%, of which it was 66.5% in urban areas and 9.2% in rural areas Simultaneously, in 2010, the ratio of the richest households using tap water is 52.6%, while that of the poorest households is only 7.4%

Table 4.4: Percentage of households by main source of drinking water

Year Private

tap

Public tap

Buying water

Drill well with pump

Hand dug well, constructed well

Filtered spring water

Other well

Rain water Others Total

at their home

CHAPTER 5 EMPIRICAL RESULTS

5.1 Non-parametric analysis

5.1.1 Descriptive Statistics

Table 5.1: Descriptive statistics of all variables

From table 5.1, it shows that the average expenditure on education is about 1,792.98 thousand dong, in which there are households who do not have any expenditure on education and the maximum amount paid for education is 43,275 thousand dong The average times of health treatment at hospital is 5, in which some households do not have any health treatment at hospital while there are households with the times of health treatment up to 71 times The average amount

of clean water expenditure is 24.38 thousand dong, in which the maximum amount

of expediture is 960 thousand dong and there are households who do not have any expenditure on clean water The ratio of households with water tap reaching their house is 10 percent (363 households over the total of 3615 households)

Among 3,615 households in the sample size, there are 22 percent (787) participants in rural credit program and 78 percent (2,828) non-participants

About the characteristics at household head level, table 5.1 shows that the average age is 47 (the minimum age is 18 and the maximum age is 99); there are 83 percent (2,984) female household heads and 17 percent (631) female household head; the average years of going to school is 1.17 (the minimum year is 0 year and the maximum is 12 years); there are 2 percent (57) single household heads and 98 percent (3,558) household heads who are married, or divorced, or widows; and there are 4 percent (114) household heads who have the leadership status in the communes

About the characteristics at household level, table 5.1 shows that the average

of household size in each household is about 4 members (the minimum number is 1 and the maximum number is 12); the average of dependency rate is 0.74 (the minimum rate is 0 and the maximum rate is 6); the average of house value is 145,417.90 thousand dong (the maximum value is 8.000.000 thousand dong, and the minimum value is 0); the average of land owned or managed is 9,858.96 thousand dong (the maximum value is 2,848,072 thousand dong, and the minimum value is 0); and there are 20 percent (731) poor households while the number of noon-poor households are 2,884 (80 percent)

About the characteristics at commune level, table 5.1 shows among 3,615 households, there are 33 percent (1,208) households living in the far distant regions; there are 27 percent (986) households living in the commune135; there are 87 percent (3,139) households living in the communes with road for car to access to; there are 90 percent (3,245) households living in the communes that have post office The average distant for households to banks is 11.86 kilometers (the farthest distant is 280 kilometers while there are some households with banks right in their area)

5.1.2 Participation in the rural credit program of the poor

Table 5.2: Participation in the rural credit program of the poor

Source: Calculated from VHLSS2010

From table 5.2, it is found that rural credit programs may not serve the poor

households Among the households who can access to rural credit program, the proportionate of the poor households is only 41 percent (320/787), while that of the non-poor is 59 percent (467/787) Simultaneously, among the poor households in this data set, only 44 percent (320/731) of them can access to rural credit program, while 56 percent (411/731) of these households cannot borrow from this program

5.1.3 Impact evaluation using two-sample t-test methods

Table 5.3: Impact of rural credit on living standard of rural households using Independent Sample T-Test Method

Source: Calculated from VHLSS2010

From table 5.3, it is found that the rural households who can borrow from credit program are more likely to access to education than the rural households who cannot access The treatment households can pay 183,000 VND for education more than the control ones The impact is rather remarkable; however, it is not statistically significant

Table 5.3 shows that the rural households who can access to credit program have higher accessibility to health treatment than the rural households who cannot access, and the difference is very small However, this impact is not statistically significant

From table 5.3, the treatment households are less likely to access to clean water The impact is very small and it is not statistically significant Similarly, there

is no evidence to conclude that participation in rural credit can increase the accessibility to tap water

Figure 5.1: The comparison about educost, health and waterexp between participants and non-participants

Source: Drawn from VHLSS2010

In summary, via using two-sample t-test, it is found that households who participate in rural credit program have higher accessibility to health treatment at hospital, education than households who do not participate in rural credit program; meanwhile, the participants in credit program are less likely to access to clean water than the non-participants However, these impacts are statistically insignificant Therefore, it is concluded that there is no impact of rural credit program on health care, education, clean water and tap water accessibility of households living in rural areas

However, the difference in outcomes between the treatment and control households does not result from the impact of rural credit program because these two groups of households are not similar in terms of household characteristics Therefore, the results from this method may not be precise

Credit

0 1 0 1 0 1

5.2 Results of determinants of the participation of the rural household in rural credit programs

Table 5.4: Correlation Matrix of the continuous independent variables in PROBIT model

age age2 edu hsize depend hvalue land distance

Source: Calculated from VHLSS2010

The correlation coefficients of most variables are quite small (Table 5.4) and VIF values of most factors are less than 10 (Table 5.5), it is likely that the degree of multicolinearity is low in the PROBIT model

For the case of the variable age and age squared, correlation coefficient is very high (about 0.985) and their VIF values are greater than 10, which indicates that there exists the multicolinearity in the model

Ramanathan (2002) stated that multicolinearity may be accepted if it does not cause serious consequence That is, when we add a variable which has high correlation with an existing variable, it will cause a change in sign or a great variation in coefficient of the other variables in the model

Table 5.5: Determinants of participating in rural credit program

Dependent Variable : Participation in rural credit program (Credit)

Note : *** significant at 1%; ** significant at 5%; * significant at 10%

Source: Calculated from VHLSS2010

However, when model 2 (without variable age2) and model 3 (without variable age) are estimated for comparison, the sign of the remaining variables in the models do not change and the difference in estimators is not remarkable (Table 5.5a) Therefore, it can be concluded that the multicolinearity in this model does not cause serious consequence Moreover, Ramanathan (2002) also insisted that the selection of right function is more important The research applied the function with age and age2 variables on the basis of the research by Edith (2012) and Berg and Schrader (2012)