Luận văn thạc sĩ UEH determinants on households’ partial credit rationing an analysis from VARHS 2008

Introduction

Research Context

Since 1986, Vietnam Government has initiated the economic reform that has transformed the nation from the central planning to market oriented economy Major achievements in terms of economic growth and poverty reduction have been attained as a result of the reform However, the large gap between rural and urban areas has still existed To ensure the sustainability of economic development and the stability of political environment, rural and agriculture development are therefore considered as a priory goal in the nation development strategy

Providing access to finance to the poor or microfinance has been considered as a tool for economic development and poverty reduction (Morduch & Haley, 2002; Khandker, 2003) It is the interest of many policy makers and researchers in recent years Thus, in this strategy, rural credit, which aims at ensuring rural households having access to financial services, is regarded as an important component The Government has launched many credit programs supporting the development of rural area such as preferential credit for the poor, agriculture forestry and fishery encouragement through special state own banks or government agencies The credit program has some significant impact to economic development of Vietnam rural areas; however there still exist some issues such credit rationing in the program When credit rationing occurs, credit suppliers ignore to offer loan to some borrowers to avoid the risk of default, thus it limit the credit accessibility of the poor Due to it implication to the economic development in general, and to the effectiveness of Government credit for the poor program in particular, this paper will aim at examining the factors that affect to credit rationing, particularly focus on the cases of partial credit ration, with the hope of revealing some potential implications for policy makers.

Research Problem

Rural credit market is an importance factor that helps to foster the economic development in rural areas, thus improving the poor living standard and supporting poverty alleviation One of its function is funding household’s credit demand

However, the degree to which rural credit impacts on the rural area welfare depends on how well the rural credit market operates, however the problem of credit rationing could have negative effect on the performance of rural credit market Credit rationing could be described as the cases in which credit lenders refuse to offer loan to borrowers, or offer an amount of loan that is less than borrower’s request, even though the borrowers willing to accept higher level of interest rate to help the lender to cover the default risk (Barham, Boucher, & Cater, 1996; Buchenrieder, 1996; Heidhues & Schrieder, 1998; Zeller, 1993) The higher the probability of credit rationing, the more difficulties for household to satisfy their credit demand In other word, if credit rationing is a common practice in the area, it may lead to the inefficiency of the credit market in the area as a consequence

As Stiglitz, J and A Weiss (1981) pointed out, asymmetric information is an explanation for the problem of the credit rationing In rural credit market, which is characterized by numbers of poor households and the difficulties to evaluate their credit worthiness, is concealed by a fog of asymmetry information between lenders and borrowers In other word, lenders are reluctant to lend as they are uncertain about the loan repayment probability To overcome this problem, lenders require different kinds of information about their borrowers such as household’s dependency ratio, household size, land value, social position, etc., to assess their repayment ability and make a basis for lending decision

As such kinds of information may affect to the probability and the degree to which a borrower be credit rationed, a question has been raised by many researchers is that what factors determined lenders’ decision of credit ration The answers are different depending on the period and location under examination of a study For this paper, the research aim to examine the determinants of credit rationing – especially for the case of partial credit ration, concentrating on 12 provinces Ha

Tay, Nghe An, Khanh Hoa, Lam Dong, Phu Tho, Quang Nam, Long An, Dac Lac, Dac Nong, Lao Cai, Dien Bien, Lai Chau period 2006-2008.

Research Objectives

The general objective of this study is to examining the relationship between partial credit rationing and its determinants in rural area of Vietnam in period 2006-2008

The general objective could be archived by meeting following sub-objectives:

 Identify key factors that affects the household’s credit accessibility

 Identify key factors that affects the degree of partial credit rationing

 Identify key determinants on the probability of partial credit rationing

 Suggest policy implication to reduce partial credit rationing practice

Research Questions

The research aims at answering the following questions:

 What are the key determinants on the credit accessibility of rural households?

 What are the key determinants on the partial credit ration probability of rural households?

 What are the key determinants on the degree of partial credit rationing of rural households?

Scope of Study

This study focuses on the issue of partial credit rationing (the case in which credit lenders constraint the amount of loan, thus borrowers do not fully satisfy their credit demand) at household level for 12 provinces in Vietnam including Ha Tay, Nghe An, Khanh Hoa, Lam Dong, Phu Tho, Quang Nam, Long An, Dac Lac, Dac Nong, Lao Cai, Dien Bien, Lai Chau period 2006-2008 using Vietnam Access to Resources Household Survey (VARHS) 2008 data set.

Thesis Structure

The remainder of this paper is organized into 4 chapters Chapter two is literature review which aims to provide a basis for rural credit understanding, theoretical and empirical framework for the study Chapter three describes VARHS 2008 data source, discusses about the issue of sample selection of the data set and methodologies, which including Heckman two-stage probit model and Bivariate probit with sample selection model, applied in this study Chapter four presents major finding revealed by methods of descriptive statistics and econometric regression, as well as providing results discussion Chapter five, the last one, is for concluding remarks, limitation of the study and policy implication.

Literature Review

Rural credit

Rural Credit is referred to the credit offered to farmers to fund their agricultural and other rural relating activities It is estimated that 90 percent of rural finance activities is rural credit (Pham, T., 2010)

2.1.2 Characteristics of rural credit market

Rural credit market has some distinct characteristics:

Transaction costs in rural credit market is high due to several reasons: the dispersion of local users, wide segments of the farming community, small loan value, the value of time lost, travel costs, and other noninterest costs in getting and repaying loans and making deposits, high information and marketing costs due to low developed infrastructure for transport and communication

Another characteristic of rural credit market is high risk, for the reasons of vulnerability due to unfavorable climate and weather, low return on investment of agricultural activities, need of household consumption, chain effect due to concentrated in small geographic rural areas, price changes causing further variability in farmers' income and the related repayment capacity, high probability of default, little acceptable loan collateral, property rights to mortgaged land may be uncertain and hard to enforce, the weak legal system and the ineffective reinforcement arrangements

Rural credit providers can be categorized into three sectors – formal, semi-formal and informal credit suppliers

Including commercial banks, branches of foreign banks, joint-stock banks and joint venture banks Some examples are Vietnam bank of agriculture and rural development (VBARD) is the major commercial source of credit for rural households Bank of social policy (BSP) is government-owned and non-profit bank, providing credit mainly to poor, ethnic minority, social policy households

Providing loans through socio-political unions in rural areas and the level of activity of this sector in a region is related to priority programs of the government, consignment services of the bank and the activity of unions, for example: PCF, Women’s Union, Farmers’ Association etc

Their loan is often low interest rates, small loan amounts, short-term

Informal sources have been traditional providers of credit in rural areas and are the result of an underdevelopment formal credit market Four forms of informal credit sources: mutual lending among friends and neighbors, rotating savings and credit associations, specialized moneylenders including pawnbrokers, and traders Loan from informal sector is often high variety in interest rates and loan amounts.

Asymmetric Information and Credit Rationing

Asymmetric information is a common problem in rural credit market The issue could be understood as a situation in which one party has more information than the other in a transaction

For instance, in credit market, borrowers may know their credit worthiness better than their lenders, as the information such as income for repayment or loan use is on hands of the borrowers

Two main problems related to information asymmetry:

1 Adverse selection- immoral behavior that takes advantage of asymmetric information before a transaction For example, credit borrowers know their project is high risk and high return, so they may readily accept a high level of interest rate that the lenders offer them

2 Moral hazard - immoral behavior that takes advantage of asymmetric information after a transaction For instance, when the loan has been disbursed to borrowers, lenders may have difficulty in monitoring on how their lending money is used, and the borrowers may use the loan for purposes other than the one they stated in the loan contract The purposes may earn more return to the borrowers, but riskier for the lenders

2.2.2 Problems of lenders in context of asymmetric information

To understand the behaviour of credit lenders in context of asymmetric information, it is necessary firstly to understand credit lenders expected return function According to Jafee, D &

Stiglitz, J.E (1990), the expected return to the bank is a function of quoted interest rate, graphically represented by an concave curve

Figure 1 – Credit Supplier Expected Return

Credit lenders reach the highest level of expected return when it charges the loan at interst rate r *

At the level of interst rate higher or lower than r * , the expected return to the lenders will fall, thus it is reluctant for them to variate their interst rate away from the optimum level of r *

An important question arises is that what is the basis underlying the assumption of the concave curve of the credit lenders’ expected return, which is the key building block of the whole Jafee &

Stiglitz’s (1990) explanation for the behavior of credit lenders In other words, why the increasing in level of interest rate may lead to a fall in the expected return to the lenders The answer is imperfect information problem in credit market, in particular it is the result of adverse selection and adverse incentive effects r * Quoted interest rate Expected return to lender

The adverse selection effect could shape credit suppliers’ expected return curve into concave form in which as interest rate raises above the optimum level, the expected return begins to fall

As the interest rate is increased, the lending portfolio of lenders will change adversely, along with the risk of default of the portfolio That is due to the fact that safe potential borrowes who need credit to undertake the projects with low risk - low return, are unable to pay the high interst rate and consequently drop out of the market In constrast, the number of risky borrowers, who need fund to finance their high risk – high return projects, will increasingly take place in the lending portfolio Consequently, risk of default in their lending will increase, thus decreasing their expected return (Stiglitz, J & A Weiss, 1981)

Adverse incentive (or moral hazard) is another effect that could shape credit suppliers’ expected return curve into concave form In this case, the action of borrowers tends to change in response to high interest rate after getting lending contracts approval That is the applicants do not follow their commissions in the lending contract in term of undertaking riskier projects rather than the ones stated in the contract, so that they can seek higher return to offset the high interest

However, it is in turn increasing the risk of lending portfolio in an unexpected way, and therefore lowering lenders’ expected return Although it is the function of lenders’ monitoring practices to keep borrowers on track with their contract obligation, it is costly and never perfect

Thus, for those reasons of information asymetry, it is reluctant for credit suppliers to variate their interst rate away from the optimum level r * ; and it is this rational behavior of lenders that lead to a situation that although the high demand for credit may lead to a raise in the level of interest rate from credit suppliers due to the law of supply and demand, the equilibrium interest rate in the market do not adapt to change away from the credit lenders’ optimum level of interst rate r * In this case a proportion of credit borrowers, who willing to pay a high level of interest rate in order to satisfy their credit demand, will be unable to raise the amount of credit supply in the market, and therefore getting unapproval for their loan application In other word, they are credit rationed by the credit suppliers

Figure 2 - Rationing in Credit Market

Lending in context of asymmetric information, credit suppliers face three main issues: (1) how to identify high risk borrowers and put credit constraint on them (screening), (2) how to monitor and prevent the loan from miss-uses (incentives), and (3) enforce them to repay the loan when they have ability (enforcement) Thus, to help credit lenders solve those issues, two screening mechanisms, i.e indirect and direct screening mechanism are frequently applied

Credit suppliers may charge high interest to cover the risk of default on borrowers However this type screening may lead to the issue of moral hazard and adverse selection

Credit lenders may give threat of cutting off credit or contractual terms in other exchanges to monitoring the loan use and enforce repayment of borrowers r * r

Amount of Credit Rationed Interest

In context of asymmetric information, credit lenders can adopt direct screening mechanism to decide whether to approve a loan by ensuring their clients’ repayment probability Credit suppliers can control the risk of default by three following approaches:

Firstly, collecting and evaluate necessary information about risk of their clients such as income, education level, ages, etc In this type of screening, the lenders can directly ration borrowers when they do not have enough required information to evaluate risk of the loan

Secondly, credit suppliers can enforce borrowers to inter-linkages with other markets such as input and output market to ensure the loan is used for right purposes Or limiting the range of lending in a particular geography and kinship group residents in a given region, or individuals with whom they trade

Credit Rationing

According to Hoff and Stiglitz (1990) – “Credit rationing is broadly defined as a situation in which there exists an excess demand for loans because quoted loan rates are below the Walrasian market clearing level” In other words, when credit borrowers are credit rationed, loan demand of those credit borrowers in the market cannot be fulfilled as credit lenders limit their lending to them even though the borrowers willing to accept a higher level of interest rate than the one that credit lenders set

There could be various types of credit rationing depend on how the term - “excess demand for loan” is defined

It could be excess demand in term of a borrower receives a smaller loan size than the one requested at a given loan rate; and the borrower has to accept a higher rate in order to obtain a larger loan The case is classified as interest rate – or price ration (Jafee, D & Stiglitz, J.E.,

In another case, the excess of loan demand comes to exist in the circumstance that some individuals are unable to get their loan application approved at the level of interest rate that they supposed to be appropriate with their risk of default It is the case of divergent views rationing (Jafee, D & Stiglitz, J.E., 1990)

Redlining is also a type of credit rationing in which “given the risk classification, a lender will refuse to grant credit to a borrower when the lender cannot obtain its required return at any interest rate Moreover, loans which are viable at one required rate of return (as determined by the deposit rate) may no longer be viable when the required return rises.” (Jafee, D & Stiglitz, J.E., 1990)

Pure Credit Rationing : This is a form of credit rationing that arises as an effect of imperfect information problem In this instance, there is discrimination in the credit lenders’ loan approval decision between two apparently identical groups of borrowers, although they have precisely the same terms in loan contracts One group is accepted for loan, while the other one do not

According to Jafee and Stiglitz (1990) when it is the case, “changes in the availability of credit, not change in the interest rate, may determine the extent of borrowing”

According to Barham, Boucher and Cater (1996), Buchenrieder (1996), Heidhues and Schrieder

(1998), Zeller (1993), the case of being credit rationed can fall into three situations:

Figure 3 - Identify Case of being Credit Rationed

For those who has demand for loan but don’t contact with credit lenders because for some reasons, they know that their credit worthiness are not qualify enough so that their loan applications are unable to get approval

For those who has demand for credit and make loan applications, but their applications are fully rejected by credit lenders, so they cannot get any amount of credit that they requested

In this case, loan applications from credit borrowers are accepted, but loan size is not fully granted In other words, the credit borrowers receive an amount of credit less than the one they requested Petric (2003) said that - “farm household are credit rationed by formal lenders in the sense that they cannot borrow as much as needed to finance inputs, investment and indispensable consumption expenditure.”

2.3.3 Impact of Credit Rationing in Rural Area

Many studies have investigated the impact of credit rationing in rural area welfare and showed that credit rationing has been negatively affecting the efficiency function of credit in term boosting the economic performance supporting social welfare in rural areas

Firstly, efficient credit market can improve the productivities in rural area Pham and Izumida

(2002) found that credit had a considerable impact on household production For the case of rural area in Ethiopia, it was estimated to increase agricultural productivity in high potential, favorable condition producing areas by 11% if credit constraint was eliminated (Ali, D., & Deininger, K.,

Secondly, a well-functioning rural credit market may help reduce poverty and contribute to rural household income growth As Józwiak (2001) shown, in general higher income growth and a greater extent of increasing family labors used tend to be the case of farmers who could get borrowing Krandker and Faruqe (2003) also gave proof on the contribution of credit on the farm welfare improvement

In contrast, in a reasearch of Li et al (2013), credit rationing was found to cause a loss in net income of 15.7% and a loss in consumption expenditure of 18.2% for households in China rural areas Feder et al (1990) also shown negative impact of credit rationing on farm profitability as well.

Empirical Studies

To understand behavior of lenders and borrower such as credit rationing or credit accessibility in credit market, it is essential to examine the forces of credit demand and credit supply This section aims to review earlier studies about determinants of credit demand and credit supply, providing an empirical framework for the factors of partial credit rationing

For various studies, age is essentially a factor that may affect credit demand Mpuga (2004) showed that the youngs have higher demand for credit than the olds for the reason that the youngs are more active engaging in doing business and need credit as a source of funding, while the old are more rely on their saving However, Tang et al (2010) revealed a contradict finding in which the old farmers, with wide social network and social capital, are more likely to get borrow than the youngs Okurut et al (2005) also found the same result with Tang et al (2010) in Uganda

Credit demand may depend on which gender of borrower is Women in rural area are oftern seen as responsible for housework rather than market-oriented activities, thus their demand for credit is not much necessary as man’s (Nwaru, 2011)

Education is realized as having effect on credit demand Tang et al (2010) study indicated that highly educated individuals are more likelly to borrow, especially in formal credit sectors

However, it may not be the case at higher education level such as four year universtiy level, as upper level educated people tend to rely more on their high income rather than credit (Chen &

Labor structure of household may affect their credit demand For instance, number of adults normally positively related with loan amount borrowed (Barslund & Tarp, 2008) Pham and Izumida (2002) argued that the need for expanding production in households with large number of adults leads them find credit market as source of funding Higher dependency households may also demand more for credit (Pham & Izumida, 2002; Okurut et al., 2005), for the reason that household seek more credit to smooth economic burden bore by large number of dependents in their family

Household assets such as livestock, farming area were found to have positive impact on credit demand (Pham & Izumida, 2002) This is due to the need of working capital to raise livestock or farming The finding was also confirmed in the work of Hussain and Khan (2011) However, study of (Dulflo et al., 2008) found a contradict result that households who had large number of livestock demanded less for credit as those who has large livestock are normally in better economic postion and thus not relying much on credit

Other factors such as household shock (illness), or social position (household having social responsibility in community) were also found to have positive effect on credit demand (Zeller,

Empirical studies showed that major credit rationing determinants are demographic characteristic, i.e individual characteristics, borrowers’ skill, credit history, household head’s reputation, dependency ratio, gender, education, and collateral (Petric, 2003; Bester, 1987;

Diamond, 1989; Pham & Izumida, 2002; Craigwell, 1992; and McKee, 1989) Borrowing purposes and loan size also affect the chance of being credit ration of households (Pham &

Izumida, 2002) Political and social network was found to have affect credit rationing behavior (Ali & Deininger, 2012) Land holding, livestock and durable goods possession, and village infrastructure level may also determine whether household being credit rationed or not (Chaudhuri & Cherical, 2012)

Collateral plays an important role in rural credit market It acts as a signaling mechanism in which low risk borrowers are identified as those who willing to secure their loan contracts with a high amount of collateral Moreover, rationing due to problem of moral hazard is limited as higher degree of collateralization can induce investment in safer projects In either case, rationing occurs in case of lacking collateral (Bester, 1987) Land, livestock, and asset are normally supposed relating the credit constraint Land is conventional collateral used in credit market, and the investigation often concentrated on this kind of collateral (Petric, 2003; Barslund

& Tarp, 2008; Vuong et al., 2012; Aguilera, 1990; Ali & Deininger, 2012; Li, Huang & Zhu, 2013; Ping, Heidhues & Zeller, 2010) Land was confirmed as a significant factor of credit rationing probability in various researches According to Petric (2003), household whose farming with more rented land would be likely to be credit rationed; as rented land, unlike owned land, was not qualified collateral to secure the loans Livestock was also considered as collateral in rural credit market (Okurut, 2005; Barslund, & Tarp, 2008)

Regarding to human capital, the effect of education or farming experience on credit constraint were normally examined (Petric, 2003; Chaudhur & Cherical, 2012; Pham & Izumida, 2002;

Vuong et al, 2012; Barslund & Tarp, 2008; Zeller, 1994, Ping, Heidhues & Zeller, 2010) As education was supposed to be related to productivity of a person and educated people tend to get respect from society, higher education level means higher loan repayment ability and higher credit worthiness

Concerning to household characteristics, Petric (2003) found that gender had a significant influence on the chance of being credit rationed As women tended to be responsible more for housework rather than income generating activities, credit repayment ability was low in those households with more women and credit was more constraint to them as a consequence Oppose to the result of Petric (2003), Chaudhur and Cherical (2012) showed that female head households had a higher chance of loan approval However, larger familiy size reduce the probability of receiving in case of loans from banks

Kereta (2007) found that young and old people in Ethiopia are less likely to access credit than the middle age Chaudhur and Cherical (2012) showed that age factor was positive relate to the chance of getting credit approval while the research of Pham and Izumida (2002) resulted in negative relationship; however those two researches are not showed a significant impact of age on lenders’ rationing decision

For various researches, dependency ratio, which measures the ratio between numbers of dependent over household size, has appeared to be a key determinant of ration decision by credit lenders Higher dependent ratio may lead to higher rate of credit ration (Pham & Izumida,

2002) The interpretation was that households with large number of dependents are normally poor as the more dependents means the more economic burden for the households

Lenders’ rationing decision may also be influenced by the factor of household reputation Pham and Izumida (2002) found that those households with low reputation are likely to be rationed

Another argument of Diamond (1989) that reputation has effect on interest rate between lenders and borrowers Political and social network was found to have affect credit rationing behavior as well in the study of Ali and Deininger (2012)

Regarding to the factor of loan contract, Pham and Izumida (2002) confirmed that households who requested large loan size are likely to be rationed for reasons such as low repayment ability in case of large loan For loan purposes, Chaudhur and Cherical (2012), and Diagne (1999) found that loan for production and farming were less likely rationed, while it was contrast in the study of Kedir, Abbi, Gemal and Torres (2007).

Methodology

Data Source and Features

The research will employ Vietnam Access to Resources Household Survey (VARHS 2008) as primary source for its sample data set to econometrically conduct the investigation on determinants of rural credit rationing in Vietnam Started in 2002, Vietnam Access to Resources Household Survey (VARHS), began to survey with around 1000 households in 4 provinces of

Ha Tay, Phu Tho, Quang Nam and Long An Then, the survey is repeated each 2 years with expanded surveyed samples In 2006 VARHS implemented in 12 provinces with 2,324 households and in 2008 VARHS implemented in 12 provinces with 3,223 households The next survey will be implementing in 2010 in the framework of this project

VARHS has been supported by Vietnamese Government to conduct nationwide investigation in order to provide detail information on situation of rural household access to resources such as land, credit, S&T, market information as well as other material resources for economic and livelihood development Started in 2002, Vietnam Access to Resources Household Survey (VARHS) implemented a survey with around 1000 households in 4 provinces of Ha Tay, Phu Tho, Quang Nam and Long An; and was repeated every 2 years with expanded the scope of survey In 2006, VARHS conducted the investigation in 12 provinces with 2,324 households and increased survey sample to 3,223 households in 2008 The 12 provinces in VARHS 2008 was area including Ha Tay, Nghe An, Khanh Hoa, Lam Dong, Phu Tho, Quang Nam, Long An, Dac Lac, Dac Nong, Lao Cai, Dien Bien and Lai Chau

Figure 4 - Survey Site Mapping for VARHS 2008

VARHS 2008 gathered different types of household's information categorizing as human capital (household’s roster information – age, education, labor, employment), social capital (group membership, social network, political connection, trust and cooperation), liquidity and asset (house and land, asset, income, expenditure, saving, insurance, credit situation), and production (agriculture production, livestock, aquaculture, agriculture services, access to market, irrigation, production disaster suffering)

For the purpose of the research, the data from VARHS is selectively extracted as follow:

Cover page Q1 Is the household classified as rural or urban Dummy Variable Rural/Urban

Q2 What is the ethnicity of this household Identify Ethnicity variable 1A - Roster 1 Q2 What is the relationship of [NAME] with the Identify Household head

Q3 What is the gender of [NAME]? Identify Household head's gender variable

Q4 What year was [NAME] born ? Identify Household head's age, Identify number of adult member, dependency ratio 1A - Roster 2 Q16 What is the highest diploma [NAME] has Identify Household head's education variable

1B - Housing Q3 How many square meters does your household occupy, including bedrooms, dining rooms, living Identify House's area variable Q7 Does your household own this dwelling? Identify household's asset variable 2A - Land 5 Q2 Do you have a red book for this land? Identify household's asset variable

Q3 Whose name(s) are in the red book? Identify household's asset variable

2 - Land 2 Q3 What is the total area of the plot? Identify Land's are variable 5H - Household income Q10 Total Net Income Identify Household's income

7C - Saving Q3 What was the money value of this saving/asset 12 months ago ? Identify household's asset variable

8A - Credit 1 Q1 Has your household borrowed money or goods

(including seeds or fertilizer) from any source

Identify Credit demand of household (have demand or not)

Q3 Which member(s) of the household applied for the loan?

Identify borrowers and their characteristics that may affect to credit rationing possibility (age, education, …)

Q4 How much did this person apply for? Identify Partial credit rationing cases (amount of credit receive is less than the amount

Q5 How much did this person receive (cash or cash equivalent)?

Identify Partial credit rationing cases (amount of credit receive is less than the amount

Q9 From which institution or individual was the loan obtained?

Identify type of credit institution that household access to

Q11 What was the stated purpose of the loan? Identify loan purpose variable

8A - Credit 2 Q13 Did your household have to offer assets as collateral for this loan Identify collateral variable

Q14 What kind of asset did your household have to offer as collateral? Identify collateral variable Q16 What was the total value of the assets offered as Identify collateral variable

Q17 If there was a guarantor, what is the relationship of the guarantor to the member of the household Dummy guarantor variable

8A - Credit 3 Q20 How many times, if any, has the person responsible for this loan failed to make a due Identify credit worthiness variable

Q22 How many times, since 1 July 2006 have you had a loan rejected? Identify the case of being credit rationed

Q23 What were the three main reasons for this? If these were clearly state, do we need to run regression to examine the other variables???

Does any member of your household hold any office or other positions of public responsibility in the Commune, or higher levels of

10A - Groups1 Q1 Are you a member of any groups, organizations, or associations? Identify social capital variable

Issue of Data Bias (Sample Selection Problem)

Statistically, the sample collection design should ensure the objective representation of the larger population’s characteristics; otherwise the inferences extracted from the sample may not valid for the population

However in some circumstances, the sample does not objectively reflect the population due to selection issues, i.e systematically selecting a sample base on particular criteria, or in other words - non-random selection on the dependent variable

The selection issues arise in the VARHS-2008 data set as the dependent variable which indicates cases of credit rationing was not randomly selected from the population In detail, the partial credit rationing households are identified only for those observations that recorded as having credit, while it is un-identifiable for those who do not have credit In other word, the population is divided into two groups (those households who have credit and those who do not in which the sample is representative for one group only rather than the whole population

Figure 5 - Sample Distribution Source: Author Calculation from VARHS 2008

In particular, only for those households who got credit (991 observations), were the information about interest rate, credit institutions, loan size, loan purposes, collateral and credit worthiness collected; while for other households who did not get credit (1146 observations), those information was not observed Therefore, when the regression was conducted to estimate the effect of, for instance, interest rate, credit institutions, loan size etc on the possibility of being partial credit rationed, only 991 observations are taken into account, while the other 1146 observations are ignored Consequently, the estimation returns the results for those 991 observations only, and the inference making for the whole population (2137 observations), which includes the remained 1146 observations, based on that result could lead to biases conclusion Heckman (1979)

No Condition Access to Credit

No Credit Access (Credit Balance = 0)

Heckman Two-Stages Model

This section aims to present Heckman (1979)’s theory about sample selection problem and his suggested treatment This model is appropriate for the case in which the dependent variable is in outcome equation is in continuous form, while the other in selection equation is dichotomous

The determinants on degree of partial credit rationing will be examined via this model

3.3.1 Sample Selection Bias vs Omitted Variables Bias

According to Heckman (1979), if the sample used in the regression model only represents specific groups and does not objectively reflect the whole population (sample selection), then biases will arise in the estimation result and may lead to error inference for the whole population

In detail the logic between sample selection and bias estimation result can be demonstrated as follow:

Assume the Degree of Partial Credit Rationing Function as:

Where 𝑌 1𝑖 is the continuous variable, defined as the difference between loan size received and loan size applied of households (i.e loan size apply – loan size received) If the value of 𝑌 1𝑖 is larger, that means the more extreme the household got partial credit rationed In addition, 𝑌 1𝑖 is observed only for those household who were recorded as got credit between year 2006 and 2008 in VARHS 2008 data set

𝑋 1𝑖 is a vector of observed variables relating to the i th household’s characteristics such as credit institutions, loan size, loan purposes etc

Assume the Credit Access Function as:

𝑌 2𝑖 ∗ is the amount of credit that the household got in the year 2006 and 2008 If the loan amount larger than 0 (𝑌 2𝑖 ∗ >0), then 𝑌 2𝑖 =1, and 𝑌 2𝑖 =0 otherwise

𝑋 2𝑖 is a vector of variables that supposed to determine the loan size of household, such as household size, house size, household shock, land value, livestock value, etc

That is the two error term – ε and u are both normally distributed with zero means, constant variances (σ 2 ε,σ 2 u), and they are correlated with the correlation coefficient as ρεu

The population regression function for the equation (1) can be presented as:

While the regression function for the subsample of available data, i.e the data of 𝑌 1𝑖 and 𝑋 1𝑖 that are only observed when 𝑌 2𝑖 ∗ >0

As h(ε, u) is a bivariate normal density, using well known results

Where 𝜙 and Φ are, respectively, the density and distribution function for a standard normal variable, and

𝜆 𝑖 is called the inverse of Mill’s ratio, in other words, it is the ratio of the probability density function over the cumulative distribution function of a distribution The full statistical model for normal population disturbances can now be developed The conditional regression function for selected samples may be expressed as:

Compare with equation (1), the problem of regression under sample selection condition can be treated as omitted variable; specifically the regression has ignored the effect of 𝜎 𝜀𝑢

√𝜎 𝑢𝑢𝜆 𝑖 to the dependent variable in the estimation The biases arise from omitted variable issues can cause the estimation of population variance of the repressors’ coefficients downward biased and lead to overestimation the significant level

The problem of sample selection bias was mathematically treated as omitted variable problem, so Heckman proposed to extract the inverse Mill ratio from equation (2) and insert it to equation (1) as if it represents for the omitted regressor

To extract the inverse Mill ratio, Heckman conducted the estimation as follow:

𝜎 𝜀𝑢 1 )), we have the reduced form of 𝜆 𝑖 :

And the function can be expressed shortly as:

So, if one knew 𝑍 𝑖 and hence 𝜆 𝑖 , one can enter 𝜆 𝑖 as a regressor in the equation:

𝑌 1𝑖 = 𝐸(𝑌 1𝑖 | 𝑋 1𝑖 , 𝑌 2𝑖 ∗ > 0) + 𝑉 1𝑖 in which 𝑉 1𝑖 is an error term

However, as one does not know 𝛽 2 , the value of 𝑍 𝑖 is also unknown Heckman (1979) suggested that:

Firstly, one can use the known value of 𝑋 2𝑖 and 𝑌 2𝑖 to estimate 𝛽 2 using probit regression in the equation 𝑌 2𝑖 = 𝛽 2 𝑋 2𝑖 + 𝑢 𝑖 with 𝑌 2𝑖 = 1 𝑖𝑓 𝑌 2𝑖 ∗ > 0 and 𝑌 2𝑖 = 0 otherwise, then with the estimator of 𝛽 2 – i.e 𝛽̂ 2 , one can estimate 𝑍 𝑖 and hence compute 𝜆̂ 𝑖 – the estimator of 𝜆 𝑖 Secondly, estimate 𝛽 1 and 𝜎 𝜀𝑢 by OLS method basing on the data of 𝑌 1𝑖 , 𝑋 1𝑖 , and 𝜆̂ 𝑖

3.3.3 Application to study & Model Specification

Bivariate Probit with Sample Selection Model

In case of the two dependent variables in outcome equation and selection equation are both dichotomous, the bivariate probit with sample selection model is appropriate This model is similar to Heckman’s sample selection model in term of correcting the problem of non-random sample However, rather than dealing with one probit regression (for selection equation) and one OLS regression (for outcome equation) in Heckman’s model, the bivariate probit with sample selection deals with the two equations both regressing in probit model (Nicoletti & Peracchi,

2001) The model is applied to examine the determinants on probability of partial credit rationing

The model of bivariate probit with sample selection can be illustrated as follow:

Where 𝑌 1𝑖 ∗ is the latent variables represent for the difference between loan amount received and loan amount applied, i.e amount applied – amount received (or the degree of partial credit ration) that credit borrowers got 𝑋 1𝑖 is the vector of explanatory variables for 𝑌 1𝑖 ∗ , 𝛽 1 is vector of explanatory parameters, and 𝑢 1𝑖 is error term

That means cases of partial credit ration (𝑌 1𝑖 = 1) are identified if the value of loan size applied – loan size received larger than 0

Where 𝑌 2𝑖 ∗ is the latent variables represents for loan amount that credit borrowers got 𝑋 2𝑖 is the vector of explanatory variable for 𝑌 2𝑖 ∗ , 𝛽 1 is vector of explanatory parameters, and 𝑢 2𝑖 is error term

That means cases of credit access (𝑌 2𝑖 = 1) are identified if loan amount borrowers got larger than 0

Assume: 𝑢 1𝑖 , and 𝑢 2𝑖 are independent and identical standard normally distributed, i.e

𝑢 1𝑖 , 𝑢 2𝑖 ~ 𝑖𝑖𝑑 𝑁(0,0,1,1), and 𝑐𝑜𝑟𝑟(𝑢 1𝑖 , 𝑢 2𝑖 ) = 𝜌, their joint probability distribution function (pdf) will be: φ = φ(𝑢 1 , 𝑢 2 ) = 1

Then, their joint cumulative distribution function (cdf) is ϕ = ϕ(𝑢 1 , 𝑢 2 ) = ∫ ∫ φ(𝑢 1 , 𝑢 2 , 𝜌)𝑑

In case of 𝑢 1 and 𝑢 2 are independent – that is 𝑐𝑜𝑟𝑟(𝑢 1𝑖 , 𝑢 2𝑖 ) = 0, the estimation of 𝛽 1 can be done by employing conventional probit model for 𝑃𝑟(𝑌 = 1|𝑋 )

However, in case of sample selection, there is likely to exist a relationship between two error terms, i.e 𝑐𝑜𝑟𝑟(𝑢 1𝑖 , 𝑢 2𝑖 ) = 𝜌 ≠ 0; and the estimation of 𝛽 1 cannot simply base on 𝑃𝑟(𝑌 1𝑖 1|𝑋 1𝑖 ), but rather 𝑃𝑟(𝑌 1𝑖 = 1|𝑋 1𝑖 , 𝑌 2𝑖 = 1), 𝑃𝑟(𝑌 1𝑖 = 0|𝑋 1𝑖 , 𝑌 2𝑖 = 1), and 𝑃𝑟(𝑌 2𝑖 = 0)

The probabilities for three types of observations in a sample are as follow:

In which 𝜙(∙) is the univariate standard normal cumulative distribution function, and 𝜙(∙ , ∙ , 𝜌) denotes the bivariate standard normal cumulative distribution function with correlation 𝜌

Given the error terms are independent and identically distributed as a standard bivariate normal with correlation 𝜌, i.e 𝑢 1𝑖 , 𝑢 2𝑖 ~ φ(0, 0,1,1, 𝜌), the probability of 𝑃𝑟(𝑌 1𝑖 = 1|𝑋 1𝑖 , 𝑌 2𝑖 = 1) can be stated as follow:

In order to estimate the vector of model parameters is 𝜃 = (𝛽 1 , 𝛽 2 , 𝜌), the Maximum likelihood method is applied, in which the following sample log-likelihood derived from (6) is maximized:

3.4.2 Application to study & Model Specification

𝑌 1𝑖 takes value of 1 or 0, 𝑌 1𝑖 = 1 if the household i th is identified as partially credit rationed, otherwise 𝑌 1𝑖 = 0

𝑌 2𝑖 takes value 1 or 0, 𝑌 2𝑖 = 1 if the household i th is identified as having accessed to credit, otherwise 𝑌 2𝑖 = 0

𝑋 1 is a vector of explanatory variables for the probability of credit access, including:

Ethnicity, household size, head age, head gender, head education, number adult, dependency ratio, house size, land value, livestock value, income, shock, social position

𝑋 2 is a vector of explanatory variables for the probability of partial credit ration, including: household size, head age, head gender, head education, dependency ratio, house size, land value, livestock value, income, loan size applied, loan purpose for production, loan purpose for consumption, loan purpose for investment, collateral value, social position, credit institution.

Multicollinearity Test

Multicollinearity is a problem that the correlation between explanatory variables with each other may bias the regression result such as larger variances, unstable parameter estimates, sign of coefficient bias (Menard, 2002) Thus multicollinearity test should be an essential step in analyzing regression result, and should be conducted as an initial step in multiple regression analysis (Mansfield & Helms, 1982)

In this study, the issue of multicollinearity will be detected via a correlation matrix which represents the correlation value between independent variables The correlation has min and max value of 0 and 1; and the indication for multicollinearity is that the correlation gets value above 0.8 The solution for multicollinearity is to drop the one of the two variables that has high correlation value (above 0.8) (Grewal, Cole & Baumgartner, 2004).

Analytical Framework

 Ethnicity – dummy variable that takes value 1 in case of household i th is Kinh ethnicity and 0 otherwise

 Household Size – variable records the number of members in a household

 Household Head Age – variable records the age of households’ head

 Household Head Gender – dummy variable identifies the households’ head gender The variable takes value of 1 if household head is male and 0 otherwise

 Household Head Education – variable records the education level of households’ head, ranging from lowest level 1 to the highest level 6

 Number of Adult – variable records the number of adult member in a household The adult member is identified if household member is older than 18

Head Age Head Gender Head Education

Income Household Shock Social Position

Probability of Partial Credit Ration

Degree of Partial Credit Ration

 Dependency Ratio – ratio of number of dependent member over household size The dependent member is identified if household member age is younger than 18 or older than

 House Size – variable records the total house area that the household owns in square meter for the year 2006

 Total Land Value – variable records the total value of land that the household owns The value was the price that the household willing to sell at

 Total Live Stock value – variable records the total value of livestock that the household raised The value was the price the household willing to sell at

 Income – variable records the total net income of the household

 Loan Size Apply – variable records the loan amount that the household requested

 Loan Purposes – variable indicates 4 types of purposes that the household stated when apply for the loan – Production purposes (including loan for raising rice, crop production, livestock, forestry, fishery, non-farm activity), investment purposes (including loan for build / buy house, buy land, buy another asset), consumption purposes (including loan for pay for wedding/funeral, education expenses, health expenses, general consumption), and other purposes (including loan for credit repayment, other purposes)

 Total Collateral Value – variable record for the value of the collateral

 Credit Institution – dummy variable that identifies formal (including social policy bank, bank of agriculture and rural development, other state owned bank, local authorities, private bank, farmer union, veterans union, women’s union, people credit fund, other credit associations) and informal credit institutions (including private trader, private money lender, friend / relatives, informal credit scheme, others) The variable takes value

1 if the credit institution that the household apply the loan to is formal, and 0 if informal

 Social Position – dummy variable that identifies whether household have any member working for government If the household has at least 1 member working for government, the variable takes value of 1, and 0 otherwise

 Household shock – variable that indicates whether household suffered any shock (such as economic, production, illness, etc.)

Hypothesis

3.7.1 Hypothesis for the probability and degree of partial credit rationing

 Household head age: age of household head could either negatively or positively affect to the chance of being partial credit rationed Chaudhur and Cherical (2012) showed that age factor positive related to the chance of getting credit approval while the research of Pham and Izumida (2002) resulted in negative relationship Some lenders may perceive old people as more maturity and trustworthy than the young, while others may see the young have higher income generation capability to repay the debt

 Household head education level: education of household head is expected to have negative relation to probability of household’s partial credit ration (Petric, 2003;

Chaudhuri & Cherical, 2012; Pham & Izumida, 2002) That means if the household’s head is well educated the chance to be credit ration by credit lenders will be lower

 Household size: the effect of household size on probability of partial credit rationing could be positive as it may bear a high cost of running large family, thus negatively relate to loan repayment ability of the household (Arene, 1992); or it could be negative as large household may have more labor to generate income (Vuong et al., 2012)

 Dependency ratio: number of dependent is expected to have positive coefficient The higher the number of dependent in a household, the more economic burdens that household have to take responsibility for, and the less likely they can pay back their loan in full (Pham & Izumida, 2002)

 Household head gender: household head with male gender are expected to have reversed relationship with the likelihood of being credit rationed (Petric, 2003)

 Reputation and social status: household reputation and social status are also have the expectation of negative correlated with household credit rationing possibility High reputation and respect position in society may be considered as more credit worthiness to approve for a loan (Pham & Izumida, 2002; Ali & Deininger, 2012)

 Total house size, total asset value, total land value, total livestock value are assumed to have negative coefficient These factors represent quality of the collateral that the credit institutions required from household to compensate for the risk of credit default This kind of factors is important for credit institutions as they help them to control the problem of asymmetric information Thus, the higher they are, the better monitoring loan can the credit lenders implement to ensure they can retrieve the loan In a research related to credit rationing in rural credit market of India, these factors also empirically affirmed their significant impact to the probability of credit rationing with the same effect (Chaudhuri &

Cherical, 2012; Barslund & Tarp, 2008; Ping, Heidhues & Zeller, 2010; Aguilera, 1990;

 Income: the higher income, the less likely being credit rationed as higher income household’s cash flow is more stable, promisingly credit payback (Ping, Heidhues &

 Collateral value: the higher the value of collateral, the less probability of credit rationed

As collateral is a tool of credit lender to enforce credit borrowers to pay back their debt

Higher collateral value means higher level of enforcement (Bester, 1987)

 Loan size applied: the amount of loan applied hypothetically has positive relation with the possibility of being credit rationed It is reasonable to assume that the higher the loan amount be lend, the riskier could it be fully recover Moreover, the credit supply constraint may also limit the amount of credit being disbursed (Pham & Izumida, 2002)

 Loan purposes: Loan for production and investment purposes are expected to have negative relationship with possibility of credit rationing As production and investment may generate income, thus the household applies for the loan may have positive cash flow to pay back their debt (Chaudhuri & Cherical, 2012; Diagne, 1999).Loan for consumption purposes may have positive correlation with the chance of being credit rationed As credit for consumption means the loan will be spent away and the cash flow to payback is not

 Credit institutions: Formal Credit sectors are supposed to credit rationed than informal credit sector due to their strictness in loan approval procedures, monitors and loan enforcement

3.7.2 Hypothesis for the probability of access to credit

 Age and education of household head are expected to have positive relation to probability of household’s credit access As the household’s head is more mature and well educated their demand for credit to expand business is higher Tang et al (2010) Moreover, mature and well education individuals are easy to earn more credit worthy to lenders (Barslund &

 Number of dependent may have positive or negative coefficient On the demand side, the higher the number of dependent in a household, the more economic burdens that household have to take responsibility for, thus the more likely they rely on credit market as a solution for their liquidity requirement (Pham & Izumida, 2002; Okurut et al., 2005)

However, on supply side, credit providers may reluctant to lend to high dependent ratio households, as those with large number of dependents are normally poor and low repayment ability (Pham & Izumida, 2002)

 Number of adult and household size are expected to have positive relationship with the possibility of credit assessment The reason is that a higher the number of adult or members in a household a higher demand for money to fund activities such as doing business, study, farming, expenditure etc (Barslund & Tarp, 2008) Moreover, As they have more workers to generate income in their family, they gain credit worthy in the perceive of credit suppliers as well

 Ethnicity: households who were classified as Kinh are expected to access to credit easier than the others, as Kinh community is more popular in Vietnam and they share the same economic and business practices (Vuong et al., 2012)

 Household head with male gender are expected borrow more as male is normally the main pillar in a family and perform different activities that require funding Women tend to be credit rationed as they are perceived as being responsible more for housework rather than income generating activities, and their credit repayment ability is lower than men (Petric,

 Reputation and social status also have the expectation of positive correlated with household credit access as they have more demand for credit and are less likely to be credit rationed (Zeller, 1994; Pham and Izumida, 2002)

 Household Shock – those who have any shock such as suffering natural disaster, economic downturn, illness, unemployment etc tend to seek for loan in credit market to fulfill their liquidity need Therefore, household who have shock tend to have higher possibility of accessing credit (Zeller, 1994)

Model Specification

No Variables Variable Denote Credit Access (Yes) Partial Credit Ration

Degree of Partial Credit Ration

5 Head Gender (Male) head_gend1 + - -

6 Number of Adult num_adult +

9 Total Land Value total_landvalue + - -

10 Total Livestock Value total_livestock_value + - -

12 Loan Size Apply loansize_apply + +

13 Loan Purpose - Production loan_purpose_prod - -

14 Loan Purpose - Investment loan_purpose_invest - -

15 Loan Purpose - Consumption loan_purpose_consump + +

17 Credit Institution (Formal) credit_inst_dummy + +

18 Social Position (Yes) social_position1 + - -

Results and Discussion

Characteristics of Borrowers by Credit Rationing

This section generally examines the correlation between the dependent variable and explanatory variables to provide an intuitive relationship between them

The under-examined dependent variable includes “Credit Access” and “Partial Credit Ration”

Credit access refers to cases in which household got credit over the year 2006 and 2008 In other words, their credit balance account is positive in case of credit access and zero in case of no- credit access Partial Credit ration refer to the situation in which households received loan amount less than the amount the y requested

Overview of Credit Rationing Situation

Figure 7 - Credit Access & Credit Ration Source: Author’s calculation from VARHS 2008

Credit Access No Credit Access

Non_Partial Ration Partial Ration

Credit Access & Partial Credit Ration

According to the data from the sample, 56% of the household had positive credit balance, and 44% of them had zero credit balance Among those who had credit access, 11.24% of them were partial rationed However, there is no record for partial ration in case of no credit access

Credit Access and Household Head’s Age

Figure 8 - Credit Access & Household Head Age Source: Author’s calculation from VARHS 2008

The household head age structure was distributed as follow The largest proportion of sample was 41-50 years old age group with 30.9% of the total sample, the next is 31-40 age group with 23.9%, then 19.2% for 51-60, 17.8% for the age group older than 60 and, finally the younger than 30 age group took 8.3%

Accordingly, rate of having credit access measured by the percentage of the number of age group who got credit over the total number of observations in that age group was distributed as follow

The highest rate belongs to 41-50 age group (62.3%), follow by 31-40 age group (47.8%), then

Rat e o f G e tt in g Cr e d it b y A g e G ro u p ( % )

51-60 age group with 38.7.2% and age group older than 60 with 34.6%, the lowest one is age group younger than 30 (16.6%)

Generally, the young (less than 30 years old) were the least taking credit; the older take most of the credit The middle age between 31 and 50 tend to access credit more, and this proportion reduce when household head get older (from 51 to more than 60)

Figure 9 - Household Head Age & Credit Ration Source: Author’s calculation from VARHS 2008

The correlation between head age and rate of partial ration is not clear Rate of partial ration (Number of partial ration / total number of observation of each age group) appears to be more or less around 10% across age groups It is 10.3% for age group less than 30, 9.02% for 31-40, the highest one 12.9% for 41-50, 12.7% for 51-60, and 9.22% for older than 60

R a te o f Pa rt ia l R a ti o n e d b y Ag e G ro u p

Credit Access and Household Head Education Level

Figure 10 - Credit Access & Household Head Education Level Source: Author’s calculation from VARHS 2008

Education level is decoded as following:

Level 1 – No diploma, level 2- Short-term vocational training, level 3 - Long-term vocational training, level 4 - Professional high school, level 5 - Junior college diploma, and level 6 - Bachelor degree Most of the household head education is low educated with education level 1 takes 93.2% proportion of the sample

The highest rate of credit access by education level, which is the percentage of the number of credit access cases over the total number of observations in each level of education, is taken by the education level 5 groups (60%) those who were educated at diploma level normally have high demand for credit and are favored by lenders as they are well trained and have better productive skill The lowest one is level 6 (23.5%), it could be said that those who were

Rate of Credit Access by Education Level (%)

Credit Access & Head's Education Level educated at university level or higher not rely much on credit as other lower education level for other education level, the rates are not much different, fluctuate around 45%

Figure 11 - Household Head Education Level & Credit Ration Source: Author’s calculation from VARHS 2008

The correlation between education and parital ration is not clear The rates of partial ration, which is measured by the ratio of number of partial ration observations over the total number of observation in each education level in percentage, are highest for education level 4 (25.9%) and

6 (25%) While it is 0% for level 5 and 3, however, as participation proportion in credit market of those two groups are low, the rate of parital ration may not truly reflect the reality situation

Finally, the rate of partial ration is low for education level 1(11%) and 2 (10%)

R a te o f Pa rt ia l R a ti o n b y Ed u c a ti o n L e v e l (% )

Head Education Level & Partial Ration

Credit Access and Loan Purposes

Figure 12 - Credit Access & Loan Purposes Source: Author’s calculation from VARHS 2008

The allocation of credit varies among different loan purposes The survey conducted in VARHS

2008 shows that a majority of households used credit for argricultural production in which credit for animal husbandry purpose takes the higest proportion of 38.9% over the total number of examined households, then 15.7% for crop production other than rice and 11.2% for rice cultication Meanwhile, credit access for general consumption, pay for wedding/funeral, and repay other loan just takes small shares, respectively 1.42%, 0.755%, and 1.04% In general, most of credit access was allocated to productive purposes, and a minor of it was for non- productive purposes

Rate of Credit Access by Loan Purposes (%)

Other (please specify) General consumption Health expenses Education expenses Pay for wedding/funeral Buy another asset Buy land Build/buy house Repay other loan Non-farm activity Fishery Forestry Animal husbandry Other crop production (including inputs)

Figure 13 - Loan Purposes & Credit Ration Source: Author’s calculation from VARHS 2008

On the other hand, for the relationship between partial rationing and loan purposes, it is shown a different picture A high proportion of household who were partial rationed falls into non-income generating loan purposes such as paying for wedding/funeral (the highest one – 37.5%), repaying other loan (36.4%), health expense (22.2%), education expense (19.7%), general consumption (13.3%) Meanwhile, for those who borrowed for productive puposes, they were less likely to be rationed and the average rate of credit ration is just around 11% For instance, it is 10% and 14.8% for fishery and non-farm activities, 5.04% for rice cultication, 15.1% for other crop production, 9.95% for animal husbandry, and 0% for forestry In general, loan for production has a higher probability to get rid of credit ration than the case of loan for non-productive purposes

Other (please specify) General consumption Health expenses Education expenses Pay for wedding/funeral Buy another asset Buy land Build/buy house Repay other loan Non-farm activity Fishery Forestry Animal husbandry Other crop production (including inputs)

Rate of Partial Ration by Loan Purposes (%)

Figure 14 - Credit Access & Credit Institutions Source: Author’s calculation from VARHS 2008

Determinants of Credit Accessibility

Table 3 - Determinants of Credit Accessibility

Source: Author calculation from VARHS 2008 Note: *, **, *** denote 10%, 5%, and 1% level of significance, respectively

The first stage of Heckman two stages regression reveals results of the selection equation, ie - the probit estimation of the credit access probability

It appears that the factor of ethnicity is positively affected the probability of credit access of the household and this assumption is strongly confirmed at 1% significant level This indicates that Kinh households are likely to have more chance to access credit in the examined regions The sign of the estimation result is consistent with expectation about the relationship between ethnicity and credit access but contrasts to Vuong et al.’s (2012) result

Household size bears a positive and statically significant at 5% level This implies the more members a households has, higher is the probability of credit access The estimation returns an expected correlation between household size and credit access possibility

Head age is significantly at 5% and carrying (-) sign The negative relationship between credit accessibility indicates that the older the household head, the less likely that household access to credit The result contrasts with Chen and Chiivakkul’s (2008) finding in which old people were favor clients of credit lenders as their repayment ability was assumed to be higher than young

Selection Equation: Dependent Variable - Credit Access = 1

Household Head Education Level -0.045 Household Head Gender (Male =1) 0.007

Total Land Value (VND1000) 0.000 * Total Livestock Value (VND1000) 0.000 Household Income (VND1000) 0.000 Household Shock (Shock=1) 0.152 ***

At least 1 Member working for Government=1 0.272 *** borrowers The explanation could be that the older don’t need much credit as they rely on their saving more Tang et al (2010)

The correlation between dependency ratio and accessibility of credit turns out to carry negative sign and statistically significant at 1% confident level This means that the higher the number of dependent members comparing to the number of working adults in a household, the lower the probability the household access to credit is The reason might be that credit providers may reluctant to lend to high dependent ratio households, as those with large number of dependents are normally poor and low repayment ability (Pham & Izumida, 2002)

Total land value has a positive correlation with the dependent variable as expected This relationship is fit with presumption, however it is weakly confirmed at 10% confident level It implies that the higher fixed asset value, especially land, is, the higher possibility of credit access is for the household Comparing to the result of Pham and Izumida (2002), Zeller (1994), Aghion and Morduch (2004), the finding is well consistent

The estimation also returns a significant result at 1% confident level for household shock, and the positive sign of the outcome coefficient confirms the assumption of – households who suffered economic shock such as unemployment, bad harvest, disaster or personal shock such as illness, etc tend to rely on credit as a source of fund to get over the unexpected situation (Zeller,

Social position is positively significant at 5% level of confident as presumed, thus indicating that household who has members working as government or local authorities have more chance to access credit The result also fits well with finding of Zeller (1994) and Pham and Izumida

For other parameters such as number of adult members, household head education, household head gender, and livestock value, there is not enough evidence to confirm their significant impact on the accessibility of credit.

Determinants of Partial Credit Rationing Probability

Table 4 - Determinants of Partial Credit Rationing Probability

Source: Author calculation from VARHS 2008 Note: *, **, *** denote 10%, 5%, and 1% level of significance, respectively

Probit model with sample selection Number of Observations 2390

Outcome equation: Dependent Variable - Loan Size Apply & Loan Size Received Differences

Household Size -0.015 Household Head 's Age -0.003 Dependency Ratio -0.092 Household Head Education Level 0.022 Household Head Gender (Male =1) -0.087

House Size -0.003 * Total Land Value (VND1000) 0.000 Total Livestock Value (VND1000) 0.000 Household Income (VND1000) 0.000

Loan Size Applied 0.000 * Loan Purposes (Production =1) -0.239 Loan Purposes (Consumption =1) 0.082 Loan Purposes (Investment =1) -0.366 *

At least 1 Member working for Government=1 -0.324 *

Selection Equation: Dependent Variable - Credit Access = 1

Household Head Education Level -0.045 Household Head Gender (Male =1) 0.007

Total Land Value (VND1000) 0.000 * Total Livestock Value (VND1000) 0.000 Household Income (VND1000) 0.000 Household Shock (Shock=1) 0.152 ***

At least 1 Member working for Government=1 0.272 ***

LR test of indep eqns (Chi2) 11.79 ***

Varieties of factors supposed to affect a household’s probability of being credit rationed are put under examination Besides the group of factors that determined the credit accessibility of households, the list of explanatory variables includes other factors that are supposed to be specifically related to probability of credit rationing such as credit institutions and characteristics of loan contract The regression result turns out to be statistically significant with house size, loan size applied, household social position, loan for investment and type of credit institution

Firstly, households owned larger houses might have lower chance of being partial credit rationed comparing to those living in urban areas This factor shows a negative statistically significant level at 10%, the finding is consistent with the assumption and results of Ali and Deininger (

Secondly, loan size requested has a positive relationship with probability of partial credit ration

It shows a weakly statistical significant level at 10% This result is in consistent with presumption as well as the evidence found by Pham and Izumida (2002)

Thirdly, the regression result shows that household social position negatively affect the chance of being credit rationed – significant at 10% confident level That means if household has at least one member working for government, public office, or being local authorities, they are less likely they will receive full amount of loan requested This finding confirms the presumption and is consistent with the result of Pham and Izumida (2002) and Ali and Deininger (2012)

Fourthly, loan for investment (building or buying house, land and assets) negatively correlated with probability of partial credit ration – significant at 10% confident level, borrowers would likely to be offered full amount of loan for investment purposes The finding confirms the presumption and Pham and Izumida (2002)

Finally, formal credit institutions showed a strong and positive correlation with the credit rationing status in the examined areas at 1% significant level That mean, the group of state own banks, private banks and unions tends to have a higher rate of rejecting to offer the full amount of loan demand by their borrowers This might reflect the strictness of formal credit institutions in term of loan approval criteria

The log-likelihood test of independence between outcome equations and selection equation fails to reject the null hypothesis that two equations are independent with each other at 1% confident level It indicates that sample selection problem may bias the result if the selection equation were not taken into account

Wald test also fails to reject the null hypothesis of all factor simultaneously equal 0 at 10% confident level.

Determinants of Partial Credit Rationing Degree

The statistical results show that degree of partial credit ration was affected by household characteristics Household size, head age and dependency ratio appear to be significant at 10%, 5% and 5% confident level respectively

Household size is hypothesized as an unclear factor; its effect may be positive or negative

Regression result showed that household size bears a positive sign in the relationship with the degree of partial credit ration in the regression It implies that as the household size increase, the more constraint on the loan size This finding is consistent with Arene (1992)’s result

Head age is a vague factor that might positive or negative affects the degree of partial credit rationing In this case, head age appeared to have a negative coefficient indicating that the older the household heads, the smaller extent their loan demand is not fully satisfied This result reflects part of Kereta, (2007) finding that the young and old people in Ethiopia had a higher credit constraint than the middle age The reason might be that the older the household head, the higher credit worthiness in view of credit lenders is

It also reveals that dependency ratio negative correlated with magnitude of partial credit ration

As expected, the finding implies the more dependent members in a household, the less amount of credit they received comparing to their requested amount This finding is also well consistent with the result of Pham and Izumida (2002)

The impact of collateral to the degree of partial credit ration is also disclosed in the regression results House size and collateral value are respectively significant at 1% and 10% Those two factors, as hypothesized, are negatively correlated with the amount of loan being credit rationed; livestock value also negatively affected to the degree of partial credit ration but was not statistically significant The result is well fit with hypothesis and other studies such as Petric

Consistently with earlier assumption, household income negatively related to the degree of partial credit ration but not significant That means credit lenders would not constraint much on loan amount relatively to the loan demanded by households who had higher income This finding well fit with the result of Ping, Heidhues and Zeller (2010)

Table 5 - Determinants of Partial Credit Rationing Degree

Source: Author calculation from VARHS 2008 Note: *, **, *** denote 10%, 5%, and 1% level of significance, respectively

As presumed, the size of loan requested by the household positively correlates with the amount of credit being partial rationed The result is significant at high level of confident – 1%, implying that the larger loan size the borrowers requested, the larger the difference between their loan size demand and loan size received is This result matches with Pham and Izumida (2002)

Heckman selection model two-step estimates Number of Observations 2390

Outcome equation: Dependent Variable - Loan Size Apply & Loan Size Received Differences

Household Size 225.595 * Household Head 's Age -46.018 **

Household Head Education Level -232.529 Household Head Gender (Male =1) -258.764

Total Land Value (VND1000) 0.001 Total Livestock Value (VND1000) -0.006 Household Income (VND1000) -0.009

Loan Purposes (Production =1) 173.144 Loan Purposes (Consumption =1) 1736.836 * Loan Purposes (Investment =1) -1620.190

At least 1 Member working for Government=1 110.808

Selection Equation: Dependent Variable - Credit Access = 1

Household Head Education Level -0.048 Household Head Gender (Male =1) 0.002

Total Land Value (VND1000) 0.000 * Total Livestock Value (VND1000) 0.000

At least 1 Member working for Government=1 0.276 ***

For the factor of loan’s purposes, the differences between loan size received and loan size requested tend to get larger for those who borrowed for consumption purposes The result shows a positive coefficient as expected and significant level of 1% It has reached similar conclusion with the study of Diagne (1999) Loan for productivity purposes appeared to positively correlate with the degree of partial ration but this explanatory variable is not statistically significant

Wald test also fails to reject the null hypothesis of all factor simultaneously equal 0 at 1% confident level (Chi 2 = 0.0000)

The significant level of Mill ratio is 1%, indicating that there is a potential sample selection bias unless the selection equation is not taken into account.

Multicollinearity Test

The correlation matrix shows that there is not any correlation value between explanatory variables higher than 0.7 As the criteria for multicollinearity detecting is correlation value higher than 0.8, multicollinearity can be seen as an insignificant issue for three regression model above.

Conclusions and Policy Implications

Conclusions

5.1.1 Findings, answers for research questions

Based on VARHS 2008 data set, the research has adopted Heckman two-step and Bivariate probit with sample selection model to identify the significant factors of partial credit rationing degree and probability of being partial credit rationed in 12 provinces in Vietnam - Ha Tay, Nghe An, Khanh Hoa, Lam Dong, Phu Tho, Quang Nam, Long An, Dac Lac, Dac Nong, Lao Cai, Dien Bien and Lai Chau in period 2006-2008

The statistic result shows that credit accessibility is statistically significant affected by factors of ethnicity (higher proportion of household had credit was Kinh household), household size, land value, household shock, and household social position (positive) and household head age, dependency ratio (negative)

For the probability of partial ration, factors of social position, formal credit institutions, loan size applied and loan for building or buying house, land and assets are key determinants

For the degree of partial credit ration, household size, loan size applied, loan for non-income generation purposes positively affected the degree of partial credit ration, while the effect of household head’s age, dependency ratio, house size, and collateral value were negatively

5.1.2 Conclusions on degree of solving research objectives

In summary, the research has examined partial credit rationing in its two aspects – degree and probability of partial credit ration The result showed that:

Household characteristics especially impact on the degree of partial credit ration The larger household size is, the smaller the loan amount they received On the other hand household household’s head’s age, dependency ratios were negatively related to the degree of partial credit ration

Collateral is another factors affect to partial credit ration Households who own larger houses, or offer higher value of collateral would less suffer from partial credit ration - they were likely to satisfy the credit as they requested

Households who had at least one member working as government authorities or who owned larger houses had lower chance of being partial credit rationed On the other hand, household who had large loan size requested or borrowed from formal credit institutions including social policy bank, bank of agriculture and rural development, other state owned bank, local authorities, private bank, farmer union, veterans union, women’s union, people credit fund and other credit association, would significantly tend to be partially credit rationed

Finally, the probability of partial rationing was lower for loan of building or buying house, land and assets

Due to issue of sample selection of VARHS 2008 arises in the scope of this research, treatments has been adopted to minimize bias on final result However, it should be better if the research was based on non – sample selection data, so that the result could be estimated directly.

Policy implications

From the statistical results, it appears that households who have members working as government officers or local authorities have more advantages than others in credit market, i.e they were more likely to get borrow and less likely to be credit rationed The discrimination may raise the issue of inefficient credit allocation in which sources of fund may not benefit those who actually need but do not have much title in community The issue may become severe in case of government authorities can make use of their entitlement or political relationship to exploit cheap credit from social policy programs This is an issue of moral hazard in social policy implementation, thus it is important to monitor such social policy programs well

Formal credit sector, particularly social policy and bank of agriculture and rural development, took large share of credit market (about 59% of the market) and play an important role in rural credit market, but households applied for credit in this sector may have high probability of being constrained As the main suppliers in the market, their constraint could erode the benefit of credit on the development of rural areas The poor who cannot find support from formal sector will have to rely on informal credit sector However, loan in this sector are not appropriate for business purposes to generate income as they normally offer high interest rate but small loan size contracts Screening to identify credit worthy borrowers could be a major reason of credit constraint in this sector

Collaterals such as house and livestock are still important criteria for credit lenders to make decision about the loan This screening mechanism unfavors the poor who normally do not have capital endowment, makes them more difficult to find support from credit Enforcement to collect the loan repayment could be a main explanation for this issue

To minimize the credit constraint issue and direct credit flow to right targets efficiently, developing micro-finance services to solve the problems of screening and enforcement could be an appropriate solution

Micro finance services such as micro-credit have some unique characteristics, i.e required no collateral and group-lending scheme As collaterals are not required, the poor who do not have capital endowment have better chance to get credit While, group-lending provides credit for a whole group and require them to monitor each other, allowing the control and monitoring the use of loan and repayment via peer to peer mechanism Thus, micro-finance can somehow fulfill the gap of formal credit sectors, especially state own banks, in rural areas with lower cost, but well organized than the informal sectors

Recently, form of micro-finance such as women union, farmer unions and veteran unions have played a small role in the examined areas It took about 12% market share comparing to 24% of informal sector and 59% of the two state own banks - social policy and bank of agriculture and rural development (Author’s calculation from VARHS 2008) The role of micro-finance organizations in rural credit market should be expanded more than current status, and regulations about micro-finance should provide an appropriate legal environment to facilitate the participation of those organizations

The study has examined the determinants of partial credit ration one of the three aspect of credit constraint The other two situations i.e completely ration and discourage could be potential research objectives to develop better understanding about credit ration Effect of credit ration on households’ welfare could be an interesting topic as well

Vietnam micro-finance regulation is another topic worth to study Good legal framework can promote the development of micro-finance market, thus minimizing the issue of credit constraint

Analyzing the advantages and disadvantages of recent regulation could provide a basis for developing better one

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Appendix 1- Sample Distribution by Partial Ration

Appendix 2 - Sample Distribution by Credit Access

Appendix 3 – Sample Distribution by Head Age

Appendix 4 - Head Age & Credit Access

Partial Ration Num Observations Percent Cummulative (%)

Credit Access Num Observations Percent Cummulative (%)

Head Age Num Observations Percent Cummulative (%)

Head Age Credit Access No Credit Access Total

Appendix 5 - Head Age & Partial Ration

Appendix 6 - Sample Distribution by Education

Head Age Non_Partial Ration Partial Ration Total

Education Level Num Observations Percent Cummulative (%)

Education Level Credit Access No Credit Access Total

Education Level Non_Partial Ration Partial Ration Total

Appendix 9 - Loan Purpose & Credit Access

Appendix 10 - Loan Purpose & Partial Ration

Loan Purpose Credit Access Total

Loan Purpose Non_Partial Ration Partial Ration Total

Appendix 11 - Credit Institution & Credit Access

Appendix 12 - Credit Institution & Partial Ration

Credit Institution Credit Access Total

Credit Institution Non_Partial Ration Partial Ration Total

Appendix 13 - Determinants of Partial Ration Degree - Heckman two-steps regression sigma 7339.4511 rho 0.42755 lambda 3137.949 1431.486 2.19 0.028 332.2878 5943.61 mills social_position1 2756147 1159383 2.38 0.017 0483798 5028496 household_shock1 185849 0543501 3.42 0.001 0793249 2923732 income -8.40e-07 6.51e-07 -1.29 0.197 -2.11e-06 4.36e-07 total_livestock_value -1.71e-06 1.32e-06 -1.30 0.195 -4.31e-06 8.78e-07 total_landvalue 2.07e-07 1.14e-07 1.81 0.070 -1.66e-08 4.30e-07 home_size2006 0001754 0008392 0.21 0.834 -.0014694 0018201 head_gend1 0019275 0704814 0.03 0.978 -.1362136 1400686 head_edu -.0477131 0380228 -1.25 0.210 -.1222365 0268102 depend_ratio -.5457069 1797477 -3.04 0.002 -.8980059 -.193408 num_adult -.0274953 0435316 -0.63 0.528 -.1128156 057825 head_age -.0113385 0020522 -5.53 0.000 -.0153607 -.0073162 hhsize 0773077 0284967 2.71 0.007 0214553 1331602 ethnicity_dummy 4418059 0607402 7.27 0.000 3227573 5608545 credit_borrow1 credit_inst_dummy 208.656 510.3437 0.41 0.683 -791.5992 1208.911 social_position1 110.8083 929.0784 0.12 0.905 -1710.152 1931.768 collateral_value_f -.0011311 0006781 -1.67 0.095 -.0024603 000198 loan_purpose_invest -1620.19 986.0945 -1.64 0.100 -3552.9 312.5194 loan_purpose_consump 1736.836 1022.778 1.70 0.089 -267.7718 3741.443 loan_purpose_prod 173.1439 820.245 0.21 0.833 -1434.507 1780.794 loansize_apply 1538791 0098343 15.65 0.000 1346041 173154 income -.0085702 0066153 -1.30 0.195 -.0215359 0043955 total_livestock_value -.0062135 0131442 -0.47 0.636 -.0319756 0195486 total_landvalue 001322 0009491 1.39 0.164 -.0005381 0031821 home_size2006 -21.77689 7.296857 -2.98 0.003 -36.07847 -7.475317 head_gend1 -258.7641 638.9062 -0.41 0.685 -1510.997 993.469 head_edu -232.5285 340.7968 -0.68 0.495 -900.478 435.4209 depend_ratio -2688.717 1247.758 -2.15 0.031 -5134.279 -243.1557 head_age -46.01776 19.83446 -2.32 0.020 -84.89258 -7.142933 hhsize 225.5947 132.582 1.70 0.089 -34.26122 485.4506 loansize_differ2

Coef Std Err z P>|z| [95% Conf Interval]

Uncensored obs = 1056(regression model with sample selection) Censored obs = 1334Heckman selection model two-step estimates Number of obs = 2390

Appendix 14 - Determinants of Partial Ration Probability - Bivariate with Sample Selection Regression

LR test of indep eqns (rho = 0): chi2(1) = 11.79 Prob > chi2 = 0.0006 rho -.7546602 1187572 -.9094582 -.4161321 /athrho -.9836932 2758664 -3.57 0.000 -1.524381 -.4430049 social_position1 2719706 1160187 2.34 0.019 0445781 4993631 household_shock1 1521098 0529814 2.87 0.004 0482681 2559515 income -7.91e-07 6.48e-07 -1.22 0.223 -2.06e-06 4.80e-07 total_livestock_value -1.79e-06 1.33e-06 -1.35 0.177 -4.40e-06 8.11e-07 total_landvalue 2.03e-07 1.13e-07 1.80 0.071 -1.76e-08 4.24e-07 home_size2006 0002097 000838 0.25 0.802 -.0014327 0018521 head_gend1 0068741 0705937 0.10 0.922 -.1314869 1452351 head_edu -.0450151 0378799 -1.19 0.235 -.1192584 0292282 depend_ratio -.5629536 1750563 -3.22 0.001 -.9060576 -.2198496 num_adult -.0332163 0415389 -0.80 0.424 -.1146311 0481985 head_age -.0105294 0020448 -5.15 0.000 -.0145372 -.0065216 hhsize 0802778 0275004 2.92 0.004 026378 1341777 ethnicity_dummy 412709 0621666 6.64 0.000 2908648 5345533 credit_borrow1 credit_inst_dummy 3696375 1103649 3.35 0.001 1533262 5859487 social_position1 -.3236198 18601 -1.74 0.082 -.6881926 0409531 collateral_value_f -5.84e-07 4.46e-07 -1.31 0.190 -1.46e-06 2.89e-07 loan_purpose_invest -.3655781 1890317 -1.93 0.053 -.7360734 0049172 loan_purpose_consump 0821521 1694673 0.48 0.628 -.2499978 414302 loan_purpose_prod -.2389276 1471759 -1.62 0.105 -.527387 0495318 loansize_apply 3.12e-06 1.69e-06 1.85 0.064 -1.86e-07 6.43e-06 income 1.62e-06 1.16e-06 1.40 0.162 -6.50e-07 3.90e-06 total_livestock_value -1.86e-06 3.04e-06 -0.61 0.539 -7.81e-06 4.09e-06 total_landvalue 3.88e-08 1.83e-07 0.21 0.832 -3.21e-07 3.98e-07 home_size2006 -.0025328 0014999 -1.69 0.091 -.0054725 000407 head_gend1 -.0866814 1208897 -0.72 0.473 -.3236209 1502581 head_edu 0218606 0598485 0.37 0.715 -.0954403 1391615 depend_ratio -.0920622 2410132 -0.38 0.702 -.5644393 3803149 head_age -.0026863 0038546 -0.70 0.486 -.0102413 0048686 hhsize -.0154793 0261824 -0.59 0.554 -.066796 0358373 partial_ration

Coef Std Err z P>|z| [95% Conf Interval]

Log likelihood = -1941.077 Prob > chi2 = 0.0566 Wald chi2(16) = 25.82

Uncensored obs = 1056 Censored obs = 1334Probit model with sample selection Number of obs = 2390