Poverty Targeting and Impact of a National Micro-Credit Program in Vietnam*

Nguyen Viet Cuong†

Vu Thieu‡

First version: November 2006

Current Version: February 2006

* This study is supported by a grant from Poverty and Economic Policy (PEP) Research Network. We would like to thank other three members of our research teams, Pham Minh Thu, Duong Khanh Toan, Pham Minh Nguyet for provision of documents and data for the analysis and their comments on the interim report. Since the paper is also related to a Ph.D. study of Nguyen Viet Cuong, it has benefited remarkably from his supervisors, Marrit Van den Berg and David Bigman (Wageningen University, the Netherlands). Comments from Arrar Abdelkrim, Duclos Jean-Yves, John Cockburn (PEP) and a referee are very helpful for improvment of the paper. † Faculty of Trade Economics, National Economics University, Hanoi, Vietnam. Tel: (84) 904159258; Email: c_nguyenviet@yahoo.com ‡ Master Program in Development Economics, National Economics University, Hanoi, Vietnam. Tel: (844) 8694742 ; Email: c_nguyenviet@yahoo.com

1

Abstract

It is argued that without collateral the poor often face binding borrowing constraints in the formal credit market. This justifies a micro-credit program, which has been operated since 1995 by the Government of Vietnam to provide the poor with preferential credit. This paper examines poverty targeting and impact of the micro-credit program. It is shown that the program is not pro-poor in terms of targeting. Among the participants, the non-poor account for a larger proportion. The non-poor also tend to receive larger amounts of credit compared to the poor. As a result, the non-poor access the program more proportionally than the poor. However, the program has positive impact on poverty reduction of the participants. The results suggest that the program can be expanded taking into account reducing the leakage rate.

Keyword: Micro-credit, poverty, impact evaluation, treatment effect, propensity score matching.

JEL classification: I38; H43, H81

2

1. Introduction

Although the Vietnam has experienced remarkable reduction in poverty over the past 10 years, nearly 20% of the population still lives below the poverty line (Table 1). It is often argued that micro-credit is an important tool for smoothing consumption and promoting production, especially for poor households (e.g. Zeller, et. al. 1997; Conning and Udry, 2005). However, without collateral the poor can face binding constraints in the credit market. Because of this, the Vietnamese government launched a micro-credit program to provide the poor with small production loans without collateral in 1995. A household belonging to the list of poor households in their commune and needing capital for production can borrow an amount of money with favorable interest rates. At the beginning, the program was undertaken by the Bank for the Poor, which belonged to the Bank for Agriculture and Rural Development. Since early 2003, the program has been implemented by a separate bank – the Vietnam Bank for Social Policies (VBSP).

The Government has spent a huge amount of money in the VBSP program. During the period 1995-2005, the program provided more than 3 million households with credit. Until September 2005, the total outstanding loans for poor households were VND 13428 billion (Vietnam Bank for Social Policies, 2005).1 Since the program is designed for the poor, it is expected that the program is pro-poor, i.e., the poor benefit proportionally more than the rich from the program. The role of credit in increasing household welfare in the developing countries has been found in many empirical studies, e.g., Morduch, 1995, Pitt and Khandker, 1998, Coleman, 2002. There is, however, little research on quantitative evaluation of the micro-credit program impact in Vietnam. Most of evaluation reports simply describe the implementation and outputs of the program. Questions on causal impact of the program remain unanswered so far. Information on the quantitative assessment of a program can be of interest for two main reasons. Firstly it is very helpful in determining whether the program should be expanded or terminated. Secondly, detailed quantitative assessment, including impact and poverty targeting evaluation, can provide useful information for modification of the program.

The main objective of this paper is to examine how well the VBSP program reaches the poor, and to what extent the program has an impact on household welfare and poverty reduction. The analysis relies on data from two Vietnam Household Living Standard Surveys (VHLSS) in 2002 and 2004, which are conducted by the General Statistical Office of Vietnam (GSO).

The main problem in measuring impact of a micro-credit is the endogeneity of borrowing. The borrowing of credit can be correlated with unobserved characteristics of households such as motivation for higher income or abilities in business. Failure to control for unobservable factors affecting borrowing, the program impact estimation is no longer unbiased. To solve this problem, the paper employs two alternative econometric methods. The first is the instrumental variables regression using single cross-section data of the 2004 VHLSS. The second is the difference-in-difference with matching method using panel data of the 2002-2004 VHLSSs. Program impact is measured by two parameters: the Average Treatment Effect on the Treated (ATT) and the Average Marginal Treatment Effect on the Treated (AMT).

1 1 USD ≈ 15000 VND in January 2005.

3

The paper is structured in 5 sections. The second section introduces the data sources and examines the poverty targeting of the VBSP program. The third section presents the methodology of impact evaluation. Next, empirical findings on program impact are presented in the fourth section. Finally the fifth section concludes.

2. Poverty Targeting of the VBSP Program

2.1. Data Sources

The study relies on data from the two VHLSSs, which were conducted by GSO with technical support from the World Bank (WB) in the years 2002 and 2004. The surveys collect information through household and community level questionnaires. Information on households includes basic demography, employment and labor force participation, education, health, income, expenditure, housing, fixed assets and durable goods, participation of households in poverty alleviation programs, and especially information on loans that households have obtained during the 12 months before the interview. Information on commune characteristics consists of demography and general situation of communes, general economic conditions and aid programs, non-farm employment, agriculture production, local infrastructure and transportation, education, health, and social affairs.

The full household sample of the 2002 VHLSS covers 75000 households, of which 30000 households were asked for detailed information on consumption expenditure and income. Similarly, the full sample of the 2004 VHLSS covers 45000 households, of which around 9160 households were surveyed on expenditure and income. The selection of the samples followed a method of stratified random cluster sampling so that the households are representative for the national, rural and urban, and regional levels. Data on expenditure and income were collected using very detailed questionnaires. Information on small and detailed expenditure and income categories was collected and then aggregated into expenditure and income per capita. Data from the 2002 VHLSS sample of 30000 households and the 2004 VHLSS sample of 9160 households have been released by the GSO. It is very interesting that the small samples of 2002 and 2004 VHLSSs include a panel of 4000 households, which are representative for the whole country, and regions of large population. In the paper, the sub-samples of the 2002 and 2004 VHLSSs are used to evaluate the VBSP program. Since the sampling of the sub-sample followed a method of stratified random cluster sampling, we need to take into account sampling weights when estimating parameters of interests.

Information on commune characteristics was collected from 2960 and 2181 communes in the 2002 and 2004 surveys, respectively. Commune data can be linked with household data to assess relationship between characteristics of households and characteristics of communes in which the households are located.

2.2. Description of the VBSP Program

In 1995, the Bank for the Poor was established under the control of the Bank for Agriculture and Rural Development (BARD) with the purpose to provide poor households with favorable credit. The poor can borrow at low interest rates without collateral. Since the beginning of 2003, the program has been undertaken by VBSP, which is independent of BARD. The program has been a sub-program of the National Targeted Program for Hunger Eradication and

4

Poverty Reduction (HEPR). The HEPR program, which was launched by the Government in 1998, aims at eliminating hunger and reducing poverty. The Ministry of Labor, War Invalids and Social Affairs (MOLISA) is assigned as the standing body to assist the Government in the execution of the program. During the period 1995-2005, VBSP provided credit to nearly 3 million households . At the end of 2005, total outstanding loans for borrowing households were 13428 billion VND (Vietnam Bank for Social Policies, 2005).

The VBSP program is designed as a group-based lending scheme. The argument for the group-based design is monitoring between credit members, which would result in high repayment rates (e.g., Coleman, 1999). In order to borrow credit from VBSP, a household should join a credit group in their locality. A credit group should include from 5 to 50 members located in the same village. If the number of members in a village is lower than 5, they should join a group in another village. Each credit group sets up a management board, which is responsible for borrowing and credit use of its members.

There are several criteria that a household should meet to become a member of a credit group:

- The household has a long-term residence permit at the locality in which the credit group is located.

- The household has someone who is able to work (working force).

- The household is classified as the poor by local authority.2

- The household has a demand for credit. The credit needs to be used in production, or consumption necessary for subsistence.3

Once being a member of a credit group, a household can apply for loans of the VBSP. Total loan size is not more than 7 million VND. A household can borrow many times, but the total outstanding loans cannot be large than this number. Firstly, they send a formatted? letter to their credit group. In the letter the household specifies the amount and purpose of the loan that they intend totake. When receiving the application , the credit group will arrange a meeting of all members to consider the relevance of the loan. The credit group determines which household is able to borrow, and amount and terms of each loan . A list of borrowing households will be prepared by the credit group and send to the People’s Committee in that commune. Once the list is ratified by the People’s Committee, it will be sent to a VBSP branch for loan provision. Basically, the VBSP often agrees with the list sent by the People’s Committee. Households can receive loans at a VBSP branch in their locality or the VBSP staffs bring the loans to the households.

It is obvious that the credit group and the People’s Committee have a very important role in determining who gets credit from the VBSP. They are also highly responsible for the repayment of credit group members. Thus, the credit group and commune heads tend to exclude very poor

2 The procedure to classify a household as poor by the local authority is rather complicated. Basically, it depends on the income poverty line - which is set up by Ministry of Labor, Invalid, and Social Affairs - and other specific criteria set up by each commune. 3 Specifically, the loan can be used into the following activities: Production, business, and service, which can generate income in the future; home repair in case of serious damage; educational cost for primary and secondary school pupils.

5

households who might not be able to repay loans (Dufhues, et. al., 2002). On the other hand, non-poor or even better-off households can get loans from VBSP, since they are expected to have higher capacity to repay the loans.

2.3. Poverty Targeting of the VBSP Program

In this study, a household is classified as poor if their per capita expenditure is below the poverty line which is set up by WB and GSO. The poverty line is equivalent to the expenditure level that allow for nutritional needs and some essential non-food consumption such as clothing and housing. This poverty line was first estimated in 1993. It means that the consumption basket used for construction of poverty line is for 1993. Poverty lines in other years are estimated by deflating the 1993 poverty line using the consumer price index. 4 Table 1 presents the poverty line of GSO-WB approach over the period 1993-2004.

Poverty has declined remarkably in Vietnam. Table 2 shows that the proportion of people with per capita expenditure under the poverty line dropped dramatically from 58.1% in 1993 to 37.4% in 1998. The poverty rate continued to decrease to 28.9% and 19.5% in 2002 and 2004, respectively.5 The poverty reduction occurs in all regions. However the poverty rate remains very high in some regions, especially the mountainous and highland regions.

To analyze the poverty targeting of the VBSP program, we used data from the 2004 VHLSS, and divided the population into three groups: the poor (whose expenditure per capita is below the poverty line), the middle whose expenditure per capita is between the poverty line and the absolute value of 365 USD, and the rich whose expenditure per capita is above 365 USD. Thus the non-poor consist of the middle and rich. The selection of 365 USD implies the consumption of 1 USD per day which is also an absolute poverty line in some countries. We do not use the expenditure quintiles, since there will be some group with only a few observations. Table 3 presents the proportion of each group by eight regions. It is shown that the middle group accounts for 57.7% of the population, while this figure for the rich is about 22.8%.

The poverty targeting of the VBSP program is examined in Table 4. Only 11.8% of the poor households borrowed from the VBSP. This means that the coverage rate of the program was low: nearly 90% of poor households did not use the favorable credit. The coverage rate for the middle and the rich is 7.3% and 2.3%, respectively. The poor tend to receive smaller amounts of credit than the middle and the rich. The loan size per poor household recipient wais VND 3167 thousands, which was rather lower than the amount of VND 3628 and 4641 thousands that middle and rich recipients borrowed from VBSP. In addition, the VBSP program had very high leakage rates. Among the borrowing households, poor households accounted for only 29.8%. In other words, a large proportion of borrowing households were non-poor. In addition, about 11.5% of credit recipients were rich households.

4 The poverty lines in Vietnam have been calculated to take account of regional price differences and monthly price changes over the survey period. 5 The poor are classified based on the expenditure poverty line constructed by WB-GSO. The poverty lines in the years 1993, 1998, 2002, and 2004 are equal to 1160, 1790, 1917, and 2077 thousands VND, respectively.

6

There can at least two reasons why the VBSP program did not reach the poor households well. The first is the difference in poverty definition between the GSO-WB approach and local authorities. In a commune, a household is classified as poor if their income is below the income poverty line constructed by MOLISA and they meet several criteria such as lacking food or living in damaged house. These criteria are set up by each commune, and they can be very different from one commune to another. As a result, the poverty classification of commune authorities is not consistent across communes and over time. Table 5 estimates the distribution of groups classified based on expenditure per capita among households who are identified as poor by commune authorities. It is showed that the middle and rich households accounted for 34.9% and 2.9% of the households who are classified as poor by commune authorities.

The second reason is mentioned in Dufhues, et al. (2002). Credit groups and commune heads were reluctant to include poor households in the list of credit applicants. Non-poor could find it easier in borrowing credit, since they were expected to be more reliable in using credit effectively and repaying credit.

One important issue in examining the effectiveness of the credit is the usage of credit. Table 6 tabulates loan size by usage purpose. For all groups of the poor, middle and rich, a large proportion of credit (more than 40%) was used for production capital. The poor used 21.4% of credit amount for capital investment, while the middle and rich used only 6.6% and 5.9% for capital investment, respectively. Credit was also used for important needs such as house construction, healthcare and education.

Detailed data on the use of production and capital investment loans is presented in Table 7, which shows that the poor used most of credit in the agriculture and fishery. Credit used in business and services accounts for small fraction. In contrast, the rich spend credit more in business and service which are expected to have higher rates of return.

3. Methodology of Impact Evaluation

In this section we present the methodology employed to estimate the impact of the micro-credit program. Two methods that are used to estimate program impact in the paper are instrumental variables and difference-in-difference with matching using panel data. The main problem in measuring impact of a particular micro-credit program as well as the VBSP program is endogeneity of the borrowing of credit. Widely-used methods to solve this problem include sample selection models, instrumental variables, and panel data models. We do not use parametric sample selection models, since it requires assumption on the joint distribution of errors in the outcome and treatment equations.6

3.1. Evaluation of Program Impact: Problems and Parameters of Interest

The main objective of impact evaluation of a program is to assess the extent to which the program has changed outcomes of subjects.7 To make the definition of impact evaluation more explicit, suppose that there is a program assigned to some people in population P. For simplicity, 6 Although there are several nonparametric estimators in sample selection methods, it is difficult to write software programs to implement the estimation. 7 In literature of impact evaluation, a broader term “treatment” instead of program/project is sometimes used to refer an intervention whose impact is evaluated.

7

let’s assume there is a single program, and denote bD as a binary variable of participation in the program of a person, i.e., bD equals 1 if she/he participates in the program, and Db equals 0 otherwise. Further let Y denote the observed value of the outcome of interest. This variable can receive two potential values corresponding to the values of the participation variable, i.e., 1YY =

if 1=bD , and otherwise.0YY = 8 Then the program impact on a person i is defined as:

01 iii YY −=∆ . (3.1)

There is wide consent that it is almost impossible to estimate program impact for each person individually (Heckman et. al., 1999). In fact, program impact can best be estimated for a group of subjects. The most popular parameter of the program impact is Average Treatment Effect on the Treated (ATT) (Heckman, et al., 1999), which is the expected impact of the program on the actual participants:9

)DY(E)DY(E)DYY(E)D(EATT bbbb 1111 0101 =−===−=== ∆ .10 (3.2)

Since the size of the loan taken by a household can be regarded a continuous variable, one can be interested in addition impact of program when the size of loan changes by an amount, denoted byδ . Denote cD as a continuous variable indicating the size of loan that a household

borrow. Since cD is a continuous variable, the denotation of the potential outcomes are not

simple. For simplicity, denote as outcome of person i corresponding to the value of

variable

)D(Y ci

cD . Thus the impact of the program at a particular amount on person i is equal to:

dDc =

)D(Y)dD(Y)dD( ci

ci

ci 0=−===∆ . (3.3)

When the amount of the program is , the program impact is: δ+= dDc

)D(Y)dD(Y)dD( ci

ci

ci 0=−+==+= δδ∆ . (3.4)

Thus we can measure the change in program impact due a change in the amount of credit from to

dδ+d :

)dD(Y)dD(Y)dD()dD( ci

ci

ci

ci =−+===−+= δ∆δ∆ . (3.5)

Since we cannot estimate (3.5) for each person, we are interested in its average:

[ ] [ ] [ ])dD(YE)dD(YE)dD()dD(E cccc =−+===−+= δ∆δ∆ . (3.6)

Expectation in (3.6) can be conditional on those who participate in the program:

[ ] [ ]00 >=−+==>=−+= cccccc D)dD(Y)dD(YED)dD()dD(E δ∆δ∆ . (3.7)

8 Y can be a vector of outcomes, but for simplicity let’s consider a single outcome of interest. 9 There are other parameters such as average treatment effect (ATE), local average treatment effect, marginal treatment effect, or even effect of “non-treatment on non-treated” which measures what impact the program would have on the non-participants if they had participated in the program, etc. 10 In some formulas, the subscript i is dropped for simplicity.

8

We can divide the right-hand side of (3.7) by δ to obtain a parameter called the Average Marginal Treatment Effect on the Treated (AMT):

[ ]δ

δδ

0>=−+==

ccc

),d(

D)dD(Y)dD(YEAMT , (3.8)

which measures how the average program impact changes due to a change in the amount of credit, denoted by from to d δ+d .

More generally, we can allow ATT and AMT(d,δ) to vary across a vector of the observed variables X:

( ) )D,X|Y(E)D,X|Y(EATT bbX 11 01 =−== , (3.9)

( )[ ]

δ

δδ

0>=−+==

ccc

,d,X

D,X)dD(Y)dD(YEAMT . (3.10)

If we consider [ ]0>cc D,X)D(YE as a real function of cD and X, and denote this function by

, the AMT can be represented by the derivative of with respect to )X,D(f coDc >

)X,D(f coDc >

cD :

( ) c

cD

X D

)X,D(fAMT

c

∂

∂= (3.11)

The relation between the conditional parameters and the unconditional parameters are given by the following express:

∫ ===

11

bD|X

b)X( )D|dF(XATT ATT , (3.12)

∫ >>=

0bD|X

c)X( 0)D|dF(XAMT AMT . (3.13)

3.2. Instrumental Variables Method

The instrumental-variables method is a standard econometrics solution to the problem of endogenous variables. In this paper, we assume that the outcome is a linear function of the conditioning variables X, the program variable D which can be binary or continuous, and error term ε :

iiiiii DXDXY εγθβα ++++= . (3.14)

An important assumption imposed by (3.14) is the common effect of the program given X. In other words, people who have the same value of X will get the same benefits of the program, and the marginal effect of the program is unchanged across the value of D.

When D is a binary variable, i.e., we are interested in impact of the participation in the program regardless of the size of the program, we can have ATT. Note that the impact on a person i is equal to:

9

γθ iii XYY +=− 01 , (3.15)

and ATT(X) is as follows:

γθ X)D,X|YY(E ii +==− 101 (3.16)

When D is a continuous variable, the marginal treatment effect (MTE) on a person i when D change from to d δ+d is:

[ ] [ ]γθ

δγθγδθδ

iii

i XdXd)d(X)d(

MTE +=+−+++

= (3.17)

Thus AMT(X) has similar formula as the case of a binary program:

.X)D,X|MTE(EAMT i γθ +=>= 0 (3.18)

In the case of credit programs, the main problem in to get unbiased estimators of coefficients θ and γ is the correlation between the variables D and ε in equation (3.14). To estimate these coefficients consistently, the method of instrumental variables requires at least one variable Z with two properties. The first is that Z is correlated with D. The second is that Z is not correlated with the unobserved variables in the outcome equation, i.e.,ε . This variable is known as an ‘instrument’, and it introduces an element of randomness into the assignment which approximates the effect of an experiment.

These assumptions are stated formally as follows.

Assumption A.1: There is at least an instrumental variable Z for D such that:

0≠)X|Z,D(Cov ii ,

0=)X|Z,(Cov iiε . (A.1)

Under assumption A.1, all the coefficients in (3.14) can be identified and estimated consistently using parametric two-stage estimators or maximum likelihood ones.11 As a result, the conditional as well as unconditional parameters of program impact are identified and estimated. In this paper, to estimate ATT, i.e., impact of participation in the micro-credit program, we use D as the binary program that equals 1 for households who have borrowed credit from the program, and 0 otherwise. To estimate AMT, D will be the size of loan that households have borrowed. For non-borrowers, D will be equal to 0.

3.3. Difference-in-Difference with Matching Method

When panel data on the participants and non-participants in a program before and after implementation are available, we can estimate the program impacts using the method of difference-in-difference with matching. The basic idea of the matching method is to find a control group (also called comparison group) that has the same (or at least similar) distribution of

11 Examples of instrumental variables as well as detailed discussion of instrument variables methods can be seen in econometrics textbooks such as (Wooldridge, 2001), (Greene, 2003) or papers on review of impact evaluation such as (Moffitt, 1991).

10

the variables X as that of the treatment group.12 The matching method can be combined with the difference-in-difference method to allow the program selection to be based on unobserved variables. However it requires these unobserved variables to be time-invariant.

Let’s denote as the outcome and conditioning variables before the program. After

the program, the potential outcomes are denoted as corresponding to the states of no-

program and program, and the conditioning variables are denoted as .

BB0 X,Y

A1A0 Y,Y

BX

Assumption A.2: Conditional on X, the difference in the expectation of outcomes between the participants and non-participants are unchanged before and after the program, i.e.:

)D,X,X|Y(E)D,X,X|Y(E)D,X,X|Y(E)D,X,X|Y(E ABAABAABBABB 0101 0000 =−===−=

(A.2)

Proposition 2: Under assumption A.1, the conditional parameter ATT(X) and unconditional parameter ATT are identified by the matching method.

Proof:

Recall the parameter ATT(X) is equal to:

1)D ,X,X|E(Y - 1)D ,X,X|E(Y ATTATT AB0AAB1A)X,(X(X) AB==== . (3.19)

Insert equation in A.2 into (3.19) to obtain:

[ ][ ]

[ ] [ )D,X|Y(E)D,X|Y(E-)D,X|Y(E-1)D X,|E(Y )D,X|Y(E)D,X|Y(E

)D,X|Y(E)D,X|Y(E -1)D X,|E(Y - 1)D X,|E(Y ATT

BBA1A

BA

BA0A1A)X,(X AB

01011

00

000

00

00

=−=====−=+

=

]

−====

The unconditional parameters are also identified by (3.13).■

According to the method, the non-participants are matched with the participants based on their variables X before and after the program. The matched non-participants will form a comparison groups. However to find a comparison group that has similar variables X requires a so-called common support assumption:

Assumption A.3: 1110 <===< )X,X|D(P)X|D(P AB (A.3)

This assumption means that there are non-participants who have variables XB and XA similar to those of the participants in the program.

Note that the term [ ])0D,X,X|Y(E)1D,X,X|Y(E ABA0ABA0 =−= in A.1 is set equal to zero if we want to identify the program impacts using single cross-section data. This bias arises when the conditional expectation of outcome of non-participants is used to predict the conditional expectation of outcome of participants if they had not participated in the program. The matching method using single cross-section data assumes this bias equals zero once conditional on X. Thus

12 There is a large amount of literature on matching methods of impact evaluation. Important contributions in this areas can be found in researches such as (Rubin, 1977, 1979, 1980), (Rosenbaum and Rubin, 1983), and (Heckman et. al., 1997).

11

the panel data matching method is more robust than the matching method in sense that it allows this bias to differ from zero. It, however, requires that this bias be time-invariant.

To implement the matching method, we need to find a comparison group who variables are identical to those of the treatment group. The comparison group is constructed by matching each participant i in the treatment group with one or more non-participants j’s whose variables Xj are closest to Xi of the participant i. The weighted average outcome of non-participants who are matched with an individual participant i will form the counterfactual outcome for the participant i.

For a participant i, denote nic as the number of non-participants j who are matched with this participant, and w(i,j) is the weight attached to the outcome of each non-participant. These weights are defined to be non-negative and sum to 1, i.e.:

1)j,i(wicn

1j

=∑=

(3.20)

Weight can be equal weights, e.g. as in n-nearest neighbor matching or different weights e.g. kernel matching and local linear regression matching. The difference-in-difference estimator of ATT is expressed as follows:

∑ ∑= == ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡−−

⎥⎥⎦

⎤

⎢⎢⎣

⎡−=

mi

n

jBjBi

n

jAjAi

icic

Y)j,i(wYY)j,i(wYm

TTA1

011

011 ∑

(3.21)

Where:

n1 is the number of participants in the program

Yi1A and Yj0A are the observed outcomes of participant i and matched non-participant j after the program.

Yi1B and Yj0B are the observed outcomes of participant i and matched non-participant j before the program.

The remaining problem is how to select non-participants to be matched with a participant i. There will be no problem if there is a single conditioning variable X. However Xs are often a vector of variables, and finding “close” non-participants to match with a participant is not straightforward. A widely-used way to find the matched sample is the propensity score matching.13 Since a paper by (Rosenbaum and Rubin, 1983), matching is often conducted based on the probability of being assigned into the program, which is called the propensity score. The propensity score is used to balance the covariates X between the participants and matched non-participants. In this research the participants are matched with non-participants based on the closeness of the propensity score.

13 Other matching methods are subclassification (Cochran and Chambers, 1965) and (Cochran, 1968), and covariate matching (Rubin, 1979, 1980).

12

The second problem is to estimate AMT. Recall that when the program is continuous (denoted by cD ), we denote the expectation of program conditional on X and cD ,

i.e., [ ]0>cc D,X)D(YE by a real function . If we take expectation of this function

over the distribution of X, we can get an impact function of D:

)X,D(f coDc >

)D|X(dF)X,D(f)D(g c

D|X

coD

coD c cc 0

0>= ∫ > >> (3.22)

Using (3.21) we can estimate program impact for all households in sample data. Then, the estimates of program impact can be used to estimate the function . In this paper, we

assume constant marginal treatment effect, and has a simple form:

)D(g coDc >

)D(g coDc >

uD)D(g ccoDc ++=

>βα . (3.23)

As a result, MAT is equal to:

β=∂

∂= >

c

cD

D

)D(gAMT

c 0 (3.24)

4. Impact Measurement of the VBSP Program

4.1. Impact of the VBSP Program on Household Outcomes

4.1.1. Instrumental variables method

In this section, we present empirical findings of measurement of the VBSP program impact. A household is expected to use credit in production or consumption. If the credit is used effectively, their income and consumption expenditure per capita will be increased. Impact of the VBSP credit is examined on several aspects of household welfare. More specifically, we measure impact of the program on nine outcome variables, including consumption expenditure per capita, expenditure per capita on healthcare and education, schooling rate of children between 6 and 17 years old, annual working hours per working person from main and secondary employment, ratio of working people among working age people in households, probability of raising livestock, and probability of being poor. The parameters of impact are AMT and ATT.

The first method used to measure the AMT and ATT is instrumental variables. Data used for instrumental variables estimation of program impact are from the 2004 VHLSS. The key identification issue is to find a valid instrument for borrowing. The 2004 VHLSS has a question on whether a household has been informed about the HEPR program. The HEPR program is the national poverty reduction program which includes many sub-programs such as education support for the poor, credit provision, infrastructure development, etc. Table 8 shows that 57.5% of the households knew the HEPR program. This proportion was higher for those who receive credit from VBSP, at 72.2%. It is obvious that a household who are aware of the HEPR program will be more likely to know that the HEPR has a preferential credit program for the poor. Thus they are more likely to join the program than a household without any information on the HEPR program. However, not all households who actually participate in the VBSP program are aware that the HEPR program has a sub-program on preferential credit for the poor. According to Table

13

8, about 42% of the households borrowing credit from the VBSP mentioned that they have no information on any preferential credit program under the HEPR program. An instrumental variable used in this paper is “being aware of the HEPR program”.

The condition of correlation between the instrumental variable and credit borrowing can be tested by running a regression of borrowing on the instrumental variable and other explanatory variables. Table 13 reports results of selected regressions. The second and third columns show that the variable “being aware of the HEPR program” is strongly positively correlated with the borrowing of credit from the VBSP program. It should be noted that there are two variables of the credit program in Table 13: the program participation (binary variables) and the mount of credit borrowed (continuous variable).

The condition of absence of correlation between the instrumental variable and the error term in outcome equations cannot be tested, since the error term is unobserved. A household has been informed about the HEPR program through media means including television, radio and magazine, or village meetings and others. Table 8 tabulates the distribution of households across the main sources of information on the HEPR program. A large proportion of households know about the HEPR program via television (65.8%) and village meetings (25.7%). Thus if we are able to control these factors, i.e., television, radio, magazine and commune characteristics, in outcome equations, the instrumental variable would be uncorrelated with error term. The forth column in Table 13 presents results of regression of the instrument on variables of household and commune characteristics. As expected, variables on television and radio have a positive correlation with the instrument. Household education and regional dummies are also statistically significant in the regression. The low R-squared of 0.07 implies weak correlation between the explanatory variables in outcome equation and the instrumental variable.

Table 9 presents estimation of the program impact on several household outcomes using the instrumental variables regression. The dummy variable of the credit borrowing is used to estimate the ATT, and the amount of borrowed credit is used to estimate the AMT. Explanatory variables include urban and regional dummy variables, commune characteristics, household composition, education of household members, education and employment of household head and head’s spouse, housing characteristics, and assets.

It is found that the credit program has a positive impact on consumption expenditure per capita, and education expenditure per capita at the significance level of 10%. Positive impact of the program is also found for expenditure per capita on health care and expenditure per capita on education with the significant level of 10%. Participants in the program have higher ratio of working. The program also increases the probability of raising livestock. Especially, the program has statistically significant impact on poverty reduction. Borrowing credit from the program reduces the probability of being poor.

Table 10 presents test whether there is a difference in program impact between the poor and non-poor households. This is done by using an interaction variable between poverty status before the program and the credit variables. The poverty status is estimated using the 2002 VHLSS. The poverty status after the program can be affected by the program, thus it cannot be used in measuring the program impact on the poor. If program impact differs for the poor and

14

non-poor, then the coefficient of the interaction term will be statistically significant. However, this term is not statistically significant in all outcome equations. It should be noted that Tables 9 and 10 report only coefficients of the variables of interest. Full regression results for consumption expenditure per capita are reported in Table 12.

The endogeneity of borrowing can be tested using the instrument. The last two columns in Table 11 report results from regression of expenditure per capita on residuals of the first-stage regression of the borrowing variables. The residuals are statistically significant, which implies endogeneity of the credit borrowing. Hausman specification test is also conducted. It strongly rejects the hypothesis that coefficients of the borrowing variables are not different between the instrument variables regression and OLS regression.14

4.1.2. Difference-in-difference with matching method

The second method used for measurement of the VBSP program impact is the difference-in-difference with matching. This method requires panel data, and there are 4008 households in the panel VHLSS 2002-2004. The first step in the impact assessment is to predict the probability of participating in a program of all households using a logit or probit model. The main problem in the estimation is how to select explanatory variables. All variables that are exogenous to the program assignment and expected to affect the program assignment as well as outcomes should be included in the logit regression. Results of logit regression are presented in the second column of Table 13. It should be noted that the usage of the propensity score is mainly aimed to overcome the multidimensionality problem of matching by covariates. The comparison group is required to have characteristics covariates, not the propensity score, similar to those of the participant group. The quality of a constructed comparison group should be assessed by testing whether the distribution of characteristics covariates is similar between the comparison and treatment groups given the predicted propensity score. In this research two types of test are performed to examine the similarity of covariates between the matched non-participants and participants. The first is simply the test for the mean equality of covariates between the treatment and comparison groups. If a good comparison group is constructed, the hypothesis of mean equality cannot be rejected statistically significantly for all covariates. The second is the test for the mean equality of covariates within strata. This can be described as follows. Firstly the participants are sorted in ascending order of the predicted propensity score, and they are divided into 10 strata based on the propensity scores. Secondly the matched non-participants are distributed into 10 strata corresponding to their matched participants. Thirdly, t-tests are performed within each stratum to examine whether there is a statistically significant difference in mean of covariates between the participants and comparison non-participants. Ideally, all covariates should be balanced in all strata.15 If a covariate exists that is not balanced in many 14 Hausman specification test: dependent (outcome) variable is log of per capita expenditure.

IV regression OLS Difference S.E. Amount of borrowed credit 0.000135 0.000005 0.000130 0.000076 Having borrowed credit 0.498221 0.006119 0.492102 0.280014

15 The method of testing the equality in mean of covariates within stratum is proposed by Dehejia and Wahba (2002). They perform the test for all the participants and non-participants after estimating the propensity score. In this research the test is applied for the treatment and comparison groups after they are matched. Since what we need is the similarity of covariates between the treatment and comparison groups.

15

strata, e.g. three strata, the comparison group should be reconstructed by modifying the logit model of propensity score. Variables that are considered as less important in determining program participation and outcomes can be dropped from the model. Another way is to add some interaction terms or higher-order terms of characteristics variables. Once the balance of covariates is achieved, there is no concern about the problems of goodness of fit in the regressions of the program selection. Figure 1 graphs the histogram of estimated propensity score of participants and non-participants.

The second step is to construct the comparison group. There are different schemes of matching, i.e., local linear regression, kernel or n-nearest neighbors matching. Since computation of standard errors by bootstrap is very computer burdensome, we use matching by 1, 3 and 5 nearest neighbors, and kernel matching with bandwidth of 0.01 and 0.03. Results are very similar, thus we report results from 3-nearest neighbors matching and kernel matching with bandwidth of 0.01.

Table 11 shows that except for probability of raising livestock, there is no estimate that is statistically significant. Large standard errors make it impossible to assess whether program impact is positive or negative, small or large. A reason might be that a non-parametric method is often less efficient than a parametric method. In addition, while the instrumental variables method uses data of 9160 households, this method uses data of only 4008 households, of which 259 households have borrowed credit from the VBSP program. The small sample size might not be large enough to have small standard error of the program impact estimates.

4.2. Impact of the VBSP Program on Poverty

Since the VBSP program has positive impact on the consumption expenditure per capita, it is expected the program can also affect the poverty reduction. In this paper, we measure poverty by three Foster-Greer-Thorbecke poverty indexes which can all be calculated using the following formula (Foster, Greer and Thorbecke, 1984):

∑=

⎥⎦⎤

⎢⎣⎡ −

=q

i

i

zyz

nP

1

1 α

α , (4.1)

where yi is a welfare indicator (consumption expenditure per capita in this paper) for person i, z is the poverty line, n is the number of people in the sample population, q is the number of poor people, and α can be interpreted as a measure of inequality aversion.

When α = 0, we have the headcount index H which measures the proportion of people below the poverty line. When α = 1 and α = 2, we have the poverty gap PG which measures the depth of poverty, and the squared poverty gap P2 which measures the severity of poverty, respectively.

The measures of poverty in the presence of the VBSP program are observed and can be estimated from the sample data. We need to estimate counterfactual poverty, i.e., poverty indexes of the credit recipients if they had not received the credit. Then the impact of the program on poverty is given by:

)Y,D(P)Y,D(PP 01 11 =−== ααα∆ (4.2)

16

However, instead of using predicted expenditure in the absence of the program, we employed an idea from “small area estimation” by Elbers et. al., 2003.16 After estimating the expenditure equation with the variables of borrowing using the instrumental variables regression, we use the following equation to predict the expected probability that household i is poor if they had not borrowed credit:

⎥⎦

⎤⎢⎣

⎡ −=

σΦσβ i

iiYzln

],,Y|P[E 020 , (4.3)

Where Pi is a variable taking a value of 1 if the household is poor and 0 otherwise; Φ is the cumulative standard normal function; σ is the standard deviation of the error term in the

outcome equation, is predicted expenditure per capita of household i in the absence of the VBSP credit.

0Y

Then the poverty rate for a group is simply the sum of probability of being poor for the group:

∑=

⎥⎦

⎤⎢⎣

⎡ −Φ=

N

i

CiiC Xz

MmXPE

1

2 ln],,|[σ

βσβ , (4.4)

where mi is the size of household i; M is the total population of the group in question; and N is the number of households in the group.

To estimate the poverty gap index PG, and the poverty severity index P2, we employ the method proposed by Minot, et. al., 2003 to estimate the cumulative distribution of the expenditure per capita in the absence of the VBSP credit by changing the poverty line from the lowest expenditure per capita to the highest expenditure per capita in the sample. The estimated cumulative distribution is then used to estimate the poverty indexes PG and P2 (in the state of no-credit from the program). To estimate standard error of estimates, the paper uses a nonparametric bootstrap technique with 200 replications.

Table 12 presents estimation of the VBSP impact on poverty of the participants. There are two models: “the full model” means one using all possible explanatory variables in the expenditure equation, “the model without affected variables” drops variables that might be affected by the credit, e.g., variables on household assets or housing characteristics. It is shown that the program has statistically significant positive impact on the headcount index when the full model is used. There is no statistically significant impact found for the poverty gap and severity indices. When the model with affected variables is used, the program impact on reduction of the headcount index and poverty gap index is statistically significant at 1% and 5%, respectively.

5. Policy Implications and Conclusions

The paper examines the poverty targeting and impact of the preferential credit program for the poor from the Vietnam Bank for Social Policies. The program is designed to provide the poor households with credit at low interest rates without collateral. However, the program is targeted poorly at the poor. Only 11.8% of the poor have participated in the program. Meanwhile, among

16 Using predicted expenditure to estimate poverty for each households, and then adding up will lead to biased estimators of poverty indexes (Hentschel, et. al., 2000).

17

the participants, up to 70.2% are non-poor households. The poor households also receive smaller amounts of credit than the non-poor. In terms of targeting, the program is not pro-poor. The non-poor access the program proportionally more than the poor. The main explanation for this might be that heads of credit groups and communes are reluctant to verify the poor households in the list of credit applicants because of their low capacity of repayment. Clearly, lending the non-poor or even rich households is less risky in terms of repayment. Thus the Government and VBSP need to have measures to modify the lending system so that the leakage rate of the program can be reduced, and the coverage rate can be increased. Further studies on the lending system and the selection process should be conducted to have more detailed suggestions for the modification.

Program impact is statistically significant using the instrumental variables methods. The program helps to increase consumption expenditure per capita, expenditure on healthcare and education. The program also has positive impact on ratio of working people in households. It is evident that the participants have used credit in raising livestock, since the program has positive impact on probability of raising livestock. In addition, it is estimated that the impact is not different between the poor and non-poor. These results might hold in not very large samples. As sample sizes increase, the estimates of program impact for the non-poor can be larger than for the poor, since the non-poor have larger amount of borrowed credit compared with the poor.

While the method of instrumental variables shows the clear trend in the program impact, the non-parametric method of difference-in-difference with matching does not. All estimates from this method are not statistically significant. A reason would be that a non-parametric method is often less efficient than a parametric method, and the small sample size of panel data used in this method might not be large enough to have small standard error of the program impact estimates. Further studies should be done to have clear explanation and to check the robustness of the results from the instrumental variables method.

Finally, it is found that the program has positive effect on poverty reduction. The program helps to reduce the probability of being poor for participants, thereby decreasing the poverty headcount index. This can be a preliminary good signal on the effectiveness of the VBSP program on poverty reduction. As a result, the Government can expand the program to cover a larger proportion of the poor households taking into account the reduction of leakage rate.

18

References

Cochran, W. G. and S. P. Chambers (1965). The Planning of Observational Studies of Human Population. Journal of the Royal Statistical Society. Series A (General), Vol. 128, No. 2. (1965), pp. 234-266.

Cochran, W. G. (1968). The effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies. Biometrics 24, 295-313.

Coleman, B. E. (1999), "The Impact of Group Lending in Northeast Thailand", Journal of Development Economics, Vol. 60 (1999) 105-141.

Coleman, B. E. (2002), "Microfinance in Northeast Thailand: Who benefits and How much?" Asian Development Bank - Economics and Research Department Working Paper 9.

Conning, Jonathan, and Christopher Udry (2005). Rural Financial Markets in Developing Countries, Economic Growth Center, Yale University, Center Discussion Paper No. 914.

Dehejia Rajeev H. and Sadex Wahba (1998), “Propensity Score Matching Methods for Non Experimental Causal Studies”, NBER Working Paper 6829, Cambridge, Mass.

Dufhues Thomas, Pham Thi My Dung, Ha Thi Hanh, Gertrud Buchenrieder (2002), “Fuzzy Infromation Policy of the Vietnam Bank for the Poor in Lending to and Targeting of the Poor in Northern Vietnam”, Paper presented at the International Symposium “Sustaining Food Security and Managing Natural Resources in Southeast Asia – Challenges for the 21st Century”, January 2002, Chiang Mai, Thailand.

Elbers, C., Lanjouw, J. and Lanjouw, P., 2003. “Micro-level estimation of poverty and inequality.” Econometrica 71 (1): 355-364.

Foster, James., J. Greer, E. Thorbecke (1984), A Class of Decomposable Poverty Measures, Econometrica Vol.52, 1984, page: 761-765.

Heckman, J., Lalonde, R., and Smith, J., 1999, "The Economics and Econometrics of Active Labor Market Programs," Handbook of Labor Economics, Volume 3, Ashenfelter, A. and D. Card, eds., Amsterdam: Elsevier Science.

Heckman, Jame, Hidehiko Ichimura, and Petra Todd (1997), “Matching as an Econometric Evaluation Estimators: Evidence from Evaluating a Job Training Programme”, Review of Economic Studies, 64 (4), 605- 654.

Hentschel, J., Lanjouw, J., Lanjouw, P. and Poggi, J., 2000, “Combining census and survey data to trace the spatial dimensions of poverty: a case study of Ecuador”, World Bank Economic Review, Vol. 14, No. 1: 147-65

Minot, N., Baulch, B., and Epprecht, M. (2003) “Poverty and Inequality in Vietnam: Spatial Patterns and Geographic Determinants”. Final report of project “Poverty Mapping and Market Access in Vietnam” conducted by IFPRI and IDS.

Morduch, J. (1995), "Income smoothing and consumption smoothing", Journal of Economic Perspectives 9(3): 103-14.

19

20

Pitt, M., and Khandker, S. (1998), "The impact of group-based credit programs on poor households in Bangladesh: Does the gender of participants matter?", Journal of Political Economy 106(5): 958-995.

Rosenbaum, P.R., and D.B. Rubin (1983): "The Central Role of the Propensity Score in Observational Studies for Causal Effects", Biometrica, 70, 41-50.

Rubin, Donald (1977), “Assignment to a Treatment Group on the Basis of a Covariate”, Journal of Educational Statistics, 2 (1), 1-26.

Rubin, Donald B. 1979. “Using Multivariate Sampling and Regression Adjustment to Control Bias in Observational Studies.” Journal of the American Statistical Association 74: 318–328.

Rubin, Donald B. 1980. “Bias Reduction Using Mahalanobis-Metric Matching.” Biometrics 36 (2): 293–298.

Vietnam Bank for Social Policies (2005), “Quarterly Report”, 2005.

Zeller, Manfred, A. Diagne, and C. Mataya (1997). Market access by smallholder farmers in Malawi: Implications for technology adoption, agricultural productivity, and crop income, Agricultural Economics, 19 (1 – 2): 219 – 229.

List of Tables

Table 1: GSO-WB expenditure poverty lines in Vietnam

Year Currency 1993 1998 2002 2004 In VND (thousand) 1160 1790 1917 2077 In USD 92 128 128 132

Table 2: Poverty rates in the period 1993-2004 (%)

Regions 1993 1998 2002 2004Red River Delta 62.54 29.27 22.44 12.14

[4.22] [3.65] [0.92] [0.86]

North East 81.63

62.04 38.43 29.38

[4.70] [6.23] [1.45] [1.57]

North West 80.98 73.35 68.03 58.57

[6.55] [8.62] [2.44] [2.84]

North Central Coast 74.54 48.09 43.90 31.90

[4.56] [5.24] [1.68] [2.02]

South Central Coast 47.20 34.46 25.15 19.01

[7.23] [6.51] [1.65] [1.82]

Central Highlands 69.96 52.40 51.76 33.15

[14.35] [9.74] [2.56] [2.71]

North East South 36.97 12.19 10.54 5.37

[5.73] [3.07] [0.87] [0.70]

Mekong River Delta 47.10 36.92 23.37 15.85

[3.45] [3.02] [1.05] [1.08]

All Vietnam 58.12 37.37 28.84 19.49

[1.93] [1.65] [0.49] [0.51]

Sources: Estimation from VHLSS 2004 Standard error in the bracket

21

Table 3: Distribution of population across expenditure levels in 2004 Regions Poor

(Below the poverty line)

Middle (between the pov. line and 365$/year)

Rich (Over

365$/year)

Total

Red River Delta 12.14 65.26 22.61 100

[0.90] [1.41] [1.42]

North East 29.38

56.28 14.34 100

[1.77] [1.78] [1.32]

North West 58.57 33.91 7.52 100

[3.19] [2.71] [1.70]

North Central Coast 31.90 58.01 10.09 100

[2.12] [2.02] [1.16]

South Central Coast 19.01 61.73 19.27 100

[1.92] [2.12] [1.76]

Central Highlands 33.15 54.50 12.35 100

[2.96] [2.74] [1.76]

North East South 5.37 40.01 54.62 100

[0.79] [2.11] [2.28]

Mekong River Delta 15.85 66.43 17.72 100

[1.10] [1.32] [1.09]

All Vietnam 19.49 57.69 22.82 100

[0.55] [0.70] [0.66]

Sources: Estimation from VHLSS 2004

22

Table 4: Percentage of borrowing households, average credit amount and interest rate, coverage and leakage rates of the program Indicators Poor Middle Rich TotalCoverage rate: % households borrowing credit 11.84 7.27 2.26 6.78 [0.81]

[0.38] [0.31] [0.28]Amount of borrowed credit (thousands VND) 3166.9 3628.1 4641.4 3576.2

[113.0] [89.8] [282.6] [72.4]Average of monthly interest rate (%) 0.301 0.291 0.335 0.299

[0.016] [0.012] [0.031] [0.010]Leakage rate: distribution of households borrowing credit

29.78 61.80 8.43 100

[1.86] [1.99] [1.11] Leakage rate: Distribution of borrowed credit amount 29.58 58.89 11.53 100

[1.63] [1.73] [1.18]

Sources: Estimation from VHLSS 2004

Table 5: Distribution of the poor by commune classification across different groups by expenditure classification

2002 2004Poor by commune authorities Poor by commune authorities

Poor (GSO-WB)

Middle Rich

Total Poor(GSO-WB)

Middle Rich

Total

Red River Delta 70.56

27.76 1.67 100 57.26 39.69 3.06 100

North East 82.30 17.59 0.11 100 72.53 26.72 0.75 100

North West 95.55 4.45 0.00 100 95.66 4.34 0.00 100

North Central Coast 83.40 16.56 0.04 100 80.64 19.01 0.35 100

South Central Coast 68.32 31.68 0.00 100 65.19 33.86 0.94 100

Central Highlands 87.60 12.16 0.24 100 70.88 29.12 0.00 100

North East South 32.42 56.60 10.98 100 25.26 55.34 19.40 100

Mekong River Delta 60.98 38.37 0.65 100 49.95 49.80 0.26 100

All Vietnam 70.66 27.63 1.71 100 62.31 34.86 2.83 100

Sources: Estimation from VHLSS 2004

23

Table 6: Distribution of credit amount by credit usage and poverty statuses Indicators Households not

borrowing from VBSP

Households borrowing from

VBSP

All households

% households are aware of HEPR No 43.56

27.77 42.49 [0.71] [1.86] [0.69] Yes 56.44 72.23 57.51 [0.71] [1.86] [0.69] Total 100 100 100Main sources of information on HEPR Television

67.01 52.42 65.76 [0.84] [2.51] [0.82] Radio 4.71 8.43 5.03 [0.33] [1.34] [0.33] Magazine 1.93 0.51 1.81 [0.26] [0.32] [0.24] Village meeting 24.63 36.87 25.67 [0.77] [2.40] [0.76] Others 1.73 1.78 1.73 [0.19] [0.59] [0.19] Total 100 100 100% households are aware of the credit program under HEPR No 60.94

41.75 59.64 [0.71] [2.06] [0.69] Yes 39.06 58.25 40.36 [0.71] [2.06] [0.69] Total 100 100 100

24

Table 7: Distribution of credit used for production capital and capital investment by detailed usage Poor Middle Rich TotalAgricultural, Fishery 89.20 80.41 38.21 79.23

[2.76] [2.84] [10.02] [2.29] Trade, Business

5.27 10.84 24.06 10.42 [2.06] [2.25] [8.14] [1.70] Services 2.43 1.32 14.64 2.78 [1.26] [0.74] [6.09] [0.80] Others 3.09 7.43 23.09 7.57 [1.45] [1.87] [9.84] [1.58]

Total 100 100 100 100Sources: Estimation from VHLSS 2004

Table 8: Knowledge of the HEPR program

Indicators Households notborrowing from

VBSP

Households borrowing from

VBSP

All households

% households are aware of HEPR No 43.56

27.77 42.49 [0.71] [1.86] [0.69] Yes 56.44 72.23 57.51 [0.71] [1.86] [0.69] Total 100 100 100Main sources of information on HEPR Television

67.01 52.42 65.76 [0.84] [2.51] [0.82] Radio 4.71 8.43 5.03 [0.33] [1.34] [0.33] Magazine 1.93 0.51 1.81 [0.26] [0.32] [0.24] Village meeting 24.63 36.87 25.67 [0.77] [2.40] [0.76] Others 1.73 1.78 1.73 [0.19] [0.59] [0.19] Total 100 100 100Sources: Estimation from VHLSS 2004

25

Table 9: Program impact on selected outcomes using the instrumental variables method OUTCOME VARIABLES

Treatment variables

Log of expenditure per capita

Log of health care

expenditure per capita

Log of education

expenditure per capita

Schooling rate for children 6-

17

Annual working hours

of main employment per adult (16-

60)

Annual working hours of secondary employment per adult (16-

60)

Ratio of working

people among adults in

households

Probability of having

livestock

Probability of being poor

AMT 0.000135* 0.000603* 0.000941* 0.000035 -0.208626 0.078073 0.000141** 0.0000258** -0.0000209** [0.000076] [0.000350] [0.000541] [0.000032] [0.154967] [0.063123] [0.000063] [0.000011] [-0.0000091]ATT 0.498221*

2.222774* 3.470684* 0.130646 -730.1583 273.2448 0.501643** 0.1252715** -0.0986243**

[0.280073] [1.282905] [1.986873] [0.120180] [533.6868] [218.9593] [0.212716] [0.054855] [-0.041193]* significant at 10%; ** significant at 5%; *** significant at 1% Source: Estimation from VHLSS 2004

26

Table 10: Testing the difference in impact between the poor and non-poor households

OUTCOME VARIABLES

Explanatory variables

Log of expenditure per capita

Log of health care

expenditure per capita

Log of education

expenditure per capita

Schooling rate for children 6-

17

Annual working hours

of main employment per adult (16-

60)

Annual working hours of secondary employment per adult (16-

60)

Ratio of working

people among adults in

households

Probability of having

livestock

Probability of being poor

Having borrowed credit

Having borrowed credit 0.531647

0.906434 3.554927 0.035063 -1117.5757 428.7861 0.26995 5.848241 -5.071400 [0.397073] [1.815379] [2.890468] [0.150403] [728.643] [300.3543] [0.242181] [4.815504] [3.511325]Interaction between borrowing credit and poverty status in 2002 0.000023 0.00027 -0.000332 0.000008 0.326179 -0.153785 -0.000009 -0.001191 0.0008154 [0.000129] [0.000574] [0.000863] [0.000066] [0.22198] [0.104009] [0.000079] [0.001025] [0.000897]Poverty status in 2002 -0.17765*** -0.209009 -0.199088 -0.022177 -81.3963 51.63745 -0.001626 0.356393 0.4030214 [0.042990] [0.186956] [0.291836] [0.030109] [79.8044] [38.39167] [0.027224] [0.301011] [0.269643]Amount credit borrowed Having borrowed credit 0.000146

0.000249 0.000976 0.000011 -0.299226 0.114806 0.000073 0.001807 -0.0015353 [0.000109] [0.000498] [0.000794] [0.000046] [0.195512] [0.080164] [0.000066] [0.001526] [0.009787]Interaction between borrowing credit and poverty status in 2002 0.000025 0.000273 -0.000318 0.000008 0.300653 -0.143991 -0.000001 -0.001363 0.0009347 [0.000126] [0.000568] [0.000848] [0.000067] [0.210283] [0.098847] [0.000074] [0.001359] [0.008909]Poverty status in 2002 -0.17252*** -0.200254 -0.164752 -0.021693 -85.719894 53.296303 -0.000295 0.466153 0.3158476 [0.044416] [0.196973] [0.307554] [0.030757] [80.589362] [38.45688] [0.027954] [0.417893] [2.918153]* significant at 10%; ** significant at 5%; *** significant at 1% Source: Estimation from VHLSS 2004

27

28

Table 11: Program impact on selected outcomes using the difference-in-difference with matching method OUTCOME VARIABLES

Treatment variables

Expenditure per capita

Health care expenditure per capita

Education expenditure per capita

Schooling rate for children 6-

17

Annual working hours

of main employment per adult (16-

60)

Annual working hours of secondary employment per adult (16-

60)

Ratio of working

people among adults in

households

Probability of having

livestock

Probability of being poor

Three nearest neighbors matching

AMT -0.059287

-0.014323 -0.032985 -0.000003 0.000080 -0.014863 0.000004 0.000025** -0.0000001 [0.035315] [0.043638] [0.031037] [0.000005] [0.018732] [0.008840] [0.000006] [0.000011] [0.0000117]ATT -178.3456

-42.5045 -126.4773 -0.002317 38.546886 -48.797321 0.019702 0.088746** -0.006327

[134.0881] [154.3896] [115.3788] [0.020460] [70.336506] [33.950675] [0.020428] [0.040821] [0.043785]Kernel matching with bandwidth of 0.01

AMT -0.042076

-0.021750 -0.024397 -0.000005 -0.000941 -0.012360 0.000004 0.000021** 0.000001 [0.021419] [0.028312] [0.022965] [0.000004] [0.015019] [0.006494] [0.000005] [0.000008] [0.000009]ATT -129.03330

-88.33112 -95.75612 -0.008252 25.26832 -45.62064 0.022179 0.076282** -0.004318 [82.13997] [105.32400] [88.61823] [0.019109] [57.12201] [25.82931] [0.017611] [0.030879] [0.036436]

* significant at 10%; ** significant at 5%; *** significant at 1% Source: Estimation from VHLSS 2002-2004

Table 12: Program impact on poverty

FULL MODEL MODEL WITHOUT AFFECTED VARIABLES

Actual No-credit

counterfactual Difference Actual No-credit

counterfactual Difference

Headcount ratio 0.33633*** 0.43483*** -0.09849** 0.33633*** 0.43971*** -0.10338**

[0.01874] [0.04327] [0.04162] [0.01874] [0.04151] [0.04015]Poverty gap index 0.08565*** 0.10284*** -0.01719 0.08565*** 0.10893*** -0.02328**

[0.00644] [0.01273] [0.01277] [0.00644] [0.01270] [0.01284]Poverty severity index 0.03095*** 0.03814*** -0.00719 0.03095*** 0.04158*** -0.01063

[0.00309] [0.00686] [0.00689] [0.00309] [0.00716] [0.00720]

* significant at 10%; ** significant at 5%; *** significant at 1% Standard error is calculated using bootstrap (non-parametric) with 200 replications Source: Estimation from VHLSS 2004

Table 13: Regression and test of endogeneity DEPENDENT VARIABLES

Explanatory variables

vbsp (Logit)

vbsp_v (OLS)

knowVBSP (Logit)

Log of exp. pc

(IV)

Log of exp. pc (IV)

Log of exp. pc

(OLS)

Log of exp. pc

(OLS)

Log of exp. pc

(OLS)

Log of exp. pc

(OLS)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Having borrowed or owned credit from VBSP 0.498221* 0.004374 0.498221*

[0.280304] [0.012783] [0.265078]Amount of credit borrowed from VBSP 0.000135* 0.000005 0.000135*

[0.000076] [0.000003] [0.000072]Being aware of the HEPR program 0.522966*** 118.2640*** 0.015398* 0.015841*

[0.103177] [23.601754] [0.008515] [0.008521]Amount of credit borrowed from non-VBSP sources

-0.000053* -0.000794*** -0.000001 0.000001*** 0.000001*** 0.000001*** 0.000001*** 0.000001*** 0.000001***

[0.000028] [0.000256] [0.000001] [0.000000] [0.000000] [0.000000] [0.000000] [0.000000] [0.000000]North East 0.68685*** 204.0326*** 0.470951*** -0.013595 -0.016647 0.01297 0.013707 -0.013595 -0.016647

[0.167702] [54.103005] [0.117232] [0.023592] [0.024739] [0.015464] [0.015473] [0.021848] [0.023031]North West 0.571576** 152.160293 0.390510* -0.110251*** -0.113623*** -0.090439*** -0.089900*** -0.110251*** -0.113623***

[0.243032] [93.989657] [0.199690] [0.030872] [0.031629] [0.026327] [0.026364] [0.028852] [0.029660]North Central Coast 0.184844 70.788282 0.457665*** -0.075382*** -0.072628*** -0.066165*** -0.065876*** -0.075382*** -0.072628***

[0.173735] [51.968300] [0.114015] [0.016197] [0.015869] [0.014040] [0.014051] [0.014898] [0.014486]South Central Coast 0.649652*** 213.5338*** 0.139265 -0.060445** -0.050890** -0.032642* -0.031760* -0.060445*** -0.050890***

[0.193727] [59.914825] [0.122108] [0.023821] [0.020504] [0.016693] [0.016684] [0.022027] [0.019089]Central Highlands -0.108735 -62.550518 -0.112563 0.007846 0.013085 -0.000298 -0.000486 0.007846 0.013085

[0.255076] [63.896356] [0.154332] [0.024953] [0.025165] [0.022423] [0.022413] [0.022984] [0.023745]North East South -0.314493 -55.151794 -0.494362*** 0.234863*** 0.238375*** 0.227682*** 0.227507*** 0.234863*** 0.238375***

[0.241270] [40.480156] [0.121086] [0.018262] [0.018939] [0.016836] [0.016845] [0.017588] [0.018242]Mekong River Delta -0.255283 -49.61445 -0.656666*** 0.135273*** 0.138788*** 0.128813*** 0.128659*** 0.135273*** 0.138788***

[0.203120] [42.913677] [0.109288] [0.017044] [0.017781] [0.015534] [0.015550] [0.016219] [0.016880]Living in urban areas -0.124918 -27.780457 0.024012 0.131694*** 0.131695*** 0.128077*** 0.127973*** 0.131694*** 0.131695***

[0.160946] [33.134513] [0.093406] [0.014167] [0.014184] [0.013570] [0.013577] [0.013667] [0.013673]Living in communes with Program to support communes of ethnic minority and difficulties

-0.244058* -72.628535* 0.440056*** -0.023347 -0.023795 -0.032803** -0.033079** -0.023347 -0.023795

[0.143493] [42.476080] [0.099755] [0.016190] [0.016070] [0.014776] [0.014781] [0.015169] [0.015112]Living in communes reporting climate risk

0.004054 -20.710204 0.131949* -0.021098 -0.023033* -0.023794* -0.023889* -0.021098* -0.023033*

[0.127329] [36.725248] [0.080202] [0.013428] [0.013305] [0.012264] [0.012256] [0.012313] [0.012252]Living in communes reporting market risk

0.036473 -21.94647 0.282894*** 0.019110* 0.017125 0.016253 0.016153 0.019110* 0.017125*

[0.118373] [29.498631] [0.072618] [0.011078] [0.010998] [0.010320] [0.010323] [0.010424] [0.010331]

29

Ratio of household members who older than 60

-1.376162***

-311.3603*** -0.067563 -0.122503*** -0.119583*** -0.163043*** -0.164182*** -0.122503*** -0.119583***

[0.296674] [60.845276] [0.126151] [0.033380] [0.034701] [0.022726] [0.022757] [0.031752] [0.032865]Ratio of household members who younger than 16

-0.326574 -38.298007 0.161914 -0.250497*** -0.246881*** -0.255484*** -0.255595*** -0.250497*** -0.246881***

[0.265290] [73.783244] [0.147071] [0.025840] [0.026014] [0.023963] [0.023972] [0.023989] [0.024199]Household size 0.267167 -9.478869 0.164143*** -0.133741*** -0.130970*** -0.134976*** -0.134987*** -0.133741*** -0.130970***

[0.184018] [36.399593] [0.050741] [0.013614] [0.015395] [0.010498] [0.010435] [0.010488] [0.010557]Household size squared -0.016347 1.593265 -0.010163** 0.004625*** 0.004386*** 0.004832*** 0.004836*** 0.004625*** 0.004386***

[0.017691] [3.235545] [0.004475] [0.001197] [0.001363] [0.000914] [0.000908] [0.000915] [0.000928]Ratio of worker in agriculture -0.071809 -40.492182 -0.001967 -0.117869*** -0.121912*** -0.123141*** -0.123328*** -0.117869*** -0.121912***

[0.144254] [37.102500] [0.078449] [0.013394] [0.013082] [0.012189] [0.012192] [0.012555] [0.012222]Ratio of members with lower secondary education

0.077115 -33.578675 0.528702*** 0.106520*** 0.102127*** 0.102148*** 0.101984*** 0.106520*** 0.102127***

[0.232158] [66.110056] [0.124107] [0.021840] [0.021907] [0.020055] [0.020053] [0.020047] [0.020048]Ratio of members with upper secondary education

-0.753394** -159.87228** 0.759072*** 0.283042*** 0.284194*** 0.262226*** 0.261638*** 0.283042*** 0.284194***

[0.350695] [72.0555] [0.176726] [0.033130] [0.033341] [0.029406] [0.029410] [0.031569] [0.031797]Ratio of members with technical degree in 0.31863 80.322027 1.182604*** 0.314712*** 0.326304*** 0.325170*** 0.325573*** 0.314712*** 0.326304***

[0.357618] [106.1912] [0.197475] [0.032360] [0.030269] [0.028295] [0.028295] [0.029194] [0.028276]Ratio of members with post-secondary education in

-1.475911** -213.7508*** 1.859448*** 0.618658*** 0.617718*** 0.590827*** 0.590019*** 0.618658*** 0.617718***

[0.752138] [74.2069] [0.284364] [0.045953] [0.045708] [0.043780] [0.043785] [0.044973] [0.044859]Household head is ethnic (not Kinh/Chinese)

0.22064 128.807** 0.298763** -0.175451*** -0.171568*** -0.158679*** -0.158163*** -0.175451*** -0.171568***

[0.158318] [62.528217] [0.116072] [0.021431] [0.020365] [0.017804] [0.017815] [0.019893] [0.019072]Log of living areas (log of m2) 0.009549 3.545733 -0.041276 0.111811*** 0.112003*** 0.112273*** 0.112288*** 0.111811*** 0.112003***

[0.108422] [25.516440] [0.062771] [0.010355] [0.010364] [0.009858] [0.009862] [0.009873] [0.009870]Living in semi-permanent house 0.288411* 61.739036** -0.029399 -0.046569*** -0.047735*** -0.038531*** -0.038310*** -0.046569*** -0.047735***

[0.166042] [29.239514] [0.078222] [0.012342] [0.012608] [0.010835] [0.010844] [0.011631] [0.011873]Living in temporary house 0.212236 47.629203 0.016575 -0.086886*** -0.085428*** -0.080685*** -0.080494*** -0.086886*** -0.085428***

[0.205995] [49.617628] [0.105477] [0.016909] [0.016668] [0.015145] [0.015146] [0.015450] [0.015312]Have toilet (not flush type) 0.547418*** 105.6282*** 0.041478 -0.128871*** -0.124201*** -0.115118*** -0.114681*** -0.128871*** -0.124201***

[0.183332] [32.486386] [0.082904] [0.015217] [0.014051] [0.012461] [0.012473] [0.014782] [0.013621]Have no toilet 0.31859 43.973642 -0.1485 -0.134724*** -0.133050*** -0.128999*** -0.128820*** -0.134724*** -0.133050***

[0.210626] [48.560964] [0.106187] [0.017862] [0.017821] [0.016656] [0.016667] [0.016971] [0.016835]Using cleaning water (not running/tap water)

-0.058187 -23.089237 0.032837 -0.070049*** -0.070836*** -0.073055*** -0.073148*** -0.070049*** -0.070836***

[0.185582] [32.907058] [0.097030] [0.015027] [0.014973] [0.014534] [0.014542] [0.014601] [0.014574]Using other water -0.043003 6.905992 0.012708 -0.097426*** -0.097024*** -0.096527*** -0.096498*** -0.097426*** -0.097024***

[0.209264] [49.853487] [0.122589] [0.019167] [0.018956] [0.018234] [0.018241] [0.018242] [0.018244]Have color television 0.042496 24.795861 0.286349*** 0.161091*** 0.167440*** 0.164320*** 0.164468*** 0.161091*** 0.167440***

30

31

[0.125052] [37.393251] [0.073325] [0.013004] [0.012763] [0.011653] [0.011647] [0.011881] [0.011671]Have black & white television 0.286211* 96.269279* 0.328149*** 0.056505*** 0.060881*** 0.069039*** 0.069438*** 0.056505*** 0.060881***

[0.147828] [54.013732] [0.095115] [0.017287] [0.016410] [0.014009] [0.014020] [0.015928] [0.014969]Have radio 0.173675 24.539875 0.160691** 0.018058* 0.017233* 0.021253** 0.021337** 0.018058* 0.017233*

[0.115079] [29.921631] [0.063594] [0.010410] [0.010421] [0.009419] [0.009424] [0.009653] [0.009763]Have magazine -0.005291*** -0.034672 0.000062 0.000414*** 0.000411*** 0.000409*** 0.000409*** 0.000414*** 0.000411***

[0.001908] [0.039879] [0.000204] [0.000033] [0.000033] [0.000032] [0.000032] [0.000032] [0.000032]Have motorbike -0.315458*** -25.268051 0.074258 0.177595*** 0.183816*** 0.174305*** 0.174265*** 0.177595*** 0.183816***

[0.114424] [33.783654] [0.061303] [0.010068] [0.010952] [0.008961] [0.008977] [0.009075] [0.010158]Have telephone -0.079133 -14.573877 0.063576 0.194618*** 0.193447*** 0.192720*** 0.192655*** 0.194618*** 0.193447***

[0.180933] [33.323686] [0.082301] [0.013438] [0.013208] [0.012685] [0.012685] [0.012682] [0.012674]Number of day-off due to sickness per a member

0.002828 1.084039 0.002309 0.002952*** 0.003026*** 0.003093*** 0.003098*** 0.002952*** 0.003026***

[0.002975] [1.058362] [0.001851] [0.000476] [0.000468] [0.000469] [0.000470] [0.000478] [0.000473]Being classified as poor household by commune authority

0.906725*** 301.7213*** 0.532976*** -0.226796*** -0.233355*** -0.187511*** -0.186440*** -0.226796*** -0.233355***

[0.130208] [61.063662] [0.094379] [0.029849] [0.032996] [0.015717] [0.015741] [0.027586] [0.030527]Area of annual crop land (m2) -0.000015 -0.001736 0.000014*** 0.000005*** 0.000005*** 0.000004*** 0.000004*** 0.000005*** 0.000005***

[0.000009] [0.001542] [0.000005] [0.000001] [0.000001] [0.000001] [0.000001] [0.000001] [0.000001]Area of aquaculture water surface (m2) -0.000035 -0.004319** 0.000030** 0.000010*** 0.000010*** 0.000009*** 0.000009*** 0.000010*** 0.000010***

[0.000022] [0.002050] [0.000013] [0.000002] [0.000002] [0.000002] [0.000002] [0.000002] [0.000002]Area of perennial crop land (m2) -0.000003 0.000562 0.000006 0.000002** 0.000002** 0.000002** 0.000002** 0.000002** 0.000002**

[0.000011] [0.002652] [0.000005] [0.000001] [0.000001] [0.000001] [0.000001] [0.000001] [0.000001]Domestic remittance (thousand VND) -0.000016 -0.00087 0.000002 0.000011*** 0.000011*** 0.000011*** 0.000011*** 0.000011*** 0.000011***

[0.000015] [0.001032] [0.000004] [0.000003] [0.000003] [0.000003] [0.000003] [0.000003] [0.000003]Foreign remittance (thousand VND) -0.000012 -0.000803 0.000007** 0.000005*** 0.000005*** 0.000005*** 0.000005*** 0.000005*** 0.000005***

[0.000010] [0.000546] [0.000003] [0.000001] [0.000001] [0.000001] [0.000001] [0.000001] [0.000001]Residual from regression of “Have borrowed credit” (Treatment equation)

-0.493847*

[0.265651]Residual from regression of “Borrowed credit amount” (Treatment equation)

-0.000130*

[0.000072]Observations 9169 9169 9169 9169 9169 9169 9169 9169 9169R-squared 0.12 0.04 0.07 0.72 0.72 0.76 0.76 0.76 0.76

* significant at 10%; ** significant at 5%; *** significant at 1% Source: Estimation from VHLSS 2004

Figure 1: Estimated propensity scores of participants and non-participants in the VBSP program

0

Treated Untreated

.01.03

.05.07

.09.11

.13.15

.17.19

.21.23

.25.27

.29.31

.33.35

.37.39

.41.43

.45.47

.49.55

.57.59

.61.65

.71

32