Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Effects of Microfinance on Poverty Reduction in Vietnam: A
Pseudo-Panel Data Analysis
Hoai An Duong1 and Hong Son Nghiem2
The development of microfinance in Vietnam since 1990s has been coincided
with a remarkable progress in poverty reduction. Numerous descriptive
studies illustrated that microfinance is an effective tool to eradicate poverty in
Vietnam but evidence from quantitative studies was mixed. This study
contributes to the literature by providing new evidence on the impact of
microfinance to poverty reduction in Vietnam using the repeated crosssectional data from the Vietnam Living Standard Survey (VLSS) in 1992-2010.
Our results show that microfinance contributes significantly to household
consumption, income and poverty reduction.
JEL Classification: D13, D14 and O12
1 . Introduction
Microfinance has been recognised as a potentially effective tool to fight against poverty. The
spread of microfinance appears to coincide with a sharp decrease in poverty rate across
countries. However, empirical studies have not reached a consensus about the extent to which
microfinance contribute to poverty reduction. For example, one of the most widely cited study –
Pitt and Khandker (1998) – suggested that microfinance has significant and positive
contribution to poverty reduction in Bangladesh whilst Roodman and Morduch (2009) found
insignificant effects using the same data set. In Vietnam, the story is similar. Pham and
Izumida (2002), Quach (2006), and Tinh et al. (2011) have found that microfinance created
positive impacts to poverty reduction while Nghiem et al. (2012) revealed that the effect of
microfinance to poverty reduction of was negligible. This study examines the effects of
microfinance using data sets from the Vietnam Living Standard Surveys (VLSS) from 1992 to
2010. To the best of our knowledge, no previous study has investigated the development and
effects of microfinance in such a long timeframe. We also able to mitigate the self-selection
issue in microfinance participating using pseudo panel data methods.
Our results show that access to microfinance is associated with an increase of household
consumption level by 50 per cent and household income by 08 per cent, respectively. In
addition, access to microfinance is associated significantly with the reduction of poverty. We
also find that loan volumes matter: households with large loans are associated with higher
level of consumption, higher income and lower probability of being poor.
The remainder of this paper is organised as follow. Section 2 reviews the literature on
microfinance impact studies. Methodologies are presented in Section 3 whilst results and
discussions are presented in Section 4. Section 5 concludes the study.
1
Hoai An Duong , PhD student in Griffith University, Australia. Email: hoaian.duong@griffithuni.edu.au
2
Hong Son Nghiem , Research fellow, National Centre for Disability and Rehabilitation Medicine (CONROD),
The University of Queensland, Australia. Email: h.nghiem@uq.edu.au
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
2 Literature Review
This section reviews studies of microfinance impacts in Vietnam. We classify their study
according to the methods to address the selection problems: panel data, quasi-experimental
surveys and randomised experiments.
2.1 Panel data
Hao (2005) used data from the Vietnam Living Standard Survey (VLSS) data in 1993 and
1998, in which more than one thousand households were sampled repeatedly in both periods.
The author applied Probit regression to estimate determinants of credit and the Heckman twostep method to estimate the impact of credit on household welfare. The findings showed that
access to formal credit has a positive impact on consumption per capita but the magnitude of
this impact is modest. For example, one per cent increased in the volume of credit borrowed,
ceteris paribus, led to 0.07 and 0.06 per cent increases in consumption per capita in 1993 and
1998, respectively.
Lensink and Pham (2012) used panel data with a sample of about 3,200 households, obtained
from VLSS 2002 and 2004 to evaluate the impact of microcredit provided by VBSP on selfemployment profits in Vietnam. The findings indicated that microfinance had positive and
significant impacts on self-employment profits of the borrowers. More importantly, microfinance
had positive impacts on poverty reduction and these impacts were more significant for the
poorest households. In addition, the authors did not find direct impacts of credit access on
fixed investments of expenditures. Despite the achievement, some limitations still can be found
in this study. Firstly, the study only focused on VBSP – one of the current microfinance service
providers in the nation. Secondly, the research also focused only on rural areas while VLSS
data provide information of households in urban areas as well. Finally, although VLSS data
provide a wide range of information of household characteristics, the data were not designed
for specific research purposes like microfinance assessment.
In summary, panel data are very powerful for producing reliable estimates. However previous
study only took a short panel that contain only two snapshots of households, thus, their results
may not very robust.
2.2 Quasi-experimental surveys
Swain et al. (2008) employed the 2006 household survey data to estimate the impact of
microfinance on poverty reduction and compare the different impacts between three groups in
the Mekong Delta of Vietnam. The survey targeted three groups: successful members (those
who successfully escaped from poverty at the time of the research), poor members (members
who remain poor), and non-members (who did not join in any microfinance program), covering
134 households in the research area. In order to obtain in-depth data, the researchers used
participatory rural appraisal (PRA) techniques: focus group discussions and in-depth
interviews. The findings show average income per capita of the member households is up to
three times higher than that of non-members. The authors also find that members had
accumulated more and better quality assets than non-members. Member households also had
opportunities to access training programs and improve their social positions.
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Doan et al. (2011) conducted a study to evaluate the impacts of household credit on education
and healthcare spending by the poor in peri-urban areas of Ho Chi Minh City. The study
employed two major methods namely propensity score matching and multiple treatment
effects. The sample included 411 member and non-member households. The findings show
positive impacts of borrowing from formal credit sources on education and healthcare
expenditure, but informal credit has insignificant impacts. For example, on average, member
households have higher consumption on education and health care by than non-member
households by roughly 90 thousand VND (about $US 4.5) per month.
The advantage of quasi-experimental approach is that it allows researchers to apply many
techniques to avoid or mitigate self-selection and program placement biases in microfinance
impact assessment. The downside of this approach is that conducting a quasi-experiment is
time consuming and costly.
2.3 Randomised experiments
Despite much effort spent, we did not find any research using randomised experiments to
evaluate impacts of microfinance in Vietnam. Thus, we have to review studies using this
approach in other countries.
Karlan et al. (2006) conducted a randomized control study in Peru to estimate the impacts of a
microfinance package including a business-training program and credit programs by assigning
the programs in three groups: mandatory treatment, optional treatment, and control. The
sample size of these three groups was 183 banks. The results show that clients in treatment
groups had better business knowledge and business practice. Also, the business-training
program had strong and positive impacts on business results among treatment groups. For
example, sales of participating businesses were 16 per cent to 28 per cent higher than those
of control groups. The authors suggested further replications such as venue of the training
should be more convenient for most of the clients; hired professionals to train the trainees
should be taken into account to attract the attention of the trainees. One criticism of the
program is that the program placement may not strictly random: it targets experienced
businesswomen, thus results are more likely to be positive.
Banerjee et al. (2009) conducted a randomized study to evaluate the impacts of an introducing
microcredit in Hyderabad, India with Spadana – the largest microfinance institution in the
country at that time. The study selected 2800 households from 120 areas of the organisation
at the baseline survey. The design followed three steps: small pilot, full pilot, and full launch.
They found that microloans are associated with increased profits. But they find no influence of
the program on health, education and women’s decision-making empowerment.
Overall, randomised experiments provide clean estimates of microfinance impacts but the time
and resource constraints of this study prohibit us from following this path.
3 Methodology
3.1
Pseudo-panel data
The main challenge of estimating impacts of microfinance is selection bias. Individuals who
decide to participate in microfinance programs may have some unobserved characteristics
(e.g., risk attitude, entrepreneurial ability) that may also affect impacts of microfinance on
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
indicators such as income or consumption. In this study, we exploit the availability of seven
repeated cross-sectional surveys to address the selection bias issue using pseudo panel data
approach – a second best option when genuine panel data are not available.
Tracking individuals overtime is time and resource consuming, hence panel data on
development projects are often rare (Deaton 1985, Verbeek 2007). But with the availability of
repeated cross-sectional surveys one can create pseudo panel data that allow analysis with
panel data methods. Pseudo panel data have advantages (Deaton 1985). Researchers can
combine data from different sources into a single data set, as long as the cohorts in each
source are comparable. Attrition problems often found in genuine panel data can be minimised
as long as other participants in the same cohort remained in the survey. The characteristics of
cohort averages can soften impacts of response errors by individuals.
In order to construct pseudo panel data, particular individual characteristics that are steady
over time (e.g., ethnicity, year of birth, and residential locations) should be used to define
cohorts. The average values of each variable are used as observations of the pseudo panel
data.
3.2
Data
The data used in this study are drawn from the Vietnam Living Standard Survey (VLSS)
carried out in 1992, 1998, 2002, 2004, 2006, 2008 and 2010. The VLSS is a general-purpose
survey series, conducted by the Vietnam General Statistics Office (GSO) of Vietnam with
financial and technical assistance from the United Nation Development Programme (UNDP)
and the World Bank. The surveys cover a variety of characteristics of the households such as
demographic information, income, consumption, expenditure, employment, health, migration,
credit and insurance.
The sample size of VLSS 1992 covered 4,799 households in 300 villages, of which 3,839
households (80 per cent) from rural areas. The VLSS 1998 sampled 5,900 households,
including most of those surveyed in 1992 but covered wider areas with 388 villages. To make
data more representative, the GSO increased the sample size of the VLSS to 29,532
households in 2002 but this attempt was so costly that the sample size was reduced to
approximately 9,189 households in subsequent surveys.
Despite the sufficiently large sample, the VLSS data are far from ideal for this study. The
surveys were designed for general purposes, which do not include detailed information on
issues such as characteristics of microfinance institutions. Also, only some proportion of the
VLSS in 1992-1998, 2002-2004 and 2006-2008 periods were surveyed repeatedly, thus the
data series can be considered as repeated cross-sectional.
The descriptive statistics show that only 22 per cent of households surveyed live in urban
areas, reflecting the population distribution in the census. In addition, 74 per cent of
households surveyed were headed by male with the average age of 42.6 years. Households in
Vietnam in the study period earn 16 million VND per person per year, on average (or
$4.2/person/day using the 1992 exchange rate of VND10,493/USD). The average
consumption per capita is slightly lower at 14 million VND per person per year
($3.92/person/day). On average, the proportion of microfinance members and poor
households (using the national poverty line) are similar at 13 and 12 per cent, respectively.
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Table 1: Descriptive statistics
Description
Mean
Gender of household head (1=male)
0.740
Age of household head (years)
42.7
Location of household (1=urban)
0.220
Income (VND’000)
16,051
Household size (persons)
4.590
Expenditure (VND’000)
14,813
Value of loans (VND’000)
4,983
Microfinance members (1=yes)
0.130
Poverty status (1=yes)
0.120
Std. Dev.
0.4
18
0.4
42,027
1.08
35,763
20,500
0.33
0.32
(Calculated from VLSS 1992-2010. Constant prices 1992 are used)
4 . Results and Discussions
We use the following equation to measure the impacts of participation in microfinance on
welfare indicators of households.
where Y is a set of interested outcomes such as consumption and income or poverty
reduction; MF is the binary variable, representing the access to microfinance (proxied by a
dummy variable for loan size less than $US500); L represents the slope effect of access to
credit (proxied by loan volumes); X is a vector of household and community characteristics
(e.g., household size, age of household head and access to electricity),  is the set of variables
that capture time-invariant unobserved characteristics of individuals (e.g., risk attitudes, tastes
and business ability); and  is random error.
We expect that parameters 1 and 2 are positive as larger value of loans and participation in
microfinance would be associated with higher income or consumption. The parameter 2
represents the contribution of the access to microfinance to selected outcomes. It is expected
that the access to microfinance services will improve the members’ welfare. Meanwhile, the
parameter 3 represents the relationships between selected household characteristics and the
welfare indicators. Parameter  captures unobservable household characteristics, which can
affect both the decision to join microfinance programs and interested outcomes. With the
availability of panel (or pseudo-panel) data, unobserved individual characteristics  can be
eliminated using fixed-effect regressions.
We plan to disaggregate the data into cohorts using time-invariant exogenous factors such as
regions of residence, year of birth, and gender. The VLSS collected a very rich data regarding
these variables. However, data definitions and variables names in VLSS change between
surveys. We are still in the data cleaning process, thus, unable to construct a rich set of
variables to form cohorts. At the time of writing, we only use province of residence as a
criterion to form cohorts.
Our preliminary results show that both the value of loans and the access to microfinance have
positive impacts on household consumption (Table 2), income (Table 3) and poverty reduction
(Table 4). Since we take the natural logarithm on both side of the equation to mitigate effects
of their skewed distribution, parameters of continuous variables are interpreted as elasticity. In
particular, the results show that one per cent increase in loan volume lead to 0.589 per cent
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
increase in household consumption and 0.393 per cent in household income, respectively. In
addition, compared to other households, microfinance members have their consumption level
higher by 50% (i.e., e0.402-1); they also have higher income by 8% (i.e., e0.077-1). This evidence
suggests that consumption smoothing, income generating and poverty reduction are among
the main contributions of microfinance to participants. The results also show that microfinance
loans created more substantial effects on consumption than income. One possible explanation
is that consumption effects is direct and certain (i.e., use of loans to purchase consumption
items) whilst the income effects take time and affected by risks (e.g., lost of crops). But we
believe that low consumption level (about $700 per person per year) of households in the
sample would make the substantial increase in percentage possible (i.e., credit follows the law
of diminishing marginal return in the household production function).
Table 2: Microfinance Impacts on Household Consumption
Descriptions
Coef.
Std. err
Access to microfinance (1=yes)
***0.402
0.013
Value of loans (log)
***0.589
0.005
Gender of household head (1=male)
***0.020
0.006
Age of household head (log)
***0.074
0.008
Location of households (1=urban)
***0.248
0.007
Household size (log)
***0.379
0.006
Constant
***4.117
0.042
N
52,508
Adj. R2
0.911
(Calculated from VLSS 1993-2010)
Table 3: Microfinance impacts on household income
Descriptions
Coef.
Std. err
Access to microfinance (1=yes)
***0.077
0.014
Value of loans (log)
***0.393
0.004
Gender of household head (1=male)
***0.049
0.006
Age of household head (log)
***0.074
0.007
Location of households (1=urban)
***0.312
0.007
Household size (log)
***0.424
0.007
Constant
***6.154
0.041
N
45,196
Adj. R2
0.602
(Calculated from VLSS 1993-2010)
Apart from having impacts on household consumption and income, our results show that
microfinance had significant impacts on poverty reduction. Since we use Probit regression, the
results are interpreted as probability. For example, households with access to microfinance
have the probability of being poor lower by 0.11 percentage points, compared with other
households. Similarly, microfinance members, who borrowed loans have the probability of
being poor lower than non-members 0.12 per cent.
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Table 4: Microfinance impacts on poverty reduction
Descriptions
Coef.
Mar. effect
Access to microfinance (1=yes)
***-0.513
-0.108***
Value of loans (log)
***-0.885
-0.168***
Gender of household head (1=male)
***0.084
-0.016***
Age of household head (log)
***0.164
-0.031***
Location of households (1=urban)
***-0.071
-0.013***
Household size (log)
***1.295
0.245***
Constant
***-6.222
N
47,886
(Calculated from VLSS 1993-2010)
5 . Concluding Remarks
This paper has examined impacts of microfinance in Vietnam using the data from Vietnam
Living Standard Survey series from 1991-2010. Our preliminary results suggested that
microfinance produces positive and significant contributions to household consumption and
income. In addition, microfinance had significant impacts on poverty reduction. Impacts of
microfinance to other important indicators such as education and health are still under
investigation. Due to the difficulties of data cleaning and synchronization we have not been
able to examine effects of microfinance to other measures such as health and education and
controlled for a wider set of exogenous variables.
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