There is an ongoing debate about whether microfinance has a positive impact on education
and health for borrowing households in developing countries. To understand this debate,
we use a survey designed to meet the conditions for propensity score matching (PSM) and
examine the impact of household credit on education and healthcare spending by the poor
in peri-urban areas of Ho Chi Minh City, Vietnam. In addition to matching statistically
identical non-borrowers to borrowers, our estimates also control for household pre-treatment
income and assets, which may be associated with unobservable factors affecting both credit
participation and the outcomes of interest. The PSM estimates show a significant and positive
impact of borrowing on education and healthcare spending. However, further investigation of
the effects of the treatment reveals that only formal credit has a significant and positive impact
on education and healthcare spending, while informal credit has an insignificant impact on
spending. This paper contributes to the limited literature on peri-urban areas using evidence
from one of the largest and most dynamic cities in Southeast Asia.
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ed 6 to
18 years old, number of persons aged 18 to 60 years old, dummy of older than 60 years old, ward dummies, marital
status*head’s gender.
In some cases to meet the balancing property in estimation of propensity scores we added interaction terms in
specifications of the propensity score.
ATTK: Average Treatment Effect on the Treated, Kernel matching.
ATTR: Average Treatment Effect on the Treated, Radius matching.
entrepreneurial ability and skills. Our estimation
strategy, therefore, sought to reduce bias and
enhance the reliability of the PSM estimates.
Our findings are in line with those of Nguyen
(2008) and Quach and Mullineux (2007), who
have found that access to credit programmes has a
positive impact on household welfare and poverty
reduction in rural Vietnam. Spending on education
and healthcare affects the formation of human
capital. Given their limited physical and financial
assets, human capital becomes the most important
asset for the poor in peri-urban areas. Therefore,
policies aiming at reducing poverty in these areas
should consider enhancing subsidized credit to the
poor and/or providing subsidized or free universal
education and healthcare services.
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Journa l o f Southeas t As ian Economies 114 Vo l . 31 , No . 1 , Apr i l 2014
APPENDIXES
Appendix 1
Pre-treatment Income per Capita of the Poor and Their Sources of Loans
Mean income is VND3.57 million with standard deviation as VND0.844 million, and median of VND3.6 million (see
Figure 3). Monthly income per capita is just about VND300,000 (or US$18), slightly higher than the national official
poverty line (equivalent to US$ 16.4).8
FIGURE 3
Pre-treatment per Capita Income
Note: Unit of horizontal axis is VND1,000.
1.
0e
-0
4
0
2.
0e
-0
4
3.
0e
-0
4
D
en
si
ty
4.
0e
-0
4
5.
0e
-0
4
2000 30001000 5000 60004000
INCOME
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Journa l o f Southeas t As ian Economies 115 Vo l . 31 , No . 1 , Apr i l 2014
TABLE 5
Sources of Loans
Sources of loans No. of loans Percentage
Mean of loans
(VND1,000)
Formal 336 55% 9,327
VBSP 37 6% 9,622
Commercial banks 26 4% 54,923
JCSF 29 5% 4,564
Social political organizations 62 10% 4,564
HEPRF 182 30% 5,176
Informal 272 45% 5,229
Moneylenders, ROSCAs, pawnbrokers, others 51 8% 9,218
Friends, relatives, neighbors 221 36% 4,308
Overall 608 100% 7,494
source: Calculation from authors’ survey; VBSP- Vietnam Bank for Social Policies; JCSP — Job Creation Support Fund;
HEPRF — The Hunger Elimination and Poverty Reduction Funds; ROSCAs- Rotating savings and credit associations.
Appendix 2
The Average Treatment Effect on Monthly Average Education Expenditure in VND1,000 Using Matching
Estimators for the Entire Sample with Reduced Bandwidth (0.01) and Radius (0.05)
Control variables in the propensity score estimation Treated/controls Kernel matching Radius matching
Head’s gender, head’s age, head’s education, marital 86.58 95.63
status, school-aged child ratio, and ward dummies (S1) 304/107 (35.71)* (33.35)**
S2=S1plus initial income in log, initial assets in logarithm 78.95 91.25
304/101 (36.53)* (33.46)**
Head’s gender, head’s age, head’s education, marital 83.34 85.99
status, number of children from 6 to 18, and ward 304/107 (33.07)* (34.69)*
dummies (S3)
S4=S3 plus initial income in log, initial assets in 77.98* 85.07*
logarithm 304/101 (36.53) (35.46)
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Appendix 3
The Average Treatment Effect on Monthly Average Healthcare Expenditure in VND1,000 Using Matching
Estimators for the Entire Sample with Reduced Bandwidth (0.01) and Radius (0.05)
Control variables in the propensity score estimation Treated/controls Kernel matching Radius matching
Specification 1 (S1) 95.36 109.33
304/103 (49.21)* (51.07)*
S2=S1 plus initial income in log, initial assets in 94.85 99.086
logarithm 304/97 (50.91)+ (53.83)+
Specification 3 (S3) 118.799 129.242
304/107 (45.74)** (44.56)**
S4=S3plus initial income in logarithm, initial assets 103.15 114.571
in log 304/102 (49.87)* (49.24)*
Appendix 4
The Average Treatment Effect on Monthly Average Education Expenditure in VND1,000 Using Matching
Estimators for the Entire Sample with Reduced Bandwidth (0.01) and Radius (0.05)
Control variables in the
Informal credit vs. Formal credit vs. Formal vs.
propensity score estimation
Non-borrowers Non-borrowers Informal
ATTK ATTR ATTK ATTR ATTR
Specification 1 (S1) 1.64 22.07 151.72 163.46 101.26
(45.59) (37.07) (47.33)** (51.03)** (44.89)*
Specification 2 (S2) 14.42 12.94 140.52 143.62 116.54
(38.72) (41.57) (49.89)** (53.94)** (53.33)*
Specification 3 (S3) 39.05 27.92 142.33 158.14 103.75
(44.19) (39.01) (50.62)** (46.47)** (46.77)*
Specification 4 (S4) 3.59 13.17 138.56 145.36 109.70
(42.27) (40.86) (48.50)** (49.80)** (49.06)*
Notes:
ATTK: Average Treatment Effect on the Treated, Kernel matching.
ATTR: Average Treatment Effect on the Treated, Radius matching.
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Appendix 5
The Average Treatment Effect on Monthly Average Healthcare Expenditure in VND1,000 Using Matching
Estimators for the Entire Sample with Reduced Bandwidth (0.01) and Radius (0.05)
Control variables in the
Informal credit vs. Formal credit vs. Formal vs.
propensity score estimation
Non-borrowers Non-borrowers Informal
ATTK ATTR ATTK ATTR ATTR
Specification 1 (S1) 76.74 82.27 195.91 203.89 182.58
(44.28)+ (42.20)+ (92.66)* (95.21)* (101.87)+
S2=S1plus initial income in log, 62.94 65.15 171.79 177.72 190.70
initial assets in log (46.35) (44.34) (102.53)+ (98.07)+ (93.12)+
Specification 3 (S3) 67.11 68.71 210.84 195.42 171.16
(42.33) (44.44) (93.08)* (100.89)* (105.08)+
S4=S3plus initial income in log, 81.99 90.09 205.71 195.77 179.03
initial assets in log (46.22)+ (48.36)+ (91.63)* (98.01)* (102.92)+
Notes:
ATTK: Average Treatment Effect on the Treated, Kernel matching.
ATTR: Average Treatment Effect on the Treated, Radius matching.
NOTES
The authors thank, without implicating, Andrea Menclova, Asadul Islam and the participants of the Sixth Australasia
Development Economics Workshop for their helpful comments and suggestions. Any remaining errors are those of
the authors.
1. HCMC has twenty-four districts. District 9 has the fifth lowest population density, with a population of 227,816
(in 2008).
2. The list was provided by the District Department of Labour, Invalids and Social Affairs. Poor households in the
list excluded temporary migrants.
3. When we reduced the bandwidth and radius to 0.01 and 0.05 for kernel and radius matching respectively, the
results are robust. See Appendixes 2 to 5.
4. Some studies suggest that the estimation should be in the range of 0.1 to 0.9, but there are 44 observations with
scores greater than 0.9 (about 11 per cent of the sample); if these are dropped, the estimates will be misleading
(Crump et al. 2009).
5. The sets of variables used for estimating scores to draw Figures 1 and 2 are different. Each set of variables
should affect both credit participation and outcomes. The impact on education expenditure is featured in Figure
1 and the impact on healthcare expenditure is featured in Figure 2. That is why the two figures are slightly
different.
6. Basically, formal credit amounts are larger than informal ones, hence formal credit is considered as consituting a
higher level of treatment or a full treatment. We expect that if loans have an effect on outcomes (i.e., education
and health care expenditure), borrowing greater amounts will have a stronger impact on the respective outcomes.
The mean of accumulated loans per household is VND8,317 thousand (about US$500) and VND15,135 thousand
(about US$920) for informal and formal credit, respectively. The average size per loan is VND5,229 thousand
(about US$317) and VND9,327 thousand (about US$566) for informal and formal credit, respectively.
7. The impact on both expenditure and school enrolment and/or attendance should be investigated to provide a
more complete picture of the effects on education. The impact on child schooling was examined in a separate
paper in International Development Planning Review (forthcoming).
8. Average exchange rate in 2008, US$1=VND16,481.
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Journa l o f Southeas t As ian Economies 118 Vo l . 31 , No . 1 , Apr i l 2014
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Mark Holmes is Professor in the Economics Department, University of Waikato, New Zealand.
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