Should Microcredit Be a Right for the Poor? :Credit Demand of Poor Households in Laos
Chansathith Chaleunsinh*
Koichi Fujita†
Fumiharu Mieno ‡Akihiko Ono§
1. Introduction
Microcredit is commonly thought to have enormous potential as a tool for poverty alleviation
based upon the belief that the poor always hold a large demand for microcredit. However,recent evidence proves this belief to be controversial. A survey of 1,438 households inIndonesia (2002) shows that less than a quarter of the poor households, who were considered
to be creditworthy, borrowed from any formal micro-lender in the past 3.5 years, despite thefact that almost all of the surveyed households were located reasonably close to such aprovider (Johnston and Morduch, 2007). Another survey of 17,000 microenterprises in
Ecuador found that only one out of six requested a loan in the past 12 months (Magill andMeyer, 2005). In addition, the surveys of household businesses in Ecuador, Guatemala,Nicaragua, Panama, and the Dominican Republic reviewed by Navajas and Tejerina (2006)
show that only 20 percent of those businesses applied for a loan. The phenomenon of nonborrowing poor is prevailing throughout microcredit markets and is persuasive in showing theneed to re-investigate the classical belief on credit demand for the poor.
If there is a precise confirmation that all categories of the poor can make good use of
microcredit, or microcredit has a significant impact on the poor households, the problem of
unmet demand should be an issue for Microcredit providers. However, microcredit’s impacts
have been an ongoing discussion in the literature. At first, microcredit is recognized as a tool
to raise household consumption in the case of lending to women in Bangladesh (Pitt andKhandker, 1998). Khandker (2005) suggested microcredit has the ability to help theextremely poor more than helping the moderately poor. However, by using the same data set
with Pitt and Khandker, Morduch (1999) found no impact on the consumption level of theborrowers. In 2009, a joint-study of Morduch and Roodman urged that the evidence ofprevious studies is weak as they obtained opposite results with Pitt and Khandker. In thesame year, Banerjee and his team (2009) conducted a randomized evaluation of microcredit
impact in India and also found no impact on the measure of health, education or women’s
decisions.
*

Corresponding author: Pd.D. Candidate, Graduate School of International Cooperation Studies, Kobe
University. Email address: chansathit@hotmail.com†
Professor, Center for Southeast Asian Studies, Kyoto University.
‡
Professor, Graduate School of International Cooperation Studies, Kobe University.
§
Professor, School of International Politics, Economics and Communication, Aoyama Gakuin University.

1While the researchers keep working on the Impact Studies, this paper turns to consider therole of microcredit in the livelihood of the poor. With the mentioned background, we suggestthat there are alternative factors from the demand side (the poor households) that determine
the microcredit market’s outcome, and it is necessary to have a better understanding of credit
demand of the poor. This might be a crucial explanation of why they do not borrow in spite
having access to do so.
Numbers of studies have applied the credit demand function to examine the impacts ofborrowing; however this paper precisely reviews the studies which focused on thedeterminants of credit demand. A study on credit demand and credit rationing in the informal
financial sector in Uganda (F.N. OKURUT et al., 2005) found that credit worthiness of thehouseholds is a key determinant for increasing credit demand; the credit worthiness includes
household characteristics such as age and education of household head; similar findings in
Myanmar were advocated by Tomoko Kaino (2007). Jonston & Morduch (2007) alsosuggested the stock of fixed assets and holding property legal titles determine credit demandof Indonesian households. In general, credit worthiness is applied by the common lenders as atool to ration the poor households from borrowing; it is the factor for evaluating thehousehold’s ability to pay back the loan, not what influences them to have demand for credit.
However, it should be noted that the studies were conducted under conditions of a credit
rationed market where information asymmetry prevailed and the poor households can notfreely access credit due to lacking financial credibility.
In China, microcredit demand was studied by Enjiang Cheng (2006), who locates demand for
microcredit as a determinant of microfinance outreach; after addressing the credit rationed
issue in the field survey, Cheng found that farmers tend to have a demand for credit while thepoor households with wage-income do not. Cheng advocated introducing Microfinance Plussuch as vocational training and flexible financial services for the farmers; and improving offfarm opportunities and basic infrastructure might be able to reduce credit demand and keep
local labor from migrating to other areas. On the other hand, Tomoko Kaino (2007)
conducted a study on the segmentation of the credit market in rural Myanmar. Kaino showedthat the formal lenders tend to provide credit to the farming sector while the semi-formal
lenders serve multi-sectors and then concluded that those two lenders are complementary. As
for credit demand, the estimation results in Myanmar showed a higher ratio of labor to total
household members reduces the possibility to borrow. Literature in the cases of China and
Myanmar illustrated that type of occupation and proportion of labor seemingly affects credit
demand of the poor households, while they have access to credit.

2

As this paper intends to focus on poor households, we also review the previous discussions
on the livelihood of the poor and how they manage and cope with income-risk (income andconsumption smoothing). We recognize credit as a part of the risk coping mechanism for thepoor households who reside in a less constrained credit market (see also Deaton 1991,
Alderman & Paxson 1992, Morduch 1998, Dercon 2002). In other words, this paper treats
credit as a tool for the poor to smooth their consumption and mitigate shocks. Alternatively,
we bind the credit demand into the whole mechanism of risk managing and coping, andhypothesize that microcredit probably serves as a substitute to its alternative tools in themechanism, in the condition of a less constrained credit market. In many cases, the study on
the “Credit Demand Function” ended up obtaining determinants of “Credit Supply” because
the studied areas were credit constrained or the sample households were rationed by thelenders. Thus, in this study, we pay particular attention to the “condition of the credit market”
in the studied area and as a crucial assumption to identify the accurate determinants of
microcredit demand.
This paper is uses 2007 and 2008 household survey data collected in rural Laos to explore thedeterminants of credit demand of the poor who have access to any source of credit. Our goal
is to explain the existence of non-borrowing poor, as well as to prove that the poor always
hold a large demand for microcredit.
In considering determinants of credit demand, we prove that the poor households base theirdecision to borrow on availability of the alternative tools in risk managing and coping
mechanism (Income smoothing and Consumption smoothing). Where income smoothing isrepresented by income diversification (job diversification) and income skewing (non-farm
paid job or less risky job); and consumption smoothing consists of credit, financial savings
and risk sharing networks. For the poor farmers who are assumed to be at risk of income
shock, we advocate the greater creation of non-farm paid work, larger facilities for microsavings and enhancing kin and social risk sharing networks to reduce the need for
consumption credit. It also implies that improving the credit market in rural Laos will support
households who engage in non-farm self employment. Academically, we show that only
when less constrained, the credit market will exhibit the accurate credit demand of the poor,
and in this condition our hypothesis works.
The rest of this paper is organized as follows. Section 2 develops the theoretical frameworkand empirical model. Section 3 briefly introduces Village Savings and Credit Group as aprominent microfinance model in Laos. Section 4 describes the survey data and investigates
the validity of key assumptions used in this study. Section 5 discusses the estimation resultson the determinants of credit demand for the poor households. Section 6 provides theconclusion and implications.
3

2. Theoretical Framework
2.1. Risk managing and coping mechanism
Where credit market is less-constrained, one of the primary motivations for borrowing in
agricultural society is to stabilize consumption in the face of fluctuating incomes (Udry,1994). We continue to assume the fluctuation of income is a type of idiosyncratic risk
(individual risk) for the poor farmers and they commonly apply two main strategies toprevent that risk and to mitigate its consequences: (i) Income Smoothing and (ii)
Consumption Smoothing.
Income Smoothing has been known as an ex-ante strategy (risk management), which includes
income diversification and income skewing. The poor might be able to avoid income-shockby working in multi-occupations in order to diversify income-risk or by investing andworking in low-risk occupations. While consumption smoothing is an ex-post strategy (riskcoping), it is usually employed after the income-shock occurrs. The poor farmers facing
idiosyncratic shock can mitigate this shock by using their savings, selling their own assets,
and borrowing money from their kinship and money lenders (Dercon, 2002). Combining exante and ex-post strategies together as a mechanism, we can locate microcredit as acomponent of the ex-post part. In the less-constrained credit market (where the poor haveaccess to credit), being successful in acquiring any alternative tools in the mechanism maylead the poor households to have less demand for credit.
2.2 Empirical Model
Holding assumptions of a less-constrained credit market, that the poor farmers are at risk ofbeing income-shocked and they mostly borrow for smoothing consumption, following is thefunction of credit demand which consists of the demand-side factors influencing poor
households to have a demand for credit:
DMCi= F (Xi, INCS1i-, INCS2i-, CONS1i-, CONS2i-, Vj)
DMCi stands for Demand for Microcredit represented by two proxies. In our Probit
estimation, DMCi is equal to 1 if the household ith borrowed money at least once in the past
three years and 0 if otherwise. In the Tobit estimation, DMCi is the number of times thathousehold ith borrowed from a microfinance institution during the past three years. Xi is thevector of household characteristics which includes monthly income per average number ofconsumption units (includes the value of self produced and consumed rice), the adultilliteracy rate and the ratio of labor to household members (labor or adult refers to household
members at age 15-64). INCS1i is income diversification which is represented by job
diversification (number of occupation) and INCS2i is income skewing which refers to the4low-risk non-farm occupation (non-farm wage/salary paid). CONS1i stands for consumption
smoothing1 which is the amount of quasi-financial savings per ANCU (this includes theestimated value of gold, live-stock, bank-deposits and deposits in VSCG). CONS2i orconsumption smoothing 2 is presented in the form of risk sharing networks (M. Woolcock &D. Narayan, 2000), which includes two variables: (i) the number of people in the village
expected to give them financial help when they have an emergency (bridging networks) and(ii) the dummy variable represents households which experienced financial transactions with
their kinship neighbors and friends (bonding networks). Vj is the vector of villages fixed
effects and village dummies.
In the regression, we are interested in INCS1i, INCS2i, CONS1i and CONS2i, while Xi and Vj
are used as control variables. As elaborated in section 3.1, the INCS1i, INCS2i, CONS1i and
CONS2i are expected to have negative correlation with credit demand of the poor households.
In other words, we determine whether the poor people who have access to credit (without
constraints) will have less demand for credit if they are successful in using the alternative
tools for smoothing income and consumptions (INCS1i, INCS2i, CONS1i, and CONS2i).
3. A Prominent Microfinance Model in Laos
According to a comprehensive study on Rural Finance in Laos which draws on theHousehold and Supplier Surveys conducted in 2004 (Coleman and Williams, 2006), three
Microfinance Institutes have begun to demonstrate the potential of sustainably oriented
microfinance in Laos. Those institutes are (i) Savings and Credit Unions (SCUs) and Credit
Cooperatives, (ii) Village Savings and Credit Groups (VSCGs) and (iii) Village Revolving
Funds (VRFs). The SCUs and Credit Cooperatives are operating under regulations of the
Bank of Laos, while the VSCGs and VRFs (mostly in the form of multilateral, bilateral, and
INGO projects) are typically implemented through local partners and the most frequent local
partner was Lao Women’s’ Union (LWU) followed by the Ministry of Agriculture andForestry (MAF). However, Coleman and Williams claim most microfinance initiatives
demonstrated weaknesses in financial reporting.
Based on the database of the Bank of Laos as of December 2009, legal licenses were granted
to thirty Microfinance Institutions (MFIs) for operating micro-financial activities under the
regulations of the Bank of Laos (BOL): six deposit-taking MFIs, eight non-deposit taking
MFIs, twelve SCUs and Credit Cooperatives, one Fund, two workers’ unions and a postal
savings. It reported that total licensed-institutions served 63,213 clients/members, mobilizedsavings of USD 9.73 million and disbursed loans of USD 9.95 million. In which, the SCUs
and Credit Cooperatives alone covered 9,604 members with USD 0.79 million of savings andprovided in total USD 2.19 million of loans. On the other hand, there are two bilateral5

projects under LWU, which initiated the VSCG model (since the late 1990s) which are notunder the BOL’s regulation. The VSCGs have been spreading rapidly and widely. Until June
2007, there were at least 440 VSCGs that served micro-financial services to 71,539
beneficiaries in five provinces throughout Laos. Those VSCGs mobilized savings deposits intotal of USD 6.10 million and the outstanding loans of USD 5.64 million.
The VSCG model (noticed as a credit union model) is technically supported by two bilateralprojects under the cooperation of Lao Women’s Union (LWU) and two NGOs from Thailand,
the Foundation for Integrated Agricultural and Environment (FIAM) and the Community
Organization Development Institute (CODI). The VSCGs under two projects, the Small Rural
Development Project for Women (SRDP) and the Women and Community’s Empowering
Project (WCEP), employs a similar model of Credit Union. Five to six members of LWU ineach village were elected by the members to be VSCG’s management committees, therefore,
to provide financial services to all members within the village. The management committees
are supposed to be closely monitored by advisory committees (village institutions andindigenous groups). Due to the advantages of the strong administrative and local authorities,
mass organizations, and village and township leaders, the VSCGs have been rapidly
developed.
According to the available data and in terms of wide-outreach and rapid growth, we consider
the VSCGs to be a prominent microfinance model in Laos. As an outreach type of
microfinance, VSCGs do not concentrate on a mechanism to screen-out or discriminate
clients, therefore the villagers can freely access the savings and credit services with a lower
cost, more flexible procedures and at a convenient place and time (all VSCG’s members are
considered to be eligible to borrow, except the members below age 18). We believe that theresults obtained from studying the financial behavior of households served by VSCGs will
respond to our research questions and hypothesis.
4. Survey Data
4.1. Surveyed Villages
In the empirical research, we examine household data from Laos. Two times of surveys
conducted in Fall 2007 and Summer 2008, cover 684 rural households in four villages. A
semi-structure questionnaire and interview method were used to collect information from therespondents of almost all households in each village. Our enumerators are the students fromfaculty of economics in the University of Laos who were well trained on questionnaire
structure and practiced how to conduct the interviews prior to starting the survey. As for the
sample villages, the research’s team selected the ones with Village Savings and Credit
Groups (VSCGs) residing in rural the Mekong area outside the city of Vientiane (please refer6

to the map in Annex 1). Table 1 shows the basic information of four villages: Natan,
PhonNgam, Thanasa and DonNeau. The first two villages are located in the site of SRDP;
while the latter two villages are in the area of WCEP. Geographically, Thanasa and DonNeau
are located near the Mekong River which has good conditions for agricultural production.
Among the four VSCGs, Ban DonNeau’s group seems to have the greatest performance with
USD 94,532 of total funds, average savings of USD 132.4 per account and USD 542.8 perloan respectively. This is probably because it has the longest vintage of operation. However,comparing VSCGs in Natan and Thanasa, which were established in the same year (2003),
Thanasa’s VSCG seems to perform better in outreach and financial terms. Thus, there might
be other factors than the vintage of operation influencing the performance of the VSCGs that
we should be aware of in order to avoid a biased analysis.
Table 1: Basic information of surveyed villages and their VSCGs
SRDPa

WCEPa

B.NatanB.PhonNgam

B.Thanasa

B.Donneau

Xaythany

Xaythany

SangthongPakNgum

3.5

1

2.2

2

177

179

204

159

Year of Establishment

2003

2001

2003

2000

Total funds (USD)

13,8308,042

18,51994,532

Avg. Savings per account (USD)

47.6

33.3

71.1

132.4

Avg. Loan per contract (USD)

133.4

87.5

244.8

542.8

1.1. Village information
District in Vientiane Municipality
Distance from town by motorcycle (hrs)
Population (Households)b1.2. VSCGs informationca

SRDP: Small Rural Development Project for Women, WCEP: Women and Community’s Empowering Projectb

Source: National Statistic Center (NSC), 2007c

Sources: SRDP and WCEP as of December 2007.

4.1.1. Investigating Validity of Key Assumptions
Before proceeding to the empirical section, we review whether the assumptions for thestudied areas are held. Firstly, the assumption of “agrarian society” where the poor farmers
tend to be at risk of income-shock because farm earning is seasonally fluctuated, credit might
be a part of their risk coping mechanism. However, Figures 1.a and 1.b clearly show thatmost of the households in the two areas (90-92% of total households and 91-94% of the poor
households respectively) are working in the agricultural sector. Interestingly, the households
in SRDP are more likely to be employed in non-farm sectors (besides farming). Household
data show the annual agricultural income per ANCU is USD 231 for WCEP and only USD
122 for SRDP. The lower farm earning must be a reason for households in SRDP to diversify
their occupation to non-farm sectors. Regarding income share (Figure 1.c), the households in
SRDP are unlikely to depend on farm earning (only 29% of household income), while 50% of7

household income in WCEP comes from farming. Consequently, the assumption of agrariansociety may be held only for the WCEP area.
Figure 1.a: Occupation of Households in studied Areas (% of households)
90%

92%
73%

28%

Farm

51%

48%

43%
24%

Non-farm self- Non-farm wage/ Other works andemployedsalary paid
remittance
SRDP

WCEP

Figure 1.b: Occupation of the Poor in studied Areas (% of poor households)
91%

94%
71%
51%
39%
17%

Farm

48%

15%

Non-farm selfemployedNon-farm wage/ Other works andsalary paid
remittance

SRDP

WCEP

Figure 1.c: Income share by occupation of households in studied areas
16%
16%
39%

24%

Other works andremittance

12%
14%

Non-farm wage/
salary paid

50%

Non-farm selfemployed

29%
SRDP

Farm
WCEP

8

The second assumption is a “less constrained credit market”; a market where everyone canhave access to credit at competitive prices, allows us to see credit demand and its
determinants accurately. Based on Jappelli (see Annex 2), we estimate the optimum
consumption of each household and designate them as having desire for borrowing if the
optimum consumption is higher than the available assets. The Loan-desired will then beconsidered as credit constrained if the households do not obtain any loans to finance theirfinancial needs or if the loans they were provided are less than what was supposed to be
demanded. The estimation presented in Table 2 shows 76 households in SRDP had a desire toborrow, but only 28 were able to obtain loans (3 households obtained loans from Banks, 22from VSCGs and 7 from informal lenders). We find the higher rate of estimated credit
constrained in SRDP (21.22% of total households) than the rate in WCEP (11.47%). We also
observe the sizes and prices of loans provided by the accessible lenders in the two studied
areas. Reported data in Table 3 shows the households in SRDP obtained smaller sizes ofloans at comparatively high interest rates. The monthly interest rates of 4-6% reveal theexcess demand for credit in SRDP, while it is unlikely in the WCEP area (about 2-3% permonth). Therefore, the households in SRDP are seemingly more credit constrained; in otherwords, we cannot hold the assumption of a less-constrained credit market for SRDP.
However, we then check the validity of the next assumption.
Table 2: Loan-desired and credit constrained households in two
studied areas (Pool data in each area)
SRDP
76

WCEP
49

28

33

% of the loan-desired[36.84][67.34]5.3. Constrained (Households)

73

39% of the loan-desired[96.05][79.59]% of total households[21.22][11.47]5.1. Loan-Desired (Households)
5.2. Be able to get loans from all sources

Remark: It should be noted that this calculation might beoverestimated. Some villagers may be not rationed by the VSCGs,
they are probably averse to debt (have no willing to borrow) even
they are loan-desired, in fact.

9

Table 3: Loan sizes and interest rates in studied areas (Pool data in each area)
Credit Providers
Bank

Savings Group

InformalAvg. Loan Size
(USD)

Avg. Monthly Interest
(%)

SRDP

263

3.56

WCEP

288

1.52

SRDP

85

4.8

WCEP

150

2.64

SRDP

121

6

WCEP

241

1.62

Project area

The third and important assumption is “credit for consumption smoothing”, where credit is apart of the consumption smoothing mechanism and assumed to have correlation to otheralternative tools in the mechanism (savings and risk sharing networks). We examine the main
usages of loans by households in the past three years. Table 4 displays the number ofhouseholds that borrowed from three types of lenders and the percentage of time thathouseholds spent the borrowing money for multiple usages (farming, non-farming,
consumption and medical treatment, and household expenses). The number of households
who had access to credit confirms the largest outreaches of VSCGs among the lenders, it also
appears that the poor used loans from different sources differently. In SRDP, particularly thepoor, only three households borrowed money from banks and about 67% of the time thoseloans were used for consumption and medical treatment, while the poor in WCEP mostly
used the formal loans for agricultural production. However, formal loans provided in WCEP
are possibly agricultural credit because 90% of the bank borrowers reported the lender was
the Agricultural Promotion Bank (APB). We turn to consider the usages of loans taken from
VSCGs and informal lenders, the data shows 100% of the time the borrowers made use of the
VSCG loan for both agricultural and consumption purposes. Due to the limited credit supply,
the informal lenders serve poor households in SRDP for multi-purposes, while none of thepoor in WCEP used loans from informal sources for agricultural purposes. Finally, theassumption on credit for consumption smoothing is partly held under the broad definition oflenders.

10

Table 4: Main usages of loans

Banks

Overall(Pool)

VSCGs

Informal

Banks

Poor

VSCGs

Informal

Consumption &Household expenses

Medical treatment(Education & ceremony)

22%

22%

0%

87%

1%

1%

0%

101

100%

NA

100%

35%

WCEP

185

100%

NA

100%

14%

SRDP

31

19%

10%

35%

19%

WCEP

43

12%

0%

35%

7%

SRDP

3

0%

0%

67%

0%

WCEP

20

95%

0%

0%

0%

SRDP

38

100%

NA

100%

26%

WCEP

62

100%

NA

100%

13%

SRDP

14

21%

7%

36%

14%

WCEP

20

0%

0%

55%

0%

Households

Farm

Non-farm

SRDP

9

33%

WCEP

71

SRDP

The investigation insists that the SRDP area is likely to be more credit-constrained and may
not exhibit credit demand accurately, and more dependent on non-farm earning which is far
from being at risk of idiosyncratic shock. We acknowledged that two of our assumptions are
not held in the SRDP area and it is not possible for us to fix this problem in the field survey,
we need to be aware of these facts when interpreting the empirical results for SRDP.
4.2. Studied VSCGs
In Table 5, we use household’s monthly income per average number of consumption units(ANCU) to identify the poor households based on the Lao National Poverty Line, USD 22.57
per person per month (the Prime Minister’s Decree on Poverty and Development Standard
dated October 13th, 2009). The identification shows 40% of households in our sample were
living under the Poverty Line, while Ban Natan is the poorest village, as 57% of thehouseholds were living with monthly income under USD 22.57 per ANCU. In terms of wide
outreach, VSCG in Ban DonNeau achieved the largest coverage (99% of total households). A
similar story can be applied for depth outreach when 98% of the poor in Ban DonNeau wereable to access financial services provided by VSCG. The depth outreaches are lower for the
rest of VSCGs, 68% of the poor in Thanasa, 50% in PhonNgam and 48% in Natan. In
considering the rates of borrowing, the VSCGs under SRDP seem to have lower borrowing
rates (27-34% of members) compared to the VSCGs (54-59% of members) in WCEP. In

11

terms of depth outreach, the poor members in Thanasa and DonNeau tend to borrow at higher
rates as well (58-60% of the poor members).
Table 5: Participation and borrowing in surveyed VSCGs
B.NatanB.PhonNgam

B.Thanasa

B.Donneau

Total

175

169

202

138

684

100

54

77

44

275

[57%]

[32%]

[38%]

[32%]

[40%]

23.1

42.9

41.6

50.1

38.8

- Members (Households)

83

76

157

137

453

(% of total households)

[47%]

[46%]

[78%]

[99%]

[66%]

2.1. Surveyed Householdsa- Poor (Households)

C1+D1(% of total households)b- Monthly Income per ANCU (USD)
2.2. VSCG's Wide Outreach

2.3. VSCG's Depth Outreach- The poor members (Households)

C2+D2

48

27

52

43

170

[48%]

[50%]

[68%]

[98%]

[62%]

- Borrowers (Households)

28

21

92

74

215

(% of total members)

[34%]

[27%]

[59%]

[54%]

[47%]

14

5

30

26

75

[29%]

[19%]

[58%]

[60%]

[44%]

(% of poor households)
2.4. VSCG borrowers- Poor borrowers (Households)(% of poor members)

C2

a

Poor: identified as the household below Lao National Poverty Line, 192.000 kip/person/month (USD22.57), based on
Prime Minister Decree on Poverty and Development Standard, dated October 13th 2009. (USD 1= 8505 kip as of
October 13th 2009 at BCEL bank).
b

ANCU= Average Number of Consumption Units: calculated as 1 for the first adult in the household, 0.9 for other
adults, 0.4 for children below age 7 and 0.7 for children aged 7-15. And Adult is identified as a household member at age
15-64.

4.2.1 Investigating Credit Rationing in VSCGs
The comprehensive set of household data allows us to verify whether VSCGs in two project
areas ration and discriminate in lending to their poor clients. The comparison of group means
is applied to check the differences in wealth level of the borrowers and non-borrowers, andthe indicator of wealth refers to four proxies: durables, gold, income and expenditures. In
Table 6, Mb represents the mean of wealthy proxy for borrowers and Mnb stands for the meanof the non-borrowers; we also use the simple t-test to verify the significance of differences
between the group’s means (Mb and Mnb). The comparison and testing show that borrowers in
SRDP seem to be richer than non-borrowers, and oppositely the borrowers in WCEP arepoorer. This implies the poor members of VSCGs in SRDP must be credit rationed andscreened out from borrowing. Again, the borrowing information obtained from surveyed
households in SRDP is more likely to be supply-side information which may not reveal thereal credit demand of the households. Thus, our assumption on less credit-constrained for
SRDP is confirmed to be violated and our theoretical framework may not be able to12empirically proved by using data from SRDP. However, we will use SRDP as the reference
for WCEP in our empirical section.
Table 6: Comparing group means of borrowers and non-borrowers of VSCGs(Pool data in each area)

9.1. Durables (Items/capita)

9.2. Gold (Baht/household)9.3. Income (kip/ancu/month)

9.4. Expenditure ($PPP/ancu/day)

Difference (Mb-Mnb)

P-value

SRDP

0.105

0.034

WCEP

-0.104

0.053

SRDP

2.293

0.150

WCEP

-0.376

0.022

SRDP

62,289

0.102

WCEP

-96,475

0.078

SRDP

0.172

0.097

WCEP

-0.216

0.093

4.3. Sample Households
We firstly use the National Poverty Line (mentioned in section 4.1) to obtain two different
groups: poor and non-poor in each village. Information of borrowing experiences (fromBanks, VSCGs and Informal lenders) were reported during the survey; this permits us to form
the group of non-borrowing poor, D1 and D2 in Figures 2.a and 2.b. In our empirical study,
we will utilize household data from the group of (C1+D1) to examine the credit demand
function for all sources (Banks, VSCGs and Informal lenders) and the data in the group of(C2+D2) to identify determinants of demand for credit from VSCGs.
Figure 2.a: Sample Households for Demand for Credit from All Sources (C1+D1)
Sample Village
Borrowers(Banks, VSCG & Informal lenders)

Non-Borrowers(Banks, VSCG & Informal lenders)

Non-Poor

A1

B1

Poor

C1

D1

13

Figure 2.b: Sample Households for Demand for Credit from VSCGs (C2+D2)
VSCG’s Members

VSCG’s Non-Members

Borrowers

Non-borrowers

Non-Poor

A2

B2

E2

Poor

C2

D2

F2

Since the sizes of (C1+D1) and (C2+D2) are too small (compare to the number of thevariables) to separately run the regression in each village, we borrowed the findings insections 4.1 and 4.2 to cluster two groups of samples for running our Probit and Tobit
regressions: (i) the SRDP group which consists of the poor in Ban Natan and PhonNgam, and(ii) the WCEP group that includes the poor households in Ban Thanasa and DonNeau (seemore in Annex 3). We expect to use the village dummies and village fixed effect to control
for the differences in credit demand.
5. Credit Demand of the PoorAs stated in section 3.2, the demand for credit in this study is defined in two terms: (i)decision made to borrow and (ii) the frequency of borrowing in the past three years. Whilethe lender is also defined in two terms: the lender in broad terms refers to banks, VSCGs andInformal lenders (relatives, friends, neighbors and money lenders); and the lender in narrowterms means the VSCGs. The description of variables used in the regressions will bepresented in Annex 4.
5.1. Demand for Credit in General (Lender in Broad Terms)
From the surveyed data we can identify the poor households had borrowed money frombanks, VSCGs and Informal lenders at least once in the past three years, and the number ofloans taken. Based on a broad definition of lender, we firstly estimate the Probit and Tobit
models of credit demand of the poor in SRDP and WCEP (Table 7 and Table 8). Apparently,
the estimation results seem to be significant only for WCEP. A reason for the insignificant
results of SRDP can be raised (without any surprise) that the data on borrowing we obtained
from survey in this area is not represented as the accurate credit demand from the poor
households, due to the poor households being more credit-constrained and rationed by their
VSCGs.
In Table 7, the significantly negative coefficients for variables of monthly income per ANCU
in both SRDP and WCEP reveal the common issue that the poorer households are more likely
to borrow. Similar to the findings in previous studies, adult illiteracy seems to decrease the14likelihood of borrowing, while the ratio of labor to household members is less likely to have acorrelation with credit demand. As for our interested variables, job diversification (INCS1)
appears to have an insignificantly positive sign; and the estimated magnitude of this variable
varied based on the classification of type of occupation. Oppositely, type of occupation tends
to be more relevant in determining the probability to borrow. In WCEP, the poor who are
non-farm self employed tend to borrow for financing their businesses and the ones who workin non-farm wage/salary paid sectors are less likely to have a demand for loans; this isprobably because the income is regularly paid and less risky (INCS2). The significantly
negative signs of quasi-financial savings and risk sharing networks (CONS1 and CONS2) in
WCEP indicate that a 1% increase in quasi-financial savings per ANCU will reduce theprobability to borrow by 9%, and if the risk sharing networks increase by 1 person, thelikelihood of taking a loan will decrease by 3%. As for village fixed effects, yeast making as
the common non-farm activity in Ban DonNeau appears to be insignificant in our estimation.
The results for WCEP shown in the Tobit model (Table 8) confirm similar findings found in
the Probit model, credit demand (the frequency of borrowing) is determined by quasifinancial savings and the risk sharing network of the poor household. Additionally, type ofoccupation (non-farm wage/salary paid) appears to be relevant in influencing the poor
households to have less demand on credit.

15

16

Number of obs
LR chi2(8)Prob > chi2
Pseudo R2
Log likelihood

IncomeAdult illiteracy
Labor
Job diversification
Farm
Non-farm self-employedNon-farm wage/salary paid
Other works and remittance
Quasi-financial savings
Risk sharing networks(bridging)
Weaving
Yeast making
Natan
DonNeau
0.509
0.273
0.081

-0.01
0.097
0.149

CONS2

147
13.65
0.091
0.076
-83.667

0.109

0.044

CONS1

INCS2

INCS1

[1]
Marginal
P-value-2.00E-06
0.037-0.078
0.674
0.031
0.881
0.044
0.404

*

**

SRDP

0.144

-0.007
0.143

0.124
0.119
-0.005
0.119
0.041

147
14.84
0.190
0.091
-82.3

0.092

0.654
0.21

0.403
0.582
0.955
0.128
0.134

[2]
Marginal
P-value-2.00E-06
0.06-0.073
0.687
0.088
0.695

*

*

105
20.36
0.009
0.148
-60.245

0.42

0.87

-0.047-0.104

0.022

0.016
-0.033-0.091

**

**

**

*

WCEP
[1]
Marginal
P-value-2.00E-06
0.073-0.611
0.043
0.364
0.24
0.064
0.475

Table 7: Credit demand of the poor (credit from all sources: banks, VSCGs and Informal lenders): PROBIT

-0.1

-0.16

-0.0290.181
0.322-0.254
0.099
-0.09

105
24.35
0.011
0.216
-55.41

0.48

0.554

0.041

0.326
0.052
0.096
0.398
0.019

[2]
Marginal
P-value-2.00E-06
0.173
-0.71
0.024
-0.254
0.263

**

**

*

*

**

Table 8: Credit demand of the poor
(credit from all sources: banks, VSCGs and Informal lenders): TOBIT

IncomeAdult illiteracy
Labor
Farm
Non-farm self-employedNon-farm wage/salary paid
Other works and remittance
Quasi-financial savings
Risk sharing networks(bridging)
Weaving
Yeast making
Natan
DonNeau
Constant

INCS2
CONS1
CONS2

SRDP
Coef.
P-value-9.00E-06
0.055-0.032
0.978
0.714
0.575
0.752
0.402
-0.0440.949-0.098
0.857
0.447
0.314
0.227
0.157
-0.0440.677
0.886-5.711

Number of obs
LR chi2(8)Prob > chi2
Pseudo R2
Log likelihood

*

0.628
0.317
0.077
0.027
147
16.45
0.125
0.055
-142.3WCEP
Coef.
P-value-3.00E-06
0.489-1.183
0.182
0.178
0.859
0.649
0.442
0.528
0.287
-1.1340.036-0.019
0.961
-0.26
0.032
-0.1320.023-0.0480.959-0.415
4.386

0.34
0.017

**
**
**

*
**

105
26.99
0.005
0.085
-144.89

5.2. Demand for Credit from VSCGs (Lender in Narrow Terms)
Previously we confirmed that determinants of credit demand for the poor are what we
hypothesized, except for INCS1 which may be because of taking an inappropriate proxy ofjob diversification. However, in this section we reduce the scope of the sample and definitionof lender by focusing on demand for credit from VSCGs (only) to obtain clearer findings, andusing the reduced-model identification (without INCS1).

17

18

Number of obs
LR chi2(8)Prob > chi2
Pseudo R2
Log likelihood

IncomeAdult illiteracy
Labor
Farm
Non-farm self-employedNon-farm wage/salary paid
Other works and remittance
Quasi-financial savings
Risk sharing networks(bonding)
Weaving
Yeast making
Natan
DonNeau
Constant

CONS2

CONS1

INCS2

0.447

0.085

72
6.04
0.87
0.08
-38.16

0.337
0.419

-0.1120.128

SRDP
Marginal
P-value-9.00E-07
0.456
0.282
0.445
-0.082
0.783
0.149
0.519
-0.081
0.67
0.087
0.441
0.026
0.813
-0.007
0.857

82
27.68
0
0.34
-37.12

0.706

0.908

-0.034
-0.06

0.001

-0.494

WCEP
Marginal
P-value-1.00E-06
0.44-1.101
0.068
0.696
0.119
0.388
0.132
0.452
0.008
-0.478
0.006
0.203
0.177
-0.1580.015

ProbitTable 9: Determinants of demand for VSCG’s Credit: PROBIT and TOBIT

***

**

***

***

*

-1.1290.624-0.622
0.884

72
8.27
0.69
0.06
-61.96

0.791

0.39

0.383
0.391

SRDP
Coef.
P-value-6.00E-06
0.432
2.059
0.322
-0.37
0.852
1.194
0.378
-0.314
0.783
0.322
0.697
0.132
0.846
-0.0950.737-0.2994.201-0.018-0.802

Coef.
-1.00E-06
-1.34
0.177
0.63
0.564
-1.49
-0.022-0.282

Tobit

82
28.6
0
0.13
-96.71

0.431
0.021

0.982

0.03

WCEP
P-value
0.76
0.267
0.864
0.377
0.192
0.003
0.95
0.026

**

**

**

***

The Probit and Tobit estimations in Table 9 provide confirmation on what we obtained inTable 7 and Table 8 with more significant results, that credit demand of the poor who aremembers of VSCGs in WCEP is determined by type of occupation (non-farm wage/salarypaid), level of quasi-financial savings and risk sharing networks. On the other hand, theresults for SRDP remain insignificant. In WCEP, the coefficients of VSCGs’ credit demand
are more significant and larger, especially for variables of non-farm self-employed and risk
sharing networks. As for risk sharing networks, we replaced the number of people in thevillage expected to give them financial help when they face emergency expenses (bridging
networks) by the dummy variable representing households which experienced financialtransactions with their kinship neighbor and friends (bonding networks), therefore theinterpretation for this variable will be slightly different. For example, interpreting the resultsof risk sharing networks in the Probit model, the poor households who experienced financialtransactions (lending and borrowing) with the kin networks tend to have 49% less possibilityof borrowing money from VSCGs compared to the poor who do not have any financial
connection with their kinship. In comparing marginal effects among the alternative tools of
VSCG’s credit, social networks seem to be the greatest influence among the other tools (typeof occupation and savings).
6. Conclusion and Implication
This paper has focused on the phenomena of non-borrowing poor in conditions where thepoor already have access to credit; we aim to prove the common belief that the poor hold alarge demand for credit. The determinants of credit demand were identified by setting credit
as a tool in risk managing and coping mechanism, we hypothesized that being successful inusing the alternative tools in the mechanism will induce the poor to have less demand for
credit. This framework fixed our interested households to be under three main assumptions:agrarian society, less constrained credit market and credit for consumption. Before
conducting the estimation, we investigated our survey data and found that the condition in the
SRDP area violated the assumptions of agrarian society and a less constrained credit market.
Therefore using the household data from SRDP may not be consistent with our assumptions
and hypothesis, SRDP is treated as the reference for WCEP. The estimation results confirmed
our hypothesis on determinants of credit demand is correct, as expected it is true only for
WCEP (not for SRDP). The phenomena of non-borrowing poor can be explained in the waythat the non-borrowing poor might be working in less risky sectors where income flows
regularly, they may hold larger amounts of quasi-financial savings, and, importantly, theirconnection with kin and social networks seems to be stronger and wider; therefore they canrely on these options and less likely to have a demand for credit.

19

A new theoretical figure in the current paper is the comprehensive framework of creditdemand that takes account of the behavior of poor households. For consumption credit, ourstudy confirmed the previous findings on the role of wage-income (China’s case by Cheng,2006) and household savings (Deaton, 1990) in stimulating less demand for credit, and it
contributed a bullet point to acknowledge the social capital (bonding networks among kinshipand bridging social networks) as an important factor for the poor to smooth their household
consumption. For remote areas in developing countries where opportunities for non-farmwork rarely exist and facilities for savings is underdeveloped, the strong and denseconnection among kinship and social networks in a community is the most crucial option forthe poor people to avoid taking consumption credit which is costly.
The discussion in this paper emphasizes the importance of the data treatment. We advocatethat the studies on impact and outreach for any credit programs need to pay particularattention to the socio-economic condition and environment of a credit market in the studiedarea, as long as the credit demand (households) and supply (lenders) are concerned.
In Laos, the majority of the population works in the farming sector. For Lao farmers,
consumption smoothing appears to be driven largely by microcredit, precautionary savingsand common risk sharing networks. Creating opportunities for non-farm employment,
promotion of a microfinance scheme which includes both savings and credit, andstrengthening village communities seem to be the crucial needs.

Acknowledgement:
The authors thank to Dr. Phoupet Kyophilavong and many other faculties and students at Lao
National University for the cooperation to our survey. The research was financially supported
by Heiwa Nakajima Foundation and Japan Society for the Promotion of Science(No.15730160). We would also like to appreciate the G-COE project “In Search of
Sustainable Humanosphere in Asia and Africa” in Kyoto University to provide an
opportunity to print this working paper.

20

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Cheng, E., 2006. The Demand for Micro-credit As a Determinant for Microfinance Outreach- Evidence from
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Morduch, J., 1999. The Microfinance Promise. Journal of Economic Literature. Vol. XXXVII, pp. 1569–1614.
Navajas, S. and Tejerina, L., 2006. Microfinance in Latin America and the Caribbean: How Large Is theMarket?. Inter-American Development Bank. Washington, D.C.
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Bangladesh: Does the Gender of Participants Matter?. Journal of Political Economy. vol. 106, no. 5.
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Udry, C., 1994. Risk and Insurance in a Rural Credit Market: An Empirical Investigation in Northern Nigeria.
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22

23

ANNEXESAnnex 2
Jappelli (1990)
Credit constrained: C*-Y-A*(1+r)>D
C*=optimum household consumption, Y= household income, A= non-human wealth, r= realinterest rate, D= loan offered from all sources in the past one year.

Annex 3: Grouping sample of interest

C1+D1
C2+D2

Natan
100
48

SRDP
PhonNgam
54
27

Total
154
75

24

Thanasa
77
52

WCEP
DonNeau
44
43

Total
121
95

25

154
154
75
75
154
154
154
154
154
154
154
154
154
154
154
154
154
154
154

Dummy of households had borrowed from Banks VSCGs and Informal
lenders in the past three years

Number of loans households had borrowed from Banks VSCGs andInformal lenders in the past three years

Dummy of VSCGs' members who had borrowed from VSCGs in the past
three years

Number of loans VSCGs' members had borrowed from VSCGs in the past
three years

Monthly income per average number of consumption units (USD)

Adult illiteracy rate (ratio to all adult in household)Ratio of labor to total household members

Number of type of occupation

Dummy

Dummy

Dummy

Dummy

Quasi-financial savings per average number of consumption unit (USD)

Dummy variable represents household which experienced financialtransaction with their kinship

Number of people in the village expected to give them financial help when
they have an in emergency

Dummy

Dummy

Dummy

Dummy

General credit demand (Tobit)

VSCGs credit demand (Probit)

VSCGs credit demand (Tobit)

IncomeEducation

Labor

Job diversification

Farm

Non-farm self-employedNon-farm wage/salary paid

Other works and remittance

Quasi-financial savings

Risk sharing networks (bonding)

Risk sharing networks (bridging)

Yeast making

Weaving

Natan

DonNeau

Obs

General credit demand (Probit)

SRDP (Poor’s group)

Annex 4: Variable description

0

0.649

0.649

0

2.253

0

0.479

0.479

0

2.586

0.497

272

247
0.435

0.502

0.489

0.453

0.288

0.842

0.212

0.506

0.390

0.714

0.909

2.519

0.764

0.203

5.9

10.4
0.129

0.799

0.438

0.872

0.462

Std. Dev.

0.360

0.253

0.474

0.305

Mean

0

0

0

0

0

0

0

0

0

0

0

0

0.286

0

0.6

0

0

0

0

Min

0

1

1

0

20

1

1387

1

1

1

1

4

1

1

22.5

4

1

5

1

Max

26

121

Dummy of households had borrowed from Banks VSCGs and Informal
lenders in the past three years

Number of loans households had borrowed from Banks VSCGs andInformal lenders in the past three years

Dummy of VSCGs' members who had borrowed from VSCGs in the past
three years

Number of loans VSCGs' members had borrowed from VSCGs in thepast three years

Monthly income per average number of consumption units (USD)

Adult illiteracy rate (ratio to all adult in household)Ratio of labor to total household members

Number of type of occupation

Dummy

Dummy

Dummy

Dummy

Quasi-financial savings per average number of consumption unit (USD)

Dummy variable represents household which experienced financialtransaction with their kinship (bonding network)

Number of people in the village expected to give them financial help
when they have an in emergency (bridging network)

Dummy

Dummy

Dummy

Dummy

General credit demand (Probit)

General credit demand (Tobit)

VSCGs credit demand (Probit)

VSCGs credit demand (Tobit)

IncomeEducation

Labor

Job diversification

Farm

Non-farm self-employedNon-farm wage/salary paid

Other works and remittance

Quasi-financial savings

Risk sharing networks (2)Risk sharing networks (1)Yeast making

Weaving

Natan

DonNeau

121

121

121

121

121

121

121

121

121

121

121

121

121

121

121

95

95

121

Obs

WCEP: (Poor’s group)

0.364

0

0.017

0.033

3.273

0.483

0

0.128

0.180

3.483

0.451

246

192
0.281

0.502

0.357

0.373

0.234

0.602

0.188

0.479

0.149

0.165

0.942

1.736

0.740

0.211

5.8

13.1
0.095

0.945

0.495

1.185

0.487

Std. Dev.

0.821

0.589

1.058

0.620

Mean

0

0

0

0

0

0

0

0

0

0

0

1

0.333

0

2.1

0

0

0

0

Min

1

0

1

1

20

1

1122

1

1

1

1

3

1

1

22.1

6

1

8

1

Max