OSIPP Discussion Paper : DP-2009-E-002

Who borrows among the poor? MC use determinants in
Guatemala, with particular reference to social ties

January 14, 2009
Masako Hasegawa

Ph.D. student, Osaka School of International Public Policy (OSIPP)【Keywords】Micro Credit (MC), social ties, information asymmetry,creditworthiness, Guatemala,【JEL code】C25, D14, Q14【Abstract】Who borrows Micro Credit (MC) is the information needed to improve not only
MC programs but also policies of poverty alleviation. This paper analyzes the MC use
determinants taking social ties into account using data in Guatemalan Living Standard
Measurement Study. It shows that the determinants are relatively different between thepoor and the non-poor. It also shows that social ties could raise the possibility of MC
utilization especially among the poor, which would verify the function of social ties as
collateral of creditworthiness. Those excluded from MC may not be the poorest but thesocially weakest.

Address: 1-31 Machikaneyama, Toyonaka, Osaka 560-0043 Japan, Tel /Fax: +81-6-6850-5839,
E-mail address: m-hasegawa@osipp.osaka-u.ac.jp

I thank Professor Naoto Yamauchi, Osaka

school of International Public Policy (OSIPP), Dr. Tsunehiro Ootsuki, associate professor of
OSIPP, and Professor Akira Kousaka, OSIPP, for their help and encouragement during the
preparation of this paper. I also thank Prof. Naoto Yamauchi for permitting me to publish
this paper.

Who borrows among the poor? MC use determinants in
Guatemala, with particular reference to social ties
Masako Hasegawa
OSIPP, Osaka University
January 14, 2009

Abstract
Who borrows Micro Credit (MC) is the information needed to improve not only
MC programs but also policies of poverty alleviation. This paper analyzes the
MC use determinants taking social ties into account using data in Guatemalan
Living Standard Measurement Study. It shows that the determinants are
relatively different between the poor and the non-poor. It also shows that social
ties could raise the possibility of MC utilization especially among the poor, which
would verify the function of social ties as collateral of creditworthiness. Those
excluded from MC may not be the poorest but the socially weakest.

JEL Classification Codes: C25, D14, Q14
Key Words: Micro Credit (MC), social ties, information asymmetry, creditworthiness,
GuatemalaIntroduction
Access to credit is an essential measure for poverty alleviation in developing countries when
the credit is used effectively for income generation activities or risk coping strategies.
Nevertheless, risky borrowers basically are refused unless adverse selection is introduced
under extremely high interest rates. Therefore, the poorest so far have been recognized as
those excluded from credit because of their economic riskiness (Hulme and Mosley, 1996;
Van Bastelaer, 2000) 1. Then to what extent does credit for poverty alleviation such as Micro
Credit (MC) actually reach the poor as a whole? Is the widespread notion that MC favors thewealthy poor really true? In this paper, who can access MC, namely the determinants of MC
use, is investigated empirically, with MC defined as a small credit by financial institutions
mainly for the poor 2. Understanding attributes of MC borrowers could lead not only to better
designs of financial programs but also more efficient public policies enabling them.
In rural credit markets, there are two types of mechanisms to screen the trustworthy: direct
mechanisms and indirect mechanisms (Hoff and Stiglitz, 1990). The direct mechanisms
obviously rely on social ties between borrowers and lenders to overcome the information
asymmetry among them. Moreover, in rural communities that often are segmented sociallyand geographically, information is circulated all the time and tends to be shared among
almost all villagers. Under such circumstances, borrowers’ information easily reaches
lenders’ ears, besides the direct information from the close relationships between borrowers
and lenders. Furthermore, if contracts are repeated in such close-knit societies, the problem
of information asymmetry is expected to be reduced greatly. In the indirect mechanisms,
group-lending also depends theoretically on social ties (Ghatak, 1999, 2000). According toGhatak (1999), in the process of group formation of group-lending, information provided bysocial ties is utilized to select group members under the pressure of joint liability.
Thus, in both direct and indirect mechanisms in rural credit markets, social ties are the

Hulme and Mosley (1996) organize their ideas from several case studies which are
principally on income generating credits using group lending method. Van Bastelaer (2000)
refers to this idea in the case of joint-liability groups as well.
2 Following the definition of Christen et al. (2004), MC is provided by such institutions as
state-owned or agricultural banks, cooperatives, specialized microfinance institutions andnon-governmental organizations, and can be distinguished from other credits provided byindividuals.
11basis for guaranteeing credit through abundant local information (Van Bastelaer, 2000). For
the poor without collateral, social ties should function just as the substitution of collateral toguarantee their trustworthiness. Consequently, possessing abundant social ties should be an
important attribute for the poor MC borrowers. However, the social ties have not necessarily
been valued as MC determinants up to now.
At first, credit determinants were treated as control variables to analyze the impact of MC
because the borrowers’ attributes affect both selection of borrowers and impact on borrowers’
welfare (Pitt and Khandker, 1998; Coleman, 1999). In both Pitt and Khandker (1998) andColeman (1999), variables related to social ties were not considered as factors of program
participation at all. As Coleman (1999) suggests, the differences in MC access determinants
on different classes logically should affect its impact. Therefore, if only the poor clients makemost of the social ties as their credit guarantees, variables related to social ties should be
included in program participation analysis, which could change the impact analysis as aresult.
Many researchers have studied the relationships between social ties and repayment rate ingroup lending (Besley and Coate, 1995; Ghatak, 2000; Ahlin and Townsend, 2007). Most ofthem divide the function of social ties into two or three parts, which are ex-ante (selection)and ex-post (monitoring and enforcement), then analyze which function could have thegreatest effect on the repayment rate. Wydick (1999) claims the effect of peer monitoring onhigh repayment rate from his empirical analysis of group lending data in Guatemala.
Although he examines fully the role of social ties in MC 3, the selection bias is not consideredin his repayment analysis. Meanwhile, Karlan (2005) succeeds in excluding this bias andverifies that social ties affect higher repayment rates by analyzing group lending data in
Peru 4. He explains this effect by the social ties’ function that is gathering information andincreasing clients’ trustworthiness. He also indicates that cultural similarity and geographic
concentration improve group lending outcomes for the same reason. In his analysis, however,

Wydick (1999) uses the term “social cohesion” instead of social ties and defines “social ties”
as closeness or homogeneity among each group in the social cohesion.
4 Karlan (2007) uses the term “social connections”, which is defined as “the links andcommonalities that bind a group of people together and determine their social interactions.”
He regards this “social connections” as a broader form of “social capital.”
32

economic variables such as income or assets of households are not included in the estimation
model. Supposing that social ties could be necessary, especially for poor MC borrowers, it
may be important to take economic factors into account even as control variables.
Recently there have been a few papers suggesting that ethnic ties affect access to credit.
Biggs et al. (2002) have found that ethnicity facilitates access to informal trade credit byanalyzing data in Kenya. They explain its mechanisms by channeling abundant information
and reputation functioning as enforcement among ethnic networks. Fisman (2003) also
shows an effect of ethnic ties on the provisions of trade credit in four African countries. He
suggests that discrimination against trade partners with different ethnic background is areason for preferential credit provision, besides more general rationalization that ethnic ties
may channel the information about members’ creditworthiness.
Credit provision in the case of MC, namely “MC outreach” depends basically on targeting
policies of each MC institution (Ledgerwood, 1999). While many MC institutions haveattempted to target the poor as their clients, some groups of poor people with particularattributes tend to be excluded. Navajas et al. (2000) argue that MC borrowers are not thepoorest but rather those near the poverty line. Relying on Basic Human Needs 5 as the basis
for poverty measurement, they proved that not the economically poor but the severelysocially deprived people were excluded in the MC programs in Bolivia. On the other hand,
Evans et al. (1999) show in their study of an MC program of Bangladesh Rural
Advancement Committee (BRAC) that the poorer the households are, the more likely they
are to have participated in the program. They also suggest that lack of female education,
small household size, and landlessness might reduce the possibility of MC access. While
those three factors surely related to borrowers’ creditworthiness, other important factors areomitted in their analysis if social ties are the key to guarantees for borrowers’
creditworthiness, especially among the poor. Zeller (1994) has found that social ties did
affect application for informal credit in Madagascar, even though they were not too
significant so far as rationing of formal and informal credit was concerned 6. Recently, Amin
5 Navajas et al. (1999) used the Index of Fulfillment of Basic Needs (IFBN) computed by theindicators of housing, access to public services, education, and access to health services.
6 In Zeller (1994), having ancestor’s burial places in the region is recognized as the variableof social ties.

3

et al. (2003) have brought up the idea of vulnerability in analysis of MC access, anddemonstrated that MC was successful in reaching the poor, but unsuccessful in reaching thevulnerable poor by using a panel data in Bangladesh. Thus, as Honohan (2005) says, “thereis less agreement on a framework for analyzing the determinants of access for low income
households.”
The aim of this paper is to analyze of the MC use determinants, taking social ties into
account using data from the Living Standard Measurement Study (LSMS) in Guatemala 7.
Analysis of the MC use determinants could explain access barriers from the borrowers’ side
and contribute to the improvement of MC programs, as well. In addition, suggesting theimportant determinants in deferent classes could influence the impact analysis as thecontrol variables that affect both credit access and the impact.
The rest of the paper is organized as follows. Section two describes the data used mainly inthis paper. Section three explains the econometric framework and its variables in particularsocial ties. Section four presents and interprets the results in light of the guarantee ofcreditworthiness among the poor. Section five concludes and adds policy implications.

Data
The principal data source for this study is the 2000 Guatemalan Living Standards
Measurement Survey (Encuesta Nacional Sobre Condiciones de Vida, hereafter ENCOVI
2000) which was carried out by the National Statistical Institute (INE) in Guatemala as an
LSMS of the World Bank. The ENCOVI 2000 features a module of social capitalquestionnaires besides usual questionnaires, such as those about loan and deposit, whichmakes it possible to study the relationship between MC and social ties, including social
capital. In ENCOVI 2000, 7,276 households were sampled as representative of eight regions
and urban/rural areas in Guatemala based on the 1999 census. In addition, each household
was labeled as “extremely poor,” “poor,” or “non-poor,” according to its income in the data
released on the World Bank’s website 8 . This labeling suggests that 45.9 percent of
It can be obtained from the World Bank’s web site:
http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/EXTLSMS/0,,c
ontentMDK:21387262~pagePK:64168445~piPK:64168309~theSitePK:3358997,00.html.
8 Extremely poor: their annual income is below the annual cost of the food basket, which
74

households are poor or extremely poor in Guatemala and about 75 percent of all those poor
live in rural areas (referring to Table 1). The north and the northwest regions seem to havesevere poverty problems from the regional point of view.
[Insert Table 1]
In Guatemala which is famous for its Mayan ruins, more than half of the population isindigenous people coming mainly from the Mayan line. However, a small number of Spanish
whites have been dominant both politically and economically, and so-called Latinos who area mixture of the white and the indigenous comprise a middle ruling class. The poor northand northwest regions are the resident places for indigenous people; therefore, the regional
distribution of the poor may reflect the social class structure in Guatemala.
As for credits, the penetration rate of MC in Guatemala takes fourth place in the world frommy calculation, using cross-country data compiled by Christen et al. (2004) 9. In ENCOVI
2000, the ratio of credit borrowers, including informal credit, is 13.7 percent of the
population (referring to Table 2). Interestingly, the borrowers’ ratio among the extremely
poor is roughly the same as that in the poor. Geographically, there is not a big gap between
urban and rural areas; if anything, regional gaps from Metropolitan or Central districts toNortheast are relatively apparent. Since households and communities in Guatemala havevarious kinds of risk factors, such as natural disasters and fluctuating terms of trade forcommercial agricultural products (Tesliuc and Lindert, 2002), it may be necessary to use
credits in order to smooth consumption, regardless of poverty, areas, and geographicalregions.
[Insert Table 2]
Although credit outreach is similar among households in different economic situations, theirborrowing institutions are quite different (referring to Table 3). From Table 3, such informal
lenders as friends, families, and individuals can be recognized as major credit resources forsatisfies the minimum daily intake of calories per capita (Quetzal 1912 ≑ $ 251.9). Poor:
their annual income is below the annual cost necessary for the daily lives including the foodexpenses at the extremely poor level (Quetzal 4319 ≑ $ 569). Non-poor: their income is above
the annual living cost at the poor level.
9 The penetration rate here is the proportion of the number of all MC accounts in a country
to the population of the country. The first place is taken by Sri Lanka (17.84%), followed byIndonesia (13.74%), and Bangladesh (13.22%). Guatemala’s penetration rate is 8.55 percent.
5the poor and the extremely poor, which accounts for more than half of their total credits. As
MC institutions, cooperatives can be said to penetrate the poor and the extremely poor. In
addition, a mission-oriented private bank named Banrural 10 opens its door for the poor,
which is reflected in this table.
[Insert Table 3]
Among the financial institutions shown in Table 3, MC institutions are defined as privatebanks, state banks, cooperatives, associations of producers, financiers, and NGOs. Informal
lenders, such as individuals and friends/family, should not be regarded as MC institutions.
In the early years, MC was recognized as credits for small businesses. The poor in
Guatemala use MC mainly for small businesses especially for agricultural activities asshown in ENCOVI 2000, although there are some occasions in which MC is used for
consumption smoothing purposes such as emergencies or medical expenses (referring toTable 4). On the other hand, the non-poor tend to use formal credits for household purposes
especially for loans for construction and reconstruction of houses.
[Insert Table 4]With regard to guarantees, while 60 percent of the non-poor can borrow formal credit withno guarantees or only signatures on contracting, almost half of the poor are required to put
their lands as guarantees. From this data, owning land is thought to be one of MC
determinants for the poor.
According to a cross-tabulation between borrowers’ jobs and lending organizations in
ENCOVI 2000, both self-employed workers or farmers and private companies’ employees
seem to use various organizations, including informal lenders (referring to Table 5-1).
Meanwhile, government employees and employers rely on banks and cooperatives. Most day
According to Alfaro-Gramajo (2003), Banrural was established in 1998 as the reorganizedinstitution of Banco Nacional de Desarrollo Agricola (BANDESA), which was the state
agricultural development bank. Banrural has held the mission that it tries to develop
Guatemalan rural areas through the universal financial services. 70 percent of capital was
private and the rest was derived from public sector. Its sales performance has been good. For
example, its profits doubled from 1997 to 2001 and it took third place in terms of
profitability (ROE) among all Guatemalan banks in 2001. Moreover, as for outreach, thenumber of branches in rural areas has increased three times and the number of accounts
has enlarged more than thirty-five times in the years between 1997 and 2001. While moststate-owned development banks have diminished in importance since the 1980s, the key tosuccess of Banrural, according to Alfaro-Gramajo (2003), was that private citizens andcitizens’ groups participate in the capital formation and the management.
10

6

workers or unskilled workers have to borrow from informal lenders. Then, narrowing theobjects down to the poor borrowers only (referring to Table 5-2), it becomes clear that privatecompanies’ employees also depend chiefly on the informal lenders. On the other hand, thepoor self-employed workers or farmers use both formal and informal institutions. They may
have assets or social ties guaranteeing their creditworthiness to borrow from formal MC
institutions.
[Insert Table 5-1 and 5-2]
Paying attention to the relationship between credits’ purposes and borrowers’ jobs, it isobvious that the majority of credits for agricultural purposes are used by self-employedworkers or farmers (referring to Table 6-1). Narrowing the objects down to only the poor, thistrend is almost the same (referring to Table 6-2). The day workers or unskilled workers andthe employers also use credits for small business purposes (referring to Table 6-1). Thistendency is relatively stronger among the poor borrowers (referring to Table 6-2). In Table
6-1, the employees of both private companies and government are likely to use loans forhousehold purposes, such as emergencies or medical expenses, and purchasing otherhousehold items. From both Table 5-2 and Table 6-2, MC is used chiefly by the self employed
for small business purposes especially for the agriculture.
[Insert Table 6-1 and 6-2]

Model and variables
To explore the determinants of MC use in Guatemala, I analyze data in ENCOVI 2000
according to an econometric model below.
yi = α ＋ Xiβ1 ＋ Giβ2 ＋SCiβ3 + ui 11

11 Maximum likelihood function and logarithmic maximum likelihood function of this model
are as follows:( β1 , β 2 , β 3 | X i , Gi , SCi , yi ) =Π in=1 (1 − Φ ( X i β i ))1− yi Φ ( X i β i ) yi • Π in=1 (1 − Φ (Gi β 2 ))1− yi Φ (Gi β 2 ) yi • Π in=1 (1 − Φ ( SCi β 3 ))1− yi Φ ( SCi β 3 ) yiln L( β1 , β 2 , β 3 | X i , Gi , SCi , yi ) =∑ {(1 − y ) ln(1 − Φ( X β )) + y ln Φ( X β )} + ∑ {(1 − y ) ln(1 − Φ(G β )) + y ln Φ(G β )} +∑ {(1 − y ) ln(1 − Φ(SC β )) + y ln Φ(SC β )}
n

n

i =1
n

i

i =1

i

i

i

1

i

3

i

i

i =1

1
i

3

7

i

i

2

i

i

2

The dependent variable (yi) would be one if at least one family member of a household (i)borrows from MC institutions in the last twelve months. If no one uses MC in the household,(yi) would be zero. On this binary response variable, I estimate the households’ properties
which raise the possibilities of MC use applying probit model. Poor borrowers and non-poorborrowers are estimated separately because their security mechanisms could be different, as
mentioned above. The explanatory variable Xi means a set of characteristics of household i,
Gi means a set of geographical factors, and SCi means a set of household social capital
properties. The ui means error term.
Considering households without access to MC institutions, the probit model with selection
mechanisms which is called Heckman probit model also was tried to be estimated 12. But the12

The econometric model and the maximum likelihood function of Heckman probit model

are as follows:

yi: use MC or not in a household(i) (use: 1/no: 0)
xi: set of household(i)’s properties including geographical factors, household’s headcharacteristics, and household social capital properties
zi: access to MC institutions for a household(i) (access: 1/no: 0)
The set of variables in xi*is not the same as set in xi**.
Maximum likelihood function

L( β , γ , σ , ρ | xi , z i , y i ) = Π{z i = 0}P( z i ) ⋅ Π{z i = 1}P( y i , z i = 1)

= Π{z i = 0}P(z i ) ⋅ Π{z i = 1}P( y i )P(z i = 1 | y i )Logarithmic maximum likelihood functionln L(β , γ , σ , ρ | xi , z i , y i ) = ∑ ln P(z i ) + ∑ ln P(z i = 1 | y i ) + ∑ ln P( y i )zi = 0

zi =1

8

zi =1

assumption that the coefficient of correlation between error term of probit model and that of
the selection equation is zero could not be rejected(referring to Table 11 and Table 12 inappendix). Therefore, it is judged that the sample selection probit model would not beappropriate in this case.
Social capital is thought to be a crucial concept to understand people and societies indeveloping countries. It is thought to be the basis of acceptance for development aids. While
the social capital recently has attracted a lot of attention from both researchers andpractitioners, the concept itself has been recognized for a long time and may be suggested
through such terms as social ties, capacity building, network, and so on. In this paper, social
capital is defined as “the institutions, relationships, and norms that shape the quality andquantity of a society's social interactions” 13 based on the World Bank’s definition in
ENCOVI 2000. Variables of social capital in ENCOVI 2000 consist mainly of membership oforganizations and participation of collective actions. Those social capital variables could beinterpreted as the index of social ties that guarantees the poor against the risk of default.
Nowadays, membership of an organization generally is used for measuring social capital. On
the contrary, measuring social capital by participation of collective action might beparticularly relevant to developing countries because organizations themselves are notwidespread in those countries, especially in rural areas (Ibanez et al., 2002). In more detail,participating collective actions are divided into two groups according to their mainfunctions; bridging and linking collective actions. Bridging collective actions include
construction of community infrastructure, donations, and labor agreement, which could be
defined as putting importance on close connection inside groups or communities. In contrast,
linking collective actions include contacting government officials, appealing to judicial
authorities, and volunteering in election campaigns, which could be characterized by thetendency of linking outside groups or communities. Which types of social capital arerecognized as guarantees of trustworthiness may depict the situation of social ties in
Guatemala. With regard to membership of organizations, in EVCOVI 2000 all the members
in a household over 12 years are asked to provide up to three names of organizations in
13 Web site of the World Bank:
http://www1.worldbank.org/prem/poverty/scapital/whatsc.htm

9which they participate and to report the degree of their eagerness for organizations’activities. Then, based on the questions a dummy variable of the membership is made,which shows whether at least one family member belongs to at least one group or not. In
addition, the variables of both the total number of memberships in a household and degreeof eagerness for membership organizations are made up. Although these variables do notconsider family size, they are expected to suggest the abundance of social capital in ahousehold. Descriptive statistics of poor and non-poor households' variables are referred toTable 9 and Table10 in appendix.

Results and Interpretations
Table 7 shows the result of the estimation that calculates fixed effects of a household’s
properties, raising the possibility of using MC among the poor.
[Insert Table 7]
In model 1, which includes frequency of participation in social activities and a membershipdummy of social group as social capital variables, both the variables are positively
significant. It is demonstrated empirically that the richness of a household’s social capital
could raise the possibility of MC use among the poor. Therefore, the social ties can be said tofunction as one of the guarantees of trustworthiness or a factor that overcomes information
asymmetries. Moreover, the significance of an ethnic dummy could endorse the importanceof the social ties for the poor MC use in Guatemala. According to Ibanez et al. (2002),
Guatemalan indigenous communities have enjoyed traditional social norms and a strong
sense of solidarity. In those close-knit societies, credit unions or mutual credit groups haveflourished and MCs have been used frequently under the circumstances in which
information asymmetries have been overcome and mutual guarantees of creditworthinessestablished. On the contrary, a household’s consumption level is not significant, whichsuggests that the household’s economic situation measured by consumption does not affectthe MC use possibility among the poor. Since the consumption level in the non-poor
estimation shows positively significance for MC use possibilities (referring to Table 8), it
could be a distinctive trait for the poor that the degree of the poor by consumption is not a
determinant of MC use empirically. Thus, the widespread notion that the poorest are
10

excluded from MC use clearly can be negated here.
[Insert Table 8]Comparing the results of the poor and the non-poor, the determinants of MC use areconsiderably different. Although the influence of holding land assets and savings aresuggested in both consumption classes, social capital properties are more important among
the poor than among the non-poor. Ethnicity, marital status, and mother tongue are thedeterminants of MC use only for the poor, whereas gender, education level, and trust forcommunity are only for the non-poor. Those factors may function to raise thecreditworthiness in each consumption class, respectively. Family size, as Evans et al. (1999)
pointed out, performs as a factor of the creditworthiness in both classes because having morefamily members possibly leads to earning more money when being at risk under their
repayment. Conversely, small households or single households could face some disadvantage
in their need of borrowing. Therefore, it can be said that the socially weakest people areexcluded from MC in Guatemala, considering the advantage of abundant social ties anddisadvantage of single households for MC use.
Model 2 includes more detailed variables of social capital properties than those in Model 1.
Among the poor in Model 2 (referring to Table 7), bridging collective activities is significant
for raising possibilities of MC use, but linking collective activities is not significant incontrast. This may suggest that intra-community social ties are more important than social
ties connected to particular authorities outside the community to get trustworthiness andinformation in their communities. In the non-poor, only the eagerness for their membershipgroups is significant, which might suggest the high social status of the non-poor borrowers.
Because there are heteroscedasticities in both probit estimations, White’s robust varianceestimator is used for the statistical judgment. The model specification is admitted by theWald test in both poor and non-poor estimations. McFadden’s adjusted R2 index is 0.108 in
the poor estimation and 0.091 in the non-poor.

Conclusion
Who uses MC is analyzed using Guatemalan LSMS data in this paper. As a result, thedeterminants are considerably different between the poor and the non-poor. In the poor,
11social ties are an important factor to raise the possibility of MC use rather than economic
situation measured by consumption, which verifies the theory that the social ties should
function as collateral for MC borrowing. Thus, the widespread notion that the poorest are
excluded from MC use should be replaced by this finding that the socially weakest are
excluded from MC. In addition, the finding that the MC use determinants might differ indifferent classes could improve the impact analysis of MC because MC use determinants arenecessary as control variables in impact analysis of MC.
This empirical analysis could lead to some policy implications. First, it should be recognizedthat social ties are an important factor for MC programs. Therefore, it may be effective tochoose program sites from this social-tie-oriented view. To generate social ties combined withMC programs should be no less effective. When social ties are an important concept formaking policy, however, it should be taken into account that social ties possibly could belong
to communities as well as households. Narayan and Pritchett (1999) suggest that villages’social capital influences households’ income through credit, public services, and so on.
Although this paper treats only social ties belonging to household under the constraint of
the data, the impact of the community’s social ties should be considered even in the analysisof the household’s social ties, which should be explored hereafter.
Second, it should be recognized that those excluded from MC are not the poorest but thesocially weakest. Thus, MC programs should be designed to target the weakest or to provide
other poverty alleviation programs. Ibanez et al. (2002) suggested that higher educationlevels could improve social capital in Guatemala. Thus, the possibilities of MC use may beraised through the channel of social ties by education programs for the socially weakest.
In this paper, MC institutions are treated generally, regardless of their programs’ design ormanagement forms. As Karlen (2007) suggests, what kind of programs or institutions reachwhat kind of clients should be investigated in order to improve programs in this field. The
extent to which those common determinants for all MC institutions and all MC programs in
Guatemala can be generalized has to be studied from now on.

12

Tables
Table 1

Table 1 : Poverty Profile of Households in ENCOVI 2000
Total Households

Extremely poor

Poor

Non-poor

7276 (100.0)

768 (10.56)

2571 (35.34)

3937 (54.11)

Rural
Urban
Regions
Metropolitan
North
North East
South East
Central3852 (52.94)

671 (17.42)

1844 (47.87)

1337 (34.71)

3424 (47.06)

97 (2.83)

727 (21.23)

2600 (75.93)

Total Households
Rural/Urban

926 (12.73)

4 (0.43)

82 (8.86)

840 (90.71)

798 (10.97)

161 (20.18)

318 (39.85)

319 (39.97)
427 (71.29)

599 (8.23)

19 (3.17)

153 (25.54)805 (11.06)

86 (10.68)

309 (38.39)

410 (50.93)

1251 (17.19)

78 (6.24)

469 (37.49)

704 (56.27)

North West1197 (16.45)

254 (21.22)

558 (46.62)

385 (32.16)

South WestPeten

1115 (15.32)

125 (11.21)

431 (38.65)

585 (8.04)
41 (7.01)
Source : ENCOVI 2000. Instituto Nacional de Estadistica (INE) Guatemala

251 (42.91)

559 (50.13)
293 (50.09)
frequency (row percentage)

Table 2

Table 2 : Loan Penetration by Poverty, Area, and Regions
whether you have borrowed or repayed in the last 12 months

using credit

no credit

Total Households

Total Households

995 (13.68)

6281 (86.32)

7276 (100.00)

Poverty
Extremely poor
Poor

92 (11.98)

676 (88.02)

768 (100.00)

288 (11.20)

2283 (88.80)

2571 (100.00)

Non-poor

615 (15.62)

3322 (84.38)

3937 (100.00)

Rural/Urban
Rural

490 (12.72)

3362 (87.28)

3852 (100.00)

Urban

505 (14.75)

2919 (85.25)

3424 (100.00)

163 (17.60)

763 (82.40)

926 (100.00)

96 (12.03)

702 (87.97)

798 (100.00)

62 (10.35)

537 (89.65)

599 (100.00)

89 (11.06)

716 (88.94)

805 (100.00)

192 (15.35)

1059 (84.65)

1251 (100.00)

171 (14.29)

1026 (85.71)

1197 (100.00)

153 (13.72)
69 (11.79)

962 (86.28)
516 (88.21)

Regions
Metropolitan
North
North East
South East
CentralNorth WestSouth WestPeten

1115 (100.00)
585 (100.00)
frequency (row percentage)
Made by the authour based on ENCOVI 2000i

Table 3

Tabel 3 ： Financial Institutions and Informal Lenders Providing Credits
total
private banks
State banks
cooperatives
association of producers
financiers
NGOs
individuals
credit cardfriends,families,etc
other what
total

extremely poor

poor

no poor244 (21.67)

5 (5.21)40 (12.58)

199 (27.95)

87 (7.73)
165 (14.65)

7 (7.29)
14 (14.58)

20 (6.29)
37 (11.64)

60 (8.43)
114 (16.01)

8 (0.71)
17 (1.51)

1 (1.04)
1 (1.04)

5 (1.57)
7 (2.20)

2 (0.28)
9 (1.26)

54 (4.80)
155 (13.77)

9 (9.38)
25 (26.04)

22 (6.92)
54 (16.98)

23 (3.23)
76 (10.67)

1 (0.09)304 (27.00)
91 (8.08)1126 (100)0 (0)32 (33.33)
2 (2.08)
96 (8.53)

0 (0)1 (0.14)108 (33.96)
164 (23.03)
25 (7.86)
64 (8.99)
318 (28.24)
712 (63.23)
frequency (row percentage)
made by the autour based on ENCOVI 2000

Table 4

Table 4 : Purposeｓ of MC by Poverty Level (main purposes of each section)
total
extremely poor
poor
no poorsmall business purposesagri purposes200(34.72)
25(67.57)
68(51.91)
58(14.22)
raw materials or inputs
98(17.01)
19(51.35)
53(40.46)
26(6.37)
other agri activities
49(8.51)7(18.92)
24(18.32)
18(4.41)
animals22(3.82)3(8.11)4(3.05)15(3.68)
business purposes
115(19.97)2(5.4)
22(16.79)
84(20.59)
articles for business40(6.94)
1(2.7)
11(8.4)28(6.85)
raw materials or inputs
28(4.86)0(0.00)4(3.05)24(5.88)
other business activities
17(2.95)
1(2.7)
5(3.82)11(2.70)
consumption smoothing purposes
household purposes261(45.31)
3(8.11)17(12.98)
241(59.07)
construct houses
65(11.28)0(0.00)2(1.53)63(15.44)
other household items
44(7.64)0(0.00)4(3.05)40(9.80)
payment of debt
42(7.29)2(5.41)3(2.29)37(9.07)
emergency or illness
33(5.73)0(0.00)5(3.82)28(6.86)
remodel houses
32(5.56)1(2.70)2(1.53)29(7.11)576(100.00)
37(100.00)
131(100.00)
408(100.00)
frequency (column percentage)

obs:576

made by the authour based on ENCOVI 2000

ii

Table 5-1
Table 5-1: Financial Institutions Providing Credits by Borrowers' Job
helper or
privateday workers
self employed
government
domestic
workeremployers workers orcompanies' or unskilledemployees
employees
without pay inemployees workers
farmersother location
private banks
52(44.07) 52(20.00)
5(5.21)0(0.00)28(29.79)
62(19.38)
3(21.43)
State banks
19(16.10)
17(6.54)3(3.13)0(0.00)10(10.64)
30(9.38)1(7.14)
coopearives
24(20.34)
25(9.62)10(10.42)
1(14.29) 24(25.53)
54(16.88)
2(14.29)
association of producers 0(0.00)
1(0.38)0(0.00)0(0.00)0(0.00)6(1.88)0(0.00)financiers1(0.85)4(1.54)1(1.04)1(14.29)4(4.26)3(0.94)0(0.00)NGOs2(1.69)8(3.08)4(4.17)1(14.29)8(8.51)26(8.13)0(0.00)3(21.43)
individuals5(4.24)41(15.77)
22(22.92)0(0.00)5(5.32)42(13.13)
credit card0(0.00)0(0.00)0(0.00)0(0.00)1(1.06)0(0.00)0(0.00)friends,families,etc.
11(9.32)
71(27.31)
39(40.63)
4(57.14) 13(13.83)
79(24.69)
4(28.57)
other what4(3.39)41(15.77)
12(12.50)0(0.00)1(1.06)18(5.63)1(7.14)
total
118(100.00) 260(100.00) 96(100.00) 7(100.00) 94(100.00) 320(100.00)
14(100.00)
obs:995

frequency (column percentage)

made by the authour based on ENCOVI 2000

Table 5-2

Table 5-2: Financial Institutions Providing Credits for the Poor by Borrowers' Job
helper or
privateday workers
self employed
government
domestic
workeremployers workers orcompanies' or unskilledemployees
employees
without pay inemployees workers
farmersother location
private banks
1(25.00)
2(3.51)4(4.94)03(12.00)
30(16.48)
0State banks02(3.51)2(2.47)06(24.00)
16(8.79)
1(14.29)
cooperatives
03(5.26)9(11.11)1(50.00) 6(24.00)
27(14.84)
1(14.29)
association of producers
0
0
0
0
0
5(2.75)0financiers02(3.51)1(1.23)01(4.00)3(1.65)0NGOs1(25.00)
1(1.75)4(4.94)03(12.00)
19(10.44)03(42.86)
individuals018(31.58) 18(22.22)01(4.00)26(14.29)
friends,families,etc.
2(50.00) 23(40.35) 36(44.44)1(50.00) 5(20.00)
45(24.73)
2(28.57)
other what
06(10.53)7(8.64)0011(6.04)0
total
4(100.00) 57(100.00) 81(100.00) 2(100.00) 25(100.00) 182(100.00)
7(100.00)
obs:380

frequency (column percentage)

made by the authour based on ENCOVI 2000

iii

Table 6-1

Table 6-1: Purposes of Credits by Borrowers' Job (main purposes in each sections)privateday workers
government
domestic
employers
companies' or unskilledemployees
employees
employee workers

helpers or
self
workers
employed
without pay
workers orin otherfarmers
locations273(27.44)
155(15.58)
66(6.83)22(2.21)142(14.27)
52(5.23)36(3.62)22(2.21)10(8.47)5(4.24)1(0.85)2(1.69)13(11.02)
4(3.39)4(3.39)2(1.69)37(38.54)
16(16.67)
11(11.46)2(2.08)6(6.25)2(2.08)2(2.08)0(0.00)1(14.29)0(0.00)1(14.29)0(0.00)1(14.19)0(0.00)1814.29)0(0.00)31(32.98)
16(17.02)
6(6.38)4(4.26)35(37.23)
14(14.89)
8(8.51)4(4.26)

171(53.44)
101(31.56)
41(12.81)
11(3.44)61(19.06)
19(5.94)
15(4.69)7(2.19)495(49.75)
82(8.24)
129(12.96)
70(7.04)
151(15.18)
47(4.72）

96(81.36) 219(84.23) 53(55.21)
20(16.95) 27(10.38)
5(5.21)14(11.86) 57(21.92) 18(18.75)
14(11.86)
25(9.62)6(6.25)17(14.41) 54(20.77) 22(22.92)
12(10.17)
19(7.31)0(0.00)118(100.00) 260(100.00) 96(100.00)

5(71.43)0(0.00)2(28.57)0(0.00)3(42.86)0(0.00)7(100.00)

29(30.85) 87(27.19)
6(42.86)8(8.51)10(3.13)1(7.14)2(2.13)16(5.00)
2(14.29)3(3.19)10(3.13)0(0.00)6(6.38)33(10.31)
1(7.14)3(3.19)8(2.50)2(14.29)94(100.00) 320(100.00) 14(100.00)

total
small business purposesagri purposes
raw materials or inputs
other agri activities
animalsbusiness purposes
articles for businessraw materials or inputs
other business activities
consumption smoothing purposes
household purposes
construct houseother household items
payment of debt
emergency or illness
remodel house
total

17(6.54)7(2.69)3(1.15)2(0.77)24(9.23)8(3.08)3(1.15)8(3.08)

obs:995(NA:85)

6(42.86)5(35.71)1(7.14)0(0.00)2(14.29)1(7.14)1(7.14)0(0.00)frequency (column percentage)

made by the authour based on ENCOVI 2000

Table 6-2
Table 6-2: Purposes of loans of the poor by loaners'job (main purposes in each sections)
total
small business purposesagri purposes
raw materials or inputs
other agri activities
payment of debt
business purposes
articles for businessraw materials or inputs
other business activities
consumption smoothing purposesHousehold purposes
construct houseother household items
payment of debt
emergency or illness
remodel house
total

privateday workers
government
domestic
companies' or unskilledemployees
employees
employees workers

employers

self
employed
workers or
farmers

helpers or
workers
without pay
in otherlocations

190(50.00)
114(30.00)
51(13.42)
12(3.16)31(8.16)
16(4.21)8(2.11)6(1.58)1(25.00)
1(25.00)001(25.00)
01(25.00)
09(15.79)5(8.77)3(5.26)06(10.53)3(5.26)02(3.51)34(41.98)
15(18.52)
10(12.35)
3(3.70)5(6.17)2(2.47)1(1.23)000001(50.00)01(50.00)018(72.00)
12(48.00)
5(20.00)
01(4.00)
5(25.00)
4(16.00)01(4.00)124(68.13)
75(41.21)
30(16.48)7(3.85)13(7.14)4(2.20)4(2.20)3(1.65)4(57.14)3(42.86)
1(14.29)00000137(36.05)
15(3.95)
33(8.68)
20(5.26)
58(15.26)
11(2.89)2(50.00)
1(25.00)
01(25.00)004(100.00)

42(73.68)
5(8.77)9(15.79)
6(10.53)
14(24.56)
4(7.02)57(100.00)

42(51.85)
5(6.17)14(17.28)
4(4.94)17(20.99)
081(100.00)

1(50.00)01(50.00)0002(100.00)

2(8.00)45(24.73)
02(1.10)09(4.95)06(3.30)1(4.00)19(10.44)1(4.00)4(2.20)25(100.00) 182(100.00)

3(42.86)
1(14.29)001(14.29)1(14.29)7(100.00)

obs:380 (NA:22)

frequency (column percentage)

made by the authour based on ENCOVI 2000

iv

Table 7
Table 7： Properties of Poor Households Borrowing from MF
Fixed Effects in Probit Model
model 1model 2dependent variable : MC use
geographical properties
area dummy (urban:1/rural:0)
regeion dummy
metropolitan dummy
north dummy
northeast dummy
south east dummy
central dummy
northwest dummy
southwest dummy
peten dumy
househol d properties
number of family members
level of cousumption (dicimal)
land assets dummy (own:1/no:0)
savings dummy (own:1/no:0)
access time to market
househol d head's properties
age
ethnic dummy (indigenous:1/no:0)
gender dummy (man:1/woman:0)
marital status (married:1/not married:0)
education level
mother Language dummy (Spanish:1/no:0)
trust for community members (majority:2/some:1/noSocial capital properties
number of participation in social activities
bridging collective activities among social activities
linking activities among social activities
membership of social group dummy

fixed effects z-value (robust)

LR statistics
fixed effects(heteroscadasticity)

LR statistics(heteroscadasticity)-0.0207-3.73***

5.08**-0.0229-4.08***

5.48**

0.0004-0.0267-0.0106-0.0135-0.013-0.021-0.0042

0.02-4.39***
-0.77
-1.04
-0.9
-1.95*
-0.25

0.06
0.15
0.49
2.78*
0.7
1.41
3.84*

0.0043-0.0268-0.0102-0.0115-0.0099-0.0204-0.0035-0.21-3.81***
-0.7
-0.81
-0.63
-1.75*
-0.2

0.01
0.01
0.13
2.22
0.19
1.55
1.59

0.00320.00160.01550.1495-0.0278

3.18***
0.86
2.57***
3.97***
-1.33

0.49
0.38
0.18
0.72
8.91***

0.00330.00180.01650.1519-0.0294

2.93***
0.91
2.69***
4.04***
-1.34

1.26
0.56
0.19
0.2
8.53***-0.00020.02710.00460.01860.00420.01870.0063-0.83
2.97***
0.42
2.33**
1.06
1.9*
1.5

0.19
0.6
2.95*
2.53*
1.14
0.69
1.03

-0.00020.02830.00730.01890.00460.01990.0078-1.14
2.97***
0.67
2.2**
1.16
1.92*
1.79*

0
0.33
1.67
2.4
0.57
0.95
0.94

0.00352.72***

0.01
0.00570.0042

2.16**
1.27

0.54
0.06

0.0242

3.93***

0.08

total numer of houshold's membership(degree of eagerness for membership group)
Number of obs ： 3339

z-value(robust)

Log pseudolikelihood = -549.74485
Wald test(f=22)：179.69***
Pseudo R2 ：0.1440
McFadden's Adj R2： 0.108
Count R2： 0.951*significant at the 10% level、** 5% level、*** 1%level
Source : ENCOVI 2000. Instituto Nacional de Estadistica (INE.) Guatemalav

0.00322.9***
0.0022
2.92**
Log pseudolikelihood = -554.69685
Wald test(f=23)：174.48***
Pseudo R2：0.1363
McFadden's Adj R2： 0.099
Count R2： 0.952

0.01
0.05

Table 8
Table 8： Properties of Non-Poor Households Borrowing from MF
Fixed Effects in Probit Model
model 1model 2dependent variable : MC use
geographical properties
area dummy (urban:1/rural:0)
regeion dummy
metropolitan dummy
north dummy
northeast dummy
south east dummy
central dummy
northwest dummy
southwest dummy
peten dumy
househol d properties
number of family members
level of cousumption (dicimal)
land assets dummy (own:1/no:0)
savings dummy (own:1/no:0)
access time to market
househol d head's properties
age
ethnic dummy (indigenous:1/no:0)
gender dummy (man:1/woman:0)
marital status (married:1/not married:0)
education level
mother Language dummy (Spanish:1/no:0)
trust for community members (majority:2/some:1/noSocial capital properties
number of participation in social activities
bridging collective activities among social activities
linking activities among social activities
membership of social group dummy

fixed effects z-value (robust)

LR statistics
fixed effects(heteroscadasticity)

LR statistics(heteroscadasticity)

0.01211.26

0.32

0.01151.19

0.36-0.03670.02080.0011-0.005-0.0173
0.0341-0.0196-2.78***
0.9
0.06
-0.28
-1.1
1.44
-1.27

0.28
1.3
2.09
0.01
0.22
0
0.11

-0.0359
0.02280.0025-0.0039-0.0158
0.0371-0.0177-2.68***
0.96
0.13
-0.21
-0.99
1.52
-1.11

0.06
1.75
1.72
0.03
0.26
0.01
0.12

0.01080.01670.01830.0387-0.00115.07***
3.8***
2.03**
3.59***-0.09

11.15***
2.41
0.01
3.28*
0.19

0.01080.017
0.01960.0395-0.0015

4.9***
3.86***
2.17**
3.65***-0.11-0.0001-0.01220.02530.019
0.0237-0.0209
0.0158

-0.42
-0.84
2.2**
1.54
5***
-1.06
2.63***

0.1
2.24
0.11
0.88
5.34***
0.19
3.15*-0.0001-0.0109
0.02620.01910.02420.02090.0166-0.33-0.73
2.27**
1.54
5.01***-1.06
2.77***

12.54***
1.48
0
2
1.84
0.08
0.1
1.3
0.24
1.24
6.07**
0.03
4.01**

0.00291.51

0.84
0.00130.0058

0.35
1.22

0.32
0.43

0.02412.8***

1.68

total numer of houshold's membership(degree of eagerness for membership group)
Number of obs ： 3937

z-value(robust)

Log pseudolikelihood = -1684.1693
Wald test(f=22)：266.77***
Pseudo R2 ：0.1098
McFadden's Adj R2： 0.091
Count R2： 0.907*significant at the 10% level、** 5% level、*** 1%level
Source : ENCOVI 2000. Instituto Nacional de Estadistica (INE.) Guatemalavi

0.00391.63
0.0034
2.98***
Log pseudolikelihood = -1087.1919
Wald test(f=23)：261.53***
Pseudo R2：0.1073
McFadden's Adj R2： 0.088
Count R2： 0.907

2.31
1.95

Reference
Ahlin, Christian and Townsend, Robert M., Using Repayment Data to Test Across Models of
Joint Liability Lending, The Economic Journal, Vol. 17, Issue 517, 2007, pp.F11-F51.
Alfaro-Gramajo, Luis Noel, Reverting the Tendency in Developing Finance: The Case of“Banrural S.A.” in Guatemala, Paving the Way Forward for Rural Finance An
International

Conference

on

Best

Practices,2003,(http://www.basis.wisc.edu/live/rfc/cs_05b.pdf).
Amin, Sajeda, Rai, Ashok S., and Topa, Giorgio, Does microcredit reach the poor andvulnerable? Evidence from northern Bangladesh, Journal of Development Economics, 70,
2003, pp. 59-82.
Besley, Timothy and Coate, Stephen, Group lending, repayment incentives and social
collateral, Journal of Development Economics, Vol. 46, 1995, pp. 1-18.
Biggs, Tyler, Raturi, Mayank, and Srivastava, Pradeep, Ethnic networks and access tocredit: evidence from the manufacturing sector in Kenya, Journal of Economic Behavior &Organization, Vol. 49, Issue 4, 2002, pp. 473-486.
Christen, Robert P., Rosenberg, Richard and Jayadeva, Veena, Financial Institutions with a“Double Bottom Line” Implications for the Future of Microfinance, CGAP Occasional
Paper, No.8, 2004, (http://www.cgap.org/p/site/c/template.rc/1.9.2701).
Coleman, Brett E., The impact of group lending in Northeast Thailand, Journal of
Development Economics, Vol. 60, 1999, pp. 105-141.
Evans, Timothy G., Adams, Alayne M., Mohammed, Rafi, and Norris, Alison, Demystifying
Nonparticipation in Microcredit: A Population-Based Analysis, World Development, Vol.
27, No. 2, 1999, pp. 419-430.
Fisman, Raymond J., Ethnic Ties and the Provision of Credit: Relationship-Level Evidence
from African Firms, Advances in Economic Analysis & Policy, Vol. 3, Issue 1, 2003, Article
4.
Ghatak, Maitreesh, Group Lending, Local Information and Peer Selection, Journal of
Development Economics, Vol. 60, No. 1, 1999, pp. 27-50.
Ghatak, Maitreesh, Screening by the Company You Keep: Joint Liability Lending and thePeer Selection Effect, The Economic Journal, 110, 2000, pp. 601-631.
Hoff, Kara and Stiglitz, Joseph E., Imperfect Information and Rural Credit Markets:
Puzzles and Policy Perspectives, The World Bank Economic Review, Vol. 4, No. 3, 1990, pp.
i235-250.
Honohan, Patrick, Measuring Microfinance Access: Building on Existing Cross- Country
Data, World Bank Policy Research Working Paper No.3606, 2005.
Hulme, David and Mosley, Paul, Finance Against Poverty, Volume 1, London, Routledge,
1996.
Ibanez, Ana Maria, Lindert, Kathy and Woolcock, Michael, Social Capital in Guatemala: A
mixed Methods Analysis, World Bank Technical Paper, No.12, 2002.
Karlan, Dean S., Social Connections and Group Banking, Economic Journal, Vol. 117, Issue
517, 2007, pp. F52 - F84.
Ledgerwood, Joanna, Microfinance Handbook, Washington D.C., The World Bank, 1999.
Narayan, Deepa and Pritchett, Lant, Cents and Sociability: Household Income and Social
Capital in Rural Tanzania, Economic development and Cultural change, Vol. 47, 1999, pp.
871-897.
Navajas, Sergio, Schreiner, Mark, Meyer, Richard L., Gonzalez-Vega, Claidio, andRodriguez-Meza, Jorge, Microcredit and the Poorest of the Poor: Theory and Evidence
from Bolivia, World Development, Vol. 28, No. 2, 2000, pp. 333-346.
Pitt, Mark M. and Khandker, Shahidur R., The Impact of Group-Based Credit Programs on
Poor Households in Bangladesh: Does Gender of Participants Matter?, The Journal of
Policy Economy, Vol. 106, No. 5, 1998, pp. 958-996.
Tesliuc, Emil D. and Lindert, Kathy, Vulnerability: A Quantitative and Qualitative
Assessment,

GuatemalaPoverty

Assessment

Program,2002,(http://info.worldbank.org/etools/docs/library/97185/Keny_0304/Ke_0304/vulnerability.pdf).
Van Bastelaer, Thierry, Does Social Capital Facilitate the Poor’s Access to Credit? A Reviewof the Microeconomic Literature, Social Capital Initiative Working Paper No.8, The WorldBank Social Development Department, 2000.
Wydick Bruce, Can Social Cohesion be Harnessed to Repair Market Failures? Evidence fromGroup Lending in Guatemala, The Economic Journal, Vol. 109, No. 457, 1999, pp. 463-475.
Zeller, Manfred, Determinants of credit rationing: A study of informal lenders and formal
credit groups in Madagascar, World Development, Vol. 22, No.12, 1994, pp. 1896-1909.

ii

Appendix

Table 9: Descriptive Statistcs of Poor Households' Variables
Variables
borrowing MC or not (dependent variable)

No. of Obs

Mean

Std. Dev.

Min

Max

3339

0.04790.2136013339

0.24680.4312

0

1

3339
3339
3339
3339
3339
3339
3339
3339

0.02580.14350.05150.11830.16380.24320.16650.08750.15840.35060.22110.32300.37020.42910.37260.2825

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

3339
3339
3339
3339
3339
3339

6.40553.51120.80020.03410.90360.10252.5526
1.5856
0.39990.1816
0.29520.3356

1
1
0
0
0
0

18
6
1
1
1
2

3339
3339
3339
3339
3339
3339
3339

43.83020.5696
0.86160.86790.55560.49
0.702

14.88390.49520.34530.3386
0.63420.5
0.6189

17
0
0
0
0
0
0

99
1
1
1
9
1
2

3339
2.29593339
0.75923339
0.34563339
0.47053339
1.31873339
2.51253339
0.8847MC institution access dummy (accessible:1/no:0)
made by authour based on ENCOVI 2000

1.99281.07820.77810.49922.051
3.93380.3194

0
0
0
0
0
0
0

12
6
5
1
15
30
1

geographical properties
area dummy (urban:1/rural:0)
regeion dummy
metropolitan dummy
north dummy
northeast dummy
south east dummy
central dummy
northwest dummy
southwest dummy
peten dumy
household properties
number of family members
level of cousumption (dicimal)
land assets dummy (own:1/no:0)
savings dummy (own:1/no:0)
market access dummy (accessible:1/ no:0)
access time to market
household head's properties
age
ethnic dummy (indigenous:1/no:0)
gender dummy (man:1/woman:0)
marital status (married:1/not married:0)
education level
Mother Language dummy (Spanish:1/no:0)
trust for community members (majority:2/some:1/no:0)
Social capital properties
number of participation in social activities
bridging collective activities among social activities
linking activities among social activities
membership dummy of social group
total numer of houshold's membership
degree of eagerness for membership groupi

Table 10: Descriptive Statistcs of non-Poor Households' Variables
Variables
borrowing MC or not (dependent variable)

No. of Obs

Mean

Std. Dev.

Min

Max

3937

0.09300.2904013937

0.66040.4736

0

1

3937
3937
3937
3937
3937
3937
3937
3937

0.21340.08100.10850.10410.17880.09780.14200.07440.40970.27290.31100.30550.38320.29710.3491
0.2625

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

3937
3937
3937
3937
3937
3937

4.20078.46000.70180.30610.93290.13892.00031.26050.45750.46090.25020.3078

1
6
0
0
0
0

15
10
1
1
1
2

3937
3937
3937
3937
3937
3937
3937

45.0767
0.23980.78110.75791.25430.82780.610915.73190.42700.4136
0.42840.96090.3776
0.6656

16
0
0
0
0
0
0

93
1
1
1
9
1
9

3937
2.42953937
0.98223937
0.42493937
0.50723937
1.20653937
2.40113937
0.9428MC institution access dummy (accessible:1/no:0)
made by authour based on ENCOVI 2000

2.02201.16810.83770.50001.67233.41940.2322

0
0
0
0
0
0
0

13
6
5
1
13
28
1

geographical properties
area dummy (urban:1/rural:0)
regeion dummy
metropolitan dummy
north dummy
northeast dummy
south east dummy
central dummy
northwest dummy
southwest dummy
peten dumy
household properties
number of family members
level of cousumption (dicimal)
land assets dummy (own:1/no:0)
savings dummy (own:1/no:0)
market access dummy (accessible:1/ no:0)
access time to market
household head's properties
age
ethnic dummy (indigenous:1/no:0)
gender dummy (man:1/woman:0)
marital status (married:1/not married:0)
education level
Mother Language dummy (Spanish:1/no:0)
trust for community members (majority:2/some:1/no:0)
Social capital properties
number of participation in social activities
bridging collective activities among social activities
linking activities among social activities
membership dummy of social group
total numer of houshold's membership
degree of eagerness for membership groupii

Table 11： Properties of Poor H ouseholds Borrowing from MF
Fix ed Effects in Heck man Probit Model (second stag e)m odel 1m odel 2
fixed effects
dependent variable : MC use
geographical properties
area dummy (urban:1/r ural:0)-0.018regeion dummy
metropolitan dummy
0.1126
north dummy
0.0566
northeas t dummy
south east dummy
0.053
central dummy
0.0485north west dummy
0.051
south west dummy
0.0295peten dumy
0.0595househol d properties
number of family member s
0.0032level of cousumption (dicimal)
0.0011land assets dummy (own:1/no:0)
0.0169savings dummy (own:1/no:0)
0.1544access time to market-0.0224
househol d head's propert ies
age-0.0003ethnic dummy (indig enous:1/no:0)
0.0248gender dummy (man:1/woman:0)
0.0028mar ital status (mar ried:1/not marr ied:0)0.0151
education level
0.0017mother Lang uage dummy (Spanish:1/no:0)
0.0232trust for communit y members (majority:2/some:1/no0.0051Social capital proper ties
number of participation in s ocial act ivities
0.0038br idging collective activities among social activities
link ing activities among social activities
members hip of social g roup dummy
0.0239

total numer of houshold's membership(degree of eagerness for membershi p gro up)
Number of obs ： 3339
Censored obs ： 385
uncensored obs ：2954

z-value

fixed effects-3.14***-0.02-3.4***

1.25
1.26
1.44
0.92
1.23

-0.0094-0.0258-0.0132-0.0129-0.0099-0.021-0.0115-0.61-3.87***
-0.98
-0.94
-0.64
-1.88*-0.83

2.88***
0.57
2.72***
4.01***-1.66*

0.00320.00120.01790.1585-0.0239

2.63***
0.6
2.8***
4.05***-1.67*-1.22
2.47**
0.21
1.54
0.36
2.1**
1.17

-0.0003
0.02520.00530.01530.00210.02380.0068-1.45
2.42**
0.43
1.48
0.46
2.08**
1.49

0.00520.00482.08**
1.48

1.28
1.26

2.87***

3.62***

0.00352.84***
0.0023
3.56***
Log likelihood = -1217.551 Log likelihood = -1221.673
Wald tes t(f=22)：142.65*** Wald tes t(f=23)：140.08***
Wald tes t(ρ= 0) ：0.00
Wald tes t(ρ=0) ：0.03

P rope rties of P oor Househol ds Accessing MC Insti tutions

Fixed Effects in Probit model (First stage)
fixed effects z-value
LR stat is tics
dependent variable : MC accessar ea dummy
0.00490.49
9.25***
north dummy-0.0229-0.78
3.32*
north east dummy
0.070812.44***
7.89***
sout h east dummy
0.0756
8.93***
0.06
central dummy
0.02151.1
0.23
northwes t dummy
0.01640.77
38.45***
sout hwest dummy
0.0181
0.92
10.61***
peten dumy
0.04493.77***
17.61***
market access dummy (accessible:1/no:0)
0.644821.09***
0.34
Number of obs： 3339
Log likelihood = -753.22317
Wald test(f=9) ：646.50***
Pseudo R2 ： 0.3689
McFadden's Adj R2：0.361
Cragg-Uhler(Nagelkerke) R2： 0.232
Mck elvey & Zavoina's R2： 0.446
Efron's R2： 0.403
Count R2： 0.931
AIC： 0.457*s ig nificant at the 10% level、** 5% level、*** 1%level
Source : ENCOVI 2000. I nstituto Nacional de Estadistica (I NE.) Guatemalaiii

z-valueTable12： Properties of Non-Poor Households Borrowing from MF
Fix ed Effects in Heck man Probit Model (second stag e)m odel 2m odel 1
fixed effects
dependent variable : MC use
geogra phical properties
area dummy (urban:1/r ura l:0)0.0159regeion dummy
metropolita n dummy-0.0365
north dummy
0.0073northeas t dummy-0.0067
south east dummy-0.0069
centra l dummy-0.0162
northwest dummy
0.0296
southwest dummy-0.0197
peten dumy
househol d properties
number of family member s
0.0107level of cousumption (dicimal)
0.0145la nd assets dummy (own:1/no:0)
0.0158savings dummy (own:1/no:0)
0.0372access time to market
0.0005househol d head's propert ies
age-0.0001
ethnic dummy (indig enous:1/no:0)-0.0126
gender dummy (ma n:1/woman:0)0.0238mar ital status (mar ried:1/not marr ied:0)0.0161
education level0.0265mother Lang ua ge dummy (Spanish:1/no:0)-0.0191
trust for communit y members (majority:2/some:1/no:0.015
Social capital proper ties
number of participation in s ocial act iv ities
0.0035
br idging collective activities among social activities
link ing activities among social activities
members hip of social g roup dummy
0.0273

total numer of houshold's membership(degree of eagerness for membershi p gro up)
Number of obs ： 3937
Censored obs ： 225
uncensored obs ：3712

z-value

fixed effects

1.5

0.01531.43-2.51**
0.32
-0.36
-0.36
-0.95
1.23
-1.19

-0.0358
0.0088-0.0048-0.0057-0.01450.0327-0.0172-2.41**
0.37
-0.25
-0.3
-0.84
1.32
-1.01

4.55***
3.12***
1.68*
3.4***
0.04

0.01070.015
0.01720.038
0.0003

4.43***
3.21***
1.83*
3.45***
0.02

-0.44
-0.8
1.77*
1.15
5.15***-0.9
2.31**-0.0001-0.01090.02470.01630.027-0.01890.0159

-0.34
-0.68
1.84*
1.17
5.21***-0.89
2.45**

0.0025
0.006

0.64
1.17

1.71*

3***

0.00441.76*
0.0037
3.03***
Log likelihood =-1641.52
Log likelihood = -1645.057
Wald tes t(f=22)：212.31*** Wald tes t(f=23)：208.78***
Wald tes t(ρ= 0) ：0.65
Wald tes t(ρ=0) ： 0.58

P rope rties of Non-P oor Househol ds Accessing MC Insti tutions

Fixed Effects in Probit model (First stage)
fixed effects z-value
LR stat is tics
dependent va riable : MC accessar ea dummy
0.03465.2***
53.72***
metr opolitan dummy-0.0462-2.41**
64.72***
north dummy-0.0993-3.04***
8.12***
northeast dummy
0.00990.98
0.29
sout heast dummy
0.0203
2.83***
7.77***
central dummy-0.0251-1.6
1.09
northwes t dummy
0.00680.54
0sout hwest dummy-0.0343-1.91*
11.74***
peten dumymarket access dummy (accessible:1/no:0)
0.3911
12.16***
10.05***
Number of obs： 3937
Log likelihood = -613.54957
LR test(f=9) ：510.41***
Pseudo R2 ： 0.2886
McFadden's Adj R2：0.277
Cragg-Uhler(Nagelkerke) R2： 0.119
Mck elvey & Zav oina's R2： 0.280
Efron's R2： 0.259
Count R2： 0.957
AIC： 0.317*s ig nifica nt at the 10% level、** 5% level、*** 1%level
Source : ENCOVI 2000. I nstituto Na cional de Estadistica (I NE.) Guatemalaiv

z-value