micro-finance lifespans: a study of attrition and...
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Micro-finance Lifespans: A Study of Attrition and Exclusion in Self-Help Groups in India
Jean-Marie Baland
University of Namur
Rohini Somanathan
Delhi School of Economics
and
Lore Vandewalle
University of Namur
Prepared for Presentation at tbe Brookings-NCAER India Policy Forum 2007New Delhi
July 17-18, 2007
Conference DraftNot for Citation
Micro-finance Lifespans: A Study of Attrition and
Exclusion in Self-Help Groups in India
Jean- Marie Baland, Rohini Somanathan and Lore Vandewalle†
July 2007
Abstract
We study member attrition and group failure in Self-Help Groups (SHGs), the dominant
institutional form in Indian micro-finance. We use survey data on groups of women in two
rural and relatively poor areas of the country: the districts of Keonjhar and Mayurbhanj
in northern Orissa and the district of Raigarh in Chattisgarh. We find that 10% of the
1,100 SHGs created over the period 1998-2006 are no longer active and 20% of women who
joined a group are no longer part of an SHG network. We estimate duration models of
group and member survival. An important determinant of group survival is the presence of
some educated members, perhaps because they can ensure accurate accounting and provide
leadership in other aspects of group functioning. Members stay longer in groups in which
they have relatives. Although fractionalization and other measures of social cohesion do
not systematically influence group failure, they do result in more member exit. Members of
the most disadvantaged ethnic groups are particularly vulnerable to group heterogeneity.
Keywords: microcredit, collective action, social exclusion. JEL codes: I38, G21, O12,
O16∗Thanks to the PRADAN team for many useful discussions and for their support in facilitating data collection,
to Sandeep Goyal, Sanjay Prasad, Amit Kumar, Rahul Sharan and Saurabh Singhal for research assistance andto FUCID (La Fondation Universitaire de Cooperation Internationale et de Developpement) and the ARCprogram of the French speaking community in Belgium for financial support.
†CRED, University of Namur ( jean-marie.baland@fundp.ac.be), Delhi School of Economics (ro-hini@econdse.org) and CRED and FNRS, University of Namur (lore.vandewalle@fundp.ac.be )
1
1 Introduction
Micro-finance is often advocated as a solution to multiple social problems. Poor households
with access to credit can make investments that bring them out of poverty, household and
regional income and wealth disparities are reduced, and groups meetings provide forums for
collective action that improves gender relations and local governance. Over the last few years,
savings and credit groups have also helped manage some important social programs of the Indian
government, such as the distribution of food grains and school meals in state primary schools.
The number of organizations involved in the micro-finance sector is very large and contracts vary
considerably. Most loans involve some form of group lending with members sharing responsibility
for repayment. It is useful to distinguish between two principal institutional forms through
which lending takes place. In the first, micro-finance institutions organize potential lenders into
groups. Group composition may be determined by random factors, as in the case of FINCA
in Peru, or the matching preferences of members as in the Grameen Bank model.1 In all
these cases, micro-finance institutions are intimately involved in all stages of the group lending
process; group formation, rules that govern member interactions, and the provision of credit.
Bank representatives are often present at group meetings. An alternative setting is one in
which the institutions involved with group formation, group interactions and group loans are
much more loosely connected. Government and non-government agencies are involved in the
formation of groups, which determine their own rules for savings and lending. Some of these
groups subsequently borrow from commercial banks. This is the dominant model in Indian
micro-finance, in terms of both outreach and total loan disbursements.
The present structure of the micro-finance sector in India emerged in the early nineties when
the Reserve Bank of India issued guidelines to all nationalized commercial banks encouraging
them to lend to informal groups which came to be called Self Help Groups (SHGs). Since then,
such groups have been actively promoted by a number of different agencies and the National
Bank for Agriculture and Rural Development (NABARD) has provided banks with subsidized
credit for SHG lending.2 Government statistics currently report over two and a half million
groups and 32 millions households covered by the program.3
1See Karlan (2006) for a description of group operations in FiNCA and Armendariz de Aghion and Mor-
duch(2005), chapter 4 for details on Grameen Bank group lending practices.2See Reserve Bank of India (1991) and National Bank for Agriculture and Rural Development (1992) for the
original policy statements.3NABARD (2006), page 38.
2
This paper reports findings from a study of about 1,100 SHGs with a total of 16,000 women
members in selected regions of rural Orissa and Chattisgarh. The groups were formed during the
period 1998-2006 by PRADAN, a non-government organization working in these areas. We find
that 10% of all groups created over this period are no longer active and that 14% of members left
groups while it was still functioning. Some women who leave groups join other SHGs. Taking
them into account, we find that about one-fifith of members who joined a group had left the
SHG network within the 8-year period. We’re interested in the factors underlying these rates
of group survival and member attrition.
We estimate models of both group and member duration and find that factors behind group
survival are quite different from those affecting member longevity. The presence of educated
members is strongly associated with longer-lived groups, perhaps because they can facilitate
transactions and ensure that group accounts are accurate. The presence of other SHGs in the
area also has a positive effect on group duration. It may be that a dense cluster of groups allows
for the sharing of costs, provides each group with ideas for successful activities, or simply instills
in members the desire to survive, compete and be part of a larger network. Measures of frac-
tionalization and social heterogeneity that are commonin the literature do not have systematic
effects on group survival.
Both member and group characteristics matter in explaining the departure of individuals from
groups. Controlling for a large set of individual and group characteristics, we find that tribal
and scheduled caste women have shorter SHG lifespans than groups that lie higher in the social
hierarchy. In addition, higher levels of education, bigger landholdings and more relatives within
the SHG are also associated with a lower probability of exit. Group level social fractionalization
indices increase departures, especially for the Scheduled Tribes. We also find that the bulk of
the difference in member duration observed between Chattisgarh and Orissa can be attributed
to characteristics of groups in these areas; regional variations in performance are negligible once
these characteristics are incorporated in our model.
Our results suggest that it is problematic to evaluate the success of micro-finance interventions
based on conventionally reported coverage figures because they do not account for attrition.
The aggregate rates of group failure and member exit are not however not the main problem in
the evaluation of micro-credit programs in India. These rates, though sizable, are not enormous;
our main concern is their systematic relationship with measures of social disadvantage. This
makes is unlikely that women moving out of SHGs are going on to enter into individual contracts
with lending institutions. It also means that some of those in desperate need of credit cannot
obtain it from within this sector. An additional concern is that lending by commercial banks to
3
Self Help Groups is considered priority sector lending by the banking system and may therefore
crowd out other lending. Disbursements by commercial banks to SHGs were about half the
size of all direct bank credit to small farmers in 2003-2004 and SHG credit has been rapidly
rising since.4 Although the duration of membership is only one, admittedly crude, measure of
the performance of the micro-finance sector, our study suggests that survey data which follows
members and groups in this sector is critical to an assessment of Indian micro-finance.
We provide a brief institutional history of the micro-finance sector in India in Section 2. Our
survey data, some summary statistics and empirical methods are described in Sections 3 and 4
respectively. Results are presented in Section 5 and are followed by a some concluding remarks.
2 Micro-finance Institutions in India
Many detailed accounts on the characteristics of rural credit markets in India and the spread
of rural banking in the post independence period are available. A number of studies, starting
with the All India Rural Credit Survey in 1954, found that the rural poor had high levels of
indebtedness and very limited bank access.5
As part of a process of providing banking services to this population, the State Bank of India
was set up in 1955, the 14 largest commercial banks were nationalized in 1969, and the National
Bank for Agriculture and Rural Development (NABARD) was created in 1982. Each of the
nationalized banks were assigned the role of lead banks in particular parts of the country and
they were required to maintain specific ratios of urban to rural branches. A vast network
comprising thousands of cooperative banks and regional rural banks was created primarily to
provide credit to the agricultural sector. These efforts created a vast system for credit delivery
with high default rates and operating costs. 6
In the early nineties central bankers tried to revitalize the elaborate and largely inefficient
4Loan disbursements to farmers with less than 2.5 acres of land were 7953 crore rupees in 2003-4 while SHG
linked loans were Rs. 3904 crore. (Reserve Bank of India, 2006, Tables 58 and 71).5See for example, Bell(1990) for summary statistics on rural borrowing and indebtedness based on rural credit
surveys and Karmakar (1999) for recent figures on the numbers of different types of rural banking institutions.6The extent to which this system reached the poor is unclear, but there is evidence that they influenced
regional poverty rates (Burgess and Pande, 2005).
4
banking system through which rural credit came to be provided after Indian Independence. The
start of institutionalized micro-finance in India is often attributed to the circular that was issued
by the Reserve Bank to all nationalized commercial banks in 1991, announcing the objective of
linking informal groups of rural poor with the existing banking system. Some non-government
organizations at the time had organized women into informal groups that used their pooled
savings for mutual insurance and small credit needs. Based on studies of these informal groups,
it was believed that they had the “potential to bring together the formal banking structure and
the rural poor for mutual benefit”(RBI, 1991). The following year NABARD launched a pilot
project which linked 500 groups with commercial banks. The banks were offered finance from
NABARD for such lending at the rate of 6.5% per annum. It was recommended that banks either
lend directly to groups at 11.5% per annum or route their loans through voluntary agencies at
the lower rate of 8.5% in order to cover the transactions costs of these organizations (NABARD,
1992). Banks were also permitted to classify such lending under Advances to Weaker Sections,
and this category formerly accounted for a large fraction of their unprofitable loans.
Another major change came in April 1999, with the launching of the Swarnajayanti Gram
Swarozgar Yojana, popularly known as the SGSY (RBI, 1999). The program was introduced
to increase the penetration of SHGs among families below the poverty line. This reflected a
significant change in state policy : It provided direct subsidies to borrowers as only part of the
initial loan had to be repaid. It also restricted the composition of a group to families below
the poverty line. Subject to caps, rates of subsidy were 50% for borrowers from the Scheduled
Castes and Tribes and 30% for other poor households. A proper evaluation of the changes that
the SGSY brought about in the composition and performance of SHGs is yet to be undertaken.7
The NABARD pilot program of 1992 was widely regarded as successful and the number of SHGs
linked to the banking system has been rising rapidly over the last 15 years. Table 1 shows official
figures for the number of groups that have received bank loans since 1992. The total number
of groups linked is currently reported to be over 2.5 million. There is considerable variance in
the distribution of SHGs across states and an overwhelming fraction is in the 4 southern states.
Alternative models of lending have been growing in the last few years and private banks have
also entered the sector. The combined coverage of institutions in this class remains small relative
7Our own surveys indicate that the combination of restrictions of group composition and subsidies may
have been a factor causing the dissolution of some groups. Surveyed groups were asked about whether or not
they received a subsidy. Although very few of the subsidized groups failed, other groups sometimes cited their
exclusion from state subsidies as a reason for group dissolution. In some cases, a few members were excluded
from the group by the others because they were not on government poverty lists.
5
to SHG lending. This contrasts sharply with countries such as Bangladesh and Indonesia, where
each of the major specialized micro-finance institutions is larger than the combined non-SHG
sector in India. (RBI, 2005 (chapter 2), Basu, 2005).
Table 1: SHG- Commercial Bank Linkages (Cumulative), 1992-2007
Year (end-March) No. of SHGs linked Bank loans
(Rs. crore)
1992-93 255 .29
1993-94 620 .65
1994-95 2122 2
1995-96 4757 6
1996-97 8598 12
1997-98 14317 24
1998-99 32995 57
1999-00 114775 193
2000-01 263825 480
2001-02 461478 1026
2002-03 717360 2048
2003-04 1079091 3904
2004-05 1618456 6898
2005-06 a 2238565 11397
2006-07 b 2580000 14479Sources: Figures from 1992-2005 have been taken from Reserve
Bank of India (RBI) (2006) while those for 2006-2007 are from
RBI (2007).
aprovisional estimatesbupto end February 2007
It appears that the institutions that came to dominate Indian micro-finance sector resulted
from the combined presence of a vibrant non-government sector engaged in rural development
and an extensive but unprofitable network of rural banks and agricultural cooperatives that
were created with the explicit purpose of providing small loans to the rural poor.8 Policy
makers could observe the phenomenal expansions in outreach of micro-finance institutions like
the Grameen Bank in Bangladesh and other countries. The Grameen Bank alone, starting from
8Harper (2002) provides some additional reasons for why SHGs rather than Grameen type institutions are
more successful in the Indian context.
6
humble beginnings had reach almost a quarter of all Bangladeshi villages by 1991.9 The linking
of banks with SHGs seemed a natural step to take and it gave India its own particular brand
of micro-finance.
3 Data
Our data is based on surveys of SHGs created by PRADAN, one of the earliest non-government
organizations in the micro-finance sector. We surveyed a total of 1,103 SHGs created by
PRADAN in two of its field locations, one in northern Orissa and the other in central Chhatis-
garh. This included every group that has been formed since the start of the program in these
areas. In addition to group-level data, we collected information on the backgrounds and SHG
activities of 16,892 women who, at any stage, had been members of these groups. We begin this
section with a brief outline of the PRADAN SHG program. This is followed by a description of
our survey methodology and some descriptive statistics on groups and members.
3.1 The PRADAN SHG Program
The first SHG created by PRADAN was in the Alwar district of Rajasthan in 1987. In the
subsequent years most groups were formed in what now constitutes the state of Jharkhand.
More recently, new SHG projects have appeared in West Bengal, Madhya Pradesh, Orissa and
Chhatisgarh. Table 2 provides a list of PRADAN locations in each of the 7 states in which the
organization operates, together with the year of the first SHG and the total number of SHGs
in existence at the end of March 2006. 10
The groups formed by PRADAN are a small fraction of the micro-finance sector, but an impor-
tant presence in their respective areas. The SHG program operates by first targeting adminis-
trative blocks with high levels of rural poverty within particular districts and then building a
dense network of SHGs in these area over a few years. In many of the more recent PRADAN
locations, SHGs have been the first intervention in each village and group meetings were used to
9Figures for the total number of villages are from the Bangladesh Bureau of Statistics (www.bbs.gov.bd) and
those for the number covered by the Grameen Bank are available at www.grameen-info.org.10current aggregate figures for the SHG program are available at www.pradan.net
7
introduce other activities aimed at raising agricultural productivity and raising incomes. Since
it is the backward areas that are chosen for new interventions, the social composition of the
administrative blocks in which PRADAN operates are often quite different from other parts of
the state and district. PRADAN areas often have a high proportion of social groups classified
as Scheduled Tribes and lower literacy rates than the state average.
The groups themselves consist entirely of women and follow the guidelines issued by NABARD
and the Reserve Bank (RBI, 1999, NABARD, 1992). Each SHG has between 10-25 members
and is fairly homogeneous in terms of caste. Large villages often have multiple groups, one
in each hamlet. PRADAN professionals begin the process of group formation by calling a
meeting in some public space in the village. They discuss the benefits of membership and
some general principles followed by successful groups (compulsory attendance, weekly savings,
typical interest rates, bookkeeping). Interested women are enlisted, a regular meeting time is
set and the professional is usually present at meetings until membership becomes fairly stable
and all members are familiar with group practices. Each group is provided with a register for
keeping accounts and a cash box, and either designates one of the members to keep accounts or
hires an accountant. The register, cash box and keys are usually rotated across the members.
As groups mature, they get linked to other groups in the area and select representatives who
attend these cluster meetings. Smoothly functioning groups typically open a savings account
with a nearby commercial bank within a year of their inception. After this stage, PRADAN
professionals discuss possible self-employment projects with the group, some members decide on
particular projects, the total investment required is determined and the group then applies to a
commercial bank for a loan. This loan constitutes their first bank linkage. Bank funds usually
come into the group which then lends to individual members at interest rates determined by
the group. Payments are made to the group which then repays the bank on the stipulated date.
As the group gains confidence and shows that it can manage its functioning independently,
PRADAN professionals withdraw and their direct interactions with group members increasingly
take place through the larger cluster meetings and on occasions when they visit the village to
initiate other livelihood projects. Regular communication with PRADAN takes place mainly
through copies of weekly accounting transactions that are sent in to the local office. Groups are
free to determine the rules under which they operate and the stringency with which they are
implemented. These rules and interest rates vary considerably accross groups and also change
periodically within particular groups. After the start of the SGSY in 1999, some subsidies to
groups are routed through PRADAN, provided the groups satisfy the selection criteria required
by the scheme. Subsidized and unsubsidized SHGs therefore co-exist in the same area.
8
As a result of the growing independence of these groups over time, the organizations promoting
SHGs are not always informed about groups that are no longer active. Successful groups may
stop sending in accounts as they reduce their reliance on PRADAN, others may temporarily
suspend meetings because its members migrate seasonally and yet others may actually collapse.
Survey data is therefore required to correctly estimate rates of group survival.
3.2 The Survey Design
We study all PRADAN groups created in the districts of Keonjhar and Mayurbhanj in northern
Orissa and the district of Raigarh in eastern Chattisgarh. PRADAN works in a total of 8
administrative blocks in these district. Both the Orissa districts are serviced by the professionals
in Keonjhar and we henceforth refer to all groups in this area as the Keonjhar SHGs. The three
survey districts are are shown in Figure 1 and the surveyed areas within each of these districts
are indicated in Figures 2-4. Although only a small fraction of total district villages are covered
by the program, they are geographically clustered and upto 40% of the villages in some blocks
have a PRADAN SHG. These dense pockets make it easier for professionals to visit these areas
and also allow groups to benefit from frequent contact with each other. 11
The first SHGs in Keonjhar and Raigarh were started in 1998 and 1999 respectively. We survey
the census of PRADAN SHGs in these locations and therefore have group and member level
characteristics for each group formed in the area, and each member that was ever part of the
network, whether or not they were part of the group at the time of the survey.
At the group level we collected information on group rules and activities and on the timing
of various significant events. These events include the inception of the SHG, the creation of
savings accounts, the dates and sizes of bank loans, membership of SHG clusters and the date of
the last meeting in case the SHG is no longer active. Group rules include fines (for attendance
and late repayment), minimum savings requirements, interest rates and the rotation of group
office-bearers and representatives. Our data on collective activities undertaken by the group
include its involvement in village and family conflicts, government contracts to provide school
meals and various types of other activities such as the selling of forest produce or visiting a
government official.
11Some of these benefits are studied by Nair (2005)
9
Figure 1: Study Area
OrissaChattisgarh
Madhya Pradesh
Maharashtra
Andhra Pradesh
JharkhandWest Bengal
Uttar Pradesh
Legend
State Boundary
STUDY DISTRICTS
Keonjhar
Mayurbhanj
Raigarh
THE STUDY AREA MAP
10
For all present and past members, and for each accountant, we recorded the dates at which
the they entered and left the group, and the total value of loans that were taken either from
the group’s internal funds or a bank. For the members, we collected data on a standard set of
characteristics relating to their social and economic background: caste, education, age, marital
status, fertility, household landholdings and some parental information. To better understand
the determinants of group cohesion and conflict, we also created a matrix of relationship between
group members which recorded family ties between members. Since we had a census rather than
a sample of groups in each area, we were also able to record the number of other PRADAN
groups in the same revenue village.
For groups that were no longer active, we asked members the main reason for group failure and
recorded the most popular response. Similarly, we asked members who had left groups the main
reason for their departure.
Figure 2: Raigarh
11
Figure 3: Keonjhar
Figure 4: Mayurbhanj
3.3 Descriptive Statistics
Table 3 gives us the number of SHGs that were created and dissolved in our survey areas over
the period 1998-2006. The survey in Keonjhar was conducted during the summer of 2006 and
included all groups created till the end of June that year. The Raigarh survey was in January
2007 and included groups formed at any point during 2006. Of the 1,103 groups created, 10%
were no longer active by the time of the survey. A higher proportion of groups failed in Raigarh
than in Keonjhar(12% as compared with 9% ).
Table 4 contains descriptive statistics for a variety of group characteristics according to the
survival status of the group. A few of the differences between existing and inactive groups are
worth emphasizing. First, the differences in the duration of these two types of groups are not
enormous. Existing groups have been in operation for an average of about three years in both
areas, while defunct groups in had lives that were 20% shorter in Keonjhar and 45% shorter in
Raigarh. These groups do not fail soon after they are formed. Second, there are many more
homogenous groups in Keonjhar in both categories, and a these groups as a whole have higher
rates of failure. If we look more closely within homogeneous groups, we find that this pattern is
marked for the Scheduled Tribes, but not for other caste categories.Third, groups that succeed
are both more involved in village activities and in the lives of their members. Lastly, members
of these groups are, on average, more educated and have more land and more of them act as
accountants for their group. Difference in group size are negligible. The stated reasons for group
failures and member departures are summarized in Table 6. In both cases, the two principal
reasons relate to group conflicts and difficulty in either saving or repaying loans.
Tables 5-8 compares women who left SHGs with those who are either still in them, or were in
them until the time that they were active. The differences in demographics between the two
groups of members are not large. What is striking here is the importance of family and social
connections. Women with relatives in a group are more likely to stay in it and homogeneous
single caste groups are better able to retain members. We also see that even after we control for
duration in a group, members who eventually leave are given fewer loans and are less likely to
be a Chairman of the group at some point during their stay. From Table 7 it appears unlikely
that defaulting with a loan is an important reason for the exit of a member. The duration in
a group does not seem to depend on whether or not the member received a share of a loan
obtained by the group through a bank linkage.
4 Empirical Methods
Many of the more standard regression techniques that are used in economic analysis are not
appropriate to the type of data we have or the questions that we are interested in. The main
data issue is that we would like to understand group failure and member exit using a data set in
which most groups and members are still active. In other words, our data is right censored. To
see why this matters, suppose that we measure group failure (or member exit) using a binary
variable which takes the value of 1 for groups that are no longer active (or members that have
left the system) and zero otherwise. We have data on a set of covariates that we believe to be
associated with longer group (or member) lives. If groups are created at different points in time
(or members enter at different times), and older groups (and members) have more favorable
characteristics, the above method would not be able to detect the correct effect or covariates
and may even assign them the wrong sign. A similar problem would occur if we used more
continuous outcome measures such as loans taken or the number of days a group or member
remains in the system.
If we tried to get around the above problem by using only data on failed groups and departed
members, we would lose a lot of data, especially in the case of a rapidly growing program,
where a large fraction of the observations are in fact censored. What we require are methods
that allow us to use censored observations by making use of information on the censored group
or member until the time of censoring, rather than simply ignoring these observations or not
accounting for the fact that they are censored. This is precisely what the methods of survival
analysis that have been popular in the biomedical and quality control fields, are designed to do.
13
These methods are used to estimate the time until events occur; in our case, the events being
either group dissolution or member exit. We begin with some standard definitions of functions
that we are going to estimate for our data.
Denote by the nonnegative random variable X, the time until the event of interest. In our
context, this is the time until the dissolution of the group or until a member departs in the case
of the member level analysis. The distribution of X can be represented in several ways.12 The
survival function ST (t) gives the probability of surviving beyond a time t or, in other words,
the probability that the random variable T ≥ t. The hazard rate hT (t) reflects the chance
of experiencing the event in the next instant in time. The cumulative hazard rate HT (t) is
the accumulation of the hazard rates over time. For a continuous random variable T with a
probability density function f(t), we have S(t) =∫∞
tf(x)dx, the hazard function is given by
h(t) = f(t)S(t)
= −dlogS(t)dt
and the cumulative hazard function H(t) = −logS(t)
These three representations of the distribution of T can be estimated either parametric or
non-parametric methods. Non-parametric estimators are a natural choice when dealing with a
homogeneous population because of the flexibility they offer. Our population is far from homo-
geneous but we begin with these nonparametric estimates to summarize the survival behavior
of groups and members before going on to a paramteric model which allows us to easily incorpo-
rate covariates and thereby estimate the causal effects of group and member characteristics on
survival rates. A variety of different nonparametric estimators and parametric models are avail-
able. For nonparametric estimates we focus on the Nelson-Aalen estimator of the cumulative
hazard function which is shown to have desirable small sample properties and on a smoothed
hazard rate derived from this estimator which allows us a better visual examination of changes
in the instantaneous hazard. For parametric estimates we use the Weibull model for reasons
discussed below.
4.1 Nonparametric Estimates of Cumulative Hazard Rates
With right censored data, the exact lifetime is only observed if failure or exit occurs before the
time of censoring, namely the date at which the group was surveyed. In the discussion below,
we will usually refer to events as the the exit of SHG members, although the same principle
applies for group failure.
12This discussion is based on Klein and Moeschberger (2003), chapters 2 and 3.
14
Suppose that in our data, members exit groups at D distinct times t1 < t2 < ... < tD and that
at time ti there are di departures. Time, in our case, is the number of days since the member
joined the group. Let Yi represent the number of individuals who are at risk at time ti. In our
case, this is the number of members who are still part of the group at ti or who or who leave
it at ti. The ratio di/Yi estimates the conditional probability that a group or a member who
survives to time ti, experiences the event at time ti. The Nelson-Aalen estimator is then given
by:
H(t) =
{0 if t ≤ t1∑
ti≤tdi
Yiif t1 ≤ t
(1)
By smoothing the jump sizes of this estimator with a parametric kernel, estimates we can obtain
a hazard function h(t).
Observations on groups that were active at the time of the survey and on the members in them
are all right censored. Depending on the duration at that time, they are subtracted form the
relevant Yi.
To test the equality of survivor functions across groups against the global alternative that at
least one of the populations differs from the others at some time, a number of nonparametric
tests are available. These are typically based on weighted comparisons of differences between
the observed failures and those expected under the null hypothesis. We compare the survival
functions in our two regions based on the Wilcoxon-Breslow-Gehan test, which extends the
Wilcoxon rank sum test that is commonly used to compare distributions to censored data.
Although the test does not rely on any assumptions about the types of distributions being
compared, it does assume that the nature of censoring is the same in both samples.
4.2 Parametric Methods: The Weibull Model
Parametric methods of survival analysis assume particular forms for the survival function. This
additional structure, while restrictive, allows us to easily incorporate the effects of covariates
on survival time. We use the Weibull distribution to study the effects of group and member
characteristics on their survival. The Weibull survival function is given by
SX(x) = exp(−λxα),
the hazard function by
hX(x) = αλxα−1,
15
and the cumulative hazard function by
HX(x) = λxα.
By using this model we restrict hazard rates to be monotonic over time, but they can be either
increasing, decreasing or constant, depending on the value of the parameter α. If α = 1 this
model reduces to an exponential model which has a constant hazard rate, for α > 1 hazard
rates are increasing and with α < 1 they are decreasing. Notice that the natural log of the
cumulative hazard function in the Weibull model is linear as a function of the log of member
duration. Figure 1 plots these two variables for our data set (using Nelson-Aalen estimates of
H(t)). The model seems to fit the data very well except for members with very short durations
within groups.
Figure 5: Appropriateness of the Weibull Model
The Weibull model has both an accelerated failure time (AFT) representation, where the de-
pendent variable is the natural log of survival time and is a linear function of covariates, and
a proportional hazard (PH) representation. We present our results within the structure of a
proportional hazards model. Given a vector of covariates Z and corresponding coefficients β,
the hazard rate is given by
h(x|Z) = (αλxα−1) exp(β′Z).
and (αλxα−1) is referred to as the baseline hazard, h0. All our results are presented in the form
of hazard ratios corresponding to our explanatory variables. For binary variables these tell us
the factor by which the hazard function moves up or down relative to the baseline hazard. In
general, it gives us the ratio of the hazard function to the baseline hazard when for a unit change
16
in an explanatory variable. A hazard-ratio of 1 therefore suggests that the co-variate has not
effect on the risk of failure.
5 Results
5.1 Nonparametric Estimates
We first present non-parametric estimates of hazard functions separately for each geographic
area and then discuss the effects of a group and member characteristics based on the Weibull
model. Figure 6 shows Nelson-Aalen estimates for cumulative hazard functions for SHGs in
Figure 6: Nelson-Aalen Estimates of Regional Hazard Rates: SHG level
Keonjhar and Raigarh as well smoothed hazard functions for these areas. 13. The hazard
13Keonjhar refers to all groups under the Keonjhar branch office of PRADAN and therefore covers both
Keonjhar and Mayurbhanj districts
17
functions are obtained by a kernel smoothing of the hazard contributions provided by the Nelson-
Aalen cumulative hazard functions and, like all estimates obtained by kernel procedures, they
are less reliable at the end points of the the time-interval where the sample is thin.
As seen in the summary statistics in Table 3, the risk of failure is higher in Raigarh. The double-
humped hazard rate for Raigarh is interesting as it reflects two different phases in a group’s life
when it is especially vulnerable; about a year after inception, and then again after three of four
years. Hazard rates in Keonjhar change much less over a group’s life. Our summary statistics in
show that Raigarh groups are much more heterogeneous than those in Keonjhar and that group
conflict is more salient. It may be that conflict in heterogeneous groups appears periodically,
depending on the timing of events such as bank linkages or bad harvests.
Figure 7 displays hazard rates using member-level data for the two regions. The risk of member
departures in the early stages of membership are very similar, but once again, we see a second
hump in the Raigarh hazard function that does not appear in Keonjhar. The differences in the
hazard functions of members in the two areas appear less marked than the differences in group
hazard across these regions.
Figure 7: Member Level Regional Hazard Rates
18
During the survey, members who had left groups were asked for the principal reason for their
departure. Table The most frequent reason stated was difficulty in saving the amount required
by the group or in repaying loans. The second most frequent was conflict with other group
members. Figures 8 and 9 are each based on a truncated sample. For Figure 8, among the
group of members who have left groups, we include only those that left due to difficulty in
savings or repayment . Similarly, in the case of Figure 9 we keep only those that stated conflict
as their reason for leaving the group. The reversal of hazard rates across regions in these two
figures is striking. Exit due to difficulty in saving and repayment is much more important
in Keonjhar and reverses the relative position of the hazard functions, while conflict is more
important in Raigarh.
Figure 8: Hazard Due to Difficulty in Saving: Member-Level Data
5.2 Parametric Estimates
Hazard ratios from estimating the Weibull model as shown in Tables 9-12.
Of the various group characteristics that we consider, the only ones that systematically affect
19
Figure 9: Hazard Due to Member Conflict: Member Level Data
20
hazard rates are the number of other PRADAN SHGs in the area and the maximum level of
education within the group. Both these lower the risk of group failure. An additional year
of education for the most educated member of the group lowers the risk of failure by 8% and
an additional group in the same village lowers the hazard rate by 18%. It is conceivable that
the presence of an educated member facilitates interactions with banks and other officials and
ensures better book-keeping. Other groups in the village may help either through the sharing
of information or by making it more likely that PRADAN professional frequently visit the area.
We have not of course looked at these particular mechanisms directly as yet, and these remain
conjectures based on some anecdotal evidence.
Member departures are much more sensitive to variables that capture group composition. Ed-
ucated members stay longer within groups as do those who have been married and those with
children. Average age, average land owned and more initial members are all are both positively
related to survival. Fractionalization does seem to encourage exit, and this is especially true of
the groups with Scheduled Tribes.
To consider the effects of heterogeneity more carefully, we estimate the model with members
from groups with only one or two official caste categories. These results are shown in Tables
11-12. Scheduled Tribes are much more likely to exit groups if they are mixed, even in case
where groups are formed together with other tribes. Scheduled Castes do not show differential
rates of exit for homogeneous and mixed groups. For Backward Castes the probability of exit
from mixed groups is not significantly higher than for homogeneous groups except when they
are in a minority.
6 Concluding Remarks
Our analysis of group and member duration in this paper brings us towards three main conclu-
sions. First, rates of group of failure and member attrition are not insignificant. They imply
that about one-fifth of those joining an SHG network leave it within an 8-year period. These
rates in themselves are not alarming and their welfare implications are not straightforward. It
may be that participation in SHGs works best for short periods of time, after which borrowers
are able to enter into individual lending contracts with rural banks. Our study of group failure
and changes in group composition does not however support this type of an explanation and
this brings us to our other two results: Groups with educated members and and those in villages
with other SHGs are less likely to fail and it is therefore the remote, disadvantaged communities
that are most likely to be deprived of credit through these institutions. Lastly, the Scheduled
Tribes which have been widely recognized as being especially disadvantaged, are most likely to
remain in the system if they are in narrowly homogeneous groups. This poses a real challenge
to policy makers who want micro-finance institutions in India to provide disadvantaged com-
munities with access to credit: Homogeneous groups of uneducated members are likely to fail,
yet attrition rates of these members in mixed groups are high.
22
References
Armendariz de Aghion, Beatriz, and Jonathan Morduch (2005) The Economics of Microfinance
(Cambridge, MA: MIT Press)
Basu, Priya, and Pradeep Srivastava (2005) ‘Scaling up microfinance for india’s rural poor.’
World Bank Policy Research Working Paper 3646.
Bell, Clive (1990) ‘Interactions between institutional and informal credit agencies in rural india.’
World Bank Economic Review 4(3), 297–327
Burgess, Robin, and Rohini Pande (2005) ‘Can rural banks reduce poverty? evidence from the
indian social banking experimen.’ American Economic Review 95(3), 780–795
Harper, Malcolm (2002) ‘Self-help groups and grameen bank groups: What are the differ-
ences?’ In Beyond Micro-Credit: Putting Development Back into Micro-Finance, ed.
Thomas Fisher and M.S. Sriram (New Delhi: Vistar Publications)
K. G. Karmarkar, K.G. (1999) Rural Credit and Self-Help Groups: Microfinance needs and
concepts in India (New Delhi: Sage)
Karlan, Dean (2006) ‘Social connections and group banking.’ Working paper.
Klein, John P., and Melvin L. Moeschberger (2003) Survival Analysis (New York: Springer)
Nair, Ajay (2005) ‘Sustainability of microfinance self-help groups in india: Would federating
help?’ World Bank Policy Research Working Paper 3516.
National Bank for Agriculture and Rural Development (1992) Guidelines for the Pilot Project
for linking banks with Self Help Groups: Circular issued to all Commercial Banks
(www.nabard.org: Mumbai)
(2006) NABARD Annual Report, 2005-2006 (www.nabard.org: Mumbai)
Reserve Bank of India (1991) Improving access of rural poor to banking-Role of interven-
ing agencies-Self Help Groups: Circular issued to all Scheduled Commercial Banks
(www.rbi.org.in: Mumbai)
(1999) Swarnajayanti Gram Swarozgar Yojana: Circular issued to all Scheduled Commer-
cial Banks (www.rbi.org.in: Mumbai)
(2005) Internal Group to Examine Issues Relating to Rural Credit and Microfinance
(www.rbi.org.in: Mumbai)
23
(2006) Handbook of Statistics on Indian Economy (www.rbi.org.in: Mumbai)
(2007) Annual Policy Statement for the Year 2007-2008 (www.rbi.org.in: Mumbai)
24
Table 2: Number of PRADAN SHGs in India ( 31/03/06)
State Location Yeara First SHG # SHGs
Chhatisgarh Raigarh 1998 1999 532
Jharkhand Godda 1987 1989 314
Jharkhand Barhi 1992 1992 411
Jharkhand Lohardaga 1992 1995 449
Jharkhand West Singhbhum 1992 1996 363
Jharkhand Gumla 1994 1994 484
Jharkhand Dumka 1995 1989 318
Jharkhand East Singhbhum 1997 1996 392
Jharkhand Khunti 2000 1997 314
Jharkhand Koderma 2000 1992 359
Jharkhand Petarbar 2000 1998 322
Jharkhand Deogarh 2002 1989 280
Rajasthan Dausa 1999 1999 171
Rajasthan Dholpur 1999 2000 180
Madhya Pradesh Kesla 1986 1996 300
Madhya Pradesh Vidisha 2000 2000 44
Madhya Pradesh Sidhi 2002 2005 49
Madhya Pradesh Dindori 2005 2005 110
Orissa Keonjhar 1990 1998 506
Orissa Balliguda 2001 2001 201
Rajasthan Alwar 1986 1987 162
West Bengal Purulia 1987 1995 218
West Bengal Bankura 2005 2000 142
Total 6701
aThis refers to the year in which a PRADAN office was started in the area. The Deogarh and Dumka SHGs
were initially under the Godda office and the Koderma and Peterbar SHGs were managed by the Barhi office.
This is why the first SHG in these areas predates the opening of the PRADAN branch office.
25
Table 3: Year-wise formation and dissolution of SHGs: Survey Data, 1998-2006.
Year Started Dissolved Bank loan
Keonjhar Raigarh Keonjhar Raigarh Keonjhar Raigarh
1998 4 0 0 0 0 0
1999 10 18 0 0 0 0
2000 51 61 0 0 3
2001 27 36 3 5 2 7
2002 155 30 4 5 14 23
2003 89 46 11 7 100 31
2004 95 172 9 8 95 100
2005 85 160 17 23 89 140
2006 16 47 2 20 62 91
Totala 532 570 46 69 362 395
aThere are two main reasons why the totals in this table do not match those in Table 2. First, we included all
groups that were formed before the survey date, and some of these were created after March 2006. Second, Table
2 is based on administrative data that do not always account for group failures since these are not consistently
reported.
26
Table 4: Group Characteristics by Survival Status
Keonjhar Raigarh
Existing Dissolved Existing Dissolved
Number of Observations 486 46 501 69
(91) (9) (88) (12)
Average duration (days) 1103 878 1129 620
COMPOSITION
Total number of castes 87 22 61 14
Number of castes in a group 2.4 1.8 4.2 3.5
Number of caste categories (st, sc, bc, fc) 1.8 1.3 2.4 2.3
Fractionalization index 0.27 0.16 0.51 0.46
HOMOGENOUS GROUPS (%) 33 52 9 13
Scheduled Tribes (% of homogenous) 66 86 61 67
Scheduled Castes (% of homogenous) 12 10 23 33
Backward Castes (% of homogenous) 22 4 14 0
Forward Castes (% of homogenous) 0 0 2 0
GROUP ACTIVITIES LAST YEAR
Midday meals (%) 9 0 12 1
PDS (%) 3 0 4 0
Panchayat meetings (%) 34 22 56 35
Exposure trips (%) 70 41 13 6
Federation meetings (%) 12 2 2 0
Meet government officials (%) 20 7 32 16
Involvement in family or village conflicts/ help member in distress (%) 44 25 52 26
RULES
Minimum weekly saving (%) 100 100 94 96
Saving compulsory (%) 29 20 38 39
Groups with fines for absence(%) 93 67 38 26
Absence fine (Rs.) 3.1 2.6 3.8 3.2
Higher interest rates default (%) 16 13 92 91
OTHER CHARACTERISTICS
Any subsidies received (%) 13 0 5 1
Any group projects (%) 80 67 27 6
Total bank linkages 1.2 0.2 1.4 0.3
Member accountants (% accts) 68 42 59 62
MEMBERS
Average number of members 16 15 15 15
Left (%) 13 14 15 15
Literate (%) 34 14 30 25
No school (%) 58 87 64 70
Maximum education (years) 9 5 8 7
Mean education (years) 2.8 1 2 1.6
Mean land (Acres) 1.7 1.4 2 1.927
Table 5: Characteristics of Present and Past Members
Keonjhar Raigarh
present departed all present departed all
Number of women 7471 1097 8568 7048 1268 8316
(87) (13) (100) (85) (15) (100)
CASTE CATEGORY COMPOSITION
ST (%) 59 60 59 45 50 46
SC (%) 13 13 13 22 25 23
BC (%) 27 26 27 31 23 29
FC (%) 1 1 1 2 2 2
BACKGROUND
Education (number of years) 2.7 2.4 2.7 1.9 1.6 1.9
No school (%) 60 65 61 65 68 65
Read and write (%) 32 29 32 30 24 29
Father’s education (number of years) 2.1 1.4 2.0 2.1 1.3 2.0
Land (acres)
RELATION TO GROUP
Relatives within group (%)a 12 8 11 8 6 8
In homogenous groups (%) 35 33 35 10 7 10
Previous shg membership (%) 4 9 5 6 6 6
Joined other shg after leaving (%) 21 19
CHAIRMAN b
membership < 2 years (%) 0.058 0.0061 0.045 0.088 0.034 0.070
2 years < membership < 4 year (%) 0.076 0.037 0.073 0.091 0.037 0.087
4 year < membership (%) 0.083 0.027 0.082 0.088 0.051 0.085
aPercentage of members who have at least one relative in their group.bPercentage of members who have been chairman, given the duration of their membership.
28
Table 6: Stated Reasons for Group Failure and Member Exit
Keonjhar Raigarh
GROUPS
Pradan withdrew support 8 8
(17) (12)
Personal conflicts / leadership problems / accountant problems 20 27
(43) (40)
Unpaid loans / irregular savings 14 17
(31) (24)
Others 4 17
(9) (24)
Total 46 69
(100) (100)
MEMBERS
PERSONAL REASONS
Illness / dead 8.3 8.1
Left village / married / seasonal migration / going to school 17.8 12.0
RELATION TO GROUP
The family was not supportive 6.2 9.1
Could not reimburse a loan taken / difficulty in saving 29.2 17.1
Could not attend the meetings 9.8 12.8
Personal conflict with the group 15.5 20.3
Excluded by the group 4.9 1.0
OTHERS
Wanted to join another group 0.5 6.5
Othersa 7.8 13.1
aOthers includes not understanding the working of the shg, pradan official stopped visiting the
group, the group is too big and no clear reason
29
Table 7: Loans
Keonjhar Raigarh
present departed present departed
Average group loans per member years (Rs.) 1111 441 344 90
Number of members who received part of linked loan 1920 29 1596 52
if shg got only one linkage (72) (56) (85) (45)
Duration in group after first linkage if member had a share in linkage 376 341
Duration in group after first linkage if member had no share in linkage 391 390
linked loans per member years (Rs.) 4197 2564 4218 2184
(for members who were present during all linkages)
30
Table 8: Castes
Keonjhar Raigarh
Scheduled tribe 2867 2030
(57) (43)
Scheduled caste 670 1118
(13) (23)
Backward caste 1420 1525
(28) (32)
Forward caste 87 77
(2) (2)
BIGGEST CASTES
SCHEDULED TRIBE
Bhuiyans 1126 204
Kharia 15 468
Ho 444 5
Munda 533 12
Santhals 501 0
Bathundi 807 0
Gond 433 619
Ganda 372 128
Rathia 0 1602
SCHEDULED CASTE
Harijans 419 11
Chauhan 0 905
BACKWARD CASTE
Yadav 5 711
Mahanta 820 100
Kurmi 493 14
Teli 95 497
31
Table 9: Hazard Rate Estimates at SHG Level, Weibull model
(1) (2) (3)
Shape parameter 1.11 1.12 1.16
Homogenous shg, caste 1,13
(0,38)
Homogenous shg, ST 1,18 1,09
(0,41) (0,39)
Homogenous shg, SC 1,9 1,84
(0,95) (0,93)
Homogenous shg, BC 0,25 0,26
(0,26) (0,28)
Fractionalization 0,83 0,84 0,78
(0,46) (0,47) (0,44)
Average relations in group 0,9 0,84 0,91
(0,59) (0,55) (0,6)
Number of initial members 0,96 0,95* 0,96
(0,03) (0,03) (0,03)
Maximum education in group 0,92** 0,92** 0,91**
(0,02) (0,02) (0,03)
Average land (Acres) 0,95
(0,06)
Average age 0,96**
(0,02)
Average total children 1,12
(0,21)
Average separated 3,9
(3,48)
Concurrent pradan shgs 0,82** 0,82** 0,81**
(0,03) (0,03) (0,03)
Raigarh 1,61** 1,54* 1,72**
(0,37) (0,35) (0,44)
Number of observations 1061 1061 1056
Number of Departures 107 107 105(*) significant at a 10% significance level
(**) significant at a 5% significance level
32
Table 10: Hazard Rate Estimates at Member Level, Weibull model
(1) (2) (3) (3) (3) (3) (4)
ST SC BC FC
Shape parameter 0,74 0,74 0,73 0,76 0,75 0.57 0,74
Caste category, SC 1,08 1,04 0,98
(0.07) (0.06) (0.07)
Caste category, BC 0,92 0.87** 0.84**
(0.05) (0.05) (0.05)
Caste category, FC 1,15 1,03 1,04
(0.2) (0.18) (0.18)
Education (in years) 0.97** 0.97** 0.95** 1.04** 0.97** 0.84** 0.97**
(0.01) (0.01) (0.01) (0.02) (0.01) (0.05) (0.01)
Land (Acres) 0,99 1 1 0.95* 1 1,04 1,01
(0.01) (0.01) (0.01) (0.03) (0.01) (0.05) (0.01)
Age 1* 0.99** 1 1 0.99** 0.96* 1
(0.002) (0.002) (0.003) (0.01) (0.01) (0.02) (0.003)
Separated 0.83** 0.83** 0,86 0.62** 1,01 0,47 0.83**
(0.06) (0.06) (0.09) (0.12) (0.16) (0.35) (0.07)
Total children 0.89** 0.89** 0.91** 0.88** 0.89** 0.8* 0.9**
(0.01) (0.01) (0.02) (0.03) (0.03) (0.1) (0.01)
Relation 0.37** 0.09** 0.09** 0.1** 0.09** 0.001** 0.09**
(0.06) (0.02) (0.04) (0.06) (0.05) (0.003) (0.02)
Homogenous shg, castes 0,91 0,91 1,08 0,86 0,26 0.86*
(0.07) (0.09) (0.22) (0.16) (0.29) (0.08)
Homogenous shg, SC 1,07
(0.17)
Homogenous shg, BC 1,01
(0.16)
Fractionalization 1.39** 1.57** 1,53 1,08 0,61 1.35**
(0.17) (0.25) (0.42) (0.28) (0.78) (0.16)
Average relations in group 7.78** 6.41** 10.91* 5.97** 174.93** 7.56**
(2.33) (2.82) (6.99) (3.48) (426.01) (2.28)
Number of initial members 1.03** 1.04** 1,01 1.04** 1,06 1.04**
(0.01) (0.01) (0.01) (0.01) (0.06) (0.01)
Maximum education in group 1,01 1,01 1,01 0,98 1.07 1,01
(0.01) (0.01) (0.02) (0.02) (0.09) (0.01)
Average land (Acres) 0.95**
(0.02)
Average age 0.99**
(0.01)
Average total children 0,98
(0.05)
Average separated 0,92
(0.23)
Concurrent pradan shgs 1.02** 1.03** 0,99 1.03** 1,07 1.02**
(0.01) (0.01) (0.01) (0.01) (0.05) (0.01)
Raigarh 1.14** 1,03 1,05 1.31** 0.81* 1,65 1,08
(0.05) (0.05) (0.07) (0.17) (0.1) (0.82) (0.06)
Dissolved 1.36** 1.49** 1.4** 2.1** 1,3 0,92 1.47**
Number of observations 15573 15529 8064 2781 4432 252 15529
Number of Departures 2009 2004 1091 390 489 34 2004
33
Table 11: Hazard Rate Estimates at Member Level for Individual Caste Categories:Weibull
model (Restricted Sample)
ST SC BC FC
Shape parameter 0.78 0.78 0.78 1.12
One caste category 1.48** 0,8 0,92
(0,17) (0,29) (0,27)
Member of majority caste category 1.26** 1,12 1,08 0.05
(0,12) (0,21) (0,18) (0,31)
Member of minority caste category 1.35* 1,19 1.66** 0
(0,24) (0,27) (0,32) (0,04)
Concurrent pradan shgs 1,01 0,99 1.06** 0,96
(0,01) (0,02) (0,02) (0,2)
Number of Observations 6124 1590 2852 75
Number of Departures 749 195 284 7
34
Table 12: Hazard Rate Estimates at Member Level for Individual Castes/Tribes : Weibull
model (Restricted Sample)
Average Shape One caste Member of majority Member of minority Concurrent Education Number of
education parameter category caste category caste category pradan shg observations
(failures)
ST
Bhuyians 0.88 0.78 0,89 0.59* 2.86** 0.9** 0.9* 1021
(0,26) (0,17) (1.15) (0,04) (0,05) (124)
Kharia 0.95 0.91 5.96* 1,46 0,31 1.16** 0,93 327
(6,59) (1,55) (0,47) (0,04) (0,08) (53)
Ho 0.89 0.71 1,24 2.42** 1,89 1,07 0,95 375
0.61 0.95 1.32 0.05 0.08 (55)
Munda 1.10 0.88 2,02 2** 0,63 1,03 0,88 350
(0,93) (0,81) (0,51) (0,07) (0,08) (59)
Santhals 1.43 0.66 1,65 3.4** 6.84* 1.12** 1 380
(1,01) (1,59) (7,89) (0,06) (0,06) (38)
Bathundi 2.34 0.67 0,58 0,83 0,42 1,15** 0,9** 627
(0,29) (0,29) (0,44) (0,06) (0,05) (59)
Gond 2.74 0.82 0,89 1,27 2,15* 0,92* 0,98 695
(0,57) (0,4) (0,97) (0,04) (0,04) (66)
Ganda 2.98 0.92 0,61 0,76 0 0,94 0,82** 368
(0,48) (0,36) (0,001) (0,07) (0,07) (35)
SC
Harijans 3.20 0.85 2,19 0,14** 0,96 1,1 1,2** 260
(2,53) (0,08) (0,52) (0,07) (0,07) (29)
BC
Yadav 1.48 0.96 2,1 0,95 1.06 0.92 296
(1,92) (0,43) (0.05) (0.07) (27)
Mahanta 5.23 0.74 0,6 2,13** 1,28 1,13** 0,93** 675
(0,5) (0,78) (0,78) (0,04) (0,03) (63)
Kurmi 5.05 0.78 1,3E+13 0,77 0,55 1,13** 1,0 399
3,25E+16 (0,26) (0,33) (0,06) (0,04) (54)
35
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