Effect
of Loaning Procedure on Repayment
The respondents were requested to indicate procedures that
influence micro-credit loan repayment. The results are presented on (Table 4).
The results in Table 4 show that the larger part of respondents
with (Mean score = 4.30) indicated that the borrowing terms put in place and
flexibility of revolving fund institutions on loan lending influences the
influence of repayment. Respondents with (M= 4.28) indicated that a business
plan is essential to borrowing and payment should be mandatory. Both variables
had the lowest standard deviation of 0.823 and 0.829 respectively. The results
from the findings indicate a mean (above 3.00) thus clearly showing that the
loaning procedures has influence on revolving fund loan borrowing and repayment
which was in line on breaking the financial services barriers in USA which
voiced the importance of a business plan before borrowing and lending. Study
cost structure and sustainability in micro-finance institutions in Bangladesh
also voiced the importance to loan screening than monitoring lending and
borrowing as part of loaning procedure. Currently, according to report by
constituency loan offers in Murang’a County, business plan requirement was a
formality.
Group
members’ failure to save
The study intended to know the reasons why group members were
failing to save resulting to poor repayment of revolving fund repayment (Table
5).
Results
in Table 5 shows that most of the respondents (Mean = 4.37) with the lowest
standard deviation (Stdv 0.788) indicated that strong preference to current
consumption results to low savings. Lack of motivation for savings to build a
reserve fund to fall back had a standard deviation (stdv 0.909) and lack of
pre-commitment measures to ensure preference of savings dominates (stdv 0.886)
and lack of knowledge of advantages of maintain a financial buffer in the group
had a standard deviation (stdv 1.001). The mean as indicate by Table 5 were
4.25, 4.21 and 4.18 respectively. The findings were in line with the study done
on bank regulation that are changing in Kenya which had noted that saving is
hard work, and many Kenyan institutions are largely designed to make it easy to
spend, and not to save. The study recommendations of the means to improve
savings including coerced (mandated) or otherwise should enforced. Most
respondents (38.4%) supported a monthly visitation by the constituency loan
officers and others for dissemination of information [16].
Table 5: Reasons for not Saving.
|
Reasons
|
N
|
Mean
|
Std. Deviation
|
|
No Knowledge of advantages of maintaining a financial buffer
|
261
|
4.18
|
1.001
|
|
No Pre-commitment measures to ensure the preference of savings
dominates
|
261
|
4.21
|
.886
|
|
No motivation for savings to build a
reserve fund to fall back
|
261
|
4.25
|
.909
|
|
Strong preference for current
consumption to future consumption
|
261
|
4.37
|
.788
|
|
Valid N (list wise)
|
261
|
|
|
|
Source: Survey
data (2014)
|
Multiple
lending
The respondents were requested to indicate whether institutions
in the constituencies were failing to reduce multiple lending and borrowing.
Figure 2 below provides the outcome (Figure 2).
Results
from Figure 2 show that a good number of the respondents (41.%) indicated that
all the variables (ranging from irresponsible officers, lack of elaborate
plans, lack of standing requirement and not providing training to borrowers as
the key reasons that encourage multiple borrowing. About (5.4 %) indicated
that, irresponsible officers are the main cause of multiple borrowing. Among
the respondents, (19.4%) indicated that lack of providing training to borrowers
on financial matters as the main cause of multiple borrowing.

Figure 2: Multiple Lending.
Table 6: Loaning Procedure
extent to Borrowing and Repayment.
|
Category
|
N
|
Mean
|
Std. Deviation
|
Variance
|
Kurtosis
|
|
Statistic
|
Statistic
|
Statistic
|
Statistic
|
Statistic
|
Std. Error
|
|
Importance of screening on
micro - credit borrowers
|
261
|
4.68
|
.736
|
.542
|
10.314
|
.300
|
|
Micro-insurance
introduction to YEDF and WEF
|
261
|
4.44
|
.770
|
.593
|
4.143
|
.300
|
|
Every micro-institutions
should demand on business plans before issue of borrowed fund
|
261
|
4.33
|
.863
|
.745
|
2.343
|
.300
|
|
Use of technology should be applied by every revolving fund
institutions for mining data
|
261
|
4.02
|
1.259
|
1.584
|
.374
|
.300
|
|
Multiply the amount lent
out to the groups
|
261
|
4.53
|
.797
|
.635
|
5.027
|
.300
|
|
Review the administrative fee upwards
|
261
|
3.00
|
1.777
|
3.158
|
-1.547
|
.300
|
|
Valid N (listwise)
|
261
|
|
|
|
|
|
Table 7: Screening Process in
Groups Including the Interview Report.
|
Category
|
Frequency
|
Percentage
|
|
|
Loan application and integrity checks
|
225
|
86.5
|
|
Technical assessment
|
12
|
4.6
|
|
Approval by
secretariat based on ability
|
9
|
3.5
|
|
Ability to insure the loan
|
5
|
1.9
|
|
Collateral security checks
|
4
|
1.5
|
|
Proposed development
criteria checks
|
3
|
1.2
|
|
Site eligibility checks
|
3
|
.8
|
|
Total
|
261
|
100.0
|
The results supports on the study of causes of default in
government revolving fund in Uasin-Gichu District who noted high existence of
multiple borrowers and recommended the need for early detection of the multiple
borrowers.
Extent
of loaning procedure to influence borrowing and repayment
The respondents were requested to indicate the extent to which
screening mechanism, micro-insurance, demand of business plan, use of
smart-cards, amount lent out and administrative fees influence revolving fund
borrowing and repayment. The results are as indicated on (Table 6).
Results from Table 6 show that a good number of respondents (M =
4.68) and a lower standard deviation of (Std dev = 0.736) indicated the
importance of screening on revolving fund borrowers. Kortosis captures whether
the actual distribution was more peaked or flatter than the normal distribution.
From the findings, kortosis measure for impotent of screening was (K=10.34)
showing how peaked the category was comparatively. The respondents with
(M=4.44) indicated that introduction of micro-insurance to YEDF and WEF very
much needed and should be hastened. The respondents indicated that loan
screening mechanism was most vital aspect in revolving fund borrowing and
repayment compared micro-insurance, demand for business plan, use of smart
cards and administrative fees put in place to others.
Screening
process taking place in most groups
The study intended to know the screening process that takes
place in the groups the respondents belonged (Table 7).
Results from Table 7 show that most of the respondents (86.6%)
indicated that main screening process they had come across was loan application
and integrity checks, site eligibility checks had the least respondents (0.8%).
The importance of screening but warn the credit officers on borrower’s cunning
and lying that takes place in many villages.
Rounds
of borrowing and amounts
The respondents were asked to state the number of rounds they
had borrowed since the inception of both the YEDF and the WEF (Table 8).
Results from Table 4.5 show that large part of respondents
(41.4%) indicated they had borrowed in the second round, showing most of them
were not new members to the groups. (38.7%) of the respondents only borrowed in
the first round. The results were in line with the challenges that were stated
in 2009 on both YEDF and WEF status reports. The reports had indicated a
negative perception and attitude on the funds as they were established on the
eve of a general election and hence perceived as a political organization to
influence voting pattern particular among the youth and women. The individuals
concerned have proved the assertion to be not true and are now joining in
numbers. On the other hand, many did submit their borrowing documents in good
time, resulting to delay in release of funds and this accounts as to why they
were in round one. The fund was not sufficient as reported by the YEDF and WEF
reports to cater for the high demand and the expectation of the youth and
women. This was another reason for the high rate of respondents in round one in
2013 survey. On the amount borrowed as indicated by the results on Table 8,
most of the respondents (52.5%) had borrowed (between Kshs. 10,000 - 100,000).
Very few (2.7%) had borrwed above Kshs. 400,000.
Seminars
attendance and workshops for financial literacy
The researcher needed to find out whether group members attend
training/workshop/seminars for financial literacy (Table 9).
Results on Table 9 show that most of the respondents (82.5%)
indicated they have attended workshops and seminars on financial literacy. Only
(11.9%) of the respondents had not attended any seminar. The results reflect
the positive initiative taken by the WEF and YEDF initiatives but still
question on the (11.9%) who have already borrowed and have not being trained.
The expectation should be (100%) training on all the groups. The results support
on whether micro-crest programmes alleviate poverty in South Africa, who
attributed the need to external reliance, provide financial literacy education
and revolving fund institutions to work closely with village administration.
The constituency loan officers confirmed the mild involvement of the village
administration in identifying members in their villages for financial literacy
training but rarely involved during the process of loan repayment. They are
only involved when the deal does not materialize which needs to be checked [8].
Sustainability
using the operating self-sufficient ratio (SSOR)
The study computed operating self-sufficient ratio (OSSR) of the
amount borrowed and repaid by all the constituencies in Murang’a County. This
was meant to determine whether the operating revenue as a percentage of
operating and financial expenses including loan loss provision expense was
greater than 100%. If the OSSR is greater than 100%, it means that the
institution(s) in question is able to cover the costs through own operations
and therefore do not rely on contributions from other donors to survive. The
general formula as modelled for computing revolving fund sustainability was:

Table show the analysis of the loan borrowed, repaid, cost and
amount recovered including the risk level for WEF in 2012 and 2013 (Table 10).
In the researcher’s point of view in Table 10, the total amount
recovered (2013)(b) Loan cost was (c) which was 5% of the amount borrowed and
amount due to date was (a)

OSSR< 1(Not sustainable but near to 1)
The
Operating Self- Sufficiency Ratio (OSSR) for WEF year 2012 was 0.91:1. Which
was an indication of non-sustainability of the government revolving fund
initiative. The findings indicated a satisfactory trend since the OSSR was
positive from 0.91:1 to 0.95:1. The computed risk level in all the
constituencies in 2013 as reported by the public account statement (2013) on
WEF was found to be greater than 10% thus sending unsatisfactory results.
Kandara constituency was outstanding with the lowest risk level of 11%. The
risk level in Kangema constituency (62%) was rather worrying and measures
should be put in place to arrest this scenario. The Average County risk level
for WEF (2013) was computed as 33%. The OSSR for the YEDF for year (2012- 2013)
was computed as 0.417:1 and 0.540:1 (APPENDEX 6) for years 2012 and 2013
respectively. This was also an indication of non-sustainability of the YEDF but
the trend was positive. The study agrees with the results indicated in the
Youth Fund Status Report (2009) that noted the loan repayment rate in two
constituencies in Murang’a County, namely; Kandara and Maragua to be at 40%.
This rate as noted from the computation has improved but still a lot needs to
be done for YEDF to reach sustainability level.
Table 8: Rounds of Issue and
Amount Borrowed.
|
Classification
Factor
|
Frequency
|
Percent
|
|
Rounds of issue
|
Round 1
|
101
|
38.7
|
|
Round 2
|
108
|
41.4
|
|
Round 3
|
38
|
14.6
|
|
Round 4
|
9
|
3.4
|
|
Non-respondents
|
5
|
1.9
|
|
Total
|
261
|
100.0
|
|
Amount borrowed
|
Between 10,000 and 100,000
|
137
|
52.5
|
|
Between 101,000 and
200,000
|
89
|
34.1
|
|
Between 200,001 and
300,000
|
20
|
7.7
|
|
Above 400,000
|
7
|
2.7
|
|
Non-respondents
|
8
|
3.1
|
|
Total
|
261
|
100.0
|
Table 9: Attendance to Seminars
and Workshops for Information.
|
Category
|
Frequency
|
Percent
|
|
Attended training
|
221
|
82.5
|
|
Did not attend training
|
32
|
11.9
|
|
Non-respondents
|
8
|
3.0
|
|
Total
|
261
|
100.0
|
Table 10: Amount Lent out and
Repaid (WEF- 2013 and 2012 Report).
|
Constitu-ency
|
No. of
Groups
|
Amount Distributed
|
Amount due to date
|
Paid to date
|
Loan balance
|
Loan cost/ expenses 5%
|
Risk
level
|
|
(Millions)
|
|
Year
20 - -
|
|
12
|
13
|
12
|
13
|
12
|
13
|
12
|
13
|
12
|
13
|
12
|
13
|
12
|
13
| |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
%
|
%
| |
|
Gatanga
|
208
|
253
|
10.8
|
13.1
|
3.15
|
5.7
|
6.27
|
6.9
|
6.27
|
6.3
|
0.54
|
0.66
|
<1
|
25
| |
|
Kandara
|
121
|
151
|
6.34
|
8.1
|
2.2
|
3.2
|
3.7
|
4.2
|
3.7
|
3.8
|
0.32
|
0.41
|
<1
|
11
| |
|
Kangema
|
112
|
116
|
6.05
|
6.3
|
3.76
|
4.3
|
3.45
|
3.3
|
3.45
|
2.9
|
0.30
|
0.31
|
23
|
62
| |
|
Kigumo
|
40
|
42
|
1.9
|
2.1
|
1.5
|
1.6
|
0.74
|
1.2
|
0.74
|
0.9
|
0.10
|
0.11
|
23
|
50
| |
|
Kiharu
|
140
|
157
|
7.4
|
8.6
|
3.6
|
4.1
|
3.9
|
5.1
|
3.9
|
3.5
|
0.37
|
0.43
|
<1
|
24
| |
|
Maragua
|
61
|
62
|
3.3
|
3.3
|
1.8
|
2.2
|
1.6
|
2.1
|
1.6
|
1.2
|
0.17
|
0.17
|
9
|
36
| |
|
Mathioya
|
119
|
137
|
5,21
|
7.1
|
3.91
|
4.1
|
2.23
|
3.7
|
2.23
|
3.7
|
0.26
|
0.36
|
24
|
24
| |
|
Total
|
801
|
918
|
48.6
|
48.6
|
19.8
|
25.5
(a )
|
22.3
|
26.5
(b)
|
22.3
|
22.3
|
2.01
|
2.43
(c )
|
|
| |
Table 11: Correlation analysis.
|
Correlations
|
|
|
Level of sustainability
|
Loaning operation procedure
|
|
Level of loan
sustainability
|
Pearson Correlation
|
1
|
.622
|
|
Sig. (2-tailed)
|
|
.024
|
|
N
|
261
|
261
|
|
Loaning operation
procedure
|
Pearson Correlation
|
.622
|
1
|
|
Sig. (2-tailed)
|
.024
|
|
|
N
|
261
|
261
|
Table 12: Parameter Estimate of
Logit Model.
|
Loan sustainability
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
Loan operation procedure
|
-.018
|
.292
|
.004
|
1
|
.048
|
.982
|
.554
|
1.741
|
|
Socio-economic functions
|
-.838
|
.264
|
10.064
|
1
|
.002
|
.432
|
.258
|
.726
|
|
Borrower characteristic
|
-.965
|
.341
|
8.005
|
1
|
.005
|
.381
|
.195
|
.743
|
|
Use of technology
|
-.519
|
.285
|
3.317
|
1
|
.069
|
.595
|
.340
|
1.040
|
|
Constant
|
1.618
|
.230
|
49.475
|
1
|
.000
|
5.04
|
|
|
|
|
Chi-square
Predicted overall performance
-2log likelihood
Negelkerke R2
|
22.761
73.4*
344.29
0.402
|
|
|
|
0.000
|
|
|
|
Hypothesis
testing
The previous results had presented descriptive statistics on
government revolving fund repayment and sustainability however, to draw
inferences about the population on the basis of the sample, there was need to
empirically analyse data using the Pearson correlation coefficient (Table 11).
From the table 12, the loaning operation procedure was
significantly correlated to the level of loan sustainability as the
significance level was (<0.05). The Pearson correlation coefficient the two
variables are (0.622) which was positive and large. This indicates a stronger
relationship between loaning operation procedure and level of sustainability of
government revolving funds.
Measuring
of the multiple logit regression model
The result of regression analysis is as indicated below (Table
12).
The regression results of the logit model in Table 13 are
reflected by the regression coefficient standard errors t- values, Wald statistics
and p-value. The logit model generates a chi-square value of 22.761 and p-value
of 0.000 which was statistically significant because the p= value was less
than? = (0.05). The results indicated that loan operation procedure had a
significant level of 0.045 < 0.05. This called for the rejection of the null
hypothesis and adopting the alternative was; there is a relationship between
revolving fund institutional operation procedures to loan sustainability in
Murang’a County. Result on Table 4.8 above shows the logit model’s accuracy of
overall prediction was 73.4 %, showing that the overall fit of model was
satisfactory. Additionally, the logit model yielded a Negelkerke R2 is 0.402,
meaning that 40.2% of the dependent variables can be explained by the
independent variable, namely the loaning operation procedures. Asserts that a
Negelkerke R2of 0.2 and <1(excluding 1) is satisfactory. Logit model
generated a -2loglikelihood value of 344.26 which means that the model was good
(a perfect model has a -2loglikeli-hood value of zero and above). The easiest
way of assessing Wald statistics is to consider the significance value, and if
(< 0.05), it means that the null hypothesis should be rejected; that there
is no statistical relationship between the independent variable to the
dependent variable. For interpretation of Exp (B) shown on table 4.8 above,
results of values from the regression analysis is taken to account, and if the
value (>1), then the odd of an outcome occurring increases, and if the figure
is (< 1), any increase in the predictor variable leads to a drop in the odd
of the outcome occurring. From the table Loan operation procedure as a
predictor has a value of Exp (B) at 95% Confidence interval(CI) of 1.741 that
implies that, when the predictor is raised by one unit, will increase level of
sustainability by 1.741 times. The summary of hypothesis testing is provided
below [24-25] (Table 13).
Summary
of hypothesis testing
The summary of the hypothesis in Table 4.9 indicates the
significance of the coefficients tested. The results showed that the first
three variables were significant and hence the null hypotheses were rejected
and the alternative hypotheses taking effect. The regression model appears as
shown in equation 14 below;

The
modal summary and ANOVA tests
The ANOVA test which partitions the observed variance based on
explanatory variables and the general equation after substituting the
coefficients was done. It compares partitions of test significance of
explanatory variables (Ayers, 2008). The ANOVA tests results are as indicated
in Table 15 below (Table 14):
Source:
survey data (2014)
Table 13: Summary of Hypothesis
Testing.
|
Hypothesis
|
Construct
|
Result
|
Explanation
|
|
H1
|
There is no statistical significant relationship between micro credit
institutions’ operation procedures to loan sustainability
|
Reject null hypothesis
|
Significant level of 0.045 < 0.05
|
Table 14: ANOVA Test.
|
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
|
Regression
|
1.329
|
4
|
.332
|
2.508
|
.043b
|
|
Residual
|
33.912
|
256
|
.132
|
|
|
|
Total
|
35.241
|
260
|
|
|
|
|
a. Dependent Variable: level of sustainability
|
| |
|
b. Predictors: (Constant), borrower
characteristic , loaning procedure , socio-economic factors
|
| |
The results in table 15 on ANOVA test showed an F- statistics of
2.508, (significance level = 0.043) which were statistically significant at
0.05(P < 0.05). This shows that the model adopted in the study was
significant and that, the variables tested fitted well in the model. A multiple
logit regression analysis was performed to determine how the independent
variables influenced the independent variables. The logit regression model for
the study was as shown in equation 4.1 above. Results on Table 4.10 above show
that, the first three independent variables were found to be significant. The
values of betas were referred to as (?0=1.618, ?1= -0.018,. The model is
represented below:

From the model above, the coefficient of revolving fund loaning
procedures is negative but significant as p-value was less than ? = 0.05 on the
three variables (Table 14 above) and the predicted probability resulted to high
and positive results (Table 15 below). Logit coefficients are log-odds units
and cannot be read as Ordinary least squares (OLS) coefficient. To interpret,
one needs to estimate the predicted probabilities of Y= 1, using a formula
provided below as modelled.

The logit regression shows that loaning operation has positive
and statistical influence on the level of sustainability. The ?-value of (X1)
was provided as negative 0.018 as indicated in the above general equation which
means in theory that, an increase in loaning operation procedures (X1) by one
unit is associated with an increased chance ratio of micro-credit
sustainability by 0.83 computed as follows
