Brown Chitekwere BSc Thesis Presentation

1. Determinants of access to and
demand for formal credit
among rural women: a Case of
Bembeke EPA
BY BROWN CHITEKWERE
Supervisor: DR. JUMBE

2. Introduction
Empowering women to participate in all sectors of the economy
is essential to build stronger economies, achieve internationally
agreed goals for development and sustainability, and improve
the quality of life for women, men, families and communities
(UN Women, 2011)
Although women make up to 52 percent of the Malawi’s
population, but still serious gender disparities exist in terms of
control of and access to productive resources and opportunities
for participation in the country’s development (UNICEF, 2010)

3. Conti…
One of the best ways of empowering women in Malawi is by
promoting their access to credit so that they can start some businesses
of their choice or expand their already existing businesses.
The GoM acknowledged the importance of the credits in the country’s
development. As such it has formulated and implemented credit
policies, programmes and it has created institutions in order to enable
low income Malawian to access credit.

4. The some private sectors have also introduced different
kinds of credit facilities for their customers
The study therefore looks for the factors behind low credit
access and demand among women despite all these
facilities

5. Statement of a problem
Women are among the poverty vulnerable groups of
people in Malawi
Efforts are being made to empower women to become
independent and self-reliant-credit facilities.
Despite all these credit facilities, not all rural women
access these loans and there are some factors that affect
rural women’s access to credit.

6. Justification
Understanding the factors that constrain rural women’s
access to credit and factors that stimulate the demand for
credit among rural women is important for designing
credit programs and policies that makes it possible for
more rural women to access and increase demand for
credit.

7. Objectives
Main Objective
To determine the factors that affect formal credit access
and demand among rural women
Specific Objectives
To analyse the factors that affect formal credit access
among rural women
To analyze factors that affect formal credit demand among
rural women

8. Hypothesis
Communicational, demographic and institutional factors
such as age, marital status and educational level have do
not constrain women’s access to formal credit.
Socioeconomic, communicational, demographic and
institutional factors such as age, marital status, education
do not affect rural women’s demand for credit.

9. Methodology
Study Area
Bembeke EPA
Sampling Technique
Purposive and random

10. Sample size
The total sample size was determined using the formula below
𝑛 =
z2 1−p p
e2
Where; 𝑛 = sample size, z = desired degree of confidence=1.96, p
=50%, e = desired level of sample error= 10%
This gives a sample size of 96
However, a total of 160 women were interviewed

11. Empirical models
Independent Double hurdle model was used
Two decisions made
1. whether to borrow or not
2. the amount to borrow
The model assumed that the decisions to borrow formal credit
and the decision on how much to borrow and independent
decisions

12. First Hurdle (Probit model)
Index equation Ai
* = X1i β1 + Ui Ui ~ (0, 1)
Threshold index equation Ai = {1 if Ai > 0, and is 0 if Ai
< 0}

13. Conti…
1. AGE = Age of the respondent
2. LNS = Land size (ha)
3. HHS = Household size
4. MAR = Marital status (1 if married, 0 otherwise)
5. EXT = Number of times in contact with an extension worker per month
6. EDU= Educational level (1=never, 2=primary, 3=secondary)
7. MEM = Membership to a cooperative (1 if a member, 0 otherwise)
8. HDD= Family role (1 if a breadwinner, 0 otherwise)
9. EXP = Experience in the formal credit use (Years)
10. COL = Perception on collateral (1 if a challenge, 0 otherwise)

14. Second hurdle (Tobit model)
Yi
* = X’2i β2 + Vi, Vi ~ N (0. δ2)
Double-Hurdle model Yi = {Yi* if Ai = 1 and Yi
* > 0 and
is 0 if Ai ≤ 1 and Yi
*≤ 0}

15. Conti…
1. AGE = Age of the woman (years)
2. EXP = Experience in the formal credit lending (years)
3. COL = Perception on collateral (1 if a challenge, 0 otherwise)
4. HHS = Household size
5. MAR= Marital status (1 if married, 0 otherwise)
6. RPM= Loan repayment period
7. INT = Interest paid on loan (%)
8. HDD = Family role (1 if a breadwinner, 0 otherwise)
9. EXT = Number of times a woman is in contact with extension workers
10. EDU = Education level of the woman
11. LNS = Land size (ha)

16. Results and discussion
 Summary statistics
Variable Overall mean
(n=160)
Credit Access
(n=76)
No Credit Access
(n=84)
Land size (hectares) 0.91 (0.05) 0.97 (0.80) 0.84 (0.05)
Household size 4.97 (1.73) 5.22 (1.91) 4.41 (1.32)
Number of times with an
extension agent
0.74 (0.68) 1.10 (0.59) 0.39 (0.58)
Interest rate 19.17 (1.88)
Amount borrowed 11286.78 (16332.63) 23737.50 (15653.48) 00.00 (0.00)
Repayment period 4.14 (1.78) 4.14 (1.78) 00.00 (0.00)
Experience in formal loan
lending
1.01 (1.49) 1.76 (1.77) 0.25 90.44)
Education level (1=never,
2=primary, 3=secondary)
1.82 (0.51) 1.79 (0.47) 1.85 (0.55)
Age 32. 26 (8.95) 33.91 (8.36) 30.6 (9.33)

17. Conti…
Variable Overall sample
(n=160)
Credit Access
(n=76)
No credit Access
(n=84)
Marital status (married) 63.78 76.25 52.50
Family role (breadwinner) 50.66 65.20 37.50
Perception on collateral (Challenge) 55.16 38.75 70.00
Membership to a cooperative (if a
member)
54.51 83.00 28.75

18. Access to formal credit, 1st Hurdle result
LR chi2 (10) =149.24 p-value>chi2=0.0000 pseudo R2=0.6729
Variable Marginal effect Standard error z-value P- value
Age 0.001 0.142 0.10 0.921
MAR -0.306 0.177 -1.73 0.083*
EDU -0.024 0.173 -0.03 0.880
HHS 0.094 0.321 0.29 0.770
LNS 0.009 0.098 0.09 0.925
EXT 0.598 0.150 3.99 0.000***
MEM 0.603 0.149 4.24 0.000***
EXP 0.462 0.137 3.37 0.001***
COL -0.514 0.135 -3.80 0.000***
HDD 0.268 0.175 1.53 0.126

19. Demand for formal credit, 2nd hurdle
 LR chi2 (11) =110.87 p-value>chi2=0.0000 pseudo R2=0.7238
Variable Marginal effects Standard error z-value P- value
Age .0103213 .008 1.26 0.212
MAR .1876318 .075 2.51 0.015*
EDU .2408117 .099 2.44 0.018 **
HHS -.1859244 .161 -1.14 0.257
LNS .2549961 .065 3.92 0.000***
EXT .1407816 .068 2.06 0.043 **
INT .0126241 .027 0.47 0.641
EXP .0606791 .025 2.47 0.016**
COL -.1760821 -2.06 0.043 0.000***
HDD -.208689 .113 -1.85 0.069 *
RPM .424747 .039 10.77 0.000***

20. Conclusions
Factors affecting credit access among rural women are
EXT, MAR, MEM, COL and EXP
Factors that stimulate the demand for credit among rural
credit are COL, MAR, EDU, RMP, HDD, LNS, EXT and
EXP

21. Recommendations
Intensify extension service.
Encourage women to join cooperatives and associations
Increase the repayment period
Revise collateral needs

22. THANK YOU FOR YOUR ATTENTION
GOD BLESS YOU ALL