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Determinants of access to and
demand for formal credit
among rural women: a Case of
Bembeke EPA
BY BROWN CHITEKWERE
Superv...
Introduction
Empowering women to participate in all sectors of the economy
is essential to build stronger economies, achi...
Conti…
One of the best ways of empowering women in Malawi is by
promoting their access to credit so that they can start s...
The some private sectors have also introduced different
kinds of credit facilities for their customers
The study therefo...
Statement of a problem
Women are among the poverty vulnerable groups of
people in Malawi
Efforts are being made to empow...
Justification
Understanding the factors that constrain rural women’s
access to credit and factors that stimulate the dema...
Objectives
Main Objective
To determine the factors that affect formal credit access
and demand among rural women
Specific...
Hypothesis
Communicational, demographic and institutional factors
such as age, marital status and educational level have ...
Methodology
Study Area
Bembeke EPA
Sampling Technique
Purposive and random
Sample size
The total sample size was determined using the formula below
𝑛 =
z2 1−p p
e2
Where; 𝑛 = sample size, z = desi...
Empirical models
Independent Double hurdle model was used
Two decisions made
1. whether to borrow or not
2. the amount t...
First Hurdle (Probit model)
Index equation Ai
* = X1i β1 + Ui Ui ~ (0, 1)
Threshold index equation Ai = {1 if Ai > 0, and ...
Conti…
1. AGE = Age of the respondent
2. LNS = Land size (ha)
3. HHS = Household size
4. MAR = Marital status (1 if marrie...
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
i...
Conti…
1. AGE = Age of the woman (years)
2. EXP = Experience in the formal credit lending (years)
3. COL = Perception on c...
Results and discussion
 Summary statistics
Variable Overall mean
(n=160)
Credit Access
(n=76)
No Credit Access
(n=84)
Lan...
Conti…
Variable Overall sample
(n=160)
Credit Access
(n=76)
No credit Access
(n=84)
Marital status (married) 63.78 76.25 5...
Access to formal credit, 1st Hurdle result
LR chi2 (10) =149.24 p-value>chi2=0.0000 pseudo R2=0.6729
Variable Marginal eff...
Demand for formal credit, 2nd hurdle
 LR chi2 (11) =110.87 p-value>chi2=0.0000 pseudo R2=0.7238
Variable Marginal effects...
Conclusions
Factors affecting credit access among rural women are
EXT, MAR, MEM, COL and EXP
Factors that stimulate the ...
Recommendations
Intensify extension service.
Encourage women to join cooperatives and associations
Increase the repayme...
THANK YOU FOR YOUR ATTENTION
GOD BLESS YOU ALL
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Determinants of Access to and Demand for Formal Credit among Rural Womenn

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Brown Chitekwere BSc Thesis Presentation

Determinants of Access to and Demand for Formal Credit among Rural Womenn

  1. 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. 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. 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. 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. 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. 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. 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. 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. 9. Methodology Study Area Bembeke EPA Sampling Technique Purposive and random
  10. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 21. Recommendations Intensify extension service. Encourage women to join cooperatives and associations Increase the repayment period Revise collateral needs
  22. 22. THANK YOU FOR YOUR ATTENTION GOD BLESS YOU ALL
  • jagriti

    Nov. 18, 2019
  • PetitsEsrom

    Jun. 15, 2018

Brown Chitekwere BSc Thesis Presentation

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