gender and credit market participation and access among households

Gender and Credit Market Participation and Access Among

Households in Coastal Barangays in Guimaras, Philippines

Alice Joan G. Ferrer and Arthur P. Barrido University of the Philippines Visayas

Outline 1. Background2. Methodology 3. Key Results

Profile Views Participation Access

4. Conclusions

Background

• 1/4 of families in the country are poor

• Many poor are found in coastal areas – 2/3 of local government units in the country are coastal

• Fishers among the poor and vulnerable sectors

• Credit continue to play a role in reducing poverty despite failure of past microcredit programs

• But more efficient credit programs are needed

• In the design of more successful credit programs and initiatives to help the poor, there is a need for a clear understanding of their need for credit, participation and access to credit market, and utilization of credit

Background

• The study aimed to increase understanding of the views on credit and the participation and access to credit market among fishing and non-fishing households in the coastal barangays in Guimaras.

Background

Participation in the credit market happenswhen the household has a need for a creditand took steps in availing credit. The steps maybe completed or not.

Lack of access to credit is when the amountthe person can borrow is zero. A person hasaccess to credit when a positive amount can beborrowed. Credit access improves when theamount a person can borrow increases.

Background

Locale : Island Province of Guimaras

54 of 98 barangays are coastal

48% of persons with sources of income are into fishing and farming.

Methodology

1 province 5 municipalities 16 Barangays 376 participants in the survey

376 was proportionately allocated to

• 16 barangays (30% of 54 coastal barangays) in 5 municipalities

• within each barangay, the fishing and non-fishing households were represented.

• Used interview schedule that was pilot tested

Key Results

Types of Households and sex

No. %

Fishing 235 62.50 Male 95 40.43Female 140 59.57

Non-fishing 141 37.50 Male 54 38.30Female 87 61.70

Total 376 100.00Male 149 38.15Female 227 61.85

• Profile

Key Results • Profile

Fishing Non-Fishing AllMalen=95

Femalen=140

All Nf=235

Malen=54

Female

n=87

AllNnf=23

5

Male n=149

Female

n=227

AllNA=376

Age (mean) 47.95 43.24 45.14 52.61 47.17 49.26 49.64 44.75 46.69

Education

At most elementary graduate

4951.60

5136.43

10042.56

2037.03

2528.74

4531.91

6946.31

7633.48

14538.56

High school/graduate

3637.90

64 45.71

10042.56

2240.74

3337.93

5539.00

5838.93

9742.73

155 41.22

At least college level

1010.50

2517.85

3514.89

12 22.22

2933.34

4129.08

3926.17

3716.30

76 20.21

Catholic 76 80.00

9870.00

174 74.04

39 72.22

72 82.76

11178.72

11577.18

17074.89

285 75.80

Unless indicated, the first figure is frequency and the second figure is %

Key Results • Profile


Indicators Fishing Non-Fishing AllMalen=95

Femalen=140

All Nf=235

Malen=54

Femalen=87

AllNnf=235

Male n=149

Femalen=227 NA=376

Household size (mean)

4.26 5.09 4.75 4.20 4.18 4.19 4.28 4.76 4.54

Owns lot where house is standing

52 54.74

7150.71

123 52.34

2953.70

37 42.53

66 46.81

8154.36

10847.58

189 50.27

House wall made of to predominantly light materials

5962.11

9668.57

15565.96

30 55.56

4551.72

74 52.48

8959.73

14061.67

229 60.90

Have electricity at home

61 64.21

10071.43

161 68.51

35 64.81

71 81.61

10675.18

9664.43

17175.33

267 71.01

Electric bill P, mean 367.41 311.27 332.50 749.12 445.74 544.92 506.96 367.63 417.47

Drink water from owned sources

1920.00

3122.14

5021.23

1222.22

2528.74

3715.74

3120.81

5624.67

8723.71

Use mainly owned flushed toilet

61 64.21

97 69.29

158 67.23

37 68.52

67 77.01

104 73.76

9865.77

16472.25

262 69.68

Key Results • Views



Femalen=140

All Nf=235

Malen=54

Femalen=87

AllNnf=235

Male n=149

Femalen=227 NA=376

Borrowed amounts should be paid

68 71.58

110 78.57

178 75.74

39 72.22

66 75.86

105 74.47

10771.81

17677.53

283 75.27

Credit is big help to the poor

65 68.42

97 69.29

162 68.94

39 72.22

50 57.47

89 63.12

10469.80

14764.76

251 66.76

Credit can be dangerous/ a problem

52 54.74

88 62.86

140 59.57

25 46.30

54 62.07

79 56.03

7751.68

14262.56

219 58.24

Credit is needed 59 62.11

83 59.29

142 60.43

33 61.11

41 47.13

74 52.48

9261.74

12454.63

216 57.45

Borrowed money should be managed well

46 48.42

73 52.14

119 50.64

22 40.74

50 57.47

72 51.06

6845.64

12354.19

191 50.80

Borrowed money is difficult to pay

36 37.8

80 57.14

116 49.36

21 38.89

47 54.02

68 48.23

5738.26

12755.95

184 48.94

Borrow only when needed

30 31.58

65 46.43

95 40.43

21 38.89

25 28.74

46 32.62

5134.23

9039.65

141 37.50




Femalen=140

All Nf=235

Malen=54

Femalen=87

AllNnf=235

Male n=149

Femalen=227 NA=376

Credit can help 77 81.05

120 85.71

197 83.83

39 72.22

63 72.41

102 72.34

11677.85

18377.22

299 79.52

Ways credit can help Can help in putting food on the table

56 58.95

92 65.71

148 62.98

31 57.41

45 51.72

76 53.90

8758.39

13760.35

224 59.57

Can send children to school

54 56.84

93 66.43

147 62.55

29 53.70

41 47.13

70 49.65

8355.70

13459.03

217 57.71

Can help in the business

57 60.00

84 60.00

141 60.00

22 40.74

43 49.43

65 46.10

7953.02

12755.95

206 54.79

Can help during sickness

42 44.21

76 54.29

118 50.21

23 42.59

34 39.08

57 40.43

6543.62

11048.49

175 46.54

Can help in buying farm inputs

23 24.21

41 29.29

64 27.23

12 22.22

23 26.44

35 24.82

3523.49

6428.19

99 26.33




Femalen=140

All Nf=235

Malen=54

Femalen=87

AllNnf=235

Male n=149

Femalen=227 NA=376

Local government

59 62.11

86 61.43

145 61.70

30 55.56

53 60.92

83 58.87

8959.73

13961.23

228 60.64

Private sector 35 36.84

64 45.71

99 42.13

22 40.74

28 32.18

50 35.46

5738.26

9240.53

149 39.63

National government

23 24.21

47 33.57

70 29.79

10 18.52

22 25.29

32 22.70

3322.10

6930.40

102 27.13

NGO 17 17.89

36 25.71

53 22.55

7 12.96

27 31.03

34 24.11

2416.12

6327.75

87 23.14

Key Results • Need for Credit

Unless indicated, the first figure is frequency and the second figure is %.


Femalen=140

All Nf=235

Malen=54

Femalen=87

AllNnf=235

Male n=149

Femalen=227 NA=376

Number of times experienced the need for credit during the last 12 months prior to interview (mean)

5.55 5.93 5.52 4.81 5.05 4.95 5.26 5.36 5.32

Key Results • Need, Participation and Access

With Need ?

Participated?

Able to Access?

N= 376

270 (95.06%)

81 (21.54%)295 (78.46%)

283 (95.93%) 12 (4.07)

13 (4.59)

No

No

No

Yes

Yes

Yes


With Need ?

Able to Access?

N= 376

270 (91.52%)

81 (21.54%)295 (78.46%)

283 (95.93%) 12 (4.07)

13 (4.59)

No

No

No

Yes

Yes

Yes


*Unless indicated, the first figure is frequency and the second figure is %


Femalen=140

All Nf=235 Malen=54

Femalen=87

AllNnf=235

Male n=149

Femalen=227 NA=376

Needed to borrow*

6669.47

12690.00

19281.70

4379.62

6068.97

10373.05

10973.15

18681.94

29578.46

Applied for credit *

6293.94

12296.43

18495.83

4195.35

5896.97

9996.70

10394.49

18096.77

28395.93

Able to borrow**

6198.3892.42

11594.2691.26

17695.6591.67

3892.6888.37

5696.5593.33

9595.9692.23

9996.1190.82

17295.5692.47

27095.4191.53

**the first figure is frequency. the second figure is % of those who participated; third figure is % of those in need of credit


Amount Fishing Non-Fishing AllMalen=95

Femalen=140

All Nf=235

Malen=54

Femalen=87

AllNnf=235

Male n=149

Femalen=227 NA=376

needed 5,965 7,701 7,104 10,215 9,049 9,536 7,642 8,136 7,953

Applied for 5,133 6,699 6,171 7,860 8,630 8,311 6,218 7,321 6, 920

Able to borrow

4,280 6,104 5,472 8,382 6,633 8,263 5,854 6,793 6,122

*based on last loan availed in the last 6 months prior to interview

Key Results • Decision-making


Femalen=125

All Nf=190

Malen=41

Femalen=59

AllNnf=100

Male n=106

Femalen=184 NA=290

Husband 1933.92

1411.2

3317.36

1741.46

58.47

2222.00

3633.96

1910.33

5518.97

Husband and wife

3655.38

7056.00

10655.79

2253.67

2644.07

4848.00

5854.72

9652.17

15453.19

Wife 57.69

3024.00

3518.42

12.44

1830.51

1919.00

65.66

4826.09

5418.62

Others 57.69

118.8

168.42

12.44

1016.95

1111.00

65.66

2111.41

279.31

Who Decided to Apply?




Femalen=125

All Nf=190

Malen=41

Femalen=59

AllNnf=100

Male n=106

Femalen=184 NA=290

Husband 3655.39

2016.00

5629.47

2970.73

1016.95

3939.00

6561.32

3016.30

9532.76

Husband and wife

34.60

75.60

105.26

12.43

00.00

11.00

43.77

73.80

113.79

Wife 2030.77

8467.2

10454.74

1024.39

3966.10

4949.00

3028.30

12366.85

15352.76

Others 69.23

1411.20

2010.53

12.43

1016.95

1111.00

76.67

2413.04

3110.69

Who applied?




Femalen=125

All Nf=190

Malen=41

Femalen=59

AllNnf=100

Male n=106

Femalen=184 NA=290

Husband 1421.54

118.80

2513.16

1639.02

11.69

1717.00

3028.30

126.52

4214.48

Husband and wife

4264.62

7257.60

11460.00

2151.22

3355.93

5454.00

6359.43

10557.07

16857.93

Wife 46.15

3124.80

3518.42

37.31

1525.42

1818.00

73.80

4625.00

5318.30

Others 57.69

118.80

168.42

12.43

1016.95

1111.00

65.67

2111.41

279.31

Decided on the use of borrowed money




Femalen=125

All Nf=190

Malen=41

Femalen=59

AllNnf=100

Male n=106

Femalen=184 NA=290

Husband 3553.85

3830.40

7338.42

2970.73

1728.81

4646.00

6460.38

5529.89

11941.03

Husband and wife

1929.23

4132.80

6031.38

49.76

1525.42

1919.00

2321.70

5630.43

7927.24

Wife 69.23

3427.20

4021.05

717.03

1627.12

2323.00

1312.26

5027.17

6321.72

Others 57.69

129.60

178.94

12.43

1118.64

1717.00

65.66

2312.50

2910.00

In-charge of paying the loan


Key Results • Credit source


Femalen=126

All Nf=192

Malen=43

Femalen=60

AllNnf=103

Male n=109

Femalen=186 NA=295

Relatives/neighbors/friends

4771.21

8466.67

13168.23

2967.44

3456.67

6361.17

7669.72

11863.44

19465.76

Private microfinance institutions

1116.67

2419.04

3518.23

920.93

1118.33

2019.42

2018.35

3518.82

5518.64

Government credit facilities

34.55

97.14

126.25

36.98

813.33

1110.68

65.50

179.14

237.80

Others 57.58

97.14

147.30

24.65

711.67

98.73

76.42

168.60

237.80


Key Results • Uses of credit


Femalen=126

All Nf=192

Malen=43

Femalen=60

AllNnf=103

Male n=109

Femalen=186 NA=295

Buy food 3152.46

5749.76

8850.42

2051.16

2342.17

4345.92

5151.47

8047.31

13148.85

Additional working capital

2746.69

4337.54

7040.10

1128.14

1934.83

3032.04

3838.35

6236.67

10037.29

Education 1322.00

3530.56

4827.50

512.79

1120.17

1619.09

1818.17

4627.20

6423.86

Medical expenses

915.23

3127.06

4022.92

1128.14

1323.83

2425.68

2020.18

4426.02

6423.6

Start up capital

58.46

32.62

84.58

25.12

11.83

33.20

77.06

42.37

114.10


• The households were poor.

• Held different views on credit

• LGUs seen as main source of credit

• High need for credit, particularly among fishing households represented by female study participants

Summary and Conclusions

• Not all with credit need participated in the credit market but participation rate was high

• Not all who participated in the credit market were successful to avail of a loan but, in general, access to credit was also high.

• Based on the amount of last loan availed, credit access was highest among males from non-fishing households while the lowest credit access was among males from fishing households.



• Credit application and use was mainly a husband and wife decision.

• Actual credit application was delegated to the wife, while the responsibility of paying the loan rested on the husband or to both the husband and wife. These were all particularly true among fishing households.

• Although access to credit was high, the popular credit sources are informal sources such as friends and relatives that lives nearby.

• Borrowed funds were not mainly used for income generating activities or to increase production but for consumption purposes.

• For these, the participation and access to credit market by the study participants has limited chances in improving their productivity and living standards.


Recommendation

• The design of credit program should consider the views of the people towards credit

• Government credit facilities closer to the poor and target the fishing households

• Credit provisions accompanied by capacity building program, training for livelihood diversification

• Thank you.

gender and credit market participation and access among households

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