Date post: | 22-Dec-2015 |
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Time at IRRI
• October- December (2014): Mathew L, Akter S (2016) Loss and Damage Associated with Climate Change Impacts. In: Chen W, Suzuki T, Lackner M (eds) Handbook of Climate Change Mitigation and Adaptation. Springer, New York
• January- March (2015): Gender mainstreaming in agricultural extension. Worked on an existing data set and study compiled by Dr. Sonia Akter.
Gender mainstreaming in agricultural extension
Gender mainstreaming in agricultu
ral extensio
n
•Farmers in South Asia are listed according to land ownership
•Women generally don’t hold land titles
•Hence, they are not listed (or recognized) as farmers
•Consequently, women are systematically excluded from agricultural extension services
•Productivity of female farmers remain low relative to potential
Background of the
study
•Examining the implications of gender differences and constraints in designing, implementing, monitoring and evaluating agricultural extension services and outreach programs
•Taking action to remove gender based constraints
Intervention
• Donor agencies are aiming to reverse this trend• The first step towards eliminating gender gap is
to enlist women as a farmer. • Officially recognizes women’s contribution in
agriculture. • It also gives women access to inputs (e.g.
seeds, fertilizers), training, information, market and other services.
Objectives of the study
1. Identify the socio-cultural constraints of listing women as farmers
2. Understand how and to what extent this initiative is contributing to women empowerment
3. Identify the characteristics of the women who are being listed and reached out by the intervention projects
4. Understand the factors influencing the decision to list women as farmers
Study Area and Data Collection• Cereal Systems Initiative for South Asia (CSISA) intervention area• Remote, conservative area• Male dominated• Main agricultural crop is rice (during wet season)• Other crops grown include wheat, maize and pulses (during dry
season)• Study conducted from October 2014- February 2015• Structured questionnaire survey• Male and female enumerators were used to conduct the interviews• A combination of face-to-face and telephone interviews
Sample
Male [72 (33%)] Female [144(67%)]
Age (max-min) 45 (22-70) 36 (20-60)
Illiterate 65% 73%
Cultivable land (in decimal) 84 94
Value of non-land asset (in US$) 1,972 1,697
Income per capita per month (in US$) 25 22
Listed vs. Unlisted WomenListed %
No 53
Yes 47
Do you want to be a listed farmer in future?
Women’s view (%)
Men’s view (%)
No 45 33Yes 42 56Maybe 8 11I don't know 6
Why female farmers are not listed?Male perspectives• “I am listed, there is no need to engage them as well” • “She is busy with housework” • “I don’t like it”• “My mother doesn’t like it”Female perspectives• “My husband did not give my name”• “I have many things to do at home, hardly get time for
anything else”• “I don’t like the idea of being listed as a farmer”
Listed vs. Unlisted WomenListed %
No 53
Yes 47
Do you want to be a listed farmer in future?
Women’s view (%)
Men’s view (%)
No 45 33Yes 42 56Maybe 8 11I don't know 6
Why don’t women want to be listed as farmers?
"I have lots of works at home, so I don't have time"
"My husband will not allow me"
"Physically I am not fit"
"My husband/son does everything, there is no need to be listed"
"I am old, I don't understand all of these"
"I don't go outside of the home"
others
0 5 10 15 20 25 30
25
10.7
7.1
10.7
17.9
10.7
17.9
Percent
Benefits of being listed as a farmer
no such benefit Receive seeds Receive inputs Receive training Receive credit Other 0
10
20
30
40
50
60
70
80
90
%
Who takes important decisions regarding agriculture? (%)
By the women In consultation with husband By the hh head Other Total
Unlisted 8 29 36 27 100Listed 8 73 14 5 100
Women’s decision making power (Women's view)
Who takes important decisions regarding household expenditure? (women's view) (%)
By the women In consultation with husband By the hh head Other Total
Unlisted 2 23 47 29 100Listed 8 66 22 4 100
Improvement in female decision making power
Related to agriculture Women’s perspective (%) Men’s perspective (%)
No 20.8 0
Yes 75.3 96
I don't know 2.6 4
Total 98.7 100
Related to household expenditure
Women’s perspective (%) Men’s perspective (%)
No 23.4 4
Yes 72.7 92
I don't know 1.3 4
Total 97.4 100
Differences in socio-economic factors
Unlisted women
Listed women Mean Difference
Total value of non-land asset (in USD)
2219 1309 910***
Land size (in decimals) 118 60 5**
Total yearly income (in USD)
1938 1476 462
***, ** and * represent significant level at 1%, 5%, and 10% respectively
Explanatory variables Coefficient P value
Age -0.05** 0.043
Literate (illiterate=0, otherwise=1) -2.06*** 0.001
Highest education of the male member of the household -0.20 0.293
Number of infants -0.354 0.192
Non-land asset (in ‘000 Taka) -0.004* 0.091
Land size (in decimal) 0.001 0.624
Value of savings available to women 0.06* 0.057
Income (in Taka) -0.001 0.380
How many days does your wife work during Rabi season -0.006 0.414
How many days does your wife work during Aman season -0.011 0.218
Decision making power in agriculture (self or consultation with husband) 1.59** 0.013
Decision making power about household expenditure (self or consultation) 1.04 0.11
Daulatkhan 0.10 0.867
Borhanuddin 0.93* 0.099
Constant 2.51* 0.047
***,** and * represent significant level at 1%, 5%, and 10% respectively
Results of Logit Regression ModelDependent variable=Women listed (Yes=1, No=0)
Implications
• Demographic and socio-economic factors was found to highly influence the decision to list women as farmers.
• Cultural restrictions and prejudices against listing were inferred from both the male and female respondents.
• Evidence that mainstreaming women in agriculture increases their decision making ability
• Overburdening women?• Donor agencies enforcing inequalities by targeting
predominantly the female farmers in poor marginalized households?