7/27/2019 Gender mainstreaming in agricultural research: Evidence from a household survey in coastal Bangladesh
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Gender mainstreaming in agricultural
research: Evidence from a household
survey in coastal Bangladesh
In association with
Sonia Akter (IRRI), Fahmida Khanam (IRRI),
Tim Krupnik (CIMMYT), Frederick Rossi (CIMMYT)
http://www.poverty-action.org/7/27/2019 Gender mainstreaming in agricultural research: Evidence from a household survey in coastal Bangladesh
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In association with
Comparing male and female headed households is not gender
analysis. Differences between these diverse household types
cannot necessarily be attributed to the sex of the householdhead.
Background
http://www.poverty-action.org/7/27/2019 Gender mainstreaming in agricultural research: Evidence from a household survey in coastal Bangladesh
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For gender analysis, it is indispensable to interview
both men and women.
This does not necessarily mean interviewing twice as
many people or that men and women in the samehousehold must be interviewed.
For some research questions, it may be preferable to
interview one person per household and randomly
choose whether it is a man or woman.
Identifying the appropriate respondents
In association with
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Arguments against gender inclusive social survey
1 Difficult to hire female enumerators:
In societies where women are not allowed to speak with men due to
religious restrictions, female enumerators are required to gain access to
women respondents. Given low education and empowerment, recruiting
female enumerators is challenging or even impossible in some contexts.
3 Women lack information:
Although women take active part in farming practices in some capacity,
often they are not fully equipped with the cost and return information.
2 Men oppose
women s
participation:
In highly patriarchal societies, men tend to undermine womens
preferences and opinions and hence, either prohibit women from
participating in the survey, or if they allow participation, they tend to
intervene during the interview.
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Objectives of the study
Objective 1: Understanding maize farmers preferences for index-
based, savings-linked weather insurance products.Hypotheses: Set 1
1. Demand for savings-linked insurance product is higher than pure insurance or
pure savings products.
2. Demand for insurance varies across weather risks (i.e., flood, storm, hail).
3. Demand for insurance varies across risk and time preference.
Hypotheses: Set 2
1. It is harder to recruit female enumerators than male enumerators.
2. Womens participation in the survey will be lower than men.
3. Women fail to provide complete and reliable cost and return in formation.
Objective 2: Identifying the barriers and opportunities for including
female respondents in mainstream agricultural surveys.
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Attributes Levels
Insurance type (1) Pure insurance (i.e., good time payment=0)
(2) Pure savings (i.e., good time payment=deposit)
(3) Insurance-savings mix (i.e., good and bad time
payment>0)
Deposit=Premium Tk 100 (US$ 1.2) to Tk 4,000 (US$ 50)
Bad time payment Tk 1,000 (US$ 12.5) to Tk 5,000 (US$ 62.5)
Good time payment Tk 0 (US$ 0) to Tk 4,000 (US$ 50)
Risk type
Methodology: Choice experiment
Flood Wind Hail
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Pure insurance
Pure savings
Pure insurance
Insurance-savings
mixed
Example choice questions
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Survey Set-up
Male sample 299
Female sample 134
Male enumerator 12
Female enumerator 6
Survey team 6
Supervisor 6
Sub-district 3
District 1
SurveyCoordinator
Supervisor
2 Maleenumerators
1 Femaleenumerator
Training
Implementation
Management
Data quality
GPS coordinates
Village level logistics
Farmers list
Enumerators list
Sub-district level logistics
Local
collaborator
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Data collection
Survey Team Groups
Commuting
Training
MonitoringInterviews
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Sample DescriptionMale (69%) Female (31%)
Average age (max-min) 45 (18-85) 35 (20-70)
Head of household 87% 23%
Household size 6.58 5.57
Illiterate 21% 18%
Electricity connection 22% 29%
Cultivable land (in decimal) 103 45
Non-land asset (in Taka (US$)) 152,000 (US$ 1,948) 96,000 (US$ 1,230)
Agricultural decisions are made
By the respondent alone 74% 10%
In consultation with the spouse 10% 86%
Household expenditure decisions are
made
By the respondents alone 65% 8%
In consultation with the spouse 17% 88%
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An additional module to track decision
making process
Male(%)
Female(%)
Was any member of the household present during the
interview?
45 83
Was the spouse present? 16 43
Did the respondent consult with the spouse?
Not at all 25 2
Sometimes 67 58
Almost all the time 8 40
Was other household members present? 29 41
Did the respondent consult with the spouse?
Not at all 30 13
Sometimes 65 74
Almost all the time 5 13
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Decision to purchase insurance
Male (%) Female (%)
Full-sample 62 38
Pure insurance 63 38
Pure savings 62 40
Insurance-savings mix 62 33
Spouse present 60 37
Agricultural decisions are taken by the
respondent alone
63 30
Agricultural decisions are taken in consultation
with the spouse
60 40
HH expenditure decisions are taken by the
respondent alone
64 32
HH expenditure decisions are taken in
consultation with the spouse
50 38
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Latent class logit model: main-effects
Explanatory variables Group 1 Group 2
Average class probability 46% 54%
Pure insurancea -0.40 1.10***
Pure savingsa -3.60*** 0.93***
Savings-insurance mixa -1.60*** 1.06***
Premium -0.0035*** -0.0018***
Good time payment 0.0035*** 0.00125***
Bad time payment 0.0003 0.00044***
Risk type
Floodb 0.68*** 0.12
Windb 0.61*** 0.10
Note: a Base category=None; b Base category=Hail
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Latent class logit model: Intra-group
heterogeneity
Explanatory variables Group 1 Group 2 Explanatory variables Group 1 Group 2
Gender gap Farming characteristics and spatial variation
Female*Non-status quo -0.21 -1.85*** Revenue*Non-status quo 0.010 0.034**
Female*Time*Non-status quo -0.003 0.05*** Land size (high)*Non-status quo 0.0005 0.001
Female*Spouse*Non-status quo 0.25 -0.65* Land size (med)*Non-status quo 0.0015* 0.007***
Risk aversion Land size (low)*Non-status quo -0.0015 -0.0017
Risk averse*Pure insurance 0.16 -0.80*** Sub-district 2c*Non-status quo -0.45 1.31***
Risk averse*Pure savings 0.76** -0.60* Sub-district 3c*Non-status quo -0.96*** -1.07**
Risk averse*Mix -0.16 -0.41 Model fit statistics
Time preference McFadden Pseudo R-squared 0.20
Time preference*Pure insurance -0.53** -0.36 Number of groups 433
Time preference*Pure savings 0.36 -0.12 Number of observations per group 6
Time preference*Mix -0.04 0.015 Chi squared 1113.17
[p
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1. Revisiting the arguments against gender inclusive social
survey:1. Lack of female enumerators
3. Women lack information
2. Men oppose womens participation
Conclusions
2. Pragmatic and adaptive filed management practices are
crucial for gender inclusive research.
3. No evidence of resentments against gender inclusivesurveys was found at the local level.
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Next step: A follow-up survey
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