gender mainstreaming in agricultural research: evidence from a household survey in coastal...

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/


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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


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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

gender mainstreaming in agricultural research: evidence from a household survey in coastal...

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