1. Effects of Extension Services on Technology
Adoption and Productivity among female and
Male Farmers: Evidence from Ethiopia
Catherine Ragasa, Guush Berhane, Fanaye
Tadesse, and Alemayehu Seyoum Taffesse
IFPRI ESSP-II
Improved evidence towards better food and
agricultural policies in Ethiopia
November 02, 2012
Hilton Hotel, Addis Ababa
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2. Introduction I
• Agricultural extension emphasized by development
experts as crucial for achieving agricultural development
• In Ethiopia, the government has been actively investing
in its agricultural extension system in the past years.
• Ethiopia’s extension system has one of the highest
extension agent–farmer ratios found in the world.
• On the gender frontier, various attempts to reach more
women farmers have been implemented
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3. Introduction II
• However, recent reports still point to the persistence of
gender inequality in rural services, including extension
(Mogues et al, 2009).
• Limited understanding on how such disparities in
extension services contribute to improved technology
adoption and productivity levels.
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4. Data and Methods I
• This study uses the AGP baseline survey dataset collected
by the Central Statistics Agency (CSA) of Ethiopia
• The survey was conducted in 2011
• Covers the four major regions of Ethiopia—Tigray, Amhara,
Oromia, and SNNP with a sample size of 7,927 households.
• A statistical representation of female headed households
in the population (30 percent of selected households are
female headed).
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5. Data and Methods II
Gender Indicators – household headship and who makes
decisions on each plot
Extension
- Visits by and advice received from extension agents;
- Access to radio, newspaper and bulletins
- Farmers visit to demonstration plots and government
offices and
- Farmers’ participation in community meetings
Technology – Use of fertilizer, improved seed, herbicides ,
pesticides, soil conservation method and row planting
Productivity – value of yield per Hectare
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6. Data and Methods III
• The framework used is a standard empirical agricultural
production model.
• Production output expressed as a function of land, capital,
inputs and other factors.
• Extension variables and gender indicator are directly added
into the production function.
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7. Descriptive Statistics I
Community level information on change in extension service in
the past two years
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
Improved Stayed the same Deteriorated

8. Descriptive Statistics I
Extension services – household level
70
60
50
40
30 Male
20 Female
10
0
Farm community DA visit (last DA visit (last 5
demonstration meetings year) years)
plots
8

9. Descriptive Statistics I
Extension services – plot level
60
50
40
30
Male
20 Female
10
0
DA advice on fertilizer DA advice on planting DA advice on land
seeds preparation
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10. Descriptive Statistics II
Input use and technologies- plot level
35
30
25
20
15 Male
Female
10
5
0
chemical improved herbicide pesticide irrigation soil row planting
fertilizer seed conservation
technique

11. Descriptive Statistics III
• On average, the value of production per hectare of male
headed farming household is 14 percent higher than female
headed households
• There are significant gender differences in crop choice.
• Female heads are significantly more likely to grow
maize, Enset, potatoes and fruits; while male heads are more
likely to grow Teff and other pulses.
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12. Results I
Gender difference in access to extension services
• Controlling for other factors, clear difference between female
and male heads in access to visits and advice from
development agents
• Male heads are more likely to be visited by extension agents
• Male heads are more likely to attend community meetings
• Education, wealth indicators, distance to market and location
dummies - affect access to extension services
• Female headed households with higher proportion of male
members are more likely to have been visited by extension
agents.
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13. Results II
Gender difference in technology adoption and input use
• input use and adoption of improved management practices
are not significantly different between female and male
heads.
• Extension service provision in the form of advice from DAs is a
significant factor explaining input adoption.
• Receiving advice on fertilizer and improved seed
• increases fertilizer adoption by 31 percent
• Increases improved seed adoption by 2.5 percent
• difference in terms of access to resources, education and
access to extension services
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14. Results III
Gender difference in productivity
• Gender of household head and of the decision maker of the
plot are not significant in explaining productivity difference
• productivity differences explained by intensity of use of
traditional inputs as well as adoption of modern inputs
• Plots of female heads and female plot managers are as
equally productive as their male counterparts if they faced
the same level of inputs and access to improved technologies
and services.
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15. Conclusion
1. Systematic and statistical gender difference in access to
different channels of extension services
• Female heads and plot managers are less likely to get
extension services through various channels
2. Receiving advice from DA a major factor that explain the
likelihood of technology adoption and rate of input use
• Beyond the influence of gender indicator through extension
variables, gender indicators appear to be insignificant in the
technology adoption and input use models
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16. Conclusion
3. Gender variable not significant in explaining productivity
levels.
• Differentiated access to quality extension, access to
input, and quality of plot and not gender per se that explain
productivity differences.
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17. Policy implications
• Observed gender related welfare differences can be
addressed by working on improving endowment of women
• Closing the gender gap in agricultural productivity requires
programs that
• reach both women and men farmers with quality
extension services – gender target
• close the persistent women bias in access to
productive resources and inputs
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