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Effects of Extension Services on Technology Adoption and Productivity among Female and Male Farmers: Evidence from Ethiopia
 

Effects of Extension Services on Technology Adoption and Productivity among Female and Male Farmers: Evidence from Ethiopia

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International Food Policy Research Institute (IFPRI) and Ethiopian Development Research Institute (EDRI). Conference on "Towards what works in Rural Development in Ethiopia: Evidence on the Impact of ...

International Food Policy Research Institute (IFPRI) and Ethiopian Development Research Institute (EDRI). Conference on "Towards what works in Rural Development in Ethiopia: Evidence on the Impact of Investments and Policies". December 13, 2013. Hilton Hotel, Addis Ababa.

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    Effects of Extension Services on Technology Adoption and Productivity among Female and Male Farmers: Evidence from Ethiopia Effects of Extension Services on Technology Adoption and Productivity among Female and Male Farmers: Evidence from Ethiopia Presentation Transcript

    • 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 December 13, 2013 Hilton Hotel, Addis Ababa 1
    • 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 2
    • 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. 3
    • Data and Methods I • This study uses an AGP 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). 4
    • 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 5
    • 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. 6
    • 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
    • Descriptive Statistics I Extension services – household level 70 60 50 40 30 Male 20 Female 10 0 Farm demonstration plots community meetings DA visit (last year) DA visit (last 5 years) 8
    • Descriptive Statistics I Extension services – plot level 60 50 40 30 Male Female 20 10 0 DA advice on fertilizer DA advice on planting seeds DA advice on land preparation 9
    • Descriptive Statistics II Input use and technologies- plot level 35 30 25 20 Male 15 Female 10 5 0 chemical fertilizer improved seed herbicide pesticide irrigation soil row planting conservation technique
    • 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. 11
    • 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  5 percent more likely to be visited by extension agents  25 percent 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. 12
    • 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 13
    • 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. 14
    • 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 15
    • 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. 16
    • 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 17