1. Gender in Agricultural Production
Presented by Dr. Paige Miller
Tropical Legumes III Annual Meeting, 27th February 2017

2. Overview of Session
Purpose of session: provide overview of objective 1, specifically my role as a temporary gender consultant, what’s been completed as well as uncover what has been done in the different countries, questions, problems and challenges encountered
To do this:
Summary of what we know about gender and gender yield gaps in agricultural production
Open ended questionnaire-please write your name, country, and crop on the sheet of paper distributed yesterday (if you do not have a copy, please let me know).
I’d ask that you be as detailed as possible & I don’t need individual copies from everyone
--at the most general level, when we’re talking about context, we know clearly that the national context matters but more than the national context, even intranational differences can be and often are important: differences between the Northern and Southern parts of Nigeria, as just one example, have been found to be important in terms of whether or not there is a gender gap at all and the extent and causes of it. Or between the more arid and food insecure regions of Tanzania compared to the southern breadbasket. In Burkina Faso (Akresh 2008) it was found that resources were allocated between men and women within a household more efficiently for those households located in less productive zones perhaps because the danger of not doing so was greater. Taken from Peterman et al. In Uganda and Nigeria we find female-headed households are associated with lower productivity in the dry savannah area. This may occur because the dry savannah ecology increases the domestic work burdens of women and girls, reducing the time spent on farming activities. It is worth noting religious and cultural norms which may also vary by region. For example, scholars note the relatively recent emergence of Islamic Sharia Law throughout states in northern Nigeria and the accompanying adoption of the hijab and a more rigid male ‘public’ female ‘private’ dichotomy (Ludwig, 2008; Mahdi, 2009). Given that there is some geographic overlap between these northern states and the agro-ecological zones where we find female productivity is lower in Nigeria, it is worth exploring the impact of an Islamic revival on women and farming.
--the biggest issue to consider here is whether or not we are talking about cash crops or more subsistence oriented crops.
--by this what I’m referring to is is really the structure of the household. If you only ask about female heads of household rather than ask about the managers of particularly plots within the household, this will impact the outcome of any data collection efforts. From Peterman et al. Results show none of the gender indicators are significant across regressions and gender differences are lost when indicators are aggregated to a higher level (household) or when specific household structures are disregarded. This suggests that gender differences in agricultural productivity may not be revealed at higher levels of aggregation that do not correspond to the basic decision making unit in specific farming systems.
--finally, when I say it is not just about gender what I mean is that often differences within groups or women or groups of men can be greater than differences between men and women. Clearly high producing women are different from lower producing women, but beyond that older women and men likely face unique challenges in comparison to younger women and men. Additionally, it isn’t just about giving people inputs because empirical evidence also suggests that the returns on inputs are not equal across groups of women and men, suggesting that something else is going on. For instance, women tend to get less out of the labor that they do have—whether that’s family labor or hired labor. This may be due to any number of causes—maybe women can’t get the best workers because they can’t offer a competitive price, maybe they have less time to supervise the work of male labor, maybe male labor is less likely to feel compelled to work as hard for a female farmer. Or even take fertlizer. Some evidence suggests that even when the quality and quantity of fertilizer is controlled for, the returns for women tend to be less suggesting that perhaps they aren’t using it correctly. The point is that it isn’t just a matter of providing inputs.

3. Objective 1: What Do We Know About the Gender Gap in Agricultural Production?
Overview of tasks completed up to this point
To state the obvious, it’s complex
Context
It’s not just about gender or access to inputs
Having said all that, what evidence exists and what does it tell us about the extent and causes of the gap?
--at the most general level, when we’re talking about context, we know clearly that the national context matters but more than the national context, even intranational differences can be and often are important: differences between the Northern and Southern parts of Nigeria, as just one example, have been found to be important in terms of whether or not there is a gender gap at all and the extent and causes of it. Or between the more arid and food insecure regions of Tanzania compared to the southern breadbasket. In Burkina Faso (Akresh 2008) it was found that resources were allocated between men and women within a household more efficiently for those households located in less productive zones perhaps because the danger of not doing so was greater. Taken from Peterman et al. In Uganda and Nigeria we find female-headed households are associated with lower productivity in the dry savannah area. This may occur because the dry savannah ecology increases the domestic work burdens of women and girls, reducing the time spent on farming activities. It is worth noting religious and cultural norms which may also vary by region. For example, scholars note the relatively recent emergence of Islamic Sharia Law throughout states in northern Nigeria and the accompanying adoption of the hijab and a more rigid male ‘public’ female ‘private’ dichotomy (Ludwig, 2008; Mahdi, 2009). Given that there is some geographic overlap between these northern states and the agro-ecological zones where we find female productivity is lower in Nigeria, it is worth exploring the impact of an Islamic revival on women and farming.
--the biggest issue to consider here is whether or not we are talking about cash crops or more subsistence oriented crops.
--by this what I’m referring to is is really the structure of the household. If you only ask about female heads of household rather than ask about the managers of particularly plots within the household, this will impact the outcome of any data collection efforts. From Peterman et al. Results show none of the gender indicators are significant across regressions and gender differences are lost when indicators are aggregated to a higher level (household) or when specific household structures are disregarded. This suggests that gender differences in agricultural productivity may not be revealed at higher levels of aggregation that do not correspond to the basic decision making unit in specific farming systems.
--finally, when I say it is not just about gender what I mean is that often differences within groups or women or groups of men can be greater than differences between men and women. Clearly high producing women are different from lower producing women, but beyond that older women and men likely face unique challenges in comparison to younger women and men. Additionally, it isn’t just about giving people inputs because empirical evidence also suggests that the returns on inputs are not equal across groups of women and men, suggesting that something else is going on. For instance, women tend to get less out of the labor that they do have—whether that’s family labor or hired labor. This may be due to any number of causes—maybe women can’t get the best workers because they can’t offer a competitive price, maybe they have less time to supervise the work of male labor, maybe male labor is less likely to feel compelled to work as hard for a female farmer. Or even take fertlizer. Some evidence suggests that even when the quality and quantity of fertilizer is controlled for, the returns for women tend to be less suggesting that perhaps they aren’t using it correctly. The point is that it isn’t just a matter of providing inputs.

4. The Interlocking Influence of Access to and Use of Inputs
Individual Characteristics: Women who manage own plots are on average older and less well educated (Ali et al. 2015; Oladeebo & Fajuyigbe 2007; Oseni et al. 2015; Slavchevska 2015).
Technological Resources: Inorganic fertilizer, insecticide, improved seed varieties, and equipment
Natural Resources: Water, land and soil fertility, women have a less secure customary and statutory protections afforded them (Quisumbing and Pandolfelli 2010).
Human Resources: Labor, extension services
Monetary Resources & Access to Markets: Aguilar et al. (2015) household’s wealth a significant predictor of gender differences in productivity. The distance women have to travel from their plots to the marketplace impacts their lower levels of agricultural production in Tanzania.
--at the most general level, when we’re talking about context, we know clearly that the national context matters but more than the national context, even intranational differences can be and often are important: differences between the Northern and Southern parts of Nigeria, as just one example, have been found to be important in terms of whether or not there is a gender gap at all and the extent and causes of it. Or between the more arid and food insecure regions of Tanzania compared to the southern breadbasket. In Burkina Faso (Akresh 2008) it was found that resources were allocated between men and women within a household more efficiently for those households located in less productive zones perhaps because the danger of not doing so was greater. Taken from Peterman et al. In Uganda and Nigeria we find female-headed households are associated with lower productivity in the dry savannah area. This may occur because the dry savannah ecology increases the domestic work burdens of women and girls, reducing the time spent on farming activities. It is worth noting religious and cultural norms which may also vary by region. For example, scholars note the relatively recent emergence of Islamic Sharia Law throughout states in northern Nigeria and the accompanying adoption of the hijab and a more rigid male ‘public’ female ‘private’ dichotomy (Ludwig, 2008; Mahdi, 2009). Given that there is some geographic overlap between these northern states and the agro-ecological zones where we find female productivity is lower in Nigeria, it is worth exploring the impact of an Islamic revival on women and farming.
--the biggest issue to consider here is whether or not we are talking about cash crops or more subsistence oriented crops.
--by this what I’m referring to is is really the structure of the household. If you only ask about female heads of household rather than ask about the managers of particularly plots within the household, this will impact the outcome of any data collection efforts. From Peterman et al. Results show none of the gender indicators are significant across regressions and gender differences are lost when indicators are aggregated to a higher level (household) or when specific household structures are disregarded. This suggests that gender differences in agricultural productivity may not be revealed at higher levels of aggregation that do not correspond to the basic decision making unit in specific farming systems.
--finally, when I say it is not just about gender what I mean is that often differences within groups or women or groups of men can be greater than differences between men and women. Clearly high producing women are different from lower producing women, but beyond that older women and men likely face unique challenges in comparison to younger women and men. Additionally, it isn’t just about giving people inputs because empirical evidence also suggests that the returns on inputs are not equal across groups of women and men, suggesting that something else is going on. For instance, women tend to get less out of the labor that they do have—whether that’s family labor or hired labor. This may be due to any number of causes—maybe women can’t get the best workers because they can’t offer a competitive price, maybe they have less time to supervise the work of male labor, maybe male labor is less likely to feel compelled to work as hard for a female farmer. Or even take fertlizer. Some evidence suggests that even when the quality and quantity of fertilizer is controlled for, the returns for women tend to be less suggesting that perhaps they aren’t using it correctly. The point is that it isn’t just a matter of providing inputs.

5. How Might We Go Further With the Impact Survey?
WEAI
Empowerment can influence who adopts what
How then might adoption influence empowerment
--at the most general level, when we’re talking about context, we know clearly that the national context matters but more than the national context, even intranational differences can be and often are important: differences between the Northern and Southern parts of Nigeria, as just one example, have been found to be important in terms of whether or not there is a gender gap at all and the extent and causes of it. Or between the more arid and food insecure regions of Tanzania compared to the southern breadbasket. In Burkina Faso (Akresh 2008) it was found that resources were allocated between men and women within a household more efficiently for those households located in less productive zones perhaps because the danger of not doing so was greater. Taken from Peterman et al. In Uganda and Nigeria we find female-headed households are associated with lower productivity in the dry savannah area. This may occur because the dry savannah ecology increases the domestic work burdens of women and girls, reducing the time spent on farming activities. It is worth noting religious and cultural norms which may also vary by region. For example, scholars note the relatively recent emergence of Islamic Sharia Law throughout states in northern Nigeria and the accompanying adoption of the hijab and a more rigid male ‘public’ female ‘private’ dichotomy (Ludwig, 2008; Mahdi, 2009). Given that there is some geographic overlap between these northern states and the agro-ecological zones where we find female productivity is lower in Nigeria, it is worth exploring the impact of an Islamic revival on women and farming.
--the biggest issue to consider here is whether or not we are talking about cash crops or more subsistence oriented crops.
--by this what I’m referring to is is really the structure of the household. If you only ask about female heads of household rather than ask about the managers of particularly plots within the household, this will impact the outcome of any data collection efforts. From Peterman et al. Results show none of the gender indicators are significant across regressions and gender differences are lost when indicators are aggregated to a higher level (household) or when specific household structures are disregarded. This suggests that gender differences in agricultural productivity may not be revealed at higher levels of aggregation that do not correspond to the basic decision making unit in specific farming systems.
--finally, when I say it is not just about gender what I mean is that often differences within groups or women or groups of men can be greater than differences between men and women. Clearly high producing women are different from lower producing women, but beyond that older women and men likely face unique challenges in comparison to younger women and men. Additionally, it isn’t just about giving people inputs because empirical evidence also suggests that the returns on inputs are not equal across groups of women and men, suggesting that something else is going on. For instance, women tend to get less out of the labor that they do have—whether that’s family labor or hired labor. This may be due to any number of causes—maybe women can’t get the best workers because they can’t offer a competitive price, maybe they have less time to supervise the work of male labor, maybe male labor is less likely to feel compelled to work as hard for a female farmer. Or even take fertlizer. Some evidence suggests that even when the quality and quantity of fertilizer is controlled for, the returns for women tend to be less suggesting that perhaps they aren’t using it correctly. The point is that it isn’t just a matter of providing inputs.

6. Having Said That, What Evidence Exists & What Does it Tell Us About the Extent and Causes of the Gap?
Gender gaps in legume production using TL-2 Data
Malawi (2008, 2010, and 2013)
Tanzania (2008, 2010)

7. Malawi What yield gaps are evident and how do inputs used contribute?
Groundnut: Men 28% more productive*
Age, marital status, seed used and responsibility in community widen gender gap
# of months spent on farm (men and women spend about the same amount of time), distance to agricultural field officer, hired oxen, and hired labor decrease gender gap
Pigeon pea: Men 29% more productive (ns)
Responsibility in the community, education, marital status, distance to main market and plot ownership widens the gap
Distance to field officer (positive effect for women) decrease the gap

8. Tanzania
What yield gaps are evident and how do inputs used contribute?
Groundnut: Women 22.5% more productive (ns)
Age and marital status widen the gap, as do plot ownership, fertilizer used (men use more)
Plot size (women’s plots smaller), distance to agricultural field officer decrease gender gap
Pigeon pea: Men 8.4% more productive*
Marital status widens the gap
Plot size, distance to nearest market and hired labor decrease the gender gap

9. Key Driver of Gender Gap
Country
Intervention/Policy Options Showing Some Promise
Human Resources (Labor & Extension Services): Women report less use of hired farm labor-particularly male labor (Tanzania, Ethiopia, all of Nigeria; Uganda; Burkina Faso); labor may also work fewer days (Tanzania, N. Nigeria) and female labor tends to provide fewer returns for female farmers (N. Nigeria); women use more household labor than men but have fewer returns (Uganda); women less likely to receive extension advice (Ethiopia and Uganda) and have lower returns on the use of extension services (Uganda)
Tanzania, Uganda, Ethiopia, Nigeria;
Burkina Faso
--Financial vouchers or access to credit for women to hire labor;
--Connect women with agents who can then connect them with sources of labor;
--Ethiopia’s Rural Capacity Building Project;
--Farmer field schools in Uganda--the Community Knowledge Worker Initiative;
--Malawi and Mozambique have both implemented programs in which they identify women within a target community to pass along agricultural information to other men and women in their social network
--Hiring more female extension workers may work in some cases; in other cases training male extension workers to be more gender aware will work better
Technological Resources: Women use less fertilizer (N. Nigeria; Uganda; Burkina Faso), herbicide (S. Nigeria), and pesticides (Uganda); women receive lower returns from the use of fertilizer (Tanzania)
Uganda, Nigeria, Tanzania
--A voucher or layaway plan to help women afford labor saving technology--the Fertilizer Input Subsidy Programme in Malawi.
Individual Responsibilities: women tend to be older and less well educated, spend less time on farming activities which may be due to other domestic responsibilities
Ethiopia
--Provide women with access to community childcare
Natural Resources: women tend to be disadvantaged in both quality and size of land (Ethiopia); women’s land in Ghana is less fertile, in part due to their rights over land
Ghana
--Rwanda’s National Land Policy (2004) and the Organic Land Law (2005);
--Malawi has it written into its constitution that upon the dissolution of a marriage a woman has rights to a fair distribution of property held by her husband;
--Tanzania and co-titling of landholdings between husbands and wives.
Uganda Land Act: need to be sure that women and men know and understand the laws.

10. Moving Forward
Work with the national partners to begin the design and implementation of intervention(s) meant to address gender differences in adoption & yield.
Address women’s or men’s practical needs: new skills, resources, opportunities, or services
Increase gender equality of opportunity, influence or benefit such as targeted actions to increase women’s role in decision making
--at the most general level, when we’re talking about context, we know clearly that the national context matters but more than the national context, even intranational differences can be and often are important: differences between the Northern and Southern parts of Nigeria, as just one example, have been found to be important in terms of whether or not there is a gender gap at all and the extent and causes of it. Or between the more arid and food insecure regions of Tanzania compared to the southern breadbasket. In Burkina Faso (Akresh 2008) it was found that resources were allocated between men and women within a household more efficiently for those households located in less productive zones perhaps because the danger of not doing so was greater. Taken from Peterman et al. In Uganda and Nigeria we find female-headed households are associated with lower productivity in the dry savannah area. This may occur because the dry savannah ecology increases the domestic work burdens of women and girls, reducing the time spent on farming activities. It is worth noting religious and cultural norms which may also vary by region. For example, scholars note the relatively recent emergence of Islamic Sharia Law throughout states in northern Nigeria and the accompanying adoption of the hijab and a more rigid male ‘public’ female ‘private’ dichotomy (Ludwig, 2008; Mahdi, 2009). Given that there is some geographic overlap between these northern states and the agro-ecological zones where we find female productivity is lower in Nigeria, it is worth exploring the impact of an Islamic revival on women and farming.
--the biggest issue to consider here is whether or not we are talking about cash crops or more subsistence oriented crops.
--by this what I’m referring to is is really the structure of the household. If you only ask about female heads of household rather than ask about the managers of particularly plots within the household, this will impact the outcome of any data collection efforts. From Peterman et al. Results show none of the gender indicators are significant across regressions and gender differences are lost when indicators are aggregated to a higher level (household) or when specific household structures are disregarded. This suggests that gender differences in agricultural productivity may not be revealed at higher levels of aggregation that do not correspond to the basic decision making unit in specific farming systems.
--finally, when I say it is not just about gender what I mean is that often differences within groups or women or groups of men can be greater than differences between men and women. Clearly high producing women are different from lower producing women, but beyond that older women and men likely face unique challenges in comparison to younger women and men. Additionally, it isn’t just about giving people inputs because empirical evidence also suggests that the returns on inputs are not equal across groups of women and men, suggesting that something else is going on. For instance, women tend to get less out of the labor that they do have—whether that’s family labor or hired labor. This may be due to any number of causes—maybe women can’t get the best workers because they can’t offer a competitive price, maybe they have less time to supervise the work of male labor, maybe male labor is less likely to feel compelled to work as hard for a female farmer. Or even take fertlizer. Some evidence suggests that even when the quality and quantity of fertilizer is controlled for, the returns for women tend to be less suggesting that perhaps they aren’t using it correctly. The point is that it isn’t just a matter of providing inputs.

11. Moving Forward
Work with the national partners to begin the design and implementation of intervention(s) meant to address gender differences in adoption & yield.
Work with key contact points across the various objectives to understand needs and move forward with gender integration
Developing M&E system(s), as well as appropriate indicators of success or change
Open ended questionnaire-please write your name, country, and crop on the sheet of paper distributed yesterday (if you do not have a copy, please let me know).
I’d ask that you be as detailed as possible & I don’t need individual copies from everyone
Contacting me: OR
--at the most general level, when we’re talking about context, we know clearly that the national context matters but more than the national context, even intranational differences can be and often are important: differences between the Northern and Southern parts of Nigeria, as just one example, have been found to be important in terms of whether or not there is a gender gap at all and the extent and causes of it. Or between the more arid and food insecure regions of Tanzania compared to the southern breadbasket. In Burkina Faso (Akresh 2008) it was found that resources were allocated between men and women within a household more efficiently for those households located in less productive zones perhaps because the danger of not doing so was greater. Taken from Peterman et al. In Uganda and Nigeria we find female-headed households are associated with lower productivity in the dry savannah area. This may occur because the dry savannah ecology increases the domestic work burdens of women and girls, reducing the time spent on farming activities. It is worth noting religious and cultural norms which may also vary by region. For example, scholars note the relatively recent emergence of Islamic Sharia Law throughout states in northern Nigeria and the accompanying adoption of the hijab and a more rigid male ‘public’ female ‘private’ dichotomy (Ludwig, 2008; Mahdi, 2009). Given that there is some geographic overlap between these northern states and the agro-ecological zones where we find female productivity is lower in Nigeria, it is worth exploring the impact of an Islamic revival on women and farming.
--the biggest issue to consider here is whether or not we are talking about cash crops or more subsistence oriented crops.
--by this what I’m referring to is is really the structure of the household. If you only ask about female heads of household rather than ask about the managers of particularly plots within the household, this will impact the outcome of any data collection efforts. From Peterman et al. Results show none of the gender indicators are significant across regressions and gender differences are lost when indicators are aggregated to a higher level (household) or when specific household structures are disregarded. This suggests that gender differences in agricultural productivity may not be revealed at higher levels of aggregation that do not correspond to the basic decision making unit in specific farming systems.
--finally, when I say it is not just about gender what I mean is that often differences within groups or women or groups of men can be greater than differences between men and women. Clearly high producing women are different from lower producing women, but beyond that older women and men likely face unique challenges in comparison to younger women and men. Additionally, it isn’t just about giving people inputs because empirical evidence also suggests that the returns on inputs are not equal across groups of women and men, suggesting that something else is going on. For instance, women tend to get less out of the labor that they do have—whether that’s family labor or hired labor. This may be due to any number of causes—maybe women can’t get the best workers because they can’t offer a competitive price, maybe they have less time to supervise the work of male labor, maybe male labor is less likely to feel compelled to work as hard for a female farmer. Or even take fertlizer. Some evidence suggests that even when the quality and quantity of fertilizer is controlled for, the returns for women tend to be less suggesting that perhaps they aren’t using it correctly. The point is that it isn’t just a matter of providing inputs.

12. Different Kinds of Indicators
Output: measure the achievement of intended output & whether project goals are being met. Ex. The number of people trained
Outcome: measure the immediate impacts produced by the outputs. Ex. The percentage increase in average crop yield among men and women farmers each year following the intervention
Impact: measure a project’s medium or long term impacts on poverty and livelihoods. Should describe the actual change in conditions as a result of a program or project. Ex. Changes attitudes of men and women as a result of training, changed practices, or a decrease in the number of households living in poverty over 5 years

13. Issues to Consider Further: Monitoring and Evaluation
Potential indicators of gender integration (important but also context/program specific):
At the most basic level, we need gender disaggregated data collection, analysis strategy, & the need for measurable indicators for gender objectives;
A general rule is no more than 6 indicators per output or project objective
The extent to which women are involved in the crop or sector in terms of production, marketing, or processing has not decreased (or has increased) as a result of the program;
Reduction of gender disparities in access to productive resources and control of incomes as a result of the program (may matter less in households in which pooling of resources and income is the practice);
Improvement in diets or nutritional status of individuals, particularly in areas with marked gender disparities in this area;
The extent to which women are involved in the intervention/program;
Changes in time/labor requirements for women/men & boys/girls
Girls attendance at primary and secondary schools relative to attendance of their cohorts
Mechanism that enable women to join groups and remain active members including allowing non-household heads and non-land owners to be group members, timing meetings to accommodate women’s workloads
Should also have a mix of qualitative and quantitative

14. Women’s plots, men’s plots Burkina Faso: sorghum and vegetables
Author(s)
Sex of farmer
Yield
Country & Crop
% of Gap
Causes
Udry et al. (1995)
Women’s plots, men’s plots
Burkina Faso: sorghum and vegetables
18% lower on women’s plots
labor and fertilizer
type of crop grown-women tend to grow groundnut, okra, earthpeas/fonio
Oseni et al. (2015)
Vargas Hill and Vigneri (2011)
Uganda: Female headed households and male headed households;
In Ghana cocoa farmers defined as individuals managing all aspects relating to cocoa production but who were not necessarily the owner of the land.
Ghana (cocoa); Uganda (coffee)
Ghana: 16.7% in 2002 and 14.6% in 2004; Uganda 11.2%
Ghana: limited access to liquidity for purchasing inputs
Uganda: access to markets constrained
Mukasa and Salami (2015)
Used land (plot/parcel) manager instead of household head, excluded jointly managed lands.
Agricultural productivity defined as market gross value of output per unit of land (acre)
Nigeria, Tanzania, and Uganda
Women less productive by 18.6% (Nigeria), 27.4% (Tanzania) and 30.6% (Uganda)
Women hold and own less land than men with Nigeria being the most unequal (17.1% of plots female managed); 28.5% in Uganda and 34.9% in Tanzania.
Nigeria: women’s endowments need to be improved in both quality and quantity of cultivated land.
Tanzania differences in labor inputs
Uganda differences in parcel characteristics and labor inputs
While nonlabor inputs do not really emerge as important predictors of productivity differences, this is largely because they are just not used by very many farmers.
UN (2015)
Malawi, Tanzania, and Uganda
Tanzania: unconditional 16%, 30% conditional
Uganda: unconditional 13%, conditional 28%
Malawi: Unconditional 28%, conditional 31%
Tanzania and Malawi access to farm labor
Uganda it is access to farming technology
Women are also less likely to grow cash crops.
Access to productive technologies and machinery
Tiruneh et al. 2001
Male headed households and female headed households
Ethiopia tef, wheat, chickpeas, lentils, barley, faba beans, field
peas, haricot beans, maize, rough peas, and horticultural crops, mainly tomatoes
35%
Lower levels of input uses and access to extension services
Aguilar et al. (2015)
Manager level analysis, not plot level
Ethiopia: Main crops: teff, wheat, maize, sorghum, and barley
23.4%
Women use fewer livestock and tools, spend less time in agricultural activities, administer smaller plots, are less likely to use rent plots, and belong to smaller and poorer households. Extension services negatively effect female managers. Diversification, measured as the number of different crops planted, has higher returns for women than for men.
Divorced and widowed female managers are the least productive in comparison to men, whereas married female managers do not show any real difference in comparison to men.
UN Study: First time I’ve heard this terminology: conditional and unconditional gender gaps. The unconditional gender gap refers to the gap between men and women without taking into account the size of the plots farmed. Conditional gender gaps refer to the gap between male and female farmers after taking into account the size of the plot farmed. This gap tends to be larger than the unconditional gap. In Tanzania the big thing is male family labor. Women have less access to male family labor due in part to the fact that they are often widowed, divorced, or separated which is also what led to them being female plot managers to begin with.

15. TO DO
Think about a baseline survey—what questions do we want? Focus groups only? I think we can use the data that’s already been gathered and analyzed for the quantitative parts of it. Not sure we need more of that. However, for M&E purposes, we will need some quantitative data so we have something to compare over time trends to. OR we use the impact study (the survey) as our baseline data and grab a sample of men and women from that for focus groups.
In addition to tracking changes in key indicators among program recipients, M&E data can also be used to look at relationships between characteristics of program recipients, characteristics of service providers, characteristics of communities, and outcomes of interest. For example, it is possible to establish using fairly straightforward multi-variate regression analysis that a program beneficiary is more likely to have more cows if the beneficiary is more educated, if a program service provider has more training, if the community is less remote, etc.

16. Project Objectives, Output and Outcomes as They Relate to Gender Need to be Clearly Outlined
This will, of course, vary based on the national context and the interventions deployed
We need to establish a baseline (either through some quantitative survey or qualitative focus groups or both) before we establish our targets with the intervention.

17. The Gender in Agriculture Sourcebook developed by the World Bank has a number of useful objectives, indicators, and targets (pp ).