Presented by Jemimah Njuki, Jane Poole, Nancy Johnson, Isabelle Baltenweck, Pamela Pali, Zaibet Lokman and Samuel Mburu at ILRI Addis Ababa, 2 May 2011.

1. GENDER, LIVESTOCK AND LIVELIHOOD INDICATORS Jemimah Njuki, Jane Poole, Nancy Johnson, Isabelle Baltenweck, Pamela Pali, Zaibet Lokman & Samuel Mburu Presentation at ILRI Addis Ababa, 2 May 2011

3. Indicators

4. Metadata

5. Sampling

7. INDICATOR 1: ASSETS Assets: Rationale Essential information for characterizing the households Important for calculating other indicators such as productivity and income Assets give a better measure of welfare than income because it reflects household’s long term capacity to manage risk Gender disaggregating of assets helps track reductions in gender asset disparities

8. INDICATOR 1: ASSETS Asset: measurable variables Land Size Tenure Ownership (male, female, jointly) Farm and domestic assets Number Age Ownership (male, female, jointly) Livestock Species Number owned at household level Ownership (male, female, jointly) Housing Ownership Number of rooms Building materials

9. INDICATOR 1: ASSETS Level and types of analysis One of the main challenges is how to combine different assets that have different value, functions into one asset index Assigning a value and calculating value of assets Weighing assets and calculation an asset index. Movable assets (livestock and domestic assets) Weighted and controlled for age List of assets can be added based on context—however, the processing of assigning weights can be complex Disaggregating the assets by those owned by men, women shows the gender asset disparity (see page 6)

10. Quality of housing (CASHPOR House Index – CHI) An often collected indicator that has not always been analysed <5=Very poor; 5 – 9=Poor; 10 - 17 =Average; 18 – 30 : Wealthy Other variables that can be calculated: Tropical Livestock Units Contribution of livestock to household /women’s assets INDICATOR 1: ASSETS

11. INDICATOR 2: ACCESS AND USE OF TECHNOLOGIES Rationale A lot of ILI interventions has components of increasing access to information, inputs, technologies and other services Access to and use of technologies has an impact on productivity and income Who has access and uses these technologies and inputs (men and women) is important for reducing gender disparities in adoption and use of services

12. INDICATOR 2: ACCESS AND USE OF TECHNOLOGIES Access and use of technologies: Divided into 3 categories Technologies: Livestock and non livestock Services: Financial, information etc Membership to groups (social capital) For technologies and services, information included are: type of technology/service, access, use and who within the household has used These technologies need to have a time frame for use that is consistent e.g last 12 months, last 5 years etc

13. INDICATOR 3: PRODUCTION AND PRODUCTIVITY OF LIVESTOCK Rationale Changes in milk production per cow and egg production are important indicators for evaluating effectiveness of dairy and poultry intervention projects An area planned for expansion in future versions of this document

14. INDICATOR 3: PRODUCTION AND PRODUCTIVITY OF LIVESTOCK Production & productivity: Calculated variables Dairy production Milk production per animal per lactation Milk production per animal per year Milk production per household per day Eggs Egg production per hen per clutch Egg production per household (3 month period) Number of clutches in the last 3 months Number of laying hens

15. INDICATOR 3: PRODUCTION AND PRODUCTIVITY OF LIVESTOCK Milk production B A O (calving) Survey time C A Lactation length Production & productivity: Calculated variables Milk production per animal by breed per lactation Milk production per lactation can be calculated in 2 ways: 1. Fitting of the lactation curve using at least two points (milk production at calving and yesterday milk production) per cow and calculating the average area under the curve. 2. Approximation of the level of production by calculating the area (triangle OBC): lactation length (OC) x milk production at calving (OB) divided by 2 as illustrated in the figure below

16. INDICATOR 4: LABOUR USE IN LIVESTOCK SYSTEMS Rationale Changes in labour patterns are useful for understanding and identifying interventions with potential to reduce labour and generate employment across the value chain Labour data collection should be done at the same time (season, calendar month) to avoid variations in labour use

18. INDICATOR 5: LIVESTOCK AND HOUSEHOLD INCOME Rationale Contribution of livestock to both farm and household income helps in quantification of the multiple functions of livestock.

19. INDICATOR 5: LIVESTOCK AND HOUSEHOLD INCOME Livestock and household income: Measurable variables Livestock income (Sale of live animals by breed, Sale of livestock products) Other household incomes (Off farm income, Crop incomes) Calculated variables Cash income from sale of livestock over a given period (Annual) Cash income from sale of livestock products over a given period (Annual) Contribution of livestock to total farm/household income Income controlled by women

20. INDICATOR 6: LIVESTOCK AND FOOD SECURITY Rationale Livestock contributes to food security in two ways: Increased consumption of animal source food Increased income from livestock that can be used to purchase additional food for the household or that can fill periods of food deficit Three main variables used: Household /or Individual Dietary Diversity (HDDS /IDDS) A Food Consumption Score (FCS) Months of Adequate Household Food Provisioning (MAHFP)

21. INDICATOR 6: LIVESTOCK AND FOOD SECURITY Livestock and food security: Calculated variables Household/Individual dietary diversity score (HDDS/IDDS) Takes a value of 0-1 and is measured based on a 24 hour recall Can also be used to calculate proportion of households consuming at least one animal source food per day Food consumption score Based on consumption of food groups Each food group is weighted Contribution of meat, fish and milk to the food consumption score Months of adequate household food provisioning (MAHFP)- Measured over a 12 month recall period

22. INDICATOR 6: LIVESTOCK AND FOOD SECURITY The Food Consumption Score Thresholds determined based on the consumption behaviour of the country Currently using the WFP thresholds: 0-21 Poor 21.5-35 Borderline >35 Acceptable

23. META DATA: Meta data is the basis for accessing, understanding and using the data. It is also important for linking and comparing surveys across regions and projects. Meta data should include study related documents such as sampling protocol, reports, etc Meta data template

24. SAMPLING PROCESS Sampling process should be properly documented Issues to consider in sampling Target population and extent of generalizing results Objectives of the survey Sampling sites Sampling method The need for baseline data Sampling frame and household identification

25. DISCUSSIONS & ACTION POINTS