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Presented by Jemimah Njuki, Jane Poole, Nancy Johnson, Isabelle Baltenweck, Pamela Pali, Zaibet Lokman and Samuel Mburu at ILRI Addis Ababa, 2 May 2011.
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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
OUTLINE
Introduction Indicators Metadata Sampling Discussions
INTRODUCTION
Goal: To promote a common standardized data collection tools for a set of gender, and livelihood indicators that can be used across projects
Current practice: Every project develops and identifies its own indicators and therefore it is
challenging to merge/compare data from different projects even when the objectives are comparable
Inconsistency in reporting similarly collected data due to non-uniform analysis
Scope: Indicators presented here are designed for data collection at household level
• Potential for future work: the list of indicators is not exhaustive and so there is potential for expansion to cover other thematic areas.
Types of indicators covered
Assets
Access to and use of technologies
Production and Productivity
Labour use in livestock production
Contribution of livestock to cash /no cash income
Food security
INDICATOR 1: ASSETS
Assets: Rationale Essential information for characterizing the
householdsImportant for calculating other indicators
such as productivity and incomeAssets 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
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
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)
Asset (g)Number owned
Weight of asset (wg)
Age (adjustment for age shown in cell) (a)
< 3 yrs old 3 – 7 yrs old > 7 yrs old
Animal
Cattle 10
no adjustment
Horses 10
Sheep/goats 3
Poultry 1
Pigs 2
Domestic assets
Cooker 2
× 1 × 0.8 × 0.5
Kitchen cupboard 2
Refrigerator 4
Radio 2
Television 4
DVD player 4
Cell phone 3
Chairs 1
Mosquito nets 1
Gas stove 2
Transport
Car/truck 160
× 1 × 0.8 × 0.5
Motorcycle 48
Bicycle 6
Cart (animal drawn)
12
Productive
Hoes 1
× 1 × 0.8 × 0.5
Spades/shovels 1
Ploughs 4
Treadle pump 6
Powered pump 12
Sewing machine 4
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
Ownership Number of rooms Floor material Wall material Roofing Borrowed=0Rented=2Owned=6
1 to 2 rooms=02 to 4 rooms=2>4 rooms =6
Earth=0Cement=2Tiles=6
Earth/mud=0Wood/Bamboo/ Iron sheets=2Cement/Bricks=6
Grass=0Iron sheets /Asbestos=2Tiles=6
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
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
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
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
INDICATOR 3: PRODUCTION AND PRODUCTIVITY OF LIVESTOCK
Production & productivity: Calculated variables Milk production per animal by breed per lactation
A
B
Milk production
O (calving) Survey time
Lactation length
A C
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
INDICATOR 4: LABOUR USE IN LIVESTOCK SYSTEMS
RationaleChanges 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
INDICATOR 4: LABOUR USE IN LIVESTOCK SYSTEMS
Labour use: Measurable variables Type of activity by species Number of household members involved by gender and
age Number of non-household members involved by gender Uses a 7 day recall period)
Several variables can be calculated from this: Amount of labour (in hours) used in livestock by activity
and gender Total livestock labour across all livestock species and
activities Percentage of time spend on livestock activities (this
will require collection of data on other activities besides livestock production)
INDICATOR 5: LIVESTOCK AND HOUSEHOLD INCOME
RationaleContribution of livestock to both farm and
household income helps in quantification of the multiple functions of livestock.
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
INDICATOR 6: LIVESTOCK AND FOOD SECURITY
RationaleLivestock 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)
INDICATOR 6: LIVESTOCK AND FOOD SECURITY
Livestock and food security: Calculated variablesHousehold/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 dayFood 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
INDICATOR 6: LIVESTOCK AND FOOD SECURITY
Types of foods Groups WeightA. Staples or food made from staples including
millet, sorghum, maize, rice, wheat, or other local grains, e.g. ugali, bread, rice noodles, biscuits, or other foods
Main Staples(if sum of
frequencies is > 7 set to 7)
2
A. Potatoes, yams, cassava or any other foods made from roots or tubers
A. Vegetables Vegetables 1A. Fruits FruitsA. Beans, peas, lentils, or nuts? Pulses 3A. Red meat-beef, pork, lamb, goat, rabbit wild
game, liver, kidney, heart, or other organ meats?Meat and
Fish(if sum of
frequencies is > 7 set to 7)
4
A. Poultry including chicken, duck, other poultryA. EggsA. Fresh or dried fish or shellfish?A. Milk, cheese, yogurt, or other milk product Milk 4A. Oils and fats? Oil 0.5A. Sweets, sugar, honey Sugar 0.5A. Any other foods, such as condiments, coffee, tea
including milk in tea?Condiments 0
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
META DATA:
Project Title:Project / Budget code:
Contact name: Although staff come & go, good to have original contact who has the best knowledge of the survey
Name of survey database(s): Location: i.e. physical location – e.g. server name
Type of database(s): E.g. MySQL, Access, CsPro, Oracle etc.
List of related documents: Locations:
Type of survey(s) E.g. Community level, Household, Herd/Flock, Market Agent, Value-chain, NRM and Baseline, M&E, Impact assessment etc.
Year of survey:
Location of survey: Country, Administrative areas or Site Description, preferably include GPS coordinates for some key points (e.g. centre of site, main urban centre)
GPS Coordinates for each observational unit? Yes / No
GPS Coordinate system used: E.g. WGS1984*GPS Unit format
used:E.g. decimal degrees (hd.ddddd)*
Brief description of surveys:
May include: Total number of observational units and/or number in each ‘site’, Key sampling aspects (cluster random, 2-stage etc.)& reference to design and/or
sampling protocol Objectives of survey Topics/type of information covered in survey (e.g. income/expenditure of dairy cattle,
list of the species/breeds of interest etc.)
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
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
DISCUSSIONS & ACTION POINTS