Gender, livestock and livelihood indicators

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


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


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)


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


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

Gender, livestock and livelihood indicators

Technology