diana tempelman senior officer, gender and development fao regional office for africa, accra
DESCRIPTION
LAUNCHING: AGRI - GENDER DATABASE a statistical toolkit for the production of sex-disaggregated agricultural data. Diana Tempelman Senior Officer, Gender and Development FAO Regional Office for Africa, Accra. 21 st AFCAS Accra, 28 - 31 October 2009. 1 st edition October ‘09. - PowerPoint PPT PresentationTRANSCRIPT
LAUNCHING: AGRI - GENDER DATABASE a statistical toolkit for the production of
sex-disaggregated agricultural data
Diana TempelmanSenior Officer, Gender and Development
FAO Regional Office for Africa, Accra
21st AFCAS Accra, 28 - 31 October 2009
3
AGRI - GENDER DATABASE a statistical toolkit for the production of
sex-disaggregated agricultural data
RESULT OF:
Nearly 2 decades collaboration FAO & NBS-s in Africa
“joint-venture” with N = 100+ statisticians
Support of N = 10,000+++ men and women farmers responding to census / survey Q.
4
AGRI - GENDER DATABASE a statistical toolkit for the production of
sex-disaggregated agricultural data
1. Early days – first half 1990-ies
2. Developing methodology - WCA 2000
(1996-2005)
3. Consolidation - WCA 2010 (2006 – 2015)
4. Remaining challenges
* WCA = World Census of agriculture
6
1. Early days – first half 1990-ies
Thought?
Thought?
“Those feminists from Beijing!”
“Yes, women’s agricultural work doesn’t show in
statistics”
Early REACTIONS
7
1. Early days – first half 1990-ies
ACTIONS
re-analysing existing raw datadata by sex of Head of Holding
technical support to user-producers workshops availability / demand / users of
sex-disaggregated agricultural data
revision of concepts & definitions
8
1. Early days – first half 1990-ies
Awareness on need for sex-disaggregated data
Knowledge among statisticians
Openness to test collection sex-disaggregated data through
existing agricultural surveys / censuses
OUTCOME
9
2. Developing a methodology:
WCA 2000 (1996-2005)
Senegal, Guinea, Cape Verde, Burkina Faso, Mali, Lesotho, Namibia, Mozambique, Tanzania,
Cameroon, Uganda
10
Gender analysis training
Data analysis & presentation at sub-national level
Data presentation at sub-household level ALL MEMBERS’ WORK
2. Developing a methodology:
WCA 2000 (1996-2005)
ACTIONS
12
2. Developing a methodology:
WCA 2000 (1996-2005)
Thematic census reports: Tanzania, Niger
OUTCOME
14
3. EXAMPLES of Best practises from WCA 2010
Analysis of demographic data
Access to productive resources (/ sex of HoHH & individual)
Destination of agricultural produce / sex of HoHH (min.)
Credit, labour and time-use
Poverty indicators
16
AGRI-GENDER DATABASEINTRODUCTION
Data Items SECTION 1 SECTION 2
1 Agricultural population and households Questionnaire Table
2 Access to productive resources Questionnaire Table
3 Production and productivity Questionnaire Table
4 Destination of agricultural produce Questionnaire Table
5 Labour and time-use Questionnaire Table
6 Income and expenditures Questionnaire Table
7 Membership of agricultural/farmer organisations Questionnaire Table
8 Food security Questionnaire Table
9 Poverty indicators Questionnaire Table
17
Guinea
85+
80 - 84
75 - 79
70 -74
65 - 69
60 - 64
55 - 59
50 - 54
45 - 49
40 - 44
35 - 39
30 - 34
25 - 29
20 - 24
15 -19
.10 - 14
.5 - 9
> 5
Male FemaleScale maximum = 800000
Guinea – Labé Region
85+
80 - 84
75 - 79
70 -74
65 - 69
60 - 64
55 - 59
50 - 54
45 - 49
40 - 44
35 - 39
30 - 34
25 - 29
20 - 24
15 -19
.10 - 14
.5 - 9
> 5
Male FemaleScale maximum = 90000
1.1 - Demographic data: Guinea
FEMINISATION AGRICULTURAL SECTOR
DATA
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1.2 - Demographic data - NIGER Average FFH: smaller but more dependentsAverage FFH: smaller but more dependents
Average size and dependency ratio of agricultural households by sex of Head of Household at regional and national level
Source: RGAC 2004-2007, Niger
Male HoHH Female HoHH
Region
Average size Dependency
ratio Average size
Dependency ratio
AGADEZ 5,5 0,87 4,0 0,90
DIFFA 5,8 0,84 3,6 0,92
DOSSO 7,6 0,82 4,4 0,89
MARADI 7,7 0,95 3,9 0,96
TAHOUA 6,6 0,86 4,3 1,16
TILLABERY 8,3 0,83 4,5 0,99
ZINDER 5,9 0,85 3,7 1,07
NIAMEY 6,1 0,69 4,8 0,65
Total 6,9 0,86 4,0 1,03
DATA
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1.3 - Demographic data - Tanzania
labour constraints in headed HHlabour constraints in headed HH
Male active / sex of HoHH Selected regions Male HoHH Female HoHH
Dodoma 1.1 0.3 Mtwara 1.0 0.5 Iringa 1.1 0.2 Mbeya 1.1 0.3 Mara 1.0 0.5 Tanzania 1.1 0.4
Active male members / sex of HoHH, Tanzania
DATA
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Section 2 : Inventory of plots of agricultural holdings (NIGER)
Identification
Plots, farms
Family name & first name of
Plotmanager
Sex of Plot manager
Type de plot
management
Plot culture history
Type of culture
Type of land tenure
Type of Relief
1 2 3 4 5 6 7 8 9 Male 1 Individual 1 cultivated 1 Cul, pur 1 1 Inheritance 1 Plane
Female 2 Collective 2 fallow 2 Cult, mixed 2 2 Purchase 2 valley bottom 3 renting or crop sharing
2 slope
4 Loan
5 Gift
Field
Plot Write first and family name of Plotmanager,
starting with the HoHH
6 Other
|____|____|
|____|____| |____| |____| |____| |____| |____| |____|
|____|____|
|____|____| |____| |____| |____| |____| |____| |____|
2.1- Access to productive resources, LAND
21
LAND Collective management / Head of HH
Male sub-holder: Area under collective management per type of acquisition - NIGER
3%1%
7%
1%
5%
83%
Inherited
Purchased
Share-cropping
Loan
Gift
Other
Female sub-holder: Area under collective management per type of acquisition - NIGER
5%9%
11%
0%
6% 69%
Inherited
Purchased
Share-cropping
Loan
Gift
Other
DATA
22
LAND Individual management
/ active HH members
Male sub-holder: Area under individual management per type of acquisition at national level - NIGER
10%2% 1%
2%
9%
76%
Inherited
Purchased
Share-cropping
Loan
Gift
Other
Female sub-holder: Area under individual management per type of acquisition at national level, NIGER
12%1%
35%
48%3%
1%
Inherited
Purchased
Share-cropping
Loan
Gift
Other
DATA
23
Section 2 : Number of sedentary animals par kind and sex of owner
Code Kind of animal, sex and age Total number Number owned by women
1 2 3 4
10 Cattle
11 Female |____|____|____| |____|____|____|
12 Male |____|____|____| |____|____|____|
13 Castrated male |____|____|____| |____|____|____|
30 Sheep |____|____|____| |____|____|____|
40 Goat |____|____|____| |____|____|____|
Household level question
2.2 - Access to productive resources: ANIMALS
24Source: RGAC 2004-2007, Niger
Sedentary animals / type of animal / sex of owner, Niger
cattle sheep goats
Men Women Men Women Men Women
77.7 % 22.3 % 60.3 % 39.7 % 45.5 % 54.5 %
DATA
25
Ownership chicken / sex of owner, Niger
-1- Chicken
Repartition des poulets par proprietaire au niveau du Niger
32%
46%
22%
Femmes
Hommes
Enfants
-2- PINTADES
Repartition des pintades selon le proprietaire au niveau de Niger
14%
68%
18%
Femmes
Hommes
Enfants
DATA
Source: RGAC 2004-2007, Niger
26
-3- Ducks
Repartition des canards par proprietaire au Niger
22%
57%
21%
Femmes
Hommes
Enfants
-4- Pigeons
Repartition des pigeons par proprietaire au Niger
3% 14%
83%
Femmes
Hommes
Enfants
DATA
Ownership pigeons / sex & age of owner, Niger
Source: RGAC 2004-2007, Niger
27
2.3 – a - Access to credit Tanzania
Q 13.1: During the year 2002/2003 did any of the household members borrow money for agriculture? Yes or no
Q 13.2 If yes, then give details of the credit obtained during the agricultural year 2002/2003 (if the credit was provided in kind, for example by the provision of inputs, then estimate the value)
28
2.3 – b Use of CREDIT / sex HH member Tanzania
Credit details Source “a”
Use codes to indicate source |__|
Provide to Male=1, Female=2 |__|
S/N
Use of credit
Tick boxes below to indicate the use of the credit
13.2.1 Labour
13.2.2 Seeds
13.2.3 Fertilisers
13.2.4 Agrochemicals
13.2.5 Tools/equipment
13.2.6 Irrigation structures
13.2.7 Livestock
13.2.8 Other ………………………………
Source of credit 1 = Family, friend or relative 2 = Commercial bank
3 = Cooperative 4 = Savings and credit soc. 5 = Trader/trade store
6 = Private individual 7 = Religious organisation/NGO/Project 8 = Other (specify) ………………………
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Female HoHH use credit to hire labour -
Chart 7.5 Percent of Households that have access to Credit by sex of Household Head
0
10
20
30
Labour Seeds Fertili -zers
Agro-chemicals
Tools /Equip ment
IrrigationStructures
Livestock Other
Use of Credit
Per
cen
t
Male Headed Female Headed
DATA
to purchase seeds
TANZANIA
30
4 – Destination of agricultural producePart 2 – Crop usage proportions (percentages) ETHIOPIA
1 2 3 4 5 6 7 8 9
Proportions of total product for
Name of crop
Sr. No.
code
Household consumption
Seed
Sale* Wages in
kind Animal
feed Other (gifts.)
Total
0 1
0 2
0 3
0 4
0 5
Etc.
31
Destination of birds / sex of HoHH, Niger
Household consumption
Celebrations Baptism – Marriages -
funerals Other
Male Female Male Female Male Female Male Female
3,1 2,4 1,3 1,0 0,8 0,5 0,5 8,0
DATA
Source: RGAC 2004-2007, Niger
32
5 - Time-use, EthiopiaSource: Ethiopian Agricultural Sample Enumeration Miscellaneous Questions – 2001/02 (1994 E.C.)
Adults Children S/N
Activity Male
(code) Female (code)
Boys (code)
Girls (code)
21.1 Tilling |__| |__| |__| |__|
21.2 Sowing |__| |__| |__| |__|
21.3 Weeding |__| |__| |__| |__|
21.4 Harvesting |__| |__| |__| |__|
21.5 Feeding/Treating |__| |__| |__| |__|
21.6 Milking |__| |__| |__| |__|
21.7 Marketing of agricultural products |__| |__| |__| |__|
21 How much time do men and women spend in the household on eachof the following agricultural activities? Use the codes given below the table
Codes:1 = Not participated2 = One fourth of the time (1/4)3 = One half of the time (1/2)
4 = Three fourth of the time (3/4) 5 = Full time6 = Not applicable
33
Chart 5.17 Percent of Households by Type of Labour - MALE Headed Households
0% 20% 40% 60% 80% 100%
Land ClearingSoil Preparation by Hand
Soil Preparation by Oxen / TractorPlantingWeeding
Crop ProtectionHarvesting
Crop ProcessingCrop Marketing
Cattle RearingCattle Herding
Cattle MarketingGoat & Sheep RearingGoat & Sheep Herding
Goat & Sheep MarketingMilking
Pig RearingPoultry Keeping
Collecting WaterCollecting Firewood
Pole CuttingTimber Wood Cutting
Building / Maintaining HousesMaking Beer
BeekeepingFishing
Fish FarmingOff - farm Income Generation
Typ
e of
Lab
our
Percent
Head of Household Alone Adults Males Adult FemaleAdults Boys GirlsBoys & Girls All Household Members Hired Labour
Chart 5.18 Percent of Households by Type of Labour - Female Headed Households
0% 20% 40% 60% 80% 100%
Land ClearingSoil Preparation by Hand
Soil Preparation by Oxen / T ractorPlantingWeeding
Crop ProtectionHarvesting
Crop ProcessingCrop Marketing
Cattle RearingCattle Herding
Cattle MarketingGoat & Sheep RearingGoat & Sheep Herding
Goat & Sheep MarketingMilking
Pig RearingPoultry Keeping
Collecting WaterCollecting Firewood
Pole CuttingTimber Wood Cutting
Building / Maintaining HousesMaking Beer
BeekeepingFishing
Fish FarmingOff - farm Income Generation
Typ
e of
Lab
our
Percent
Head of Household Alone Adults Males Adult FemaleAdults Boys GirlsBoys & Girls All Household Members Hired Labour
5 -2 - Division of Labour, Tanzania
DATA
34
8/9 – Food security / Poverty indicators Tanzania
Source: United Republic of Tanzania – Agricultural Sample Census 2002/2003- Small holder/Small Scale Farmer Questionnaire: Section 34
Code 34.6.3 1 = Never 3 = Sometimes 6 = Always 2 = Seldom 4 = Other
34.6.1
Number of meals the household normally has per day
|__|
34.6.2
Number of days the household consumed meat last week
|__|
34.6.3
How often did the household have problems in satisfying the food needs of the household last year (code)
|__|
35
8 - Food security
Frequency of food shortages, Tanzania
Chart 9.4 Percent of Male and Female Headed Households by Frequency of Facing Food Shortages
0
10
20
30
40
50
Never Seldom Sometimes Often Always
Frequency of Food Shortage
Per
cen
t of
Hou
seh
old
s
Male Female
A higher percent male-headed HHs never has food shortage.
A higher percent of female-headed HHs has often or always food shortages.
The same pattern appears in the regions.
DATA
37
analysis of available
sex-disaggregated data
use sex-disaggregated data – policy-making, implementation & impact assessment
4.1 – Remaining challenges
Discussion points
38
integration national
statistical systems
Progress & impact indicators
Discussion points
4.2 - Remaining challenges
39
IMPROVED DATA COLLECTION
Labour
Decision-making
Responsibilities
Discussion points
4.3 - Remaining challenges