Diana Tempelman Senior Officer, Gender and Development FAO Regional Office for Africa, Accra

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

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1st edition October ‘09

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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.

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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

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1. Early days (1991-1995)

Togo, Benin, Burkina Faso, Botswana

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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

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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

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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

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2. Developing a methodology:

WCA 2000 (1996-2005)

Senegal, Guinea, Cape Verde, Burkina Faso, Mali, Lesotho, Namibia, Mozambique, Tanzania,

Cameroon, Uganda

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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

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2. Developing a methodology:

WCA 2000 (1996-2005)

Lessons learned document

OUTCOME

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2. Developing a methodology:

WCA 2000 (1996-2005)

Thematic census reports: Tanzania, Niger

OUTCOME

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3. Consolidation

WCA 2010 (2006 - 2015)

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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

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AGRI-GENDER DATABASE

ACTIONS

TODAY

29 October ‘09

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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

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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

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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

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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

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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

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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

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-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

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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)

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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

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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.

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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

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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

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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

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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)

|__|

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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

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4. Remaining challenges

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analysis of available

sex-disaggregated data

use sex-disaggregated data – policy-making, implementation & impact assessment

4.1 – Remaining challenges

Discussion points

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integration national

statistical systems

Progress & impact indicators

Discussion points

4.2 - Remaining challenges

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IMPROVED DATA COLLECTION

Labour

Decision-making

Responsibilities

Discussion points

4.3 - Remaining challenges

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4.4 – Remaining challenges

Increasingsex-disaggregated data

in COUNTRY STAT

Discussion points

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