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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 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.

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

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  1. LAUNCHING: AGRI - GENDER DATABASEa statistical toolkit for the production of sex-disaggregated agricultural data Diana Tempelman Senior Officer, Gender and Development FAO Regional Office for Africa, Accra 21st AFCAS Accra, 28 - 31 October 2009

  2. 1st edition October ‘09

  3. AGRI - GENDER DATABASEa 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 DATABASEa statistical toolkit for the production of sex-disaggregated agricultural data • Early days – first half 1990-ies • Developing methodology- WCA 2000 (1996-2005) • Consolidation- WCA 2010(2006 – 2015) • Remaining challenges * WCA = World Census of agriculture

  5. Early days(1991-1995)Togo, Benin, Burkina Faso, Botswana

  6. Early REACTIONS Early days – first half 1990-ies “Those feminists from Beijing!” Thought? Thought? “Yes, women’s agricultural work doesn’t show in statistics”

  7. ACTIONS Early days – first half 1990-ies • re-analysing existing raw data • data by sex of Head of Holding • technical support touser-producers workshopsavailability / demand / users of • sex-disaggregated agricultural data • revision of concepts & definitions

  8. OUTCOME 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

  9. Developing a methodology:WCA 2000(1996-2005)Senegal, Guinea, Cape Verde, Burkina Faso, Mali, Lesotho, Namibia, Mozambique, Tanzania, Cameroon, Uganda

  10. ACTIONS Developing a methodology:WCA 2000(1996-2005) Gender analysis training Data analysis & presentation at sub-national level Data presentation at sub-household level ALL MEMBERS’ WORK

  11. OUTCOME Developing a methodology:WCA 2000(1996-2005) Lessons learned document

  12. OUTCOME 2. Developing a methodology:WCA 2000(1996-2005) Thematic census reports: Tanzania, Niger

  13. ConsolidationWCA 2010(2006 - 2015)

  14. EXAMPLES of Best practisesfrom 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

  15. ACTIONS AGRI-GENDER DATABASE TODAY 29 October ‘09

  16. AGRI-GENDER DATABASEINTRODUCTION Data ItemsSECTION 1 SECTION 2 1 Agricultural population and households QuestionnaireTable 2 Access to productive resources QuestionnaireTable 3 Production and productivity QuestionnaireTable 4 Destination of agricultural produce QuestionnaireTable 5 Labour and time-use QuestionnaireTable 6 Income and expenditures QuestionnaireTable 7 Membership of agricultural/farmer organisations QuestionnaireTable 8 Food security QuestionnaireTable 9 Poverty indicators QuestionnaireTable

  17. Guinea – Labé Region Guinea 85+ 85+ 80 - 84 80 - 84 75 - 79 75 - 79 70 -74 70 -74 65 - 69 65 - 69 60 - 64 60 - 64 55 - 59 55 - 59 50 - 54 50 - 54 45 - 49 45 - 49 40 - 44 40 - 44 35 - 39 35 - 39 30 - 34 30 - 34 25 - 29 25 - 29 20 - 24 20 - 24 15 -19 15 -19 .10 - 14 .10 - 14 .5 - 9 .5 - 9 > 5 > 5 Male Male Female Female Scale maximum = 90000 Scale maximum = 800000 DATA 1.1 - Demographic data: GuineaFEMINISATION AGRICULTURAL SECTOR

  18. DATA 1.2 - Demographic data- NIGERAverage 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

  19. DATA 1.3 - Demographic data - Tanzanialabour constraints in headed HH Active male members / sex of HoHH, Tanzania

  20. 2.1- Access to productive resources, LAND

  21. DATA LAND Collective management / Head of HH

  22. DATA LAND Individual management / active HH members

  23. 2.2 - Access to productive resources: ANIMALS Household level question

  24. DATA Sedentary animals / type of animal / sex of owner, Niger Source: RGAC 2004-2007, Niger

  25. DATA Ownership chicken / sex of owner, Niger Source: RGAC 2004-2007, Niger

  26. 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

  29. DATA Female HoHH use credit to hire labour - to purchase seeds TANZANIA

  30. 4 – Destination of agricultural produce Part 2 – Crop usage proportions (percentages) ETHIOPIA

  31. DATA Destination of birds / sex of HoHH, Niger Source: RGAC 2004-2007, Niger

  32. 5 - Time-use, Ethiopia Source: Ethiopian Agricultural Sample Enumeration Miscellaneous Questions – 2001/02 (1994 E.C.) 21 How much time do men and women spend in the household on each of the following agricultural activities? Use the codes given below the table Codes: 1 = Not participated 2 = 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 time 6 = Not applicable

  33. DATA 5 -2 - Division of Labour, Tanzania

  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

  35. DATA 8 - Food security Frequency of food shortages, Tanzania 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.

  36. Remaining challenges

  37. Discussion points 4.1 – Remaining challenges • analysis of available sex-disaggregated data • usesex-disaggregated data – policy-making, implementation & impact assessment

  38. Discussion points 4.2 - Remaining challenges • integration national statistical systems • Progress & impact indicators

  39. Discussion points 4.3 - Remaining challenges IMPROVED DATA COLLECTION • Labour • Decision-making • Responsibilities

  40. Discussion points 4.4 – Remaining challenges Increasing sex-disaggregated data in COUNTRY STAT

  41. THANK YOU

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