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Engendering agricultural censuses, Experience from Africa

This presentation discusses the efforts made to include gender considerations in agricultural censuses and surveys. It explores the challenges faced, methodology developed, and outcomes achieved in Africa. The presentation highlights the importance of sex-disaggregated agricultural data for better understanding and addressing gender disparities in the agricultural sector.

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Engendering agricultural censuses, Experience from Africa

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  1. Engendering agricultural censuses, Experience from Africa Diana Tempelman Senior Officer, Gender and Development FAO Regional Office for Africa, Accra “Global Forum on Gender Statistics” Accra, 26 - 29 January 2009

  2. GENDER CONCERNS IN AGRICULTURAL SECTOR • Male dominated rural out-migration • Access to productive resources:land & animals • Access to agricultural inputs:seeds, fertilizer / agro-chemicals, extension / training, finances, farmers organisations (market-)information • Access to / provision of family labour • Responsibilities

  3. Engendering agricultural statisticsOutline of presentation • Early days – first half 1990-ies • Developing methodology- WCA 2000 (1996-2005) • Consolidation- WCA 2010(2006 – 2015) • Remaining challenges * WCA = World Census of agriculture

  4. Early days(1991-2005, .., ..)

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

  6. 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

  7. 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

  8. Developing a methodology:WCA 2000(1996-2005)

  9. 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

  10. 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 FEMINISATION AGRICULTURAL SECTOR

  11. DATA  feminisation of agriculture Heads of agricultural holdings / sex in selected provinces - CAMEROON

  12. DATA labour constraints in headed HH Active male members / sex of HoHH, Tanzania

  13. DATA “Gender” variation at sub-national level Area under maize, NIGER

  14. DATA “Gender” variation at sub-national level area under vouandzou, NIGER

  15. DATA Under - presentation of women farmers’ work Area cultivated / crop by sex of agricultural holder– BURKINA FASO

  16. DATA Enhanced presentation of women farmers’ work Area cultivated / crop by sex of agricultural holder & sub-holder NEW CONCEPT > PLOT-MANAGERS

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

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

  19. ConsolidationWCA 2010(2006 - 2015)

  20. 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

  21. DATA i - Demographic data - NIGER Average size and dependency ratio of agricultural households by sex of Head of Household at regional and national level Source: RGAC 2004-2007, Niger

  22. ii - Access to productive resources, LAND

  23. DATA LAND Collective management / Head of HH

  24. DATA LAND Individual management / active HH members

  25. ii - Access to productive resources: ANIMALS

  26. DATA Agricultural HH / principal activity / sex HoHH, Niger Source: RGAC 2004-2007, Niger

  27. ii - Access to productive resources: ANIMALS Household level question

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

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

  30. DATA Ownership pigeons / sex & age of owner, Niger Source: RGAC 2004-2007, Niger

  31. iii – destination of agricultural produce Part 2 – Crop usage proportions (percentages) ETHIOPIA

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

  33. iv – Credit, labour, time-use. 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)

  34. Use of CREDIT / sex of HH member, Tanzania

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

  36. Reasons for not receiving a loan or credit - UGANDA Source: Uganda – Pilot Census of Agriculture 2003 – PCA Form 2: Section 2.2

  37. iv 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

  38. DATA iv - Division of Labour, Tanzania

  39. V – Poverty indicators, Tanzania Source: United Republic of Tanzania – Agricultural Sample Census 2002/2003- Small holder/Small Scale Farmer Questionnaire: Section 34

  40. DATA 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.

  41. ACTIONS Consolidation phase WCA 2010 (2006 – 2015) Integration into: FAO STATISTICAL DEVELOPMENT SERIES

  42. ACTIONS Consolidation – WCA 2010 (2006 – 2015) Forthcoming

  43. ACTIONS Consolidation – WCA 2010 (2006 – 2015) Reinforcing sex-disaggregated data in COUNTRY STAT

  44. Remaining challenges

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

  46. Discussion points Remaining challenges • integration national statistical systems • Progress & impact indicators

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

  48. THANK YOU

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