Intra and Interhousehold Gaps in Agricultural Resources and Productivities: Insight from Mixed Farming Systems (MFS) Initiative Baseline Surveys in Malawi and Ghana Adane Tufa June 27, 2024 www.iita.org | www.cgiar.org G-LENS Webinars 2024 1 Outline Introduction Methodology Results conclusion IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org 2 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Introduction Several studies show a gender gap in agricultural productivity between women and men farmers (e.g., World Bank, 2014; Aguilar et al., 2015; Kilic et al., 2015; Ali et al., 2016; Tufa et al., 2022; Boahen et al., 2024; etc.). Women and men farmers have different levels of resources (endowment) – e.g., unequal access to agricultural inputs, technologies, labor, etc.). If women had the same access to productive resources as men, their farm yields could increase by 20–30%. These estimated gains could increase total agricultural output by 2.5–4% in low-income countries. Fig.1. Percent productivity difference between female and male farmers and associated loss (bn USD/year) 3 Productivity of female farmers (% less than male) Ethiopia Malawi Northern Nigeria Tanzania Uganda 23.4 25 28 17.5 Associated loss (bn USD/Year) Ethiopia Malawi Northern Nigeria Tanzania Uganda 1.1000000000000001 0.1 0.105 6.7000000000000004E-2 % productivity difference Productivity loss (bn USD/year) Women's agency (choices, bargaining power, preferences, capacities, aspirations) Women's access to and control over resources  (information, education, land, finance, technology, etc.) Gendered social norms  (expectations, traditions, etc.) Gendered policies, institutions, and governance Informal Formal Individual Systemic IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Even with equal access to productive resources, women would still have lower agricultural productivity than men, given gender norms, institutional constraints, and market failures that impact how effectively they use these resources. Fig.2. Percent of overall productivity loss due to differences in endowment of and structure Fig.3. Gender dimensions 4 % of endowment Ethiopia Mali Malawi Uganda 57 56 82 69.599999999999994 % of structural Ethiopia Mali Malawi Uganda 43 44 18 30.4 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Objectives To examine intra-household and inter-household differences in access to productive resources, technologies, and knowledge. Analyze crop productivity differences between male-headed households who have spouses and female-headed households with no spouses. 5 Methodology IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Baseline household surveys (SI-MFS) Malawi Survey conducted in 2022/23 a multi-stage cluster sampling 1268 randomly selected households from six districts Ghana Survey conducted in 2023 a multi-stage cluster sampling 1317 randomly selected households from 19 districts Descriptive statistics and RIF decomposition 6 Fig.4. Study sites in Malawi and Ghana 6 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Characteristics MHHS§ (n=804) FHHS¥ (n=32) FHHNS† (n=384) MHHNS‡ (n=48) Households (%) 63.4 2.5 30.3 3.8 Age (in years)           Wife 39.2 38.9 49.1     Husband 45.4 44.4   54.1 Education (# yrs)           Wife 5.2 5.7 4.6     Husband 6.5 5.3   5.8 Family size 5.3 6.1 4.5 2.7 § MHHS represents a male-headed household with a spouse; ¥ FHHS represents a female-headed household with a spouse; † FHHNS represents a female-headed household without a spouse; ‡ MHHNS represents a male-headed household without a spouse. Table 1. Demographic characteristics of the households in Malawi Marriage type MHHS FHHS FHHNS MHHNS Total Polygamous (n=62) 85.5 14.52     4.9 Monogamous (n=774) 97 3     61 *Others (n=432)     88.9 11.1 34.1 *Others represent widowed, separated, divorced, and never married. Most of the sample households are MHHS. In MHHS, husbands are more educated than wives, which is contrary to FHHS. Heads in FHHNS are less educated and older than the heads and spouses in other household types. Households in MHHS have larger families than the ones in FHHNS. Most of the sample households are monogamous, The singles constitute above one-third of the sample household. Household characteristics 7 7 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Characteristics MHHS (n=1036) FHHS (n=15) FHHNS (n=194) MHHNS (n=72) Households (%) 78.7 1.1 14.7 5.5 Age (in years)           Wife 39.8 48.8 56.7     Husband 49.7 48.6   50.7 Education (# yrs)           Wife 1.9 2.7 1.2     Husband 3.9 1.7   3.9 Family size 6.9 5.8 4.2 4.4 Table 2. Demographic characteristics of the households in Ghana Marriage type MHHS FHHS FHHNS MHHNS Total Polygamous (232) 100       17.6 Monogamous (819) 98.2 1.8     62.2 Others (266)     72.9 27.1 20.2 Most of the sample households are monogamous. More polygamous households in Ghana than in Malawi. More single households in Malawi than in Ghana. Most of the sample households are MHHS. In MHHS, husbands are more educated than wives, which is contrary to FHHS. Heads in FHHNS are less educated and older than the heads and spouses in other household types. Households in MHHS have larger families than the ones in FHHNS. Household characteristics 8 8 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Primary occupation MHHS FHHS FHHNS (n=384) MHHNS (n=48) Total (n=2106) Husband (n=804) Wife (n=806) Husband (n=32) Wife (n=32) Crop prodn 67 77.2 59.4 75 71.4 66.7 71.7 Ganyu+ 14.1 12.7 9.4 15.6 14.1 8.3 13.3 Self-employed 12.7 7.1 9.4 9.4 9.9 14.6 10 Livestock 0.5 0.5 1.04 2.1 0.6 Others* 5.7 2.6 21.9 3.64 8.3 4.3 Table 3. Occupation by household type in Malawi +ganyu (casual wage labour) *Others (unpaid household labor, non-farm employment, farm employment, office employment, unpaid farm agricultural work, fishing, unpaid household work, student, other, looking for a job, and none) Secondary occupation n=699 n=679 n=31 n=25 n=333 n=38 n=1805 Ganyu 33.5 34.8 44 35.5 42.3 42.1 36 Crop prodn 24.3 19 24 19.4 20.4 23.7 21.5 Self-employed 21 17.8 20 32.3 17.1 15.8 19.2 Livestock 9.6 5.7 4 3.2 6.61 10.5 7.4 Others 11.6 22.7 8 9.7 13.5 7.9 16 Crop production is the primary occupation for all household types. In coupled households, higher proportions of wives reported crop production as their primary occupation than their husbands. Ganyu is the second important primary occupation and the first most important secondary occupation. < 10% of households keep livestock as their secondary occupation. Occupations 9 9 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Wives in MHHS and FHHS devote longer hours to domestic activities than their husbands but devote equivalent hours to farming in both dry and rainy seasons. Husbands in MHHS devote longer hours to nonfarm activities than their wives – financial implication. Husbands in MHHS and FHHS devote longer hours to socializing and resting than their wives. Fig.5. Time devoted to different activities (1 = shortest and 5=longest) in Malawi Time allocation 10 Domestic work (cooking, collecting water, collecting firewood, sweeping, washing dishes and clothes, caring for children and elderly or sick people) § Farming work (agricultural work and animal care) † Non-farm income-generating activities (Dependent or independent remunerated work) ‡ Socializing (participation in local associations, community meetings, religious gatherings) $ Resting (time to rest or sleep during the day) 10 Dry season MHHS Husband Domestic¥ Farming§ Non-farming† Socializing‡ Resting$ 1.4 3.3 2.7 2.6 2.9 MHHS Wife Domestic¥ Farming§ Non-farming† Socializing‡ Resting$ 4.4000000000000004 3 1.4 1.3 2.5 FHHS Husband Domestic¥ Farming§ Non-farming† Socializing‡ Resting$ 1.2 2.8 3 2.8 2.7 FHHS Wife Domestic¥ Farming§ Non-farming† Socializing‡ Resting$ 4.3 2.2999999999999998 2.7 2.2999999999999998 2.2999999999999998 FHHNS Domestic¥ Farming§ Non-farming† Socializing‡ Resting$ 4.2 3 1.9 2.1 2.5 MHHNS Domestic¥ Farming§ Non-farming† Socializing‡ Resting$ 3.2 3.1 2.6 2.2000000000000002 2.5 Time devoted Rainy season MHHS Husband Domestic Farming Nonfarming Socializing Resting 1.3 4.8 2 2.2999999999999998 2.8 MHHS Wife Domestic Farming Nonfarming Socializing Resting 3.9 4.9000000000000004 1 2 2.2999999999999998 FHHS Husband Domestic Farming Nonfarming Socializing Resting 1.3 4.2 2.4 2.2999999999999998 2.6 FHHS Wife Domestic Farming Nonfarming Socializing Resting 3.6 4.5999999999999996 2.2999999999999998 1.8 1.9 FHHNS Domestic Farming Nonfarming Socializing Resting 3.7 4.9000000000000004 1.4 1.8 2.2000000000000002 MHHNS Domestic Farming Nonfarming Socializing Resting 2.9 5 1.8 1.8 2.2999999999999998 Activities Time devoted IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Fig.6. Time devoted to different activities (1 = shortest and 5=longest) in Ghana Wives in MHHS devote longer hours to domestic activities than their husbands in both dry and rainy seasons. Husbands in MHHS devote longer hours to farming activities than their wives. Husbands in MHHS and FHHS devote longer hours to socializing and resting than their wives. Time allocation 11 Dry season MHHS Husband Domestic¥ Farming§ Non-farming† Socializing‡ Resting$ 1.4 3.3 1.7 3.2 2.4 MHHS Wife Domestic¥ Farming§ Non-farming† Socializing‡ Resting$ 4.8 1.5 1.9 2.8 1.9 FHHS Husband Domestic¥ Farming§ Non-farming† Socializing‡ Resting$ 2.7 2.7 1.1000000000000001 3.1 1.8 FHHS Wife Domestic¥ Farming§ Non-farming† Socializing‡ Resting$ 2.9 3.1 1.6 3.1 1.8 FHHNS Domestic¥ Farming§ Non-farming† Socializing‡ Resting$ 4.5999999999999996 2.2999999999999998 1.7 2.5 1.8 MHHNS Domestic¥ Farming§ Non-farming† Socializing‡ Resting$ 2.2999999999999998 3.2 1.4 2.9 2.4 Time devoted Rainy season MHHS Husband Domestic Farming Nonfarming Socializing Resting 1.3 4.5999999999999996 1.4 2.9 1.7 MHHS Wife Domestic Farming Nonfarming Socializing Resting 4.5 3.8 1.6 2.4 1.2 FHHS Husband Domestic Farming Nonfarming Socializing Resting 2.6 3.9 1.3 2.9 0.6 FHHS Wife Domestic Farming Nonfarming Socializing Resting 2.8 4.5 2 2.5 1.1000000000000001 FHHNS Domestic Farming Nonfarming Socializing Resting 4.3 4 1.4 2.2000000000000002 1.3 MHHNS Domestic Farming Nonfarming Socializing Resting 2 4.5999999999999996 1.1000000000000001 2.7 2 Activities Time devoted IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Religious institutions and VSL groups are the most important community organizations in Malawi. Religious institutions are the most important community organizations for both husbands and wives in MHHS and heads in FHHNS. VSL groups are more important for women than men. Cooperatives and farmers' associations are not that popular. Fig.7. Percent participated in community organizations in Malawi Community organizations 12 12 MHHNS Others Youth association Women association Government team Local administration Input supply Farmers association/cooperatives Village savings and lending group Church/mosque/congregation 8.5 4.3 0 4.3 8.5 12.8 8.3000000000000007 8.5 46.8 FHHNS Others Youth association Women association Government team Local administration Input supply Farmers association/cooperatives Village savings and lending group Church/mosque/congregation 5.3 1.33 6.07 3.18 5.57 8.73 21.7 43.01 50.26 FHHS Wife Others Youth association Women association Government team Local administration Input supply Farmers association/cooperatives Village savings and lending group Church/mosque/congregation 0 0 6.7 6.7 6.7 3.3 36.4 56.7 19.399999999999999 FHHS Husband Others Youth association Women association Government team Local administration Input supply Farmers association/cooperatives Village savings and lending group Church/mosque/congregation 6.3 0 0 0 9.4 3.1 9.4 6.3 15.6 MHHS Wife Others Youth association Women association Government team Local administration Input supply Farmers association/cooperatives Village savings and lending group Church/mosque/congregation 4.3 0.5 10.1 1.8 1.8 7.2 13.2 46.8 43.3 MHHS Husband Others Youth association Women association Government team Local administration Input supply Farmers association/cooperatives Village savings and lending group Church/mosque/congregation 5.9 2.4 0 6 10.6 10.6 19.899999999999999 14.2 45.2 % of participant IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org VSL groups and women's associations are the most important community organizations in Ghana. VSL groups are more important for women than men. Farmers’ and youth associations are also important community organizations in Ghana. Fig.8. Percent participated in community organizations in Ghana Community organizations 13 13 MHHNS Others Youth association Women association Government team Local administration Input supply Farmers association/cooperative Village savings and lending group Church/mosque/congregation 5.8 12.9 0 1.4 5.8 0 14.3 14.4 8.5 FHHNS Others Youth association Women association Government team Local administration Input supply Farmers association/cooperative Village savings and lending group Church/mosque/congregation 0 3 35.700000000000003 0 0.6 0 13.2 52.4 15.1 FHHS Wife Others Youth association Women association Government team Local administration Input supply Farmers association/cooperative Village savings and lending group Church/mosque/congregation 0 23.1 21.4 0 0 0 7.7 53.3 7.7 FHHS Husband Others Youth association Women association Government team Local administration Input supply Farmers association/cooperative Village savings and lending group Church/mosque/congregation 0 7.7 15.4 0 0 0 20 26.7 7.7 MHHS Wife Others Youth association Women association Government team Local administration Input supply Fa rmers association/cooperative Village savings and lending group Church/mosque/congregation 1.4 1.6 33.4 0 0.3 0.4 6.9 64.3 13.6 MHHS Husband Others Youth association Women association Government team Local administration Input supply Farmers association/cooperative Village savings and lending group Church/mosque/congregation 3.7 19.399999999999999 0.5 1 3.7 1.8 26.7 23 17.3 % of participants IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Fig.9. Benefits of joining community organizations (% yes) Social capital is the main benefit of joining community organizations (counseling, entertainment, prestige, spiritual growth, and leisure). Women farmers also join community organizations to access credit. Community organizations 14 Malawi MHHNS Others Access to inputs (fertilizer, seed, pesticide/herbicide) Access to information (extension, produce markets) Access to credit (inputs, consumption) Social capital including counselling, entertainment, prestige, spiritual growth and leisure) 6.3 10.4 12.5 8.3000000000000007 64.599999999999994 FHHNS Others Access to inputs (fertilizer, seed, pesticide/herbicide) Access to information (extension, produce markets) Access to credit (inputs, consumption) Social capital including counselling, entertainment, prestige, spiritual growth and leisure) 3.6 14.1 19 41.2 65.400000000000006 FHHS Wife Others Access to inputs (fertilizer, seed, pesticide/herbicide) Access to information (extension, produce markets) Access to credit (inputs, consumption) Social capital including counselling, entertainment, prestige, spiritual growth and leisure) 6.3 6.3 34.4 50 31.3 FHHS Husband Others Access to inputs (fertilizer, seed, pesticide/herbicide) Access to information (extension, produce markets) Access to credit (inputs, consumption) Social capital including counselling, entertainment, prestige, spiritual growth and leisure) 3.1 3.1 9.4 6.3 28.1 MHHS Wife Others Access to inputs (fertilizer, seed, pesticide/herbicide) Access to information (extension, produce markets) Access to credit (inputs, consumption) Social capital including counselling, entertainment, prestige, spiritual growth and leisure) 2.1 9.1999999999999993 14.4 44.5 58.1 MHHS Husband Others Access to inputs (fertilizer, seed, pesticide/herbicide) Access to information (extension, produce markets) Access to credit (inputs, consumption) Social capital including counselling, entertainment, prestige, spiritual growth and leisure) 4.4000000000000004 10.199999999999999 22.4 16 60.9 Ghana MHHNS Others Access to inputs (e.g., fertilizer, seed, labor, pesticide/herbicide) Access to information Access to credit (e.g., inputs, consumption) Social capital including counselling, entertainment, prestige, spiritual growth and leisure) 5.56 4.17 4.17 16.670000000000002 30.56 FHHNS Other s Access to inputs (e.g., fertilizer, seed, labor, pesticide/herbicide) Access to information Access to credit (e.g., inputs, consumption) Social capital including counselling, entertainment, prestige, spiritual growth and leisure) 0.51 6.7 5.15 49.48 41.75 FHHS Wife Others Access to inputs (e.g., fertilizer, seed, labor, pesticide/herbicide) Access to information Access to credit (e.g., inputs, consumption) Social capital including counselling, entertainment, prestige, spiritual growth and leisure) 6.6 13.33 86.67 FHHS Husband Others Access to inputs (e.g., fertilizer, seed, labor, pesticide/herbicide) Access to information Access to credit (e.g., inputs, consumption) Social capital including counselling, entertainment, prestige, spiritual growth and leisure) 6.6 13.33 60 MHHS Wife Others Access to inputs (e.g., fertilizer, seed, labor, pesticide/herbicide) Access to information Access to credit (e.g., inputs, consumption) Social capital including counselling, entertainment, prestige, spiritual growth and leisure) 21.18 0 31.78 63.49 MHHS Husband Others Access to inputs (e.g., fertilizer, seed, labor, pesticide/herbicide) Access to information Access to credit (e.g., inputs, consumption) Social capital including counselling, entertainment, prestige, spiritual growth and leisure) 3.5 10.52 12.93 22.78 42.38 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Variable MHHS (n=1860) FHHS (n=58) FHHNS (n=718) MHHNS (n=95) Total (n=2731) Distribution of the plots (%) 68.1 2.1 26.3 3.5 100 Number of parcels per household 1.9 1.5 1.6 1.8 1.8 Number of plots per household 2.3 1.8 1.9 2 2.2 Landholding size (ha) 1.1 0.8 0.8 0.8 1 Plot ownership (%)           Head 49 55.3 97.5 97.6 60.4 Head and other related 0.2   2.5 2.4 0.7 Spouse 42.3 44.7     32.6 Spouse & other related 0.3       0.2 The main plot main manager (%)           Head 62.7 60 100 100 71.4 Spouse 37.3 40     28.6 Table 4. Land ownership and plot management by household types in Malawi MHHS households have larger land sizes than other household types. Husbands in MHHS own more plots and manage more than they own. Land ownership and plot management 15 15 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Variable MHHS (n=3300) FHHS (n=46) FHHNS (n=490) MHHNS (n=181) Total (n=4017) Distribution of the plots (%) 82.2 1.15 12.20 4.5 100 Number of parcels per household 2 1.5 1.7 1.7 1.9 Number of plots per household 3.2 3.1 2.5 2.5 3 Landholding size (ha) 4 3.1 1.7 2.5 3.6 Plot ownership (%)           Head 64.2 51.4 97.2 98.3 68 Head and other related 0.1   2.8 1.7 0.4 Spouse 34.7 48.7     30.8 Spouse & other related 0.1       0.1 The main plot main manager (%)           Head 89.1 60.9 100 100 90.3 Spouse 10.9 39.1     9.7 Table 5. Agricultural land ownership and plot management by household types in Ghana MHHS households have larger land sizes than other household types. Husbands in MHHS own more plots and manage more than they own. Land ownership and plot management 16 16 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org   Variable MHHS (n=1860) FHHS (n=58) FHHNS (n=718) MHHNS (n=95) Total (n=2731) Applied fertilizer on the plot 48.8 53.5 49.4 39 48.8 Type of fertilizer applied on the plot Inorg. fertilizer Inorganic fertilizer (%) 92.9 100 93 83.8 93   Ani. manure Animal manure (%) 30.5 25.8 36.3 29.7 31.2   Com. manure Compost manure (%) 6.8 29 9.3 10.8 7.9 Type of seed planted on the plot   Improved, certified 33.9 39.7 30.1 27.4 33.3   Improved, recycled 34.9 46.6 35 34.7 35.2   local, landrace 31 13.8 34.5 37.9 31.3 Used pesticides on the plot 8.9 5.2 4.3 10.5 8.9 Used hired labour on plot 20.5 15.5 17.7 17.9 19.9 Table 6. Input use on the plot by household type (% of plots) in Malawi A higher proportion of households in FHHNS than in MHHS reported applying manure on their plots, the opposite is true for improved seeds, pesticide/herbicide, and hired labor. Input use 17 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Variable MHHS (n=3300) FHHS (n=46) FHHNS (n=490) MHHNS (n=181) Total (n=4017) Applied fertilizer on the plot 33.9 28.26 41.72 37.02 34.92 Type of fertilizer applied on the plot Inorg. fertilizer Inorganic fertilizer (%) 85.9 69.2 62.8 80 82.5   Ani. manure Animal manure (%) 22.6 30.8 45.3 23.3 25.6   Com. manure Compost manure (%) 8 30.8 26.2 8.3 10.6   Others (%) Others (%) 0.1 0 0 0 0.1 Type of seed planted on plot   Improved, certified 10.5 0 4 9.7 9.6   Improved, recycled 27.3 10.9 39.8 22.6 28.3   local, landrace 62.2 89.1 56.2 67.7 62.1 Used pesticides on plot 26.3 21.7 18.6 25.4 25.3 Used hired labour on plot 61.9 63 55.6 54.1 60.8 Table 7. Input use on the plot by household type (% of plots) in Ghana In Ghana, the proportions of plots that reportedly fertilized are lower than the ones in Malawi. A higher proportion of plots owned by MHHS than those owned by FHHNS reportedly fertilized with inorganic fertilizer but got less organic manure, improved seed, and crop protection chemicals. Input use 18 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Decision-maker MHHS (n=2211) FHHS (n=34) FHHNS (n=215) MHHNS (n=89) Total (n=2549) Head 51.2 41.2 63.7 100 53.8 Spouse 18 26.5     16 Son/daughter 25.7 23.5 19.5   24.2 Son/daughter-in-law 1 2.9 4.7   1.3 Grandchild 1.4   9.8   2 Parent or parent-in-law 0.8 2.9 0.5   0.8 Nephew/Niece 0.6   0.9   0.6 Other related 1.3 2.9 0.9   1.3 Other unrelated 0.1       0.1 Table 8. The primary decision maker in selling the crop (% of crops sold) by household type in Ghana Household heads are the primary decision-makers in selling crops. In MHHS, children have a higher role than the wives in making decisions to sell crops. Decision making 19 19 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Crop  MHHS (n=804) FHHS (n=32)  FHHNS (n=384)  MHHNS  (n=48)  Total (n=1268)  Maize  98.8  100  98.7  97.9  98.7  Other cereals  10  18.8  13.5  14.6  11.4  Legumes  76  65.6  67.7  70.8  70.8  Vegetables  2.5  3.2  1.3  2.1  2.1  Roots and tubers  8.3  3.2  2.6  8.3  6.4  Traditional cash crops (tobacco & Cotton)  6.5  3.2  1.6    4.6  Table 9. Crops planted (% of households) by household types in Malawi Crop MHHS (n=1036)  FHHS (n=15) FHHNS (n=191) MHHNS  (n=75) Total (n=1317) Maize  81.7  53.3  57.1  62.7  76.7  Other cereals  55.7  40  63.9  54.7  56.6  Legumes  79.2  100  74.9  68  78.1  Vegetables  11.6  20  9.4  4  10.9  Roots and tubers  16  13.3  1.6  6.7  13.4  Table 10. Crops planted (% of households) by household types in Ghana  MHHS households exhibited more crop diversification than FHHNS households in both Malawi and Ghana. Higher crop diversification in Ghana and in Malawi. Crop diversification 20 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Livestock MHHS (n=804)  FHHS (n=32)  FHHNS (n=384)  MHHNS (n=48)  Total (n=1268)  Cattle  6.8  3.2  2.3  6.3  5.5  Chicken and other birds  68.8  62.5  55.5  45.8  63.7  Goats and sheep  36.2  37.5  32.6  20.8  34.5  Pigs  16.3  6.3  6.3  10.4  12.8  Rabbits  2.2    1.3  2.1  1.9  Table 11. Livestock ownership (% of households) by household types in Malawi Livestock MHHS (n=1036)  FHHS (n=15)  FHHNS (n=191)  MHHNS (n=75)  Total (n=1317)  Cattle  15.1  6.7  12  16  14.6  Chicken and other birds  74  80  61.3  76  72.4  Goats and sheep  72.9  73.3  64.9  62.7  71.1  Pigs  5.9  20  10.5  5.3  6.7  Rabbits  0.2        0.15  Table 12. Livestock ownership (% of households) by household types in Ghana Livestock ownership and diversification In Malawi, chickens are the most important livestock type followed by goats for all household types Ownership of livestock is higher among MHHS than FHHNS. In Ghana, chickens and goats are equally important among all household types. Ghana is more diversified than Malawi in terms of livestock production. 21 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Combination1  MHHS   (n=804)  FHHS (n=32)  FHHNS (n=384)  MHHNS (n=48)  Total (n=1268)  Count of crops  2  1.9  1.9  1.9  2  Count of livestock  1.3  1.1  1  0.9  1.2  Count of crop and livestock   2.8  2.3  1.9  1.9  2.4  Table 13. Number (counts) of crop, livestock, and crop and livestock combinations (mean per household) in Malawi The maximum count for the crop is 6, for livestock, it is also 6, and for the crop and livestock combination, it is 30.  Combination1  MHHS (n=1036)  FHHS (n=15)  FHHNS (n=191)  MHHNS (n=75)  Total (n=1317)  Count of crops  2.4  2.3  2.1  2  2.4  Count of livestock  1.7  1.8  1.5  1.6  1.6  Count of crop and livestock   4.5  4.3  3.2  3.4  4.2  Table 14. Number (counts) of crop, livestock, and crop and livestock combinations (mean per household) in Ghana Agricultural diversification More diversification of agriculture in Ghana than in Malawi 22 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Fig.10. Gender norms (% of respondents who agreed) in Ghana  23 MHHNS It is unacceptable for a woman to apply sustainable intensification technologies on her farm without first seeking approval from the landowner A woman can only get land to farm through her male relative The area of land available to a person is dependent on whether you are a man or a woman Until the work on the household head's farm is completed, no one in the family can work on anyone’s farm A husband cannot interfere with his wife’s decisions o n what crops to grow on her farm A husband cannot interfere with his wife’s decisions on how to use her own (farm and nonfarm) income It is a husband’s decision whether a woman’s livestock will be sold A woman cannot take livestock to the market to sell It is unacceptable for a man to cook for his family Doing household chores is a woman’s work A woman cannot be made a leader of a mixed group 46.5 84.5 63.4 49.3 60.6 57.7 53.5 76.099999999999994 39.4 62 26.8 FHHNS It is unacceptable for a woman to apply sustainable intensification technologies on her farm without first seeking approval from the landowner A woman can only get land to farm thro ugh her male relative The area of land available to a person is dependent on whether you are a man or a woman Until the work on the household head's farm is completed, no one in the family can work on anyone’s farm A husband cannot interfere with his wife’s decisions on what crops to grow on her farm A husband cannot interfere with his wife’s decisions on how to use her own (farm and nonfarm) income It is a husband’s decision whether a woman’s livestock will be sold A woman cannot take livestock to the market to sell It is unacceptable for a man to cook for his family Doing household chores is a woman’s work A woman cannot be made a leader of a mixed group 50.3 77.099999999999994 66.900000000000006 45.7 47.4 50.9 42.9 67.400000000000006 46.3 77.099999999999994 32 MHHS Wife It is unacceptable for a woman to apply sustainable intensification technologies on her farm without first seeking approval from the landowner A woman can only get land to farm through her male relative The area of land available to a person is dependent on whether you are a man or a woman Until the work on the household head's farm is completed, no one in the family can work on anyone’s farm A husband cannot interfere with his wife’s decisions on what crops to grow on her farm A husband cannot interfere with his wife’s decisions on how to use her own (farm and nonfarm) income It is a husband’s decision whether a woman’s livestock will be sold A woman cannot take livestock to the market to sell It is unacceptable for a man to cook for his family Doing household chores is a woman’s work A woman cannot be made a leader of a mixed group 54.5 83.4 72.400000000000006 55.4 43.9 47.3 56.8 83.9 62.1 82 32.9 MHHS Husband It is unacceptable for a woman to apply sustainable intensification technologies on her farm without first seeking approval from the landowner A woman can only get land to farm through her male relative The area of land available to a person is dependent on whether you are a man or a woman Until the work on the household head's farm is completed, no one in the family can work on anyone’s farm A husband cannot interfere with his wife’s decisions on what crops to grow on her farm A husband cannot interfere with his wife’s decisions on how to use her own (farm and nonfarm) income It is a husband’s decision whether a woman’s livestock will be sold A woman cannot take livestock to the market to sell It is unacceptable for a man to cook for his family Doing household chores is a woman’s work A woman cannot be made a leader of a mixed group 52.9 84.8 69.400000000000006 52.1 48.6 51.5 51.2 83.6 52.1 77.7 28.7 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Recentered influence function (RIF) decomposition = ()’ + ) + ) +()’ Empirical procedure 24 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Empirical results – RIF decomposition: crop productivity (MWK/ha) gender gap in Malawi   Overall Pure explained Specification error Pure unexplained Reweight error FHHNS (n=693) 353,258.1***         Counterfactual 395,176.4 ***         MHHS (n=1806) 459,312***         Total difference -106,054***         Total explained diff -41,918         Total unexplained diff -64,136         Total   -27,728 -14,190 -50,616 -13,520 MHHS FHHS n Mean n Mean Maize 1075 1342 37 993 Sorghum 35 1197 3 832 Finger millet 4 690 1 494 Bean 19 664 1 395 Soybean 280 813 6 535 Cowpea 28 867 2 1483 Groundnut 197 868 5 457 Pigeon peas 33 446 3 420 Peas 20 546 Cotton 31 984 1 988 Fig.11. Stochastic dominance analysis 25 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org   Overall Pure explained Specification error Pure unexplained Reweight error FHHNS (n=502) 30,534.5***         Counterfactual 31,496.9***         MHHS (n=3259) 53,298.1***         Total difference -22,763.6***         Total explained diff -962.3         Total unexplained diff -21,801.3         Total   -3,864.2 2901.8 -28,098.4 6297.1 Empirical results – RIF decomposition: crop productivity (GHS/ha) gender gap in Ghana MHHS FHHNS n Mean n Mean Maize 940 874 109 597 Sorghum 87 576 24 572 Finger millet 155 355 49 332 Pearl millet 115 354 23 469 Rice 363 1,523 69 684 Bean 150 319 18 323 Soybean 362 849 23 647 Cowpea 38 321 10 315 Groundnut 655 814 123 620 Peas 10 248 Bambara 46 484 Fig.12. Stochastic dominance analysis 26 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org The results of this analysis show that plots owned/managed by FHHNS are 23.1% less productive than those owned/managed by MHHS in Malawi and 42.7% in Ghana. Much of the gap is from the structural differences. These results are in line with the results of previous studies in Ghana and Malawi. In Ghana, Boahen et al. (2024) found that the total cereal production of female plot managers is 46% lower than that of their male counterparts. The results of our study also show that FHHNS have lower education levels, smaller family sizes, smaller land sizes, less control over resources, lower levels of input use, lower levels of diversification, and unfavorable gender norms compared to MHHS, which could contribute to the productivity gap. We also find differences between husbands and wives in terms of time devotion for different activities. In Malawi, in both dry and rainy seasons, wives in MHHS and FHHS devote longer hours to domestic activities than their husbands but devote equivalent hours to farming. Conclusion 27 27 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org In Malawi and Ghana, husbands in MHHS and FHHS devote longer hours to socializing and resting than their wives. Religious institutions and VSL groups in Malawi and VSL groups and women's associations in Ghana are the most important community organizations. Social capital is the main benefit of joining community organizations. In both Malawi and Ghana, VSL groups are more important for women than men. There are prevailing unfavorable gender norms in Ghana being championed not only by men but women as well. Gender transformative approaches are required to address the root causes of gender inequalities. Conclusion 28 28 IITA is a member of the CGIAR System Organization. www.iita.org | www.cgiar.org Thank you! 29 29 image1.jpeg oleObject1.bin image2.emf image3.png Microsoft_Excel_Worksheet.xlsx Sheet1 Country Productivity of female farmers (% less than male) Associated loss (bn USD/Year) Ethiopia 23.4 1.1 Malawi 25 0.1 Northern Nigeria 28 Tanzania 0.105 Uganda 17.5 0.067 Vietnam 10.3 Productivity of female farmers (% less than male) Ethiopia Malawi Northern Nigeria Tanzania Uganda Vietnam 23.4 25 28 17.5 10.3 Associated loss (bn USD/Year) Ethiopia Malawi Northern Nigeria Tanzania Uganda Vietnam 1.1000000000000001 0.1 0.105 6.7000000000000004E-2 % productivity difference Productivity loss (bn USD/year) Sheet2 Sheet3 Microsoft_Excel_Worksheet1.xlsx Sheet1 countries % productivity gap Ethiopia 23 Malawi 25 Mali 20.18 Niger 19 Nigeria 28 Uganda 17.5 Tanzania 8.1 Ethiopia Mali Malawi Uganda % of endowment 57 56 82 69.6 % of structural 43 44 18 30.4 Ethiopia Malawi Mali Niger Nigeria Uganda Tanzania 23 25 20.18 19 28 17.5 8.1 % productivity gap % of endowment Ethiopia Mali Malawi Uganda 57 56 82 69.599999999999994 % of structural Ethiopia Mali Malawi Uganda 43 44 18 30.4 image4.jpeg image5.jpeg image8.png image9.png image10.png image11.png image6.png image7.png image13.png image59.png image60.png image61.png image62.png image63.png image17.png image64.png image65.png image66.png image67.png image68.png image69.png image70.png image71.png image72.png image73.png image18.png image19.png image20.png image21.png image22.png image23.png image24.png image25.png image14.tiff image26.png image27.png image28.png image29.png image30.png image31.png image32.png image33.png image34.png image35.png image15.png image36.png image37.png image38.png image40.png image41.png image42.png image43.png image39.png image44.png image150.png image45.png image46.png image47.png image48.png image49.png image50.png image51.png image52.png image53.png image16.png image54.png image55.png image56.png image57.png image58.png image112.png image113.png image114.png image115.png image116.png image480.png image490.png image500.png image510.png image520.png image530.png image75.png image76.png image77.png image74.tiff image78.png image79.png image80.png image81.png image82.png image83.png image84.png image85.png image86.png image87.png image450.png image88.png image89.png image90.png image91.png image92.png image93.png image94.png image95.png image96.png image460.png image97.png image98.png image99.png image100.png image101.png image102.png image103.png image104.png image105.png image106.png image470.png image107.png image108.png image109.png image110.png image111.png