Peterman et al gender differences in non land assets

1,389 views

Published on

Published in: Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,389
On SlideShare
0
From Embeds
0
Number of Embeds
5
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Peterman et al gender differences in non land assets

  1. 1. A review of empirical evidence on gender differences in non-land agricultural inputs, technology and services in developing countries Amber Peterman Julia Behrman Agnes Quisumbing Poverty, Hunger and Nutrition Division (PHND), IFPRI FAO SOFA Writers Workshop – September 16, 2009
  2. 2. Gender matters <ul><li>… for implementation, effectiveness and evaluation of agricultural programs. </li></ul><ul><li>“ Failure to recognize the roles, differences and inequities [between men and women] poses a serious threat to the effectiveness of the agricultural development agenda ” </li></ul><ul><li>~ Gender and Agricultural Sourcebook (World Bank, FAO, IFAD 2009 [p2]). </li></ul><ul><li>Despite this consensus, still mixed evidence on: </li></ul><ul><li>Gender differences in, magnitudes and effects of agricultural inputs. </li></ul><ul><li>Generally research has focused on land. </li></ul><ul><li>Dated and regional specific studies. </li></ul>Page
  3. 3. In response to these gaps, this review: <ul><li>Focuses strictly on empirical household or plot-level data analyzed in program evaluations, agricultural or socio-economic research. </li></ul><ul><li>Sufficient sample sizes, attention to measurement, econometric evaluation techniques. </li></ul><ul><li>Recent papers in the last 10 years (1999 to 2009). </li></ul><ul><li>Inclusion of both published and gray literature (forthcoming, technical reports). </li></ul><ul><li>Four key areas: 1) technological resources, 2) natural resources, 3) human resources and 4) social and political capital. </li></ul><ul><li>Attempt to make contrasts and comparisons between regions to identify how women farmers face similar or different constraints (Asia, SSA, Middle East and Latin/South America). </li></ul>Page
  4. 4. Some notes on measurement and organization <ul><li>Starting point: seminal research on gender and agriculture, followed by online database searches, google scholar, website searches of agriculture organizations, emails and inquires to researchers in the field. </li></ul><ul><li>Inclusive measures of ‘gender’ (female headship, female farmer, proportion female owned, managed etc.) </li></ul><ul><li>Include studies which provide mean values (descriptive statistics) as well as those which analyze use as an outcome (and use gender as an explanatory variable). </li></ul><ul><li>Generalizations and specifics assigned to terms women’s ‘use’, ‘access’, ‘adoption’, and ‘participation.’ </li></ul><ul><li>Collect key information from each study to input to a “matrix of findings.” </li></ul>Page
  5. 5. Example of summary matrix Page authors (year) country(crop) N Input type Stats on use Outcome Gender indicator Effect size Other comments Pub?
  6. 6. 1. Technological resources <ul><li>Advancements in technological resources provide means to improve soil fertility, increase land productivity and overall crop yields. </li></ul><ul><li>Marginal benefit may be especially significant for women farmers who are more likely to be asset poor. </li></ul><ul><li>Inorganic fertilizers (including </li></ul><ul><li>vouchers). </li></ul><ul><li>2. Insecticide/pesticide </li></ul><ul><li>Improved seed varieties/seeds </li></ul><ul><li>Mechanical power/tools </li></ul>Page
  7. 7. <ul><li>Doss and Morris (2001) examines fertilizer and seed adoption among 420 Maize farmers in Ghana. </li></ul><ul><ul><li>Women farmers have lower mean inputs for both fertilizer and modern seed varieties. </li></ul></ul><ul><ul><li>Uses two stage probit models no significant differences in adoption after controlling for complementary inputs. </li></ul></ul><ul><ul><li>Sensitivity analysis on gender (female farmers within female headed versus male headed households). </li></ul></ul><ul><li>Horrell and Krishnan (2007) examine fertilizer, seed and machinery among 300 farmers in Zimbabwe (primarily maize). </li></ul><ul><ul><li>Female headed households have lower mean values for all inputs. </li></ul></ul><ul><ul><li>Distinguishes between de jure and de facto (widowed) female heads. </li></ul></ul><ul><ul><li>Using tobit models finds no significant differences between male and female headed households in usage, intensity of usage or productivity once controlling for other inputs. </li></ul></ul><ul><ul><li>Further analysis suggests de facto female headed households receive lower prices for output and have lack of access to selling/marketing consortia. </li></ul></ul>1. Key studies in technological resources Page
  8. 8. 1. Summary of results for technological resources Page <ul><li>21 studies reviewed (15 published in peer reviewed journals). </li></ul><ul><li>19 measures of inorganic fertilizer use, 11 seed varieties, 9 machinery/tools and 5 pesticide/insecticide. </li></ul><ul><li>Descriptive statistics (24 indicators) </li></ul><ul><ul><li>19 (79 percent) find men have higher mean inputs </li></ul></ul><ul><ul><li>5 (21 percent) find women have higher mean inputs </li></ul></ul><ul><li>Bivariate or multivariate analysis (34 indicators) </li></ul><ul><ul><li>19 (56 percent) find gender is insignificant </li></ul></ul><ul><ul><li>14 (41 percent) find men have significantly higher inputs </li></ul></ul><ul><ul><li>1 (3 percent) find women have significantly higher inputs </li></ul></ul>
  9. 9. 2. Natural resources <ul><li>Importance of natural resources increasing owing to growing concern with increasing population pressure and stress on environmental resources. </li></ul><ul><li>Women may have lower endowments of natural resources ; these are not always visible or easily measurable. </li></ul><ul><li>Natural soil improvement </li></ul><ul><li>(manure/composting, fallow periods, </li></ul><ul><li>alley/hedgerow cropping, intercropping). </li></ul><ul><li>2. Water (for agricultural use, irrigation). </li></ul>Page
  10. 10. <ul><li>Jagger and Pender (2003) examines adoption of natural resource management techniques (manure, crop residue and mulching) among 451 Ugandan hhlds. </li></ul><ul><ul><li>Impact of programs on NRM technology adoption using two stage probit models. </li></ul></ul><ul><ul><li>Female headship insignificant in adoption across techniques. </li></ul></ul><ul><ul><li>Number of males in household significantly associated with adoption of crop residues and manure. </li></ul></ul><ul><li>Pender and Gebremedhin (2006) examine manure/composting and burning to prepare fields among 500 hhlds in Ethiopia. </li></ul><ul><ul><li>Uses probit models. </li></ul></ul><ul><ul><li>Female headed households (22 percent of the sample) less likely to use manure/composting, equally as likely to burn fields. </li></ul></ul>2. Key studies in natural resources Page
  11. 11. <ul><li>12 studies reviewed (9 published in peer reviewed journals). </li></ul><ul><li>12 measures of soil fertility, 3 water. </li></ul><ul><li>Descriptive statistics (11 indicators) </li></ul><ul><ul><li>8 (72 percent) find men have higher mean inputs </li></ul></ul><ul><ul><li>3 (27 percent) find women have higher mean inputs </li></ul></ul><ul><li>Bivariate or multivariate analysis (12 indicators) </li></ul><ul><ul><li>9 (75 percent) find gender is insignificant </li></ul></ul><ul><ul><li>3 (25 percent) find men have significantly higher inputs </li></ul></ul><ul><ul><li>0 (0 percent) find women have significantly higher inputs </li></ul></ul>2. Summary of results for natural resources Page
  12. 12. <ul><li>Human capital endowments and investments supremely important and broad topic, with spillover effects to other social and economic sectors. </li></ul><ul><li>Agricultural labor </li></ul><ul><li>(own labor, hired labor). </li></ul><ul><li>Extension and agricultural knowledge </li></ul><ul><li> (advisory services, farmer field </li></ul><ul><li>schools, trainings etc.). </li></ul><ul><li>3. Lifecycle challenges </li></ul><ul><li>(marriage, reproductive health, </li></ul><ul><li>childcare). </li></ul>3. Human resources Page
  13. 13. 3. Key studies for human resources <ul><li>Davis et al. (2009) examines field farmer school (FFS) participation in Kenya, Tanzania and Uganda among 267 – 300 farmers. </li></ul><ul><ul><li>FFS participation equally accessible for male and female headed farmers in Kenya and Tanzania. </li></ul></ul><ul><ul><li>Female headed households in Uganda less likely to participate, due to lack of time, distance and information about FFS. </li></ul></ul><ul><ul><li>Results suggest FFS have a higher impact on productivity, crop and livestock income for female headed households as compared to male headed households. </li></ul></ul><ul><li>IFPRI Gender and Governance team (2009) examine extension services in Ethiopia, Ghana and India among 676 – 1753 households. </li></ul><ul><ul><li>Large mean differences in contact with extension services (e.g. in India 1 percent versus 27 percent). </li></ul></ul><ul><ul><li>However, multivariate analysis indicates these differences are largely accounted for by background factors (regional variation and assets). </li></ul></ul>Page
  14. 14. 3. Summary of results for human resources <ul><li>16 studies reviewed (12 published in peer reviewed journals). </li></ul><ul><li>14 measures of extension, 11 labor, 1 lifecycle. </li></ul><ul><li>Descriptive statistics (25 indicators) </li></ul><ul><ul><li>14 (56 percent) find men have higher mean inputs </li></ul></ul><ul><ul><li>11 (44 percent) find women have higher mean inputs </li></ul></ul><ul><li>Bivariate or multivariate analysis (13 indicators) </li></ul><ul><ul><li>7 (54 percent) find gender is insignificant </li></ul></ul><ul><ul><li>5 (38 percent) find men have significantly higher inputs </li></ul></ul><ul><ul><li>1 (8 percent) find women have significantly higher inputs </li></ul></ul>Page
  15. 15. <ul><li>Provide context for informal learning, creation of social safety nets, organization for regulation, protection, change and challenge of agricultural and development related factors. </li></ul><ul><li>Group membership </li></ul><ul><li>(local level agricultural focused </li></ul><ul><li>co-ops, user groups, committees). </li></ul><ul><li>Non-group informal information exchange </li></ul><ul><li> (via social networks). </li></ul><ul><li>3. Political representation </li></ul>4. Social and political capital Page
  16. 16. 4. Summary of results for social and political capital <ul><li>10 studies reviewed (3 published in peer reviewed journals). </li></ul><ul><li>18 measures of group participation, 1 non-formal information exchange, </li></ul><ul><li>0 political representation. </li></ul><ul><li>Descriptive statistics (6 indicators) </li></ul><ul><ul><li>4 (67 percent) find men have higher mean inputs </li></ul></ul><ul><ul><li>2 (33 percent) find women have higher mean inputs </li></ul></ul><ul><li>Bivariate or multivariate analysis (19 indicators) </li></ul>Page
  17. 17. <ul><li>Godquin and Quisumbing (2008) examine participation in general and production groups among 304 households in the Philippines. </li></ul><ul><ul><li>Gender does not affect group participation overall, however men more likely to be in production oriented groups as compared to women. </li></ul></ul><ul><li>IFPRI Gender and Governance team (2009) examine participation in CBOs, farmer based organizations and agricultural cooperatives in India, Ghana and Ethiopia respectively (966 – 1761 households). </li></ul><ul><ul><li>In India, female household head not significantly associated with CBO participation, however women participate mainly in self help/woman’s groups while men participate in forest groups, cooperative societies and caste associations. </li></ul></ul><ul><ul><li>In Ghana, male headed households significantly more likely to participate in farmer based orgs using probit regression. </li></ul></ul><ul><ul><li>In Ethiopia, descriptive and bivariate analysis shows men headed housholds significantly more likely to participate in co-ops (4 versus 24 percent). </li></ul></ul>4. Key studies for social and political capital Page
  18. 18. Discussion points <ul><li>What can we say about those cases where we do, indeed, find differences? Do they matter? </li></ul><ul><ul><li>Crop choice. </li></ul></ul><ul><ul><li>Division of labor. </li></ul></ul><ul><ul><li>Cultural context. </li></ul></ul><ul><li>What can we say about regional evidence? </li></ul><ul><ul><li>Approximately 80 percent of studies from SSA. </li></ul></ul><ul><ul><li>Studies from Asia have tended to focus on men’s and women’s labor inputs, rather than productivity on male and female farms, because of joint farming. </li></ul></ul><ul><ul><li>Lacking studies on Middle East and South/Latin America. </li></ul></ul><ul><li>What can we say about diversity of input evidence? </li></ul><ul><ul><li>Most evidence for technology (especially fertilizer, seeds) and human resources (extension). </li></ul></ul><ul><ul><li>Least evidence for machinery, lifecycle factors and non-formal information exchange. </li></ul></ul><ul><li>What can we say about gender measures? </li></ul><ul><ul><li>Majority disaggregate at the household head level. </li></ul></ul><ul><ul><li>Few do sensitivity analysis – however when done, evidence indicates they seem to matter. </li></ul></ul>Page
  19. 19. Summary of Key findings and recommendations <ul><li>Women are almost always disadvantaged in mean use indicators. </li></ul><ul><li>However, these differences do not necessarily translate to differences when other factors are controlled for, depending on study design, evaluation framework, etc. </li></ul><ul><li>Factors are likely to differ based on geographic area, cultural context. </li></ul><ul><li>Main question for policy: when and where do gender disparities in inputs matter? </li></ul><ul><li>Gender indicator matters . Recommendations for plot specific and sensitivity analyses. </li></ul><ul><li>Lack of regional diverse studies . Recommendation for studies on Latin/South America and Middle East. </li></ul><ul><li>Lack of attention to some inputs . Recommendation for studies which examine or include measures of machinery, lifecycle challenges and non-formal information exchange. </li></ul>Page
  20. 20. Understanding gender differences in agricultural productivity in Nigeria and Uganda (Peterman, Quisumbing, Behrman & Nkonya) <ul><li>IFPRI collected Household and plot-level data from Nigeria (2005; N = 3707) and Uganda (2003; N = 2536). </li></ul><ul><li>Female headed households in Nigeria and female owned plots in Uganda have significantly lower productivity. </li></ul><ul><li>Productivity differences persist in both counties after controlling for complementary inputs, however this varies within primary crops and within agro-ecological zones. </li></ul><ul><li>In Uganda, lowest productivity among ‘mixed gender ownership’ plots ,which may be suggestive of bargaining issues within households </li></ul><ul><li>Gender indicator matters in Uganda – use of alternate indicators at the household level (female headship, percent female managed) do not produce same results. </li></ul>Page
  21. 21. <ul><li>Comments/critiques/suggestions welcome </li></ul><ul><li>Thank you! </li></ul>Page

×