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Who is empowered? Predictors of empowerment in Bangladesh and Ghana
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This presentation was given by Elena Martinez (International Food Policy Research Institute), as part of the Annual Scientific Conference hosted by the University of Canberra and co-sponsored by the University of Canberra, the Australian Centre for International Agricultural Research (ACIAR) and CGIAR Collaborative Platform for Gender Research. The event took place on April 2-4, 2019 in Canberra, Australia. Read more: https://www.canberra.edu.au/research/faculty-research-centres/aisc/seeds-of-change and https://gender.cgiar.org/annual-conference-2019/
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Who is empowered? Predictors of empowerment in Bangladesh and Ghana
- Who is empowered? Predictors of empowerment in Bangladesh and Ghana Elena Martinez and Greg Seymour International Food Policy Research Institute CGIAR Research Program on Agriculture for Nutrition and Health Seeds of Change 2019 Canberra, Australia April 4, 2019
- What is empowerment? Agency AchievementsResources Agency AchievementsResources What resources enable women and men to make strategic choices about their own lives? Kabeer (1999)
- Data: Population-based Feed the Future baseline surveys Ghana (2012), representative for the FTF Zone of Influence (ZOI) Collected by the Institute of Statistical, Social and Economic Research and USAID Bangladesh (2011-12), nationally representative for rural Collected by DATA, IFPRI, and USAID Bangladesh Integrated Household Survey (BIHS) GHANA ZOI BANGLADESH ZOI RURAL BANGLADESH
- Women’s Empowerment in Agriculture Index (WEAI) Measures empowerment and inclusion of women in the agricultural sector Launched in 2012 by USAID, IFPRI & OPHI Survey-based index, constructed using interviews of the primary male and primary female adults in the same household An aggregate index in two parts: Five Domains of Empowerment (5DE) Gender Parity Index (GPI)
- Methods Estimated predictors of women’s and men’s empowerment score using fractional logistic regression Empowerment score = (# indicators individual is adequate in)/10 Fractional variable, range=[0,1] Estimated predictors of adequacy in each indicator of empowerment using Poisson or OLS regression Resources: individual, household, and community characteristics Individual: literacy, marital status, age Household: ethnic group, wealth/asset quintile, household hunger scale, dependency ratio, household size, household type Community: rural/urban, region
- Study population 0.61 0.71 0.61 0.80 0.0 0.2 0.4 0.6 0.8 1.0 Women Men Women Men BangladeshGhana Average empowerment score
- Study population Bangladesh Ghana Women Men Women Men Average age (years) 37 44 38 44 Currently married (%) 95 95 90 87 Literate (%) 49 47 10 25
- Empowerment score: Ghana Empowerment score = (# indicators individual is adequate in)/10 Fractional logistic regression models, 95% CIs WOMEN (N=2,784) MEN (N=3,046)
- Being literate is associated with higher empowerment score, but only for women Empowerment score = (# indicators individual is adequate in)/10 Fractional logistic regression models, 95% CIs WOMEN (N=2,784) MEN (N=3,046) Able to read and write
- Being older is associated with higher empowerment score, but only for women Empowerment score = (# indicators individual is adequate in)/10 Fractional logistic regression models, 95% CIs WOMEN (N=2,784) MEN (N=3,046) Ref=18-29
- Being married is associated with lower empowerment score, but only for women Empowerment score = (# indicators individual is adequate in)/10 Fractional logistic regression models, 95% CIs WOMEN (N=2,784) MEN (N=3,046) Currently married
- Being wealthy is associated with higher empowerment score for men and women Empowerment score = (# indicators individual is adequate in)/10 Fractional logistic regression models, 95% CIs WOMEN (N=2,784) MEN (N=3,046) Ref= Poorest
- Empowerment score: Bangladesh WOMEN (N=5,400) MEN (N=4,530) Empowerment score = (# indicators individual is adequate in)/10 Fractional logistic regression models, 95% CIs
- Being literate is associated with empowerment score for both men and women Empowerment score = (# indicators individual is adequate in)/10 Fractional logistic regression models, 95% CIs WOMEN (N=5,400) MEN (N=4,530) Able to read and write
- Being married is not associated with empowerment score Empowerment score = (# indicators individual is adequate in)/10 Fractional logistic regression models, 95% CIs WOMEN (N=5,400) MEN (N=4,530) Currently married
- Being older is associated with empowerment score for both men and women Empowerment score = (# indicators individual is adequate in)/10 Fractional logistic regression models, 95% CIs WOMEN (N=5,400) MEN (N=4,530) Ref=18-29
- Being wealthy is associated with higher empowerment score for both men and women Empowerment score = (# indicators individual is adequate in)/10 Fractional logistic regression models, 95% CIs WOMEN (N=5,400) MEN (N=4,530) Ref= Poorest
- What did we learn? Wealth, literacy, marital status, and age are associated with empowerment score, though these associations vary by country. Some of these resources matter more for women’s agency than for men’s agency. Examples: Being married was associated with lower empowerment for women in Ghana but not in Bangladesh. In Ghana, age mattered for women’s empowerment score; in Bangladesh, age mattered for both men and women.
- WEAI
- Input in productive decisions WEAI indicator: Adequate if individual participates in decisions about at least one agricultural activity Regression outcome: Number of agricultural activities the individual makes decisions about Count variable, range=[0,9] Model: Poisson regression
- Resources Input in productive decisions Direction of associations based on IRRs Bangladesh Ghana Predictor Women Men Women Men Literacy ++ ++ Marriage -- -- ++ Age ++ ++ ++ Wealth -- -- ++ Dependency ratio -- Household hunger -- -- ++ Household size ++ ++ ++ Dual-adult household ++ Positive association 0.05 level of confidence -- Negative association Blank No association
- Resources Input in productive decisions Direction of associations based on IRRs Bangladesh Ghana Predictor Women Men Women Men Literacy ++ ++ Marriage -- -- ++ Age ++ ++ ++ Wealth -- -- ++ Dependency ratio -- Household hunger -- -- ++ Household size ++ ++ ++ Dual-adult household ++ Positive association 0.05 level of confidence -- Negative association Blank No association
- Resources Input in productive decisions Direction of associations based on IRRs Bangladesh Ghana Predictor Women Men Women Men Literacy ++ ++ Marriage -- -- ++ Age ++ ++ ++ Wealth -- -- ++ Dependency ratio -- Household hunger -- -- ++ Household size ++ ++ ++ Dual-adult household ++ Positive association 0.05 level of confidence -- Negative association Blank No association
- Resources Input in productive decisions Direction of associations based on IRRs Bangladesh Ghana Predictor Women Men Women Men Literacy ++ ++ Marriage -- -- ++ Age ++ ++ ++ Wealth -- -- ++ Dependency ratio -- Household hunger -- -- ++ Household size ++ ++ ++ Dual-adult household ++ Positive association 0.05 level of confidence -- Negative association Blank No association
- Control over use of income WEAI indicator: Adequate if individual participates in decisions about how to use income from at least one activity Regression outcome: Number of activities for which the individual makes income decisions Count variable, range=[0,9] Model: Poisson regression
- Resources Control over use of income Direction of associations based on IRRs Bangladesh Ghana Predictor Women Men Women Men Literacy ++ ++ Marriage -- ++ -- ++ Age ++ ++ ++ Wealth -- -- ++ Dependency ratio -- Household hunger -- -- Household size ++ Dual-adult household ++ ++ Positive association 0.05 level of confidence -- Negative association Blank No association
- Resources Control over use of income Direction of associations based on IRRs Bangladesh Ghana Predictor Women Men Women Men Literacy ++ ++ Marriage -- ++ -- ++ Age ++ ++ ++ Wealth -- -- ++ Dependency ratio -- Household hunger -- -- Household size ++ Dual-adult household ++ ++ Positive association 0.05 level of confidence -- Negative association Blank No association
- Resources Control over use of income Direction of associations based on IRRs Bangladesh Ghana Predictor Women Men Women Men Literacy ++ ++ Marriage -- ++ -- ++ Age ++ ++ ++ Wealth -- -- ++ Dependency ratio -- Household hunger -- -- Household size ++ Dual-adult household ++ ++ Positive association 0.05 level of confidence -- Negative association Blank No association
- What did we learn? The associations between resources and agency vary for different dimensions of empowerment. The predictors of achievements in each dimension of empowerment vary by country. Use these results to think about how to target interventions. E.g., marital status and control over income
- Next steps Data from other FTF countries Mozambique, Nepal, Tanzania Other WEAI indicators Autonomy in production, leisure, speaking in public, ownership of assets, rights over assets, access to and decisions on credit Intrahousehold parity WEAI also allows us to compare women’s and men’s agency in the same household. Do men and women benefit equally from a household’s resources? What is the association between household resources and the difference between men’s and women’s agency? Qualitative How do these results compare with qualitative findings? Compare to qualitative results from the WEAI pilots.
- Thank you!
- gender.cgiar.org We would like to acknowledge all CGIAR Research Programs and Centers for supporting the participation of their gender scientists to the Seeds of Change conference. Photo: Neil Palmer/IWMI
- References Alkire, S., Meinzen-Dick, R., Peterman, A., Quisumbing, A., Seymour, G., & Vaz, A. (2013). The Women’s Empowerment in Agriculture Index. World Development, 52, 71-91. Kabeer, N. (1999). Resources, agency, achievements: Reflections on the measurement of women’s empowerment. Development and Change, 30(3), 435-464. FTF Bangladesh Baseline Report FTF Ghana Baseline Report
Editor's Notes
- There are many ways to define empowerment. In this analysis, we use Naila Kabeer’s definition (1999). She defines empowerment as an interrelated process made up of three dimensions – resources, agency, and achievements. Many studies look at how increasing women’s resources or agency can improve achievements such as income, nutrition, and health. In this analysis, we look at the associations between resources and agency. What resources and characteristics allow men and women to have more agency? In other words, what resources are needed to be able to make strategic decisions about one’s own life?
- This study was based on analysis of data collected through USAID’s Feed the Future program in Ghana and Bangladesh. Feed the Future is a program launched in 2010 to address hunger and global food security, with a heavy focus on improving women’s and children’s nutrition. In its first phase, FTF had 19 focus countries in Africa, Asia, and Latin America. In this analysis, I will focus on the baseline surveys from two of those focus countries: Ghana and Bangladesh. The FTF baseline survey in Ghana was collected in 2012 by the Institute for Statistical, Social and Economic Research. It is representative for the FTF Zone of Influence (the area where FTF programming was implemented, which mainly covers northern Ghana). The FTF baseline in Bangladesh was collected in 2011-12 as part of the Bangladesh Integrated Household Survey. The sample used for this analysis is nationally representative for rural Bangladesh, and the data were collected by DATA and IFPRI in partnership with USAID.
- We used the WEAI to measure women’s empowerment. The WEAI is… In this study, we used each individual’s empowerment score – or the proportion of the 10 indicators they are adequate in – as a measure of empowerment. We also looked more in detail at each of the 10 indicators.
- We estimated predictors of women’s and men’s empowerment score using fractional logistic regression model. The outcome – empowerment score – is the proportion of indicators in which an individual was adequate. We also looked at predictors of achievements in each indicator using Poisson and OLS regression, depending on the type of indicator. The predictors were “resources” – including human, social, and economic resources. In these surveys, we looked at individual characteristics – literacy, marital status, and age – household characteristics – ethnic or religious group, wealth quintile, household hunger scale, dependency ratio, household size, and household type – and community characteristics – geographic region and rural/urban.
- These graph show the results of fractional logistic regression models where the outcome is empowerment score of women and men in Ghana. The y-axis shows the explanatory variables and the x-axis shows the marginal association of each predictor with empowerment score, including 95% confidence intervals.
- Some of the predictors of empowerment vary between men and women. For example, being literate was associated with higher empowerment for women but not for men.
- Similarly, being older was associated with higher empowerment for women, but age did not matter for men’s empowerment.
- In contrast, being married was associated with lower empowerment for women. Again, there was no association for men.
- Living in a wealthier household was associated with higher empowerment for both men and women.
- Next, I ran a similar analysis looking at predictors of empowerment score in Bangladesh.
- In Bangladesh, literacy was associated with higher empowerment for both women and men.
- Unlike in Ghana, being married was not associated with empowerment score for either men or women.
- Being older was associated with higher empowerment score for both men and women, except for women aged 60 or older.
- Living in a wealthier household was associated with higher empowerment for both men and women, though wealth mattered more for women than for men.
- The empowerment score is an aggregate across all 10 indicators of WEAI. However, the associations between resources and agency may vary for different areas of agency. Next, we looked at the associations between resources and each indicator of empowerment. In this presentation, I’ll highlight two of the indicators.
- First, I’ll look at the indicator input in productive decisions. In WEAI, an individual is considered adequate in this indicator if they participate in decisions about at least one agricultural activity, where the activities are things like crop production, livestock production, etc. To bring out more variability in a regression model, the outcome that we used was the number of agricultural activities the individual makes decisions about. We modeled predictors of input in productive decisions using Poisson regression. Photo credit: https://goo.gl/images/4tKWHi
- Here, I am showing you the direction of significant associations with input in productive decisions for women and men in Bangladesh and Ghana. We see some interesting differences between the countries. For example, literacy was associated with higher input in productive decisions for both women and men in Bangladesh, but not in Ghana. Age was associated with higher input in productive decisions for both women and men in Bangladesh, but only mattered for women in Ghana. Some results were more difficult to interpret. Living in a wealthier household was associated with lower input in productive decisions in Bangladesh, however. This could be explained by less diversification in wealthy households. We will need to do more analysis to unpack some of these results.
- Here, I am showing you the direction of significant associations with input in productive decisions for women and men in Bangladesh and Ghana. We see some interesting differences between the countries. For example, literacy was associated with higher input in productive decisions for both women and men in Bangladesh, but not in Ghana. Age was associated with higher input in productive decisions for both women and men in Bangladesh, but only mattered for women in Ghana. Some results were more difficult to interpret. Living in a wealthier household was associated with lower input in productive decisions in Bangladesh, however. This could be explained by less diversification in wealthy households. We will need to do more analysis to unpack some of these results.
- Here, I am showing you the direction of significant associations with input in productive decisions for women and men in Bangladesh and Ghana. We see some interesting differences between the countries. For example, literacy was associated with higher input in productive decisions for both women and men in Bangladesh, but not in Ghana. Age was associated with higher input in productive decisions for both women and men in Bangladesh, but only mattered for women in Ghana. Some results were more difficult to interpret. Living in a wealthier household was associated with lower input in productive decisions in Bangladesh, however. This could be explained by less diversification in wealthy households. We will need to do more analysis to unpack some of these results.
- Here, I am showing you the direction of significant associations with input in productive decisions for women and men in Bangladesh and Ghana. We see some interesting differences between the countries. For example, literacy was associated with higher input in productive decisions for both women and men in Bangladesh, but not in Ghana. Age was associated with higher input in productive decisions for both women and men in Bangladesh, but only mattered for women in Ghana. Some results were more difficult to interpret. Living in a wealthier household was associated with lower input in productive decisions in Bangladesh, however. This could be explained by less diversification in wealthy households. We will need to do more analysis to unpack some of these results.
- Next, we’ll look at predictors of control over use of income. In WEAI, an individual is adequate in this indicator if they participate in decisions about how to use income from at least one activity. Like the last indicator, we use a count variable in the regression to bring out more variability. So, the outcome is the number of activities for which the individual makes income decisions, modeled using Poisson regression. Photo credit: https://goo.gl/images/wkhygU
- Again, we see some interesting differences between the countries. For example, literacy was associated with higher control over use of income in Ghana, but not in Bangladesh. Some results were consistent between countries, though. In both countries, being married was associated with lower control over income for women but higher control over income for men.
- Again, we see some interesting differences between the countries. For example, literacy was associated with higher control over use of income in Ghana, but not in Bangladesh. Some results were consistent between countries, though. In both countries, being married was associated with lower control over income for women but higher control over income for men.
- Again, we see some interesting differences between the countries. For example, literacy was associated with higher control over use of income in Ghana, but not in Bangladesh. Some results were consistent between countries, though. In both countries, being married was associated with lower control over income for women but higher control over income for men.
- Photo credit: ILRI Ethiopia The predictors of women’s and men’s agency vary by dimension of empowerment and country. What can we do with these results? Some of these results can help us think about how to target interventions. For example, we found that being married was associated with lower control over income for women. Of course, this does not mean that women should not get married. Rather, interventions aimed at increasing women’s control over income or changing gender norms around how income is managed should focus on married women and consider how income and spending decisions change when someone gets married.
- Some of this is already done, but I could not fit it into the presentation. So here is a preview of other elements of this work.
- Based on regression coefficients
- Based on incidence rate ratios (IRR) *Higher for lower age group difference
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