Across all countries, women respondents tend to be younger than men, although the magnitude of the age difference varies (Table 1). In Benin and Malawi, men respondents have more years of schooling, whereas in the Philippines and Bangladesh women complete more years of schooling than men. This pattern is common in the Philippines but is a relatively recent phenomenon in Bangladesh owing to policies encouraging girls’ education. Most of our respondents are married and live in dual-adult households, although about a fifth of respondents in Benin and the Philippines live in woman-only households.
Table 2 shows the 3DE score, the GPI, and the pro-WEAI score. Because our samples were drawn purposively, these results should not be interpreted as representative of the empowerment status of women and men in these countries but may be indicative of the types of households targeted for the interventions or involved in the target value chains. Except for the Philippines, women’s 3DE scores (scores across the three domains of empowerment) are consistently lower than men’s, although levels vary. Women in the Malawi sample have the highest 3DE score, followed by the Philippines, Benin, and Bangladesh, in that order. More than 90% of Bangladeshi women in our sample are disempowered, while about two-thirds of the women in the Philippines and Benin samples have not achieved empowerment. In contrast, only 27% of the women in the Malawi sample have not achieved empowerment. The GPI is highest in the Malawi sample, followed by the Philippine and Benin samples, with Bangladesh showing the least gender parity. Pro-WEAI scores range from 0.53 in Bangladesh to 0.89 in Malawi.
Correlates of empowerment and gender equality
Before turning to the regression analysis, we note a few additional details. First, the upper part of each regression table (Tables 3-10) contains the key variables of interest—those related to value chain and market participation (Bangladesh, Philippines) and value chain, training, and market outcomes (Benin, Malawi). Second, we use principal component analysis to construct an asset index from information on household assets (including indicators of quality of dwelling, ownership of productive equipment, land, and livestock) and divide households into quintiles based on their score on that index. Because asset lists vary across countries, the wealth quintiles referred to below are country and survey specific. Third, the regressions to follow are intended to capture correlations, not causation. In presenting our results, we use “correlations” and “associations” interchangeably because they do not imply causality.
Levels of individual empowerment differ by both gender and the node of the value chain (Table 3). Relative to the base category of agricultural producer households, women in both entrepreneur and wage-earner households have lower empowerment scores and are significantly less likely to be empowered. In contrast, men in both entrepreneur and wage-earner households are significantly more likely to be empowered and to have higher empowerment scores than men in producer households.
Though small in number, women in women-only households are more likely to be empowered and have slightly higher empowerment scores relative to women in dual-headed households. Women’s and men’s empowerment is positively correlated with educational attainment in our Bangladesh sample, but marital status, household size and receipt of cash or in-kind transfers are not significantly correlated with empowerment for either gender.
Women and men in wealth quintiles 2, 3 and 4 do not differ significantly from their counterparts in the poorest wealth quintile, either in the likelihood of being empowered or in the empowerment scores, with the exception of the empowerment score for men in wealth quintiles 3 and 4. However, women in the richest quintile are 6 percentage points less likely to be empowered than women in the poorest quintile (p < 0.01) and have an empowerment score than is 0.011 points lower (p < 0.01). In contrast, men in the richest quintile have, on average, an empowerment score that is 0.013 (p < 0.01) points higher than that of men in the poorest quintile. This decrease in women’s empowerment across the wealth gradient is consistent with other work in Bangladesh (Mahmud et al., 2012).
The specific node of the value chain the household is engaged in is strongly correlated with household wealth. Wage-earner households, typically without any land of their own, tend to come from the bottom two wealth quintiles. Agricultural producer households come predominantly from the middle three wealth quintiles, and entrepreneur households from the two richest, consistent with the idea that entrepreneurship is inherently risky. While it is difficult to disentangle the wealth effect from that of the specific node of the value chain, it does not appear that engaging in entrepreneurship—arguably a higher value node than production or wage labor—necessarily implies greater levels of women’s empowerment. If entrepreneurship is confined to small-scale, low-return activities, it may not bring about desired changes in women’s empowerment.
Compared to the base category of producer households, entrepreneur and wage earner households perform worse on intrahousehold measures (Table 4). Entrepreneur households are 21 percentage points (pp) less likely to achieve parity (p < 0.01). It is also 21 pp. less likely that the man and woman are equally empowered (p < 0.01), 21 pp. more likely that the man in the entrepreneur household is more empowered than the woman (p < 0.01), and 9 pp. less likely that the woman is more empowered than the man. The patterns for wage earner households are very similar, though the magnitudes are larger for every outcome measure. This corroborates our interpretation that producer households in the middle of the wealth spectrum display greater equity between men and women.
We find no correlation between intrahousehold measures of empowerment and men’s or women’s levels of education, household size or receipt of cash or in-kind transfers. There is some evidence that wealth is negatively correlated with intrahousehold empowerment measures. Compared to the poorest quintile, household in the richest quintile are 20 pp. less likely to achieve parity (p < 0.01) and 27 pp. more likely to have a man who is more empowered than the woman. Households in quintile 4 display similar trends though with smaller magnitudes; they are 9 pp. less likely to achieve parity (p < 0.05) and are 12 pp. (p < 0.05) more likely to have a man who is more empowered than the woman. Quintiles 2 and 3 are, for the most part, indistinguishable from the poorest quintile on intrahousehold measures of empowerment.
Tables 5 and 6 present similar regressions for the Philippines, indicating again that empowerment differs across nodes and types of value chains. Both women and men are least empowered in the coconut value chain, and most empowered in the seaweed value chain. Women who are engaged in processing have lower empowerment scores compared to those engaged in production and trading. The low scores of women processors may arise from their engagement in low-value and time-demanding processing activities in coconut and abaca and the poor work conditions in abattoirs. In terms of market participation, men who participate in wage employment are 8 pp. less likely to be empowered than those who are not (p < 0.05); having one’s own business may involve greater autonomy than wage work.
Being a woman in a woman-only household is not significantly correlated with greater empowerment. Women’s and men’s empowerment is positively correlated with their own education, age, being married, access to extension services, and access to community programs and projects, with some differences by gender. Education and extension services are more strongly associated with men’s empowerment than women’s. Although earlier studies in similar contexts (e.g., Samarakoon & Parinduri, 2015 for Indonesia) point to the positive association between education and women’s empowerment, in our study setting, the weaker association between education and women’s empowerment is likely attributable to the higher proportion of women who have completed secondary schooling or higher compared to men, not unusual in the Philippines. Similarly, access to extension services is associated with a 12 pp. increase in likelihood of the man being empowered and a 7% increase in their empowerment score; these effects are smaller for women with access to extension, with access to extension associated with only a 5 pp. increase in the woman’s likelihood of being empowered, and a 4% increase in her empowerment score. Even if education increases women’s bargaining power within their households, it may be insufficient to change deeply rooted societal attitudes. Interestingly, there is no strong relationship with household wealth; a woman is more likely to be empowered if she belongs to the top wealth quintile, but none of the other wealth categories are significant.
Table 6 presents regressions on the correlates of intrahousehold inequality measures in the Philippine sample. The node of the value chain matters: men’s participation in trading is correlated with a higher likelihood of his being more empowered, whereas the woman’s participation in trading is correlated with a lower likelihood that the man is more empowered. Participation in trading may involve more direct access to sales proceeds on higher value products, as well as more engagement with other market actors, which may itself be empowering. Among the four value chains, participation in the abaca value chain is correlated with a higher likelihood of the man being more empowered, relative to a condition of gender equality. The male respondent’s participation in nonfarm activities and wage employment (relative to agricultural production) is correlated with a lower likelihood that he is more empowered, relative to a condition of gender equality. Nonfarm work and wage employment may be relatively low-return sectors for men in these contexts.
Households are more likely to achieve gender parity if the woman is more educated and is older, though the marginal effects are relatively small. Participation in and access to extension and community programs often show opposite signs by gender, suggesting that increasing men’s and women’s access to services may offset each other. Men’s access to extension services increases the likelihood that the man is more empowered by 28 pp. (p < 0.10) (and lowers the likelihood that the woman is more empowered by 52 pp. (p < 0.001), and therefore is correlated with greater likelihood of inequality. Women’s access to extension services is associated with a 32 pp. (p < 0.10) reduced likelihood that the man is more empowered, and a 5.2 pp. (p < 0.05) increase in the likelihood that men and women are equally empowered. Surprisingly, women’s own access to community programs is associated with a 40 pp. (p < 0.10) increased likelihood that the man is more empowered, and a 6.3 pp. decrease in the likelihood of achieving gender parity. Men’s access to community programs is correlated with a 32 pp. decrease in the likelihood that the woman is more empowered, but this is only weakly significant. If extension services and community programs are targeted to specific individuals within the household, this could worsen gender inequality and disempower their partners, unintentionally limiting households’ participation in these programs compared to programs that could potentially empower both men and women.
Table 7 presents regressions on women’s and men’s individual empowerment scores in the Benin sample, as a function of individual and household characteristics. The regressions for Benin include additional indicators of access to agricultural training and severe food insecurity according to the Food Insecurity Experience Scale (FIES).
There were no significant differences among the empowerment scores of women who participated in soy and poultry processing compared to those participating in rice processing. The same finding was true for the men who lived in their households. Women participating in composting had a higher likelihood of being empowered relative to those participating in rice processing, and the men living in households with women who participated in compost experienced an increased likelihood of being empowered. The positive correlations are evident for women’s and men’s empowerment scores as well. Receiving ATVET4W training is correlated with a higher likelihood of being empowered and higher empowerment scores, but only for men. Because the sample does not include a randomly selected control group, this coefficient does not indicate program treatment effects.
Women residing in women-only adult households, older women, and women with higher educational attainment are significantly more likely to be empowered. There is no correlation between men’s educational attainment and their own empowerment. Belonging to a larger household is weakly correlated with a higher empowerment score for men. Greater household wealth is correlated with empowerment for men; it is only in the top two quintiles that we observe positive correlations between wealth and women’s empowerment (both the likelihood of being empowered and the empowerment score).
Very few covariates show significant associations with the household achieving gender parity (Table 8). In households where women process soy, there is a higher likelihood of achieving gender parity, while being involved in composting is weakly correlated with the woman being less empowered. Receiving ATVET4W training has a weak positive correlation with the woman being more empowered.
Interestingly, while a higher educational level of the male respondent is associated with a higher probability of a woman’s being empowered, a higher educational level of the woman is associated with a lower probability that the man is more empowered than her and a higher likelihood that the household achieves gender parity. Overall, the correlations of parity measures with wealth are weak.
In Malawi, the main value chain the household is involved in does not appear to be significantly correlated with individual men’s or women’s likelihood of being empowered or their empowerment scores (Table 9), which may be attributable to the types of value chains targeted in the study or the types of households selected to participate in an intervention that targeted couples. The ATVET4W training occurred a relatively short period of time before the survey in Malawi; perhaps because of this, receiving the ATVET4W training does not seem to be significantly associated with women’s empowerment in this early phase of implementation, although having received other agricultural training is associated with greater empowerment for both men and women.
Belonging to a woman-only household is not significantly associated with a higher likelihood of empowerment for women but is weakly associated with higher empowerment scores for women. Higher educational levels are empowering for both women and men alike, but there does not appear to be a wealth gradient with respect to empowerment for either men or women. A woman’s own age is correlated with a higher empowerment score, but not a greater likelihood of her being empowered.
Finally, we examine correlates of intrahousehold inequality in Table 10. Once again, the main value chain for the household is not significantly correlated with gender parity, or with the man or woman being more empowered. In contrast to the results in Table 9, ATVET4W training is strongly positively correlated with the woman being more empowered than the man and negatively correlated with the man being more empowered than the woman, which could be a consequence of the types of households that were selected for this training. Provision of other agricultural training is weakly correlated with the household achieving gender parity, but also with the man being less empowered, which could be the result of previous training programs targeting women but not men.
While higher educational levels for men are correlated with gender parity, they are also associated with a lower likelihood that the woman is more empowered; women’s education has no significant correlation with the gender parity measures. Women’s age is also an important factor that is correlated with gender parity; the woman is more likely to be empowered than the man in households where the female respondent is older. In contrast, a household where the man is older is likely to be one where the woman is less empowered than the man. There is also a positive correlation between wealth and the woman being more empowered, but only in quintiles 3 and 5.
Summary of findings from the four countries
Our analysis across the four countries suggest that entrepreneurship is not necessarily empowering for rural women. In our Bangladesh sample, for example, men in entrepreneurial households are more likely to be empowered, but women in those households are not. This may relate to gender norms in Bangladesh as well as the scale of the enterprise in which women entrepreneurs are involved. Small-scale enterprises with low returns (like trading) may not be empowering. Greater involvement in the market is also not necessarily associated with gender equality. For example, in our Benin sample, a decrease in the amount of the household’s main commodity sold was correlated with higher gender equality. Some commodities may provide more opportunities for empowerment. For example, high return export sectors (seaweed in the Philippines) or those that do not require large-scale operations or that can be grown close to the home (swine in the Philippines), could reduce tradeoffs between market and caregiving work.
In our samples, training and extension services are usually associated with greater empowerment but may differentially benefit men and women. In the Philippines, access to extension services seems to have a stronger correlation with men’s than women’s empowerment. In Benin, receiving ATVET4W training was associated with a higher likelihood of only the man being empowered. In Malawi, receiving the ATVET4W training was not significantly associated with individual men’s and women’s empowerment, but was associated with a higher likelihood that the woman is more empowered and that the man is less empowered, which may be a consequence of the proximity of the training to the survey and the types of couples selected for the program. In the Malawi sample, receiving other types of agricultural training was positively correlated with the probability of being empowered, with higher empowerment scores for both men and women, and with greater gender parity.
Education is associated with greater empowerment of both men and women, but the “empowerment returns” to education vary across contexts. In Bangladesh, for example, both men’s and women’s education levels are associated with a higher empowerment score, but this association is only weakly significant for women and highly significant for men. In the Philippines, education is more strongly associated with men’s empowerment than women’s empowerment. In Benin and Malawi, a woman’s own education levels are positively associated with women’s empowerment scores, but men’s education levels are not associated with their empowerment scores in Benin. With the exception of the Philippines, where women’s education is positively associated with achieving gender parity, men’s and women’s schooling is not significantly associated with the likelihood that a woman is as empowered or more empowered than the man in her household.
We also find that greater wealth is not always correlated with empowerment for women. Women’s empowerment is inversely correlated to wealth in our Bangladesh sample—but positively so in the Philippines and Benin samples. This finding implies that we cannot assume that women are going to become more empowered from wealth alone. In our Bangladesh sample, being in the top two wealth quintiles is associated both with a lower likelihood that the household attains gender parity and a higher likelihood that the man is more empowered. In contrast, there is no wealth gradient with respect to intrahousehold inequality measures in the other three countries in our sample.
All in all, culture and context determine whether participation in value chains—and which node of the value chain—is empowering. This suggests that food systems and value chains interventions that seek to empower women should consider the social and cultural contexts in which these food systems operate, so that interventions “do no harm” and do not exacerbate existing gender inequalities.