Plagiarism Checker X Originality Report

Similarity Found: 4%

Date: jeudi, mars 09, 2023

Statistics: 369 words Plagiarized / 8724 Total words
Remarks: Low Plagiarism Detected - Your Document needs Optional Improvement.
-------------------------------------------------------------------------------------------

Women, Income Use and Nutrition Quality: Evidence from Rural Households in the West
Region of Cameroon Abstract In most parts of the developing world, women are central
to family food security and nutrition. However, they face several constraints concerning
access to and decision about credit, control over the use of income, and ownership of
resources amongst others.

Using 600 surveyed rural households in the West region of Cameroon, the Abbreviated
Women’s Empowerment in Agriculture Index (A-WEAI) and the Partial Least Square
Structural Equations Modeling (PLS-SEM), are employed to investigate the effects of
women’s control over income use on household nutrition quality. By using food
consumption score nutrition quality (FCS-N) analysis, we observe that a significant
percentage of households are characterized by inadequate nutritional quality mainly in
terms of iron-rich food consumption.

Women's control over income use varies based on the source of income but remains
considerably low for all income sources. The outcome of the PLS-SEM analyses show
that women's decision-making concerning income use and women's perception of their
ability to make decisions regarding income use and expenditure is positively associated
with higher nutritional quality for the household.

However, women's perception of their ability to make decisions regarding income use
has a positive direct and total effect on nutrition quality but a negative indirect effect on
the latter but the direct positive effect outweighs the indirect effect. Enhancing women’s
control over income use has significant implications for household nutrition quality.

Keywords: Nutrition quality, women’s empowerment, PLS-SEM, Latent Variable,

Cameroon, Introduction Available data shows that the objectives of ensuring access to
sufficient, safe and nutritious food for all and of eradicating malnutrition in all its forms
(SDG target 2.1 and 2.2) are still far fetched [1,2]. Before the COVID-19 outbreak,
approximately 250 million people in Africa suffered from hunger but with the pandemic,
the situation will be exacerbated and the estimated number of undernourished people
could exceed 433.2 million people in Africa as a whole and 411.8 million people in Sub-
Saharan Africa by 2030 [3].

Also, the sustainable development goal of achieving gender equality by 2030 has been
further compromised by the socioeconomic implications of the COVID-19 pandemic [4].
The condition of women since the 2010 World Bank Gender Assessment Report has not
changed in any significant manner [5]. Despite women’s effective and inclusive
leadership in responding to the COVID-19 pandemic, they are excluded from decision-
making positions [4].

Africa as a whole is largely off-track with the three pillars of the SDGs namely economic
growth, social inclusion and the environment [3]. Concerning social inclusion, Africa
leads the world in appointing female legislators and the sub-Saharan average is greater
than the global average. However, relative to men, African women are more likely to be
in vunlnerable employment, despite the downward trend of people in vulnerable
employment generally [3]. In all developing economies, women make a vital
contribution in agriculture and rural livelihoods.

Their roles are changing permanently, varying between and within the various regions of
the world. [6]. However, gender inequalities pose a major threat to agricultural and rural
development and eliminating these inequalities is fundamental to achieving sustainable
food systems [7].

For instance, in Africa there is a wide male-female disparity in terms of food insecurity
and ownership of assets such as land given that only close to 15% of landowners are
women [3]. Cameroon is a typical African economy facing several challenges, including
revitalizing the economy, improving the security climate, strengthening the sociological
fabric and improving the livelihoods of the population in a sustainable manner [8].

In Cameroon, about 16% of households in Cameroon are food insecure and at the
regional level, food insecurity is more acute in the Far North region, followed by the
North West with and the West regions [9]. The WFP in its comprehensive food security
and vulnerability analysis (CFSVA) shows that food insecurity is more acute amongst
rural households than households in urban centres. Also, in rural Cameroon, close to
27% of households have inadequate diets and about 5% of these households consume
poor diets [9].

In Cameroon, most households with poor or borderline food consumption do not

consume protein and vitamin A-rich foods frequently [9]. Close to 5% of rural
households do not consume vitamin A rich foods on weekly basis, 6.2% do not consume
protein-rich foods on weekly basis and 22.3% do not consume iron-rich foods on weekly
basis.

Iron deficiencies are more acute in the Far-North, Centre and West regions with the
share of households who never consume iron-rich foods are estimated at 35.6%, 20.1%
and 19.1%, respectively [9]. At the regional level, the West region has the highest
percentage of households consuming a poor diet (9.3%) in Cameroon and the
increasing level of food insecurity in the West region is attributed to low consumption of
milk with an average consumption of 1.5

days per week and low consumption of meat with an average of 0.9days per week. In
the Bamboutos division, for instance, close to 13% and 55% of households have a poor
and borderline food consumption score respectively [10]. Whereas, about 71% of the
population in the West region rely on agriculture for income earning and employment
[9].

In most parts of Sub-Saharan Africa (SSA) including Cameroon, women carryout the
essential tasks in the production processes [11]. They produce 80-90% of household
food [12], and are considered the principal agents of food security and household
welfare in rural areas [13] but they have less access to and control over productive
resources such as land, education, labour, livestock, technology, financial and extension
services that lead to the final output [11,12].

Despite this, across regions, rural women still face major gender-based constraints that
limit their potential as economic agents and their capacity to reap the full benefits of
their work. The root cause of these discriminations lies in social norms, attitudes and
beliefs, which shape how women and men are expected to behave, the opportunities
that are offered to them and the aspirations they can pursue [7].

It is expected that if the gender disparities in terms of access to and control over
productive resources are leveled, women would be able to produce more [13], [14].
However, empirical evidence on the linkages between rural women’s control over
income and household food and nutrition security in the Cameroonian context remains
relatively scarce and limited.
Against this background, the study seeks to close the existing empirical gap by
providing supplementary empirical evidence on the role women's control over use could
play in the quest for improved nutrition and food security in rural settings. The study
aims to provide empirical evidence on the level of women’s control over income use;
determine the household nutrition quality and unearth the nutritional implications of
increased women’s control over income used in the West region of Cameroon.

The study hypothesizes that increasing women’s control over income use is instrumental
in raising household nutrition quality. Also, the findings of this study are important for
policymakers and civil society actors targeting gender equality, rural women
empowerment as well as food and nutrition security. Women, Income and Nutrition: The
Nexus .

There is increasing interest in how to address the co-occurrence of malnutrition and

food insecurity in farming households by improving nutrition through agriculture [15].
The translation of increased production into better child nutrition depends on a series of
intra-household factors and processes, including women’s status, decision-making
power, control over income, and access to and use of health and sanitation services [16].

Also, improving women’s status and empowerment through agriculture are constantly
cited as essential in strengthening the linkages between agriculture, diets and other
nutritional outcomes [17]. According to the FAO, women’s empowerment in agriculture
is essential given that they are in charge of food selection and preparation, placing them
at the heart of family food and nutrition security [13].

Eventually, women's empowerment leads to greater influence at household level aspects

such as income use and women, in their role as main caregivers, spend a higher
percentage of their income on food, health and care, which leads to nutrition security
[14]. However, reducing gender discrimination could lead to women's preferences
gravitating toward those of men, which might result in reduced spending on children or
other family-related expenses (Doepke and Tertilt, 2011 as cited in [14].

Linkages between women's empowerment in agriculture and nutrition differ across

cultures due to the context specificity of gender norms and differences in levels of
empowerment [17]. Gendered inequalities within the agricultural sector may limit the
sector’s potential to provide nutritious diets and improve nutrition outcomes [17].

There are several linkages between gender inequities in agriculture and nutrition
outcomes, one of which is women’s increased control over household spending
decisions which raises investment in women’s and children’s nutrition [18]. In Africa, Asia,
and Latin America, there is evidence that there is a greater tendency for women than
men to spend additional income in their children's health and nutrition. The income and
resources that women control are therefore central for health and nutrition outcomes
[16].

Hence, the resources and income that women command by engaging in agriculture is a
pathway that carries special significance for nutrition, especially among children. The
recent availability of a standardized measure of women's empowerment in agriculture
likes the Women's empowerment in agriculture index [19]; the abbreviated women’s
empowerment in agriculture [20]; the women’s empowerment in livestock index [21]; the
project-level women’s empowerment in agriculture index [22]; and most recently
women’s empowerment in nutrition index [23] have greatly increased the use of
women's empowerment measures in surveys and in the analytical work looking at the
mediating or mitigating role of women's empowerment in agriculture for nutrition
outcomes at the household and individual levels [17].

The Women's Empowerment in Agriculture Index (WEAI) and its derivatives build up a
multidimensional empowerment profile for each man and woman that reflects their
overlapping achievements in different domains and aggregates. The WEAI's indicators
are also unique and reflect the overlapping kinds of agency at the individual level they
can be broken down by subnational region, age, and social group, as well as by each
indicator.

The index builds on identical questions for female and male decision makers and covers
decisions in the productive and economic aspects [19]. The WEAI can be adapted to
measure the empowerment of women in rural areas more generally, whether they are
farmers, agricultural or non-agricultural wage workers, or engaged in non-farm
businesses [19].

Systematic studies employing the WEAI in several developing countries identify access
to and decision about credit, control over the use of income, autonomy in income-
related decisions, excessive workload, lack of group membership and ownership of
assets as the major constraints to women empowerment in agriculture [24,25]. Control
over income reflects whether a person can benefit from her or his efforts and is a core
domain for exercising choice.

This is especially important in agriculture because often even where women produce
crops or livestock, they are marketed by men who then keep most of the income.
Monitoring this component of the WEAI could help track changes in the control of
income [19]. Limited control over the income use is one of the main causes of women's
disempowerment in agriculture in several developing countries [24–26].

However, in a multi-country analysis of women’s empowerment and gender equity in

agriculture carried out by Akter et al., [27] in Southeast Asia, it appears that the income
of the husband and wife is pooled as family income and is in most cases is managed by
the wife. Also, women are responsible for decisions related to saving, food and non-
food expenditure, education and health expenses.

Hence, control over household income is disproportionately concentrated towards

women. Women make the majority of household expenditure decisions alone, and men
only occasionally take part in decision-making on major expenditures [27]. Women’s
ability to make decisions about income is limited and varies by the source of income
[26].

In several income-generating activities, women are more involved in making decisions

about money, such as non-farm economic activities and raising animals (generally
chickens). Empirical findings have shown that in Honduras, 88.3% of women are
adequately empowered the income domain [26]. Whereas, in Burkina Faso, only 41% of
women have control over at least one source of income [28].

This study will therefore assess the proportion of women empowered in terms of control
over income use with an emphasis on the various income sources and expenditures over
which rural women have control in the West region of Cameroon In most cases, women
have limited control over agricultural income relative to men, favouring men’s spending
choices, which on average involve less expenditure on food and education.

Hence, increasing women's decision-making authority over agricultural income raises

expenditures on child care and leads to improvements in nutrition and educational
achievements [29]. Also, Women in male-headed households are less likely to take part
in income sharing due to their low bargaining power [30]. For instance, [30] in an
analysis of intra-household decision-making over income in banana-producing
households report that 41% of the decisions on how income is spent are jointly made,
31% are made by the husband while the wife handles 28% of the decisions.

Several studies hold that women and female-headed households spend a larger
proportion of their income on varieties of household goods, in particular food and
education and this has positive implications for nutrition and education outcomes for
children [29]. The positive effects of increases in women’s income on childhood nutrition
and dietary practices appear most pronounced among the lowest income groups and
among households with high dependency ratios in which a large proportion of
household members are nonearning dependents [16].

Women’s empowerment is considered a potential pathway for improved nutrition [14]

and there is the expectation that women’s control over assets and income is linked to
improvements in family welfare [31–33]. Empirical studies and systematic analysis
highlight significant linkages between women empowerment in agriculture and
increased food production, household-level nutrition quality, and hunger [13, 26, 31–34].
Hence, removing the barriers faced by women will address some of the challenges faced
in the food systems.

Against this background, this study investigates the extent to which women's control
over income use can improve household nutrition quality in the West region of
Cameroon. According to Quisumbing et al. [2], Women's control over income is central
for their own empowerment and has implications for food and nutrition security, as well
as poverty reduction.

In the same line of ideas, Larson et al. [26] posit that when women have control over the
use of income, dietary diversity is enhanced. As evidence, in Nigeria, women's
empowerment in terms of group membership, control over income and workload raises
the extent of food security among farming households [35].

Similarly, In Tanzania, women's control over assets and income increase women's ability
to produce or purchase more diverse and nutritious foods thereby improving dietary
diversity and nutrition [36]. Also, in Bangladesh, women’s empowerment in agriculture
has positive implications for household dietary diversity. Likewise, in Nepal, adequacy in
terms of group membership, control over income, workload, and overall empowerment
are positively associated with better maternal nutrition[37]..

On the other hand, in Pakistan women’s empowerment in the income domain has
negative implications for food security that is enhanced income control by women raises
food insecurity [38]. The authors justify this by the fact that women generally devote an
important part of their income to household food security and child well-being but
when women are empowered, the male household head may reduce the household
food budget in response to the women's income contribution and this negatively affect
household food security.

Also, it is believed that in a classic patriarchal society where women's seclusion remains
common, there may be a negative association between women’s income-earning and
household diet [39]. 3. Materials and Methods 3.1 Study Area and Data Collection The
field survey was conducted in the West Region (5°30'0"N, 10°30'0"E) of the Republic of
Cameroon.

The region covers a surface area of 13,892 km2and is located in the central-western
portion of the Republic of Cameroon [40, 41]. It is the smallest region of Cameroon's ten
regions in terms of surface area, but has one of the highest population densities [41].
This region is chosen for the study because it is the highest percentage of households
consuming poor diets whereas about 71% of its population are farmers and one-quarter
of the population is involved in livestock production. The main crops produced are
maize, beans, groundnut and potato [9].

The data for the study were collected from selected rural households involved in
agricultural activities with the help of a well-structured questionnaire. The survey
respondents consisted of the primary members responsible for decision-making, both
social and economic, within the household as recommended by the A-WEAI
instructional guide. The focus was on the main female decision-maker within the rural
households.

The respondents were selected in the region through a multi-stage sampling method. In
the first stage, four divisions (Noun, Menoua, Nde and Haut-Plateau) were randomly
selected out of the eight in the region; in the second stage, three sub-divisions were
randomly selected per division meanwhile in the fourth stage, two villages per sub-
division were randomly selected and in the fifth stage, 25 households were purposively
chosen. Hence, a total of 600 households were selected for the study.

During the process, the main female decision-maker within the household, assisted by
the survey agent was required to fill out the questionnaire provided. In the case of
polygamous households, the choice of female decision-maker was done by randomly
selecting a wife or by selecting the wife available at the time of the interview. 3.2

Method of Data Analysis The analytical approach applied in this study is the agency-
achievement framework (see figure 1) adapted from the resource-agency-achievement
framework applied by [39]. Agency has to do with women's control over income use
(control over income from various sources) and the potential ability to make decisions
on income from various sources. Achievement on the other hand refers to the
household nutrition quality measured through the consumption of vitamin A, Hem-iron
and protein-rich foods.

/ Figure 1: Conceptual framework showing the Agency–Achievement framework Source:

Adapted from [39]. To achieve the study objectives, women’s control over income use
was evaluated through the income domain of the Abbreviated Women’s Empowerment
in Agriculture Index. Here, particular attention is on how the female household decision
maker is involved in making decisions about how to use the income generated from
food and cash crop production, livestock farming, non-farm activities, fishing,
wages/salaries; and the extent to which she feels she can participate in making decisions
about income from a food crop, cash crop and livestock production, non-farm activities,
fishing, wages/salary, major and minor household expenses [20].

Here, three latent variables (LVs) were built. The first latent variable termed income
empowered (INCD) measures women's effective decision-making on income obtained
from various sources (food and cash crop production, livestock farming and non-farm
activities,).

The second latent variable feel income empowered (FEMPW) captures women’s feelings
about their ability to make decisions and participate in making decisions about income
from a food crop, cash crop and livestock production, non-farm activities, as well as
major and minor household expenses. On the other hand, the third latent variable
nutrition quality (NQ) is built with the use of the household's consumption frequency of
vitamin A, protein and Hem iron-rich foods. 3.2.1

Household nutrition quality appraisal The food consumption score nutritional quality
analysis (FCS-N) is employed to evaluate household nutrition quality. The FCS-N
provides essential information on households' dietary diversity, and food frequency and
provides an additional level of information on the nutritional value of the households'
diet [42]. The FCS-N is suitable for this study because it highlights nutrient inadequacies
in households.

The FCS-N analyses are based on three food groups Vitamin A rich foods (Dairy, Organ
meat, Eggs, Orange vegetables, Green vegetables and Orange fruits); Protein-rich foods
(Pulses, Dairy, Flesh meat, Organ meat, Fish and Eggs); and Hem iron-rich foods (Flesh
meat, Organ meat, and Fish) [42]. Once the nutrient-rich food groups are created, to
conduct the FCS-N analysis, the various food groups are categorized and the
frequencies of consumption of these nutrients are recorded on a 7-day recall basis as
presented in table 1.

Table 1: Categories of FCS-N food groups Vitamin A rich food group Protein-rich food
group Hem iron-rich food group Vitamin A rich foods n ? n Protein rich foods n ? n
Hem iron rich foods n ? n Diary Pulses/nuts Flesh meat Organ meat Dairy Organ
meat Eggs Flesh meat Fish Orange vegetables Organ meat Green vegetables
Fish Orange fruits Eggs Source: Author; adapted from [42] Note n= frequency
(number of days the food group was consumed by the household within the last 7 days),
and ? n= sum of frequency.

After aggregating and categorizing the various food groups into nutrient-rich food
groups, the sum of frequencies is obtained and ranked in the following thresholds 0
days, 1-6 days and 7 days [42]. From the frequency thresholds, the following categories
of food consumption are obtained: 0 day: food group never consumed; 1-6 days: food
group consumed sometimes; 7 (and/more) days: food group consumed daily.

For analysis, the consumption frequencies of each nutrient-rich food group are then
recoded into three categories: 0 = 0 time (never consumed) 1 = 1-6 times (consumed
sometimes) 2 = 7 times or more (consumed at least daily). 3.2.2 Empirical Specifications
To analyse the effects of Women’s control over income use on food nutrition quality. The
latter is an endogenous construct – Nutrition Status.

The exogenous variable (control over income use) is made up of two constructs: the
income decision and the feeling of empowerment in the different incomes of production
activities. Because each construct of empowerment is a decomposable structure
consisting of many indicators, it can be easily analysed using partial least squares (PLS).

Partial least squares SEM (PLS-SEM) is employed in a novel way to evaluate the
empowerment of women and the food security relationship. The basic idea for this
method was developed by Wold in 1966 for multivariate analysis and then extended its
application to SEM. PLS-SEM relies on two different conceptual models.

The measurement model assesses the validity and reliability between the observed
variables and the latent causal constructs. In contrast, the structural model tests the
significance of the relationship among the latent constructs, the predictive power of
different variables and the variance of the endogenous variables [43].

The following equations are related to the two sub-models: (1) (2) M and p express the
latent variables (LVs) and the manifest variables (MVs). The ?, x, B, ?, t and d specify the
LV and MV vectors, the path coefficients of the LVs, the factor loading joining the MV to
the LV, and the errors terms, respectively. Higher-order LVs were not considered in
Wold’s basic PLS model.

Two popular approaches for dealing with hierarchical constructs are the repeated
indicators approach [44] and the two-step approach [45]. Four hierarchical constructs
were considered in the previous studies, and these constructs were used to show
different relations between the HOC, Low ordered constructs (LOCs) and their indicators
[46]; reflective-formative, reflective-reflective, formative-reflective and formative-
formative [47, 48].

In this study, the formative-formative type HOC model is based on the formative
indicators in both the HCMs and the LCMs. This model is infrequently used in the
structural model. PLS-SEM is the standard framework to measure this hierarchical model.
3.2.2.1 Repeated indicators approach In 1989, Lohmoller suggested an approach for
modelling hierarchical constructs known as the repeated indicators approach.

This approach replicates MVs numbers at each construct [49]. A meaningful

precondition for this approach is that the models should have all reflective indicators in
the first and second-order factors. The following three equations were used to model
the repeated indicator approach: (3) (4) (5) In the model, m and p are the subscripts
for the number of first-order LVs and the MVs. The number of second-order LVs is
denoted by subscript q.

The vectors ?I, ?II, x, ?, d and e are the LVs, the first and second-order MVs and the
structural and the errors terms of measurement, respectively. The matrices B, ?I, and ?II,
expresses the path coefficients joining the LVs and the factor loading joining the MVs to
the LVs of the first and second-order constructs, respectively. Using PLS estimation,
variables of a similar kind link together and create bias, which is a disadvantage of this
approach. 3.2.2.2

Two-step approach This approach is another alternative way to build HOCs. Notably, the
indicators at all constructs are formative. It is a well-known best-fit approach for
analyzing formative indicators in HOC models [49, 50]. In this approach, the LVs in the
model are primarily estimated without second-order constructs [45].

The LV scores are used as indicators for analysis in a separate higher-order structural
model. This approach is implemented in two stages. The first stage considers the first-
order LVs estimations in the measurement model: (6) In the second stage, the estimated
scores ?ˆand I, acquired in the first step, are employed as indicators for the second-order
LVs.

(7) One disadvantage of this approach is that it does not consider stage two’s
construction when evaluating the scores of LV during stage one. However, Wilson has
demonstrated that second-order construct consistency does not rely on the method
employed. Moreover, compared to the repeated indicators approach, this approach, in
the case of small samples, does not produce biased or unreliable estimates.

Therefore, this study followed a two-stage path aligned with previous studies [45, 49, 50]
and data were analysed with STATA and R [51]. 4. Results 4.1 Household socioeconomic
characteristics Table 2 depicts the characteristics of participants and their households.
The results showed that sampled women belonging to a dual-headed households were
67.7%, while women belonging to single female-headed households 19.7% and 12.7%
were members of single male-headed households.

The household structure has important implications for women's agency and therefore
affects their control over income use. Statistics further indicate that the women had an
average age of approximately 45 years with an average household size of close to six
persons per household. The statistics further depict that 06% of women are illiterates.
About 22% and 55% of the women have received primary and secondary education
respectively.

Women with a university level of education are only 17.2% of the surveyed respondents.
Women's education level plays a significant role in food management and women's
agency as well. The majority of respondents are married (71%) and the most common
religious belief is Christianity (59.8%).

Crop production appears as the main agricultural activity for most respondents
(81%.7%) whereas only 43% receive agricultural extension services and 22.5% are
members of producer's organisations. Table 2: Household characteristics (n = 600)
Variable Definition % Mean Std. Deviation Household structure Dual headed 67.7
1.45 0.7082863 Single female-headed 19.7 Single male headed 12.7

Age of household head Continuous 44.94833 11.86579 Household size Continuous

5.688333 3.757845 Female decision makers' level of schooling No formal education
6.0 2.835 0.7759587 Primary education 21.7 Secondary 55.2 University 17.2
Households’ main economic activity crop production 56.0 1.835 1.081538 Livestock
production 16.5 Trade 15.5

Public service 12.0 Marital Status Single 12.0 2.186667 0.8162512 Married 71.0
Divorced 3.3 Widower 13.7 belief Christian 59.8 1.786667 1.013015 Muslim 6.0
Traditionalist 29.8 Other 4.3 Membership in producers' organisations No 77.5 1.225
0.4179307 Yes 22.5 Household’s main agricultural activity crop 81.7 1.183333
0.3872624 livestock 18.3 Access to agricultural extension services No 57 1.43
0.4954888 yes 43 Table 3 shows the state of household nutrition quality household
measured through the consumption frequency of vitamin A-rich foods, protein and
hem-iron rich foods. It appears that in the study area, the nutrition quality was not much
worse than the national average roughly 6.2% of households in the study area do not
consume vitamin A foods, 4.5% do not consume protein-rich foods and 8% do not
consume iron-rich foods.

This result tends to differ from those obtained by the [9] in 2017 which showed that
Approximately 4.8% of rural households do not consume vitamin A rich foods on weekly
basis, 6.2% do not consume protein-rich foods on weekly basis and 22.3% do not
consume iron-rich foods on weekly basis.

While noting improvements concerning protein and iron-rich foods, we note that the
situation for vitamin A foods has rather worsened when compared to the national
average. Table 3: Household Nutrition quality for respondents and their households
(n=600) Food Group Definition % Mean Standard deviation Vitamin A Never
consumed 6.2 1.63 0.5975185 Consumed sometimes (1-6 times) 24.7 Consumed at
least daily (7 times or more) 69.2 Protein Never consumed 4.5

1.678333 0.5556183 Consumed sometimes (1-6 times) 23.2 Consumed at least daily
(7 times or more) 72.3 Hem-Iron Never consumed 8.0 1.181667 0.5560388
Consumed sometimes (1-6 times) 65.8 Consumed at least daily (7 times or more) 26.2
Concerning women's control over use, about 23.5% of women regularly made decisions
about income earned from non-farm activities, 20.50% on income earned from food
crop production, 17% on income from cash crop production, 13.83% from livestock
production and wages & salary occupations respectively and 03% on income from
fishing (table 4).

The relatively low proportion of women making decisions on income utilization from
various sources can be because food crop production is mainly carried out for
subsistence purposes and only the excess is usually sold to settle specific problems such
as health issues and sponsor children's education. On the other hand, cash crop and
livestock production are mainly controlled by male household heads who therefore
dispose of the money earned without necessarily consulting their female counterparts.

Also, few women are involved in wage and salaries occupations in the rural milieu (those
who do are limited to temporary jobs such as farm workers during planting and
harvesting periods) and more to that, the practice of fishing is very limited in the study
area (only close to 6% of respondents are involved into fishing). Table 4: Women's
participation in decision-making for income use Women’s control over income use
Percentage Decision-making on Income earned Makes Decisions on income from food
crop production 20.50 Makes Decisions on income from cash crop production 17.00
Makes Decisions on income from livestock production 13.83 Makes Decisions on
income from non-farm activities 23.50 Makes Decisions on income from wages &
Salaries 13.83 Makes Decisions on income from fishing 03.00 Feel can make a decision
on income and expenditures Feel can make a decision on income from food crop
production 63.33 Feel can make a decision on income from cash crop production 45.50
Feel can make a decision on income from livestock production 47.67 Feel can make a
decision on income from non-farm activities 49.17 Feel can make a decision on income
from fishing 05.50 Feel can make a decision on income from wage/salaries activities
28.33 Feel can make a decision on major household expenditure 72.50 Feel can make
decision on minor household expenditure 75.50 With respect to women's impression
about their ability to make decisions about income use, a majority of women declare
feeling they can make decisions about income from food crop production (63.33%),
major household expenditure (72.5%) and minor household expenditure (75.5%).

About half of women reported they feel they can make decisions on income from non-
farm activities (49.17%), income from livestock production (47.67%), and income from
cash crop production (45.5%). The above results align with the previous study of [52]
who have demonstrated that in Burkina Faso only 41% of women have control over at
least one source of income.

Also, as suggested by [26], women's ability to make decisions about income is restricted
and varies by the source of income. Women appear to be more autonomous when it
comes to decision over income from non-farm activities, and food crop production
sometimes because their male counterparts consider such income as being relatively
insignificant in terms of volume and seasonal.

Our findings are therefore not consistent with those of [26] who posit that in Honduras,
88.3% of women are adequately empowered in the income domain. 4.2 Structural Model
A reflective measurement model was used to estimate the latent variables LVs in the first
step. When considering the items' reliability, the constructs' (Latent variable).

When considering the items' reliability, the constructs’ (latent variables) relevance and
convergence validity as well as the model’s discriminatory validity, we conclude that the
reflective measurement model as a whole is of good quality. Table 5: Standardized
loadings of measurement model - (n= 600) Reflective: Income decision Reflective: Feel
can make decision Reflective: Nutrition quality Decision on income from a non-farm
activity 0.655*** Decision on income from livestock 0.717*** Decision on income
from food crop 0.788*** Decision on income from cash crop 0.790*** Feel can make
decision on income from a non-farm activity 0.582*** Feel can make decision on minor
household expenses 0.807*** Feel can make decision on major household expenses
0.782*** Feel can make decision on income from livestock 0.649*** Feel can make
decision on income from food crop 0.799*** Feel can make decision on income from
cash crop 0.714*** Consumption of Vitamin A rich foods 0.953*** Consumption of
Protein rich foods 0.915*** Consumption of Hem iron rich foods 0.656*** Cronbach
0.721 0.817 0.828 DG 0.827 0.869 0.886 rho_A 0.728 0.821 1.081 Model general
characteristics Average R2 0.30292 Average commonality 0.57948 Path Absolute
Gof 0.41897 Relative GoF 0.75149 Average redundancy 0.16624 (significant at 1%
level); Average R-squared In this study, only indicators with factor loadings above 0.5

were maintained (see table 5) and all indicators whose factor loadings were below 0.40
were automatically excluded from the estimations. This is because the theory
recommends loadings above 0.708 as they provide acceptable item reliability [53]. On
the other hand, indicators with very low factor loadings (below 0.40) should always be
eliminated from the measurement model [54].

Hence, all indicators used in this study have factor loadings above 0.5 which guarantees
the reliability of items in the model. The analyses also show that the model has a
moderate in-sample predictive power with an R2 of approximately 0.303 (table 5). It
implies that about 30.3% of nutrition quality was accounted for by women’s control over
income use (making decisions about income use and impression about the ability to
make decisions about income use).

The size of the R2 in this study may appear to be low but its size is a function of the
number of predictor constructs such that the greater the number of predictor constructs,
the higher the R2. Hence, when measuring concepts such as physical processes, R2
values of 0.90 might be plausible but similar R2 value levels in a model that predicts
human attitudes, perceptions and intentions likely indicate an over fit [53].

The internal consistency reliability of the model is assessed with Cronbach’s alpha.
Internal consistency reliability refers to the extent to which indicators measuring the
same construct are associated with each other. When making use of Cronbach’s alpha,
the higher the values the higher the levels of reliability thus, the Cronbach’s alpha which
ranges between 0.72 and 0.83 shows that the internal consistency reliability is
satisfactory. The convergence validity is assessed with the analysis of the average
variance extracted (AVE).

In our model, the means vary between 0.546 and 0.725 (table 6). The following table is
used to verify the discriminant validity criterion. The discriminant validity on the other
hand is the extent to which a construct is empirically distinct from other constructs in
the structural model [53]. Table 6: Discriminant validity - Squared interfactor correlation
vs.

Average variance extracted (AVE) Income Decision Feel can make decision Nutrition
Quality Income decision 1.000 0.598 0.001 Feel can make decisions 0.598 1.000 0.006
Nutrition Quality 0.001 0.006 1.000 AVE 0.546 0.529 0.725 Table 7 shows the direct
relationship between women’s control over income use and nutrition quality with their
respective path coefficients.

The results indicate that the hypothesized paths of income decision and feeling can
make a decision are highly significant (p < 0.05). The results in Table 7 demonstrate the
strong positive association between income decision and household nutrition quality
(ß=0.63, p = 0.030), and suggest that household nutrition quality can be improved
through this component.

The income decision in this study is women's decision-making on income earned from
food crop production, cash crop production, livestock production, and non-farm income
activities. Hence, increasing women's income decision making power can raise the
likelihood of having adequate nutrition quality by 63%. Moving forward to women's
feelings about their ability to decide on income use if they wanted to, the results hold
that this aspect has a relatively weak but positive and significant effect on household
nutrition quality (ß=0.129, p = 0.046) and this implies that self-confidence about the
ability to make a decision over income earned from various sources raises the
probability of having a better nutrition quality by 12.9%.

Women's feeling about their ability to make decisions is strongly and positively
associated with their effective decision-making over income use (ß=0.773, p = 0.030).
This suggests that a direct and an indirect effect may exist between women's feelings
about their ability to make decisions over income and expenditure and household
nutrition quality. Table 7: Standardized path coefficients of latent variables Variable
Income decision Nutrition Quality Income decision 0.63** (0.030) Feel can make
decision 0.773*** 0.129** (0.000) (0.046) Adjusted R2 0.597 0.55 p-values in
parentheses (*** and **= significant at 1% and 5% respectively) These results are
consistent with prior studies showing that women’s control over income has important
implications for food and nutrition security.

As revealed in previous studies, when women have control over income use, dietary
diversity is enhanced [26] and there’s an increase in their ability to purchase more
diverse and nutritious foods for the household [36]. Table 8 presents the construct’s
direct, indirect and total effects on the target construct (nutrition quality).

The findings at this stage reiterate that income decisions and feeling can make decisions
have positive direct effects on household nutrition quality as suggested in table 7.
However, women's feeling about their ability to make decisions over income and
expenditure has both a direct and an indirect effect on the household's nutrition quality.
Meanwhile, the direct effect is positive (ß=0.129), the indirect effect is rather negative
(ß=-0.048) and the total effect is therefore positive but with a relatively weak coefficient
(ß=-0.080).

This implies that women's feeling about their ability to make a decision about income
use reduces the probability of having adequate nutritional quality by 4.8% (indirect
effect) but at the same time, it raises the likelihood of having a better nutrition quality
by 8% (total effect). Table 8: Decomposition of total effects Effect Direct Indirect Total
Income decision -> Nutrition Quality 0.63 0.63 Feel can make decision -> Income
decision 0.773 0.773 Feel can make decision -> Nutrition Quality 0.129 -0.048 0.080
The negative indirect effect of women's feelings about their ability to control income use
may be because increased women's self-confidence may disrupt the household's gender
dynamics and women may therefore face restrictions concerning their contribution at
various levels within the household.

Also, [38] exhibited a similar outcome but revealed that women generally devote an
important part of their income to household food security and child well-being but
when they are empowered, the male household head may reduce the household food
budget in response to the women’s income contribution and this negatively affect
household food security. 5.

Conclusion and policy implication Over the previous year, food and nutrition security as
well as women's empowerment and gender equality have been some of the top
priorities on the development agenda for most governments and international
development agencies. In the context of rural Cameroon, no previous study explored the
nature of women’s empowerment in agriculture in general and women’s control over
income use in particular through the Abbreviated Women’s Empowerment in agriculture
index methodology and its effects on household food and nutrition security.

This study reiterates the role of women’s control over income use in enhancing
household nutrition quality in rural settings. The household nutrition quality analysis
highlights an inadequate consumption of iron-rich foods for most households and
limited control over income used for the majority of women in the West region of
Cameroon.

The study result shows that rural women in the study area are vulnerable to
discrepancies in terms of decision-making over income use and expenditure. Whereas,
women’s ability to make decisions regarding income use and their perceived ability to
make decisions over income use and expenditure enhance household nutrition quality.
To improve the food and nutrition security conditions of the rural population, nutrition
education efforts should be reinforced through various channels.

The caveat of this study is that women's control over income and agency with respect to
income and expenditure-related issues should be considered as areas of focus when
designing rural development strategies. Finally, societal biased attitudes should be
eliminated to enable women fully exploit their potential and enhance development at
the family, community and national levels. Acknowledgements We thank the Menoua,
Noun, Nde and Haut-Plateau agricultural administration offices for facilitating our access
to the target population.

Furthermore, we express our gratitude to the development agents and informants of the
study area for providing us with all the important information and data during the study.
Funding This research did not receive any specific grant from funding agencies in the
public, commercial, or not-for-profit sectors. References [1] FAO, IFAD, UNICEF, WFP, and
WHO, The State of Food Security and Nutrition in the World 2020. FAO, IFAD, UNICEF,
WFP and WHO, 2020. doi: 10.4060/ca9692en.

[2] A. R. Quisumbing, R. S. Meinzen-Dick, and J. Njuki, 2019 Annual trends and outlook
report: Gender equality in rural Africa: From commitments to outcomes, International
Food Policy Research Institute, Washington, DC, 2019. doi: 10.2499/9780896293649. [3]
SDGC/A, AFRICA 2030: SDGs Within Social Boundaries - Leave no One Behind Outlook
Report, SDGC/A, Kigali, Rwanda, 2021.

[4] United Nations, Progress towards the Sustainable Development Goals Report of the
Secretary-General, United Nations Economic and Social Council, 2022. [5] World Bank,
Women Economic Empowerment Study, The World Bank Group, Washington DC, 2018.
[6] SOFA TEAM and C. Doss, The role of women in agriculture, FAO, Rome, Italy, Working
Paper 11–02, 2011. [7] FAO, FAO Policy on Gender Equality 2020–2030, Rome, Italy, 2020.

[8] MINEPAT Cameroon, National Development Strategy 2020-2030 For structural

transformation and inclusive development, MINEPAT, Yaounde, Cameroun, 2020. [9] WFP,
Cameroon : Comprehensive Food Security and Vulnerability Analysis (CFSVA), United
Nations World Food Programme, Rome, Italy, 2017. [10] B. Soh Wenda, D. Fon, and N. H.
Moluh, 2019.

Integrating nutrition in agriculture extension information and services and its

implications for rural household food security in the Bamboutos division, West region of
Cameroon, J. Agric. Econ. Ext. Rural Dev., vol. 6, no. 7, Art. no. 7. [11] D. Fon and T.
Edokat, 2012. Marginalization of Women’s Role in Sub-Saharan Africa towards Crop
Production: A Review’, Agric. Sci. Res. J., vol. 2, no. 9, pp. 499–505. [12] F. Engwali and M.

Nformi, 2016. Access and Use of Agricultural Extension Information and Services by
Rural Women in Cameroon’, Asian J. Agric. Ext. Econ. Sociol., vol. 8, no. 4, pp. 1–9, Jan,
doi: 10.9734/AJAEES/2016/22544. [13] FAO, Ed., Women in agriculture: closing the
gender gap for development. Rome: FAO, 2011. [14] N. Verhaart, A. van den Wijngaart,
M. Dhamankar, and K.

Danielsen, Bringing agriculture and nutrition together using a gender lens, Netherlands
Development Organisation (SNV) and the Royal Tropical Institute (KIT), Amsterdam, The
Netherlands, 2016. [15] K. J. Fiorella, R. L. Chen, E. M. Milner, and L. C. H. Fernald, 2016.
Agricultural interventions for improved nutrition: A review of livelihood and
environmental dimensions, Glob. Food Secur., vol. 8, pp. 39–47, doi:
10.1016/j.gfs.2016.03.003.

[16] C. Hawkes and M. Ruel, From Agriculture to Nutrition: Pathways, Synergies and
Outcomes, 40, (2008). [Online]. Available:
https://openknowledge.worldbank.org/handle/10986/9511 [17] M. T. Ruel, A. R.
Quisumbing, and M. Balagamwala, 2018. Nutrition-sensitive agriculture: What have we
learned so far?, Glob. Food Secur., vol. 17, pp. 128–153, doi: 10.1016/j.gfs.2018.01.002.
[18] K. Cunningham, M. Ruel, E. Ferguson, and R. Uauy, 2015.

Women’s empowerment and child nutritional status in South Asia: a synthesis of the
literature: Women’s empowerment and child nutrition: South Asia’, Matern. Child. Nutr.,
vol. 11, no. 1, pp. 1–19, doi: 10.1111/mcn.12125. [19] S. Alkire, R. Meinzen-Dick, A.
Peterman, A. Quisumbing, G. Seymour, and A. Vaz, 2013. The Women’s Empowerment in
Agriculture Index’, World Dev., vol. 52, pp. 71–91, doi: 10.1016/j.worlddev.2013.06.007.
[20] H. Malapit, C. Pinkstaff, K. Sproule, C.

Kovarik, A. Quisumbing, and R. Meinzen-Dick, The Abbreviated Women’s Empowerment

in Agriculture Index (A-WEAI), International Food Policy Research Institute, Washington,
DC., 2017. [21] A. Galiè et al., 2019. The Women’s Empowerment in Livestock Index’, Soc.
Indic. Res., vol. 142, no. 2, pp. 799–825, doi: 10.1007/s11205-018-1934-z. [22] H.

Malapit et al., 2019. Development of the project-level Women’s Empowerment in

Agriculture Index (pro-WEAI), World Dev., vol. 122, pp. 675–692, Oct. 2019, doi:
10.1016/j.worlddev.2019.06.018. [23] S. Narayanan, E. Lentz, M. Fontana, A. De, and B.
Kulkarni, 2019. Developing the Women’s Empowerment in Nutrition Index in Two States
of India’, Food Policy, vol. 89, p. 101780, doi: 10.1016/j.foodpol.2019.101780. [24] R.
Meinzen-Dick, A. Quisumbing, C. Doss, and S. Theis, 2019. Women’s land rights as a
pathway to poverty reduction: Framework and review of available evidence, Agric. Syst.,
vol. 172, pp. 72–82, doi: 10.1016/j.agsy.2017.10.009. [25] H. Malapit, C. Ragasa, E. M.
Martinez, D. Rubin, G. Seymour, and A. Quisumbing, 2020. Empowerment in agricultural
value chains: Mixed methods evidence from the Philippines, J. Rural Stud., vol. 76, pp.
240–253, doi: 10.1016/j.jrurstud.2020.04.003.

[26] J. B. Larson, P. Castellanos, and L. Jensen, Gender, household food security, and
dietary diversity in western Honduras, Glob. Food Secur., vol. 20, pp. 170–179, 2019, doi:
10.1016/j.gfs.2019.01.005. [27] S. Akter et al., Women’s empowerment and gender equity
in agriculture: A different perspective from Southeast Asia, Food Policy, vol. 69, pp. 270–
279, 2017, doi: 10.1016/j.foodpol.2017.05.003.

[28] R. P. Nikiema and K. C. Kponou, Impact of Women Empowerment in Agriculture on

under Five Children Nutritional Outcomes in Rural Burkina Faso, African Economic
Research Consortium, Nairobi, Kenya, AERC POLICY BRIEF, 2021. [29] C. L. Anderson, T.
W. Reynolds, P. Biscaye, V. Patwardhan, and C. Schmidt, 2021.

Economic Benefits of Empowering Women in Agriculture: Assumptions and Evidence, J.

Dev. Stud., vol. 57, no. 2, pp. 193–208, doi: 10.1080/00220388.2020.1769071. [30] V.
Nyabaro, J. Mburu, and M. Hutchinson, 2019. Factors enabling the participation of
women in income sharing among banana ( musa spp.) producing households in South
Imenti, Meru County, Kenya, Gend. Technol. Dev., vol. 23, no. 3, pp. 277–292, doi:
10.1080/09718524.2019.1669104. [31] F.

Clement et al., 2019. From women’s empowerment to food security: Revisiting global
discourses through a cross-country analysis, Glob. Food Secur., vol. 23, pp. 160–172, doi:
10.1016/j.gfs.2019.05.003. [32] H. J. L. Malapit and A. R. Quisumbing, 2015. What
dimensions of women’s empowerment in agriculture matter for nutrition in Ghana?’,
Food Policy, vol. 52, pp. 54–63, doi: 10.1016/j.foodpol.2015.02.003.

[33] E. Sraboni and A. Quisumbing, 2018. Women’s empowerment in agriculture and

dietary quality across the life course: Evidence from Bangladesh’, Food Policy, vol. 81, pp.
21–36, doi: 10.1016/j.foodpol.2018.09.001. [34] E. Sraboni, H. J. Malapit, A. R.
Quisumbing, and A. U. Ahmed, 2015. Women’s Empowerment in Agriculture: What Role
for Food Security in Bangladesh?, World Dev., vol. 61, pp. 11–52, doi:
10.1016/j.worlddev.2014.03.025. [35] M. O.

Kehinde, A. M. Shittu, A. G. Adeyonu, and M. G. Ogunnaike, 2021. Women

empowerment, Land Tenure and Property Rights, and household food security among
smallholders in Nigeria’, Agric. Food Secur., vol. 10, no. 1, p. 25, doi: 10.1186/s40066-
021-00297-7. [36] A. Galiè et al., 2019. Women’s empowerment, food security and
nutrition of pastoral communities in Tanzania’, Glob. Food Secur., vol. 23, pp.

125–134, doi: 10.1016/j.gfs.2019.04.005. [37] H. J. L. Malapit, S. Kadiyala, A. R.

Quisumbing, K. Cunningham, and P. Tyagi, 2015. Women’s Empowerment Mitigates the
Negative Effects of Low Production Diversity on Maternal and Child Nutrition in Nepal, J.
Dev. Stud., vol. 51, no. 8, pp. 1097–1123, doi: 10.1080/00220388.2015.1018904. [38] N.
Aziz, Y. Ren, K. Rong, and J. Zhou, 2021.

Women’s empowerment in agriculture and household food insecurity: Evidence from

Azad Jammu & Kashmir (AJK), Pakistan, Land Use Policy, vol. 102, p. 105249, doi:
10.1016/j.landusepol.2020.105249. [39] S. S. Sinharoy, J. L. Waid, M. Ali, K. M. Yount, S. H.
Thilsted, and A. W. Girard, 2019. Resources for women’s agency, household food security,
and women’s dietary diversity in urban Bangladesh, Glob. Food Secur., vol. 23, pp. 1–8,
doi: 10.1016/j.gfs.2019.03.001.

[40] G.-R. Kamga et al., 2017. Important progress towards elimination of onchocerciasis
in the West Region of Cameroon’, Parasit. Vectors, vol. 10, no. 1, p. 373, doi:
10.1186/s13071-017-2301-7. [41] M. N. Katabarwa et al., 2013. Fifteen Years of Annual
Mass Treatment of Onchocerciasis with Ivermectin Have Not Interrupted Transmission in
the West Region of Cameroon, J. Parasitol. Res., vol.

2013, pp. 1–12, 2013, doi: 10.1155/2013/420928. [42] WFP, Food Consumption Score
Nutritional Quality Analysis Guidelines (FCS-N), First Edition. Rome - Italy, 2015. [43] J. F.
Hair, G. T. M. Hult, C. M. Ringle, and M. Sarstedt, A primer on partial least squares
structural equation modeling (PLS-SEM). Thousand Oaks: Sage, 2017. [44] J.-B.
Lohmöller, ‘Predictive vs. Structural Modeling: PLS vs.

ML’, in Latent Variable Path Modeling with Partial Least Squares, Heidelberg: Physica-
Verlag HD, 1989, pp. 199–226. doi: 10.1007/978-3-642-52512-4_5. [45] R. Agarwal and E.
Karahanna, 2000. Time Flies When You’re Having Fun: Cognitive Absorption and Beliefs
about Information Technology Usage, MIS Q., vol. 24, no. 4, p. 665, doi:
10.2307/3250951. [46] J. F. Hair, C. M. Ringle, and M. Sarstedt, 2014.

Corrigendum to “Editorial Partial Least Squares Structural Equation Modeling: Rigorous

Applications, Better Results and Higher Acceptance” [LRP 46/1-2 (2013) 1–12], Long
Range Plann., vol. 47, no. 6, p. 392, Dec. 2014, doi: 10.1016/j.lrp.2013.08.016. [47] C. B.
Jarvis, S. B. MacKenzie, and P. M. Podsakoff, 2003. A Critical Review of Construct
Indicators and Measurement Model Misspecification in Marketing and Consumer
Research, J. Consum. Res., vol. 30, no. 2, pp.

199–218, doi: 10.1086/376806. [48] Wetzels, Odekerken-Schröder, and van Oppen, 2009.
Using PLS Path Modeling for Assessing Hierarchical Construct Models: Guidelines and
Empirical Illustration, MIS Q., vol. 33, no. 1, p. 177, 2009, doi: 10.2307/20650284. [49] W.
Reinartz, M. Krafft, and W. D. Hoyer, 2004. The Customer Relationship Management
Process: Its Measurement and Impact on Performance, J. Mark. Res., vol.

41, no. 3, pp. 293–305, Aug. 2004, doi: 10.1509/jmkr.41.3.293.35991. [50] A.

Diamantopoulos and H. M. Winklhofer, 2001. Index Construction with Formative
Indicators: An Alternative to Scale Development, J. Mark. Res., vol. 38, no. 2, pp. 269–277,
doi: 10.1509/jmkr.38.2.269.18845. [51] M. Mehmetoglu and S. Venturini, Structural
equation modelling with partial least squares using Stata and R, 1st ed. CRC Press, 2021.
[52] R. P.

Nikiema and K. C. Kponou, Impact of Women Empowerment in Agriculture on under

Five Children Nutritional Outcomes in Rural Burkina Faso, African Economic Research
Consortium, Nairobi, Kenya, AERC POLICY BRIEF, 2021. [53] J. F. Hair, J. J. Risher, M.
Sarstedt, and C. M. Ringle, 2019. When to use and how to report the results of PLS-SEM,
Eur. Bus.

Rev., vol. 31, no. 1, pp. 2–24, Jan. 2019, doi: 10.1108/EBR-11-2018-0203. [54] J. F. Hair, G.
T. M. Hult, C. M. Ringle, and M. Sarstedt, A primer on partial least squares structural
equation modeling (PLS-SEM), 3rd ed. Thousand Oaks: Sage, 2022.

INTERNET SOURCES:
-------------------------------------------------------------------------------------------
<1% - www.fao.org › publications › sofi
<1% - www.researchgate.net › profile › Boris-Soh-Wenda
<1% - resources.vam.wfp.org › data-analysis › quantitative
<1% - www.emerald.com › insight › content
<1% - ajcn.nutrition.org › article › S0002-9165(23)27762-X
<1% - www.unfpa.org › sites › default
<1% - www.statista.com › statistics › 1305284
<1% - www.unwomen.org › en › digital-library
<1% - news.un.org › en › story
<1% - sustainabledevelopment.un.org › content › documents
<1% - www.prisonpolicy.org › blog › 2021/03/18
<1% - www.unwomen.org › en › docs
<1% - www.researchgate.net › publication › 318018970_The
<1% - reliefweb.int › sites › reliefweb
<1% - docs.wfp.org › api › documents
<1% - www.cdc.gov › coronavirus › 2019-ncov
<1% - www.csis.org › urbanization-sub-saharan-africa
<1% - www.worldbank.org › what-is-food-security
<1% - www.wfp.org › news › empowerment-rural-women
<1% - www.semanticscholar.org › paper › Women-control-over
<1% - generations.asaging.org › improving-health-equity
<1% - documents1.worldbank.org › curated › zh
<1% - gh.bmj.com › content › 5
<1% - www.worldbank.org › en › news
<1% - www.feedthefuture.gov › the-womens-empowerment-in
1% - www.sciencedirect.com › science › article
<1% - assets.publishing.service.gov.uk › media › 57a08a
<1% - publish.illinois.edu › womenanddevelopment › files
<1% - www.jstor.org › stable › 24866864
<1% - www.un.org › en › un75
<1% - 1library.net › article › repeated-indicators
<1% - www.researchgate.net › publication › 235967400
<1% - 1library.net › article › step-approach-based
<1% - www.ncbi.nlm.nih.gov › pmc › articles
<1% - pubmed.ncbi.nlm.nih.gov › 35011064
<1% - www.resilience.org › stories › 2020/10/14
<1% - www.researchgate.net › publication › 338937363
<1% - pdfs.semanticscholar.org › d156 › 8a44bb34fa1861bed
<1% - scholar.google.com › citations
<1% - us.sagepub.com › en-us › nam