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Determinants of Gender Dynamics: Evidence From East Kenya

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Gender equality is currently a topic of intense debate by many researchers and policy makers. This is because gender inequalities and lack of attention to gender in agriculture and development have contributed immensely to the current low productivity, rising levels of poverty and malnutrition (FAO, 2011; World Bank, 2012; Quisumbing, 2003).

In many developing countries, including Kenya, agricultural roles and responsibilities, asset ownership and control, power and decision making, and access to key services in agriculture are sharply divided along gender lines with high degree of gender discrimination in favor of men (UN Millennium development Goals Report, 2006; Kabeer, 2003). These divisions can be based on crops, tasks or both (Doss, 2001; Heritz, 2004; McPeak and Doss, 2006).

Notwithstanding these skewed gender inequalities, women continue to play critical roles in agriculture as farmers, labourers and entrepreneurs but these are not recognised or rewarded. At aggregate women make up about 43% of the agricultural labor force in developing countries ranging from approximately 20% in Latin America to almost 50% in Sub-Saharan Africa, and Eastern and Southeastern Asia (FAO, 2011). Clearly, gender roles vary significantly between and within regions along diverse ecological sub-zones, farming systems, social class, age and stages in the family cycle (Khurana and Lal, 2011; Kibere et al., 2013). Recently, SOFA and Doss (2011) have shown that these trends are changing rapidly in many parts of the world, where economic and social forces are transforming the agricultural sector.

A number of studies (Peterman et al., 2011; Goldstein and Udry, 2008; Kinkingninhoun-Mudagbe et al., 2008; Gilbert et al., 2002; Akresh, 2005; Tiruneh et al., 2001) have documented this variance and attributed it to women’s limited access to productive resources. For instance, Doss et al. (2014) found that although women own land jointly with their husbands, they normally have fewer land rights compared to their husbands. On the other hand, Peterman et al., 2009 argues that even where women have full access to land they often lack other resources mainly technological resources such as fertilizer, improved seed, irrigation, insecticide and mechanical power necessary to farm productively.

These patterns of results however, are not universal. After controlling for inputs, studies from Nepal, Gambia and Zimbabwe have found no significant difference in productivity between men and women (Horrell and Krishnan, 2007; Thapa, 2008; Aly and Shields, 2010). Empirical evidence from these studies and others suggest that women are in fact not worse farmers than their male counterparts. Arguably, if women had equal access to productive resources as men, farm productivity would increase by about 20%-30% (Croppenstedt et al., 2013; FAO, 2011). Furthermore, a growing body of empirical evidence suggest that increasing the resources under the control of women yields positive effects on a range of key development outcomes such as food security, child nutrition, and education (Quisumbing, 2003; Quisumbing and Maluccio, 2003; Skoufias, 2005).

Whereas a clear simplistic solution according to Quisumbing (2013) might be to increase input application by women, the idea might not be viable as we still do not fully understand why inputs are lower on women’s plots. But since the gender relations are dynamic and change with changing conditions both within and between countries (Doss, 2001; Ibnouf, 2011), one way of increasing women access to productive resources is through empowerment. Kabeer (1999) defines empowerment as expanding people’s ability to make strategic life choices particularly in the context in which the ability had been denied. As Deutsch (2007) point out, when changes in gender relations happen they are accurately noticed when men and women, boys and girls do not follow the traditional scripts. This signify the importance of empowerment in outlining the factors leading to gender dynamics.

This study defines gender as the set of socially constructed roles, behaviors, responsibilities, and attributes a society considers appropriate for men and women. Unlike sex which is biologically determined and never changing, gender is dynamic which can change over time and varies according to geographical location, social and cultural context (FAO, 2001a). Therefore, building on the work of Deutsch (2007) this study defines the gender dynamics to include changes in the standard gender relations which include; gender roles, norms, control of benefits, decision making power and participations in programs. These changing conditions impact on rural livelihoods strategies leading to changes in gender-based practices, responsibilities and behaviours.

This study recognises that household types are heterogeneous. Therefore, explaining gender dynamics involves analysis beyond the changing patterns of activities between men and women to also looking at the changing patterns of activities between different categories of women as mediated by factors such as household type. A general consensus by Finley (2007) and Garcia and Oliveira (2011) is that female-headed households are a heterogeneous category, reflecting both the context and the ways in which they are formed. To account for the heterogeneity, we disaggregate female-headed households into three categories which include de jure and de facto, that is, households headed by unmarried, divorced or widowed adult women, households headed by adult women in their husbands’ absence, we also look at the women in male headed household. Male-headed households on the other hand connote those households headed by adult men who could be married, unmarried, divorced or widowers.

Explaining Intra-household Gender Dynamics

Gender dynamics are influenced by a number of factors key among them include agricultural commercialization (Kaaria and Ashby, 2001; Farnworth et al., 2013) and extensive migration (De Janvry and Sadoulet, 2000) both of which result to adjustments in the farming households. With commercialisation, a dramatic shift is seen where, when crops that were predominantly controlled by women “female crop” become attractive in the market, ownerships and control switch to male (Kaaria and Ashby, 2001; Farnworth et al., 2013). Similarly, commercialization comes along with integration of communities and individuals into markets. With this move, modern technologies and innovations create high external-input dependent systems which have often bypassed women mainly due to gender gaps in access to productive resources (Fischer and Quim, 2012). In many cases, these development trends have had a neutral effect on women, or have led to the displacement of women’s agricultural activities. Women had to move to increasingly marginal land, leading to the replacement of local crops and animal breeds with commercial production by men.

While rural-urban migration has received considerable attention and has been the subject of extensive research, the gender dimensions of migration, particularly from the point of view of resulting gender dynamics have been largely neglected. These dimensions include, migration-induced changes in gender relations within the farm household and their demographic effects, encompassing the demographic behaviour of female-headed farm households. Male rural-to-urban migration or migration to off-farm activities results not only in changes in family structure, but usually leads to adjustments in family roles, and more importantly, in the division of labour as well as in the way labour is utilised in the households. As a result of these adjustments, women often assume major responsibilities, and in some countries become the backbone of subsistence food production, a situation termed as the `feminization of agriculture' and in the management of their families' livelihoods. While the above transformations can be observed, they manifest themselves differently conditioned by the household types, a clear indication that a typical farming households in sub-Saharan Africa is a complex institution.

Although there is considerable literature documenting the gender differentials in agricultural production, the gender dynamics that ensue have not been rigorously explored, particularly the factors that lead to gender dynamics. The contribution of this paper to the knowledge gap is two-fold, first it identifies the areas where gender dynamics are observed and secondly it answers the question what determines gender dynamics at household level using a rich data from Machakos and Makueni counties.

To understand the intra-household dynamics, the process of decision making in a household has an important bearing. Researchers have proposed a number of models to analyze household decision making processes. For a long time, the traditional neo-classical economists have applied the unitary model which assume that households behave as a single decision making unit. This model views the household as a single economics unit where members have identical preferences and the household income is pooled, hence individual preferences and bargaining weights for time and income do not matter (Katz, 1996; Udry, 1996; Marchant, 1997). Based on this assumption a large proportion of the studies have used the gender of the household head as the gender identifier. This way of analysis has been criticised as having methodological and empirical limitations (Vermeulen, 2002; Quisumbing, 2003; Doss, 2013). Instead, there is growing consensus that a collective model of a household is more relevant to analyze household relations in a more realistic manner (Quisumbing, 2013; Arora and Rada, 2013). The collective model to household behaviour identifies a household to consist of different members who undergo an intra-household bargaining process in the allocation of resources and decision making.

Njuki et al., 2013 identifies two types of collective household models, cooperative and non-cooperative bargaining models. In the non-cooperative bargaining model, a household member acts in a way to maximize his or her own utility while in the cooperative bargaining model the household acts as a unit to maximize members’ welfare (Njuki et al., 2013). To explain gender dynamics this study adapts the cooperative bargaining model as its theoretical basis. In the study therefore, a household is seen as a unit consisting of sub-units, with agency, who bargain about the distribution of assets, benefits, including income among household members.

As stated earlier, one of the central concept in understanding changes in gender relations is empowerment (Kabeer, 1999). Building on this, Aslop (2006) and Alkire (2005) describe empowerment as having two components, agency, which refers to the ability to act on behalf of what one values including the processes of making such decisions and the institutional environment such the household structure that give people opportunities to exert agency productively. Evidently, the process of empowerment is incomplete if it does not address people’s ability to act, the institutional structure in this study household structure and other non-institutional changes that are instrumental to increased agency (Ibrahim and Alkire, 2007).

While acknowledging the distinct importance of the household structures, this paper seeks to document the domains of empowerment that result to gender dynamics using the five domains of empowerment (5DE) in the recently developed Women’s Empowerment in Agriculture Index (WEAI) Alkire et al. (2013). The WEAI is a new index tool used for monitoring gender gaps in agricultural production and development projects. It measures the empowerment, agency and inclusion of women in the agricultural sector. Diverging from earlier studies that measured empowerment as one global measure (Alkire, 2007), WEAI measures empowerment based on five domains of empowerment commonly referred to as 5DE. These 5DE include production, resources, income, leadership and time.

Production domain; involves understanding decision-making roles and responsibilities within the household about agricultural production activities which helps to discern power relations within the household.

Resource domain; the purpose of this domain is to determine ownership, access and control over productive assets, including land, livestock, equipment and technology, extension services and credit.

Income domain; this domain concerns sole or joint control over the use of income benefits and expenditures

Leadership domain; this domain addresses empowerment through social capital. The main goal is to assess the gendered differences in social or economic group membership.

Time domain; this is a strategy of gender analysis to assess the gendered differences and similarities regarding the time allocated for agricultural tasks. In identifying the time constraints/allocations for each household member.

These domains closely reflect the sustainable livelihood framework (Scoones, 1998; DFID, 2000), with some identified relationships with the five capitals; human assets, natural assets, physical assets, financial assets and social assets in the sustainable livelihood framework.

Description of the KARI-McGill Food Security Project

The KARI-McGill project started in 2011 with an objective of contributing to improved food security among women and men in the hunger-prone communities by understanding and overcoming the barriers to farmer adoption of resilient farming systems. The project is implemented in Machakos, Makueni and Tharaka-Nithi Counties with a research team from KARI and McGill University who work with the Ministry of Agriculture (MoA), Freshco Seed Company, Kenya Medical Research Institute (KEMRI), CASCADE, smallholder farmers and other local stakeholders.

Project Organization

In Machakos and Makueni counties, the project operates four sub counties Yatta and Mwala in Machakos county and Makueni and Kathonzweni in Makueni county. Each sub county has three research site referred to as focal research development areas (FRDAs) making a total of twelve FRDAs in the two counties. Each FRDA has three research implementation areas commonly referred to as Primary Participatory Agricultural technology Evaluation (PPATE). There are a total of thirty six PPATEs in Machakos and Makueni counties. A PPATE is made up of 10-30 farmers who were selected at the beginning of the project to evaluate the technologies. The aim was to ensure that farmers participate in research, innovation, discovery and joint learning. Members of PPATEs are trained on various aspects of diverse crops and indigenous chicken management, linkages between farmers and markets and other service provision. The PPATEs have done evaluations of the different technologies and have gained substantial competencies and knowledge. Through a local learning networks, the PPATEs are expected to extend the knowledge gained to other farmer groups which are referred to as Secondary Participatory Agricultural Technology Evaluations (SPATEs). There are 133 SPATEs in operation each comprising of 10-30 members who identify a technology or set of technologies from the PPATE sites practice and learn from it on continuing basis.


Each PPATE group identified an accessible location in one of the member’s farm where they set up their demonstration plots for the different crops they ranked to be of great importance to them. The project supplied the PPATE groups with seed varieties of the crops they ranked and fertilizer for demonstration. The PPATEs similarly identified a member’s household where the chicken house was erected. The PPATEs were then supplied with a flock of 11 improved indigenous chicken (10 pullets and 1 cockerel) of 2-3 months old. These flocks are used as learning platform to evaluate improved chicken management practices.

Since the inception of the project, a number of impacts have been witnessed from both the PPATE and SPATE members. These include change in crop priority to high value traditional crops such as green grams, cow peas and sweet potatoes due to the perceived high performance and income potential relative to maize. Farmers have now embraced the use of resilient seeds and are more willing to use chemical fertilizer. Access to markets has been seen to improve through collective marketing (market opportunity groups) particularly for green grams, cowpeas and indigenous chicken. This has resulted to farmers both within and outside the PPATEs and SPATEs to increase production and allocate specific plots to grow the target crops, the flocks of indigenous chicken is also report to be rising. This is particularly because of the availability of trained service providers of poultry-related information and services including vaccination in the community.

Materials and Methods

The data for the study will collected from a household survey which will include households who participate in the KARI-McGill project and those who do not participate in the project but benefit from the normal government extension services. Multi-stage sampling technique will be used to select the representative sample for the study. The first stage of the sampling procedure is the purposive sampling of Machakos and Makueni counties which are among the three counties where the KARI-McGill food security projected is implemented. The two counties were purposively selected because of the existence of a variety of food security interventions (Lemba et al., 2013). The next stage will involve a random selection of two FRDA from each of the four sub-counties to give a total of eight FRDAs. In the third stage, one PPATEs and one SPATEs will be selected randomly from each of the eight selected FRDAs to give a total of eight PPATEs and eight SPATEs. In the fourth stage, proportionate random sampling of 160 respondents from PPATEs and SPATEs of KARI-McGill food security project will be selected. Concurrently, respondents who are not participants of the KARI-McGill food security project will be sample from locations where the project PPATEs and SPATEs are not found. A sample of 160 respondents will be selected randomly. A total of 320 respondents will be selected for the study.

Results and Discussions

The analysis will use a number of empirical tools and methods. Quantitative data will be analysed using descriptive statistics, including proportions, t-tests and chi square tests to make comparisons between, male-headed and female-headed households, between different categories of female-headed households and between KARI-McGill food security project participants and non-participants. Economic modelling and analysis will also be done to identify the factors determining gender dynamics in the study area.

Data categories

  1. Respondents socio-economic characteristics by household type
  2. Distribution of asset ownership by household type
  3. Distribution of access to service (credit, extension) by household type
  4. Calculation of tropical livestock units (TLU) and household domestic asset index
  5. Econometric results of the determinants of gender dynamics

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