Analysis of Human And Social Capitals And Community Participation In Development Initiatives Among The Rural Households In Coastal Region Of Kenya
1Ong’ayo, A H. and 2Hassan, F. A.
1,2 Pwani University
Corresponding Author: e-mail: aongayo@hotmail.com
Abstract
Community participation in development initiatives as beneficiaries of the intended outcomes is important to the Government and development partners as implementing agencies. Community capitals which include cultural, human, social and built capitals play a crucial role in determining the extent communities participate in projects and programmes implemented by development agencies. The present study sought to establish the human and social capitals that determine community participation in projects and programmes implemented by the Kenya government and development partners among the rural households within the coastal region of Kenya. The research was carried out in the three counties of the coastal region. Multi-stage sampling techniques namely purposive, proportionate random and simple random sampling was used to select the study area and the study sample. Data were collected using semi-structured questionnaire Focus Group Discussion and observation schedules. The data analysis was done using descriptive statistics and regression analysis with the help of the Statistical Package for Social Sciences Version 22. The findings revealed that households heads with human capital namely Primary and Secondary education, training, and occupation have a higher likelihood of participating in development initiatives. While households with the social capital namely membership to groups engaged in economic activities and have linkages with development agencies, have a likelihood of participating in development initiatives. Key policy recommendations for county government and development partners includes: encourage the community members to enrol in adult education, provide support for vocational and technical training, register as members in existing groups or form groups based on common interest and engage in economic activities. The county government to enhance advisory services to ensure close contact with professional who will facilitate training, meetings and interactions with groups consequently members’ empowerment.
Keyword: Household, Participation, Human capital, Social capital, Development initiatives
Introduction
Community participation has been recognized by many international development agencies as a vital component for sustainable development (Cornwall, 2009). The concept of community participation originated about 40 years ago from the community development movement of the late colonial era in parts of Africa and Asia. The concept has been recognized as an important element since the early 1990s as a means of improving local welfare, training people in local administration and extending government control through local self-help activities ((Ayman, 2011; McCommon, 1993).Today, community participation has developed as one of the major models of development gaining acceptance across the spectrum of development actors as a means of improving development practice related to grassroots community development initiatives and viewed as a basis for project success (Cornwall, 2009). In recognition of its vitality to community development, community participation has been referred to as the heart that pumps the community life blood (Reid, 2000).
The concept of participation came to be popularized and institutionalized in the 1990s as a novel, common-sense way of addressing development discourses and practices of many mainstream development organizations. It has earned its status as an orthodoxy with promises of giving ‘the poor’ a voice and a choice in development and an essential ingredient in getting development interventions and policies right (Cornwall, 2006). Participation is commonly understood as the collective involvement of local people in assessing their needs and organizing strategies to meet these needs in partnership with the national government, county government, local organizations and external development partners. (Zaku and Lysack, 1998 cited in Cuthill, 2010).
Community participation in development initiatives is associated with attainment of benefits. The accrued benefits include: a) enhancement of the relevance of programmes to ensure that they are all suited for the needs and circumstances of the beneficiaries (Kironde & Kihirimbanyi,2002 cited in Cuthill, 2010); b) ensures that the views of many stakeholder groups are represented in the development process (Cullen, Coryan & Rugh, 2011); c) expectations that the programmes decisions that feed on the insights of many stakeholders are not just relevant to the beneficiaries, they are generally smarter (Weaver & Cousin, 2004, Cullen et al., 2011); d) greater programme outcomes such as greater access to social services (Bedelu, Ford, Hilderbrand & Reuter, 2007), consumption and demand for services (Kilpatrick et al., 2009); e) programme sustainability due to greater sense of ownership and responsibility for programmes activities by stakeholders by willing, able to mobilize and commit local resources to continue some or all of the programmes proceeds after external support is withdrawn or reduced (Oakley, 1992).
In an attempt to understand effective community participation in development initiatives implemented by government and development partners either on their own or in partnership to attain the benefits associated with it, it is important to examine the factors that influence their participation. Research on community participation in development has focused on demographic and socio-economic factors among other s as factors that influence community participation. For instance, Bauma et al., (2000) argues that the level of participation in social and civic community life is significantly influenced by individual socio-economic status and other demographic characteristics. Supporting this line of thought, Plummer (2002) describes factors such as skills and knowledge, employment, cultural beliefs, gender, education and literacy social and political marginalization to be key in affecting community participation. Recent research on community participation in development has broadened focus and included community capitals namely: human, social and institutional factors and the interaction among these components of the community (Cote, 2001, cited in Cuthill, 2010). A theoretical analysis of community participation by Nkwake, Trandafili, & Hughey (2013) revealed that Communities have seven types of capital which influence community or individual participation in development initiatives. Community capitals include cultural capital, social capital, human capital, built capital, natural capital and political capital. Assessing levels of community capital is an effective way of measuring a community capacity to participate in development initiatives for change (Flora & Flora, 2008). It is important to examines the extent the community capitals influence community participation in development initiatives among households.
Human capital is defined as a key factor of individuals’ Cadile et al. Human capital includes characteristics of individuals that strengthen one’s ability to earn a living and provide for one’s community, family and self-improvement. It consists of one’s personal assets such as health of individual, formal education, skills, intelligence, leadership and talents (Flora & Flora. 2008). While human capital consists of a variety of personal assets, Becker (2002) states that human capital which includes, schooling, on-the-job training, health information and research, is the most important form of capital in economies of success of individuals and the whole economies which depend on how extensively and effectively people invest in themselves. Becker (2002) asserts that human capital stimulates technological innovations and high tech sector and identifies education and training as the most essential forms of human capital which are associated with individual occupation. In the theoretical analysis of the scientific literature, Ciutiene and Railaite (2014), concludes that human capital includes a wide range of different components, such as knowledge, experience, competency, health among others which are necessary for achieving development.
While there are many definitions of social capital, Fine (2001) defines social capital as the development of networks in which community residents can identify problems, share information, and implement strategies designed to solve problems for the benefit of all. Putnam (1993) defines social capital as features of social organizations such as networks, norms and trust that improve performance of a society by facilitating coordination actions for mutual benefits. Social capital is manifested in the relations among people (Coleman, 1988). According to Coleman, social capital resides in people’s minds and influences their relationships with each other or plan to interact and may produce potential benefits, advantage and preferential treatment from another person of group beyond that expected in an exchanged relationship. Narayan and Pritchett (1997, cited in Lindon et al., 2002) and Heller (1996) argue that increased social capital leads to increased community cooperative action and solves local community property problems and economic development, strengthens linkages among the individuals that speed the diffusion of innovations, quantity and quality of information, reduces transaction costs, pools risks and allows households to pursue more risky and higher return activities. Social capital is within two context of economic development policy. The one that is bottom-up development depends on intra-community ties which is referred to as integration and extra-community networks referred to as linkages. The other is top-down development which involves state-society relations referred to synergy and institutional coherence, competence and capacity which are called organizational integrity (Woolcock, 1998). In other words, social capital is inherent in individuals and interaction with others.
In Kenya today, participation of the community is mostly done through structures such as groups namely: Community Based Organizations (CBOs), Common Interest Groups (CIGs), and Faith Based Groups (FBGs) which according to Putnam (1993, cited in Cuthill, 2010) are social capital specifically formed for the purposes of achieving common good projects (Hassan et al., 2018; Ong’ayo et al., 2017) and which are among the growing mechanism for channelling development assistance (Khwaja, 2004). The groups have served as instruments for consultation with supposed beneficiaries about planning and implementation of community projects (Hassan et al., 2018; Ong’ayo et al., 2017). The groups are formed on the basis of interest and for the purpose of sharing of technologies and information on new innovations, networking, forming linkages with other like minded individuals, groups and professional the viability of the groups is determined by the both acquired and inherent in the individual (Ong’ayo et al., 2017) The participation is strengthened by both inherent and acquired individual ability and anticipated gains which include literacy levels, gender, skills, knowledge, and training (Flora & Flora, 2008).
The Kenya government both at national and county level and development agencies have implemented various development initiatives at the coastal region with the goal of alleviating poverty among the rural households. About 69.7% percent of the coastal population live below the poverty line, with some areas such as Ganze in Kilifi scoring an alarming 84 percent making it the second poorest region of Kenya’s eight regions after North Eastern with 73.9 percent (Government of Kenya, 2008). Many development initiatives have been implemented with a focus on ensuring community participation for empowerment. The projects include Kenya Coastal Development Project (KCDP), Hazina Ya Maendeleo ya Pwani sub-component of KCDP, Health Service Project (HSP) funded by Danish Development Agency (Danida), Agricultural Sector Projects (ASP) funded by Kenya Government in collaboration with development partners, Regional Water Development Projects, United Nation Development Programmes (UNDP) among others (Danida Ministry of Foreign affairs, 2004).
Objective of the Study
The study was guided by the following specific objectives: To identify the human and social capitals of the households, and to establish the extent the two forms of capitals determine rural households’ participation in development initiatives implemented among them by the government and development partners and organizations.
Methodology
The study was carried out in three counties of the coastal region of Kenya namley namely: Tana River, Kwale and Kilifi. The climate of the region varies with distance from the coast and it becomes drier towards the inland from the ocean and from south to north (Nicholson et al., 1999).Covering an area of approximately 83,000km2, the coast region has a population of approximately 3.3 million people with a birth rate of 3% (Government of Kenya, 2009). About 69.7% percent of the coastal population lives below the poverty line, with some areas such as Ganze in Kilifi scoring an alarming 84% making it the second poorest region of Kenya’s eight regions after North Eastern with 73.9% (Government of Kenya, 2013).
The target study population was an estimated 3.3 million people of the communities living in coastal region currently (GoK, 2009). The accessible population was the 2,160 community members drawn from the groups that participated in different development initiatives implemented in the region by the government either on its own or in partnership with development partners.
The study used a combination of simple random sampling, proportionate random sampling, purposive sampling and techniques. First simple random sampling was used to select three Counties since participatory approaches have been used for implementation of development initiatives in all the six counties. Purposive sampling was used to select the three sub-counties. Proportionate random sampling was used to select households. Two hundred and twenty six households were sampled. According to Kathuri and Pals (1993), a sample of 100 respondents or more is appropriate for a survey study. This is large enough for data collection. With a large sample, the researcher is confident that if another sample of the same size were to be drawn, findings from the two samples would be similar to a high degree (Bordens & Abbort, 2008). Sampling Frame for households from the selected sub-counties was obtained and arrangements made on when to visit the field and administer the questionnaire to the selected household heads.
For successful data collection in the field, one set of semi-structured questionnaire, Focus Group Discussion schedule were used. The questionnaire was administered to households to collect personal profile of the respondents which included demographic data, individual characteristics which included: socio economic diversification, frequency of interaction with development professionals and to obtain suggestions from households on the way forward on development initiatives implemented in the field by the government and development partners, NGOs and CBOs. Observation schedule was used to collect data on the performance of socio economic activities for various categories of respondents and Focus Group Discussion was used to elicit more information from groups of households converged by the researcher. Data collected were analysed using descriptive statistics namely percentages and frequencies and inferential statistics regression with the help of the SPSS version 20.0. Regression analysis was used to determine the influence of human and social capitals on household participation in development initiatives.
In this study, human capital is captured in terms of the education level, training, and occupation and years of work experience. The data analysis was done using the following regression function predictor equation
CP = ß0 + ß1Ag + ß2Ms +ß3Ed + ß4Sa + ß5Occ + ß6Exp + Ɛ (1a)
CP is not observable but what is observable is defined by
CP = 1 if HP ˃ 0 (1b)
0 if HP ≤ 0
Where
Ag = 1 if the household member 26 years, 0 if otherwise.
Ms = 1 if married, 0 if otherwise.
Educational level
Ed = A vector of dummy variables indicating household member’s level of education
These are:
Primary = If household member has primary level of education
Secondary = If household member has secondary level of education
Tertiary = If household member has tertiary level of education
(Base category: no schooling)
Training
Trn = A vector of dummy variables indicating household member’s type of training
These are:
Vocational = If household member attended vocational training
On-job training = If household member attended on-job training
(Base category: no training)
Occ = 1 if the household member is engaged in socio-economic activities, 0 if otherwise.
Exp = 1 if the household member is has 2 years of experience, 0 if otherwise.
ßs are the coefficients to be estimated from equation (1b), while Ɛ is the error term with the assumption CP (Ɛ) = 0.
Equation (1b) can be estimated using a Probit model because the dependent variable is binary.
The characteristics of the household such as education, age and gender of the individual may have either positive or negative relationships with PC. Households with basic or higher levels of education may influence positively the degree with which they participate in development because it enhances ones chances of participating in training such as workshops and seminars and other development initiatives. Individual marital status may also influence the participation training and access to funds for economic activities due to lack of collaterals.
Social capital is captured in terms of the membership to groups, interaction with other groups and linkages with development agencies. The data analysis was done using the following regression function:
CP = ß0 + ß1Mg + ß2Ig +ß3Lda + Ɛ (1a)
CP is not observable but what is observable is defined by
CP = 1 if SP ˃ 0 (1b)
0 if SP ≤ 0
Where
Mg = 1 if the household member to a group, 0 if otherwise.
Ig = 1 if interacts with other groups, 0 if otherwise.
Lda = 1 if interacts with development agencies, 0 if otherwise.
ßs are the coefficients to be estimated from equation (1b), while Ɛ is the error term with the assumption CP (Ɛ) = 0
Results and Discussion
The bio data of the respondents are shown in Table 1.
Table 1:Bio data of the respondents
|
Variables |
Frequency (n) |
Percentage (%) |
|
Age: |
|
|
|
<25 Years |
3 |
0 |
|
26 – 30 Years |
20 |
7 |
|
31 – 50 Years |
151 |
53 |
|
>50 Years |
111 |
40 |
|
Gender: |
|
|
|
Male |
124 |
44 |
|
Female |
161 |
56 |
|
Marital status: |
|
|
|
Married |
135 |
47.2 |
|
Single |
70 |
24.6 |
|
Widow/widower |
80 |
28.2 |
|
Membership to Group |
160 |
55.9 |
|
Interaction with other groups |
146 |
51.1 |
|
Linkages with Devt agencies |
170 |
58.5 |
|
Household size |
|
|
|
1 – 5 Persons |
122 |
42.8 |
|
6 – 10 Persons |
118 |
41.4 |
|
11 – 15 Persons |
24 |
8.4 |
|
16 Persons and above |
21 |
7.4 |
|
Level of education: |
|
|
|
None |
89 |
31.2 |
|
Primary school |
96 |
33.7 |
|
Secondary School |
70 |
24.6 |
|
College |
22 |
7.7 |
|
University |
8 |
2.8 |
|
Training |
|
|
|
Vocational training |
90 |
24.6 |
|
Informal training |
129 |
45.0 |
|
None |
67 |
23.4 |
|
Socio-economic activities |
|
|
|
Farming |
183 |
64.2 |
|
Fishing |
02 |
0.7 |
|
Trading |
54 |
18.9 |
|
Formal employment |
23 |
8.1 |
|
Others |
23 |
8.1 |
Field survey data, 2018
Majority (53%; n =151) of the respondents fell within the age group of 31 – 50 years, whereas an additional 40% (n = 111) were above 50 years of age and only 3 respondents were below 20 years (Table 3). More than half (56%, n = 161) of the respondents were females, while 46% (n = 133) were males. More than half (56%, n = 161) of the respondents were single, widows and widowers. In terms of household sizes, slightly more (42.8%, n = 122) of the respondents had small households of 1 – 5 persons while 41.4% (n = 118) had household size of 6 – 10 persons. Very few respondents (7.4%, n = 214) had household sizes of 11 – 15persons. The educational attainments of respondents were relatively low. Only 7.7% (n = 22) and 2.8% (n = 8) had college and university education. More than 70% (24.6% & 45%) had undergone training. Interaction with other groups was over 50% of the households while 51% had linkages with development agencies. About 64% (n = 183) of the respondents engaged in farming as their main source of livelihood. Very few respondents engaged in fishing (0.7%, n=2). Given the fact that the region is endowed with marine and specifically fishery resources this finding is of great concern. Versleijen and Hoorweg (2008) confirm that challenges such as reduced catches, more competition from fellow artisanal fishermen as well as foreign fishermen, tourism and human settlement have made many fishermen to resort to other income-generating Households Participation in Development Initiatives in relation to Human and social capitals
Table 2 and 3 presents human and social capitals attributed to community participation in development initiatives in three counties of the coastal region of Kenya. The table shows the results of the Probit estimations.
Community Participation by Human Capitals
Using a Probit regression, the study assessed the influence of bio data comprising age, marital status, level of education, training, type of economic activity and experience attained by the household member on community participation in development initiatives (Table 2). In this model the reference category was “those who did not participate”. Table 2 and 3 show the output from the Probit model and the z-statistics.
Table 2: Human Capital influencing Household Participated in Development Initiatives
|
Variables |
Probit dF/dx. |
z-stat |
|
If aged above 26 years |
-0.16* |
-1.67 |
|
Education level: |
|
|
|
Primary school |
-0.16** |
4.57 |
|
Secondary School |
-0.14* |
1.60 |
|
Tertiary |
0.13 |
-1.44 |
|
Training ( base no training): |
|
|
|
If attended Vocational training |
0.35*** |
-0.18 |
|
If attended on-job training |
0.23*** |
0.29 |
|
If engaged in Socio-economic activities: |
0.53*** |
4.95 |
|
F-stat (wald chi2) R2 (Pseudo-R2 Number of observation |
97.40*** 0.529 286 |
|
The coefficients on dummy variables indicate changes in probability for each outcome category when the value of the dummy variables changes from zero to one. The second column reports the z-statistics based on robust standard error.
*, **, and *** denotes significant at 10%, 5% and 1% significant levels respectively.
According to the results, tertiary level of education does not predict the likelihood of household head’s participation in development initiatives. Household heads who are younger (25 years or below) are more likely to participate in development initiatives. The probability of participating is 16 percent each. Although these results are weakly significant at 10 percent level, the results for age are consistent with those in table 1. The household head’s with primary education, have attended vocational and on-job training, and are engaged in socio economic activities have the probability of 35 percent, 23 percent and 53 percent respectively have a higher likelihood of participating in development initiatives. In overall, the results show that household heads who have attained primary education and have undergone vocational or on-job training and are engaged in economic activities predict with higher probabilities the chance of participating in development initiatives. This therefore means that basic education is a determinant of community participation in development projects.
Table 3: Social Capital influencing Household Participated in Development Initiatives
|
Variables |
Probit dF/dx. |
z-stat |
|
If member of a group |
0.51** |
2.21 |
|
Interaction with other groups |
0.23* |
0.29 |
|
Linkages with Devt agencies |
0.44** |
1.95 |
|
F-stat (wald chi2) R2 (Pseudo-R2 Number of observation |
92.40** 0.519 286 |
|
The coefficients on dummy variables indicate changes in probability for each outcome category when the value of the dummy variables changes from zero to one. The second column reports the z-statistics based on robust standard error.
*, **, and *** denotes significant at 10%, 5% and 1% significant levels respectively.
The Probit results show that households who are members to groups and have linkages with government and development agencies that include private, NGOs and CBOs have higher likelihood of 51 percent and 44 percent of participating in development initiatives. The interaction with other groups has a lesser likelihood of the individual participating in development projects and programmes. Groups as a social capital provides ground for developing sense of belong and empowerment of individuals. This is achieved when the groups hold meetings and it is at these meetings that learning skills takes place either through interaction or from invited professionals.
Discussions
Although on-job training which include workshops and seminars are used by many organizations a avenues for gaining knowledge, skills and competency in performing various activities, the results show that training has a less probability than the workshops and seminars. This could be attributed to the methods used in the dissemination of knowledge and information. The beneficiaries who participate in projects are adults whose level of education is majorly basic education. The category of participants requires more interactive and dialectic process of knowledge acquisition. The interaction allows for sharing of knowledge, information, creation of awareness of new ideas and manipulation. The three aspects have a long lasting impact on the knowledge and skills the development initiatives intend to involve the community. Kwon (2009) argues that human capital is based on the knowledge and skills that are received during the learning process. The knowledge, skills and competency are among the important human capitals acquired during the learning that takes place during the training and workshops. Human capitals are inherent in an individual and active participation in any development process or activities gives the individual the chance opportunity to acquire them.
Human capitals are inherent in an individual and active participation in any development process or activities gives the individual the chance opportunity to acquire them. The knowledge, skills and competency are among the important human capitals acquired during the learning that takes place during the training. The type of activity undertaken by an individual or household during the training influences to a great extent the development of human capital and as such it may imply that the income level was a factor determining individual participation.
Kwon (2009) argues that human capital is based on the knowledge and skills that are received during the learning process. Tanner et al. (2002) states that vocational training are an effective means of producing changes in practice especially in relation to acquisition of individual human skills. The lack of predictability of the likelihood of development of human capital by the participation of stakeholders in evaluation of projects is could be due of professions or extension workers lack of facilitation skills. According to Nweke et al. (2013), lack of the likelihood of community participating in evaluation of projects is alluded more to the professionals development workers playing a leading role even if the aim is to build the capacity of communities or empowering them. High education level can also be a hindering factor in community participation as explained by Dorsner (2004) in which she indicates that educated members of the communities at times are not available for their community even if they have interest as they tend to have other business commitments.
The study findings contradict with those of Aworty (2012) who asserts that education as a human capital is in itself is not entirely a determining variable in community participation. He asserts that many uneducated households scored even better than those with secondary school education in variables such as: membership of community organization, attendance at meetings and participation in planning while those with good education level speak more in meetings than those without education do.
According to Seferiadis et al. (2015), Membership to groups strengthens the social fabric. It’s a network that enables individuals to access resources and information and achievement of common goals. Social capitals provide an avenue for collective action. through different mechanisms, development projects are able to strengthen social capital for positive development outcomes which includes human competency and acquisition of skills and access to information. One way this is achieved is through group meetings often organized when development initiatives are set up. At these meetings, learning of skills takes place. Brodie et al. (2009) found out that the socio economic group a person belongs to has an impact on his/her level of participation as people from lower socio economic groups often have less access to resources and practical support making their participation in community development initiatives rather difficult.
Conclusion
The study has shown that individual participation in development initiatives requires a set of human and social capital. Social and human capitals are intertwined. Human capital is associated with active and interactive engagement of the individual in development activities such as workshops, training and other practical activities. The interactive process inherent in group activities increases individual members’ ability to acquire knowledge and skills which are essential for decision making on the use of new ideas introduced to them for longer period and improved welfare. Although education as a human capital is necessary especially in acquisition of technical knowledge, one does not require tertiary education to participate in development initiatives implemented in the community.
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