Economy Challenges and Opportunities Assessment

Drivers of Marine Fishery Dependence: Micro-level Evidence from the Coastal Lowlands of Kenya

Mohamed Idris Somoebwana a, Oscar Ingasia Ayuya a and John Momanyi Mironga b

Don't use plagiarized sources. Get Your Custom Essay on
Economy Challenges and Opportunities Assessment
Just from $9/Page
Order Essay

a Department of Agricultural Economics and Agribusiness Management, Egerton University, P.O. Box 536-20115, Egerton, Kenya

b Department of Geography, Egerton University, P.O. Box 536-20115, Egerton, Kenya

E-mail: [email protected]


Poverty and inequality remain a development challenge for most fishery-dependent households. This has prompted the current interest in the ocean-based economic model as Kenya became the first to host a global conference on the sustainable blue economy, which was held in Nairobi in November 2018. To provide a more comprehensive understanding of the blue economy approach’s challenges, we used a fractional response model to analyze drivers of ocean fishery dependence. Our case study, involving 384 randomly selected households from Coastal lowlands of Kenya, specifically Kilifi County, revealed that ocean fishery is the highest livelihood option exhibiting gender differences. The livelihood participation rates were approximately 68%, 36%, 31%, 25%, and 9% for ocean fishery and related activities, agriculture, self-employment (excluding agriculture and marine fishery), wage employment, and remittances, respectively. Many livelihood options were pursued independently, implying a low level of diversified livelihood strategies in the region. The analysis of the determinants of ocean fishery dependence indicated that education level, productive agricultural assets, access to credit, group membership, security of tenure, flood shock, and fish price shock significantly influenced ocean fishery dependence. The study, therefore, recommends government intervention in marine governance and development programs such as infrastructural development, capital availability and extension services. This will enable the dependent households to diversify to alternative livelihood options and contributes to sustainable marine fishery dependence.

Keywords: Ocean fishery dependence, fractional response model, land tenure, shocks, Kenya


Mohamed Idris Somoebwana is a student at Egerton University, Kenya, pursuing a Master of Science degree in Agricultural and Applied Economics. He is a beneficiary of the African Economic Research Consortium (AERC) scholarship and shared facility program that occurred between September and December 2018 in the University of Pretoria, South Africa. His research areas include rural development, environmental economics, agricultural marketing, climate change, food security and nutrition, agricultural project analysis and agricultural innovation. This study provides information on the factors influencing participation in the marine fishery and related activities. Its aim is to provide effective policy recommendations for building sustainable livelihood options among fishing communities.


The marine fishery is one of the major economic activities along the coastal region of Kenya. Despite its vast potential to sustain livelihoods and contribute to the national economy, most marine fishery-dependent households are poor. Therefore, it was of great interest to explore factors associated with marine fishery participation. This paper established that socioeconomic, institutional and climatic factors influence participation in marine fishery dependence. To attain sustainable livelihood option among fishing communities, the study recommends government intervention in marine resource governance, capital availability, and infrastructural development.



  1. Introduction

Marine resources contribute to food security, livelihood, and mitigation of climate change, as well as enhanced economic growth through trade (Ding et al., 2017; Amevenku et al., 2019; Funge‐Smith, and Bennett, 2019; Satumanatpan and Pollnac, 2020). They also serve as the safety net for the poor, providing nutrition and income, particularly in the period of financial hardships. Estimates show that ocean resources generate employment for millions of coastal inhabitants and nearly one billion people worldwide (Teh and Sumaila, 2013; Fabinyi et al., 2019; Salas et al., 2019). Therefore, the contribution of marine fishery activities to the national economy is inevitable and multifaceted. Apart from improving food security, they contribute to the gross domestic product (GDP), and they are the source of hard currency (FAO, 2014).

In Kenya, marine resource generates employment to over two million Kenyans through fishing activities, boat building, fish processing, equipment repair, and tourism (CGOK, 2018; Muigua, 2018). The fishing activities in Kenya’s coastal lowlands are mainly small scale and rely heavily on traditional and simple methods. Also, since the ocean fishery-dependent households have low literacy levels and the fishing proceeds are affected by seasons, they are more vulnerable to shocks. The higher vulnerability in fishing communities results in a poverty-environment trap, which is higher among the poor compared to the non-poor (Narain et al., 2008; Soltani et al., 2014; Nguyen et al., 2018; Kiwanuka-Tondo et al., 2019). More importantly, ocean fishery dependence is under the threat of climatic and idiosyncratic shocks. These include fluctuating prices, changing economic policies, rising sea levels, aridity of the climate, and biological constraints. Therefore, poverty in fishery could be explained by biological limits and natural shocks of the ocean fishery resource.

Many of the coastal residents depend on marine resource due to landlessness and insecure land right systems (Van Hoof and Stein, 2017). The squatter problem has become a development concern in the coastal region of Kenya, which traces its way back to the colonial administration. Most importantly, the enactment of the Registration of Documents Ordinance, together with Crown Land Ordinances of 1902 and 1915, redefined crowned land owned by natives. This resulted in a declined community land. With the lack of efficient ownership rights, Africans at the coast remained dispossessed and forced to live as tenants at will. Further, individualization of land tenure and other post-independence laws and policies created rural elite phenomenon and intense squatter problem. The land transfer was grounded within the willing seller willing buyer principle that favored politicians leading to the unfair allocation of land and land grabbing concept of the 1980s (Githunguri, 2017). Kenya has begun to embrace the blue economy concept for sustainable development. Therefore, an effective legislative framework that enhances the restoration of an individual’s entitlement to land is inevitable. Through this, ocean fishery-dependent households will diversify to alternative livelihood strategies and contribute to a sustainable fishery.

As the Kenyan population increases, the availability of agricultural land struggles to keep up with its demand. As a result, the importance of the marine resource base for sustainable development and income generation for the households to meet their basic needs has increased. This has inspired a wide range of empirical studies in Kenya to understand marine resource opportunities and challenges facing the blue economy approach (Cinner et al. (, 2012; Benkenstein, 2018). However, these studies failed to explore fishery dependence and how the shift to blue revolution affects fish biomass concerning both the incidental and target catch. More so, the coastal lowland of Kenya is affected by historical land injustices. Nevertheless, the previous studies failed to consider the nexus between ocean fishery dependence and land tenure. Given the higher incidence of shocks among natural resource-dependent households, over dependency on marine resources may make households vulnerable to poverty. Therefore, this paper aims at contributing to a better understanding of factors influencing ocean fishery dependence, with a special focus on land tenure and shocks. The information obtained is critical to comfort policies pioneered towards promoting inclusive economic growth through sustainable and diversified livelihood options. Further, we are not aware of any previous study in natural resource literature using the fractional response model (FRM) to analyze dependency. FRM offered a robust framework for handling the proportional dependent variable and overcoming the observed limitations in linear regression models, Tobit model and non-linear models.

The remainder of the article is structured as follows; the second section presents a literature review; the third section presents material and methods for the study; section fourth presents the econometric results, and section five presents the conclusions and policy implications of the study.

  1. Literature review

This section presents a review of the past research and findings on the determinant of marine fishery dependence. Allan Degen et al. (2010) and Cinner et al. (2012) found that fishers’ number has increased gradually in Kenya. This is because of poverty linked to poor education, inadequate employment opportunities, lack of access to credit, poor agro-ecological conditions, and unfavourable climate. In this sense, failure to diversify to alternative livelihood options is usually observed, especially in regions where there is marginalization and a lack of social protection of the fishing communities. This implies that a weak institutional framework to capacitate the rural livelihoods may be the missing link towards diversified and sustainable livelihood options (Cinner et al., 2012). Further, Daw et al. (2012) established that failure to exit marine fishery is attributed to occupational attachment, ethnicity, household size, education, age and attitudinal factors.

A growing body of empirical literature suggests that natural resource dependence is associated with socioeconomic characteristics, distance to the extracting ground, and asset value (Nguyen et al., 2015; Nguyen et al., 2018; Tamale et al., 2017; Amevenku et al., 2019). In addition, market access, financial capital, access to health, and membership to formal or informal institutions are likely to affect the dependence on these resources (Béné and Friend, 2011; Soltani et al., 2012). Hence, given climate variability, institutional bottlenecks, inadequate infrastructure, and land degradation (Neiland and Béné, 2013), the households may be attracted to the fishery resource due to both the short-term and long-term shocks. These conditions create a downward spiral of overexploitation, which leads to poverty, and poverty results in overexploitation (Cinner et al., 2012; Stanford et al., 2013).

  1. Materials and methods

3.1 Description of the study area

The study was carried out in Kilifi County, which is among the six counties in Kenya’s coastal region. Kilifi County lies in the East and South of the Indian Ocean and Mombasa County (Figure 1). It is characterized by a bimodal rainfall pattern averaging between 300mm and 1,300mm. The main topographic features of the County include coastal plain, foot plateau, coastal range, and Nyika plateau (CGOK, 2013). With poor agro-ecological zones and massive blue economy investment potential arising from the Indian Ocean coastline of 265km and its 200 nautical, fishing and tourism have emerged as the dominant economic activities in the area. Most importantly, the Ocean fishery dependence has been long linked to the Bajun ethnic group that is being regarded as the fishers’ par excellence. However, since the 1960s, Mijikenda has engaged in this economic activity (Allan Degen et al., 2010). This is due to a significant level of poverty related to historical injustices of land and social exclusion. Therefore, to improve the welfare of the dependent households, studying the dimensions of the ocean fishery dependence in the County is vital. This will help formulate appropriate policy options that take into account socioeconomic constraints, land tenure, and multiple shocks observed. As a result, the wealth generated from fishery can contribute to rural development through fishing proceeds and employment multiplier effects due to the cash crop nature of fish (Béné et al., 2007).

Figure 1. Map of the study area

Source: Geography Department, Egerton University-Kenya

3.2. Research design, sampling, and data management

The cross-sectional research design was used in this study to explore the drivers of ocean fishery dependence among households in Kilifi County. Apart from enabling assessment of the link between covariates and the outcome, this approach allowed a simultaneous comparison of different variables in the study sample and is relatively faster and inexpensive.

The selection of the respondents for this study was arrived at using a multistage sampling technique. Kilifi County was purposively selected in stage one due to its large Indian Ocean coastline, the higher rate of historical land injustices in the region, and significant poverty level (CGOK, 2018). The second stage involved a purposive sampling method to select 4 Sub-Counties (Malindi, Kilifi North, Magarini, and Kilifi South). Later 4 wards were purposely selected from the 4 Sub-Counties (Shella-Malindi, Watamu-Kilifi North, Gongoni-Magarini, and Shimolatewa-Kilifi South) from a population of 35 because they offer important ground for artisanal fishing as they are located along the Indian Ocean. In the last stage, simple random sampling was used to select 384 households spread over the 4 wards. The household sample size was determined using Eq. (1) (Cochran, 1977).



Where:  is the minimum estimated sample size, z is the value of the t-distribution, which equals the alpha of 0.05= 1.96, p represents population proportion estimate, and e is the margin of error. When the population proportion is unknown, p is put at 0.5 and e at 0.05 (Cochran, 1977).

The primary data was collected through face-face interviews with the household heads. However, this was after the questionnaire was subjected to a pretesting procedure to determine its validity and competency. We ensured quality control in data by carefully selecting and training the enumerators to equip them with knowledge sufficient to portray the research objectives. More so, to avoid a biased data collection process, respondents who took part in the pretest exercise were considered ineligible in the actual household survey. Further, the descriptive and fractional response models were applied in this study to gain clear insights into the dimensions of ocean fishery dependence. Data were analyzed using Stata econometric software.

3.3. Ocean fishery dependence

The level of ocean fishery dependence of a particular household was expressed as the proportion of the income derived from the ocean fishery and related (fishing, fish trading and processing, boatbuilding, and selling of fish equipment) to the total household income as described in Eq. (2).


Y* is the ocean fishery dependence ranging between 0 and 1 with a higher value indicating high dependence (Nguyen et al., 2018). The numerator represents household income generated from ocean fishery and related activities, while the denominator reflects total household income; (net income from crop production, livestock rearing, fishery and related activities, business), wages, salary, and remittances (Mathenge et al., 2010).

3.4. Analytical framework 

In determining the factors influencing ocean fishery dependence, the present study used a fractional response model (FRM). Most importantly, FRM was preferred to linear regression models such as Ordinary Least Square (OLS) because they are not fit to estimate fractional dependent variables and, therefore, may produce estimates outside the unit interval (Chegere, 2018). The non-linear models such as logit and probit transformations may be applicable in this case; however, they do not consider observations that lie at the boundaries and hence may result in a truncation problem. Further, the Tobit model may seem appropriate for bounded dependent variables; however, the values outside the unit interval are not feasible in proportional data. Therefore, FRM remains the best model for handling the proportional dependent variable and overcoming the observed limitations in other econometric models. According to Papke and Wooldridge (1996), the model is a synthesis and an extension of quasi-maximum likelihood (QML) methods and generalized linear models (GLM) as described;


Where yi is the dependent variable defined as (0 ≤ yi ≤ 1), xi represents explanatory variables for household i, and G(.) is the logistic regression function that will be estimated directly using QML based on Bernoulli log-likelihood function defined as;


Given Bernoulli distribution is a linear, exponential family (LEF), the estimator of QML, θ will be given by;


Therefore, θ is normally distributed, and the method can generate consistent estimates of the fractional response variable. The variables in the fraction response model are presented in Table 1 from the review of past studies;

Table 1. Variables used in the fractional response model

Variable Description Measurement Expected sign    
Dependent variable
FISDep Ocean Fishery Dependence The ratio of household income from ocean fishery resource to Total household income  
Independent variables
Age Age in Years of the household head Continuous +/-
Gen Gender of the household head Dummy=1 if male, 0=female +/-
Educ Years of education of the household head Discrete
Asset Value of agricultural productive assets Continuous +/-
HSize Number of households members Discrete +
Grp Group membership of the household head Dummy=1 if Yes, 0=No +/-
Credit Whether credit is a constraint Dummy=1 if Yes, 0=No
Land Security of land tenure Dummy=1 if Yes, 0=No
Land size Size of the land owned Continuous
Mark Walking time in minutes to the market Continuous +/-
Dist Distance to the fishery resource in kilometer Continuous
Rainfall Rainfall index Dummy=1 if best, 0=Otherwise
Flood If the household experienced a flood in the last 3 years Dummy=1 if Yes, 0=No +
Health Health shock Dummy=1 if household reports illness of member(s), 0=Otherwise +
Price Price shock Dummy=1 if household faces price fall of fish, 0=Otherwise +
  1. Results and discussion

4.1. Descriptive statistics on ocean fishery dependency

The total number of households that participated in ocean fishery and related activities was approximately 68%, about 36% pursued agriculture, and 31% participated in self-employment (non-farm and non-fishery). Further, approximately 25% of the households participated in wage employment (non-farm and non-fishery), while households with income from remittances were about 9%. In terms of the level of dependency, ocean fishery and related activities, agriculture, self-employment, wage employment, and remittances had about 0.63, 0.02, 0.12, 0.19, and 0.01, respectively. Diversification is a critical approach to ensure a consistent flow of income. However, most households who participated in ocean fishery and related activities find it difficult to find the next best diversification livelihood option due to insecure land ownership rights and lower education levels. This is clearly indicated by a higher dependence on ocean fishery compared to other livelihood options.

The willingness to pursue additional livelihood options among the households was low. Cultural preference and the time-consuming nature of the fishing activities hampered the households to practice diversified strategies. Most importantly, the dependency on ocean fishery extends beyond personal income and employment. The conducted interviews suggest that there is a working culture in this particular livelihood strategy defined by reciprocity and interpersonal relationships, where individuals engage in ocean fishery through social ties and form a significant part of their identity. Additionally, most of the households dependent on the ocean fishery were Bajuns and were culturally bounded by self-employment. They felt that ocean fishery provided a sufficient form of self-employment and did not like wage employment due to too much control from employers. These elements offered a critical pathway in the survival and growth of the fishing industry in the coastal region (Carter and Garaway 2014). With the government at the heart of transforming the sector and organizing beach management units (BMUs), reciprocal interdependencies have become more evident.

Further, gender-disaggregated analysis on the livelihood options was conducted. Results indicated that marine fishery is a male-dominated livelihood option, with males taking the largest share at 79.69% while females at 20.31%. This could have been attributed to the cultural belief that fishery suits males since it requires higher strength. Males were also more likely to engage in wage employment than females at 78.57% and 21.43%, respectively. Further, the participation rate of males and females in self-employment was 81.51% and 18.49% accordingly. The higher likelihood of males engaging in wage employment and self-employment is because of their exposure to education and entrepreneurial behaviors than females who for a long time have been marginalized. However, it is worth mentioning that females were more likely to participate in agriculture at 79.14% compared to males at 20.86. This is because of the higher tendency of females to provide unpaid farm labor and ensuring food security for households.

4.2. Descriptive statistics of the variables used in the econometric model

Table 2a shows descriptive statistics for the dependents and non-dependents households in the ocean fishery for continuous variables. There are significant differences with regard to some of the covariates. On average, dependent households have a lower level of education at a mean of about 7.9 compared to 12.5 for non-dependent households. Majority of the dependent households tend not to value education and usually drop out of school because ocean fishery serves as an immediate and direct source of income (Ndhlovu et al. 2017; Nguyen et al. 2018), which reduces the individual’s incentive to pursue further education for future employment.

Participating households in ocean fishery and related activities have significantly fewer agricultural productive assets averaging KES 10417.2 than non-participating households, KES 71321.3, as shown in Table 2. Asset poverty usually compels individuals to diversify into common pool resources (Nguyen et al., 2020), and as such, they pursue the livelihood option as a safety net in response to idiosyncratic and climatic shocks. In terms of group membership, there are significant differences between the two groups. Further, there are significant differences with respect to distance to the ocean and distance to the fishery market. Households closer to the ocean and market tend to engage more in the marine fishery and related activities due to social identity and reduction in transaction costs. On the other hand, an increase in the distance lowers the expected net economic value, which ultimately discourages the decision to participate (Kyando et al., 2019).

Table 2a: Socioeconomic characteristics of households stratified by participation in Ocean Fishery (continuous variables)

  Households that participate in ocean fishery and related activities Households that do not participate in ocean fishery and related activities Difference in means
Variable  Mean (µ1)     SD       Mean (µ0)              SD        (µ1– µ0)
Age (years)       41.2069       12.5971         44.0244          12.8538         -2.8175
Education level (years of schooling         7.8620         2.2828         12.4552            4.9708         -4.5932***
Household size (number)         6.1533         2.7996           5.8780            3.0824           0.2753
Land size (Acres)         0.7527         3.2339           1.2297            2.1270          -0.6685
Agricultural assets (KES) 10417.2410 42299.6200   71321.3820  158167.4000  -60904.9680*
Distance to the ocean         3.2811         3.3087           7.0267            4.5196          -3.7456***
Distance to the fishery market         2.3088         2.9035           6.8037            4.4596          -4.4949***
Rainfall         0.1900         0 .2275           0.1553            0.1216            0.0347*

Note: *significant at 10%, **significant at 5%, ***significant at 1%

Table 2b reports findings for the socioeconomic characteristics of households stratified by participation in Ocean Fishery for categorical variables. The results indicate that there are also significant differences in credit access. Households that have poor access to credit are more likely to participate in ocean fishery and related activities. The logic follows that lack of access to credit limits capital availability and investment in entrepreneurial activities, making common pool resource the only viable livelihood option for the affected households. This can be further affirmed by a lower proportion of security of tenure for the participating households, 36%, preventing the right to land usage, loan acquisition and long-term investment. Further, group membership was higher among the participating households compared to those that do not pursue the livelihood option at about 86% and 4%, respectively. Participation in local institutions affects the decision to participate in fishery resource due to peer influence and awareness of economic surplus extracted from the commons.

Table 2b: Socioeconomic characteristics of households stratified by participation in Ocean Fishery (categorical variables)

Variable Households that participate in ocean fishery and related activities n=261(67.96%) Households that do not participate in ocean fishery and related activities n=123 (32.04%) Chi2 Test
Gender (%Male) 79.6935 80.4878    0.0329
Credit Access (%Yes) 32.9502 81.1301  78.2533***
Security of tenure (%Yes) 36.2874 57.7236  16.0768***
Group membership (%Yes) 86.9732   4.0650 240.2922***
Flood (%Yes) 31.8007   4.0650   37.1391***
Health (%Yes) 88.6179 85.4406     0.7208
Price (%Yes) 94.2528 12.1951 258.5654***

Note: *significant at 10%, **significant at 5%, ***significant at 1%

The differences in shocks relating to rainfall, flood, and price are also significant between the two groups. Price uncertainty and the fluctuation nature of the ocean fishery returns due to seasons and climatic conditions expose these households to financial and weather shocks. In response to these shocks, households usually take advantage of their right of opportunity-freedom of the commons by increasing fishing activities (Jentoft et al., 2010). In such instances, coping strategies such as adopting illegal gears, exploiting threatened and protected species, or extending fishing periods are deployed. This results in overexploitation, which exposes poor households to heavy risks given that the majority of the dependent households do not enjoy the insurance offered by land ownership against a sudden loss of livelihood option. Efforts have been put to organize the ocean fishery under beach management units (BMU) with the primary objective of achieving capacity building and reduce sensitivity to shocks through information sharing and provision of marketing facilities. However, more than half of the conducted interviews publicly criticized BMUs due to their failure to mobilize resources and instead focusing on restrictive fishing regulations that are not even equally implemented.

4.3. Econometric analysis of the factors influencing ocean fishery dependence

To determine the factors influencing ocean fishery dependence, the fractional response model was estimated. The results are presented in Table 3. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) was 208 and 271, respectively. The likelihood ratio test of the model had a p-value of less than 0.001, indicating a better fit (Shin and McCann, 2018). Pseudo R2 of 65% was also higher compared to the set statistical threshold of 20%, and hence ocean fishery dependence is well explained by the proposed covariates (Power et al., 2015). The result in Table 3 indicates that several factors significantly influenced the level of ocean fishery dependence among households. They include education level, group membership, credit, the security of tenure, flood, fish price, and value of agricultural productive assets.

Table 3: Fractional response model results on factors influencing ocean fishery dependence

Variable Coefficients Robust standard errors z p>Z
Socioeconomic factors        
Age -0.0106 0.0125 -0.84 0.3990
Gender -0.5500 0.4062 -1.35 0.1760
Education level -0.8539*** 0.2536 -3.37 0.0010
Household size -0.0264 0.0486 -0.54 0.5860
Log Agric. productive assets  0.0402** 0.0191  2.10 0.0360
Institutional factors        
Group membership  2.5241*** 0.3364  7.50 0.0000
Access to credit -0.8721** 0.3202 -2.72 0.0060
Security of land tenure -0.5424* 0.3199 -1.70 0.0900
Land size  0.0129 0.0168  0.77 0.4430
Distance to the fishery market -0.0551 0.0450 -1.22 0.2210
Distance to the ocean resource -0.0329 0.0506 -0.65 0.5150
Covariate and idiosyncratic shocks        
Rainfall shock  0.4722 0.6602  0.72 0.4740
Flood shock  0.8028* 0.4350  1.85 0.0650
Health shock  0.6650 0.4080  1.63 0.1030
Price shock  3.0132*** 0.3220  9.36 0.0000
Constant  0.5877 0.9777  0.60 0.5480
Goodness of fit        
Number of observation 384.0000      
Log pseudo-likelihood     0.6520      
Wald chi2(15) 206.6500      
Prob > chi2     0.0000      
AIC 208.4608      
BIC (df=16) 271.6711      

Note: *significant at 10%, **significant at 5%, ***significant at 1%

The education level of the household head was negatively associated with ocean fishery dependence at a 1% significant level. Lower education level limits opportunity in formal employment, and fishing provides a livelihood option as it does not require higher levels of education. This finding is consistent with previous studies (Daw et al., 2012; Garekae et al., 2017; Amevenku et al., 2019; Selig et al., 2019), where education had a negative effect on both relative and absolute environmental income attributed to higher education opening up alternative employment opportunities in public and private sector jobs. Moreover, education also enhances income diversification and labour diversity (Jin et al., 2018; Do et al., 2019) since it enriches access to information and skills and hence off fishery-related employment opportunities.

The amount of agricultural productive assets positively and significantly affected ocean fishery dependence at a 5% significant level. The results have been attributed to the majority of the households diversifying into agricultural production. However, agriculture was pursued in this region, mainly for subsistence with pockets of commercialized agricultural production. This serves as a coping strategy against the perceived risk and a livelihood diversification approach in sustaining consumption. The finding is consistent with prior evidence (Ndhlovu et al., 2017; Nguyen et al., 2018), where a positive relationship between asset values and natural resource dependence implies an attempt by the households to build adaptive capacity. This is because the fishery is associated with various disruptions, which could result in fluctuations in the income derived. Therefore, increased asset value is critical as a coping strategy and in sustaining consumption among dependent households.

Group membership in agricultural-related activities positively and significantly influenced the level of ocean fishery dependence at a 1% significant level. Beach management units (BMU) are the main groups in the region related to agriculture and, therefore, positively influenced ocean fishery dependence. This is because beach management units are directly related to ocean fishery, and its membership is critical in providing information, skills, knowledge, fishing gears, and market resources that enable fishers to be more efficient. Additionally, membership to beach management units enhanced repeated social cohesion and peer influence, which influences members’ behaviour to participate in common economic activity (Alexander et al., 2018).

Access to credit negatively and significantly influenced ocean fishery dependence at a 5% significant level. Credit access increases financial resources, reduces cash constraint, and enhances participation in off-fishery investment opportunities, which will reduce dependence on ocean fishery.  The results are in line with the work of Kimengsi et al. (2019) and Amevenku et al. (2019), who found that lack of access to credit increases dependence on natural resources. The reason for this is that access to credit prompts households to pursue more lucrative livelihood strategies that are not labour intensive. It has been established that households with efficient access to credit tend to promote non-farm activities rather than increasing investment in fishing (Amevenku et al., 2019). Similarly, Kleih et al. (2019) reported that small scale and medium actors in the fishery sector in Egypt have poor access to credit due to the information gap and lack of suitable collateral.

Security tenure of land negatively and significantly influenced the level of ocean fishery dependence at a 10% significant level. Ceteris paribus, the security of tenure, decreases ocean fishery dependence by the probability of 0.5424%. Land ownership right prompts households to invest in agriculture and off-fishery employment opportunities. Security of tenure provides the right to usage, which encourages individuals to engage in entrepreneurial livelihood strategies outside the ocean fishery, such as business investment and intensive agricultural production. Another possible explanation relates to the land and squatter issue facing the coastal community. The problem was contributed by the colonial era, the non-recognition of the land tenure security system, and hence dispossession of indigenous people by successive regimes. Despite the enactment of the 2010 constitution, the squatter problem continues to persist due to a lack of independent enforcement mechanisms and political goodwill (Githunguri, 2017; Klopp and Lumumba, 2017). The destabilizing political effect of land reforms in Kenya’s coastal region has increased land inequality among households. As a result, local individuals are exposed to land-related violence, social division, and impeded economic growth, which has enhanced dependence on natural livelihood options. The result is consistent with Teshager et al. (2019), who established that insecure land ownership rights significantly influence the lack of diversification.

Ocean fishery dependence was further influenced by shocks related to flood at a 10% significant level. The results indicated that households that experienced floods have a higher likelihood of depending on ocean fishery and related activities. This is particularly true in Malindi during the rainy season when the river Sabaki moves into the Indian Ocean. Mixing waterfronts of different densities raise the sea level and result in high and strong tides disrupting fishing activities. Similar findings were found by Nguyen et al. (2018), who founded that households that are dependent on natural resources have faced a significantly higher number of shocks, such as floods. Ndhlovu et al. (2017) argued that the small scale sector is vulnerable to climate change and through flooding events and fluctuating water levels.

Further, Price shock had a positive and significant effect on ocean fishery dependence at a 1% significant level. The reason for this is due to the seasonal nature of the livelihood option. Ocean fishery catches depend on seasons, with the kusi season, which occurs from April to July being the poorest. During this period, rough seas and heavy rainfall hamper the supply of fish and subsequently result in a higher price. On the contrary, kaskaz season, which occurs from August to March, is suitable for fishing activities since the ocean is relatively calm. However, due to the higher supply of fish, price decreases, ultimately reducing absolute and relative income from ocean fishery and related activities. The findings indicated how the vicious cycle between weather shocks and price uncertainty results in natural resource dependence. Given credit constraint, most households in the region used risk-sharing strategies such as advance payment to cope with flood events and price fluctuation in the fishing industry.

  1. Conclusion and policy implications

This study examined the drivers of ocean fishery dependence among households in the coastal lowlands of Kenya using the fractional response model. The key factors influencing marine fishery dependence were education level, access to credit, security of tenure, group membership, agricultural productive assets, flood shock, and fish price shock. Given that poor households mainly depend on marine fishery and related activities, there is the need for government intervention in non-formal education programs, financial support, beach management units, and infrastructural development. Public policies built around these aspects will enable adequate marine conservation, sound financial management, loan acquisition, and entrepreneurial behaviours. As a result, the dependent households will diversify to alternative livelihood options that will subsequently increase their adaptive capacity and achievement of the blue economy approach.

Further, efficient land right system is considered critical for sustainable livelihood options. Slow adjudication has been observed to jeopardize settlement programs contributing to insecure land tenure in the Country. Therefore, establishing the administrative and legal framework that addresses historical claims and regions with squatter problems is critical to solving the landlessness problem. More generally, reducing or alleviating chronic poverty in fisheries requires public services, social protection, and individual and collective assets enhancement. This implies that a more comprehensive governance option remains significant for responsible fishing and improving household welfare in the coastal lowlands of Kenya.

This study can be extended to other regions in Kenya to allow for generalization of the research findings for effective policy recommendation critical to contribute to the achievement of the blue economy approach. Areas for further research include determining factors influencing the vulnerability of the coastal fishing communities and evaluating the effect of migrant fishers on local fishers’ income and wellbeing.


The authors would like to sincerely thank the African Economic Research Consortium (AERC) for funding this research work. They would also like to acknowledge the chairpersons of Beach management units and residents of Kilifi County for guidance, support, and cooperation during data collection.


Alexander, S., Bodin, Ö. and Barnes, M. (2018). Untangling the Drivers of Community Cohesion in Small-scale Fisheries. International Journal of the Commons, 12(1), 519-547.

Allan Degen, A., Hoorweg, J. and Wangila, B. C. (2010). Fish Traders in Artisanal Fisheries on the Kenyan Coast. Journal of Enterprising Communities: People and Places in the Global Economy, 4(4), 296-311.

Amevenku, F. K. Y., Asravor, R. K. and Kuwornu, J. K. M. (2019). Determinants of Livelihood Strategies of Fishing Households in the Volta Basin, Ghana. Cogent Economics & Finance, 7(1), 1595291.

Béné, C. and Friend, R. M. (2011). Poverty in Small-scale Fisheries: Old Issue, New Analysis.   Progress in Development Studies, 11(2), 119-144.

Béné, C., Macfadyen, G. and Allison, E. H. (2007). Increasing the Contribution of Small-Scale Fisheries to Poverty Alleviation and Food Security (No. 481). Food & Agriculture Organization.

Carter, C. and Garaway, C. (2014). Shifting Tides, Complex Lives: the Dynamics of Fishing and Tourism Livelihoods on the Kenyan Coast. Society & Natural Resources, 27(6), 573-587.

CGOK. (2013). First Kilifi County Integrated Development Plan 2013- 2017, Finance and Economic Planning, County Government of Kilifi.

CGOK. (2018). Kilifi County Integrated Development Plan 2018 -2022, Economic Planning and Finance, County Government of Kilifi.

Chegere, M. J. (2018). Post-harvest losses reduction by small-scale maize farmers: The role of handling practices. Food Policy, 77, 103-115.

Cinner, J. E., McClanahan, T. R., Graham, N. A., Daw, T. M., Maina, J., Stead, S. M., Wamukota, A., Brown, K. and Bodin, Ö. (2012). Vulnerability of Coastal Communities to Key Impacts of Climate Change on Coral Reef Fisheries. Global Environmental Change, 22(1), 12-20.

Cochran, W. G. (1977). Sampling Techniques. John Wiley & Sons. New York.

Daw, T. M., Cinner, J. E., McClanahan, T. R., Brown, K., Stead, S. M., Graham, N. A., & Maina, J. (2012). To fish or not to fish: factors at multiple scales affecting artisanal fishers’ readiness to exit a declining fishery. PloS one, 7(2), e31460.

Ding, Q., Chen, X., Hilborn, R. and Chen, Y. (2017). Vulnerability to Impacts of Climate Change on Marine Fisheries and Food Security. Marine Policy, 83, 55-61.

Do, T. L., Nguyen, T. T. and Grote, U. (2019). Nonfarm Employment and Household Food Security: Evidence from Panel Data for Rural Cambodia. Food Security, 11(3), 703-718.

Fabinyi, M., Dressler, W. and Pido, M. (2019). Access to Fisheries in the Maritime Frontier of Palawan Province, Philippines. Singapore Journal of Tropical Geography, 40(1), 92-110.

FAO. (2014). The State of Food and Agriculture 2014. Rome: FAO. Available online at Accessed on 25th march 2019.

Funge‐Smith, S. and Bennett, A. (2019). A Fresh Look at Inland Fisheries and their Role in Food Security and Livelihoods. Fish and Fisheries, 20(6), 1176-1195.

Garekae, H., Thakadu, O. T. and Lepetu, J. (2017). Socioeconomic Factors Influencing Household Forest Dependency in Chobe Enclave, Botswana. Ecological Processes, 6(1), 40.

Githunguri, F. W. (2017). Evaluation of the Mandate of the Relevant Institutions in Addressing the Squatter Problem in Kenya (Doctoral dissertation, Strathmore University). Available online at 20the. %20mandate%20of%20the% 20relevant%20institutions.pdf?sequence=1&is Allowed=y. Accessed on 25th march 2019.

Jentoft, S., Onyango, P., & Islam, M. M. (2010). Freedom and poverty in the fishery commons. International Journal of the Commons, 4(1).

Jin, S., Waibel, H., Min, S. and Huang, J. (2018). Livelihood Responses of Smallholder Farmers in Southwest China to the Decline in Rubber Prices.

Kimengsi, J. N., Pretzsch, J., Kechia, M. A. and Ongolo, S. (2019). Measuring Livelihood Diversification and Forest Conservation Choices: Insights from Rural Cameroon. Forests 2019, 10(81), 1-16.

Kiwanuka-Tondo, J., Semazzi, F., Pettiway, K. and Casadevall, S. R. (2019). Climate risk communication of navigation safety and climate conditions over Lake Victoria basin: Exploring perceptions and knowledge of indigenous communities. Cogent Social Sciences, 5(1), 1588485.

Kleih, U., Linton, J., Marr, A., Mactaggart, M., Naziri, D. and Orchard, J. E. (2013). Financial Services for Small and Medium-scale Aquaculture and Fisheries Producers. Marine Policy, 37, 106-114.

Klopp, J. M. and Lumumba, O. (2017). Reform and Counter-reform in Kenya’s Land Governance. Review of African Political Economy, 44(154), 577-594.

Kyando, M. T., Nyahongo, J. W., Røskaft, E. and Nielsen, M. R. (2019). Household Reliance on Environmental Income in the Western Serengeti Ecosystem, Tanzania. Environment and Natural Resources Research, 9(1), 54-63.

Lopez-Feldman, A., Mora, J. and Taylor, J. E. (2007). Does Natural Resource Extraction Mitigate Poverty and Inequality? Evidence from Rural Mexico and a Lacandona Rainforest Community. Environment and Development Economics, 12(2), 251-269.

Mathenge, M., Place, F., Olwande, J. and Mithoefer, D. (2010). Participation in Agricultural Markets among the Poor and Marginalized: Analysis of Factors Influencing Participation and Impacts on Income and Poverty in Kenya. Unpublished Study Report Prepared for the FORD Foundation.

Muigua, K. (2018). Harnessing the Blue Economy: Challenges and Opportunities for Kenya. Available at Accessed on 22nd March 2019.

Narain, U., Gupta, S. and Van’t Veld, K. (2008). Poverty and Resource Dependence in Rural India. Ecological Economics, 66(1), 161-17.

Ndhlovu, N., Saito, O., Djalante, R. and Yagi, N. (2017). Assessing the Sensitivity of Small-scale Fishery Groups to Climate Change in Lake Kariba, Zimbabwe. Sustainability, 9(12), 2209.

Neiland, A. E. and Béné, C. (Eds.). (2013). Poverty and Small-scale Fisheries in West Africa. Springer Science & Business Media.

Nguyen, T. T., Do, T. L. and Grote, U. (2018). Natural Resource Extraction and Household Welfare in Rural Laos. Land Degradation & Development, 29(9), 3029-3038.

Nguyen, T. T., Do, T. L., Bühler, D., Hartje, R. and Grote, U. (2015). Rural Livelihoods and Environmental Resource Dependence in Cambodia. Ecological Economics, 120, 282-295.

Nguyen, T. T., Nguyen, T. T. and Grote, U. (2020). Multiple Shocks and Households’ Choice of Coping Strategies in Rural Cambodia. Ecological Economics, 167, 106442.

Papke, L. E. and Wooldridge, J. M. (1996). Econometric Methods for Fractional Response Variables with an Application to 401 (k) Plan Participation Rates. Journal of Applied Econometrics, 11(6), 619-632.

Power, R. A., Steinberg, S., Bjornsdottir, G., Rietveld, C. A., Abdellaoui, A., Nivard, M. M. and Cesarini, D. (2015). Polygenic Risk Scores for Schizophrenia and Bipolar Disorder Predict Creativity. Nature Neuroscience, 18(7), 953-955.

Salas, S., Chuenpagdee, R. and Barragán-Paladines, M. J. (2019). Drivers and Prospects for the Sustainability and Viability of Small-Scale Fisheries in Latin America and the Caribbean. In Viability and Sustainability of Small-Scale Fisheries in Latin America and The Caribbean (pp. 543-559). Springer, Cham.

Satumanatpan, S. and Pollnac, R. (2020). Resilience of Small-Scale Fishers to Declining Fisheries in the Gulf of Thailand. Coastal Management, 48(1), 1-22.

Selig, E. R., Hole, D. G., Allison, E. H., Arkema, K. K., McKinnon, M. C., Chu, J. and Ingram, J. C. (2019). Mapping Global Human Dependence on Marine Ecosystems. Conservation Letters, 12(2), e12617.

Shin, D. W. and McCann, L. (2018). Analyzing Differences among Non-adopters of Residential Stormwater Management Practices. Landscape and Urban Planning, 178, 238-247.

Soltani, A., Angelsen, A. and Eid, T. (2014). Poverty, Forest Dependence and Forest Degradation Links: Evidence from Zagros, Iran. Environment and Development Economics, 19(5), 607-630.

Soltani, A., Angelsen, A., Eid, T., Naieni, M. S. N. and Shamekhi, T. (2012). Poverty, Sustainability, and Household Livelihood Strategies in Zagros, Iran. Ecological Economics, 79, 60-70.

Stanford, R. J., Wiryawan, B., Bengen, D. G., Febriamansyah, R. and Haluan, J. (2013). Exploring Fisheries Dependency and its Relationship to Poverty: A case study of West Sumatra, Indonesia. Ocean & Coastal Management, 84, 140-152.

Tamale, A., Ejobi, F., Muyanja, C., Naigaga, I., Nakavuma, J., Drago, C. K. and Amulen, D. R. (2017). Sociocultural Factors Associated with Fish Consumption in Lake Albert Fishing Community: Guidelines for Lead and Mercury. Cogent Environmental Science, 3(1), 1304604.

Teh, L. C. and Sumaila, U. R. (2013). Contribution of Marine Fisheries to Worldwide Employment. Fish and Fisheries, 14(1), 77-88.

Teshager Abeje, M., Tsunekawa, A., Adgo, E., Haregeweyn, N., Nigussie, Z., Ayalew, Z.  and Berihun, D. (2019). Exploring Drivers of Livelihood Diversification and its Effect on Adoption of Sustainable Land Management practices in the Upper Blue Nile Basin, Ethiopia. Sustainability, 11(10), 2991.

Van Hoof, L. and Steins, N. A. (2017). Mission Report Kenya: Scoping Mission Marine Fisheries Kenya (No. C038/17). Wageningen Marine Research.





Get Professional Assignment Help Cheaply

Buy Custom Essay

Are you busy and do not have time to handle your assignment? Are you scared that your paper will not make the grade? Do you have responsibilities that may hinder you from turning in your assignment on time? Are you tired and can barely handle your assignment? Are your grades inconsistent?

Whichever your reason is, it is valid! You can get professional academic help from our service at affordable rates. We have a team of professional academic writers who can handle all your assignments.

Why Choose Our Academic Writing Service?

  • Plagiarism free papers
  • Timely delivery
  • Any deadline
  • Skilled, Experienced Native English Writers
  • Subject-relevant academic writer
  • Adherence to paper instructions
  • Ability to tackle bulk assignments
  • Reasonable prices
  • 24/7 Customer Support
  • Get superb grades consistently

Online Academic Help With Different Subjects


Students barely have time to read. We got you! Have your literature essay or book review written without having the hassle of reading the book. You can get your literature paper custom-written for you by our literature specialists.


Do you struggle with finance? No need to torture yourself if finance is not your cup of tea. You can order your finance paper from our academic writing service and get 100% original work from competent finance experts.

Computer science

Computer science is a tough subject. Fortunately, our computer science experts are up to the match. No need to stress and have sleepless nights. Our academic writers will tackle all your computer science assignments and deliver them on time. Let us handle all your python, java, ruby, JavaScript, php , C+ assignments!


While psychology may be an interesting subject, you may lack sufficient time to handle your assignments. Don’t despair; by using our academic writing service, you can be assured of perfect grades. Moreover, your grades will be consistent.


Engineering is quite a demanding subject. Students face a lot of pressure and barely have enough time to do what they love to do. Our academic writing service got you covered! Our engineering specialists follow the paper instructions and ensure timely delivery of the paper.


In the nursing course, you may have difficulties with literature reviews, annotated bibliographies, critical essays, and other assignments. Our nursing assignment writers will offer you professional nursing paper help at low prices.


Truth be told, sociology papers can be quite exhausting. Our academic writing service relieves you of fatigue, pressure, and stress. You can relax and have peace of mind as our academic writers handle your sociology assignment.


We take pride in having some of the best business writers in the industry. Our business writers have a lot of experience in the field. They are reliable, and you can be assured of a high-grade paper. They are able to handle business papers of any subject, length, deadline, and difficulty!


We boast of having some of the most experienced statistics experts in the industry. Our statistics experts have diverse skills, expertise, and knowledge to handle any kind of assignment. They have access to all kinds of software to get your assignment done.


Writing a law essay may prove to be an insurmountable obstacle, especially when you need to know the peculiarities of the legislative framework. Take advantage of our top-notch law specialists and get superb grades and 100% satisfaction.

What discipline/subjects do you deal in?

We have highlighted some of the most popular subjects we handle above. Those are just a tip of the iceberg. We deal in all academic disciplines since our writers are as diverse. They have been drawn from across all disciplines, and orders are assigned to those writers believed to be the best in the field. In a nutshell, there is no task we cannot handle; all you need to do is place your order with us. As long as your instructions are clear, just trust we shall deliver irrespective of the discipline.

Are your writers competent enough to handle my paper?

Our essay writers are graduates with bachelor's, masters, Ph.D., and doctorate degrees in various subjects. The minimum requirement to be an essay writer with our essay writing service is to have a college degree. All our academic writers have a minimum of two years of academic writing. We have a stringent recruitment process to ensure that we get only the most competent essay writers in the industry. We also ensure that the writers are handsomely compensated for their value. The majority of our writers are native English speakers. As such, the fluency of language and grammar is impeccable.

What if I don’t like the paper?

There is a very low likelihood that you won’t like the paper.

Reasons being:

  • When assigning your order, we match the paper’s discipline with the writer’s field/specialization. Since all our writers are graduates, we match the paper’s subject with the field the writer studied. For instance, if it’s a nursing paper, only a nursing graduate and writer will handle it. Furthermore, all our writers have academic writing experience and top-notch research skills.
  • We have a quality assurance that reviews the paper before it gets to you. As such, we ensure that you get a paper that meets the required standard and will most definitely make the grade.

In the event that you don’t like your paper:

  • The writer will revise the paper up to your pleasing. You have unlimited revisions. You simply need to highlight what specifically you don’t like about the paper, and the writer will make the amendments. The paper will be revised until you are satisfied. Revisions are free of charge
  • We will have a different writer write the paper from scratch.
  • Last resort, if the above does not work, we will refund your money.

Will the professor find out I didn’t write the paper myself?

Not at all. All papers are written from scratch. There is no way your tutor or instructor will realize that you did not write the paper yourself. In fact, we recommend using our assignment help services for consistent results.

What if the paper is plagiarized?

We check all papers for plagiarism before we submit them. We use powerful plagiarism checking software such as SafeAssign, LopesWrite, and Turnitin. We also upload the plagiarism report so that you can review it. We understand that plagiarism is academic suicide. We would not take the risk of submitting plagiarized work and jeopardize your academic journey. Furthermore, we do not sell or use prewritten papers, and each paper is written from scratch.

When will I get my paper?

You determine when you get the paper by setting the deadline when placing the order. All papers are delivered within the deadline. We are well aware that we operate in a time-sensitive industry. As such, we have laid out strategies to ensure that the client receives the paper on time and they never miss the deadline. We understand that papers that are submitted late have some points deducted. We do not want you to miss any points due to late submission. We work on beating deadlines by huge margins in order to ensure that you have ample time to review the paper before you submit it.

Will anyone find out that I used your services?

We have a privacy and confidentiality policy that guides our work. We NEVER share any customer information with third parties. Noone will ever know that you used our assignment help services. It’s only between you and us. We are bound by our policies to protect the customer’s identity and information. All your information, such as your names, phone number, email, order information, and so on, are protected. We have robust security systems that ensure that your data is protected. Hacking our systems is close to impossible, and it has never happened.

How our Assignment Help Service Works

1. Place an order

You fill all the paper instructions in the order form. Make sure you include all the helpful materials so that our academic writers can deliver the perfect paper. It will also help to eliminate unnecessary revisions.

2. Pay for the order

Proceed to pay for the paper so that it can be assigned to one of our expert academic writers. The paper subject is matched with the writer’s area of specialization.

3. Track the progress

You communicate with the writer and know about the progress of the paper. The client can ask the writer for drafts of the paper. The client can upload extra material and include additional instructions from the lecturer. Receive a paper.

4. Download the paper

The paper is sent to your email and uploaded to your personal account. You also get a plagiarism report attached to your paper.

smile and order essay GET A PERFECT SCORE!!! smile and order essay Buy Custom Essay

Place your order
(550 words)

Approximate price: $22

Calculate the price of your order

550 words
We'll send you the first draft for approval by September 11, 2018 at 10:52 AM
Total price:
The price is based on these factors:
Academic level
Number of pages
Basic features
  • Free title page and bibliography
  • Unlimited revisions
  • Plagiarism-free guarantee
  • Money-back guarantee
  • 24/7 support
On-demand options
  • Writer’s samples
  • Part-by-part delivery
  • Overnight delivery
  • Copies of used sources
  • Expert Proofreading
Paper format
  • 275 words per page
  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

Our guarantees

Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.

Money-back guarantee

You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.

Read more

Zero-plagiarism guarantee

Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.

Read more

Free-revision policy

Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.

Read more

Privacy policy

Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.

Read more

Fair-cooperation guarantee

By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.

Read more
error: Content is protected !!
Open chat
Need assignment help? You can contact our live agent via WhatsApp using +1 718 717 2861

Feel free to ask questions, clarifications, or discounts available when placing an order.
  +1 718 717 2861           + 44 161 818 7126           [email protected]
  +1 718 717 2861         [email protected]