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

^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]

ABSTRACT

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

ABOUT THE AUTHOR

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.

PUBLIC INTEREST STATEMENT

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.

2. 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).

3. 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).

 

(1)

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).

(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;

(3)

Where y_i is the dependent variable defined as (0 ≤ y_i ≤ 1), x_i 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;

(4)

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

(5)

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		+

4. 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.

5. 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.

Acknowledgements

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.

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