Amevenku et al., Cogent Economics & Finance (2019), 7: 1595291
https://doi.org/10.1080/23322039.2019.1595291
ECONOMETRICS | RESEARCH ARTICLE
Determinants of livelihood strategies of fishing
households in the volta Basin, Ghana
F. K. Y. Amevenku1, R. K. Asravor2* and J. K. M. Kuwornu3
Received: 13 November 2018 Abstract: The main objective of this study is to identify livelihood strategies of
Accepted: 07 March 2019 fishing households in response to prevalent vulnerabilities they face within the Volta
First Published: 01 April 2019
Basin. Questionnaires were administered to fishery households in the Basin using
*Corresponding author: R. K. Asravor,
Ghana Technology University, Ghana the multistage sampling technique. A non-hierarchical k-means cluster analysis
E-mail: rkasravor@yahoo.com partitioned the households into four livelihood strategies on which the multinomial
Reviewing editor: logit regression was performed. These four identified strategies are fishery only,
Xibin Zhang, Econometrics and
Business Statistics, Monash fishery and farming, fishery and non-farm, and fishery, non-farming and farming.
University, Melbourne, Australia The results from the multinomial logit regression revealed that marital status of
Additional information is available at head of household, number of months of food shortage experienced by a household
the end of the article per year, access to credit, access to extension services, distance to regular markets
and district capital as well as experience in fishery were the major determinants of
livelihood strategies. Implications for policy include the need for public extension
services and training to invigorate fishery households’ income. As majority of the
fishery households combined fishing and non-fishing strategies, livelihood inter-
vention programmes should prioritize improvement of the non-fishing activities and
lead to opening other opportunities for rural development. This will take pressure off
the fish stock by facilitating the regeneration of fish stock.
ABOUT THE AUTHORS PUBLIC INTEREST STATEMENT
F. K. Y. Amevenku is a Senior Research Scientist The major dams in Ghana have contributed in
at the Council for Scientific and Industrial Water many ways to the social and economic develop-
Research Institute, Fisheries and Aquaculture ment of inhabitants, but the resultant changes in
Division, Ghana. He holds a PhD in Agricultural the environment have led to losses of livelihoods.
Economist and Policy. His research interest is in After five decades of existence of the first of these
the Socioeconomics of Fisheries and dams, this article describes the types and factors
Aquaculture, Economics of the Environment and that have influenced livelihood strategies adopted
Project Analysis. by households in response to changes made to
R. K. Asravor is a research consultant with the original Volta River. The results show that
special interested in agricultural, environmental many of the households combined fishing and
and health economics. He holds a PhD in non-fishing approaches for survival. The decision
Agricultural Economist and a Master of on what approach to choose was determined by
Philosophy degree in economics. He is a research variables such as marital status of head of
assistant in applied econometrics for PhD stu- household, number of months of food shortage
dents at the University of Ghana, Department of experienced by a household per year, access to
Agricultural Economics and Agribusiness, credit, access to extension services, distances to
College of Basic and Applied. regular markets and district capital and number
J. K. M. Kuwornu is an Associate Professor of of years spent fishing. An appreciation of these
Agribusiness Management in the Department of variables could assist to guide future interventions
Food, Agriculture and Bioresources, School of and policies to construct dams on rivers.
Environment, Resources and Development,
Asian Institute of Technology, Pathum Thani
12120, Thailand.
© 2019 The Author(s). This open access article is distributed under a Creative Commons
Attribution (CC-BY) 4.0 license.
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Subjects: Agricultural Development; Agricultural Economics; Fisheries Science
Keywords: volta Basin; cluster analysis; k-means; livelihood strategy; diversification
1. Introduction
Diverse livelihood portfolios are often viewed as an essential part of household economies in
developing countries, especially rural economies. The relationship between fishing and liveli-
hood diversification is important because fishing is an important component of rural liveli-
hoods of households in the coastal areas. The Big Numbers Project (BNP) (2008) estimate that
between 93 and 97 million of rural households in developing countries are either directly or
indirectly involved in fishing or are into the processing and marketing of small-scale fisheries.
Fishing is a highly gender-segregated profession, with men catching fresh fish, and women
processing fish (Britwum, 2009). Despite the importance of fishery to the rural economy,
steady increases in the number of rivers being dammed have been reported to affect the
aquatic ecosystems that provide important environmental and socioeconomic benefits (Liu
et al., 2013). Analyzing the role of natural resource-based occupational changes along the
spectra of socioeconomic and infrastructure development in coastal communities in East
African, Cinner and Bodin (2010) suggest that increases in household-level specialization in
most occupational sectors, including fishing and farming. Cinner and Bodin (2010), further
reported that at the community-level, rural coastal economies have diversified due to infra-
structural and developmental changes. According to Prado, Seixas, and Berkes (2015), many
coastal communities in developing countries have been going through complex dynamics of
change relating to the degradation of ecosystems and changing government policies. Many of
these policies severe consequences on the livelihood strategies of coastal residents.
Prior to the construction of the dam on the Volta River in Ghana, Volta River supported sub-
stantial local fishery especially in the Lower Volta Area. The Volta basin, as an ecosystem, func-
tioned primarily for food production, supporting fishery, flood protection, water infiltration and
groundwater recharge. An economic assessment by Lawson in 1963 showed that an estimated
1500 to 2000 women were involved in clam picking along the river between Tefle and Torgome.
These women were earning an average income of approximately £100,000.00 (1963 British
Pounds) annually (Lawson, 1968). However, the damming of the Volta River has led to the
alterations of the livelihood strategy and income of coastal households. This has negatively
affected inhabitants of the riparian communities in Ghana and their livelihoods. Shepherd, Kessy,
Higgens, Scott, and Luvanda (2011) stated that the capacity to diversify livelihoods and income
sources under such situations has become crucial for the survival of households. The postconstruc-
tion habitat modifications and resource decline started after the coming to being of the Akosombo
dam but worsened upon the formation of the Kpong dam. This manifesting changes in the
ecosystems of the Lower Volta Area as diminished income of households and lead to losses of
livelihood opportunities.
The construction of these two dams have led to activities such as fuelwood harvesting, charcoal
burning (Tonah, 2008), palm wine tapping, local gin distilling, mat weaving, sand winning (Obour,
Owusu, Agyeman, Ahenkan, & Madrid, 2016) and stone quarrying in the Volta basin. These
activities are extractive in nature and depended on locally available resources. The intensity of
the exploitation of these extractive resources has raised questions of sustainability and resilience
of livelihood strategies as they expose future generations to significant ecological risks and
scarcities (Tonah, 2008; Tsikata, 2005). In the context of the Volta basin in Ghana, the over-
dependence on fishing as a source of livelihood was fast depleting the natural resource and
deepening poverty (Asante, 2006). Fabio et al. (2003a, 2003b) diagnostic study on the poverty
profiling and the diversification strategies of households in the Volta Basin is the only studies on
the entire international Volta Basin of Ghana.
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Within the interplay of the ecological changes occasioned by the creation of these two dams and
its attendant livelihood loses, the pertinent questions worth asking are;
● Which sustainable livelihood strategies have evolved among fishery households?
● What principal factors influence the choice of those strategies?
The broad objective of this paper, therefore, is to examine the livelihood strategies of fishery
households within the Volta Basin in Ghana. The specific objectives are:
● To identify the major livelihood strategies adopted by fishery households;
● To examine the determinants of the choices of fishery households livelihood strategies.
The remainder of the paper proceeds as follows: the next section discusses theoretical and
empirical literature on livelihood diversification. This is followed by a narration of the sampling
technique, study areas, data collection and analytical methods adopted. Section three is
a presentation of the detail results gathered from the analyzed data. The discussions of the results
with reference to the literature presented are also made in this section. The last section of the
study presents the conclusions and recommendations of the study.
2. Brief literature review
While empirical studies on diversification may be widely applicable in the economic and socio-
cultural contexts, results from empirical studies differed between the different regions of the
world. In spite of this, it is generally agreed in the empirical literature that the purpose of
diversification is to develop portfolios of income generating activities with low covariate risk
(Asravor, 2017; Ellis, 2000). The literature also shows that numerous factors affect the diversifica-
tion strategies of household in agriculture and aquaculture but in a general context, these factors
can be categorized into “pull” (positive) or “push” (negative) factors (Brugère, Holvoet, & Allison,
2008; Ellis, 2000). According to Asravor (2017), the pull factors are favorable factors or opportu-
nity-led and leads to diversification of livelihood strategies, whereas push factors are survival-led
or harsh conditions that force farm households to diversify their income activities off their main
income generating activity.
Empirical studies devoted to the diversification of fishery households are limited as many
studies have focused on crop and livestock farmers (Asravor, 2017; Eneyew, 2012). Martin,
Lorenzen, and Bunnefeld (2013) examined the relationship between fishing, livelihood diversifi-
cation and poverty in the lower Mekong basin, in Laos. From their analysis of the household
survey data, Martin et al. (2013) found that participation in fishing is common and positively
associated with higher occupational diversity and more agricultural activities. Additionally,
Martin et al. (2013) reported that alternative livelihoods within the rural setting are unlikely to
cause fishers to leave fishery, but instead strengthen the livelihood portfolio as a supplementary
activity. Fishing forms a greater proportion of income, employment and food security for the poor
in the coastal areas.
Cinner, McClanahan, and Wamukota (2010) posited that household livelihoods in tropical coastal
communities are not dependent on a single livelihood strategy but often a multiplicity of occupa-
tional sectors, such as agriculture, fisheries, and informal economic activities. These non-fishing
and agricultural economic activities include small shops, transportation, and mechanic work
among others. To understand the diversification of smallholder farmers’ in Ghana, Asravor
(2017) employed the Margalef index of diversification. The results revealed that households had
diversified on their farm and in informal economic activities. It also showed that social capital
(dependency ratio, marital status), and human capital (extension services) significantly affected
the type of diversification strategy practiced.
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3. Materials and methods
3.1. Study area
The study was undertaken in selected coastal administrative districts in Ghana. These selected
districts are located in the segments labeled LV1, LV2 and LV3 included North, Central and South
Tongu, Ada East, Asuogyaman, and Lower Manya (Figure 1). In addition, the Pru district in the
Brong Ahafo region, which constitute Stratum VII was also sampled for this study (Figure 1). The
characteristic of the study area is presented in Table 1.
3.2. Sampling technique
The multistage sampling technique was employed to sample the fishing households. The Volta
Basin encompasses four administrative regions, that is, the Greater Accra, Volta, Eastern
regions and Brong Ahafo region. The first stage of the multistage sampling involved the
selections of districts in these regions. The districts were purposively selected because they
were known to have many communities and households involve in fishery, hence are affected
by any activities along the Volta Basins. After purposively selecting these districts, the second
stage involved the use of the simple random sampling technique to select the various com-
munities that can be found in these selected districts. To sample these communities, District
Assemblies were first contacted for the list of all the communities that fall within each of these
districts. At the community level, all the households within each community were numbered.
The “sample” command in R was then employed to select households that were administered
with the questionnaire. Before the administration of the questionnaire to each household,
opinions and community leaders were each informed about the survey and permission were
sought. On average 2 h was spent administering the questionnaire to each household. In all,
the 802 households were selected for the study.
Figure 1. Study Areas: LV , LV , THE VOLTA LAKE AND ITS MAJOR TRIBUTRIES IN GHANA1 2
LV3 and the principal strata of
B U R K I N A F A S O 
11°00'N
the Volta Lake.
10°00'N
9°
6°30'N VIII N
Source: CSIR Water Research
Institute, Accra
Gyakiti
VII
LOWER
VOLTA
Ajena V
SCALE
Akosombo 0 20 40 60 80 8°
Adomi
(LV3) VI
Abotia
Senchi
Kpong IV
Amedeka Adidome
Asutsuare Mepe
Volivo Bator Bakpo 7 III
(LV2) °Sogakope
6°00'N
Tefle (LV1)
Sokpoe
Agodome
I
Agave II
Agotaga
Big Ada
Anyanui
A 6°kp A A W
Anl
l u oa d ti ti gb a ta e
a
O n ti
ld y aN
ingo LEGEND
Kpong River / Stream
Road
0 100 Km.
Town
Bridge
Scale 5°
Sampling Point
0 5 10 15 20km
5°30'N 1° 0° 1°
0°00' 0°30'E 1°00'E
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C O T E             D'    I V OI R E
T      O      G        O
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Table 1. Characteristics of the segments of the Volta basin studied
Segment Characteristics
LV1 Formation of sandbar at the estuary limiting inflow of seawater into the river at high tide;
(Lower Volta Area I) Reduction of floodplain agriculture;
Explosion of aquatic weeds that blocked fishing grounds;
Infestation of creeks, streams and main fishing channels by aquatic weeds;
LV2 Collapse of floodplain farming;
Lower Volta Area II Cessation of influx of seawater at high tide leading to collapse of shell fish fishery;
Trapped between the 2 Volta dams;
Receives limited inflow of seawater;
LV3 Experiences reduced water flow (speed) in the main channel;
Lower Volta Area III Collapsed flood plain agriculture;
Weed-choked creeks, streams and main channel limiting extent of fishing;
Constitutes the most riverine segment of the Volta Lake;
Stratum VII Under the influence of large inflows thus, providing large volumes of water and fish;
Constitutes the hub of fishery on the Volta Lake;
Benefited from a number of national and international interventions aimed at improving livelihoods of households
Sources: Institute of Aquatic Biology [IAB] (1995); Petr (1974); Fisheries Department (1995) and Henle and Eckert (1970); FAO (2008).
Amevenku et al., Cogent Economics & Finance (2019), 7: 1595291
https://doi.org/10.1080/23322039.2019.1595291
3.3. Data collection
A total of 802 household heads constituted the respondents from which data was obtained
through the administration of a structured pretested set of questionnaires from July 2015 to
December 2015 in the study area. However, after data cleaning, during which nonresponses and
unrelated feedbacks were eliminated, 715 respondents became the sample on which the analysis
was based.1
3.4. Estimated model
In this study, analytical technique and model utilized are presented in this section. The analytical
technique employed to measure the diversification strategies of the fishery households was the
k-means while the multinomial logit model was used to examine the determinants of the fishery
household diversification strategies.
3.4.1. Diversification measure
The income-based approach is the main basis for the analysis of identifying household livelihood
strategies (Barrett, Bezuneh, Clay, & Reardon, 2005; Brown, Stephens, Ouma, Murithi, & Barrett,
2006). Specifically, the k-means Cluster Analysis procedure was employed in this study. The
k-means approach used the Euclidean measure to determine dissimilarities, that is, the distance
between the final cluster points. It further assigned each observation (xi) to only one cluster
(Marzban & Sandgathe, 2005). Thus, more succinctly, given a set of observation ðxi; x2; . . . :; xnÞ
with each observation being a d-dimensional of a real vector, the purpose of k-means clustering is
to partition the sampled observations (n) into k (≤ n) sets f ¼ ðfi; f2; . . . :; fkÞ in order to minimize the
variance or within-cluster sum of squares (WCSS). Thus, the within-cluster is written as:
k k
argfmin ∑ ∑ k x μ 2i k ¼ argfmin ∑ jfijVar fi (1)
i¼1 x2fi i¼1
where the mean of points in fi is given by μi and is equivalent to the minimization of the pairwise
squared deviations of points in the same cluster:
k 1
argfmin ∑ j j ∑ kx yk
2 (2)
i¼1 2 fi x;y2fi
Using the identity of equation (3) we deduce the equivalence. From the law of total variance,
where the total variance is assumed to be a constant equation (3) and is equivalent to maximizing
the sum of squared deviations between points in different clusters (between-cluster sum of
squares).
∑ 2x2f kx μik ¼ ∑i x
y2f ðx μiÞðμi  yÞ (3)
The choice of k-means over the wards and hierarchical clustering approaches was because the
k-means approach helped the researcher identify strategies which are “similar” and are “dissim-
ilar” to the other strategies belonging to other clusters. Thus, the k-mean approach enabled the
researcher to segregate groups with similar traits (livelihood strategies) and assign them into
clusters (strategy). The k-means was used in this study because it is conceptually simple and is
computationally fast. According to Kaushik and Mathur (2014), there is no clear evidence that any
other clustering algorithm performs better in k-means general as it has the advantage of cluster-
ing large data sets with its performance increasing as the number of clusters increases.
3.4.2. Determinant of diversification
To identify the determinants of rural household’s decision to engage in various livelihood strate-
gies, the researchers assumed that a rational household would choose among mutually exclusive
livelihood strategy alternatives that offered them the maximum utility (Eneyew, 2012; Eneyew &
Bekele, 2012). From the random utility argument, fishery households’ decision to choose
a livelihood strategy is categorized as a function of a set of livelihood strategies. Given the four-
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alternative choices (fishery only; fishery and farming; fishery and non-farming; and fishery, farming
and non-farming), we applied the multinomial logit model to estimate the livelihood choice with
discrete dependent variable.
Given the random utility model (RUM), we assume that the decision-maker (fishers) choose from
a set of mutually exclusive alternatives j ¼ 1; 2; 3; . . . ; J and obtains a certain level of utility Uij
from each alternative. Since we do not observe the decision makers’ utility, but observe some
attributes of the alternatives faced by households, then the utility is decomposed into determinis-
tic ðVijÞ and random (εijÞ) part:
Uij ¼ Vij þ εij (4)
A fisher selects livelihood strategy U ¼ 1 if
Uik > Uk (5)
Where Uik denotes a random utility associated with the livelihood strategy j ¼ k, and Vik is an index
function denoting the fishers’ average utility linked with this alternative. The second term εik is
a random error which is specific to a producer’s utility preference (McFadden, 1974). The livelihood
strategies modeled is stated as:
Lij ¼ βjXij þ εij (6)
where Lij is a vector of the livelihood choices j ¼ 1 for fishery only; 2 for fishery and farming; 3 for
fishery and non-farming; and 4 for fishery, farming and non-farming of ith fisher, βj is a vector of
channel-specific parameters. εij is the error term assumed to be normally distributed (mean 0 and
variance 1) and Xij is a vector of fishers’ characteristics.
If we make Y to be the unordered categorical dependent variable that takes on a value of zero or
one for each of th
e J choices, then the general multinomial logit model is given as:
exp β0jXi
PrðYi ¼ jÞ ¼ 
  for J ¼ 1;2;3 (7)
∑jj¼0exp β
0
jXi
Where:
PrðYi ¼ jÞ is the probability of choosing either fishing only, fishing and farming, fishing and non-
farming, and fishing, farming and non-farming; j is the number of livelihood activities engaged in;
Xi is a vector of explanatory factors conditioning the choice of the jth alternatives; β is a vector of
the estimated parameters.
The estimated equations provide a set of probabilities for the jþ 1 choice restricted for a decision-
maker with characteristics. In order to remove an indeterminacy in the model, a convenient normal-
ization that solves the problem is β0 ¼ 0. Therefore, one can define the general form of the probability
that individual ith choose
thealternative j
th in the following way:
exp β0X
ð ¼ Þ ¼ j iPr Yi j=Xi 
  for all J > 0 (8)
1þ ∑jj¼0exp β0jXi
The MNL coefficients are difficult to interpret and associating the βj with the j outcome is tempting
and misleading. To interpret the effects of explanatory variables on the probabilities, marginal
effects are u"sually used#and derived as (Greene, 2003):
j h i
θj ¼
@Pj ¼ Pj β  ∑ Pjβ ¼ pj β  β (9)
@X j j jj j¼1
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The marginal effects measure the expected change in the probability of a specific outcome being
made with respect to a unit change in an explanatory variable (Greene, 2003).
The choice of the multinomial logit model over the ordered logit and the mixed logit or the probit
or logit model is because the predictor variables are individual specific, and the choices facing the
household are unordered. STATA version 13 was the statistical package used to analyze the data
gathered from the survey.
3.5. Definitions of variables
Table 2 presents the definition of variables used in the estimation of the multinomial logit model.
In terms of the socioeconomic characteristics of the households, the statistics shows that approxi-
mately 79.2% of the household heads were married. The household with the minimum members
had the household size of three (3) persons whereas the highest household size was nine (9)
persons. The number of dependents within the sampled households ranges from 0 to 8. This is an
indication that there were households without any dependent.
The human capitals or assets of the sampled study showed that the average fishing experience of
the fishing households in the study area was 21 years while theminimum experience was 9 years and
the maximum fishing experience was 33 years. Many of the household heads do not have any formal
education (69.4%). The summary of the explanatory variable on agricultural extension services shows
that very few of the fisher households had access to agricultural extension service.
Table 2. Socio-demographic and economic characteristics the fishing households
Explanatory variables Descriptions
Social capitals/assets:
Marital status 1 = Married, 0 = otherwise 0.792
Household size Number of persons in the 6.0 ± 3.31
households
Dependency ratio Level of dependency on household 1.0 ± 0.90
workforce
Human capitals/assets:
Fishing experience (HH) Years of fishing 21.0 ± 12.43
Formal education (HH) 1 = Formal education, 0 = 0.694
otherwise
Extension services 1 = Access to extension service, 0 = 0.021
otherwise
Financial capitals/assets:
Receipt of remittances 1 = Receives remittance, 0 = 0.07
otherwise
Access to credit 1 = Access to credit, 0 = otherwise 0.84
Natural capitals/assets
Fishing seasons 1 = distinct number of fishing 0.161
seasons, 0 = otherwise
Food shortage Number of months of food 3.84 ± 1.13
shortage
Physical capitals/assets
Distance to regular fishing site Kilometres 2.77 ± 1.94
Distance to regular market Kilometres 6.54 ± 3.78
Value of fishing assets Value in Ghana Cedis 5,444.00 ± 4,767.99
Distance to district capital Kilometres 9.13 ± 4.31
Sources: Authors computation (2016).
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The analysis in terms of financial capital or asset showed that most fisher households had
access to credit (84%) for their fishing activities. Very few expressed receiving remittances (7%)
from household members or relatives leaving in the big cities.
The natural capitals/assets indicated a fishing regime (season) with no distinct number of fishing
seasons. An indication that fisher households have an all-year-round fishing activity. On average,
three months of food shortage was experience within a year by respondents.
The distance to the regular fishing site, regular market and district capital averaged 2.77, 6.54 and
9.13 km respectively. In addition, physical capital or asset showed that the value of fishing asset
owned by the household averaged GHC 5,444.00 but range between 5,444.00 ± 4,767.99.
The continuous variables of all the binary variables reported in Table 2 were also collected during
the data collection period. Table 32 shows some of the analysis.
The empty spaces imply that households did not provide the researcher with the needed information.
Table 3 shows that on average the entire Volta Basin recorded 3 days of extension visit, an average of
GHc100 remittance and GHc120 worth of credits. Table 3 shows that majority of the responding house-
holds did not provide details of the amount of money received in the form of remittance or credit. For
majority of households visited, these, were not applicable or they could not recall the amount received.
4. Results
4.1. Livelihood strategies
Table 4 presents the livelihood strategies identified through non-hierarchal (k-means) cluster
analysis for the Volta Basin, Ghana. The results show that most of the household in the Volta
Basin are engaged in fishery and non-farming (56.81%)
The least livelihood diversification strategy identified in this study was fishery and farming (8.20).
The farming practices of households are the cultivation of crop and the rearing of livestock and
Table 3. Continuous variables of the Dummy responses
Volta Basin LV1 LV2 LV3 Stratum7
Number of extension visit 3 2 1
(days)
Average remittances received 100 100
(GHc)
Amount received as credit 120 120
(GHc)
2These were dropped by Stata during the course of the analysis, hence we settled on using the dummy responses.
Table 4. Livelihood Strategies identified through Non-hierarchal (k-means) cluster analysis for
the Volta Basin, Ghana
Cluster 1 2 3 4
Name of Fishery only Fishery and Fishery and Fishery, Farming
strategy Farming Non-farming and
Non-farming
Number of HH 198 56 388 41
% of HH 28.99% 8.20% 56.81% 6.00%
Source: Authors compilation (2016).
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poultry birds. The common crops cultivated by these households are maize and cassava while the
common livestock reared were sheep, goats and pigs. The main poultry birds kept were chicken
while a few kept ducks and guinea fowls. These farming practices were undertaken on smallholder
bases, and most farm produce are kept for consumption purposes. There were fewer cases where
farm produce was sold in the market for cash.
The non-farm livelihood activities engage in by the households were generally petty trading.
Women were mostly involved in the sale of food stuffs and toiletries while men were mainly
mechanics and carpenters.
4.2. Choice of livelihood strategy in the volta basin
Table 5. presents the result of the choice of livelihood strategies in the Volta Basin. The result
indicated that 14 of the explanatory variables were significant at 1%, 5% and 10% significant
levels. Overall, the diagnostics statistics shows that the model was statistically significant at 1%
while the pseudo R-square show that 17% of the variation in the dependent variables was
explained by the changes is the independent variables.
The base strategy for the determinant of livelihood diversification strategies was fishery, non-
farm and farming. Detail results presented in Table 4 show that compared to households in the
base category, household heads who were married decreased their participation in fishery only
strategy by 19%, whereas married household heads increased their participation in fishery and
non-farming strategies by 25%.
The coefficient of household size was positive for fishery and farming. This implies that com-
pared to households in the base category, an increase in household size is more likely to increase
the probability of engaging in fishery and farming strategies by 0.8% at the 5% level of signifi-
cance. The study also reports that compared to the base category having access to credit
significantly reduce the participation of households in fishery strategy by 31%.
Table 5 also shows that compared to the base category, an increase in the dependency ratio
significantly leads to a 3% reduction in the adoption of fishery and farming strategies. Receipt of
remittances from member of the households living outside the community was found to adverse
impact in the adoption of fishery plus farming strategies by approximately 7%.
Compared to the base category, an increase in the number of fishing seasons positively affected
the participation in fishery only but negatively affected the participation in fishery and non-farming
livelihood strategy. Thus, the finding shows that a month increase in the fishing season leads to
a 24% increase in the fishery only livelihood strategy and a 24% decrease in fishery plus non-
farming livelihood strategy of households in the Volta basin.
The distance to the regular market center and also the distance to the regular fishing site were
significant and directly influence the participation in fishery plus farming strategy and fishery plus
non-farming strategy by 0.08% and 0.36%, respectively. The availability of extension services had
a direct relationship on diversification into fishery plus farming (96%) and fishery plus non-farming
plus farming activities (46%).
The finding also shows that compared to the base category, an increase in the value of fishery
assets will lead to a decrease in the participation of fishery only as a livelihood diversification
strategy but increase the participation in fishery and non-farming livelihood diversification
strategy.
5. Discussion
The households studied can be said to be diversified in their livelihood activities. This corresponds
with the assertion by Smith, Nguyen Khoa, and Lorenzen (2005) and Martin et al. (2013) that
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Table 5. Estimates of choice of livelihood strategy by fishery households of the Volta basin of Ghana
Strategy3 Fishery only Fishery and farming Fishery and non-farming
Explanatory Coefficients Marginal effects Coeffients Marginal effects Coefficient Marginal effects
variables
Marital status 1.0592** (0.5227) −0.1859 (0.0826)** 0.0616 (0.1838) −0.0322 (0.0454 1.0592 (0.229)*** 0.2471 (0.0686)***
Formal education (HH) −0.1126 (0.0991) −0.0350 (0.25838) −0.0653 (0.2537) −0.0014 (0.0065) −0.3781 (0.6021) −0.1348 (0.0998)
Household size 0.4929*** (0.1834) −0.2172 (0.31880) 0.1216 (0.0357)*** 0.0084 (0.0027)**) 0.3252 (0.4030) 0.0115 (0.10377)
Dependency ratio −0.2289 (0.2519) −0.1976 (0.2829) 0.3493 (0.1614)* −0.0269 (0.0124)* −0.0658 (0.0452) −0.0826 (0.1218)
Access to credit 15.7478 (4.6684)*** −0.3067 (0.0105)*** 0.0136 (0.0924) −0.3807 (0.2664) 0.0119* (0.0066) 0.0756 (0.1093)
Number of months of 0.2137 (0.0947)** −0.0476 (0.0159)** −0.063 (0.1007) −0.3058 (0.2553) −0.0149 (0.0153) −0.0811 (0.1116)
food shortage
Receipt of remittances −0.1977 (0.2887) −0.0060 (0.0063) 1.3602 (0.6712)* −0.0668 (0.0214)** −0.3502 (0.2685) −0.0146 (0.0334)
Number of fishing 1.1763 (0.3919)** 0.2369 (0.0870)** 0.4355 (0.5911) 0.0063 (0.0087) 1.1763 (0.3952)** −0.2383 (0.0967)**
seasons
Distance to regular 0.1840 (0.0616)** −0.0348 (0.0122)** −0.5003 (0.3885) −0.0364 (0.0874) 0.1840 (0.0620)** 0.0036 (0.0013)**
fishing site
Distance to regular 0.0028 (0.0069) 0.0889 (0.10166) 0.1127 (0.0424)** 0.0084 (0.0016)*** 0.0011 (0.0028) 0.0022 (0.0035)
market
Value of fishing assets −0.00003 (1.45E-05)* −7.10e-06 (3.28E-06)* 0.0209 (0.0362) 0.0233 (0.06972) 0.00003 (1.2E-05)** 4.16e-06 (1.27E-06)**
Fishing experience (HH) −0.031 (0.0123)** −0.0058 (0.0023)** −0.0457 (0.0340) −0.0242 (0.097) 0.0310 (0.0107)** 0.00003 (9.38E-06)**
Distance to district 0.1717* (0.0867) 0.0267 (0.01827) 0.0349 (0.0161)* 0.0024 (0.0012)* 0.0058 (0.0354) −0.1337 (0.0863)
capital
Access to extension 0.0534 (0.0902) 0.0047 (0.0027) 17.9287 (3.9322)*** 0.9617 (0.2136)*** −0.0232 (0.0384) −0.10777 (0.0860)
services
Wald Chi2 3749.54*** 3749.54*** 248.63***
Pseudo R2 0.1702 0.1702 0.1702
Prob>Chi2 = 0.0000
Number of observations4 = 683
Note: The marginal effects are defined by the dy/dx values and ***, **, * are, respectively, significant at <1%, 5% and 10% probability level.
3 The base strategy is Fishery, non-farm and farming.
4Though 715 observations were used for the MLR, some observation were dropped hence 683 observations were used for the final estimation.
Amevenku et al., Cogent Economics & Finance (2019), 7: 1595291
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specialization in fisheries rarely happens in developing countries and majority of fishery house-
holds diversified their livelihood strategy.
Marital status is an important determinant of the diversification behavior of household heads.
Marriage is usually accompanied by numerous responsibilities, especially child care and the pay-
ment of bills. Household heads will, therefore, diversify their income, hence the decrease in the
reliance of fishery only to the diversification into fishery plus non-farming strategy. As indicated by
Asravor (2017) farming is seen as a very risky business, hence many farm households diversify
their farming activities away from farming by entering into non-farm. Similarly, it could be that the
households studied were avoiding the riskiness of relying on fishery alone by diversifying their
activities. Those with formal education were better enlightened to efficiently manage their
activities.
In developing countries, such as Ghana, increasing household size is associated with more labor
for agriculture activities. Households with more family labor are more diversified than those with
very little labor (Asravor, 2017). In the fishing communities, diversification in both fishery and
farming strategies are paramount to larger households since it provides these households with
alternative sources of income and consumption of balanced diets (protein and carbohydrate diet).
The higher dependency ratio is associated with a lower level of participation in fishery livelihood
strategy. The plausible explanation was that an increase in dependency ratio increased the number of
householdmemberswithin the bracket (less than 18 andgreater than 65 years) were not able to either
farm or fish or do both. Additionally, fishers who had access to credit reduced their reliance on fishery
only strategy. Access to financial credit has been documented to lead to diversification (Jansen,
Damon, Pender, Wielemaker, & Schipper, 2003) among rural households. With access to credit,
households have basic capital to diversify into either farming or non-farming business or both.
Jansen et al. (2003) argue that households with large access to credit promote nonfarm activities
rather than increased investments in inputs for their basic or main fishery livelihood activity.
Extension services significantly influenced the diversification strategies of households. This
finding was confirmed by Asravor (2017). Extension services gave the fisher folks the knowledge,
training and skills needed to either diversify or specialized their livelihood activities. In most cases,
fisher households with access to agricultural extension service have a higher tendency to diversify
their farming activities than those with very little access to extension services.
The empirical findings of this study broadly confirmed the empirical literature, which posits that
despite the risk involved in fishing, households located in coastal areas of developing countries
preferred fishing (Cinner, Daw, & McClanahan, 2009). The importance of fisheries to the small-scale
families is that being a safety net and its contributions to pro-poor growth.
From the random utility maximization perspective, we argue that fishery households’ decision to
choose a particular livelihood strategy is a function of the set of livelihood choice available. The choice
made by the household is, therefore, based on the maximum satisfaction that the head of household
derives (in terms of the income generated) from the livelihood activity(ies) subject to a set of constraints
that he or she faces. This largely confirms the assertion of Eneyewand Bekele (2012) and Eneyew (2012).
6. Conclusions and recommendations
This study undertook an appraisal of the livelihood strategies of households in the Volta Basin of Ghana.
The study concludes that although fishing is risky and laborious venture, it remains the major occupa-
tions preferred by households in the Volta Basin of Ghana. Our study concludes that the riskiness of
fishing had ledmany fishing households in the Volta Basin to diversified their livelihood activities. in their
livelihood strategies, although fishery activities remained central in the composition of all strategies. The
fourmain livelihood strategies employed by the fishery households are fishery only, fishery plus farming,
fishery plus non-farming and fishery plus farming plus non-farming. The study concludes that most
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households in the Volta Basin engage in an additional livelihood activity apart fishery. Fishery plus non-
farming was the dominant strategy employed whereas the least strategy used was fishery, farming and
non-farming. On the aggregated, the decision to adopt a particular strategy was mostly influenced by
the demographic characteristics of the households. In spite of this, the human capital, financial capital,
and natural capital also play important roles in the livelihood diversification strategy of households. In
details, the conclusion is that the marriage household heads, household heads with larger family size
and a higher number of dependents were more diversified. Furthermore, livelihood diversification
strategies were significantly influenced by access to credit, food insecurity, remittances, fishing season,
distances to fishery activity grounds and markets, value of fishery assets, experience of head of house-
hold, distance to district capital, and extension contact. In the sub aggregated Lower Volta south of the
Akosombo dam, again marital and formal educational statuses of the head of household, household
size, dependency ratio, access to credit, distance to fishery activity grounds, distance to markets;
experience of head of household and extension contact.
Based on these conclusions, we recommend that livelihood intervention programmes should prioritize
the improvement of non-fishing activities as it led to the opening of other opportunities for rural
development and take pressure off the fish stock to facilitate regeneration. To be successful, however,
support in the form of credit and training must go together if alternative income sources of the house-
holds are to be relied upon.
7. Limitation of the study
Although there is no doubt that this study is of importance to the research community in general,West
Africa and Ghana in particular, there is a limitation in using the binary data. The reliance on binary data
for some variables might lead to omissions of revealed subtle differences between households. Binary
outcomemeasures are less sensitive to change than continuousmeasures; thus, binarymeasuremay
impact the relative efficacy of estimates. Binary outcomes as used in this paper are widespread and
perceived to be advantageous due to its simplicity in the interpretation. The challenge of using it is the
loss of information which leads to the loss of power to detect relationships, and the probable inflation
of type I error. To overcome these challenges, attempts were made to use some of the richer
disaggregated data collected during the study (see Section 3.5). These variables were automatically
dropped by the software whiles running the multinomial logit model.
Funding Note
The authors received no direct funding for this research. 1. During the analysis using the MNL, Stata automatically
dropped some variables. Thus, the analysis was based
Author details on 683 respondents.
F. K. Y. Amevenku1
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