Research & Publications
Working Paper Series
2025
Alhassan Abdul-Wakeel Karakara, James Atta Peprah & Isaac Dasmani
Social Resilience and the Blue Economy: A Study on Fishermen in Coastal Communities in Ghana Working paper Forthcoming
Forthcoming.
Abstract | Links | BibTeX | Tags:
@workingpaper{nokey,
title = {Social Resilience and the Blue Economy: A Study on Fishermen in Coastal Communities in Ghana},
author = {Alhassan Abdul-Wakeel Karakara, James Atta Peprah & Isaac Dasmani},
url = {http://cepgaafrica.org/wp-content/uploads/2025/10/Social-Resilience-and-the-Blue-Economy_A-Study-on-Fishermen-in-Coastal-Communities-in-Ghana.pdf},
year = {2025},
date = {2025-00-00},
pages = {35},
abstract = {Fishing is the most notable human activity in the ocean because many people, including the
poor, vulnerable, and less advantaged, earn their living directly or indirectly. However, fishing
practices have been recognized to have an effect on the sustainability of the ocean, which calls
for concern (referred to as the blue economy). The social resilience of marine communities is
key to achieving a blue economy and an essential aspect of sustainability in environmental
management, particularly in resource-dependent communities. Previous studies on social
resilience have neglected the social resilience state of marine communities, the determinants
of such social resilience, and its relationship to the blue economy. We employed a convergent
parallel mixed-methods research design to collect and analyze data on 491 coastal artisanal
fishermen across nine semi-urban, two urban, and 16 villages in Ghana. Principal Component
Analysis was employed to determine the factors contributing to the fishermen's social
resilience. At the same time, a binary logistic model was employed to examine the relationship
between social resilience and demographic characteristics. Using a five-point Likert scale
(strongly agree, agree, don’t know, disagree, and strongly disagree) on four major components,
fishermen self-assess their expected well-being. Social resilience of fishermen in the study can
be explained by four broad characteristics: the risk perception emanating from change,
planning, learning, and reorganization ability; how people perceive their ability to cope with
change; and the interest level of individuals in a prospective change. Also, demographic
variables significantly determine the state of social resilience. Specific policy measures for
strengthening social resilience at the local level could target building community social capital
by helping fishermen form self-help associations and developing community economic and
social infrastructures that could provide an alternative source of livelihood.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {workingpaper}
}
Fishing is the most notable human activity in the ocean because many people, including the
poor, vulnerable, and less advantaged, earn their living directly or indirectly. However, fishing
practices have been recognized to have an effect on the sustainability of the ocean, which calls
for concern (referred to as the blue economy). The social resilience of marine communities is
key to achieving a blue economy and an essential aspect of sustainability in environmental
management, particularly in resource-dependent communities. Previous studies on social
resilience have neglected the social resilience state of marine communities, the determinants
of such social resilience, and its relationship to the blue economy. We employed a convergent
parallel mixed-methods research design to collect and analyze data on 491 coastal artisanal
fishermen across nine semi-urban, two urban, and 16 villages in Ghana. Principal Component
Analysis was employed to determine the factors contributing to the fishermen's social
resilience. At the same time, a binary logistic model was employed to examine the relationship
between social resilience and demographic characteristics. Using a five-point Likert scale
(strongly agree, agree, don’t know, disagree, and strongly disagree) on four major components,
fishermen self-assess their expected well-being. Social resilience of fishermen in the study can
be explained by four broad characteristics: the risk perception emanating from change,
planning, learning, and reorganization ability; how people perceive their ability to cope with
change; and the interest level of individuals in a prospective change. Also, demographic
variables significantly determine the state of social resilience. Specific policy measures for
strengthening social resilience at the local level could target building community social capital
by helping fishermen form self-help associations and developing community economic and
social infrastructures that could provide an alternative source of livelihood.
poor, vulnerable, and less advantaged, earn their living directly or indirectly. However, fishing
practices have been recognized to have an effect on the sustainability of the ocean, which calls
for concern (referred to as the blue economy). The social resilience of marine communities is
key to achieving a blue economy and an essential aspect of sustainability in environmental
management, particularly in resource-dependent communities. Previous studies on social
resilience have neglected the social resilience state of marine communities, the determinants
of such social resilience, and its relationship to the blue economy. We employed a convergent
parallel mixed-methods research design to collect and analyze data on 491 coastal artisanal
fishermen across nine semi-urban, two urban, and 16 villages in Ghana. Principal Component
Analysis was employed to determine the factors contributing to the fishermen's social
resilience. At the same time, a binary logistic model was employed to examine the relationship
between social resilience and demographic characteristics. Using a five-point Likert scale
(strongly agree, agree, don’t know, disagree, and strongly disagree) on four major components,
fishermen self-assess their expected well-being. Social resilience of fishermen in the study can
be explained by four broad characteristics: the risk perception emanating from change,
planning, learning, and reorganization ability; how people perceive their ability to cope with
change; and the interest level of individuals in a prospective change. Also, demographic
variables significantly determine the state of social resilience. Specific policy measures for
strengthening social resilience at the local level could target building community social capital
by helping fishermen form self-help associations and developing community economic and
social infrastructures that could provide an alternative source of livelihood.
Alhassan Abdul-Wakeel Karakara Osman John A. T. Froko, Arimiyaw Zakaria; Gyingyi, Victor Kwame
Predicting Human Development Using Machine Learning: Evidence from African Countries Working paper Forthcoming
Forthcoming.
Abstract | Links | BibTeX | Tags:
@workingpaper{nokey,
title = {Predicting Human Development Using Machine Learning: Evidence from African Countries},
author = {Osman John A. T. Froko, Alhassan Abdul-Wakeel Karakara, Arimiyaw Zakaria and Victor
Kwame Gyingyi},
url = {http://cepgaafrica.org/wp-content/uploads/2025/10/Predicting-Human-Development-Using-Machine-Learning-1.pdf},
year = {2025},
date = {2025-00-00},
urldate = {2025-00-00},
abstract = {This study employs Random Forest machine learning to predict the Human Development Index
(HDI) for 41 African countries, with separate models developed for West Africa (comprising
16 countries) and Southern Africa (comprising 12 countries). It analyses an imbalanced panel
dataset with 6.6% missing data, processed to 83 predictors from the ND-GAIN dataset after
removing highly correlated features. The results identify economic wealth, environmental
management, infrastructure, health, and governance as key drivers of the HDI. West Africa
emphasizes rural areas, education, and health, reflecting its agrarian economy and early-stage
development, while Southern Africa prioritizes economic output, governance, and
urbanization, driven by advanced economies like South Africa. The models perform strongly
(testing R-squared: 0.83–0.94), capturing complex, non-linear patterns missed by traditional
methods. These findings support policymakers in targeting investments in education, health,
and infrastructure, guide ECOWAS and SADC in developing regional strategies for agriculture,
education, and governance, and assist the African Union in promoting human development and
unity through evidence-based policies.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {workingpaper}
}
This study employs Random Forest machine learning to predict the Human Development Index
(HDI) for 41 African countries, with separate models developed for West Africa (comprising
16 countries) and Southern Africa (comprising 12 countries). It analyses an imbalanced panel
dataset with 6.6% missing data, processed to 83 predictors from the ND-GAIN dataset after
removing highly correlated features. The results identify economic wealth, environmental
management, infrastructure, health, and governance as key drivers of the HDI. West Africa
emphasizes rural areas, education, and health, reflecting its agrarian economy and early-stage
development, while Southern Africa prioritizes economic output, governance, and
urbanization, driven by advanced economies like South Africa. The models perform strongly
(testing R-squared: 0.83–0.94), capturing complex, non-linear patterns missed by traditional
methods. These findings support policymakers in targeting investments in education, health,
and infrastructure, guide ECOWAS and SADC in developing regional strategies for agriculture,
education, and governance, and assist the African Union in promoting human development and
unity through evidence-based policies.
(HDI) for 41 African countries, with separate models developed for West Africa (comprising
16 countries) and Southern Africa (comprising 12 countries). It analyses an imbalanced panel
dataset with 6.6% missing data, processed to 83 predictors from the ND-GAIN dataset after
removing highly correlated features. The results identify economic wealth, environmental
management, infrastructure, health, and governance as key drivers of the HDI. West Africa
emphasizes rural areas, education, and health, reflecting its agrarian economy and early-stage
development, while Southern Africa prioritizes economic output, governance, and
urbanization, driven by advanced economies like South Africa. The models perform strongly
(testing R-squared: 0.83–0.94), capturing complex, non-linear patterns missed by traditional
methods. These findings support policymakers in targeting investments in education, health,
and infrastructure, guide ECOWAS and SADC in developing regional strategies for agriculture,
education, and governance, and assist the African Union in promoting human development and
unity through evidence-based policies.