Urban Expansion, Governance, and Environmental Quality: Decoding the Drivers of Human Development in SADC Countries (2000-2020).

Abstract

Human Development Index (HDI) serves as a crucial measure of socio-economic progress, yet the nuanced relationships between HDI and key factors such as economic growth, urbanization, institutional quality, environmental pollution, and corruption control remain underexplored. This study examines the impact of economic growth, urban development, institutional quality, environmental pollution and control of corruption on HDI in the Southern African Development Community (SADC), a region with diverse economic and social challenges. To achieve this, the study employs advanced econometric techniques, specifically the Dynamic Common Correlated Effects (DCCE) and Augmented Mean Group (AMG) estimators. This research analyzes panel data spanning from 2000 to 2020 across 16 SADC countries, addressing cross-sectional dependence and heterogeneous slopes. The findings indicate that economic growth has a consistently positive impact on HDI in several countries, including Botswana, Comoros, Eswatini, Lesotho, Madagascar, Malawi, and the overall panel emphasizing its vital role in enhancing human development. Urbanization effects vary, with both positive and negative outcomes observed in countries like Zimbabwe and Seychelles. Institutional quality is positively linked to HDI in Lesotho and Mauritius, reinforcing the importance of effective governance. Environmental pollution shows a complex impact, benefiting HDI in Angola but impairing it in Zimbabwe. Corruption control also exhibits mixed effects, with negative impacts on HDI in Lesotho and Seychelles. This study highlights the need for tailored policy interventions that address specific regional and national contexts. It recommends enhancing institutional quality and tackling environmental pollution to promote sustainable human development across the SADC region.

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Original Research
SAGE Open
April-June 2025: 1–22
ÓThe Author(s) 2025
DOI: 10.1177/21582440251335192
journals.sagepub.com/home/sgo
Urban Expansion, Governance,
and Environmental Quality:
Decoding the Drivers of Human
Development in SADC Countries
(2000-2020).
Erick Okoth
1
and Abdulmalik Egesa Omar
2
Abstract
Human Development Index (HDI) serves as a crucial measure of socio-economic progress, yet the nuanced relationships
between HDI and key factors such as economic growth, urbanization, institutional quality, environmental pollution, and cor-
ruption control remain underexplored. This study examines the impact of economic growth, urban development, institutional
quality, environmental pollution and control of corruption on HDI in the Southern African Development Community
(SADC), a region with diverse economic and social challenges. To achieve this, the study employs advanced econometric
techniques, specifically the Dynamic Common Correlated Effects (DCCE) and Augmented Mean Group (AMG) estimators.
This research analyzes panel data spanning from 2000 to 2020 across 16 SADC countries, addressing cross-sectional depen-
dence and heterogeneous slopes. The findings indicate that economic growth has a consistently positive impact on HDI in
several countries, including Botswana, Comoros, Eswatini, Lesotho, Madagascar, Malawi, and the overall panel emphasizing its
vital role in enhancing human development. Urbanization effects vary, with both positive and negative outcomes observed in
countries like Zimbabwe and Seychelles. Institutional quality is positively linked to HDI in Lesotho and Mauritius, reinforcing
the importance of effective governance. Environmental pollution shows a complex impact, benefiting HDI in Angola but
impairing it in Zimbabwe. Corruption control also exhibits mixed effects, with negative impacts on HDI in Lesotho and
Seychelles. This study highlights the need for tailored policy interventions that address specific regional and national contexts.
It recommends enhancing institutional quality and tackling environmental pollution to promote sustainable human develop-
ment across the SADC region.
Keywords
HDI, urbanization, governance, environmental impact, SADC, economic growth
Introduction
The Southern African Development Community
(SADC) is a regional economic community comprising
16 countries in Southern Africa (Figure 1A). The SADC
region is rich in natural resources and tourist attractions,
making it a key destination for global investors (Zheng,
2024). With a population of approximately 340 million
and a combined GDP of US$720 billion, SADC plays a
vital role in promoting sustainable and equitable eco-
nomic growth, socio-economic development, good gov-
ernance, and lasting peace and security among its
member states (Omay et al., 2017; Southern African
Development Community [SADC], 2024). The commu-
nity aims to boost intra-regional trade, foster regional
trade integration, and enhance economic cooperation
1
Sakarya University, Serdivan, Sakarya, T€
urkiye
2
Istanbul Medeniyet University, Kadiko
¨y, Istanbul, T€
urkiye
Corresponding Author:
Erick Okoth, Faculty of Social Sciences, Department of Economics, Sakarya
University, Esentepe Campus, Serdivan/Sakarya 54050, T€
urkiye.
Email: erick.okoth@ogr.sakarya.edu.tr
Data Availability Statement included at the end of the article
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License
(https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of
the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages
(https://us.sagepub.com/en-us/nam/open-access-at-sage).

among its members (Moyo, 2023). The SADC region is
notable for its significant economic activities, especially
in the mining sector, which contributes more than 10%
of GDP in countries such as Angola, Namibia,
Botswana, and Zimbabwe (Isheloke & Blottnitz, 2023).
One of the key responsibilities of governments within the
SADC is to improve the welfare of their populace.
Article 5 of the SADC Treaty emphasizes that the main
objectives of SADC are to foster economic growth and
socio-economic development, eradicate poverty, and
ensure peace, security, and democracy through regional
cooperation and integration (SADC, 2022; Seleteng &
Motelle, 2016). SADC shares this vision and aims to
achieve economic well-being, improve living standards,
enhance social justice, and promote peace and security
for the people of Southern Africa (Joseph, 2022).
The Human Development Index (HDI) often the mea-
sure used to assess this progress. The HDI, developed by
the United Nations Development Programme (UNDP),
is a composite measure used to assess and compare
development levels across countries (Jaman, 2020). It
serves as a benchmark for evaluating quality of life and
well-being within regions or countries, offering a compre-
hensive snapshot of human progress (Urzu´ a & Vilbert,
2023). The HDI includes indicators such as life expec-
tancy, education levels, and standard of living, providing
a holistic view of human development achievements
(Ðord
evic
´et al., 2022). According to Helen Suzman
Foundation (2019), SADC countries have a median HDI
of 0.58. Only Mauritius and Seychelles surpass the global
median, with Mauritius and Seychelles having HDI val-
ues above 0.700. Botswana and South Africa fall between
0.600 and 0.699, Namibia and Eswatini between 0.500
and 0.599, while Zambia, Angola, Zimbabwe, Comoros,
Tanzania, Madagascar, and Lesotho are below 0.600,
and Malawi, DRC, and Mozambique have HDI values
below 0.500. These figures demonstrate how societal wel-
fare levels are diverse in the region. Income inequality is
a growing global concern, with people across the political
spectrum believing it should be reduced. However,
inequalities in human development are even more pro-
found, as shown by stark differences in education and
life expectancy between children born in high and low
human development countries, as well as significant life
expectancy gaps within countries based on income levels.
For example, while over half of 20-year-olds in high-
human development countries are enrolled in higher edu-
cation, only 3% are in low human development coun-
tries, and children in the latter are far more likely to die
before age 20 (United Nations Development Programme
[UNDP], 2019). In the SADC region, HDI is an impor-
tant measure of holistic progress beyond economic
output into dimensions that are fundamental to human
well-being and sustainable development. It integrates life
expectancy, education, and per capita income; each one
of these dimensions addresses directly SADC’s objectives
for the promotion of equitable growth, social welfare,
and economic integration among its member states.
Given the vast differences in income and development
across this region, the HDI provides a standardized sys-
tem through which these improvements in quality of life
and access to basic resources are quantified. In addition,
in countries characterized predominantly by features of
inequality, health, and education disparities-which, for
the most part, pertains to the many countries in the
SADC region-HDI becomes particularly handy in point-
ing out disparities that are camouflaged by economic
indicators. As member states of SADC strive for regional
integration and the attainment of goals on sustainable
development, HDI offers a useful yardstick against
which progress in efforts toward inequality reduction
and human welfare enhancement-both core priorities of
SADC’s Regional Indicative Strategic Development Plan
(2020-2030)-can be measured. Based on this assertion the
HDI level of a country is thus of great importance, con-
sequently, understanding its determinants is crucial not
only for policymaking but also for policy implementation
in the SADC region.
Economic growth is crucial for advancing human
development, as it provides the resources necessary for
investment in education, healthcare, and infrastructure,
thereby improving living standards. Higher income lev-
els, often resulting from economic growth, are linked to
better access to education and healthcare. Research indi-
cates that economic development can enhance institu-
tional quality in lower-middle and low-income countries,
contributing to overall human development (Redmond
& Nasir, 2020). This underscores the need for sustainable
economic policies and investments in key sectors to fos-
ter population well-being and prosperity. Urbanization,
a prominent trend in the SADC region, impacts human
development in diverse ways. Effective urbanization can
enhance access to essential services and economic oppor-
tunities, while inadequate infrastructure and overcrowd-
ing in urban areas may impede progress (Vitenu-Sackey,
2023). Institutional quality also plays a pivotal role in
shaping human development outcomes. Strong govern-
ance structures and effective institutions enhance
resource allocation and contribute to better human
development. Studies consistently highlight the positive
impact of institutional quality on human development,
highlighting the need for robust governance frameworks
to achieve social and economic objectives (Ariu et al.,
2016). Improving institutional quality can boost public
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service delivery, reduce corruption, and support sustain-
able development in SADC member countries. Effective
governance, transparency, and adherence to the rule of
law are crucial for translating economic gains into mean-
ingful improvements in human development.
Corruption significantly undermines institutional
quality, economic growth, and human development out-
comes. It weakens public institutions by diverting
resources from essential services and hindering socio-
economic progress. Therefore, effective anti-corruption
measures are crucial for ensuring transparency, account-
ability, and good governance, all of which are vital for
sustainable development (Thi Cam Ha et al., 2023).
Environmental pollution is a major challenge to human
development in the SADC region, adversely affecting
health, well-being, and overall quality of life. Addressing
environmental degradation and adopting sustainable
practices are critical for protecting human development
outcomes. Research indicates that environmental factors,
such as pollution, can adversely impact human develop-
ment indicators, highlighting the urgent need for proac-
tive environmental policies and conservation efforts
(Hashmat et al., 2024).
This study seeks to answer the question—what is the
effect of economic growth, urban development, institu-
tional quality, environmental pollution and corruption
control on Human Development Index (HDI) in SADC
member countries? The study aims to provide insights
into the determinants of human development in the
region, offering valuable guidance for policymakers
striving to enhance development outcomes in Southern
Africa. While HDI is widely recognized for encompass-
ing welfare aspects in measuring overall economic devel-
opment, the complex interplay between HDI and various
environmental and socio-economic factors remains
underexplored. For instance, the direct relationship
between environmental pollution and HDI has not been
thoroughly investigated, with existing research, such as
Gyawali et al. (2023), primarily offering comprehensive
reviews of scholarly articles without delving into empiri-
cal analysis. These studies highlight the detrimental
effects of pollution, including increased incidences of dis-
eases such as cancer and cardiovascular conditions.
However, the lack of empirical data and econometric
models to determine the nature of the relationship
between environmental pollution and HDI may result in
an incomplete representation of the issue.
Moreover, previous studies, including Hardi et al.
(2023), Akinbode et al. (2020), Tripathi (2021), Nguea
(2023), Sarabia et al. (2020), Sangaji (2016), Nginyu et al.
(2025), and Thi Cam Ha et al. (2023), have often over-
looked cross-sectional dependence by failing to consider
correlations across different entities within the panel.
This is particularly critical in the SADC region, where
countries such as Lesotho and Eswatini, though indepen-
dent, share borders with South Africa and maintain close
trade relations. This study addresses these gaps by
employing advanced econometric techniques, the
Dynamic Common Correlated Effects (DCCE) and
Augmented Mean Group (AMG), which are well-suited
for handling panel data with cross-sectional dependence
and heterogeneous slopes. Additionally, given the choice
of variables, the time frame of the study, recent global
developments, and the impact of COVID-19, this
research is timely. Unlike previous studies, such as
Akinbode et al. (2020), which focused on government
health spending and effectiveness on HDI before
COVID-19 (2015-2018), and Nginyu et al. (2025), which
examined government effectiveness on HDI from 2010 to
2017, this study spans a more comprehensive period from
2000 to 2020. It incorporates five independent
variables—economic growth, urban development, institu-
tional quality, environmental pollution, and corruption
control—and one dependent variable, HDI, providing a
more holistic analysis of human development in the
SADC region.
Literature Review
This section reviews literature on human development in
relation to urbanization, control of corruption, institu-
tional quality, economic growth, and environmental pol-
lution. To broaden the scope of development beyond
purely economic measures and include social dimensions,
the United Nations Development Programme (UNDP)
introduced the Human Development Index (HDI) as
part of its Human Development Report. Conceptualized
by Mahbub ul Haq, the HDI is a composite index mea-
suring average achievement in three key dimensions: life
expectancy, education, and standard of living. The intro-
duction of the HDI marked a significant shift in develop-
ment discourse, attracting considerable attention to
welfare issues at a time when many experts believed that
welfare levels would automatically improve with GDP
growth. Since its inception, the HDI has garnered
increasing attention in the literature, with researchers
and policymakers exploring its relationship with various
other variables. Notable studies in this domain include
Saybasachi (2019), who investigated the impact of urba-
nization on HDI values using random effect Tobit panel
data estimation from 1990 to 2017. The study found that
total urban population, percentage of urban population,
and percentage of urban population living in million-plus
agglomerations positively affect HDI, even after control-
ling for other significant determinants. Similarly, Selcxuk
(2006) examined the relationship between corruption and
human development using a sample of 63 countries for
the year 1998. Employing three different corruption
Okoth and Omar 3

indices, the study concluded that there is a statistically
significant negative relationship between corruption and
human development, with more corrupt countries exhi-
biting lower levels of human development. Further
research on these variables is summarized in Table 1.
Evaluation
Most studies do not address cross-sectional dependence
among panel data, potentially weakening the robustness
of their findings. This is particularly relevant for the
SADC region, where economic, social, and environmen-
tal factors are highly interconnected across borders,
ignoring these dependencies can lead to biased estimates,
as some models employed in prior research overlook
cross-sectional dependence. Additionally, while environ-
mental pollution is widely recognized as a determinant
of health outcomes and mortality, its specific effects on
HDI remain underexplored in empirical analyses.
Although existing research highlights the impact of envi-
ronmental degradation on development, the literature
lacks models that quantitatively analyze this relationship
within HDI frameworks, particularly for the SADC
region. This gap underscores the need for empirical,
data-driven approaches to provide a more comprehen-
sive understanding of environmental factors on human
development.
Data and Model
Data
The Human Development Index (HDI) measures prog-
ress in health, education, and standard of living. This
article explores how economic growth, urban develop-
ment, institutional quality, environmental pollution, and
control of corruption affect the HDI in SADC member
countries over the period 2000 to 2020. The period is
selected primarily based on availability of data. The
region is selected due to its economic potential and simi-
larity of their political and social environment. The study
adopts these determinants as justified by existing studies.
Economic growth is crucial for human development,
providing resources for education, healthcare, and infra-
structure, and positively correlates with the HDI
(Arisman, 2018). Existing research shows that there is a
positive relationship between higher economic growth
and improvement in HDI, enhancing income levels, life
expectancy, and educational attainment (Ramesh &
Abebe, 2016). For instance, the study by Ramesh and
Abebe (2016) found that economic growth in Ethiopia
had positive impacts on HDI through betterment in
income and life expectancy, signifying that economic
advancement can result in superior outcomes in human
development. In addition, according to Suyanto (2023),
in improving the quality of human life, economic growth
plays a vital role; hence, solidifying its position as a key
determinant of HDI. Urban development improves
access to services and employment, positively impacting
HDI (Ghifara et al., 2022).For instance, a study by Jiang
et al. (2021) revealed that while most often uncontrolled,
the rise in urban land in Africa provides increased eco-
nomic activities and living standards if well managed.
Urbanization ensures better access to education and
health, which are key components of HDI. It also brings
about challenges like poor infrastructure and degrada-
tion of the environment, hence requiring sustainable
urban planning for maximization of its gains (Manteaw,
2020). This duality in urbanization makes it very impor-
tant as a variable impacting HDI. Institutional quality,
marked by effective governance and rule of law,
enhances HDI by improving policy implementation and
reducing corruption. Runtunuwu et al. (2023) supports
this by showing an illustration of how institutional qual-
ity directly relates to HDI improvements through the
need for strong governance frameworks. Strong institu-
tions are the environment needed for economic develop-
ment and growth in human development, as it ensures
that resources are well allocated to reach set goals equi-
tably. Corruption undermines economic growth and
human development, making anti-corruption measures
essential for improving HDI (Y. Kurniawan et al., 2020).
Hope (2021) observes that corruption is an antithesis to
sustainable development and a deterrent to progress in
critical sectors pivotal to HDI, such as health and educa-
tion. Corruption is one of the significant malaises facing
most of the countries in Africa and is associated with
negative impacts on both economic growth and human
development. It has been found that corruption diverts
resources from the provision of basic public goods and
services that are vital to increasing HDI (Hope, 2020).
For instance, Mlambo et al. (2019) point out that cor-
ruption not only inhibits economic growth but also
embeds the level of poverty and inequality, hence ham-
pering HDI. The presence of effective anti-corruption
must therefore be met in order to enhance HDI as it
allows for a better allocation of resources toward health,
education, and infrastructure development (Muhammad
et al., 2023). Environmental pollution negatively affects
health and quality of life, highlighting the need for sus-
tainable practices. According to Safitri (2023), pollution
is known to cause health problems and result in lower
life expectancy and general well-being according to
Manteaw (2020). Rapid urbanization in Africa has the
marks of inadequate waste management and pollution;
hence, good sustainable practices are needed to reduce
these impacts. Environmental sustainability has to be
ensured since it directly impacts HDI, as health out-
comes and living conditions depend on it. Said
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Table 1. Summary of Literature Review.
Author Region Period Methodology Findings
Hardi et al. (2023) Indonesia 1995-2022 FMOLS,DOLS,MRA, VECM FMOLS and DOLS: Corruption does not have a
direct effect on HDI, Government expenditure
has a positive long-term effect on HDI, Tax
revenue has a negative long term effect on HDI
MRA: All variables do not have a strong direct
impact on HDI
VECM: There is no multivariate causality
between corruption and HDI.
Becherair and Tahtane (2017) Middle East and North
African Countries
1996-2012 Granger Causality Test There is a unidirectional causality between
human development and corruption. Human
development causes corruption in MENA
countries indirectly through political and health
expenditure channels.
Akinbode et al. (2020) Sub-saharan Africa 2005-2018 SGMM Lagged HDI, government effectiveness,
economic growth rate and government health
spending had a significant positive effect on
human development while corruption and its
interaction with government effectiveness had a
negative but insignificant effect on human
development.
Tripathi (2021) 187 Countries 1990-2017 Random effect Tobit and dynamic
panel data estimations
Adult literacy rate, FDI, per capita GDP, life
expectancy at birth and percentage urbanization
have a positive and statistically significant effect
on HDI. CO2 emissions, fertility rate, GDP
growth rate, Gini index and inflation rate have a
negative and statistically significant effect on the
HDI.
Nguea (2023) 33 African countries 1990-2019 Driscoll-Kraay, IV-GMM Panel
Quantile regression methods
Urbanization is significantly associated with
improved human development.
Acar and Topdag
˘(2022) 128 Countries 2019 Quantile regression Per capita income, democracy, urbanization, and
IMF loan use have a positive effect on human
development. Infant mortality reduces on human
development.
Sarabia et al. (2020) 28 EU countries 2023-2017 Linear regression There is high correlation between human
development and the level of corruption.
Sangaji (2016) Buddhist countries 2010-2014 Panel regression random effect model Life expectancy and birth, per capita GDP have
positive significant effect on HDI while inflation
and fertility rates have a negative signs.
Stryzhak et al. (2022) 188 Countries 2017-2019 Spearman Rank Order and Kendall Tau
Correlations
The link between HDI and Worldwide
Governance indicators is directly positive.
Kamalu and Ibrahim (2022) 14 Developing countries 1991-2019 DCCE, ADF (CADF) AND IPS (CIPS) Institutional quality has a positive and statistically
significant long run effect on human
development.
(continued)
Okoth and Omar 5

Table 1. (continued)
Author Region Period Methodology Findings
Balcerzak and Pietrzak (2015) 24 European Union Countries 2004-2010 TOPSIS method with Panel analysis Quality of institutions has a positive influence on
human development.
Nginyu at al. (2025) 39 Sub-Saharan African countries 2010-2017 System-GMM estimation technique Institutional Quality increases human
development.
Li and Xu (2021) China 2004-2017 Simultaneous Equations Model There is an inverted U-shaped relationship
between Environmental Degradation Index and
HDI
B. Kurniawan et al. (2023) 7 ASEAN member countries 2012-2020 Multiple Linear Regression and Moderate
Regression Analysis
GDPPC has a statistically insignificant positive
influence on HDI while Corruption has a
statistically significant negative effect on HDI.
Gyawali et al. (2023) Global scale Descriptive Pollution remains a significant contributor to
human mortality worldwide with low-income
countries being disproportionately affect.
Thi Cam Ha et al. (2023) 143 Countries 2002-2019 GMM There is a significant positive relationship
between FDI and HDI. The impact of HDI-FDI
nexus is larger in countries with moderately
high-quality institutions. Good governance plays
a crucial role in enhancing human development.
Amponsah (2023) Africa 2000-2019 PCSE, FGLS Environmental pollution is proportional to level
of human development, EKC hypothesis is only
valid for high-HDI African countries
Raj et al. (2024) 26 States and union
territories in India
1990-2019 Convergence analysis Economically poor and low HD states have
continued to lag the economically rich and high-
HD states.
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differently, the selected independent variables—
economic growth, urbanization, institutional quality,
corruption, and environmental pollution—are justified
on the basis of their established relationships with HDI
in prior studies. Each of these variables adds to a com-
prehensive understanding of the determinants of human
development in Africa and thus provides a need for inte-
grated approaches to fostering sustainable developments.
Further details on the data used is available in Table 2.
Model
The Dynamic Common Correlated Effects (DCCE) and
Augmented Mean Group (AMG) techniques can be
applied to the analysis of dynamic panel data for coun-
tries with interdependent economies, especially in consid-
eration of including unique national characteristics and
cross-country interdependencies. Multi-country studies
need to take great care in accounting for both the com-
mon economic or environmental influences and the indi-
vidual country differences, in which case DCCE and
AMG both have particular advantages. The DCCE
model handles cross-sectional dependence effectively by
factoring in the potential impact events in one country
may have on others, mostly through economic or policy
channels. This is achieved by adding cross-sectional
averages, which net out common influences and, there-
fore, isolate country-specific dynamics, reducing the esti-
mation bias. This method is most helpful in a long-panel-
data setting as in this case, SADC region, where coun-
tries face common external shocks, so that country-
specific findings are not clouded by global movements.
Further, the AMG approach addresses the heteroge-
neity of responses of individual countries to similar
determinants. Since economic or environmental impacts
vary across countries, it allows unique slope estimation
for each country, permitting nuanced appreciation of
local responses to global issues. The AMG enhances the
robustness of the estimates by accounting for unobserved
common factors and allowing for different national tra-
jectories. This is especially useful in studies of economies
with great diversity, where individual responses to
policies—like carbon reduction strategies—may vary a
lot, which shall give the policymakers more tailored and
specific insights.
The Dynamic Common Correlated Effects (DCCE;
Chudik & Pesaran, 2015) and Augmented Mean Group
(AMG) (Eberhardt & Stephen, 2009; Eberhardt & Teal,
2010) estimators are advanced econometric techniques
well-suited for handling panel data with cross-sectional
dependence, and heterogeneous slopes. In dynamic
panels, the Common Correlated Effects (CCE) estima-
tor, typically consistent in nondynamic settings, faces
challenges due to the presence of lagged dependent vari-
ables, which introduces endogeneity. As such, where the
errors are weakly cross-sectionally dependent, and the
lagged dependent variable is no longer strictly exogen-
ous, the CCE estimator may often be inconsistent. The
DCCE mean group model applied in this study follows
the dynamic Equation 1 below:
yit =ai+liyi,t1+b0
ixit +X
rt
l=0
dilztl+eit ð1Þ
Where rt is the floor of ffiffiffiffi
T
3
plags of the cross-sectional
averages added into both the dependent variables and
the strictly exogenous variables, to achieve consistency of
the estimators. zt=(

yt1,
x_
t) with the MG estimator
given by ^pMG =1
=
NPN
I=1
^pi. For the consistency of the
Mean Group (MG) estimates, both N (the cross-sectional
Table 2. Summary of Variables.
Variable Acronym Measurement Source
Human development index HDI Index World Bank (2024)
https://databank.worldbank.org
Economic growth GDP GDP per capita USD constant 2015 World Bank (2024)
https://databank.worldbank.org
Urban development URB Urban population
percent of total population
World Bank (2024)
https://databank.worldbank.org
Institutional quality IQ Index Knoema (2023) https://knoema.com/QOGI
SD2024FEB/quality-of-government-institute-stan
dard-dataset
Environmental pollution EP CO
2
emissions metric tons per capita Our World in Data
Global Carbon Project (2023)
https://ourworldindata.org/grapher/annual-co2-
emissions-per-country
Control of corruption CORR Percentile rank World Bank (2024)
https://databank.worldbank.org
Okoth and Omar 7

dimension) and T (the time dimension) must jointly grow
to infinity (N,T) !N. The cross-sectional dimension N
approaches infinity to account for the heterogeneous
coefficients across units while the time dimension grows
to reduce the time series because of the lagged dependent
variable ensuring that the variability in the data is ade-
quately captured.
The AMG, unlike the CCEMG estimator, where
unobservable common factors are treated as nuisances,
the AMG approach considers these unobservables as
representing total factor productivity (TFP), which is
crucial in cross-country production functions. The AMG
procedure involves three key steps: A pooled regression
model augmented with year dummies is estimated using
first-difference OLS. The coefficients on the differenced
year dummies represent the estimated cross-group aver-
age of the unobservable TFP’s evolution over time,
referred to as the ‘‘common dynamic process.’’ The
group-specific regression model is then adjusted by
including the estimated TFP process. This can be done
either by explicitly adding it as a variable or by imposing
it on each group member with a unit coefficient, effec-
tively subtracting the estimated process from the depen-
dent variable. An intercept is also included in each
regression model to account for time-invariant fixed
effects, representing TFP levels. Similar to the MG and
CCEMG estimators, the group-specific model para-
meters are averaged across the panel, with optional
weighting applied to the averages. This approach allows
for a more nuanced analysis of the role of TFP in cross-
country production functions, accounting for both com-
mon dynamics and group-specific effects. The AMG fol-
lows the empirical Equation 2 as below:
yit =bixit +uit ð2Þ
Where uit =ait +lift+eit and xit =ait +lift+
gigt+eit the regression involves observable variables yit
and xit with country-specific slopes bi. The error term uit
in the model includes unobservables like group fixed
effects ait and a common factor ftwith heterogeneous
loadings li, accounting for both time-invariant and time-
variant heterogeneity, as well as cross-sectional depen-
dence. The unobserved factors ftand gt, which affect
both the dependent and independent variables, are not
restricted to linear or stationary forms, complicating
cointegration analysis. Furthermore, since the regressors
xit are influenced by the same common factors as the
observables, this induces endogeneity in the estimation
equation, presenting challenges for accurate estimation.
The DCCE and AMG estimators are adept at han-
dling panel data where variables are stationary at first
difference, as they manage non-stationary data by differ-
encing variables, thus ensuring reliable regression results
(Kredo et al., 2012). They are also proficient in capturing
long-run relationships among cointegrated variables,
providing insights into equilibrium dynamics
(Moirangthem & Nag, 2021). Furthermore, these estima-
tors address cross-sectional dependence, accounting for
correlations across different entities within the panel to
ensure robust estimation results (Pretorius et al., 2021).
Additionally, they accommodate heterogeneous slopes,
allowing for the estimation of individual-specific effects
and capturing the varying impacts of independent vari-
ables on the dependent variable across different panel
entities (Mbulawa, 2015). In this study the general equa-
tion followed is given by Equation 3:
logHDI =logGDP +logURB +logIQ +logEP
+logCORR +eit ð3Þ
1. Descriptives
2. Cross-sectional
Dependency tests
Unit root tests 3. Homogeneity
tests
Cointegration tests
4. Long-run
Estimates
DCCE, AMG
Figure 1. Analysis strategy.
8SAGE Open

Methodology and Empirical Results
Methodology
The study adopts a four-stage methodological approach
(Figure 1), firstly descriptives are reported, then cross-
sectional dependency and stationarity is assessed. In the
third stage, slope homogeneity is examined, followed by
cointegration tests. Finally, DCCE and AMG long run
estimators are obtained.
In the first phase of the study, descriptive statistics are
examined to determine the basic characteristics of the
dataset including mean, variance and skewness. Over the
period of study, the HDI of the SADC countries was
0.544, and a maximum and minimum of 0.808 and 0.303
respectively. The regions GDP per capita averaged at
3165.9USD with a minimum of 293.23USD and a maxi-
mum of 19481.6USD. Detailed descriptives are presented
in Table 3. Additionally graphical distribution of the
variables for the 16 countries over the period of study
are given in Figures 2A to 7A (in the Appendix).
In the second phase of the study, cross-sectional
dependence among the variables under study is exam-
ined. This is necessitated by the fact that the economies
being studied are interconnected and may exhibit corre-
lations across different countries due to shared regional
characteristics, economic linkages, and common external
shocks, when not accounted for, such interconnections
may lead to biased estimates and unreliable statistical
inference. In this context, the Pesaran test of cross-
sectional dependence (CD) is used (Jianguo et al., 2022).
It is observed from the results presented in Table 4, that
cross sectional dependence was present in HDI, GDP,
URB, and EP.
The next diagnostic test conducted by the study is the
stationarity test. A time series is stationary if its statistical
properties—such as mean, variance, and autocorrela-
tion—are constant over time. In a stationary series, val-
ues fluctuate around a fixed mean level without a trend,
and the spread of values (variance) remains stable.
Stationarity is important in time series analysis because it
allows for more reliable modeling and forecasting, as
future behavior can be inferred based on past behavior
without shifts in the underlying structure of the data. To
differentiate between a true unit root process and a sta-
tionary process influenced by random independent
shocks, a unit root test incorporating white noise into the
model is employed. It is essential to ascertain whether the
data series is trend stationary( stationary around a deter-
ministic trend) or difference stationary (stationary after
differencing). The CIPS (Cross-Sectionally Augmented
Im-Pesaran-Shin) and Breitung and Das (2005) tests are
employed due to their robustness in handling cross-
sectional dependence (Woldu & Szaka
´lne
´Kano
´, 2023).
As shown in Table 4, all variables are stationary at first
difference.
The third phase of the study, the Pesaran and
Yamagata (2008) Delta Test is used to test the hypoth-
esis that the coefficients of the explanatory variables are
the same across all cross-sections in a panel data model.
This helps determine if there is significant heterogeneity
in the relationship between the dependent and indepen-
dent variables across different cross-sections. As shown
in Table 4, the data exhibited heterogenous slopes.
Cointegration refers to a relationship between two or
more non-stationary time series that share a common
stochastic trend. If two series are cointegrated, they may
individually wander over time (non-stationary), but there
exists a linear combination of them that is stationary.
This means that while each series may be trending or
drifting, they maintain a stable, long-term equilibrium
relationship, allowing for meaningful modeling of their
relationship despite their non-stationary nature. The
Pedroni and Westerlund cointegration tests are used in
this context to establish presence of long run relationship
among the variables in the panel. It is confirmed that
cointegration is present in the data (Table 4).
Empirical Results
In the final stage of the analysis, DCCE and AMG long
run estimators are obtained and shown in Table 5. From
the robust findings, in Botswana, Comoros, Eswatini,
Lesotho, Madagascar, Malawi and in the overall panel,
GDP had a positive effect on human development index.
Table 3. Descriptive Statistics.
Variables Obs Mean Std. Dev. Min Max Skew. Kurt.
HDI 336 0.544 0.118 0.303 0.808 0.618 2.494
GDP 336 3,165.899 3,665.861 293.232 19,481.646 1.948 6.950
URB 336 38.591 14.27 14.61 70.877 0.448 2.267
IQ 322 35.384 24.85 0.900 84.6 0.303 1.723
EP 336 1.496 2.054 0.030 8.447 1.938 5.941
CORR 320 41.262 24.287 0.532 89.524 20.109 1.843
Note. Table of descriptives of the variables-HDI, GDP, URB, IQ, EP, and CORR used in the study.
Okoth and Omar 9

Table 4. Diagnostics.
Cross-sectional dependency test Unit root tests
Variables CD-statistic
CIPS Breitung and Das (2005)
I(0) I(1) I(0) I(1)
logHDI 47.734
a
22.457 24.273
a
4.8026 26.160
a
logGDP 33.902
a
21.638 23.800
a
20.2906 26.1280
a
logURB 25.347
a
21.994 -2.706
c
0.2789 22.2390
b
logIQ 0.375 22.560 24.504
a
0.2122 29.8552
a
logEP 10.973
a
22.147 24.208
a
21.2644 211.0688
a
logCORR 1.072 21.751 23.677
a
1.5586 26.6901
a
Slope homogeneity
Delta test Test stat.
D
_13.195
a
D
_
Adj 16.366
a
Cointegration tests
Pedroni test for cointegration
Test Test stat.
Modified Phillips-Perron t4.1121
a
Phillips-Perron t24.3675
a
Augmented Dickey-Fuller t22.1123
b
Westerlund test for cointegration
Test Test stat.
Variance ratio 1.9703
b
Note. a, b, and c imply the rejection of null hypothesis at the 1%, 5%, and 10% significance levels. In conducting the CIPS stationarity test, the Portmanteau
test for white noise is included, as well as both trend and constant deterministic terms. The critical values for significance decision criteria were at 22.63,
22.73, and 22.92 for the 10%, 5%, and 1% levels, respectively. A value is significant if more negative than the critical values at corresponding significance
level.
Table 5. Long-run Estimate Results.
Robustness tests: Long-run estimates
Countries Method logGDP logURB logIQ logEP logCORR
Angola DCCE/AMG 0.014/0.017 0.918/1.725
a
0.007/0.009 0.065
a
/0.085
a
20.002/20.006
Botswana DCCE/AMG 0.095
b
/0.147
a
0.011/0.608
a
0.042/0.019 -0.021
c
//-0.015 0.066/0.070
Comoros DCCE/AMG 0.098
b
/1.178
a
0.002/2.379
a
20.001/20.022
a
0.001/20.040 20.000/0.008
DRC DCCE/AMG 0.156/0.247
b
0.054/0.606
a
20.001/20.002 0.002/20.009 20.001/20.002
Eswatini DCCE/AMG 0.296
a
/0.446
a
0.365/2.027
a
0.004/0.009 20.010/0.061
a
0.013/0.043
Lesotho DCCE/AMG 0.102
a
/0.191
b
0.169
b
/0.372
a
0.033
b
/0.058
c
20.006/20.021 -0.101
b
/-0.372
a
Madagascar DCCE/AMG 0.147
a
/0.300
a
20.158
b
/0.328
a
20.011/20.054
a
0.017/0.003 20.005/0.034
b
Malawi DCCE/AMG 0.133
a
/0.572
a
20.318
b
/0.806
b
-0.013
c
/20.018 0.013/20.102
a
0.0004/20.000
Mauritius DCCE/AMG 0.148
a
/0.069 4.092
a
/22.136
a
0.043
b
/0.052
c
0.029/0.035 0.004/0.008
Mozambique DCCE/AMG 0.097/0.502
a
0.083/0.762
a
0.005/0.017 20.000/20.096
b
0.012/20.005
Namibia DCCE/AMG 0.054
b
/20.005 0.038/0.307
a
0.025/0.139
a
0.009/0.017 0.002/20.094
a
Seychelles DCCE/AMG 0.020/0.017 0.617
b
/0.500
a
0.076/0.067 0.0002/0.009 -0.063
c
/-0.059
c
South Africa DCCE/AMG 0.134/0.039 0.181/0.872
a
0.105/0.159 0.0005/20.021 20.082/20.161
a
Tanzania DCCE/AMG 0.023/0.240 20.069/0.179 20.008/0.047
a
20.009/20.004 0.017
b
/20.007
Zambia DCCE/AMG 0.151/0.299
a
0.262/0.851
a
0.007/0.005 20.018/20.038
a
0.005/0.018
Zimbabwe DCCE/AMG 0.344
a
/0.398
a
22.340
a
/24.884
a
20.034
b
/20.023 -0.104
a
/-0.155
a
20.012/20.014
PANEL DCCE/AMG 0.126
a
/0.247
a
0.244/0.647
a
0.017
c
/0.022 20.002/20.011 20.009/0.006
Note. These are findings from the DCCE and AMG estimators obtained from Stata output. a, b, and c imply 1%, 5%, and 10% significance levels. The values
in colored frames are robust and significant results.
10 SAGE Open

These findings suggest that economic growth is a critical
factor in enhancing human development outcomes. In
the context of SADC, the economic integration and
growth facilitated by regional trade agreements have
been instrumental in improving HDI. For instance, the
SADC has actively pursued policies aimed at reducing
trade barriers, which has led to increased economic activ-
ity and investment in member states. This economic
dynamism is reflected in the HDI improvements
observed in countries such as Botswana and Eswatini,
where GDP growth has been linked to better health, edu-
cation, and living standards (Egu & Aregbeshola, 2017;
Thow et al., 2015). The presence of South African multi-
national corporations (MNCs) in the region has also
contributed significantly to economic development, pro-
viding capital and innovation that bolster local econo-
mies and, consequently, human development (Egu &
Aregbeshola, 2017). Moreover, the HDI, which incorpo-
rates GDP per capita as a key component, underscores
the importance of economic resources in enhancing well-
being. The UNDP’s methodology for calculating HDI
emphasizes that GDP per capita accounts for a substan-
tial portion of the index, thereby establishing a direct
correlation between economic performance and human
development (Haq & Zia, 2022). This relationship is evi-
dent in the SADC region, where countries with higher
GDP per capita tend to exhibit better HDI scores,
reflecting improved access to education, healthcare, and
overall quality of life. Empirical studies have reinforced
this connection, demonstrating that increases in GDP
are associated with significant improvements in HDI
across various SADC nations. For example, research
focusing on the determinants of economic growth in the
SADC region indicates that economic expansion directly
correlates with enhancements in human development
metrics (Musora & Matarise, 2023). This finding is con-
sistent with broader economic theories that advocate for
the role of GDP as a predictor of human development
outcomes (Bechtel, 2018, 2019).
Urbanization recorded varied effects, a negative effect
in Zimbabwe, positive effects in Lesotho and Seychelles
while it exhibited mixed effects in Madagascar, Malawi,
and Mauritius. The impact of urbanization on HDI in
various countries within the SADC is very complex and
has varied widely depending on the socio-economic con-
text, institutional frameworks, and stages of urban devel-
opment for each country. This complexity is particularly
revealed through case studies of Madagascar, Malawi,
Mauritius, Zimbabwe, Lesotho, and Seychelles. In
Madagascar, Malawi, and Mauritius, urbanization pre-
sents opportunities and challenges that contribute to
ambiguous effects on HDI. Urbanization can improve
access to basic services like healthcare, education, and
work, which are all included in HDI. However,
inadequate urban planning and insufficient investment in
infrastructure may cause overcrowding of urban centers
with slimmed-down public services, bringing out mixed
results in human development (Patt et al., 2010). For
example, whereas Mauritius has relatively more progres-
sive development policies that harness urbanization for
economic development, thus the ambiguous effect war-
rants further investigation. In Madagascar and Malawi,
urbanization often overshoots infrastructure develop-
ment, creating variable and unpredictable consequences
for HDI (Kounou, 2020). This duality of urbanization in
these contexts raises the importance of good governance
and strategic planning that maximizes benefits but also
minimizes negative outcomes. Conversely, Zimbabwe’s
experience with urbanization is marked by negative
impacts on HDI, as indicated by both the DCCE and
AMG methods. The country’s economic instability, gov-
ernance challenges, and deficiencies in urban planning
contribute to this negative correlation. High rates of
urbanization without adequate support for job creation
and public services exacerbate inequality and strain
resources, ultimately hindering human development
(Soheylizad et al., 2016). Urban areas in Zimbabwe are
characterized by housing shortages, high unemployment
rates, and inadequate healthcare services, illustrating
how urbanization can impede progress when not accom-
panied by robust socio-economic support mechanisms
(Suryanto et al., 2022). In contrast, there are positive
links between urbanization and HDI in Lesotho and
Seychelles. In Seychelles, urbanization comes with better
access to health, education, and economic opportunities,
enabled by strong policy frameworks effective in manag-
ing urban growth and reducing poverty (Coburn &
Blower, 2017). Similarly, urbanization policies in
Lesotho are aligned with development goals, particularly
through investment in education and health infrastruc-
ture, which significantly raises HDI (Crush et al., 2017).
Taken together, the heterogeneous effects of urbaniza-
tion on HDI in SADC countries show that the relation-
ship is not homogenous but instead mediated by the
different policy regimes, economic structures, and insti-
tutional capacities of each country. In addition, effective
governance, economic stability, and investment in infra-
structure are important for urbanization to act as a cata-
lyst for human development. This is consistent with
broader literature that emphasizes the need for enabling
frameworks to realize the potential of urbanization while
mitigating its adversities (Silva et al., 2022).
Institutional quality consistently had positive effects
on human development effects in Lesotho and
Mauritius. The benefits associated with good quality
institution in enhancing human development in Lesotho
and Mauritius spring from a number of interrelated fac-
tors such as good governance, sound policies and their
Okoth and Omar 11

application that are aimed toward enhancing the HDI.
In the case of Lesotho, the institutional context is a sig-
nificant determinant of the level of human development
attained. The Lesotho National Development
Corporation (LNDC) is one of the main institutions cre-
ated to provide guidance to the development of the man-
ufacturing sector in the country. Its success depends on
the institutional arrangements in place and development
or growth of Private Sector Economies (Makhetha et al.,
2022). Strong institutions result in good governance
which is important in promoting policies that drive eco-
nomic growth and provision of social services leading to
better HDI (Thamae, 2015). In addition, the introduc-
tion of public private partnerships (PPPs) within the
health care system has its own benefits as it has
improved the quality of services offered as well as man-
agement systems which are key in the improvement of
health and human development in general (Hellowell,
2019). Similarly, high quality of governance plays a role
in the enhancement of human well being in Mauritius.
This island nation is known for good governance, which
is based on transparency, accountability, and concern
for the well-being of its citizens. This allows Mauritius to
follow policies that respond to the needs of the popula-
tion as a result health, education and economic growth
improves (McGuire et al., 2024). The governance of reg-
ulatory regimes in Mauritius has also been particularly
effective with regards to telecommunication services and
other similar businesses.
Environmental pollution had a positive effect on HDI
in Angola while the effect is negative in Zimbabwe. The
effects of environmental pollution on HDI are contradic-
tory in Angola and Zimbabwe which could be attributed
to their different socio-economic and governance reali-
ties. In Angola, some indicators of environmental pollu-
tion may be positively associated with HDI due to the
country’s reliance on natural resource extraction, which
can drive economic growth and improve living standards
despite the associated environmental degradation. In this
case, however, the picture cannot be painted black and
white because any form of enhancement can be realized
at a very high health cost, which Nazeer et al. (2022)
argues can be counter-productive in the long run. On the
other hand, Conversely, Zimbabwe experiences a nega-
tive impact of environmental pollution on HDI. The
country’s economic instability and governance challenges
exacerbate the adverse effects of pollution, leading to
deteriorating public health and reduced quality of life.
High levels of pollution, coupled with inadequate infra-
structure and public services, contribute to health crises
that undermine human development (Odiete, 2020). The
lack of effective environmental regulations further com-
pounds these issues, resulting in a situation where pollu-
tion detracts from overall human development outcomes
(Nahar et al., 2021). Thus, while Angola may leverage
pollution for economic gain, Zimbabwe’s context illus-
trates how environmental degradation can severely hin-
der human development.
Control of corruption recorded a negative effect on
HDI in Lesotho and Seychelles. The negative effect of
corruption control measures on the Human
Development Index (HDI) in Lesotho and Seychelles
can be understood through a nuanced examination of
the socio-political contexts and the implementation
dynamics of these measures. While corruption control is
generally aimed at enhancing governance and equitable
resource distribution, its short-term impacts can be dis-
ruptive, particularly in environments where corruption is
deeply embedded in economic and political systems. In
Lesotho, the implementation of stricter corruption con-
trols may initially lead to a decline in HDI due to the dis-
ruption of established patronage networks that have
historically facilitated access to public resources and ser-
vices. These entrenched systems often provide essential
services to communities, and their dismantling can result
in temporary service delivery interruptions, adversely
affecting health and education outcomes, which are criti-
cal components of HDI (Amate-Fortes et al., 2015).
Moreover, the challenges associated with effectively
enforcing anti-corruption policies can lead to bureau-
cratic inefficiencies, further complicating the landscape
of public service delivery and limiting access to resources
necessary for human development (Amate-Fortes et al.,
2015). Seychelles, despite its relatively higher economic
stability, faces unique challenges where corruption con-
trol initiatives can indirectly impact HDI. The govern-
ment’s focus on strengthening regulatory frameworks
may divert resources away from critical sectors such as
healthcare and education, especially in a small economy
with limited fiscal capacity (Ortega et al., 2013). This
redirection of funds can temporarily hinder the provision
of essential services, thereby negatively affecting HDI
components. Additionally, the adjustment period
required for institutions to adapt to new anti-corruption
measures can exacerbate these challenges, as public ser-
vices may struggle to maintain quality during the transi-
tion (Ganda, 2020). These findings underscore the
complexity of the relationship between corruption con-
trol and human development in specific African con-
texts. While anti-corruption initiatives are essential for
long-term development, their immediate effects can be
counterproductive if not managed carefully, particularly
in countries like Lesotho and Seychelles where corrup-
tion has been intertwined with socio-economic struc-
tures. The interplay between the implementation of these
measures and the capacity of institutions to maintain ser-
vice quality is crucial in determining the overall impact
on HDI (Qaiser et al., 2018).
12 SAGE Open

Discussion
The empirical findings from the Dynamic Common
Correlated Effects (DCCE) and Augmented Mean Group
(AMG) estimators align with existing literature, offering
nuanced insights into the determinants of HDI across the
SADC region. Broadly, the results echo existing studies
highlighting the positive link between economic growth
and HDI, such as Tripathi (2021) and Amponsah (2023),
which emphasize that increased income facilitates invest-
ments in key development sectors like education and
health. However, similar to Hardi et al., (2023), this study
recognizes the importance of sustainable growth, espe-
cially for countries heavily reliant on fluctuating commod-
ities, a critical point for ensuring long-term human
development. Urbanization’s impact on HDI in this study
reflects the diverse findings in previous literature, with
both positive and negative outcomes contingent on local
contexts. Saybasachi (2019) and Nguea (2023) observed
positive associations between urbanization and HDI, attri-
buting this to better access to essential services. This aligns
with positive findings in Lesotho and Seychelles, where
managed urbanization appears to enhance human devel-
opment. Conversely, the negative effect in Zimbabwe, mir-
roring Akinbode et al. (2020), suggests that challenges like
overcrowding, inadequate infrastructure, and insufficient
public services may hinder development gains from urba-
nization. The mixed results in Madagascar, Malawi, and
Mauritius further substantiate that urbanization’s impact
is context-sensitive, as supported by varying country
experiences in urban governance and resource allocation.
The study’s findings on institutional quality, showing
positive impacts on HDI in Lesotho and Mauritius, are
consistent with Kamalu and Ibrahim (2022) and Nginyu
et al. (2025), who underline the role of strong governance
in fostering human development. Effective institutions
enhance policy execution, reduce corruption, and
strengthen public service delivery, ultimately promoting
health, education, and economic security. The conver-
gence with literature underscores the imperative for
robust institutional frameworks in the SADC context to
achieve sustained improvements in HDI. The divergent
impact of environmental pollution on HDI across coun-
tries, such as the positive effect in Angola, supports Li
and Xu (2021)’s findings on the complex, often nonlinear
relationship between economic growth, environmental
pollution, and HDI. Short-term economic gains from
industrial activities may momentarily elevate HDI,
despite environmental costs. However, as Zimbabwe’s
case demonstrates, long-term environmental degradation
can undermine HDI through health impacts, validating
findings by Gyawali et al. (2023). These outcomes
emphasize the necessity for development policies balan-
cing economic growth with environmental sustainability,
especially for countries like Zimbabwe where pollution’s
adverse health effects directly diminish HDI.
Unexpectedly, the observed negative effect of corrup-
tion control on HDI in Lesotho and Seychelles warrants
further exploration. This outcome deviates from Selcxuk
(2006) and Sarabia et al. (2020), who find that reduced
corruption generally correlates with higher HDI. A possi-
ble explanation, as suggested, may involve short-term inef-
ficiencies arising from anti-corruption efforts that disrupt
entrenched systems, temporarily hindering public service
delivery. Alternatively, the delayed benefits of institutional
reforms might explain this lag, with improvements in HDI
becoming apparent only after sustained anti-corruption
measures. This finding suggests the importance of a
balanced approach to anti-corruption, where reforms are
carefully implemented to mitigate potential adverse effects
on human development outcomes.
Conclusion and Recommendations
Conclusion
The study aims to examine the impact of economic
growth, urbanization, institutional quality, environmen-
tal pollution, and corruption control on the Human
Development Index (HDI) in 16 SADC member coun-
tries from 2000 to 2020. Given that HDI represents a
broad measure of well-being, it is crucial to understand
how these variables—often emphasized in development
theory—specifically shape human development out-
comes in the Southern African region. In exploring this
topic, the study addresses gaps in previous literature,
such as the lack of empirical analysis on environmental
pollution’s direct effects on HDI, which has mostly been
studied in theoretical or review-based contexts. Further,
prior research has often disregarded the importance of
cross-sectional dependence, despite the shared borders
and strong trade linkages among SADC countries, par-
ticularly between countries like Lesotho, Eswatini, and
South Africa. This research employs the Dynamic
Common Correlated Effects (DCCE) and Augmented
Mean Group (AMG) estimators, which account for
cross-sectional dependence and heterogeneity—both
essential in capturing unique regional dynamics. The use
of DCCE and AMG methods proved invaluable for cap-
turing the complexity of these relationships, which would
be challenging to detect with traditional models.
Additionally, by covering a more extended period (2000-
2020), the study offers insights into how these relation-
ships may have evolved, particularly capturing the con-
text of recent events like the COVID-19 pandemic.
Unlike previous studies that used shorter time frames or
limited variables, this study provides a comprehensive
analysis by including five key predictors of HDI, thus
Okoth and Omar 13

ensuring a holistic examination of development determi-
nants in SADC. In Botswana, Comoros, Eswatini,
Lesotho, Madagascar, Malawi, and the overall panel
GDP positively influenced the Human Development
Index (HDI). This reinforces the vital role of economic
progress in enhancing human development, but it also
suggests that for growth to translate effectively into
human development, it must be accompanied by robust
institutional frameworks and anti-corruption measures.
Urbanization showed varied effects: a negative impact in
Zimbabwe, positive impacts in Lesotho and Seychelles,
and mixed results in Madagascar, Malawi, and
Mauritius; indicating that the benefits of urbanization
depend on factors like governance quality, planning, and
environmental policies. In cases where rapid urbaniza-
tion led to environmental degradation, gains in HDI
were offset by the associated health and social costs.
Institutional quality consistently had a positive effect on
HDI in Lesotho and Mauritius. Environmental pollution
positively impacted HDI in Angola but had a negative
effect in Zimbabwe. Control of corruption negatively
affected HDI in Lesotho and Seychelles. These nuanced
results underscore the need for country-specific policies
rather than blanket solutions. Policymakers should tailor
interventions to each country’s unique conditions, balan-
cing the goals of economic growth, urban development,
and environmental management with the need for high
institutional quality and effective corruption controls.
Recommendations for Policy
To enhance human development in the Southern African
Development Community (SADC) region, policymakers
must implement multifaceted strategies tailored to the
unique challenges and opportunities of each country. In
Botswana, the Comoros, Eswatini, Lesotho,
Madagascar, and Malawi, where a positive relationship
between GDP and Human Development Index (HDI)
has been identified, it is imperative to diversify the eco-
nomic base by reducing reliance on extractive industries
and promoting sectors such as manufacturing, tourism,
and digital services, thereby fostering a more resilient
economy capable of sustaining human development
gains amidst external shocks. Investment in human capi-
tal is crucial; thus, policymakers should allocate
resources toward education, healthcare, and social pro-
tection, prioritizing budgets for primary healthcare, early
childhood education, and vocational training that align
with local job market needs. Additionally, strengthening
social safety nets through targeted welfare programs
offering unemployment benefits, health insurance, and
affordable housing support can significantly reduce vul-
nerability among low-income populations. For countries
like Zimbabwe, where rapid urbanization has adversely
affected HDI, the focus should shift to urban infrastruc-
ture and service provision, necessitating the development
of integrated urban plans that anticipate population
growth, allocate land for housing, and expand essential
infrastructure, including water, sanitation, and waste
management facilities. Furthermore, affordable housing
initiatives and investments in public transportation are
essential to mitigate overcrowding and ensure equitable
access to urban resources, while Lesotho and Seychelles
should leverage urbanization for economic growth by
enhancing urban infrastructure and fostering employ-
ment opportunities, which can be supported by initia-
tives for small and medium-sized enterprises (SMEs),
expanded digital access, and the creation of urban zones
that attract investment and skilled labor. Given the posi-
tive impacts of institutional quality on HDI in Lesotho
and Mauritius, governance improvements across SADC
should encompass capacity building for public institu-
tions through training programs for civil servants, judi-
cial reforms to enhance accountability, and the
modernization of bureaucratic processes to improve ser-
vice delivery. Engaging the community and enhancing
transparency are vital; thus, governments should adopt
participatory governance models and strengthen public
oversight mechanisms, including public expenditure
tracking and digital transparency initiatives. Context-
sensitive anti-corruption measures must be prioritized,
recommending a phased approach to implementing
reforms that mitigate short-term disruptions while ensur-
ing long-term benefits. Moreover, to address the mixed
effects of pollution on HDI, particularly in Angola and
Zimbabwe, it is essential to strike a balance between eco-
nomic growth and environmental health, promoting
clean energy initiatives and strengthening pollution regu-
lation in high-emission industries, while providing gov-
ernment subsidies for clean technology and incentivizing
private sector compliance with environmental standards.
The Environmental Kuznets Curve (EKC) hypothesis
may serve as a strategic framework for high-HDI coun-
tries, allowing for a phased transition from pollution-
intensive growth to stricter environmental controls as
HDI improves, supplemented by public health cam-
paigns to raise awareness about pollution impacts.
Finally, while anti-corruption measures offer long-term
benefits for HDI, it is critical to ensure that these inter-
ventions do not disrupt immediate service delivery; thus,
gradual and transparent institutional reforms, pilot pro-
grams in key public sectors, and robust monitoring and
feedback systems should be established to assess the
short-term effects of anti-corruption initiatives, allowing
for timely adjustments to minimize negative impacts on
HDI. Enhanced transparency in public funds and pro-
curement processes can serve as an effective entry point
for anti-corruption policies, fostering accountability and
14 SAGE Open

facilitating the effective implementation of these multifa-
ceted strategies aimed at improving human development
across the SADC region..
Limitations and Recommendations for Further Research
This study’s shortcomings stem from its dependence on
publicly accessible data, which might not adequately
capture the complex dynamics of the many variables
affecting the Human Development Index (HDI) within
the Southern African Development Community (SADC)
area. The possible exclusion of unobserved variables that
might have a substantial impact on the results and result
in partial or distorted interpretations of the correlations
among the components under study is one of the main
limitations. Furthermore, variations in definitions, mea-
suring techniques, and data quality between nations may
contribute to the difficulties in cross-country compari-
sons, making it more difficult to interpret the findings
and ensure the validity of the conclusions drawn.
Furthermore, the varied effects of urbanization and envi-
ronmental degradation underscore the need for more in-
depth investigations into these relationships, particularly
how contextual elements drive these outcomes. This
necessitates the application of qualitative methodologies,
such as case studies or ethnographic research, particu-
larly in countries exhibiting unique patterns in HDI, to
capture the local realities and socio-political dynamics
influencing development. The unexpected negative
impacts of corruption control on HDI also warrant fur-
ther scrutiny, ideally through longitudinal studies that
assess the temporal lag between anti-corruption initia-
tives and their subsequent effects on human development
outcomes. Future research should consider expanding
the scope to include additional nations beyond the
SADC, as well as extending the timeframes under inves-
tigation to test the generalizability of these findings. This
could enable a more comprehensive understanding of the
dynamic interactions between the variables impacting
HDI. Moreover, alternative methodologies, such as
mixed-methods approaches that combine quantitative
analysis with qualitative insights, could provide a richer
exploration of the complexities surrounding human
development and yield more nuanced policy recommen-
dations tailored to the specific contexts of each country.
Future research could also consider widening the scope
to include other nations and longer timeframes to test
the generalizability of the findings and investigate the
dynamic interactions between these variables
ORCID iDs
Erick Okoth https://orcid.org/0000-0002-9568-2862
Abdulmalik Egesa Omar https://orcid.org/0009-0009-4913-
2550
Funding
The author(s) received no financial support for the research,
authorship, and/or publication of this article.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Data Availability Statement
The datasets used or analyzed during the current study are
available from the corresponding author on reasonable request.
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Appendix
Figure 1A. Map of SADC member countries (Vassilakos & Martin, 2023).
Figure 2A. HDI in SADC member countries.
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Figure 3A. Economic growth in SADC member countries.
Figure 4A. Urbanization in SADC member countries.
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Figure 5A. Environmental pollution in SADC member countries.
Figure 6A. Institutional quality in SADC member countries.
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Figure 7A. Control of corruption in SADC member countries.
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