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American Journal of  Applied 
Statistics and Economics (AJASE)

Migration Dynamics in West Africa: The Nigeria Experience with Internet Access and 
Human Capital Investment

Obomeghie Adamu Muhammed1*, Obomeghie Adamu Inusa2

Volume 4 Issue 1, Year 2025
ISSN: 2992-927X (Online)

DOI: https://doi.org/10.54536/ajase.v4i1.5542
https://journals.e-palli.com/home/index.php/ajase

Article Information ABSTRACT

Received: June 19, 2025

Accepted: July 22, 2025

Published: November 14, 2025

The advent of  the internet has led to increased cross-border interactions and transnational 
activities. Similarly, the increase in human capital investment has further given rise to digital 
migrants further highlighting the role of  internet access in altering traditional migration 
dynamics. This study therefore examines the migration dynamics in West Africa using 
the Nigeria scenario. The dynamic OLS is used to analyze the data collected from various 
statistical bodies and agencies such as; the Nigeria CBN statistical bulletin as well as, the World 
Bank’s world development indicator databases. The data collected is from the period ranging 
from 2008 to 2023. Findings indicates that internet access and human capital investment 
both contributes negatively to migration dynamics in West Africa, as it significantly fuels 
the widespread emigration of  skilled West Africa nationals. The insights gained in this 
work can inform evidence-based strategies to optimize the benefits and mitigate the risks 
associated with complex migration phenomenon. This increase in emigration has further 
exacerbated brain drain and human capital flight, meaning that West Africa countries would 
face significant challenges to its economic development, social cohesion, and long-term 
prosperity if  migration issues are poorly managed. It is recommended that policy makers in 
collaboration with relevant agencies should, invest in expanding broadband infrastructure 
and ensuring affordable internet services with the view to using the technology to discourage 
brain drain and encourage digital migrant. It is further recommended that government 
should promote vocational and technical training programs aligned with domestic market 
needs with the view to encourage brain circulation. 

Keywords

Brain Drain, Digital Technology, 
Internet, Migration Dynamics

1 Department of  Statistics, Auchi Polytechnic, Auchi, Edo State, Nigeria
2 Department of  Polymer Engineering, Auchi Polytechnic, Auchi, Edo State, Nigeria
* Corresponding author’s e-mail: maoisdg@yahoo.com

INTRODUCTION
Migration has served as a driving force behind cultural 
exchange, economic growth, demographic changes, and 
the spread of  innovations. Migration is the movement 
of  people from one place to another, involving a change 
of  residence across borders or within a country, often 
driven by economic, social, political, or environmental 
factors. It encompasses various forms such as voluntary 
or forced migration, internal or international migration, 
and seasonal or permanent movement (IOM, 2022). 
According to Castles and Miller (2009), it fosters cultural 
diversity and understanding through the exchange of  
traditions, languages, and customs, enriching social 
fabric and fostering intercultural dialogue. The UN 
report (2010) noted further that, migration influences 
population dynamics by balancing age structures and 
addressing labor shortages in aging societies, thus 
impacting social services and economic sustainability. 
One may conclude that, migration is a vital component 
of  human society that shapes demographic patterns, 
fosters economic and cultural development, and 
addresses societal challenges.
The internet has revolutionized human life by serving as a 
global communication network that connects individuals, 
organizations, and governments across the world. Since 
its inception in the late 20th century, the internet has 
become an integral part of  daily existence, transforming 
the way people communicate, learn, work, and access 

information. It has bridged geographical gaps, facilitated 
instant communication, and enabled the dissemination 
of  knowledge on an unprecedented scale. Studies such 
as, Castell, (2010), noted that the Internet has made 
it possible for individuals to communicate instantly 
regardless of  their physical location, fostering greater 
social interaction and global connectivity. Social media 
platforms such as, email and other messaging apps enable 
real-time communication, thus strengthening personal 
and professional relationships.
The internet has significantly enhanced migration 
processes in West Africa by facilitating various aspects of  
migration, from information access to communication 
and service provision. The internet provides West 
Africa nationals with vital information about migration 
opportunities abroad, including visa requirements, job 
openings, and educational prospects. Online platforms 
and social media groups often share firsthand experiences, 
tips, and updates, enabling prospective migrants to make 
informed decisions (Maqsood et al., 2025). Many West 
Africa nationals seeking to migrate or work abroad utilize 
online job portals, social media, and recruitment websites. 
These platforms streamline the job search process 
and connect prospective migrants with employers or 
recruitment agencies, making migration more accessible 
and efficient (Obi-ani et al., 2020). 
The historical relationship between human capital and 
migration dynamics is a complex and evolving topic that 



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has garnered significant scholarly attention. Broadly, this 
relationship can be understood through the lens of  how 
the movement of  people influences, and is influenced by, 
the distribution of  skills, education, and expertise across 
regions and countries (Obomeghie, 2025). Migration 
was often driven by economic necessity, conflict, or 
colonization, with less emphasis on human capital 
considerations. However, skilled migration, such as the 
movement of  artisans or scholars, did occur and influenced 
local development (Docquier & Marfouk, 2004).
With globalization and technological advancements, 
migration increasingly became a mechanism for the 
transfer of  human capital. Countries with advanced 
economies attracted highly educated migrants, leading 
to what is often termed “brain drain” from developing 
nations and “brain gain” for developed nations (Docquier 
& Rapoport, 2012).

Statement of  Problem
Migration dynamics in West Africa are complex and 
influenced by multiple factors, including economic 
opportunities, education, infrastructure, and technological 
development. Despite the growing penetration of  the 
internet and increasing investments in human capital, 
there is limited understanding of  how these variables 
specifically influence migration flows within the region. 
While some studies suggest that internet access facilitates 
access to information, social networks, and opportunities, 
thereby potentially influencing migration decisions 
(Irele & Bababunmi, 2024), others highlight the risk of  
brain drain and uneven development (Maharaj, 2014). 
Similarly, investments in human capital are believed to 
shape migration patterns by either encouraging skilled 
migration or promoting retention of  talent within 
countries (Ozulumba et al., 2024). However, empirical 
evidence on the combined impact of  internet usage and 
human capital development on migration in West Africa 
remains fragmented and underexplored.
This research deficit poses a challenge for policymakers 
seeking to harness technological and educational 
advancements to promote balanced migration and regional 
development. Without a comprehensive understanding of  
these relationships, strategies aimed at leveraging internet 
infrastructure and human capital investments to optimize 
migration flows and minimize negative consequences 
may be ineffective or counterproductive.

Objectives of  The Study
To evaluate the influence of  internet access on migration 
dynamics in West Africa.
To identify the role of  human capital in the flow of  
migration in West Africa.
To provide policy recommendations for leveraging 
internet technologies to improve safe and informed 
migration in West Africa
Overall, these objectives aim to generate comprehensive 
empirical evidence on how internet usage impacts migration 
patterns, experiences, and outcomes in West Africa.
Significance of  the Study

The significance of  studying the impact of  internet access 
and human capital development on migration dynamics 
in West Africa lies in understanding how technological 
advancements and educational investments influence 
migration patterns within the region. This research can 
provide valuable insights for policymakers, educators, 
and development agencies aiming to foster sustainable 
development and regional integration. Understanding the 
role of  internet access and human capital in migration can 
help design targeted policies to manage migration flows, 
reduce brain drain, and promote regional development 
(Ejemeyovwi et al., 2019).
Insights from the study can help mitigate negative 
effects such as brain drain and social dislocation, while 
maximizing benefits of  human capital mobility (Maharaj, 
2014). Finally, an understanding these dynamics supports 
regional cooperation and development strategies aimed 
at balancing migration flows and promoting inclusive 
growth (World Bank, 2022).

LITERATURE REVIEW
Conceptual Framework
Internet access refers to the ability to connect to the 
internet, allowing individuals to access information, 
connect with others and take advantage of  various 
opportunities in areas such as, education, healthcare 
business etc. (Majumder, 2019). The proliferation 
of  internet technology has particularly significant 
implications for migration processes, as it facilitates 
the flow of  information, connects migrants with their 
families, and provides platforms for migration-related 
services. Nigeria, as one of  Africa’s largest economies 
with a growing population of  potential migrants, has 
experienced an increasing reliance on internet technology 
to support various aspects of  migration, including 
information seeking, social networking, and online 
recruitment. According to Obomeghie and Ugbomhe, 
(2021), the advent of  the internet has increased the spate 
of  globalization.
In the context of  migration, internet usage has been 
identified as a vital facilitator that enhances migrants’ 
access to information about migration opportunities, 
legal requirements, and living conditions in destination 
countries. It also fosters social support networks that can 
ease the migration process and integration (Godin et al., 
2025). 
Migration is typically defined as a move that crosses 
a specified political boundary, such as a county, or 
moves into a different labor market for the purpose of  
establishing a new place of  residence. Migration within a 
country is referred to as internal migration, and migration 
that crosses a national boundary is called immigration or 
emigration (Toney & Bailey, (2014). 
According to IOM (2022), migration can be categorized 
in various ways such as:

Internal Migration
Movement within a country’s borders (e.g., rural-to-urban 
migration, inter-state movement).



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International Migration
Movement across national borders, leading to a change 
of  country of  residence.

Rural-to-Urban Migration
A common form of  internal migration driven by the 
perceived economic opportunities and amenities in urban 
centers.

Chain Migration
A process where migrants are assisted in their move by 
family members or friends who have already settled in the 
destination area, often through social networks.
Migration is not just an individual decision but is heavily 
influenced by social and economic networks. These 
networks, often facilitated by modern communication 
technologies, provide information, financial support, and 
emotional sustenance to migrants, both before, during, and 
after their journey (Akanle et al., 2020). 
Human capital development refers to the process of  
improving the skills, knowledge, health, and overall 
capabilities of  individuals, which enhances their productivity 
and potential contribution to economic growth and social 
well-being (Obomeghie, 2025). In the context of  migration 
dynamics, human capital development plays a crucial role 
in shaping migration patterns, decisions, and outcomes 
within and across regions.
When individuals acquire advanced skills and education 
through investments in health, education, and training, 
they become more mobile, often seeking opportunities 
in regions where their skills are in demand (Elsayed et 
al., 2025). This phenomenon can lead to brain drain, 
where highly skilled individuals migrate from their home 
countries to more developed regions, potentially resulting 
in a loss of  human capital for the origin country (Docquier 
& Rapoport, 2012). In West Africa, efforts to develop 
human capital are intertwined with migration dynamics, 
as improved education and skills influence whether 
individuals choose to migrate, return, or stay. Human 
capital development can thus serve as both a driver and a 
consequence of  migration, impacting regional economic 
integration, labor markets, and development trajectories 
(Adepoju, 2010).

Theoretical Review
Studying the impact of  internet access on migration 
dynamics in West Africa is strongly underpinned by several 
theoretical frameworks. These theories help to explain 
why and how the internet influences migration decisions 
and experiences, offering a deeper understanding beyond 
mere observation. 

Social Capital Theory
This theory posits that the internet facilitates the building 
and strengthening of  social networks, which are critical 
in migration processes. Online platforms enable migrants 
and potential migrants to access social capital that can 

provide information, emotional support, and assistance 
during migration and integration. For example, migrants 
use social media to connect with family and community 
members, reducing uncertainties and risks associated with 
migration (Godin et al., 2025).

Information and Communication Technologies 
(ICT) Diffusion Theory
This framework explains how the adoption and usage 
of  internet technologies spread within communities, 
influencing migration patterns. Increased access to 
ICTs leads to greater information dissemination about 
migration opportunities, legal requirements, and living 
conditions, which can encourage migration or facilitate 
safe migration practices (Rogers, 2003). 

Network Theory (and Transnationalism)
Network theory posits that migration is sustained and 
perpetuated through social ties connecting migrants, non-
migrants, and institutions across sending and receiving 
areas. These migrant networks reduce the costs and risks 
of  migration, influencing the magnitude and direction 
of  flows (Massey et al., 1993). Transnationalism, as a 
related concept, describes the sustained cross-border ties 
and activities of  migrants that link their home and host 
countries, often enabled by technology (Vertovec, 1999). 
Cheap and instant communication (WhatsApp, video calls) 
reduces the economic and emotional costs of  maintaining 
transnational ties, strengthening family and community 
bonds across borders (Dekker & Engbersen, 2014).

Human Capital Theory
This foundational theory posits that individuals invest 
in their own human capital through education, training, 
and health, to enhance their productivity and earning 
potential. Migration is often viewed as a response to 
disparities in human capital returns across regions; 
individuals move from areas with lower returns to those 
with higher opportunities, seeking better economic 
benefits (De Haas, 2019).

Neoclassical Economic Theory of  Migration
Building on human capital theory, this perspective 
suggests that migration is driven by rational economic 
decisions aimed at maximizing income. Human capital 
plays a crucial role as migrants move to regions where 
their skills and qualifications are valued more highly, thus 
increasing their lifetime earnings (De Haas, 2007).

New Economics of  Labor Migration (NELM)
This theory emphasizes household decision-making 
rather than individual rationality. It considers migration 
as a strategy for risk diversification, investment in human 
capital, and overcoming market failures. Migration 
can facilitate the transfer of  human capital through 
remittances, skill development, and knowledge exchange 
upon return (Sako, 2002).
Skill Transfer and Brain Circulation



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Recent frameworks highlight the concept of  “brain 
circulation,” where migration is not solely a loss but also 
a channel for skills transfer, remittances, and knowledge 
exchange, contributing positively to human capital 
development in origin countries (Clemens et al., 2020).
These theories, often used in conjunction, provide a 
robust framework for understanding the multifaceted 
relationship between internet access and migration 
patterns. They highlight that the internet is not a neutral 
tool but an active agent that mediates, transforms, and 
complicates migration processes.

Empirical Review
Recent empirical studies have examined how internet 
access influences migration dynamics, including decision-
making, migration flows, integration, and transnational 
ties. For instance, Godin et al. (2025) found that migrants 
in Nigeria extensively use social media and online forums 
to gather information before migrating, which influences 
their destination choices and preparation.
Research by Koser (2007) indicates that increased internet 
penetration correlates with higher migration flows, 
especially in regions where information barriers previously 
limited mobility. They observed that in Southeast Asia, 
the internet has facilitated chain migration by enabling 
migrants to maintain contact with their networks abroad, 
encouraging others to follow. Furthermore, empirical 
evidence by Ruyssen & Salomone (2017) demonstrated 
that migrants who actively use digital communication tools 
maintain stronger ties with their home countries, leading to 
increased remittances and sustained migration links.
Specifically, some empirical studies confirm a positive 
correlation between internet access/usage and migration 
aspirations and intentions in Nigeria. Grubanove et al. 
(2021), in a broad study that included African countries, 
found that having internet access is positively associated 
with both the desire to move abroad and preparations 
to migrate. Ufuophu-Biri (2020) specifically found that 
Nigerian youths in Edo and Delta states, highly exposed 
to migration information on the internet, showed a 
high propensity to travel abroad due to internet-driven 
migratory motivation.
Odulami (2025) conducted a quantitative survey in 
Ogun State, Nigeria, revealing that while socio-economic 
conditions are the primary drivers of  migration, social 
media significantly reinforces migration aspirations 
by “amplifying idealized ‘Japa’ narratives and under-
representing the complexities of  return.” This supports 
the idea that social media creates a compelling, 
often glamorized, image of  life abroad, influencing 
youth’s desire to emigrate. This aligns with the Social 
Amplification of  Risk Framework, where perceived 
opportunities are amplified. Finally, an empirical review 
of  the impact of  internet access on migration in Nigeria 
reveals a complex and often contradictory picture, 
confirming that the internet acts as a dual-edged sword, 
both facilitating and complicating migration processes, 
although comprehensive, large-scale empirical studies 

specifically on Nigeria are still emerging. 
Empirical research indicates that human capital 
development significantly influences migration patterns 
at both regional and international levels. Investments in 
education, health, and skills tend to increase individual 
mobility, as more educated and skilled individuals are 
more likely to migrate in search of  better opportunities 
(Docquier & Rapoport, 2012). 
Studies such as, Beine et al. (2014) have shown that higher 
levels of  education and skills correlate positively with 
migration propensity. They found that countries with 
higher human capital levels tend to export more skilled 
labor, leading to brain drain but also to the potential for 
remittances and knowledge transfer. Equally, research 
by Docquier & Rapoport (2012) reveals that countries 
with a well-developed human capital base tend to attract 
migrants, especially skilled workers, contributing to 
regional migration flows and global talent distribution.
Research works by Maharaj (2014), noted that, the 
disparity in human capital levels between regions often 
drives migration from less developed to more developed 
areas, exacerbating regional inequalities . This pattern is 
evident in West Africa, where migration often stems from 
disparities in educational attainment and employment 
opportunities.

Research Gaps
While many studies examine internet penetration broadly, 
few explore how disparities in internet access across urban 
and rural areas influence migration decisions and human 
capital retention. Understanding how digital inequalities 
affect migration patterns remains under-explored (Irele 
& Bababunmi, 2024). Equally, most research provides 
cross-sectional analyses, lacking longitudinal studies 
that track how changes in internet access and human 
capital over time impact migration flows in West Africa. 
Long-term data is crucial to establish causality and 
observe trends (Ozulumba et al., 2024). In summary, the 
key research gaps involve the need for more nuanced, 
longitudinal, and country-specific studies that consider 
digital inequalities, informal sectors, and policy impacts, 
as well as the interplay between internet use and human 
capital in shaping migration dynamics in West Africa. 
Addressing these gaps can lead to more targeted and 
effective development strategies.

MATERIALS AND METHODS
Research Design
The research design adopted in this study is the descriptive 
analysis, this is in order to provide reviewers with clear, 
concise insights that aid in planning and operational 
decisions. As well, descriptive analysis allows analyst to 
better understand the data landscape, which is essential 
for accurate and effective decisions.(Umoru, et al., 2023).

Method of  Data Collections
Time series data is used for the study, the data were 
collected from the CBN statistical bulletin (2024) and the 



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World Bank’s World Development Indicators database 
(2024). the data were from 2003 to 2023.

The Model of  the Study
The general form of  a DOLS model is:
Yt = β0 +β1X1+∑i=-pqγi ∆X1 t-1+ β2X2+∑i=-pqγi ∆X2 t-1+ 
∑j=1k δj∆Zjt+ εt )
Where:
Yt Migration Dynamics for Nigeria within the period of  
study 
Xt1 Internet access in Nigeria within the period of  study.
Zjt Control variables (GDP and Inflation rate) within the 
period of  study .
∆X1 t-1 First differences of  the internet usage variable, 
with leads (i<0) and lags (i>0). This captures dynamic 
effects and accounts for potential endogeneity.
∆X2 t-1 First differences of  the Human capital investment, 
with leads (i<0) and lags (i>0). This captures dynamic 
effects and accounts for potential endogeneity.
The choice of  p and q (number of  leads and lags) is 
determined within the  study. 
∆Zjt First differences of  the control variables, also with 
appropriate leads and lags 
β1 β2 The long-run coefficient of  interest, representing the 
impact of  internet access and human capital investment 
on migration flow.
εt Error term.

The variables used in this study are defined below:
Migration dynamics (NMIG) = data for net migration 

is used to represent the difference between the number of  
people immigrating to Nigeria and the number of  people 
emigrating from Nigeria within the period under review.

Internet access (ITU) = Internet access ability to connect 
to the internet to accomplish different tasks or activities.

Human capital index (HCI) = A measure that quantifies 
the human capital in Nigeria, reflecting the health, 
education, and skills of  its population.

Economic growth (GDP) = GDP expressed in US 
dollars to enable international comparisons of  living 
standards and economic prosperity.

 Inflation rate (INF) = the level of  inflation in Nigeria

Method of  Data Analysis
The dynamic OLS estimation is used in the analysis 
because it extend the OLS regression by including leads 
and lags of  the first differences of  the regressors to 
correct for endogeneity and serial correlation (Stock & 
Watson, 1993; Banerjee et al., 1993).

Analytical Framework
The analytical framework for the study is hypothesizes in 
the table 1 below;

Justification of  the Chosen Method

Table 1: Hypothesized analytical framework
Variable Expected sign Rational
ITU Negative (-) Internet usage provides Nigerians with access to information about 

job opportunities, education, and living conditions abroad, potentially 
encouraging emigration (Adepoju, 2010). 

HCI Negative (-) Increased HCI can lead to higher international emigration of  skilled 
individuals (brain drain) if  opportunities abroad are better (Adegoke, 2023). 

GDP Positive (+) Higher GDP through improved living standards may potentially reduces the 
motivation for migration (Yemisi & Tosho, 2020).

INF Positive/Negative (+/-) Increase inflation rate may increase immigration flow because increase in the 
price of  goods and services encourage foreign investment. It may also lead 
workers to find better paying jobs abroad (Ejemeyovwi, 2019). 

Source: Authors compilation.

Migration flows, internet usage, human capital investment 
and some macroeconomic variables often exhibit non-
stationary properties (trends over time). If  they are 
cointegrated, it means they have a long-run equilibrium 
relationship. DOLS is designed to estimate this long-run 
relationship in the presence of  cointegration. Equally, 
DOLS addresses potential endogeneity by including leads 
and lags of  the first-differenced regressors. This accounts 
for the dynamic interactions and feedback effects between 
the variables, ensuring that the estimated coefficients 
represent the true long-run impact. In addition, DOLS 
often performs better than other cointegration techniques 
(like Engle-Granger or Johansen) in small samples (Stock 
& Watson, 1993).
RESULTS AND DISCUSSIONS

The descriptive statistics of  our analysis is presented 
below in table 2.
From table 2 which represents the descriptive statistics, 
it can be seen that GDP has the highest Mean with a 
value of  376.9806 while NMIG has the lowest mean with 
a value of  -5985.0021. NMIG has the highest standard 
deviation with a value of  52021.93 while HCI with a value 
of  0.028825 has the lowest standard deviation.
From table 3 which depicts the stationarity situation using 
the Phillip-Peron test, it can be seen that all our variables 
are stationary at first difference.
Table 4 shows the cointergration result which shows that 
there exist a long-run relationship between our dependent 
variable and the selected independent variables
The F- Statistics with a prbability value of  0.0000, 



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Table 2: Descriptive statistics
NMIG ITU GDP HCI INF

Mean -5985.002  18.54755  376.9806  0.502988  12.87919
Median -11604.67  19.10000  406.5567  0.506667  12.46000
Maximum  78685.00  39.20000  574.1800  0.560000  24.66000
Minimum -116162.0  0.560000  104.7400  0.440000  5.390000
Std. Dev.  52021.93  11.50099  117.4289  0.028825  3.706971
Skewness -0.037921  0.125708 -0.708301 -0.235144  0.506302
Kurtosis  1.942201  1.808384  2.686781  1.823910  3.406984
Jarque-Bera  11.29376  14.89339  21.13640  16.11042  11.95965
Probability  0.003529  0.000583  0.000026  0.000317  0.002529
Sum -1442386.  4469.960  90852.31  121.2200  3103.885
Sum Sq. Dev.  6.50E+11  31745.47  3309493.  0.199410  3297.992
Observations  241  241  241  241  241

Source; Author’s computation from e-views output

Table 3: Staionarity test.
Variable Order PP value Prob Conclution 
NMIG I (I) -4.121448 (0.0000) Stationary
ITU I (I) -2.970367 (0.0392) Stationary
GDP I (I) -2.607344 (0.0091) Stationary
HCI I (I) -3.030018 (0.0025) Stationary
INF I (I) -4.048571 (0.0001) Stationary

Source; Author’s computation from e-views output

Table 4: Cointegration test
Series: NMIG ITU GDP HCI INF 
Hypothesized Trace 0.05
No. of  CE(s) Eigenvalue Statistic Critical Value Prob.**
None *  0.148627  94.95461  69.81889  0.0002
At most 1 *  0.116584  56.98106  47.85613  0.0055
At most 2  0.078922  27.72671  29.79707  0.0851
At most 3  0.034131  8.325125  15.49471  0.4313
At most 4  0.000549  0.129591  3.841466  0.7189
Hypothesized Max-Eigen 0.05
No. of  CE(s) Eigenvalue Statistic Critical Value Prob.**
None *  0.148627  37.97354  33.87687  0.0153
At most 1 *  0.116584  29.25435  27.58434  0.0303
At most 2  0.078922  19.40159  21.13162  0.0858
At most 3  0.034131  8.195534  14.26460  0.3592
At most 4  0.000549  0.129591  3.841466  0.7189

Source; Author’s computation from e-views output

Table 5: Staionarity test.
Wald Test:
F-statistic  9.733709 (3, 222)  0.0000
Chi-square  29.20113  3  0.0000

Source; Author’s computation from e-views output

indicates that the over-all fit of  the model is adequate.
From table 6, it can be noticed that there is a significant 
but negative relationship between internet access and 
migration dynamics as the p-value of  0.0001 is less than 
0.05 which indicates that for every one-unit increase in 
ITU, migration is estimated to decrease by -4799.270 



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Table 6: the Dynamic OLS estimates
Dependent Variable: NMIG
Variable Coefficient Std. Error t-Statistic Prob. 
ITU -4799.270 512.3348 -9.367450 0.0000
GDP 341.1918 53.03061 6.433866 0.0000
HCI -290251.7 48553.14 -5.978021 0.0000
INF 6906.044 1183.578 5.834889 0.0000
D(ITU) -4670.782 231325.0 -0.020191 0.9839
D(ITU(1)) 84103.74 167561.9 0.501926 0.6162
D(ITU(-1)) -19552.96 169485.2 -0.115367 0.9083
D(GDP) -218.8061 2958.983 -0.073946 0.9411
D(GDP(1)) 5575.992 2167.413 2.572648 0.0107
D(GDP(-1)) -3729.752 2149.571 -1.735115 0.0841
D(HCI) -32439.78 3334979. -0.009727 0.9922
D(HCI(1)) 3186840. 2453895. 1.298686 0.1954
D(HCI(-1)) -1227553. 2465193. -0.497954 0.6190
D(INF) -749.3552 30704.40 -0.024405 0.9806
D(INF(1)) -9079.442 22817.58 -0.397914 0.6911
D(INF(-1)) 1087.682 23185.19 0.046913 0.9626
R-squared 0.430137
Adjusted R-squared 0.391632

Source; Author’s computation from e-views output

units, holding other variables constant. This is in line 
with our a priori expectation similar to the study by 
Brynjolfsson et al. (2020).
In the case of  human capital investment, it can be seen 
that there is a negative relationship between HCI and 
migration dynamics, the p-value of  0.0001 is less than 
0.05 which indicates that for every one-unit increase in 
HCI migration is estimated to decrease by -29025.7 units, 
holding other variables constant. This is in line with our a 
priori expectation (Adegoke, 2023).
For GDP, it can be observed that there is a positive 
relationship between GDP and migration dynamics the 
p-value of  0.0001 is less than 0.05 which indicates that for 
every one-unit increase in GDP, migration is estimated 
to increase by 341.1918 units, holding other variables 
constant. This is in line with our a priori expectation and 
supported by similar study by Adepoju (2016).
Finally, it can be observed that there is a positive 
relationship between inflation rate and migration dynamics, 
the p-value of  0.0001 is less than 0.05 which indicates that 
for every one-unit increase in INF, migration is estimated 
to increase by 6906.044 units, holding other variables 
constant. This is in line with our a priori expectation and 
supported by similar study by Ekhorugue et al. (2024).

CONCLUSION
A negative relationship between internet access and 
migration dynamics suggests that as internet access 
increases, emigration increases. This means that enhanced 
internet access provides residents with better access to 

foreign education, job information, government services, 
and social networks. This can increase desire to migrate 
elsewhere, leading to decreased net migration (Goolsbee 
& Syverson, 2008). This trend suggests that better 
connectivity increases outward migration by supporting 
telecommuting.
On the other hand, a negative relationship between 
human capital investment and migration dynamics 
suggests that enhancing human capital through education, 
skills development, and health can encourage emigration. 
Better education and skills can boost employment 
prospects internationally, which can lead to “brain drain.” 
According to Umeokwobi et al. (2025), West Africa has 
historically experienced significant out-migration of  
skilled health workers, engineers, and academics seeking 
better opportunities abroad. 
A positive relationship between GDP and migration 
dynamics suggests that as GDP increases, more foreigners 
move into the country. Higher GDP levels, often driven by 
economic growth, can make the country more attractive to 
migrants seeking better opportunities. 
Finally, a positive relationship between inflation rate and 
migration dynamics suggests that higher inflation rates 
are associated with increased immigration because rising 
inflation encourage investors to migrate and invest in the 
country. 

Reccommendations
Arising from the outcome of  this study, it is recommended 
that policy makers in collaboration with relevant agencies 



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should invest in expanding broadband infrastructure 
and ensuring affordable internet services with the view 
to using the technology to discourage brain drain and 
encourage digital migrant. Policies aimed at expanding 
digital connectivity and leveraging the internet access for 
economic and social development should be encouraged. 
With respect to human capital investment, it is 
recommended that policy makers should promote 
vocational and technical training programs aligned with 
market needs with the view to encourage brain circulation. 
Encourage public-private partnerships to improve skill 
acquisition, implement policies that ensure competitive 
salaries and benefits for skilled workers. As well as, offer 
incentives for expatriates to return or contribute remotely 
and promote diaspora engagement programs.
In the case of  GDP, it is recommended that policy 
makers in the sub-region should make favorable policies 
and incentives to attract foreign investors and skilled 
expatriates. Promote the region as a destination for 
business, innovation, and entrepreneurship through 
international marketing campaigns. Simplify business 
registration and improve ease of  business to sustain 
economic growth and migration inflows. 
Finally, policy makers in the sub-region should plan policies 
that will help to channel investment influx into productive 
sectors, facilitate entry and operation of  businesses that 
can benefit from inflationary environments, such as real 
estate, as well as develop targeted policies to protect 
vulnerable populations from inflationary impacts and 
prevent undesirable outflows. 

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