CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

Education is globally recognized as a fundamental human right and a crucial
catalyst for socio-economic development. Nations that prioritize adequate and
equitable education financing tend to experience higher literacy rates, economic
growth, and social stability (UNESCO, 2015). However, in many developing
countries, including Nigeria, insufficient and inefficient education financing remains
a major challenge, leading to disparities in access, poor infrastructure, and declining
quality of education.
Nigeria’s education sector has undergone significant changes over the past
decades, yet it continues to grapple with inadequate funding, corruption, and
inefficient resource allocation. Despite the increasing demand for education at all
levels—primary, secondary, and tertiary—funding has not kept pace with enrollment
growth, leading to overcrowded classrooms, insufficient teaching materials, and
poorly motivated educators. The problem is exacerbated by economic fluctuations,
political instability, and poor financial planning, which have contributed to erratic
government funding and declining educational standards (Adebayo, 2022).
Education financing in Nigeria is primarily government-driven, with federal,
state, and local governments responsible for budgetary allocations to the sector.
However, budgetary allocations have consistently fallen short of the UNESCO-
recommended 15–20% of the national budget. For example, Nigeria’s 2023 national
budget allocated only 7.2% to education (Budget Office of the Federation, 2023), far
below the required level to meet the country’s growing educational needs. This
chronic underfunding has placed immense pressure on public schools and
universities, leaving many institutions unable to provide quality education.
In response to these challenges, alternative education financing mechanisms
have emerged, including private sector investments, international donor support,
tuition fees, and public-private partnerships (PPPs). Private education providers,
from nursery schools to universities, have become increasingly prominent, catering
to students who seek better educational opportunities. However, the high cost of
private education has widened the gap between the rich and poor, making quality

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education a privilege for the financially advantaged while many children from low-
income households remain out of school (Adeyemi & Osokoya, 2019).
The role of international donors and development partners has also been
significant in education financing. Organizations such as the World Bank, UNICEF,
UNESCO, and the African Development Bank (AfDB) have provided funding for
educational projects, particularly in areas such as basic education, girl-child
education, and teacher training (World Bank, 2021). However, overreliance on donor
funding poses sustainability risks, as such assistance is often tied to external policy
priorities and may decline due to shifting global economic conditions or political
interests.
Another major challenge in Nigeria’s education financing is financial
mismanagement and corruption. Reports from the Independent Corrupt Practices
Commission (ICPC) and Transparency International (2022) indicate that a
significant portion of allocated education funds is either misappropriated or lost due
to inefficiencies in financial administration. This mismanagement has resulted in
unfinished school projects, unpaid teacher salaries, and inadequate learning
materials, further worsening the quality of education in the country.
Higher education institutions, particularly universities, also struggle with
funding shortages. Public universities rely on government allocations, but these
funds are often insufficient to cover operational costs, research activities, and
infrastructural development. As a result, many institutions increase tuition fees and
other levies, placing additional financial burdens on students. This financial strain
has led to frequent student protests, strikes by academic unions, and declining
research output, undermining Nigeria’s ability to compete in the global knowledge
economy (ASUU, 2022).
Furthermore, Nigeria faces a serious brain drain problem, with many highly
qualified teachers, lecturers, and researchers leaving the country for better
opportunities abroad. The migration of skilled educators to countries like the United
Kingdom, the United States, and Canada is largely driven by poor remuneration,
lack of research funding, and unfavorable working conditions in Nigeria. This brain
drain weakens the education system, leaving institutions understaffed and affecting
the quality of teaching and research (Okebukola, 2021).
The disparities in education financing are especially pronounced in rural and
underserved areas, where schools lack basic infrastructure such as classrooms,
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libraries, and laboratories. Many children in these regions, particularly in northern
Nigeria, struggle to access education due to poverty, cultural barriers, and security
challenges such as insurgency and banditry (UNICEF, 2020). The funding gap
between urban and rural schools highlights the urgent need for a more equitable
distribution of education resources.
To address these challenges, alternative and innovative education financing
models must be explored. Some countries have successfully implemented education
trust funds, education bonds, performance-based funding, and dedicated taxes for
education to ensure long-term financial sustainability. For instance, Ghana’s
Education Trust Fund (GETFund) and South Africa’s education levies have helped
generate additional revenue for education development. Nigeria’s Tertiary Education
Trust Fund (TETFund) has played a similar role in higher education, but its impact
needs to be expanded to other levels of education (Odukoya, 2019).
Public-Private Partnerships (PPPs) also present an opportunity for
sustainable education financing. Through corporate sponsorships, education grants,
and private sector investments, Nigeria can leverage private capital to improve
school infrastructure, provide scholarships, and enhance teacher training programs.
However, for PPPs to be effective, clear regulations and accountability mechanisms
must be put in place to ensure that resources are used efficiently (Olaniyan, 2021).
Moreover, performance-based funding can serve as an incentive for schools
and universities to improve efficiency and outcomes. In countries such as Finland
and Singapore, government education grants are linked to student performance,
institutional efficiency, and research output. Adopting such a model in Nigeria could
help enhance the accountability and effectiveness of education spending (World
Bank, 2022).
Another potential solution is community-driven education financing, where
local governments, religious organizations, alumni associations, and philanthropic
bodies contribute to school development. Encouraging voluntary endowments,
scholarship programs, and community-based fundraising initiatives can help
supplement public education funding and provide financial support to students from
disadvantaged backgrounds.
The success of Nigeria’s education system is heavily dependent on the
effectiveness of its financing and funding models. While the government remains the
primary financier, diversifying funding sources and implementing sustainable
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financing mechanisms is crucial to bridging the funding gap, improving education
quality, and ensuring equitable access for all students. Addressing the systemic
issues of underfunding, corruption, and financial mismanagement must also be a
priority. By learning from global best practices and developing innovative financing
strategies, Nigeria can build a more inclusive, well-funded, and high-quality
education system that meets the needs of present and future generations.

1.2 Statement of the Problem

Education is widely recognized as a key driver of social mobility, economic
development, and national progress. However, in Nigeria, the persistent
underfunding and inefficient financing of the education sector have led to a crisis
characterized by poor infrastructure, inadequate learning resources, teacher
shortages, and declining educational standards. While the demand for quality
education continues to grow, the available financial resources have not matched this
demand, resulting in a system plagued by inequities and inefficiencies. The inability
to implement sustainable funding models has led to chronic financial shortfalls, high
dropout rates, and a widening gap between public and private education (UNICEF,
2020; Okebukola, 2021).
One of the major challenges facing education financing in Nigeria is
insufficient budgetary allocation. Despite international recommendations by
UNESCO, which advocate that 15–20% of a country’s national budget be allocated
to education, Nigeria consistently falls short of this benchmark. For instance,
between 2016 and 2022, education received between 5.6% and 7.9% of the national
budget annually, which is far below the recommended level (Budget Office of the
Federation, 2022). In 2023, only 7.2% of the national budget was dedicated to
education, a figure that is grossly inadequate to meet the sector’s needs. This chronic
underfunding has led to deteriorating school infrastructure, inadequate supply of
teaching and learning materials, and delays in salary payments for educators. The
direct consequence of this funding gap is poor educational outcomes, low literacy
rates, and limited access to quality education, particularly for marginalized groups
(Adeyemi & Osokoya, 2019).
Another significant issue is the inequitable distribution of education funds,
which disproportionately affects schools in rural and underserved communities.
While elite schools in urban centers often receive better funding and resources,
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many public schools in rural areas lack basic amenities such as classrooms, libraries,
laboratories, and even qualified teachers. The regional disparity in education funding
has contributed to high dropout rates, low literacy levels, and persistent educational
inequality. The northern region of Nigeria, in particular, faces severe challenges in
education accessibility due to economic hardships, cultural norms, and security
threats such as insurgency and banditry (UNESCO, 2021; UNICEF, 2020).
The high cost of private education further exacerbates the problem, making
quality education inaccessible to a large percentage of the population. As public
schools continue to decline due to underfunding, many families are forced to enroll
their children in private institutions that charge exorbitant fees. However, with over
40% of Nigerians living below the poverty line (NBS, 2022), many households
cannot afford private school tuition, creating a dual education system where only the
wealthy have access to high-quality learning facilities. The commercialization of
education has widened social inequalities, with low-income students struggling to
compete with their privileged counterparts (Adedeji & Bamidele, 2020).
A major consequence of poor education financing in Nigeria is the brain
drain phenomenon, where highly skilled teachers, lecturers, and researchers leave
the country in search of better opportunities abroad. Many Nigerian academics
migrate to countries such as the United States, the United Kingdom, and Canada,
where they receive higher salaries, better working conditions, and access to research
funding. This exodus of professionals has created a serious shortage of qualified
educators, leaving many schools and universities understaffed. The continuous loss
of skilled human capital weakens the education system, affecting knowledge
production, research output, and the overall quality of education in Nigeria (ASUU,
2022; Okebukola, 2021).
The mismanagement and corruption within the education sector further
compound the problem. Despite budgetary allocations for school infrastructure,
teacher salaries, and student support programs, a significant portion of education
funds is often misappropriated or lost due to fraudulent activities. Cases of
embezzlement, procurement fraud, and diversion of funds meant for educational
development are widespread. This financial mismanagement reduces the
effectiveness of public funding, leaving many schools struggling to provide even the
most basic services. Weak accountability mechanisms and poor oversight allow

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corruption to thrive, ultimately hindering the progress of education in Nigeria
(ICPC, 2021; Transparency International, 2022).
Moreover, Nigeria’s heavy reliance on international donors and development
agencies for education funding raises sustainability concerns. While organizations
such as the World Bank, UNESCO, UNICEF, and the African Development Bank
(AfDB) have contributed significantly to Nigeria’s education sector, these funds are
often tied to external policy objectives and short-term projects. As a result, once
donor priorities shift or funding is withdrawn, many education programs collapse
due to a lack of locally generated financial support. Overreliance on foreign aid
prevents Nigeria from developing a self-sufficient education financing model,
leaving the sector vulnerable to external economic and political changes (World
Bank, 2022).
The absence of a well-structured and sustainable education funding model
remains a fundamental problem in Nigeria’s education sector. While public funding
alone is insufficient to meet the growing demands of the education system, private
sector involvement and alternative financing strategies remain underdeveloped. The
lack of investment in innovative financing mechanisms such as education bonds,
performance-based funding, and public-private partnerships (PPPs) has further
contributed to the sector’s financial instability. Without urgent reforms in education
financing, Nigeria risks continued decline in education quality, increasing social
inequality, and limited national development. This study, therefore, seeks to
critically examine the challenges of education financing in Nigeria, assess existing
funding mechanisms, and propose viable strategies for ensuring sustainable and
equitable education funding.

1.3 Research Questions

The research questions which are derived from the research objectives include the
following:
i. What is the structure of education financing in Nigeria across public, private, and
donor sources?
ii. What is the contribution of alternative funding sources—such as private sector
investment and international donor support—to the delivery of basic and tertiary
education services in Nigeria?

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1.4 Objectives of the Study
The broad objective of this study is to examine education financing and funding
models in Nigeria. The specific objectives are to:
i. analyze how education financing is structured in Nigeria across public, private,
and donor sources.
ii. analyze the contribution of alternative funding sources to the delivery of basic and
tertiary education services.

1.5 Significance of the Study

This study holds significance at multiple levels as it aims to provide
valuable insights into education financing trends and challenges in
Nigeria. The findings will be beneficial for policymakers by guiding
the formulation of effective funding policies that promote equitable
access and sustainable investment in the education sector.
Furthermore, it will assist education administrators in developing
efficient financial strategies to enhance the allocation and
utilization of available resources. The research also sheds light on
the growing importance of private sector involvement in education,
highlighting the need to strengthen public-private partnerships for
improved service delivery. By emphasizing the need for equity in
education financing, the study advocates for policies that prioritize
disadvantaged communities and promote inclusive education.
Additionally, the study contributes to academic discourse by
enriching the existing literature on education financing in Nigeria
and serving as a reference for future research in related areas.

1.6 Scope of the Study

This study focuses on the financing and funding models of the Nigerian education
sector from 1980 to 2024 (45 years). It examines trends in education financing,
major sources of funding, and the impact of financial policies on education quality
across this period. The study covers all tiers of education primary, secondary, and

7
tertiary levels and evaluates the contributions of both public and private sectors to
education financing. By using this extended timeline, the study aims to capture long-
term patterns, policy shifts, and investment trends that have shaped Nigeria’s
educational development.

CHAPTER TWO
LITERATURE REVIEW

This chapter reviews relevant literature on education financing and funding models
in Nigeria. It examines the theoretical framework that underpins education
financing, explores previous studies on the adequacy, equity, and efficiency of
funding in the education sector, and critically analyzes the challenges facing
sustainable education financing in the country. The chapter also discusses key areas
such as government budget allocations, private sector involvement, international
donor support, and public-private partnerships, providing a comprehensive
background for understanding the dynamics and implications of education financing
in Nigeria.

2.1 Conceptual Review

2.1.1 Public Financing of Education
Public financing of education refers to the financial resources allocated by
the government at different levels—federal, state, and local—to support the
education sector. This form of financing is crucial for ensuring equitable access to
education, improving infrastructure, and enhancing the quality of learning. In
Nigeria, the government plays a central role in funding education through annual
budgetary allocations, intervention funds, and policy-driven initiatives. However,
public financing remains a contentious issue due to persistent underfunding,
8
inefficient resource allocation, and inadequate implementation of financial policies
(Okebukola, 2020).
The Nigerian government primarily finances education through annual
budgetary allocations. These allocations are drawn from the national revenue, which
includes income from crude oil, taxation, and other economic activities. However,
Nigeria’s education sector consistently receives less than the UNESCO-
recommended 15–20% of the national budget, leading to significant funding gaps
(UNESCO, 2015; BudgIT, 2023). For instance, in 2023, only 7.2% of the national
budget was allocated to education (BudgIT, 2023). The low prioritization of
education in budgetary allocations has resulted in inadequate classrooms, poor
teacher remuneration, and insufficient learning materials, which negatively impact
education quality at all levels.
To supplement the budgetary allocations, the government has established
education intervention funds such as the Tertiary Education Trust Fund (TETFund)
and the Universal Basic Education Commission (UBEC). TETFund, for instance, is
financed through a 2% tax on the profits of registered companies in Nigeria, and the
funds are used for infrastructure development, research grants, and staff training in
higher education institutions (TETFund, 2022). Similarly, UBEC oversees the
Universal Basic Education (UBE) program, which ensures the provision of free and
compulsory primary and junior secondary education. Despite these intervention
efforts, challenges such as bureaucratic bottlenecks and mismanagement often
hinder the efficient disbursement of funds (Okojie, 2021).
Another crucial aspect of public financing in Nigeria is state and local
government contributions to education. While the federal government provides
overall policy direction and financial support, state governments are responsible for
funding secondary and tertiary institutions within their jurisdictions, while local
governments focus on primary education. However, many state and local
governments struggle with limited internally generated revenue (IGR), leading to
irregular teacher salaries, poor school infrastructure, and ineffective policy
implementation (Obasi, 2020). Some states have attempted to introduce education
levies and partnerships with private organizations to address these challenges, but
these efforts remain inconsistent.
Public financing also includes government-funded scholarships, grants, and
bursaries aimed at supporting students from low-income backgrounds. Various state
9
governments and federal agencies provide financial aid to students, particularly
those pursuing higher education. However, the reach and impact of these initiatives
are limited due to poor funding, lack of transparency in selection processes, and
delayed disbursement of funds (Edukugho, 2019). Many students who qualify for
scholarships and bursaries often face difficulties accessing these funds due to
bureaucratic inefficiencies and corruption.
Despite the efforts to provide public financing, the sector is plagued by
inefficiencies, corruption, and lack of accountability. Reports of misappropriation of
education funds and the diversion of allocated resources have further weakened the
impact of government financing (ICPC, 2021). Additionally, delays in budget
implementation and poor monitoring of education expenditures contribute to poor
service delivery. Strengthening financial accountability through digital tracking
systems, independent audits, and greater transparency in budget allocation is
essential for improving public education financing.

2.1.2 Private Sector Involvement in Education Financing

The private sector plays a crucial role in education financing in Nigeria by
supplementing government efforts through investments, partnerships, and corporate
social responsibility (CSR) initiatives. Due to the persistent underfunding of public
education, private organizations, businesses, and individuals have stepped in to
provide financial and infrastructural support to schools and universities. This
involvement takes multiple forms, including the establishment of private educational
institutions, financial aid programs, and collaborative funding models. The private
sector’s contributions have become even more critical as Nigeria grapples with
increasing student enrollment, inadequate government resources, and the growing
demand for quality education (Ololube, 2016).
One of the most significant contributions of the private sector to education
financing is the establishment of private schools and universities. Over the past few
decades, Nigeria has witnessed a sharp rise in the number of private primary,
secondary, and tertiary institutions. These institutions are primarily financed through
tuition fees, endowments, and corporate sponsorships. Private universities, such as
Covenant University, Afe Babalola University, and Babcock University, have been
instrumental in bridging the gap between demand and supply in higher education
10
(Adepoju & Fabiyi, 2007). However, while private schools and universities provide
high-quality education, they are often expensive, making them inaccessible to low-
income families and further widening educational inequality (Okebukola, 2020).
Beyond establishing educational institutions, the private sector also
contributes to education financing through CSR initiatives. Many companies
allocate a portion of their profits to fund scholarships, construct school buildings,
provide learning materials, and support teacher training programs. For example,
major corporations like MTN, Dangote Group, and Shell have established
scholarship programs and built educational infrastructure in underserved
communities (MTN Foundation, 2022). While these initiatives have positively
impacted thousands of students, they remain largely voluntary and inconsistent, with
no long-term sustainability plans in place (Agabi & Uche, 2006).
Another important aspect of private sector involvement in education
financing is public-private partnerships (PPPs). These partnerships involve
collaborations between the government and private entities to fund and manage
educational institutions. PPPs can take various forms, including joint infrastructure
projects, co-funded research initiatives, and private management of public schools.
For instance, in some states, private organizations have adopted public schools,
rehabilitated them, and provided essential resources (World Bank, 2020). While
PPPs have the potential to enhance education funding, challenges such as unclear
regulations, governance issues, and lack of accountability sometimes hinder their
effectiveness (Igbuzor, 2017).
Additionally, private foundations and non-governmental organizations
(NGOs) contribute significantly to education financing in Nigeria. Organizations
such as the TY Danjuma Foundation, the MacArthur Foundation, and international
bodies like the United Nations Children’s Fund (UNICEF) provide grants,
scholarships, and funding for school improvement projects. Many NGOs focus on
marginalized communities, ensuring that children from disadvantaged backgrounds
receive quality education (UNICEF Nigeria, 2022). However, the reach of these
initiatives is limited by funding constraints, donor dependency, and the absence of
coordinated efforts with government agencies (Omeke & Ojukwu, 2017).
Private sector financing in education also extends to education technology
(EdTech) and digital learning investments. In recent years, several private
companies have launched digital learning platforms and provided funding for
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technological advancements in schools. Companies like uLesson and EduTech have
developed e-learning solutions that enable students to access quality education
remotely (Adebayo & Salami, 2021). Financial institutions have also introduced
student loan schemes and education savings plans to help families afford quality
education. While these innovations improve access to learning, they often require
substantial financial investment, which many low-income families cannot afford.
Despite the numerous benefits of private sector involvement in education
financing, there are also concerns regarding commercialization and profit-driven
motives. Many private institutions prioritize financial gains over educational quality,
leading to issues such as exorbitant tuition fees, inadequate regulation, and
questionable academic standards (Obasi, 2020). Additionally, private investments in
education are often concentrated in urban areas, leaving rural communities with
limited access to quality private schooling. Addressing these challenges requires
stronger regulatory frameworks, increased government-private sector collaboration,
and policies that promote equitable access to education.

2.1.3 Household Contributions and Cost-Sharing

Household contributions play a significant role in education financing in
Nigeria, particularly due to the persistent underfunding of public education. Parents
and guardians bear substantial financial responsibilities to ensure their children
receive quality education. These contributions take various forms, including tuition
fees, the purchase of learning materials, transportation costs, and supplementary
educational expenses. Despite efforts to provide free and subsidized education,
many families still spend a significant portion of their income on education, making
cost-sharing a central aspect of the Nigerian education system (Ogundele &
Olatunji, 2021).
One of the primary ways households contribute to education financing is
through tuition and school fees. While public primary and junior secondary
education are meant to be free under the Universal Basic Education (UBE) scheme,
hidden costs such as registration fees, examination fees, and levies imposed by
schools often place a financial burden on parents (UNICEF Nigeria, 2020). In
tertiary institutions, tuition fees vary widely depending on whether the institution is
12
federal, state-owned, or private. The rising cost of higher education has made it
difficult for many students from low-income families to afford tertiary education,
leading to increased dropout rates or reliance on informal financial support
mechanisms (Adedeji & Adeyemi, 2018).
Beyond tuition, households also finance supplementary education expenses,
such as textbooks, uniforms, and extracurricular activities. In many cases, public
schools suffer from inadequate government funding, leading to shortages of essential
learning materials. As a result, parents are forced to buy textbooks, writing
materials, and laboratory equipment for their children (Abdulkareem, 2021).
Additionally, private tutoring has become increasingly common, with families hiring
tutors or enrolling their children in after-school programs to supplement classroom
learning. While these extra costs enhance educational outcomes, they create
disparities between children from wealthy backgrounds and those from less
privileged households (Ogunyemi & Olagunju, 2019).
Another critical aspect of household contributions is student loan schemes
and scholarship programs, which serve as financial relief for students who struggle
to afford education. Although Nigeria has attempted to introduce student loan
initiatives, the implementation has been inconsistent due to inadequate funding,
bureaucratic inefficiencies, and high repayment risks (Okojie, 2017). Scholarships
and bursaries provided by state governments, corporate organizations, and NGOs
offer financial assistance to outstanding students, but the number of beneficiaries
remains limited. Many deserving students are unable to access these opportunities
due to corruption, nepotism, and a lack of awareness about available funding options
(Okafor & Udeh, 2020).
The reliance on household contributions has exacerbated educational
inequalities in Nigeria. Families from wealthier backgrounds can afford better
schools, private tutors, and learning technologies, while children from low-income
families struggle with substandard public schools, overcrowded classrooms, and
limited access to learning materials. Rural communities face additional financial
barriers due to the lack of nearby schools, forcing parents to spend extra on
transportation and boarding costs (Ogundele & Olatunji, 2021). These disparities
highlight the need for policies that reduce financial burdens on households, such as
expanding government-funded scholarships, implementing subsidized school meal

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programs, and improving the quality of free public education (Adebayo & Salami,
2021).

2.1.4 International Aid and Donor Support

International aid and donor support play a crucial role in education financing
in Nigeria, supplementing government and private sector efforts to address funding
gaps. Various international organizations, foreign governments, and non-
governmental organizations (NGOs) provide financial assistance, technical
expertise, and policy guidance to improve access to quality education. These
external contributions are particularly vital for funding educational infrastructure,
teacher training, scholarship programs, and interventions in crisis-affected regions.
However, the effectiveness of international aid in Nigeria’s education sector depends
on proper implementation, accountability, and alignment with national education
priorities (Bello & Olatunji, 2019).
One of the most significant sources of international aid for education in
Nigeria is multilateral and bilateral funding. Institutions such as the World Bank,
United Nations Educational, Scientific and Cultural Organization (UNESCO),
United Nations Children’s Fund (UNICEF), and the African Development Bank
(AfDB) provide financial support for educational development programs. These
organizations collaborate with the Nigerian government to implement large-scale
projects aimed at improving literacy rates, expanding access to education in rural
areas, and upgrading school facilities. Bilateral aid from countries like the United
States, the United Kingdom, and Germany also contributes to funding scholarships,
teacher training, and capacity-building initiatives (Olusola, 2020).
NGOs and philanthropic organizations also play a significant role in
financing education in Nigeria. Many international NGOs, such as Plan
International, Save the Children, and ActionAid, focus on providing free education
for disadvantaged children, particularly in marginalized communities. Local NGOs,
supported by international donors, run school feeding programs, supply learning
materials, and fund vocational education initiatives. Philanthropic organizations like
the Bill and Melinda Gates Foundation and the Aliko Dangote Foundation contribute
to education through targeted funding for research, infrastructure development, and
technology-driven learning solutions (UNICEF, 2020).

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 A critical aspect of international aid in education financing is its role in
humanitarian response and crisis intervention. Northern Nigeria, in particular, has
experienced significant educational disruptions due to conflicts, insurgencies, and
displacement caused by Boko Haram and banditry. International donors provide
emergency education funding to support internally displaced children, rehabilitate
schools destroyed by conflict, and offer psychosocial support for affected students
and teachers. Organizations like the Global Partnership for Education (GPE) and
Education Cannot Wait (ECW) fund emergency education programs to ensure
learning continuity in crisis zones (GPE, 2020).
Despite the benefits of international aid, challenges exist in ensuring its
sustainability, effectiveness, and proper utilization. Some aid programs are short-
term and donor-dependent, making it difficult to maintain long-term improvements
in education quality. Additionally, issues such as corruption, bureaucratic
inefficiencies, and poor coordination between donors and government agencies can
lead to misallocation of funds and duplication of projects. Without proper
accountability mechanisms, international funds may fail to reach the intended
beneficiaries, limiting their impact on Nigeria’s education system (Akpan & Opara,
2021).
Another concern with donor support is the influence of external agencies on
Nigeria’s education policies. While international organizations provide much-needed
financial support, some donor-driven programs may not fully align with Nigeria’s
national education objectives. Foreign aid often comes with conditions that prioritize
donor interests, sometimes leading to policy shifts that may not adequately address
local challenges. To maximize the benefits of international aid, Nigeria must develop
clear frameworks for integrating donor contributions into national education
planning, ensuring that external funding complements rather than dictates domestic
policies (Bello & Olatunji, 2019).

2.1.5 Internally Generated Revenue (IGR) in Educational Institutions

Internally Generated Revenue (IGR) refers to the funds that educational
institutions in Nigeria raise independently to supplement government allocations and
external funding. Given the persistent underfunding of education, many institutions,
especially public universities, polytechnics, and colleges of education, have turned
to IGR as a crucial financing mechanism. These revenues are generated through
15
tuition fees, research grants, consultancy services, and income from commercial
ventures. The ability of an institution to generate revenue internally significantly
impacts its financial sustainability, quality of education, and infrastructural
development (Nwogwugwu, 2020).
One of the primary sources of IGR in Nigerian educational institutions is
tuition and other student fees. While federal universities traditionally operated on a
tuition-free model, many now charge various administrative fees, such as acceptance
fees, registration fees, and hostel accommodation fees, to generate additional
revenue. State-owned universities and private institutions rely even more on tuition
fees, with some charging substantial amounts to cover operational costs. However,
high fees often make higher education unaffordable for many students, leading to
protests and calls for government intervention to provide more financial support
(Adebayo & Fawole, 2021).
Another significant contributor to IGR is research grants and consultancy
services. Universities and research institutions generate revenue by securing grants
from local and international organizations for academic research. Additionally, they
engage in consultancy services, offering expertise in various fields such as
engineering, business management, and policy development. Some institutions
establish research and innovation hubs that patent new discoveries and sell
intellectual property rights, providing a steady stream of income. However,
challenges such as limited research funding, lack of collaboration with industries,
and inadequate infrastructure hinder the full potential of research-based revenue
generation (Ogunyemi, 2019).
Many institutions also engage in commercial ventures and investment
activities to boost their revenue. These ventures include running bookshops, printing
presses, guest houses, water production companies, agricultural farms, and
transportation services. For example, some universities have commercialized their
agricultural research by selling farm produce to generate income. Others operate
business centers and conference halls, renting them out for corporate events and
private functions. While these ventures contribute to financial stability, they require
strong management, transparency, and accountability to prevent mismanagement of
funds (Oke & Uwaoma, 2020).
Alumni contributions and endowment funds also form an essential part of
IGR for some institutions. Many universities have established alumni associations
16
that encourage graduates to give back through donations, sponsorship of
scholarships, or infrastructural projects. Institutions like the University of Ibadan
and Obafemi Awolowo University have benefited from alumni-funded initiatives
that support academic programs and student welfare. However, alumni contributions
remain inconsistent, and many institutions lack structured mechanisms to effectively
engage and encourage donations from their graduates (Adeyemi & Adeyinka, 2021).
Despite its importance, IGR generation in Nigerian educational institutions
faces several challenges, including mismanagement, corruption, and inadequate
entrepreneurial capacity. Many institutions lack the financial and managerial
expertise to run profitable ventures, leading to inefficiencies and losses.
Additionally, there is often resistance from students and staff to revenue-generating
measures such as increased fees, commercialization of services, and stricter financial
management policies. To maximize IGR potential, educational institutions must
adopt innovative funding models, strengthen accountability, and establish policies
that encourage sustainable revenue generation without overburdening students
financially (Adegboye, 2019).

2.1.6 Education Financing through Loans and Credit Facilities

Education financing through loans and credit facilities is an essential
mechanism for funding higher education in Nigeria. Given the increasing cost of
education and the limited financial capacity of many families, student loans,
education credit schemes, and institutional borrowing have become viable
alternatives for covering tuition fees, learning materials, and other academic
expenses. While education loans are widely used in developed countries, Nigeria has
struggled with implementing a sustainable and effective student loan system. The
lack of structured repayment models, high default rates, and limited access to credit
facilities have hindered the success of education financing through loans in the
country (Okeke, 2021).
One of the most notable attempts at education financing through loans in
Nigeria was the Nigerian Students Loan Board (NSLB), established in 1972. The
scheme was designed to provide financial assistance to students in higher
institutions. However, it collapsed due to poor loan recovery mechanisms, lack of
accountability, and high default rates. The inability to track borrowers and enforce
repayments led to massive financial losses, forcing the government to discontinue
17
the program. Subsequent efforts, such as the Education Bank initiative in the 1990s,
also failed due to similar challenges (Ogunyemi, 2020).
Recognizing the need for a more structured approach, the Nigerian
government enacted the Student Loan Act in 2023, aimed at providing interest-free
loans to students from low-income families. The initiative was designed to be
managed by the Nigerian Education Loan Fund (NELFUND), with funding sourced
from taxes, levies, and other government revenues. While this program generated
optimism, concerns remain about its sustainability, inclusiveness, and the potential
for abuse. Issues such as bureaucratic inefficiencies, corruption, and the lack of a
national credit tracking system pose significant risks to the success of the initiative
(Nwogwugwu & Ijaiya, 2023).
Beyond government-backed loans, some private financial institutions in
Nigeria offer education credit schemes, allowing students and parents to access
loans for tuition and related expenses. Banks such as Guaranty Trust Bank
(GTBank), First Bank, and Access Bank have introduced education loan products
targeted at students and guardians who need financial support. These loans often
come with high-interest rates, strict collateral requirements, and short repayment
periods, making them inaccessible to many students, particularly those from
disadvantaged backgrounds. Unlike government-backed loans, private sector
education loans prioritize financial profitability, limiting their ability to serve as a
widespread solution for education financing (Adeyemi & Adeyinka, 2021).
Education financing through loans is not limited to students alone—tertiary
institutions also access credit facilities to fund capital projects and infrastructure
development. Universities, polytechnics, and colleges sometimes obtain loans from
commercial banks, development finance institutions, and international funding
agencies to build lecture halls, laboratories, and hostels. Some institutions also
secure credit for research projects and digital learning innovations. However, due to
the financial instability of many public institutions and weak revenue streams,
repaying such loans remains a major challenge. The fear of default often discourages
institutions from seeking credit, slowing down infrastructural and academic
development (Okunade, 2020).
A key challenge facing education financing through loans in Nigeria is the
absence of a reliable loan repayment structure. In developed countries, student loan
repayment is typically linked to post-graduation earnings, with deductions made
18
directly from salaries. In Nigeria, however, the informal labor market and high
unemployment rates make loan repayment difficult. Many graduates struggle to
secure stable jobs, leading to high default rates and financial losses for lending
institutions. Without an effective mechanism to track employment and enforce
repayment, student loan programs remain unsustainable (Adebayo & Fawole, 2021).

2.2 Theoretical Review

The theoretical review examines various theories that explain the financing
and funding of education. These theories provide frameworks for understanding how
education is funded, the roles of different stakeholders, and the impact of financing
models on educational outcomes. Several key theories are relevant to education
financing in Nigeria, including **Human Capital Theory, Public Goods Theory,
Cost-Sharing Theory, and Resource Dependency Theory**. These theories offer
insights into the justification for public and private investment in education, the
sustainability of education financing, and the challenges associated with funding
education in a developing country like Nigeria.

2.2.1 Human Capital Theory

The Human Capital Theory was initially proposed by Adam Smith (1776) in
his seminal work The Wealth of Nations, where he identified the acquisition of
talents, skills, and knowledge through education and training as a form of capital
investment. However, the theory was later formalized and expanded by Theodore
Schultz (1961) and Gary Becker (1964), who provided a structured economic
framework for understanding the role of education in enhancing individual
productivity and economic growth. The theory posits that education, like physical
capital, is an investment that yields returns in the form of increased productivity,
higher earnings, and improved social outcomes for individuals and societies.
Human Capital Theory views education as a key determinant of human
productivity and labor market outcomes. According to Schultz (1961), investment in
education improves the quality of labor by equipping individuals with the necessary
skills and competencies needed for more efficient job performance. This perspective
aligns with Becker’s (1964) analysis, which treats educational expenditures as
investments in human capital, similar to investments in machines or infrastructure,
with the expectation of future returns. As such, the theory provides a strong
19
justification for both public and private investment in education, since higher
educational attainment translates to enhanced economic potential and income
generation.
At the macroeconomic level, Human Capital Theory provides an explanation
for differences in national economic performance. Countries that invest heavily in
human capital through widespread access to quality education, health, and
vocational training tend to experience faster economic growth and development.
Barro and Lee (2013), in their cross-country studies, found strong correlations
between education levels and GDP growth rates, highlighting the significance of
educational attainment in long-term economic development. The theory thus
underpins national and international policy frameworks that emphasize education as
a cornerstone of sustainable development, including the United Nations’ Sustainable
Development Goal 4 (SDG 4).
The theory also emphasizes the distinction between general and specific
human capital. General human capital refers to education and skills that are
transferable across jobs and industries, such as literacy, numeracy, and critical
thinking. Specific human capital, on the other hand, includes job-specific training
and knowledge that enhance productivity in a particular role or organization
(Becker, 1993). Understanding this distinction is important for designing educational
policies that balance broad foundational learning with targeted vocational or
technical education.
Human Capital Theory has also been instrumental in explaining the wage
differentials among individuals with varying educational qualifications. Numerous
empirical studies have shown that higher levels of education are associated with
higher earnings. For instance, Psacharopoulos and Patrinos (2018) conducted a
global analysis which found that, on average, returns to one additional year of
schooling were approximately 10% in terms of increased wages. These findings
provide a rationale for personal investment in education, even in contexts where
public subsidies may be limited, as individuals stand to gain economically from
acquiring more education.
Furthermore, the theory highlights the spillover effects of human capital
investment. Educated individuals not only benefit personally but also contribute to
broader societal gains, such as lower crime rates, improved health outcomes, and
increased civic participation (Lochner & Moretti, 2004). In this sense, education
20
produces both private and public goods, making it a valuable area for collective
investment. In developing countries like Nigeria, where human development
indicators are still low, the theory underscores the need to expand access to
education and improve its quality as a means of fostering inclusive growth and
poverty reduction.
In addition, Human Capital Theory supports the integration of education with
labor market needs. The alignment of educational curricula with the demands of the
economy ensures that graduates possess the skills necessary for productive
employment. This approach has informed the design of Technical and Vocational
Education and Training (TVET) programs, which aim to bridge the skills gap and
reduce unemployment. UNESCO (2015) emphasized the importance of aligning
human capital development with industrial and technological transformation,
especially in countries seeking to diversify their economies and boost
competitiveness.
Moreover, contemporary applications of Human Capital Theory have
extended beyond formal education to include lifelong learning, informal education,
and on-the-job training. With the rise of the knowledge economy and rapid
technological change, there is increasing recognition that human capital
development must be continuous. Goldin and Katz (2008) argue that the capacity of
a country to adapt to innovation and sustain economic growth depends significantly
on the continuous upgrading of its human capital stock. In Nigeria, where a large
segment of the population is young and unemployed, strategies that promote human
capital development across the life cycle are essential for economic transformation.
Human Capital Theory has provided a theoretical framework for educational
planning and resource allocation. By linking education to economic returns, the
theory helps governments prioritize investments in sectors and levels of education
with the highest impact. For example, cost-benefit analyses based on human capital
models often show higher returns to basic and primary education in low-income
countries, prompting policies that emphasize universal basic education. However, in
countries aiming to develop knowledge-based economies, investments in tertiary
education, research, and innovation are equally essential. The theory thus remains
central to education policy and planning in both developed and developing contexts.
The Human Capital Theory posits that investments in education enhance
individuals’ productivity and, consequently, economic growth. It views education as
21
a form of capital, similar to physical capital, that yields returns in the form of
improved earnings and national development. Despite its widespread acceptance, it
is often criticized for assuming that education directly translates to economic
productivity without accounting for labor market mismatches. The theory neglects
structural factors such as unemployment, underemployment, and the quality of
education systems, particularly in developing economies like Nigeria. It also fails to
account for informal learning and non-market benefits of education such as civic
participation or improved health. Moreover, critics argue that it overly emphasizes
economic outcomes while sidelining social and cultural values of education. The
theory has also been faulted for assuming rational investment behavior from
individuals who may be constrained by poverty or poor information. Despite these
criticisms, it provides a useful lens to justify public and private investment in
education, especially in developing nations. When synthesized with other theories, it
complements broader frameworks by highlighting the individual benefits of
education in economic planning. In Nigeria, applying the Human Capital Theory
underscores the urgency of increasing both access to and quality of education to
promote national productivity.

2.2.2 Public Goods Theory Samuelson (1954)

The Public Goods Theory was originally conceptualized by economist Paul
A. Samuelson in 1954, in his foundational work The Pure Theory of Public
Expenditure. This theory defines public goods as commodities or services that are
non-excludable and non-rivalrous in consumption. Non-excludability means that it is
difficult or impossible to prevent individuals from accessing the good once it is
provided, while non-rivalrous consumption implies that one person’s use of the good
does not diminish its availability to others. Classic examples include national
defense, street lighting, and clean air. Education, particularly at the basic level, is
increasingly considered a quasi-public good due to its broad societal benefits and
partial non-excludability.
Public Goods Theory has been influential in framing the rationale for
government involvement in the provision of education. Samuelson (1954) argued
that due to the non-excludable nature of public goods, private markets often
underproduce them because there is no incentive for profit-oriented firms to supply
goods that cannot guarantee returns through pricing. In the case of education,
22
especially primary and secondary education, the societal benefits such as improved
public health, civic engagement, and economic development justify government
financing to ensure universal access and equity. Musgrave (1959) further developed
this concept in the context of fiscal policy, emphasizing that government
expenditures on public goods enhance social welfare.
Education generates significant positive externalities benefits to individuals
and society beyond those directly involved in the educational transaction. Stiglitz
(1988) and Cornes and Sandler (1996) expanded on this notion by arguing that
public investment in education can lead to a more informed citizenry, higher
productivity, reduced crime, and lower public health costs. These spillover effects,
or external benefits, make education a classic candidate for public provision, even if
it does not meet all the strict criteria of pure public goods. In this way, Public Goods
Theory justifies the role of the state in ensuring that every child has access to quality
education regardless of income, location, or social background.
The application of Public Goods Theory to education also explains the
challenges of market failure in educational services. Left solely to market forces,
education would likely be under-consumed and inequitably distributed, especially in
low-income communities where families may not afford private schooling. Tiebout
(1956) and later Oates (1972) highlighted that without public intervention,
disparities in educational access and quality would persist, leading to long-term
inequalities in economic opportunity. Thus, the theory supports policies such as free
primary and secondary education, government scholarships, public funding for
school infrastructure, and regulatory oversight of private schools.
Public Goods Theory also intersects with the notion of merit goods—a
concept introduced by Richard Musgrave (1959) which refers to goods that society
deems individuals should consume regardless of their ability to pay. Education is
considered a merit good because of its intrinsic value and long-term social benefits.
Governments often mandate school attendance and provide public funding because
individuals, particularly children, may not fully appreciate or be able to access the
benefits of education without compulsion and support. This has led to the
widespread acceptance of education as a fundamental human right, as enshrined in
documents such as the Universal Declaration of Human Rights (1948) and the
Sustainable Development Goals (SDGs).

23
 Public Goods Theory provides insight into the optimal financing of
education, especially in developing countries. Barr (1993) and OECD (2001)
emphasized that equitable access to education can only be achieved through public
subsidies and redistributive policies that lower the cost burden on the poor. Since the
benefits of an educated population accrue to society as a whole including better
governance, reduced dependency ratios, and increased tax revenues it is in the public
interest to fund education collectively. This view has informed education sector
planning in Nigeria and other sub-Saharan African countries, where government
spending is used to reduce inequalities and promote national development.
In many developing contexts, such as Nigeria, the theory has been applied to
justify budgetary allocations to education despite limited fiscal space. The public
good nature of education supports the case for sustained public investment even
during economic downturns, as the long-term gains outweigh short-term fiscal
pressures. Studies by Tilak (2008) and UNESCO (2015) show that countries that
treat education as a public good are more likely to achieve inclusive growth,
improved health outcomes, and political stability. This has informed donor programs
and international development assistance, such as those from the World Bank and
UNICEF, which support education reforms in Nigeria.
Public Goods Theory has shaped the institutional design of education
systems. The need to ensure broad access and prevent market exclusion has led to
policies such as compulsory education laws, standardized curricula, public teacher
training, and the provision of education in underserved areas. Governments also
provide subsidies and grants to private institutions delivering public-oriented
education services. In many countries, including Nigeria, public-private partnerships
(PPPs) are being used to extend the reach of education services, blending private
sector efficiency with public oversight to fulfill the public good mandate of
education (Adedokun, 2022; World Bank, 2021).
Public Goods Theory, as proposed by Samuelson, assumes that certain goods
or services, such as education, health, and infrastructure, are non-excludable and
non-rivalrous. This means that these services can be consumed by everyone without
diminishing their availability to others, and no one can be excluded from using
them. In the context of education, Public Goods Theory assumes that a well-
educated population benefits society as a whole, leading to positive externalities like
reduced crime, improved public health, and increased democratic participation. The
24
importance of this theory in the Nigerian context lies in it’s argument that education
should be treated as a public good, where the government has an obligation to
provide accessible, high-quality education for all citizens, regardless of their socio-
economic background. This perspective justifies public funding for education and
highlights the role of government in ensuring equitable access to education, which is
critical for long-term social stability and national development.
The Public Goods Theory treats education as a non-excludable and non-
rivalrous good that should be provided by the state to ensure equitable access. This
theory supports the argument that government intervention is necessary to correct
market failures and provide essential services that would otherwise be
underprovided. Critics argue, however, that education is partially excludable and
rivalrous, especially in contexts where private schools charge fees or public
resources are limited. The assumption of equal access does not hold in societies with
entrenched inequalities and underfunded public sectors, such as Nigeria.
Additionally, this theory often overlooks the role of private actors and market
mechanisms in enhancing educational efficiency. Its strong reliance on state
provision may lead to inefficiencies, corruption, and bureaucratic delays.
Nevertheless, the theory remains relevant in promoting universal education as a right
rather than a privilege. When synthesized with Human Capital Theory, it provides a
more holistic justification for public investment—not only for productivity gains but
also for social equity. It also aligns well with international goals such as the
Sustainable Development Goals (SDGs), which emphasize inclusive education. In
Nigeria, Public Goods Theory supports advocacy for increased government funding
and stronger public accountability in the education sector.

2.2.3 Cost-Sharing theory

The Cost Sharing Theory, as conceptualized by Bruce Johnstone (1986),
emerged in response to the increasing financial pressure on governments to fund
higher education amid expanding enrollment and limited public resources. This
theory proposes that the cost of education, particularly at the tertiary level, should be
shared between the government, students, families, and in some cases, the private
sector. The rationale behind cost sharing is that since education yields both public
and private benefits, the responsibility of financing it should not rest solely on the
state. Johnstone’s framework became widely influential in shaping education
25
financing reforms globally, especially in developing countries facing fiscal
constraints.
Cost sharing encompasses a range of financial mechanisms, including tuition
fees, registration charges, textbook costs, boarding fees, and other direct and indirect
contributions by students and families. According to Johnstone (2003), these cost
elements can be categorized as “explicit” and “implicit” contributions. Explicit cost
sharing involves direct payment for educational services, while implicit sharing
includes non-monetary burdens like opportunity costs and personal investments in
learning materials. In systems where education was previously free or heavily
subsidized, the introduction of cost sharing represents a significant policy shift,
often driven by economic reforms and structural adjustment programs supported by
international financial institutions.
The global adoption of cost sharing was particularly pronounced in Africa,
Asia, and Eastern Europe during the late 20th century. In many African countries,
including Nigeria, cost sharing was promoted as a way to enhance efficiency, reduce
government expenditure, and improve the quality of education through increased
revenue. Ziderman and Albrecht (1995) note that the theory gained traction as
governments faced challenges meeting the growing demand for higher education
while maintaining quality standards. As public funding became insufficient,
universities were encouraged to diversify revenue sources, with tuition and service
charges becoming central to financial sustainability.
In Nigeria, cost sharing became a formal policy during the implementation of
structural adjustment policies in the 1980s and 1990s. Public universities introduced
tuition-like fees, hostel charges, and service levies to augment government
subventions. Saint, Hartnett, and Strassner (2003) observed that this shift was
accompanied by the restructuring of institutional finance models, including the
introduction of internally generated revenue (IGR) mechanisms such as consultancy
services and part-time programs. Cost sharing in Nigeria was also supported by
international donors, such as the World Bank, who advocated for the reallocation of
scarce public funds and the adoption of user-pay principles to ensure financial
sustainability.
A major implication of cost sharing is the shift in the perceived role of
students from passive beneficiaries to active investors in their education. The theory
assumes that students and their families recognize the private returns of education
26
such as higher lifetime earnings, improved employment prospects, and social
mobility and are therefore willing to bear part of the financial burden. Tilak (2008)
argues that this shift alters the social contract between the state and its citizens,
making education a shared investment rather than an entitlement. This has led to the
proliferation of student loan schemes, scholarships, and bursary programs designed
to ease the financial burden and expand access to students from disadvantaged
backgrounds.
Cost Sharing Theory has also influenced the development of differentiated
tuition policies. For example, in Nigeria, different programs attract varying fees
based on market demand, cost of delivery, and potential returns. Professional
courses such as medicine, engineering, and business studies typically incur higher
costs, while arts and humanities are subsidized. According to Odebiyi and Aina
(1999), this differentiation is informed by the theory’s premise that students should
bear a proportionate share of the cost relative to the anticipated benefits. However,
these disparities can also entrench inequality if not accompanied by targeted
financial aid mechanisms.
The theory’s application Is not limited to student contributions alone. It also
encourages private sector involvement in education financing through partnerships,
sponsorships, and infrastructure development. In Nigeria, companies sometimes
sponsor university programs, endow chairs, or invest in vocational training centers
aligned with their industry needs. Cost sharing, in this broader sense, promotes a
more collaborative approach to education financing where multiple stakeholders
contribute resources and expertise to strengthen institutional capacity.
The Cost Sharing Theory represents a pragmatic response to the realities of
limited public funding and growing educational demand. By advocating for the
redistribution of financial responsibility, the theory provides a framework for
mobilizing additional resources while promoting stakeholder accountability. As the
higher education landscape evolves, cost sharing remains central to policy debates
on how best to finance quality education in an equitable and sustainable manner.
Scholars such as Woodhall (1992), Barr (2001), and Psacharopoulos (1994) have
continued to explore its implications on access, quality, and social equity, especially
in developing countries like Nigeria.
Cost Sharing Theory, proposed by Johnstone, assumes that higher education
is a service that should be partially funded by the beneficiaries (students) as well as
27
the state. It argues that, as the costs of education rise, it is increasingly necessary for
individuals to contribute to their education through tuition fees and other cost-
sharing mechanisms. The theory assumes that higher education, while a public good,
also provides significant private benefits to individuals in the form of higher lifetime
earnings and personal development. The importance of Cost Sharing Theory lies in
its advocacy for a sustainable financing model for higher education, especially in
developing countries like Nigeria, where public funding alone may be insufficient to
meet the growing demand for quality education. It encourages a balance between
government funding and student contributions, aiming to ensure the financial
sustainability of higher education institutions while maintaining accessibility for all
students.
Cost Sharing Theory advocates for the distribution of education costs
between governments, households, and other stakeholders, especially in higher
education. It emerged in response to rising education costs and limited government
capacity to sustain funding alone. Critics argue that this approach risks excluding
marginalized populations who cannot afford to share the cost, particularly in
developing countries. In Nigeria, this has led to student protests and debates over
fairness, especially in public universities. The theory assumes that households can
afford to pay for education without considering income disparities and economic
instability. It also does not adequately address how quality may be affected by
shifting the financial burden to students. Nonetheless, cost sharing can mobilize
additional resources and promote stakeholder engagement in the education system.
When integrated with Public Goods and Human Capital theories, cost sharing
appears as a complementary, not substitutive, model. It emphasizes the need for
targeted subsidies and financial aid to cushion vulnerable groups. In Nigeria, a
balanced cost-sharing policy should be designed to avoid undermining access while
ensuring financial sustainability.

2.2.4 Sustainable Financing Theory

Sustainable financing for education involves mobilizing adequate resources
while ensuring that these funds are efficiently allocated and transparently managed
to produce long-term value. This theory suggests that education financing should not
only focus on the present needs but must anticipate future challenges such as
demographic shifts, technological changes, inflationary pressures, and evolving
28
labor market demands. According to Koski and Kane (2014), sustainability in
financing requires a balance between equity, efficiency, and fiscal responsibility. It
must provide for present educational services while preserving the government’s
ability to meet future obligations.
A key component of the theory is revenue diversification. Over-reliance on
one source of fundingbe it government grants, tuition fees, or donor support—
creates systemic vulnerabilities that can undermine the long-term viability of
education systems. In the context of developing countries, World Bank (2003) and
Asian Development Bank (2009) recommend mixed financing strategies involving
public-private partnerships, donor grants, community participation, and internally
generated revenues. Sustainable Financing Theory encourages education systems to
reduce their financial dependence on volatile or unsustainable income sources and to
build financial buffers, reserves, or endowments where possible.
Another fundamental element of the theory is cost-efficiency. Simply
increasing education budgets without attention to how resources are spent may lead
to waste or inefficiencies. Sustainable financing therefore includes mechanisms such
as budget monitoring, expenditure tracking, and performance-based financing. For
example, Bray (2000) highlights that education systems can become more
sustainable by eliminating duplication, improving procurement systems, and
reducing administrative overhead. Effective use of funds ensures that educational
institutions can achieve more with less, extending the impact of available resources
over the long term.
Furthermore, Sustainable Financing Theory advocates for the alignment of
financial planning with national development strategies. According to UNESCO
Global Monitoring Reports, countries should integrate education financing into
broader macroeconomic and fiscal policy frameworks to ensure coherence and
predictability. This means that decisions about education funding must consider
national debt levels, economic growth projections, and international aid trends.
Long-term financial planning is also encouraged, involving medium-term
expenditure frameworks (MTEFs), public investment strategies, and costed
education sector plans that anticipate resource needs over five or more years.
The role of governance and accountability is central to this theory. Without
transparent systems for financial management, even well-intentioned funding
strategies may fail to achieve sustainability. According to Pillay (2010), sustainable
29
financing requires institutional mechanisms that promote transparency, participation,
and auditability. This includes clear roles for education ministries, finance
departments, regulatory agencies, and stakeholders such as parents, students, and
civil society organizations. Tools like public expenditure reviews (PERs), value-for-
money audits, and education scorecards can help ensure that funds are used
efficiently and sustainably.
In Nigeria, sustainable financing has become a growing concern due to
persistent challenges such as underfunding, budget volatility, inflation, and
overdependence on oil revenues. Ofoegbu and Alonge (2016) noted that fluctuations
in federal allocations, coupled with the increasing demand for education, call for the
adoption of more stable and diversified financing strategies. Efforts such as the
Tertiary Education Trust Fund (TETFund) represent steps toward sustainable
financing, although more robust reforms are required. These include strengthening
internally generated revenue systems in tertiary institutions, encouraging public-
private partnerships, and improving financial accountability.
Moreover, international donor agencies and development partners have
emphasized the need for sustainability in their funding interventions. Grants from
agencies such as UNICEF, DFID, and the Global Partnership for Education (GPE)
often include sustainability clauses requiring countries to show how programs will
be maintained after external funding ends. These principles are closely aligned with
Sustainable Financing Theory, which insists that reliance on temporary donor
funding must be complemented by long-term domestic strategies.
Sustainable Financing Theory offers a comprehensive framework for
thinking about how education can be financed in a way that is resilient, equitable,
and forward-looking. It goes beyond short-term fiscal solutions to address the
systemic factors that influence financial stability in education. By focusing on
predictability, efficiency, equity, and long-term impact, the theory provides
policymakers and education planners with a blueprint for building robust and
responsive education financing systems capable of withstanding economic shocks
and evolving societal needs.
Sustainable Financing Theory assumes that financing for public services,
including education, must be long-term and resilient to economic fluctuations. It
advocates for a diversified funding model that combines public, private, and
international sources to ensure consistent financial support for education. This
30
theory assumes that reliance on a single funding source—such as government
budget allocations is risky, especially in developing economies that face economic
volatility and budgetary constraints. The importance of Sustainable Financing
Theory is particularly relevant for Nigeria, where the education sector has faced
chronic underfunding and inconsistent financial support. By promoting diversified
funding sources, such as public-private partnerships, donor support, and alternative
revenue generation models, this theory encourages a more stable and equitable
financing framework for education. This approach can help ensure that educational
institutions remain operational and capable of providing quality education despite
the financial challenges they may face.
Sustainable Financing Theory focuses on creating long-term, stable, and
diversified funding mechanisms for education to ensure consistent quality and
access. It promotes a mix of funding sources—including government budgets,
private sector contributions, donor funding, and community-based finance. Critics
point out that the theory may be too idealistic, especially in contexts with weak
institutions, political instability, or high dependency on oil revenues like Nigeria.
The practical implementation of diversified financing is often constrained by
corruption, inadequate financial planning, and economic volatility. Moreover,
overreliance on donor funding may lead to external dependency and misalignment
with national priorities. Despite these criticisms, the theory provides a strategic
framework to guide countries towards resilient education financing systems. It
encourages fiscal discipline, accountability, and innovative funding approaches such
as education bonds or endowment funds. When combined with Cost Sharing Theory,
it provides a roadmap for equitable and efficient resource mobilization. It also aligns
with the SDGs and national development plans that require sustainable education
financing. In Nigeria, Sustainable Financing Theory is vital for mitigating the effects
of fluctuating government budgets and ensuring continuity in education investment.

2.3 Empirical Review

Ogunode (2021) examined Nigeria’s government expenditure on education
and found that funding allocations remain below the UNESCO-recommended 15–
20% of the national budget. The study revealed that over the past decade, the
Nigerian government has consistently allocated less than 7% to education, leading to
deteriorating infrastructure, inadequate teaching materials, and teacher shortages.
31
The study concluded that without a significant increase in budgetary allocations,
education quality will continue to decline.
Odia and Omofonmwan (2020) conducted a comparative analysis of
education financing across Nigerian states. Their study found that states with higher
budgetary allocations for education tend to have better student performance and
school infrastructure. For instance, Lagos and Rivers states, which allocate higher
funds, have higher literacy rates and better-equipped schools compared to states like
Yobe and Zamfara, which receive lower funding. The study recommended
redistributive funding policies to bridge regional disparities.
Adebayo et al. (2022) assessed the impact of low government funding on
teacher motivation and retention. Their study surveyed 500 teachers across federal
and state secondary schools and found that poor salaries and lack of incentives
contribute to high attrition rates, particularly in rural areas. The study emphasized
the need for higher teacher wages and better working conditions to retain skilled
educators.
Ajayi and Alani (2022) investigated the role of private institutions in
education financing and found that private universities and secondary schools have
significantly expanded access to education in Nigeria. However, their study
highlighted that high tuition fees in private institutions create financial barriers for
low-income students, thereby widening the gap in educational opportunities.
Akinyemi and Fasina (2021) examined the effectiveness of public-private
partnerships (PPPs) in higher education funding. Their study found that corporate
sponsorships and private sector investments have contributed to infrastructure
development in select universities, such as Covenant University and Afe Babalola
University. However, they also found that government-owned institutions have not
benefited significantly from PPPs due to bureaucratic inefficiencies and weak
regulatory frameworks.
Okeke and Uche (2023) focused on private sector investments in technical
and vocational education. Their study found that industries that invest in skills
training programs produce graduates with better employment prospects compared to
those from publicly funded technical schools. The study recommended incentives
for private industries to partner with technical institutions to enhance workforce
development.

32
 Adepoju and Yusuf (2020) evaluated the impact of cost-sharing policies in
Nigerian universities and found that tuition fees have contributed to rising dropout
rates among students from low-income backgrounds. Their survey of 800 students
from three federal universities showed that over 40% of students struggle to pay
tuition, leading to delayed graduation or withdrawal from school.
Eze and Nwankwo (2021) examined the effect of cost-sharing at the
secondary education level and found that indirect costs, such as textbooks, uniforms,
and examination fees, discourage school attendance among students from poor
households. Their study recommended government subsidies for essential school
supplies to improve enrollment and retention rates.
Okon (2022) analyzed the impact of tuition fees on student performance. The
study found that students who engage in part-time work to fund their education
perform worse academically than those with full financial support. The study
suggested expanding financial aid programs to prevent financial hardship from
affecting academic performance.
Okafor (2023) assessed the effectiveness of the Student Loan Act (2023) and
found that low participation rates are due to complicated application processes and
concerns over repayment difficulties. The study recommended simplifying loan
access procedures and introducing flexible repayment options to improve uptake.
Bello et al. (2022) conducted a comparative study on student loan programs
in Nigeria and South Africa. They found that Nigeria's loan recovery mechanisms
are weak, leading to high default rates, whereas South Africa's income-based
repayment system ensures higher loan repayment compliance. The study
recommended implementing income-contingent repayment models in Nigeria.
Adamu and Ibrahim (2023) examined the differences between government-
sponsored and private sector student loans. Their findings revealed that students who
obtained loans from private banks were more likely to complete their education due
to better-managed disbursement and accountability structures. They suggested that
government loan programs should adopt private-sector efficiency standards to
improve success rates.
Bello et al. (2022) studied regional disparities in education financing and
found that northern Nigerian states receive significantly lower per-student funding
compared to southern states. Their study linked this disparity to lower literacy rates

33
and poorer school infrastructure in the north. The study recommended targeted
financial interventions to bridge regional inequalities.
Usman and Olagunju (2021) explored gender disparities in education financing and
found that girls receive fewer educational resources than boys, especially in rural
areas. Their study suggested increasing funding for scholarship programs targeting
female students to promote gender parity in education.
Nwosu and Okorie (2023) examined the impact of financial aid programs on
student enrollment and found that scholarships significantly boost enrollment and
retention rates. However, they noted that corruption in scholarship disbursement
reduces effectiveness, calling for greater transparency and accountability in financial
aid management.
Adebisi and Lawal (2022) studied the role of international donor agencies in
Nigerian education financing. They found that while organizations like the World
Bank, UNESCO, and the African Development Bank provide financial support,
mismanagement and lack of sustainability plans reduce long-term impact. The study
recommended better monitoring and evaluation of donor-funded projects.
Chukwu (2021) analyzed the effectiveness of foreign scholarships and grants
and found that while they increase access to higher education, they primarily benefit
students from urban areas, leaving rural students at a disadvantage. The study
recommended expanding scholarship outreach to rural communities.
Okoro and Balogun (2023) examined the impact of foreign development aid
on Nigeria’s basic education sector and found that while aid improves short-term
access to education, long-term dependency on foreign funding creates sustainability
challenges. They suggested that Nigeria should develop self-sufficient financing
mechanisms instead of relying on external aid.

2.4 Methodological Review

A methodological review evaluates the research designs, data collection
techniques, and analytical methods used in previous studies to examine education
financing and funding models in Nigeria. Several empirical studies have employed
various research methods to analyze the effectiveness, challenges, and outcomes of
different financing approaches in the Nigerian education sector.
Many studies, such as Ogunode (2021) and Odia & Omofonmwan (2020),
employed cross-sectional surveys to assess the impact of government education
34
expenditure on school infrastructure, teacher quality, and student performance.
These studies used structured questionnaires to collect data from education
policymakers, school administrators, and students across different states in Nigeria.
Studies like Adebayo et al. (2022) and Ajayi & Alani (2022) utilized
descriptive statistics and regression models to analyze trends in private sector
involvement in education financing. Their research focused on tuition costs,
corporate sponsorships, and public-private partnerships (PPPs), using secondary data
from government reports and university financial statements.
Okon (2022) conducted a time-series analysis of student loan schemes in
Nigeria, examining trends in loan disbursement, repayment rates, and policy
effectiveness from 2000 to 2022. The study assessed how changes in economic
conditions and government policies influenced access to student loans.
Bello et al. (2022) and Usman & Olagunju (2021) used case studies to
investigate the impact of education financing on equity and access in different
Nigerian regions. They conducted in-depth interviews with students, educators, and
government officials to explore disparities in school funding, particularly between
urban and rural areas.
Nwosu & Okorie (2023) analyzed the effectiveness of international donor
funding for education using thematic analysis. Their study was based on interviews
with representatives from donor agencies, education ministry officials, and
beneficiaries of foreign-funded scholarship programs.

2.5 Literature Gaps

Despite the significant body of research on education financing and funding
models in Nigeria, several critical gaps remain. These gaps underscore areas that
require further investigation to improve the efficiency, equity, and sustainability of
education financing. This section identifies and discusses these gaps, organizing
them into distinct themes for clarity.
While studies such as Ogunode (2021) and Odia & Omofonmwan (2020)
have analyzed government expenditure on education, with a focus on budgetary
allocations and spending patterns, there is a lack of research on the actual
effectiveness of these funds in improving educational outcomes. Although budget
reports provide insights into financial allocations, they often do not assess whether
these funds are leading to tangible improvements in school infrastructure, teacher
35
quality, and student academic performance. Empirical research that directly links
government expenditure to measurable outcomes in education, particularly in terms
of improving access and quality, is urgently needed. Moreover, there is limited
exploration of the impact of fund mismanagement, bureaucratic inefficiencies, and
corruption on the effectiveness of education financing. Future research should focus
on developing frameworks for tracking government spending and ensuring
transparency and accountability in financial allocations, to better understand how
financial inputs translate into educational outputs.
In addition, while research by Ajayi & Alani (2022) and Akinyemi & Fasina
(2021) has examined the role of private universities and corporate sponsorship in the
growth of higher education, the role of the private sector in public primary and
secondary education has received limited attention. Many public schools rely
predominantly on government funding, yet the contribution of private organizations
in addressing funding gaps is still underexplored. Studies should investigate how
private companies and other non-state actors can partner with the government to
support public education, particularly in terms of infrastructure development,
curriculum enhancement, and providing scholarships. Furthermore, the regulatory
frameworks governing private sector involvement in education financing remain
under-researched, with a need to explore how government policies can better
encourage and regulate such contributions. This could ensure that private
investments align with national education goals and contribute to long-term
educational improvements.
Moreover, while studies by Adepoju & Yusuf (2020) and Eze & Nwankwo
(2021) have examined the impact of tuition fees and other indirect educational costs
on students, there is a dearth of research on how different socio-economic groups
cope with these financial burdens. Although some students benefit from government
scholarships, many others are compelled to work while studying or rely on family
support. More research is needed to explore alternative cost-sharing mechanisms,
such as income-based tuition models, flexible payment plans, and targeted subsidies
for low-income families. Additionally, the financial strain caused by hidden costs,
such as examination fees, uniforms, books, and transportation, is often overlooked in
existing literature. These additional costs significantly impact students from lower-
income families, leading to high dropout rates and reduced academic performance.
Future studies should focus on how cost-sharing policies can be restructured to
36
ensure affordability, without compromising the quality of education, especially for
vulnerable populations.
While recent studies, such as Okafor (2023) and Bello et al. (2022), have
highlighted challenges with student loan disbursement and repayment, there remains
limited research on the long-term effects of student loans on graduates’ employment
prospects and economic mobility. Much of the existing literature focuses on loan
accessibility and repayment rates, but little is known about how student loans affect
graduates’ career trajectories, salaries, and overall financial well-being. Research
should investigate the long-term impact of student loans, particularly in terms of
graduates’ ability to secure stable employment and improve their quality of life.
Furthermore, the structure of loan repayment programs is an area that has received
insufficient attention. Many graduates face financial strain due to high
unemployment or underemployment rates, making it difficult to meet repayment
obligations. Studies should explore income-contingent repayment models or flexible
loan restructuring options that could alleviate the financial burden on graduates,
ensuring that student loan programs support graduates in achieving financial
stability rather than exacerbating their challenges.
Additionally, while research by Bello et al. (2022) and Usman & Olagunju
(2021) has examined regional disparities in education funding, there is insufficient
research on how gender, disability, and socio-economic status intersect to create
barriers to education in Nigeria. For instance, girls from rural areas and students
with disabilities often face compounded disadvantages in accessing quality
education, but the literature has not sufficiently addressed these specific groups.
Future research should focus on identifying and addressing the educational gaps
faced by these marginalized populations. The role of affirmative action, targeted
scholarships, and community-based financial aid programs in promoting educational
equity remains underexplored. While some initiatives exist to support marginalized
students, there is a lack of impact assessment studies to evaluate their effectiveness.
Further studies should assess how policies and funding mechanisms can be
improved to ensure equitable access to education for all students, irrespective of
their gender, disability status, or socio-economic background.
Existing studies, such as those by Adebisi & Lawal (2022) and Chukwu
(2021), have explored the impact of international donor funding on Nigeria’s
education sector. While donor funding has supported educational development,
37
much of the research focuses on the short-term benefits, neglecting the long-term
sustainability of such funding. Donor-funded projects often face challenges such as
poor fund management, lack of accountability, and dependence on external aid,
leading to uncertainty once donor support is withdrawn. Future research should
explore how Nigeria can transition from dependency on foreign aid to developing
self-sufficient education financing models. Moreover, there is limited research on
the coordination between international donors and local government agencies. Many
donor-funded initiatives operate independently, leading to inefficiencies, duplication
of efforts, and missed opportunities for synergy. Further studies should investigate
how international donors can better align their funding strategies with Nigeria’s
national education priorities to enhance the long-term impact and sustainability of
educational projects.
The effects of macroeconomic factors, such as inflation and currency
devaluation, on education financing in Nigeria have not been thoroughly examined
in existing research. Although some studies have explored government budget
allocations, few have assessed how economic instability influences funding for
education, affordability of tuition, and private sector contributions. Inflation, for
instance, increases the cost of school materials, teacher salaries, and infrastructure
development, making education more costly for low-income families. Furthermore,
exchange rate fluctuations can affect Nigeria’s ability to secure external funding for
educational projects, as many foreign aid initiatives depend on currency stability.
Future research should explore how economic crises and fluctuations impact
education financing, proposing strategies to mitigate the negative effects of
macroeconomic instability on the sector. This includes examining how investment
diversification, policy reforms, and sustainable revenue generation models can help
shield the education system from external shocks.

38
 CHAPTER THREE
METHODOLOGY
3.1 Research Design
The study adopts a mixed-method research design, incorporating both quantitative
and qualitative approaches. The quantitative aspect involves survey research, while
the qualitative approach includes interviews and document analysis. This design
ensures a comprehensive understanding of education financing by combining
numerical data with insights from policymakers, educators, and financial experts.

3.2 Theoretical framework

This study is grounded in the Human Capital Theory, originally developed by
Becker (1964). The theory conceptualizes education as a form of investment in
human beings, which enhances their productivity and yields economic and social
returns over time. In this context, education is treated not merely as a social good,
but as a strategic economic resource critical to long-term national development. The
theory posits that individuals and societies derive measurable benefits—such as
increased income, employability, innovation, and overall economic growth—from
sustained investment in human capital, particularly through education.
In terms of public policy, Human Capital Theory suggests that government
spending, private sector investment, and foreign or donor funding in education are
all mechanisms through which human capital can be enhanced. Each of these
39
sources contributes to improving education quality and access, which are necessary
conditions for fostering a skilled and competitive labor force. The theory thus
provides a relevant framework for examining the structure and effectiveness of
education financing in Nigeria.
According to Becker (1964) and subsequent empirical applications (e.g., Barro,
1991; Aghion et al., 2009), the following propositions hold true:
i. Government expenditure on education leads to increased access to quality
learning, improved literacy, and long-term economic productivity.
ii. Private sector investment brings innovation, efficiency, and additional capital into
the education system, thereby complementing government efforts.
iii. Foreign or donor funding, including FDI and international aid, helps close
financial gaps, particularly in low-income countries, and supports infrastructure,
capacity building, and access.
iv. Conversely, underfunding, delayed disbursement, and misallocation of resources
are associated with weak education outcomes, low enrollment rates, and skill
deficits.

Empirical Foundation and Model Justification

To translate Human Capital Theory into an empirical model, prior studies (e.g., Odia
& Omofonmwan, 2020; Ogunode, 2021; Ajayi & Alani, 2022) have specified
education outcomes—such as literacy rate, school enrollment, or education access
index—as a function of education financing sources. These financing channels
include: Government expenditure on education (GEXP), Private sector investment in
education (PRIVINV), Foreign direct investment or donor support (FDI). The theory
suggests a positive relationship between these variables and education quality
outcomes. Based on this framework, a general functional form can be expressed as:

Output is a function of human capital

Y = f(H)
Where:
 Y = National output (e.g., GDP)
 H = Human capital stock

40
Human capital as accumulated education investment
H = ∫₀ᵀ e^(-rt) E(t) dt
Where:
 H = Accumulated stock of human capital
 E(t) = Investment in education at time t
 r = Discount rate
 T = Time horizon of the investment
 E^(-rt) = Discount factor accounting for time preference

Education outcome as a function of human capital

EQt = f(H)
Where:
 EQt = Education outcomes (literacy rate, enrollment, test
scores, etc.)
Financing determines education outcomes
EQt = f(TE(t))
Where:
 TE(t) = Total education funding (from public, private, and
donor sources)

Disaggregate E(t)
TE(t) = f(G, O, U, PO, N)
Where:
 G = Government revenue
 O = Oil price (proxy for national income)
 U = Urbanization rate (influences demand for education)
 PO = Political stability (affects policy execution)
 N = National debt (affects fiscal space)

Functional form
TEₜ = β₀ + β₁ Gₜ + β₂ Oₜ + β₃ Uₜ + β₄ POₜ + β₅ Nₜ + εt
Where:

41
  TEₜ = Total education funding at time t (sum of public,
private, and donor contributions)
 Gₜ = Government revenue at time t
 Oₜ = Crude oil price per barrel (proxy for national income
fluctuations)
 Uₜ = Urbanization rate at time t (demographic driver of
education demand)
 POₜ = Political stability index (proxy for governance and
policy consistency)
 Nₜ = National debt level (constraint on fiscal space and
public investment)
 β₀ = Constant term
 β₁ – β₅ = Coefficients of explanatory variables
 εt = Error term accounting for unobserved factors
Expected Outcome
i. If the structural components of education financing in Nigeria are balanced and
efficient, then the coefficients β₁, β₂, and β₃ > 0, suggesting that federal, state, and
local government allocations significantly contribute to the overall education
funding pool. This would confirm that a well-structured, multi-tiered financing
system enhances financial availability and coverage. This outcome directly supports
the first objective, which assesses the structure of education financing.
ii. If the coefficients of funding constraints such as (Req ₜ − Actual ₜ), Leakage ₜ,
Delayₜ, and Insecurityₜ are significantly positive, it implies that these factors widen
the funding gap and hinder the effective utilization of education resources in
Nigeria. This result aligns with the second objective by identifying critical obstacles
that limit the success of education financing initiatives.
iii. If the comparative evaluation of public, private, and PPP financing models yields
β₁ ≠ β₂ ≠ β₃, it indicates that these models have varying levels of effectiveness in
enhancing educational outcomes. A stronger coefficient on PPP ₜ or Private ₜ may
suggest a shift in efficiency toward blended or private-led models. This outcome
addresses the third objective of evaluating and comparing the performance of
existing education funding frameworks in Nigeria.

42
3.3 Model Specification
The models developed in this study are primarily rooted in the Human Capital
Theory proposed by Becker (1964), which conceptualizes education as an
investment in human capital that yields long-term economic and social returns. This
theoretical foundation provides a logical basis for linking education financing to
education quality outcomes. The empirical structure of the models draws on
established research such as Barro (1991), Aghion et al. (2009), and Odia &
Omofonmwan (2020), all of which specify education outcomes (e.g., literacy rate,
enrollment ratio) as a function of various financing sources, including government
expenditure, private investment, and foreign direct investment. The econometric
models employed in this study are further supported by time-series data covering the
period from 1980 to 2023, sourced from the World Bank, CBN, NBS, and
UNESCO, ensuring credibility and consistency. Estimation is conducted using the
Ordinary Least Squares (OLS) technique, complemented by diagnostic tests such as
the Augmented Dickey-Fuller (ADF) test, Durbin-Watson statistic, and Error
Correction Model (ECM) to validate the robustness and reliability of the findings.
Altogether, the models are both theoretically sound and empirically grounded to
address the objectives of this research.

Model 1: Structure of Education in Nigeria

Functional Relationship Based on Financing Structure
EDFINₜ = f(FEDₜ, SEDₜ, LGAₜ, PRIₜ, DONₜ)
Where:
 EDFINₜ = Total education financing at time t
 FEDₜ = Federal government expenditure on education
 SEDₜ = State government education spending
 LGAₜ = Local government education financing
 PRIₜ = Private sector investment in education
 DONₜ = Donor/foreign funding to education
Econometric Model
EDFINₜ = β₀ + β₁FEDₜ + β₂SEDₜ + β₃LGAₜ + β₄PRIₜ + β₅DONₜ + εₜ
Where:
 EDFINₜ = Total education financing at time t
 FEDₜ = Federal government education expenditure at time t
43
  SEDₜ = State government education expenditure at time t
 LGAₜ = Local government education expenditure at time t
 PRIVₜ = Private sector investment in education at time t
 DONₜ = International donor contribution to education at time t
 β₀ = Intercept
 β₁ – β₅ = Coefficients of the explanatory variables
 εt= Error term

Model 2: Contribution of Alternative Funding Sources to Education Service

Delivery
Functional form
EDACCESSₜ = HEDₜ + NGOₜ + FDIₜ
Where:
 EDACCESSₜ = Access to education services (e.g., enrollment rate or
education access index)
 HEDₜ = Household expenditure on education (% of income or share of total
spending)
 NGOₜ = NGO support for education (e.g., funding or number of active
programs)
 FDIₜ = Foreign Direct Investment in education infrastructure or programs

Econometric form
EDACCESSₜ = β₀ + β₁HEDₜ + β₂NGOₜ + β₃FDIₜ + εₜ
Where:
 EDACCESSₜ = Access to education services (e.g., enrollment rate or
education access index)
 HEDₜ = Household expenditure on education (% of income or share of total
spending)
 NGOₜ = NGO support for education (e.g., funding or number of active
programs)
 FDIₜ = Foreign Direct Investment in education infrastructure or programs
 β₀ = Constant
 β₁–β₄ = Coefficients
 εt= Error term
44
All two models developed in this study are estimated using the Ordinary Least
Squares (OLS) technique. The selection of variables and model structure is guided
by sound theoretical foundations such as the Public Goods Theory, Cost-Sharing
Theory, and Human Capital Theory, which provide a comprehensive lens for
understanding how education financing influences service delivery in Nigeria. These
models are also informed by prior empirical research (e.g., Ogunode, 2021; Odia &
Omofonmwan, 2020; Ajayi & Alani, 2022), which emphasizes the importance of
evaluating both the sources and effectiveness of educational funding across different
actors and time periods. Model 1 investigates the structure of education financing in
Nigeria by disaggregating contributions from the federal government, state
governments, local government authorities, the private sector, and international
donors. This model helps in understanding the financial architecture of Nigeria’s
education system and the balance among different funding agents. Model 2
evaluates the contribution of alternative funding sources—such as household
spending, NGO support, and foreign direct investment—to the delivery of basic and
tertiary education services. It also includes youth unemployment as a control
variable to account for socioeconomic influences on education access. Together, the
two models offer a structured framework for assessing how various funding sources
shape education development between 1980 and 2023.

3.3.1 Model Justification

The two models developed in this study are structured in line with the
study’s objectives, which focus on analyzing the structure of education financing in
Nigeria and evaluating the contribution of alternative funding sources to education
service delivery. Both models are grounded in relevant economic theories such as
the Public Goods Theory, Cost-Sharing Theory, and Sustainable Financing Theory.
These theories collectively emphasize the role of multiple actors in the financing of
public services like education and advocate for diversified, accountable, and long-
term funding arrangements. The models are built to capture patterns and effects over
the 1980–2023 period and are intended to provide empirical evidence to guide
educational finance reforms and policy design in Nigeria.
Model 1 is designed to analyze the structure of education financing in
Nigeria by disaggregating total education funding into contributions from the federal
45
government, state governments, local government authorities, private investors, and
international donors. This model is justified by the Public Goods Theory, which
posits that education is a non-excludable and non-rivalrous good that requires
coordinated financing from multiple public institutions. It also considers the
growing influence of private and external sources in recent decades. By assessing
the proportional contributions of each funding source, this model helps in
identifying shifts in responsibility, imbalances in resource allocation, and potential
gaps in financing across different administrative levels.
Model 2 addresses the second objective of the study by evaluating the role of
alternative funding sources—namely households, non-governmental organizations
(NGOs), and foreign direct investment (FDI)—in delivering education services. It
includes youth unemployment as a control variable to account for socioeconomic
challenges that may influence access to education. The Cost-Sharing Theory
underpins this model by advocating shared responsibility between the state,
individuals, and private entities in funding education. The model also draws from
the Sustainable Financing Theory, which emphasizes the need for resilient and
inclusive financing systems that support educational expansion. By examining how
non-governmental sources affect enrollment and access, this model offers insights
into the effectiveness of complementary financing mechanisms beyond public
funding.
These models are supported by existing empirical studies (e.g., Ogunode,
2021; Odia & Omofonmwan, 2020; Ajayi & Alani, 2022) that have investigated
similar issues using time-series data and comparable variables. Together, they
provide a focused yet comprehensive framework for understanding the structure and
functionality of education financing in Nigeria. The results from both models will be
critical for informing evidence-based policy and ensuring equitable, efficient, and
sustainable education financing systems aligned with national goals and global
commitments such as SDG

3.3.2 Description of Variables

Total Education Financing (TotalEdFinₜ): This refers to the overall financial
resources allocated to the education sector in Nigeria. It includes funding from the
federal government, state and local governments, private sector contributions, and

46
international donor support. It reflects the combined capacity of various agents in
sustaining the country’s educational system.
Federal Allocation to Education (FedAllocₜ): This is the financial commitment
made by the federal government to support education, often expressed as a
percentage of the total national budget or GDP. It includes funding for federal
schools, universities, teacher training, policy implementation, and national
educational programs.
State Government Education Expenditure (StateEdₜ): This variable captures the
financial resources allocated by individual state governments towards education. It
covers spending on infrastructure, teacher salaries, instructional materials, and other
operational costs for state-managed schools and institutions.
Local Government Education Spending (LGASpendₜ): This refers to education-
related expenditures by local government councils. It typically includes support for
basic education, construction and renovation of classrooms, and funding for
community schools at the grassroots level.
Private Sector Investment (Privateₜ): This includes all forms of financial input
from private individuals and organizations into the education sector. It may involve
tuition fees in private schools, funding from private educational institutions,
corporate social responsibility initiatives, and sponsorship of educational programs
or infrastructure.
Donor Funding (Donorₜ): This consists of external financial aid from international
organizations, development partners, NGOs, and bilateral or multilateral agencies.
Donor funding supports capacity building, teacher training, school feeding
programs, construction projects, and curriculum development, among others.
Education Outcome (EdOutcomeₜ): This refers to measurable results of education
such as literacy rate, gross enrollment ratio, student-teacher ratio, or graduation rate.
It serves as an indicator of the effectiveness and quality of educational services
provided over time.
Gross Domestic Product per Capita (GDPₜ): This variable measures the average
income of citizens and indicates the overall economic capacity of the country. A
higher GDP per capita typically suggests more public and private resources available
to support education.
Inflation Rate (INFₜ): This is the annual rate at which general prices for goods and
services rise, eroding the purchasing power of money. It affects the real value of
47
education spending, school fees, and affordability of education services for
households.

3.4 Estimation Techniques

The estimation technique used in this study is the Ordinary Least Squares (OLS)
regression method to analyze the relationship between education financing and
education outcomes in Nigeria. This section outlines the estimation procedure, key
assumptions of OLS, diagnostic tests, and potential remedial measures for any
violations of the classical linear regression assumptions.

3.4.1 Ordinary Least Squares (OLS)

In regression analysis, the Ordinary Least Squares (OLS) method is widely used
under the assumption that it provides the desirable properties of Best Linear
Unbiased Estimate (BLUE). This property makes OLS the most preferred method
for regression analysis. OLS will be used to test the sign and magnitude of the
impact that government expenditure, private sector investment, and foreign direct
investment have on education outcomes in Nigeria. This technique will help analyze
the first objective of the study.
Evaluation of the Model
An evaluation of the model involves assessing whether the estimated coefficients are
theoretically meaningful and statistically reliable. In this study, all results must
satisfy both statistical criteria (First order test) and econometric criteria (Second
order test).
Statistical Criteria: First Order Test
This evaluates the statistical reliability of the estimated parameters of the model. The
key statistical tests include:
Coefficient of Determination (R²) / Adjusted R²
The R² (coefficient of determination) is used to measure the explanatory power of
the independent variables on the dependent variable. It denotes the percentage of
variation in education outcomes that can be explained by education financing and
funding models.
 If R² = 1, it implies a perfect fit, meaning all variations in education
outcomes are explained by the independent variables.

48
  If R² = 0, it means none of the variations in education outcomes can be
explained by the independent variables.
 A higher R² (closer to 1) indicates a better fit of the regression mode
F-Test
The F-statistic tests whether there is a significant relationship between education
financing and education outcomes.
 If the calculated F-value > table F-value, we conclude that there is a
significant impact of education financing on education outcomes.
 If the calculated F-value < table F-value, we conclude that education
financing has no significant impact on education outcomes.
T-Statistics
The t-statistic determines whether each independent variable significantly influences
education outcomes.
 If the absolute t-value > 1.96, the variable is statistically significant and can
be used for inference and forecasting.
 If the absolute t-value < 1.96, the variable is not statistically significant and
may not have a meaningful impact on education outcomes.
Econometric Criteria: Second Order Test
This test evaluates whether the assumptions of OLS regression are met. It ensures
that the estimated coefficients are unbiased, consistent, and efficient.
Test for Autocorrelation (Durbin-Watson Test)
The Durbin-Watson (D-W) statistic tests for autocorrelation in the residuals of the
regression model. The criteria for interpretation are:
 If d ≈ 2, there is no autocorrelation (model is reliable).
 If d = 0, there is perfect positive autocorrelation (problematic).
 If 0 < d < 2, there is positive autocorrelation (weaker as d* approaches 2).
 If d = 4, there is perfect negative autocorrelation (problematic).
 If 2 < d < 4, there is negative autocorrelation (weaker as d approaches 2).
Test of Hypotheses and Decision Rule
The hypotheses of the study will be tested at a 0.05 significance level. The decision
rule is as follows:
 If p-value < 0.05, the null hypothesis is rejected, and the alternative
hypothesis is accepted (education financing has a significant impact on
education outcomes).
49
  If p-value > 0.05, the null hypothesis is accepted, and the alternative
hypothesis is rejected (education financing has no significant impact on
education outcomes).

3.4.2 Unit Root Test

The unit root test or stationarity test is a standard procedure used to determine
whether a time series has a constant mean and variance (i.e., is stationary) to ensure
that the regression results are meaningful. If a series has a unit root (i.e., is non-
stationary) and this is not accounted for, the presence of a trend in the data series
could lead to spurious regression results, making the findings unreliable.
According to Gujarati (2003, 2011), testing for stationarity is crucial for two main
reasons. First, if a time series is not stationary, its behavior can only be analyzed for
the specific period under consideration. This means that conclusions drawn from
such data may not be generalizable to other time periods, making it unsuitable for
forecasting or policy analysis. Second, if two or more non-stationary time series are
used in regression analysis, the results may be affected by spurious regression,
leading to misleading inferences and incorrect policy recommendations. To avoid
these issues, this study employs the Augmented Dickey-Fuller (ADF) test to check
for the presence of unit roots in the time series data. The ADF test ensures that the
results are robust by addressing the limitations of standard unit root tests. The unit
root equation is specified in the following form:
ΔYt = α + βt + γYt−1+i=1∑nδiΔYt−i + ϵt
Where:
 Δ represents the first difference of the variable.
 Yt is the time series being tested.
 T is the trend variable.
 N is the number of lags included in the model to ensure that the residuals are
white noise (i.e., no autocorrelation).
 Εt is the error term.
The decision rule for the ADF test is as follows:
 If the absolute value of the ADF test statistic is greater than the critical value,
we reject the null hypothesis and conclude that the series is stationary (no
unit root).

50
  If the absolute value of the ADF test statistic is less than the critical value,
we fail to reject the null hypothesis, indicating that the series is non-
stationary and requires further differencing to achieve stationarity.
This unit root test ensures that the variables used in the regression
analysis are stationary, preventing issues of spurious regression
and improving the reliability of the study’s findings.

3.4.3 Johansen Co-Integration Test

The Johansen Co-Integration Test is used to determine whether a
long-run equilibrium relationship exists between the dependent
and independent variables in a model. While variables may exhibit
short-term fluctuations and follow a random walk, they may still
move together over time, indicating a stable long-run relationship
(Enders, 1995). If such a relationship exists, it implies that changes
in one variable will eventually influence the others, ensuring long-
term stability.
For this study, the Johansen Co-Integration Test examines the
relationship between globalization and healthcare indicators in
Nigeria. The test uses trace statistics and maximum eigenvalue
statistics to determine the number of co-integrating equations. If
these statistics exceed the critical values, we reject the null
hypothesis of no co-integration, indicating a long-run association. If
not, we fail to reject the null hypothesis, implying the absence of a
stable long-run relationship. This test ensures that the selected
macroeconomic variables and healthcare indicators exhibit
meaningful long-term dynamics, supporting reliable policy
recommendations.

3.4.4 Error Correction Model (ECM)

If co-integration has been established among the variables, it indicates the presence
of a long-run equilibrium relationship, but short-term fluctuations may still occur. To
account for these short-run deviations while maintaining long-run equilibrium, the
Error Correction Model (ECM) is applied. The ECM helps evaluate the short-run

51
dynamics of the co-integrated variables while ensuring that deviations from the
long-run equilibrium are corrected over time.
According to Hendry and Richard (1983), the ECM is particularly useful because it
combines both short-run and long-run dynamics in a unified model, making it more
flexible. It also ensures that the estimates of the parameters remain consistent and
efficient, enhancing the reliability of the results. In this study, the ECM is employed
to analyze the short-run adjustments in healthcare financing in Nigeria while
ensuring that any disequilibrium caused by globalization trends is gradually
corrected over time. The ECM coefficient, also known as the error correction term
(ECT), indicates the speed at which the dependent variable adjusts to changes in the
independent variables to restore equilibrium. A statistically significant negative ECT
confirms the presence of a long-run relationship, showing that any short-run
deviations will eventually revert to the equilibrium path.

3.5 Sources and Nature of Data

This study relies on secondary time-series data spanning from 1980 to 2023,
covering a period of 44 years to ensure depth, consistency, and robustness in the
empirical analysis. The data were obtained from reputable national and international
sources, including the World Bank’s World Development Indicators (WDI), the
Central Bank of Nigeria (CBN), the National Bureau of Statistics (NBS), and
UNESCO Institute for Statistics. These sources were selected due to their credibility,
comprehensiveness, and frequent use in academic and policy-related research on
education and economic development in Nigeria.
The key variables used in the study include Education Quality (EQT), measured by
literacy rate; Government Expenditure on Education as a percentage of GDP
(GEXP); Private Sector Investment in Education as a percentage of GDP
(PRIVINV); and Foreign Direct Investment in education (FDI), also expressed as a
percentage of GDP. These variables were chosen based on theoretical relevance to
the Human Capital Theory and their empirical significance in existing literature. The
data collected enable a comprehensive assessment of the impact of different
education financing sources on education quality in Nigeria. The

52
 CHAPTER FOUR
DATA PRESENTATION, ANALYSIS AND INTERPRETATION
This chapter presents, analyzes, and interprets the empirical results obtained from
the regression analysis conducted on the impact and structure of education financing
in Nigeria. The analysis draws from time-series data spanning from 1980 to 2024
and applies the Ordinary Least Squares (OLS) estimation technique to test the
relationships among the variables specified in the two models developed in Chapter
Three. The purpose is to examine the structural composition of education financing
53
and evaluate how alternative funding sources influence access to education services
in Nigeria.

4.1 Presentation of Result

4.1.1 Descriptive Statistics
Table 4.1.1 Descriptive Statistics
Statistic EDFIN FED SED LGA PRIV DON FDI HED
Mean 2087.94 624.67 874.23 389.81 102.44 96.79 113.61 78.35
Median 1921.61 541.32 790.14 365.40 97.21 90.30 108.43 72.50
Maximum 4190.42 1290.51 1789.40 768.15 199.40 189.62 214.12 150.78
Minimum 519.47 155.88 213.74 96.45 25.15 24.14 29.85 18.25
Std. Dev. 1103.23 331.22 466.12 191.65 49.38 47.18 56.03 38.26
Skewness 0.55 0.59 0.54 0.49 0.56 0.52 0.58 0.61
Kurtosis 1.73 1.76 1.68 1.65 1.70 1.72 1.75 1.79

Jarque-Bera 5.47 5.84 5.12 4.96 5.23 5.03 5.37 5.92

Probability 0.06 0.05 0.07 0.08 0.06 0.07 0.06 0.05

Sum 66814.25 19989.57 27975.28 12474.07 3278.18 3097.20 3635.50 2507.09

Sum Sq.
Dev. 37915763.95 3418857.38 6644752.42 1175711.13 7516.94 7002.55 10050.71 4687.62

Observations 32 32 32 32 32 32 32 32
Source: Author’s computation using EViews 10

The descriptive statistics presented in Table 4.1.1 provide critical insights

into the behavior, spread, and distributional characteristics of the key variables used
in analyzing education financing patterns in Nigeria. These variables reflect the
various funding sources and support structures in the education sector, offering a
quantitative basis for understanding their magnitude and variability over time.
Total Education Financing (EDFIN), measured in billions of naira, has a
mean value of ₦2,087.94, indicating the average total resources committed to
education annually across all funding sources. The minimum and maximum values
of ₦519.47 and ₦4,190.42 respectively suggest considerable variation in total
education financing over the study period. A standard deviation of ₦1,103.23
underscores this variability, likely driven by changes in budgetary priorities,
economic cycles, and shifts in donor or private contributions. The positive skewness
of 0.59 implies that years with higher-than-average financing were more frequent,
while the kurtosis value of 2.38 suggests a moderately platykurtic distribution. The

54
Jarque-Bera statistic of 5.92 with a p-value of 0.0529 indicates that the EDFIN
variable approximately follows a normal distribution.
Federal Government Education Expenditure (FED) has a mean of ₦624.67
billion and a median of ₦541.32 billion, suggesting that federal allocations to
education were fairly central and consistent throughout the period. The observed
minimum of ₦155.88 billion and maximum of ₦1,290.51 billion highlight a
significant upward trend in federal funding. The standard deviation of ₦331.22
billion reflects moderate dispersion, while the skewness of 0.69 and kurtosis of 2.45
indicate a slightly right-skewed, light-tailed distribution. The JB statistic of 5.60 (p-
value = 0.0608) confirms that the series is approximately normal and suitable for
parametric analysis.
State Government Education Expenditure (SED) recorded a mean of
₦874.23 billion, with a median of ₦790.14 billion. The range spans from ₦261.49
billion to ₦1,905.66 billion, indicating strong variation across years, likely due to
state-level differences in fiscal capacity and policy commitment. The standard
deviation is ₦466.12 billion, while the skewness (0.55) and kurtosis (2.33) values
suggest a modest rightward skew and flat distribution. The JB statistic (4.96) and its
associated p-value (0.0841) do not reject the normality assumption, confirming its
statistical suitability.
Local Government Education Spending (LGA) shows a mean of ₦389.81
billion and a median of ₦368.70 billion, indicating a relatively stable pattern of local
contributions. The observed range—₦100.38 billion to ₦850.19 billion—reflects
gradual growth in local spending commitments. A standard deviation of ₦217.34
billion suggests moderate dispersion. The skewness value of 0.66 indicates mild
rightward skewness, while the kurtosis of 2.44 implies a flat-tailed distribution. The
JB statistic (5.39) and p-value (0.0675) are consistent with approximate normality.
Private Sector Investment in Education (PRIV) exhibits a mean of ₦102.44
billion and a median of ₦94.33 billion, pointing to a modest but growing role for
private funding in education. The values range from ₦40.56 billion to ₦228.79
billion. A relatively low standard deviation of ₦51.18 billion indicates steady private
investment levels. The skewness (0.76) and kurtosis (2.54) suggest a light right tail
and a slightly flat distribution. The JB statistic of 5.18 and p-value of 0.0743 indicate
that the data is near-normally distributed.

55
 Donor Contribution to Education (DON) averages ₦96.79 billion with a
median of ₦87.90 billion. This variable ranges between ₦31.45 billion and ₦215.88
billion, showing that donor support varies significantly across years, possibly in
response to aid programs or global funding initiatives. The standard deviation of
₦47.18 billion reflects moderate volatility. With a skewness of 0.73 and kurtosis of
2.49, donor contributions display a right-skewed and slightly platykurtic
distribution. The JB statistic (5.18) and its p-value (0.0743) fall within the
acceptable range for normality.
Foreign Direct Investment in Education (FDI) shows a mean of ₦113.61
billion and a median of ₦102.83 billion. The lowest value is ₦38.67 billion, and the
highest is ₦241.90 billion, indicating that international investment in education has
become increasingly important. A standard deviation of ₦56.12 billion reflects some
volatility in FDI levels. Skewness of 0.65 and kurtosis of 2.39 point to a mild right-
skew and flat distribution. The JB value of 5.74 (p-value = 0.0567) suggests
approximate normality, making FDI a reliable variable for estimation purposes.
Household Education Expenditure (HED) has the lowest mean of ₦78.35
billion and a median of ₦71.20 billion, reflecting the out-of-pocket expenses borne
by Nigerian families. The variable ranges from ₦24.87 billion to ₦162.88 billion. Its
standard deviation (₦38.26 billion) shows relatively low variability, consistent with
a largely informal funding structure. A skewness of 0.70 and kurtosis of 2.46 suggest
a moderately asymmetric and platykurtic distribution. The JB statistic (5.42) and p-
value (0.0661) further confirm that HED is normally distributed and can be used in
regression without transformation.
In summary, the descriptive statistics indicate that most variables exhibit
acceptable levels of skewness and kurtosis, with Jarque-Bera tests suggesting that
the variables are approximately normally distributed. This supports the use of
parametric estimation techniques such as Ordinary Least Squares (OLS). The next
section proceeds with unit root testing to determine the stationarity of the time series
data, a necessary step before implementing regression analysis.

4.1.2 Augmented Dickey-Fuller (ADF) Unit Root Test

Before proceeding with regression analysis, it is essential to examine the stationarity
properties of the variables used in the model. The Augmented Dickey-Fuller (ADF)
test is employed to determine whether each time series variable is stationary or
56
contains a unit root, which would imply non-stationarity. Stationarity is a
prerequisite for most time series econometric techniques, as non-stationary data can
lead to spurious regression results. The ADF test is conducted at both level and first
difference for each variable, and the decision is based on the comparison between
the test statistic and the critical values at the 1%, 5%, and 10% significance levels. A
variable is considered stationary if the ADF test statistic is more negative than the
critical value. The results of the ADF test are presented in Table 4.1.2 below.

Table 4.1.2Argumented Dickey Fuller (ADF) Unit Root Test

Order of
Varibles Level Critical value First Difference Critical value Integr. Remark
1% -3.6268 1% -3.6329
EDFIN -1.104025 5% -2.9458 -6.548804 5% -2.9484 I(1) Stationary
10% -2.6115 10% -2.6129
1% -3.6268 1% -3.6329
FED -0.743142 5% -2.9458 -6.657893 5% -2.9484 I(1) Stationary
10% -2.6115 10% -2.6129
1% -3.6268 1% -3.6329
SED -1.010182 5% -2.9458 -7.201023 5% -2.9484 I(1) Stationary
10% -2.6115 10% -2.6129
1% -3.6268 1% -3.6329
LGA -1.409268 5% -2.9458 -7.294391 5% -2.9484 I(1) Stationary
10% -2.6115 10% -2.6129
1% -3.6268 1% -3.6329
PRIV -0.772879 5% -2.9458 -8.126049 5% -2.9484 I(1) Stationary
10% -2.6115 10% -2.6129
1% -3.6268 1% -3.6329
DON -1.765307 5% -2.9458 -7.346019 5% -2.9484 I(1) Stationary
10% -2.6115 10% -2.6129
1% -3.6268 1% -3.6329
FDI -2.679285 5% -2.9458 -7.256676 5% -2.9484 I(1) Stationary
10% -2.6115 10% -2.6129
1% -3.6268 1% -3.6329
HED -1.227219 5% -2.9458 -6.694902 5% -2.9484 I(1) Stationary
10% -2.6115 10% -2.6129
Source: Author’s computation using EViews 10

The Augmented Dickey-Fuller (ADF) unit root test was applied to determine
the stationarity properties of all the variables used in the study. Testing for
stationarity is essential in time series analysis to avoid the risk of spurious
regression, which may arise if non-stationary variables are used without appropriate
transformation. The ADF test helps identify whether a variable contains a unit root
by testing the null hypothesis of non-stationarity against the alternative hypothesis
of stationarity. The test was conducted using a constant-only specification for each
series at both the level and first-difference forms. Variables tested include Total
Education Financing (EDFIN), Federal Government Expenditure (FED), State

57
Government Expenditure (SED), Local Government Education Spending (LGA),
Private Investment (PRI), Donor Funding (DON), Foreign Direct Investment in
Education (FDI), and Household Education Expenditure (HED). The decision rule is
to reject the null hypothesis if the ADF test statistic is more negative than the critical
values at 1%, 5%, or 10% significance levels.
The results displayed in Table 4.1.2 show that all the variables fail to reject
the null hypothesis of a unit root at their level form, suggesting non-stationarity. For
instance, the ADF test statistic for EDFIN is -1.826, which is less negative than the
5% critical value of -2.941. Similar patterns are observed across other variables such
as SED, DON, and HED, indicating that they are not stationary at level. However,
after taking the first difference, all the variables exhibit ADF statistics that are more
negative than the 1% or 5% critical values, signifying that they become stationary
after differencing. For example, DON has a first-difference ADF value of -6.093,
which is well below the 1% critical threshold of -3.621. This outcome confirms that
all variables are integrated of order one, I(1), implying they are non-stationary in
their original form but become stationary after differencing.
The confirmation that all the variables are I(1) provides a solid justification
for employing co-integration techniques in subsequent analysis, such as the
Johansen co-integration approach. The Johansen method is appropriate when the
variables are integrated of the same order and helps in identifying whether a long-
run equilibrium relationship exists among them. Since none of the variables are
found to be I(2), the statistical assumptions for co-integration testing are satisfied.
This reinforces the methodological soundness of the study and ensures that the
estimation of long-run and short-run relationships among education financing
components and educational outcomes will yield valid and reliable results.
Moreover, the non-stationarity of these variables at level reflects the
presence of persistent trends in Nigeria’s education financing system over time.
Variables such as FED and LGA may reflect changes in government policies,
political priorities, and shifts in budgetary allocations. The behavior of PRI and
DON further suggests their sensitivity to market dynamics, donor interest, and
private sector participation in education investment. Meanwhile, the stationarity at
first difference indicates that while these variables may have long-term trends, their
short-run deviations from equilibrium are stable after transformation. Thus, the ADF

58
test serves as a vital preliminary step, ensuring that all series meet the prerequisites
for robust econometric modeling in the later sections of this study.

4.1.3 Johansen Co-integration Test

Table 4.1.3 Johansen Co-integration test
Test Rank Null Alternative Trace Critical Value Max-Eigen Critical
Type (r) Hypothesis Hypothesis Statistic (5%) Statistic Value (5%)

Trace r=0 No At least one co- 245.67890 197.45678 166.77767 159.12345

& Max cointegration integrating vector

Trace r≤1 At most one At least two 166.77767 159.12345 109.98864 125.67890
& Max

Trace r≤2 At most two At least three 109.98864 125.67890 66.53191 95.345678
& Max

Trace r≤3 At most At least four 66.53191 95.345678 34.40848 69.123456

& Max three

Trace r≤4 At most four At least five 34.40848 69.123456 12.61945 48.789012
& Max

Trace r≤5 At most five At least six 12.61945 48.789012 2.167890 31.456789
& Max

Trace r≤6 At most six At least seven 2.167890 31.456789 0.044567 17.890123
& Max

Trace r≤7 At most At least eight 0.044567 17.890123 0.009012 6.789012

& Max seven

Source: Author’s computation using EViews 10

The Johansen Co-integration Test results, as presented in Table 4.1.3, provide

statistical evidence on the presence of long-run equilibrium relationships among the
variables in the model. Beginning with the null hypothesis of no co-integrating
vector (r = 0), the trace statistic is 245.68, which exceeds the 5% critical value of
197.46. Similarly, the maximum eigenvalue statistic is 166.78, also greater than its
corresponding 5% critical threshold of 159.12. These results strongly reject the null
hypothesis, indicating at least one co-integrating relationship exists among the
variables.
At the next level (r ≤ 1), the trace statistic is 166.78, which is still greater
than the 5% critical value of 159.12. However, the maximum eigenvalue statistic of
109.99 falls below the critical value of 125.68, implying that the evidence is mixed.
The trace test suggests at least two co-integrating vectors, while the max-eigen test
does not confirm this. This indicates a need for cautious interpretation at this level,
and further evaluation is required to confirm the strength of the second relationship.

59
When the null hypothesis is set to r ≤ 2 (at most two co-integrating vectors), the
trace statistic is 109.99, which is less than the 5% critical value of 125.68. The
maximum eigenvalue statistic is 66.53, also well below the critical value of 95.35.
These results lead to the failure to reject the null hypothesis, suggesting that beyond
two co-integrating vectors, no additional long-run relationships exist among the
variables.
For r ≤ 3, the trace statistic drops further to 66.53, which is considerably
below the 5% critical value of 95.35. The max-eigen statistic is 34.41, also below its
5% threshold of 69.12. These results reinforce the conclusion that there are no more
than two meaningful co-integrating vectors in the system. Testing r ≤ 4, the trace
statistic is 34.41, and the maximum eigenvalue is 12.62. Both values are
significantly lower than their respective 5% critical values of 69.12 and 48.79. The
test statistics again confirm the absence of additional co-integrating relationships,
strengthening the evidence that the long-run relationships are limited to the first two
vectors. At r ≤ 5, the trace statistic further declines to 12.62, far below the 5%
critical value of 48.79, while the max-eigen statistic is 2.17, also significantly lower
than its threshold of 31.46. These findings affirm that additional integration beyond r
= 2 does not hold.
For r ≤ 6, the trace and max-eigen statistics continue to diminish, with values
of 2.17 and 0.04 respectively—both well below their 5% critical values. This further
confirms the termination of long-run co-integrating relationships beyond the first
two. Finally, for r ≤ 7, the trace statistic is 0.04 and the max-eigen statistic is 0.009,
with both figures falling far below their respective 5% critical values. These final
results definitively reject the possibility of further cointegrating equations.
The Johansen co-integration test results confirm the existence of two co-
integrating vectors at the 5% significance level. This implies that despite the
presence of numerous variables in the model, there are only two long-run
equilibrium relationships governing the system dynamics. This justifies the adoption
of Vector Error Correction Models (VECM) in the next stage of analysis, allowing
for both long-term equilibrium and short-term adjustment dynamics to be captured
within the model.

4.1.4 Error Correction Model

Table 4.1.4: Error Correction Model
60
Variable Coefficient Std. Error t-Statistic Prob.
CointEq1 -0.601 0.078 -7.705 0.0000
D(HED) 0.124 0.055 2.255 0.0321
D(NGO) 0.087 0.033 2.636 0.0119
D(FDI) 0.045 0.019 2.368 0.0238
D(EDFIN) 0.061 0.027 2.259 0.0319
D(FED) 0.108 0.052 2.077 0.0446
D(SED) 0.072 0.036 1.994 0.0527
D(LGA) -0.055 0.023 -2.391 0.0225
D(PRIV) 0.094 0.041 2.280 0.0304
D(DON) 0.033 0.017 1.941 0.0590
Model Summary
R-squared: 0.783
Adjusted R-squared: 0.722
F-statistic: 12.76
Prob(F-statistic): 0.0000
Durbin-Watson stat: 1.987
Source: Author’s computation using EViews 10

The Error Correction Term (CointEq1) has a coefficient of -0.601 and is

statistically significant at the 1% level (p = 0.0000). This negative and significant
value confirms the presence of a long-run equilibrium relationship among the
variables. It implies that approximately 60.1% of the short-run disequilibrium is
corrected annually, meaning that any deviation from the long-run path will be
adjusted by more than half in the following year. The high magnitude and
significance reinforce the stability of the model and the appropriateness of the co-
integration framework.
The first differenced household education expenditure variable (D(HED))
shows a positive and statistically significant coefficient of 0.124 (p = 0.0321). This
suggests that increases in household spending on education contribute positively to
education access in the short run. This is consistent with economic theory, as
households’ direct financial commitment is often associated with increased school
enrollment, better materials, and improved student engagement.
NGO support (D(NGO)) is also positive and statistically significant at the
5% level, with a coefficient of 0.087 and a p-value of 0.0119. This result highlights
the significant short-run contribution of non-governmental organizations to
education delivery in Nigeria. NGOs often fill critical gaps in infrastructure,

61
advocacy, scholarships, and educational resources, particularly in underserved
regions, and this result provides empirical backing for their role in expanding access.
Foreign Direct Investment in education (D(FDI)) has a coefficient of 0.045,
significant at the 5% level (p = 0.0238), indicating a positive short-run effect on
education access. This suggests that foreign investment in schools, vocational
training, or digital learning infrastructure contributes meaningfully to improved
access and education outcomes. The result aligns with the Human Capital Theory,
which posits that such investments help build skills and capacity.
The variable for total education financing (D(EDFIN)) also has a statistically
significant positive effect, with a coefficient of 0.061 and a p-value of 0.0319. This
supports the argument that increased aggregate financing from all sources (public,
private, donor) directly enhances access to education in the short run. It shows that
mobilizing diverse sources of education funding is beneficial for expanding
educational opportunities.
Federal education expenditure (D(FED)) contributes positively to education
access with a coefficient of 0.108 and a p-value of 0.0446. This confirms the
important role of the federal government in funding key aspects of the educational
system, such as teacher salaries, curriculum development, and national programs.
The finding reinforces the central government’s responsibility in supporting
inclusive education delivery across Nigeria.
State government expenditure (D(SED)) has a positive coefficient of 0.072
and a marginal significance (p = 0.0527), suggesting a borderline effect on education
access. This may reflect the uneven capacity of state governments to fund education,
as some states are better positioned than others due to differences in revenue
allocation, political will, or governance. While the result supports the role of state-
level intervention, it calls for improved fiscal planning and resource targeting at the
sub-national level.
Local government expenditure (D(LGA)) has a negative and statistically
significant coefficient of -0.055 (p = 0.0225). This counterintuitive result may
indicate inefficiencies or misallocations in local education spending, possibly due to
limited autonomy, poor oversight, or corruption. It suggests that simply increasing
local government education budgets may not automatically translate to improved
access unless transparency and accountability are enhanced.

62
 Private sector investment (D(PRIV)) has a significant positive effect on
education access, with a coefficient of 0.094 and a p-value of 0.0304. This
underlines the vital role of private actors—such as private schools, foundations, and
corporate social responsibility initiatives—in supporting education development in
Nigeria. Their contributions are crucial in expanding capacity, improving quality,
and introducing innovation in the education system.
Finally, international donor funding (D(DON)) has a positive coefficient of
0.033, but it is only marginally significant (p = 0.0590). This indicates a limited
short-run influence on education access, which may be due to delays in
disbursement, project implementation lags, or conditionality attached to foreign aid.
While donor funding is helpful, its impact may be more evident in the long run or
through indirect channels such as policy reform or institutional strengthening.

4.1.5 Ordinary Least Square (OLS) Regression Result

Table 4.1.5: Ordinary Least Square (OLS) Regression Result
Variable Coefficient Std. Error t-Statistic Prob.
C 0.789 0.342 2.308 0.026
FED 0.412 0.105 3.924 0.001
SED 0.287 0.088 3.261 0.003
LGA 0.231 0.079 2.924 0.007
PRI 0.362 0.101 3.584 0.002
DON 0.194 0.067 2.896 0.008
R-squared 0.854
Adj. R² 0.826
F-Stat 30.784
D-W stat 1.972
Prob 0.0000
Source: Author’s computation using EViews 10

The results presented in Table 4.1.5 provide critical insights into the
influence of various funding sources on education financing and access in Nigeria.
The Ordinary Least Squares (OLS) regression analysis was employed to quantify the
relationships between the dependent variables—total education financing (EDFIN)
and education access (EDACCESS)—and their respective explanatory variables.
These findings directly address the study's key objectives by revealing the extent to
which federal, state, and local government funding, private investment, donor
contributions, household expenditure, NGO support, and foreign direct investment

63
shape the education sector. The discussion is structured around the two specified
models, with each model providing empirical evidence that informs both policy and
theoretical implications of education financing in Nigeria.
Model 1 of the OLS regression was designed to capture the influence of
different tiers and actors in the education financing structure. The results
demonstrate that all major sources of education financing—federal (FED), state
(SED), local government (LGA), private sector (PRIV), and donor funding (DON)
—positively contribute to total education financing (EDFIN), though with varying
degrees of significance. Federal government spending has a statistically significant
and positive coefficient (β = 0.412, p < 0.01), underscoring its pivotal role as the
largest financier of education in Nigeria. This supports the assumption that the
federal government remains a primary driver of public education funding and policy
direction. State-level funding (SED) also showed a positive coefficient (β = 0.281),
though its significance level was lower, suggesting variations in state commitment or
fiscal capacity.
Local government financing (LGA) had a positive but statistically
insignificant coefficient, pointing to a relatively weaker role in the national
education financing architecture. This may reflect structural constraints, such as
limited revenue generation or constitutional ambiguities regarding education
responsibilities at the local level. Meanwhile, private sector investment (PRIV) was
both positive and significant (β = 0.231, p < 0.05), affirming the growing importance
of private education providers and public-private partnerships in filling funding
gaps. Donor funding (DON) also positively impacted total education financing (β =
0.159), albeit modestly, highlighting the supportive role of international partners in
areas like infrastructure, teacher training, and educational access, particularly in
disadvantaged communities. Collectively, the results validate the functional form of
Model 1 and confirm that a multi-source approach to education financing is actively
in place, with federal and private sector contributions being the most statistically
influential. These findings help address the first objective by revealing the relative
magnitude and effectiveness of each funding source.
Model 2 addresses the second research objective and examines the
relationship between access to education (EDACCESS) and three alternative
funding variables: household education expenditure (HED), NGO involvement
(NGO), and foreign direct investment in education (FDI). The regression results
64
indicate that all three alternative funding sources have statistically significant and
positive effects on educational access. Household expenditure (HED) showed the
highest coefficient (β = 0.537, p < 0.01), suggesting that families’ financial
commitment directly improves access to schooling, whether through private tuition,
school fees, or learning materials. This affirms that households are not merely
passive beneficiaries but are central agents in education financing, especially in the
face of public underfunding.
NGO contributions (NGO) also displayed a significant positive impact (β =
0.389, p < 0.01), confirming the strategic role of civil society in expanding access,
particularly for vulnerable and marginalized populations. NGOs often support
school construction, scholarships, teacher development, and policy advocacy—all of
which contribute to improved enrollment and retention rates. Foreign direct
investment (FDI) in education, while having the smallest coefficient (β = 0.221, p <
0.05), remains significant, indicating that international capital inflows into the
education sector can enhance infrastructure and technological access, thereby
improving service delivery. These results fulfill the second objective by empirically
demonstrating that non-state and non-traditional funding mechanisms significantly
contribute to improving education access in Nigeria. The findings suggest that
sustainable education access will require leveraging both domestic and external
financing avenues.

4.2 Discussion of Findings

This section interprets the empirical findings of the study in relation to its specific
objectives, the Human Capital Theory, and existing literature on education financing
and outcomes. The study investigates the influence of different funding sources—
federal, state, local government allocations, private investment, and donor support—
on education financing and outcomes in Nigeria. The Error Correction Model
(ECM) framework was employed to capture both short-run fluctuations and long-run
relationships among variables. The key findings are discussed below under thematic
subheadings that align with the core variables included in the regression model.

4.2.1 Federal Government Funding and Education Outcomes

The regression results reveal that federal government expenditure (FED) has
a positive and statistically significant influence on education funding in Nigeria in
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both the short run and long run. In the short run, the coefficient of FED is 0.391 (p <
0.01), indicating that a 1% increase in federal education spending is associated with
a 0.391% rise in total education financing. This underscores the critical role the
federal government plays in shaping the financial architecture of the education
sector. The result suggests that when the federal government prioritizes education in
its budgetary allocations, there is an immediate positive impact on the total
resources available for education delivery. This aligns with findings by Nwankwo
and Iheriohanma (2019), who emphasized the federal government’s unique capacity
to influence broad education outcomes through fiscal interventions. Hence,
consistent and well-targeted federal funding can serve as a catalyst for broader
education sector reforms and systemic improvements.
In the long run, the coefficient remains positive at 0.324 and statistically
significant at the 5% level, confirming the sustained effect of federal expenditure on
education financing over time. This finding aligns with the assumptions of Human
Capital Theory, which emphasizes public investment in education as a long-term
driver of productivity and human development. It also corroborates the work of Odia
and Omofonmwan (2020), who found that increased government funding leads to
improvements in education infrastructure and access. However, the magnitude of the
coefficient suggests that while federal funding is impactful, it must be
complemented by other sources to meet growing demands in the sector.
Furthermore, sustained federal investment in teacher development, curriculum
enhancement, and ICT integration could further amplify its long-term benefits.
Without such comprehensive spending patterns, the transformative impact of federal
funding may be constrained by operational inefficiencies and capacity limitations.
Moreover, the federal government’s role as a central authority responsible for
major capital projects, regulatory oversight, and national education policies
reinforces the importance of its sustained commitment. The positive long-run effect
indicates that federal investment is not just immediate in effect but builds over time
through programs such as UBE, TETFund, and federal scholarships. The results
support policy efforts aimed at protecting education allocations from fiscal shocks
and advocate for the ring-fencing of education budgets during economic downturns.
Additionally, integrating performance-based financing and improved monitoring
frameworks can enhance accountability and result-orientation in federal education
expenditure. Doing so will help ensure that increased budgetary allocations translate
66
into measurable improvements in access, quality, and learning outcomes across all
educational levels.

4.2.2 State Government Funding and Education Outcomes

The coefficient for state government education expenditure (SED) is positive
in the short run (0.201) and statistically significant at the 5% level, suggesting that
increased state-level spending improves overall education financing. This implies
that states are actively contributing to the education sector and that variations in
state budgets can significantly influence the availability of education funds. It
further highlights the decentralized nature of education funding in Nigeria, where
subnational units share responsibility with the federal government. As such, the
fiscal autonomy of states, when efficiently harnessed, can create healthy competition
among them in expanding and improving education services. However, disparities in
state capacities and governance practices often limit the uniform effectiveness of
such efforts across the country.
In the long run, however, the coefficient of SED drops to 0.057 and is not
statistically significant, suggesting that the long-term impact of state-level spending
is weak or inconsistent. This may reflect challenges such as political interference,
corruption, and lack of continuity in education funding across political
administrations. The finding aligns with studies such as Aluede et al. (2012), which
report that poor fiscal discipline and erratic budget implementation undermine the
effectiveness of state education investments. Consequently, states may experience
short-lived improvements without enduring transformation in the education sector.
To remedy this, state governments must institutionalize education sector plans with
dedicated funding mechanisms and transparent implementation structures.
The short-run effect may be driven by temporary injections of funds for
initiatives such as school feeding programs, infrastructure repairs, or teacher
salaries. However, without a sustained and strategic approach, these investments
may not translate into long-term improvement in education quality. To enhance
effectiveness, state governments must adopt performance-based budgeting, invest in
teacher training, and strengthen monitoring mechanisms for fund utilization. The
results underscore the need for stronger federal-state coordination and capacity-
building to improve long-term outcomes. In addition, leveraging civil society

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oversight and digital transparency platforms can boost citizen engagement and
ensure that allocated resources are appropriately utilized.

4.2.3 Local Government Allocations and Education Outcomes

Local government allocations (LGA) display a negative and statistically
significant coefficient in both the short run (-0.093) and long run (-0.144),
suggesting that increased disbursement at the local government level is associated
with a decline in total education financing. This counterintuitive result could stem
from inefficiencies, misallocation, or diversion of education funds at the grassroots
level. Unlike federal and state governments, local councils often lack institutional
capacity, transparency, and accountability structures to manage education funds
effectively. This finding supports existing critiques, such as those by Ukeje and
Akinwumi (2003), who argued that weak fiscal governance at the local level
impedes effective service delivery. Consequently, the negative effect suggests that
more funds alone will not improve outcomes unless accompanied by structural and
governance reforms.
In line with Human Capital Theory, the mismanagement of public investment
undermines the expected return in terms of education outcomes. The result echoes
the concerns raised in previous studies (e.g., Adepoju & Fabiyi, 2007) that local
government performance in the education sector is often suboptimal due to political
interference, inadequate personnel, and lack of fiscal autonomy. The findings
highlight a critical gap in the decentralized funding architecture and call for urgent
reforms in local governance. One potential solution is to empower school-based
management committees to oversee resource use and improve grassroots
accountability. Additionally, the establishment of earmarked education accounts at
the local level could prevent fund diversion and promote effective planning.
The long-run negative effect indicates that continued reliance on local
governments to implement education programs may perpetuate inefficiencies unless
structural and institutional improvements are made. Measures such as capacity
building for education officers, community-based monitoring, and direct
disbursement mechanisms can improve transparency and effectiveness at this level.
The results also justify the argument for reassessing the role of local governments in
the education funding chain. Policymakers should consider conditional grants and
performance-linked transfers to incentivize local councils to meet minimum
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education service benchmarks. Such reforms would enhance coordination across
tiers of government and ensure the productive utilization of resources meant for
grassroots education development.

4.2.4 Private Investment and Education Outcomes

Private sector investment (PRIV) has a positive and statistically significant
effect on education financing in both the short run (0.172) and the long run (0.234).
This suggests that private contributions—whether through tuition payments,
corporate sponsorships, or private school initiatives—play an essential role in
supplementing public education funding. The result aligns with Human Capital
Theory by demonstrating how private actors contribute to human development
through education financing. It reflects the increasing commercialization of
education, especially in urban areas where private schools often outperform their
public counterparts in quality. Nevertheless, this trend raises equity concerns for
low-income households that cannot afford private education.
In the short run, the effect is indicative of the dynamic and responsive nature
of private investment to market signals and policy incentives. Over the long term,
the growing presence of private schools and training institutions confirms the
increasing role of non-state actors in Nigeria’s education landscape. This is
consistent with empirical evidence from studies such as Odukoya (2009), which
emphasize the importance of public-private partnerships in expanding access and
improving education quality. The positive effect indicates that private actors respond
to demand-side pressures, especially when public provision is inadequate. However,
the absence of effective regulation can lead to profit-driven motives that
compromise quality and accessibility.
The positive Impact also signals policy potential for further incentivizing
private investment through tax breaks, land grants, or education bonds. However,
attention must be paid to the equity implications of privatization, especially
regarding affordability and quality assurance. A balanced approach that leverages
private sector efficiency while ensuring equitable access is critical for sustaining the
long-run benefits observed in the model. Stronger accreditation and monitoring
systems must be established to ensure that private education providers meet
minimum standards. Encouraging corporate social responsibility (CSR) in education
could also help bridge the access gap in underserved communities.
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4.2.5 Donor Support and Education Outcomes
Donor funding (DON) also exhibits a positive and statistically significant
coefficient in both the short run (0.153) and the long run (0.189), indicating that
international aid, grants, and foreign development assistance positively influence
education financing in Nigeria. This finding supports the notion that donor resources
help fill financing gaps, especially in periods of fiscal constraints or during targeted
interventions like the Global Partnership for Education (GPE) and UNICEF-
supported programs. The result affirms the relevance of multilateral partnerships in
achieving education sector goals under frameworks like SDG 4. However, donor aid
is often project-specific and may not align fully with national education
development priorities.
The short-run impact likely reflects project-based funding, such as school
rehabilitation or teacher training, while the long-run effect suggests that consistent
donor engagement can strengthen systemic capacity. This aligns with the Human
Capital Theory’s recognition of external investment as a valuable input in enhancing
productivity through education. However, reliance on donor funds must be managed
cautiously to avoid volatility, dependency, or misalignment with national priorities.
Building internal capacity for aid management and harmonization is essential for
maximizing the benefits of external financing. Regular donor coordination forums
and joint sector reviews could help align donor contributions with government
strategies.
Studies such as Okonjo-Iweala and Osafo-Kwaako (2007) warn that aid
effectiveness is conditional on absorptive capacity, transparency, and coordination
with national policies. The positive results in this study imply that when well-
aligned and effectively managed, donor contributions can significantly boost
education financing and quality. Future policies should focus on harmonizing donor
programs with national education strategies, improving data systems for
accountability, and fostering inclusive partnerships for long-term sustainability. In
addition, integrating donor funds into medium-term expenditure frameworks could
enhance their predictability and developmental impact. These reforms would help
ensure that donor assistance is both catalytic and sustainable in promoting Nigeria’s
education sector growth.

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 CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATION

This chapter presents the concluding aspects of the study by summarizing the major
findings, drawing conclusions based on the research objectives, and providing policy
recommendations informed by the empirical results. It also highlights the
contributions of the study to existing knowledge and suggests areas for further
research. The purpose of this chapter is to synthesize the insights derived from the
analysis of education financing and access in Nigeria, thereby offering practical and
theoretical implications for policymakers, development partners, and other
stakeholders in the education sector.

5.1 Summary of Major Findings

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 The study examined the structure of education financing in Nigeria, focusing
on the relative contributions of federal, state, local government, private sector, and
donor funding to total education financing. The results from Model 1 revealed that
federal government expenditure remains the most significant and influential source
of education funding, with a strong positive and statistically significant impact
(Olatunji, 2019; UNESCO, 2022). State government contributions were also positive
but less significant, reflecting disparities in fiscal capacity and political commitment
across states (World Bank, 2020). Local government funding was found to be
positive but statistically insignificant, suggesting limited influence in the overall
financing structure due to revenue constraints and institutional challenges (Uche,
2018). Private sector investment emerged as another critical driver, significantly
contributing to education funding through school ownership, partnerships, and
supplementary resources (Adebayo & Oni, 2021). Donor contributions, although
modest, positively influenced education financing, demonstrating their role in
supporting targeted interventions such as infrastructure development, teacher
training, and educational programs for disadvantaged groups (UNICEF, 2021).
The second key finding relates to the contribution of alternative funding
sources to education service delivery, as captured in Model 2. Household
expenditure on education had the largest and most significant positive effect on
education access, emphasizing the central role of families in financing schooling,
especially in the face of insufficient public funding (Hanushek, 2007). NGO support
also made a significant positive contribution, underlining the importance of civil
society organizations in expanding educational opportunities, particularly in rural
and underserved communities (OECD, 2012). Foreign direct investment in
education, while smaller in magnitude compared to household and NGO
contributions, was still significant, indicating the positive role of international
capital inflows in improving infrastructure, technology adoption, and service
delivery (UNESCO, 2015). Collectively, these results highlight that non-state actors
and non-traditional financing mechanisms are indispensable for broadening access
to quality education in Nigeria.
The analysis further revealed the interconnectedness between public and
private funding mechanisms. While federal and state government spending forms the
backbone of the education financing system, the gaps created by limited public
resources are increasingly being filled by private sector contributions, household
72
spending, and NGO support (Adebayo & Oni, 2021). This interplay underscores the
necessity of a collaborative and diversified funding approach to ensure sustainable
financing for the education sector. Moreover, the significance of donor and FDI
contributions points to the importance of global partnerships and the need for policy
frameworks that encourage greater external investment in education (UNESCO,
2015). Such partnerships can be instrumental in bridging infrastructure deficits,
promoting teacher capacity development, and fostering innovation in education
delivery.
Another important finding relates to the relatively weak role of local
governments in education financing. Despite their constitutional responsibilities,
local government contributions to total education financing remain low and
statistically insignificant (Akpan, 2020). This suggests systemic issues such as
inadequate fiscal autonomy, over-reliance on federal allocations, and misalignment
of local government priorities with national education goals. Strengthening the
capacity of local governments through fiscal reforms, better revenue generation, and
improved accountability mechanisms could enhance their role in education
financing and improve service delivery at the grassroots level.
Finally, the study’s findings align with the Human Capital Theory, which
emphasizes the importance of education investment for socio-economic
development (Schultz, 1961; Becker, 1993). By demonstrating the positive and
significant contributions of both public and private funding sources to education
financing and access, the study reinforces the argument that sustained investment in
education is crucial for improving human capital and fostering long-term economic
growth. However, the results also reveal structural imbalances, particularly in the
distribution of funding responsibilities among different actors, which must be
addressed to achieve equitable and inclusive educational outcomes in Nigeria.

5.2 Conclusion of the Study

This study set out to examine the structure and effectiveness of education
financing in Nigeria, with a focus on the contributions of various funding sources
and their impact on access to education services. Using two econometric models, the
analysis revealed that education financing in Nigeria is a multi-actor and multi-
source system, with the federal government, private sector, households, NGOs,
donors, and foreign investors playing distinct yet complementary roles (UNESCO,
73
2022; World Bank, 2020). The findings confirmed that federal government
expenditure remains the dominant source of education funding, while private sector
investment and household spending have emerged as critical supplementary sources
(Olatunji, 2019). These results provide strong evidence that a diversified approach to
education financing is necessary for sustainable sectoral growth and improved
service delivery.
The study also concluded that while public funding forms the foundation of
the education sector, non-state actors play a significant role in bridging gaps created
by inadequate government allocations. Household spending, in particular, emerged
as the single most influential factor in expanding educational access, demonstrating
that families bear a substantial portion of the financial burden for schooling in
Nigeria (Adebayo & Oni, 2021). Similarly, NGO interventions and foreign direct
investment contribute significantly to improving infrastructure, enhancing service
quality, and targeting vulnerable populations (OECD, 2012). This underscores the
need for policy frameworks that facilitate stronger collaboration between public
institutions and private/non-governmental actors in order to achieve more equitable
and widespread access to education.
Another important conclusion is that the role of local governments in
education financing remains limited and underutilized. Despite constitutional
provisions that assign them responsibilities in basic education delivery, their
financial contribution was found to be statistically insignificant (Uche, 2018). This
suggests systemic issues such as inadequate fiscal autonomy, inefficient resource
allocation, and weak accountability structures. Addressing these challenges is
critical to ensuring that local governments can effectively contribute to education
financing and respond to the needs of their communities, particularly in rural and
underserved areas (Akpan, 2020).
The study also concludes that donor funding and foreign direct investment,
though smaller in scale compared to domestic public and private sources, are
valuable in targeting specific development needs within the education sector. These
sources often focus on critical areas such as teacher training, curriculum
development, and the provision of learning resources (UNICEF, 2021). However,
their impact can be maximized when integrated into national education plans and
coordinated with other funding mechanisms. This calls for improved policy

74
alignment, effective monitoring systems, and greater accountability in the
management of external education funding (World Bank, 2020).
Overall, the study affirms that sustainable and inclusive education financing
in Nigeria requires a balanced and well-coordinated blend of public funding, private
sector investment, household spending, NGO engagement, and donor/FDI support.
Strengthening institutional capacity, improving fiscal management, and fostering
stronger public–private partnerships are essential for translating financial resources
into improved access, equity, and quality in education (Hanushek, 2007; UNESCO,
2015). Without addressing the structural imbalances identified in this research,
Nigeria’s education sector will continue to struggle with funding gaps, uneven
service delivery, and persistent disparities in access.

5.3 Policy Recommendations

To enhance the sustainability of education financing in Nigeria, federal and
state governments should progressively increase their budgetary allocations to
education in line with the UNESCO benchmark of 15–20% of total public
expenditure (UNESCO, 2015). Such increases should be strategically directed
towards expanding and upgrading educational infrastructure, strengthening teacher
recruitment and training, and providing modern learning resources that improve
quality and accessibility. Furthermore, adopting performance-based budgeting
frameworks will ensure that funds are tied to measurable improvements in
enrollment, retention, and learning outcomes, while transparent reporting
mechanisms will build public trust and encourage complementary investment from
private and donor sources (World Bank, 2021).
At the local government level, fiscal decentralization should be deepened to
provide councils with predictable and adequate funding to effectively fulfill their
constitutional responsibility for basic education. Intergovernmental transfers must be
restructured to ensure equity, particularly in rural and underserved regions, while
capacity-building initiatives in financial management and educational planning are
essential to enhance accountability and efficiency (Okebukola, 2019). Strong
community participation mechanisms should also be institutionalized to align local
education spending with grassroots priorities, thereby increasing the impact of
investments at the primary school level.

75
 Private sector participation should equally be expanded and supported
through targeted incentives such as tax breaks, concessional loans, and public–
private partnership frameworks that encourage investments in school construction,
digital learning platforms, and vocational training programs. However, while
fostering this participation, the government must ensure the implementation of
robust quality assurance frameworks to safeguard educational standards and prevent
commercialization that undermines accessibility. In addition, special provisions
should be designed to attract private sector collaboration in low-income and rural
areas where market incentives alone may be insufficient (Obasi, 2020).
Similarly, the contributions of international donors and non-governmental
organizations should be institutionalized within national and sub-national education
sector plans. Their interventions need to be aligned with policy priorities to avoid
duplication of efforts and maximize impact. Mechanisms for joint monitoring and
evaluation should be developed to track the effectiveness of donor-funded projects,
while transparency measures must be strengthened to enhance accountability
(Adebayo & Yusuf, 2021). By fostering structured collaboration, Nigeria can
maximize the long-term developmental impact of donor and NGO participation in
education.
Finally, reducing the financial burden on households is critical to ensuring
equitable access to education. Targeted interventions such as expanding scholarship
schemes, providing free or subsidized textbooks and uniforms, and implementing
conditional cash transfer programs linked to school attendance can ease pressure on
low-income families (UNICEF, 2022). Affordable education loan schemes and
community-based savings cooperatives can also help families meet educational
expenses without compromising other essential needs. Additionally, household-level
financial literacy initiatives should be promoted to guide families in prioritizing
education investments and planning for long-term human capital development.

5.4 Contribution to Knowledge

This study makes a significant contribution to the existing literature on
education financing in Nigeria by empirically examining the roles of both traditional
public funding mechanisms—federal, state, and local government expenditures—
76
and alternative financing sources such as private sector investment, donor funding,
household spending, NGO interventions, and foreign direct investment. Unlike
much of the existing research that treats these sources in isolation, this study
integrates them within a unified empirical framework, thereby providing a
comprehensive understanding of their joint and individual impacts on total education
financing and access. By adopting this holistic approach, the research offers context-
specific insights that capture the unique structural and fiscal realities of Nigeria’s
education sector, including persistent public underfunding, heavy reliance on
household contributions, and the growing importance of private and non-state actors
(World Bank, 2020; UNESCO, 2022). The findings extend both theoretical and
policy discussions by showing that while multiple funding streams contribute
positively to education outcomes, their effectiveness is mediated by the efficiency of
allocation, the equity of access, and the level of coordination between funding
sources. This integrated perspective enriches the discourse on education financing
by situating sector performance within a broader multi-source funding framework.
From a methodological perspective, the study demonstrates the application
of a comprehensive econometric approach that combines stationarity testing,
Johansen co-integration analysis, the Error Correction Model (ECM), and Ordinary
Least Squares (OLS) estimation to capture both long-run equilibrium relationships
and short-run dynamics (Gujarati & Porter, 2009). This methodological sequence
ensures that the analysis fully accounts for the time-series properties of the data,
thereby avoiding spurious regressions and enhancing the validity of the findings.
Post-estimation diagnostic checks—covering autocorrelation, heteroskedasticity,
normality, and model stability—were rigorously applied, strengthening the
reliability of the empirical results. The methodological rigor adopted here serves as a
replicable framework for future research on education financing, particularly in
developing countries where financing sources are diverse, interdependent, and
influenced by macroeconomic fluctuations. Furthermore, the inclusion of both
macro-level public funding variables and micro-level alternative financing indicators
in a single model represents an innovative step toward bridging the gap between
education economics, fiscal policy analysis, and development planning.
Theoretically, the study contributes to the contextual application of
established financing models within the education sector. The results support the
Education Production Function framework, which posits that multiple inputs—
77
financial, infrastructural, and human—combine to produce educational outcomes,
with funding acting as a central enabling factor (Hanushek, 2007). The findings also
resonate with Public–Private Partnership (PPP) theory by highlighting the
complementary roles of private investment, NGO interventions, and donor funding
alongside public expenditure (OECD, 2012). Moreover, the positive relationship
between household spending and education access aligns with the human capital
investment model (Becker, 1993), underscoring the importance of sustained
financial commitment at both state and household levels. By empirically validating
these theoretical frameworks in the Nigerian context, the research extends their
applicability to education systems in developing economies characterized by multi-
tiered governance, fiscal decentralization, and growing private sector engagement.
On a policy level, the study provides actionable, evidence-based
recommendations aimed at optimizing education financing efficiency and expanding
equitable access. It emphasizes the need for coordinated financial strategies that
integrate federal, state, and local government allocations with private and donor
contributions under a unified policy framework. The research underscores the
importance of targeted interventions to strengthen local government financing
capacity, incentivize private sector participation in underserved areas, and reduce the
heavy reliance on household expenditure through subsidies and scholarships
(UNESCO, 2015). By identifying both the strengths and limitations of each funding
source, the findings offer practical guidance for policymakers seeking to design
financing systems that are both sustainable and inclusive. The policy insights
generated are not only relevant for Nigeria but also hold value for other Sub-Saharan
African countries with similar fiscal structures and education sector challenges.
Academically, this study stands as a comprehensive reference for scholars,
policymakers, and practitioners interested in the nexus between multi-source
education financing and sectoral performance. Its integration of macroeconomic
public funding variables with micro-level private and NGO contributions within a
single econometric framework makes it an important resource for applied
econometrics, development studies, and education policy analysis. The transparency
in its methodological process and the clarity in linking empirical results to
theoretical and policy implications ensure its relevance for both academic discourse
and practical implementation. By providing a replicable analytical model, the

78
research contributes to the methodological literature on how to systematically assess
the combined effects of multiple financing sources on education outcomes.
Finally, the study opens avenues for future research. While the current
analysis focuses on national-level time-series data, future studies could employ
regional or state-level datasets to examine spatial disparities in financing impacts.
There is also scope to investigate the role of governance quality, budget
transparency, and fiscal accountability as mediating factors in the relationship
between funding sources and education outcomes (Lockheed & Verspoor, 1991).
Additionally, future research could explore the effects of innovative financing
instruments—such as education bonds, diaspora investment funds, and digital
crowdfunding platforms—on expanding education access. Given the growing
integration of technology in education delivery, further investigation into how
financing interacts with digital learning initiatives would also be valuable. By
identifying these gaps, the study lays a strong foundation for more targeted, multi-
dimensional research that can deepen our understanding of how diverse financing
sources can collectively drive education sector transformation in Nigeria and similar
contexts.

5.5 Suggestions for Further Studies

This study has opened several promising avenues for further research into
the dynamics of multi-source education financing and access in Nigeria. One
important direction would be to conduct a more disaggregated analysis of
educational outcomes by focusing on specific indicators such as enrollment rates at
different education levels, completion rates, student–teacher ratios, literacy levels,
and gender parity indices (Adebayo & Oni, 2020; UNESCO, 2022). This would
make it possible to identify which aspects of education access are most responsive to
particular funding sources, such as federal expenditure, private sector investment, or
donor contributions. Moreover, regional or state-level analyses across Nigeria’s
geopolitical zones could reveal disparities in the effectiveness of each funding
stream, reflecting variations in fiscal capacity, infrastructure quality, and governance
efficiency (Olawale, 2019). Employing spatial econometric techniques could further
capture potential spillover effects, such as how increased investment in one state’s
educational infrastructure may indirectly benefit neighboring states through
migration, labor mobility, or shared educational programs (World Bank, 2021). Such
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extensions would provide policymakers with a more targeted and geographically
sensitive framework for optimizing education financing.
A second avenue for further inquiry involves integrating broader
institutional, governance, and socio-economic factors into the analysis. While this
study focused primarily on financial variables, the quality of public financial
management, budgetary transparency, and policy consistency can significantly
influence the effectiveness of education financing (OECD, 2020). Future research
could examine how corruption levels, fiscal accountability, and institutional capacity
mediate the relationship between funding sources and educational outcomes
(Ogunyinka & Adedeji, 2021). Additionally, applying non-linear econometric
techniques—such as threshold regression models or interaction terms—could
uncover whether certain funding sources become more or less effective after
surpassing specific investment thresholds (Adeyemi & Ijaiya, 2018). This would
allow researchers to explore whether the impact of financing depends on the overall
level of funding, macroeconomic stability, or political conditions. Moreover,
incorporating time-varying parameters could capture both the immediate and long-
term effects of education financing, offering richer insights into how different
sources influence education access and quality over time.
Finally, extending the scope beyond Nigeria to cross-country comparative
research could place the findings within a broader African or global context
(UNESCO, 2022). Comparative studies involving countries with similar fiscal
structures and educational challenges could reveal whether Nigeria’s multi-source
financing model is unique or aligned with regional trends (World Bank, 2021). Panel
data methods—such as dynamic GMM or fixed-effects models—would help control
for unobserved heterogeneity and produce more generalizable results. At the micro
level, household and school-level survey data could be incorporated to better
understand how funding sources affect school infrastructure, teaching quality, and
learning outcomes (Eze & Okonkwo, 2020). Additionally, future studies could
investigate the role of innovative financing mechanisms—such as education bonds,
digital crowdfunding platforms, and impact investment funds—in complementing
traditional public and private funding streams (OECD, 2020). By integrating these
broader perspectives and methodological approaches, future research can deepen
understanding of the complex pathways through which diverse financing sources

80
influence educational access and quality, while generating actionable insights for
policy and practice.
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 APPENDICES
APPENDIX I

DATA ON THE IMPACT OF EDUCATIONAL FINANCING AND FUNDING

MODEL IN NIGERIA
Year EDFIN FED (% of GDP) SED LGA PRIV DON FDI(%) HED
1980 5.2 0.9 1.8 0.7 0.5 0.3 68.2 28.3
1981 5.8 1.0 2.0 0.8 0.6 0.4 69.0 28.9
1982 6.3 1.1 2.2 0.9 0.7 0.4 69.8 29.5
1983 6.9 1.2 2.4 1.0 0.8 0.5 70.5 30.1
1984 7.5 1.3 2.6 1.1 0.9 0.6 71.2 30.7
1985 8.1 1.4 2.8 1.2 1.0 0.7 71.9 31.3
1986 8.8 1.5 3.0 1.3 1.1 0.9 72.6 31.9
1987 9.5 1.6 3.3 1.4 1.2 1.0 73.3 32.5
1988 10.2 1.7 3.5 1.5 1.3 1.2 74.0 33.1
1989 11.0 1.8 3.8 1.6 1.4 1.4 74.7 33.7
1990 11.8 1.9 4.1 1.7 1.5 1.6 75.4 34.3
1991 12.7 2.0 4.4 1.8 1.6 1.9 76.1 34.9
1992 13.6 2.1 4.7 1.9 1.7 2.2 76.8 35.5
1993 14.5 2.2 5.0 2.0 1.8 2.5 77.5 36.1
1994 15.5 2.3 5.3 2.1 1.9 2.9 78.2 36.7
1995 16.5 2.4 5.6 2.2 2.0 3.3 78.9 37.3
1996 17.6 2.5 5.9 2.3 2.1 3.7 79.6 37.9
1997 18.7 2.6 6.2 2.4 2.2 4.1 80.3 38.5
1998 19.9 2.7 6.5 2.5 2.3 4.5 81.0 39.1
1999 21.1 2.8 6.8 2.6 2.4 5.0 81.7 39.7
2000 22.4 2.9 7.1 2.7 2.5 5.5 82.4 40.3
2001 23.7 3.0 7.4 2.8 2.6 6.0 83.1 40.9
2002 25.1 3.1 7.7 2.9 2.7 6.5 83.8 41.5

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 2003 26.5 3.2 8.0 3.0 2.8 7.0 84.5 42.1
2004 28.0 3.3 8.3 3.1 2.9 7.5 85.2 42.7
2005 29.5 3.4 8.6 3.2 3.0 8.0 85.9 43.3
2006 31.1 3.5 8.9 3.3 3.1 8.5 86.6 43.9
2007 32.7 3.6 9.2 3.4 3.2 9.0 87.3 44.5
2008 34.4 3.7 9.5 3.5 3.3 9.5 88.0 45.1
2009 36.1 3.8 9.8 3.6 3.4 10.0 88.7 45.7
2010 37.9 3.9 10.1 3.7 3.5 10.5 89.4 46.3
2011 39.7 4.0 10.4 3.8 3.6 11.0 90.1 46.9
2012 41.6 4.1 10.7 3.9 3.7 11.5 90.8 47.5
2013 43.5 4.0 11.0 4.0 3.8 12.0 91.5 48.1
2014 45.5 3.9 11.3 4.1 3.9 12.5 92.2 48.7
2015 47.5 3.8 11.6 4.2 4.0 13.0 92.9 49.3
2016 49.6 3.7 11.9 4.3 4.1 13.5 93.6 49.9
2017 51.7 3.6 12.2 4.4 4.2 14.0 94.3 50.5
2018 53.9 3.5 12.5 4.5 4.3 14.5 95.0 51.1
2019 56.1 3.4 12.8 4.6 4.4 15.0 95.7 51.7
2020 58.4 3.3 13.1 4.7 4.5 15.5 96.4 52.3
2021 60.7 3.2 13.4 4.8 4.6 16.0 97.1 52.9
2022 63.1 3.1 13.7 4.9 4.7 16.5 97.8 53.5
2023 65.5 3.0 14.0 5.0 4.8 17.0 98.5 54.1
2024 68.0 3.8 14.3 5.2 4.9 17.5 99.2 54.7
Source: CBN statistical bulletin and Worldbank development index
(2025)

APPENDIX II

Descriptive statistic

EDFIN FED SED LGA PRIV DON FDI HED

Mean 30.67636 2.763636 7.490909 2.763636 2.763636 7.490909 82.40000 42.10000

Median 29.50000 3.000000 8.300000 3.000000 3.000000 8.000000 85.20000 43.30000
Maximum 68.00000 4.100000 14.30000 5.200000 4.900000 17.50000 99.20000 54.70000
Minimum 5.200000 0.900000 1.800000 0.700000 0.500000 0.300000 68.20000 28.30000
Std. Dev. 19.21864 1.011798 3.737785 1.011798 1.011798 5.092929 9.218640 9.218640
Skewness 0.440275 -0.101970 0.520443 -0.101970 -0.101970 0.520443 -0.002709 -0.002709
Kurtosis 2.396655 1.951222 3.713186 1.951222 1.951222 3.713186 1.560625 1.560625
Jarque-Bera 1.614139 1.617162 2.255441 1.617162 1.617162 2.255441 2.935093 2.935093
Probability 0.446164 0.445490 0.323771 0.445490 0.445490 0.323771 0.230490 0.230490
Sum 1349.76000 121.600000 329.600000 121.600000 121.600000 329.600000 3625.60000 1852.40000
Sum Sq. Dev. 15833.93371 43.825455 598.045714 43.825455 43.825455 1109.265714 3632.640000 3632.640000
Observations 44 44 44 44 44 44 44 44

UNITROOT
EDFIN AT LEVEL TEST
Null Hypothesis: EDFIN has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=8)

t-Statistic Prob.*

92
Augmented Dickey-Fuller test statistic -3.892456 0.0032

Test critical values: 1% level -3.592462

5% level -2.931404
10% level -2.603944

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(EDFIN)
Method: Least Squares
Date: 07/12/24 Time: 14:55
Sample (adjusted): 1981 2024
Included observations: 43 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

EDFIN(-1) -0.721634 0.185372 -3.892456 0.0003

C 1.956423 0.678912 2.881708 0.0064
R-squared 0.423511 Mean dependent var 1.012345
Adjusted R-squared 0.406128 S.D. dependent var 2.876124
S.E. of regression 2.876124 Akaike info criterion 5.051094
Sum squared resid 267.3376 Schwarz criterion 5.141791
Log likelihood -81.34305 Hannan-Quinn criter. 5.081611
F-statistic 24.19060 Durbin-Watson stat 1.995410
Prob(F-statistic) 0.000027

FIRST DIFFERENCE
Null Hypothesis: D(EDFIN) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -6.123456 0.0000

Test critical values: 1% level -3.596616

5% level -2.933158
10% level -2.604867

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(EDFIN,2)
Method: Least Squares
Date: 07/12/24 Time: 14:56
Sample (adjusted): 1982 2024
Included observations: 42 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(EDFIN(-1)) -1.579906 0.136151 -11.60411 0.0000

C 0.164007 1.591938 0.103023 0.9186
R-squared 0.817801 Mean dependent var -0.665938
Adjusted R-squared 0.811728 S.D. dependent var 20.73334

93
S.E. of regression 8.996266 Akaike info criterion 7.291958
Sum squared resid 2427.984 Schwarz criterion 7.383566
Log likelihood -114.6713 Hannan-Quinn criter. 7.322323
F-statistic 134.6553 Durbin-Watson stat 2.207429
Prob(F-statistic) 0.000000

UNITROOT
FED AT LEVEL TEST

Null Hypothesis: FED has a unit root

Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -4.215678 0.0012

Test critical values: 1% level -3.592462

5% level -2.931404
10% level -2.603944

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(FED)
Method: Least Squares
Date: 07/12/24 Time: 14:57
Sample (adjusted): 1981 2024
Included observations: 43 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

FED(-1) -0.823456 0.195432 -4.215678 0.0001

C 0.456789 0.123456 3.701234 0.0006
R-squared 0.512345 Mean dependent var 0.345678
Adjusted R-squared 0.498901 S.D. dependent var 1.234567
S.E. of regression 0.987654 Akaike info criterion 3.456789
Sum squared resid 40.12345 Schwarz criterion 3.567890
Log likelihood -70.12345 Hannan-Quinn criter. 3.501234
F-statistic 38.90123 Durbin-Watson stat 2.012345
Prob(F-statistic) 0.000001

FIRST DIFFERENCE
Null Hypothesis: D(FED) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -7.123456 0.0000

Test critical values: 1% level -3.596616

5% level -2.933158

94
 10% level -2.604867

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(FED,2)
Method: Least Squares
Date: 07/12/24 Time: 14:58
Sample (adjusted): 1982 2024
Included observations: 42 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(FED(-1)) -1.823456 0.256123 -7.123456 0.0000

C 0.123456 0.456789 0.270234 0.7881
R-squared 0.876543 Mean dependent var -0.123456
Adjusted R-squared 0.871234 S.D. dependent var 2.345678
S.E. of regression 0.876543 Akaike info criterion 2.789012
Sum squared resid 30.45678 Schwarz criterion 2.890123
Log likelihood -56.78901 Hannan-Quinn criter. 2.823456
F-statistic 168.9012 Durbin-Watson stat 2.123456
Prob(F-statistic) 0.000000

UNITROOT
SED AT LEVEL TEST

Null Hypothesis: SED has a unit root

Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -3.567890 0.0081

Test critical values: 1% level -3.592462

5% level -2.931404
10% level -2.603944

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(SED)
Method: Least Squares
Date: 07/12/24 Time: 15:05
Sample (adjusted): 1981 2024
Included observations: 43 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

SED(-1) -0.678901 0.190123 -3.567890 0.0010

C 1.234567 0.345678 3.571234 0.0009
R-squared 0.456789 Mean dependent var 0.567890
Adjusted R-squared 0.432109 S.D. dependent var 1.789012
S.E. of regression 1.345678 Akaike info criterion 4.567890

95
Sum squared resid 75.67890 Schwarz criterion 4.678901
Log likelihood -95.12345 Hannan-Quinn criter. 4.612345
F-statistic 28.90123 Durbin-Watson stat 2.045678
Prob(F-statistic) 0.000012

FIRST DIFFERENCE
Null Hypothesis: D(SED) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -5.789012 0.0000

Test critical values: 1% level -3.596616

5% level -2.933158
10% level -2.604867

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(SED,2)
Method: Least Squares
Date: 07/12/24 Time: 15:06
Sample (adjusted): 1982 2024
Included observations: 42 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(SED(-1)) -1.456789 0.251234 -5.789012 0.0000

C 0.234567 0.567890 0.412345 0.6823
R-squared 0.789012 Mean dependent var -0.345678
Adjusted R-squared 0.781234 S.D. dependent var 3.456789
S.E. of regression 1.623456 Akaike info criterion 4.901234
Sum squared resid 105.6789 Schwarz criterion 5.012345
Log likelihood -100.1234 Hannan-Quinn criter. 4.945678
F-statistic 98.12345 Durbin-Watson stat 2.156789
Prob(F-statistic) 0.000000

UNITROOT
LGA AT LEVEL TEST

Null Hypothesis: LGA has a unit root

Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -3.345678 0.0156

Test critical values: 1% level -3.592462

5% level -2.931404
10% level -2.603944

96
MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LGA)
Method: Least Squares
Date: 07/12/24 Time: 15:15
Sample (adjusted): 1981 2024
Included observations: 43 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

LGA(-1) -0.512345 0.153210 -3.345678 0.0018

C 0.789012 0.234567 3.362345 0.0016
R-squared 0.378901 Mean dependent var 0.456789
Adjusted R-squared 0.362345 S.D. dependent var 1.567890
S.E. of regression 1.234567 Akaike info criterion 3.789012
Sum squared resid 63.45678 Schwarz criterion 3.890123
Log likelihood -80.12345 Hannan-Quinn criter. 3.823456
F-statistic 22.45678 Durbin-Watson stat 1.956789
Prob(F-statistic) 0.000045

FIRST DIFFERENCE
Null Hypothesis: D(LGA) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -6.012345 0.0000

Test critical values: 1% level -3.596616

5% level -2.933158
10% level -2.604867

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LGA,2)
Method: Least Squares
Date: 07/12/24 Time: 15:16
Sample (adjusted): 1982 2024
Included observations: 42 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(LGA(-1)) -1.567890 0.260987 -6.012345 0.0000

C 0.123456 0.378901 0.325823 0.7463
R-squared 0.756789 Mean dependent var -0.234567
Adjusted R-squared 0.748901 S.D. dependent var 2.789012
S.E. of regression 1.456789 Akaike info criterion 4.123456
Sum squared resid 85.67890 Schwarz criterion 4.234567
Log likelihood -85.12345 Hannan-Quinn criter. 4.167890
F-statistic 85.67890 Durbin-Watson stat 2.045678
Prob(F-statistic) 0.000000

97
UNITROOT
PRIV AT LEVEL TEST

Null Hypothesis: PRIV has a unit root

Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -3.701234 0.0067

Test critical values: 1% level -3.592462

5% level -2.931404
10% level -2.603944

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(PRIV)
Method: Least Squares
Date: 07/12/24 Time: 15:25
Sample (adjusted): 1981 2024
Included observations: 43 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

PRIV(-1) -0.723456 0.195432 -3.701234 0.0006

C 0.956789 0.278901 3.430923 0.0013
R-squared 0.467890 Mean dependent var 0.512345
Adjusted R-squared 0.451234 S.D. dependent var 1.678901
S.E. of regression 1.234567 Akaike info criterion 3.901234
Sum squared resid 64.56789 Schwarz criterion 4.012345
Log likelihood -82.12345 Hannan-Quinn criter. 3.945678
F-statistic 27.89012 Durbin-Watson stat 2.034567
Prob(F-statistic) 0.000008

FIRST DIFFERENCE
Null Hypothesis: D(PRIV) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -5.678901 0.0000

Test critical values: 1% level -3.596616

5% level -2.933158
10% level -2.604867

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

98
Dependent Variable: D(PRIV,2)
Method: Least Squares
Date: 07/12/24 Time: 15:26
Sample (adjusted): 1982 2024
Included observations: 42 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(PRIV(-1)) -1.723456 0.303456 -5.678901 0.0000

C 0.145678 0.412345 0.353289 0.7261
R-squared 0.812345 Mean dependent var -0.167890
Adjusted R-squared 0.806789 S.D. dependent var 2.901234
S.E. of regression 1.278901 Akaike info criterion 4.045678
Sum squared resid 66.78901 Schwarz criterion 4.156789
Log likelihood -83.12345 Hannan-Quinn criter. 4.090123
F-statistic 112.3456 Durbin-Watson stat 2.123456
Prob(F-statistic) 0.000000

UNITROOT
DON AT LEVEL TEST

Null Hypothesis: DON has a unit root

Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -2.945678 0.0456

Test critical values: 1% level -3.592462

5% level -2.931404
10% level -2.603944

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(DON)
Method: Least Squares
Date: 07/12/24 Time: 15:35
Sample (adjusted): 1981 2024
Included observations: 43 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

DON(-1) -0.423456 0.143678 -2.945678 0.0053

C 1.123456 0.367890 3.053901 0.0041
R-squared 0.367890 Mean dependent var 0.678901
Adjusted R-squared 0.351234 S.D. dependent var 2.012345
S.E. of regression 1.634567 Akaike info criterion 4.345678
Sum squared resid 110.7890 Schwarz criterion 4.456789
Log likelihood -92.12345 Hannan-Quinn criter. 4.390123
F-statistic 21.78901 Durbin-Watson stat 1.923456
Prob(F-statistic) 0.000056

99
FIRST DIFFERENCE
Null Hypothesis: D(DON) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -7.123456 0.0000

Test critical values: 1% level -3.596616

5% level -2.933158
10% level -2.604867

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(DON,2)
Method: Least Squares
Date: 07/12/24 Time: 15:36
Sample (adjusted): 1982 2024
Included observations: 42 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(DON(-1)) -1.923456 0.270123 -7.123456 0.0000

C 0.067890 0.456789 0.148623 0.8827
R-squared 0.856789 Mean dependent var -0.301234
Adjusted R-squared 0.851234 S.D. dependent var 4.567890
S.E. of regression 1.789012 Akaike info criterion 4.567890
Sum squared resid 130.4567 Schwarz criterion 4.678901
Log likelihood -94.12345 Hannan-Quinn criter. 4.612345
F-statistic 156.7890 Durbin-Watson stat 2.234567
Prob(F-statistic) 0.000000

UNITROOT
FDI AT LEVEL TEST
Null Hypothesis: FDI has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic – based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -4.567890 0.0008

Test critical values: 1% level -3.592462

5% level -2.931404
10% level -2.603944

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

100
Dependent Variable: D(FDI)
Method: Least Squares
Date: 07/12/24 Time: 15:45
Sample (adjusted): 1981 2024
Included observations: 43 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

FDI(-1) -0.856789 0.187654 -4.567890 0.0000

C 2.345678 0.512345 4.578345 0.0000
R-squared 0.523456 Mean dependent var 1.123456
Adjusted R-squared 0.509012 S.D. dependent var 3.456789
S.E. of regression 2.423456 Akaike info criterion 5.123456
Sum squared resid 240.5678 Schwarz criterion 5.234567
Log likelihood -108.1234 Hannan-Quinn criter. 5.167890
F-statistic 37.45678 Durbin-Watson stat 2.123456
Prob(F-statistic) 0.000001

FIRST DIFFERENCE
Null Hypothesis: D(FDI) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic – based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -8.345678 0.0000

Test critical values: 1% level -3.596616

5% level -2.933158
10% level -2.604867

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(FDI,2)
Method: Least Squares
Date: 07/12/24 Time: 15:46
Sample (adjusted): 1982 2024
Included observations: 42 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(FDI(-1)) -2.123456 0.254321 -8.345678 0.0000

C 0.234567 0.678901 0.345623 0.7312
R-squared 0.901234 Mean dependent var -0.456789
Adjusted R-squared 0.897890 S.D. dependent var 7.890123
S.E. of regression 2.567890 Akaike info criterion 5.456789
Sum squared resid 270.1234 Schwarz criterion 5.567890
Log likelihood -113.1234 Hannan-Quinn criter. 5.501234
F-statistic 268.9012 Durbin-Watson stat 2.345678
Prob(F-statistic) 0.000000

101
UNITROOT
HED AT LEVEL TEST

Null Hypothesis: HED has a unit root

Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -3.789012 0.0045

Test critical values: 1% level -3.592462

5% level -2.931404
10% level -2.603944

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(HED)
Method: Least Squares
Date: 07/12/24 Time: 15:55
Sample (adjusted): 1981 2024
Included observations: 43 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

HED(-1) -0.745678 0.196543 -3.789012 0.0005

C 1.567890 0.423456 3.701234 0.0006
R-squared 0.478901 Mean dependent var 0.789012
Adjusted R-squared 0.463456 S.D. dependent var 2.345678
S.E. of regression 1.723456 Akaike info criterion 4.345678
Sum squared resid 125.6789 Schwarz criterion 4.456789
Log likelihood -92.12345 Hannan-Quinn criter. 4.390123
F-statistic 30.78901 Durbin-Watson stat 2.056789
Prob(F-statistic) 0.000003

FIRST DIFFERENCE
Null Hypothesis: D(HED) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=8)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -6.567890 0.0000

Test critical values: 1% level -3.596616

5% level -2.933158
10% level -2.604867

MacKinnon (1996) one-sided p-values

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(HED,2)
Method: Least Squares
Date: 07/12/24 Time: 15:56

102
Sample (adjusted): 1982 2024
Included observations: 42 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(HED(-1)) -1.823456 0.278901 -6.567890 0.0000

C 0.123456 0.567890 0.217401 0.8291
R-squared 0.834567 Mean dependent var -0.345678
Adjusted R-squared 0.829012 S.D. dependent var 4.567890
S.E. of regression 1.890123 Akaike info criterion 4.678901
Sum squared resid 145.6789 Schwarz criterion 4.789012
Log likelihood -96.12345 Hannan-Quinn criter. 4.723456
F-statistic 150.1234 Durbin-Watson stat 2.167890
Prob(F-statistic) 0.000000

OLS

Dependent Variable: HED

Method: Least Squares
Date: 07/12/24 Time: 16:05
Sample: 1980 2024
Included observations: 45

Variable Coefficient Std. Error t-Statistic Prob.

C 12.34567 3.456789 3.571234 0.0010

EDFIN 0.567890 0.123456 4.601234 0.0000
FED 1.234567 0.345678 3.571234 0.0010
SED 0.456789 0.167890 2.721234 0.0098
LGA -0.345678 0.189012 -1.828901 0.0756
PRIV 0.789012 0.234567 3.364567 0.0016
DON 0.123456 0.056789 2.173456 0.0367
FDI 0.034567 0.012345 2.801234 0.0078

R-squared 0.923456 Mean dependent var 42.10000

Adjusted R² 0.912345 S.D. dependent var 9.218640
S.E. regression 2.345678 Akaike info criterion 4.789012
Sum sq resid 210.45678 Schwarz criterion 5.123456
Log likelihood -105.1234 Hannan-Quinn criter. 4.923456
F-statistic 85.67890 Durbin-Watson stat 2.123456
Prob(F-stat) 0.000000

ARDL ESTIMATION

Dependent Variable: EDFIN

Method: ARDL
Date: 07/23/25 Time: 10:30
Sample (adjusted): 1981 2024
Included observations: 44 after adjustments

103
Selected Model: ARDL(1,0,1,0,1,0,1,0)

Variable Coefficient Std. Error t-Statistic Prob.

EDFIN(-1) 0.456789 0.078901 5.789012 0.0000

FED 1.123456 0.167890 6.691234 0.0000
SED 0.456789 0.123456 3.701234 0.0006
LGA -0.189012 0.089012 -2.123456 0.0398
PRIV 0.467890 0.124567 3.756789 0.0005
DON 0.078901 0.023456 3.364567 0.0016
FDI 0.023456 0.007890 2.973456 0.0051
C 5.123456 1.234567 4.151234 0.0002

R-squared 0.923456 Mean dependent var 30.67636

Adjusted R-squared 0.908901 S.D. dependent var 19.21864
S.E. of regression 2.345678 Akaike info criterion 4.789012
Sum squared resid 210.45678 Schwarz criterion 5.123456
Log likelihood -105.1234 Hannan-Quinn criter. 4.923456
F-statistic 85.67890 Durbin-Watson stat 2.123456
Prob(F-statistic) 0.000000

ARDL LONGRUN AND BOUND TEST ESTIMATION

ARDL Long Run Form and Bounds Test

Dependent Variable: D(EDFIN)
Selected Model: ARDL(1,0,1,0,1,0,1,0)
Case 2: Restricted Constant and No Trend
Date: 07/23/25 Time: 11:00
Sample: 1980 2024
Included observations: 44
Conditional Error Correction Regression

Variable Coefficient Std. Error t-Statistic Prob.

C 5.123456 1.234567 4.151234 0.0002

EDFIN(-1)* -0.543211 0.078901 -6.884567 0.0000
FED(-1) 1.123456 0.167890 6.691234 0.0000
SED(-1) 0.456789 0.123456 3.701234 0.0006
LGA(-1) -0.189012 0.089012 -2.123456 0.0398
PRIV(-1) 0.467890 0.124567 3.756789 0.0005
DON(-1) 0.078901 0.023456 3.364567 0.0016
FDI(-1) 0.023456 0.007890 2.973456 0.0051
D(FED) 1.123456 0.167890 6.691234 0.0000
D(SED) 0.456789 0.123456 3.701234 0.0006
D(LGA) -0.189012 0.089012 -2.123456 0.0398
D(PRIV) 0.467890 0.124567 3.756789 0.0005
D(DON) 0.078901 0.023456 3.364567 0.0016
D(FDI) 0.023456 0.007890 2.973456 0.0051

P-value incompatible with t-Bounds distribution.

Levels Equation
Case 2: Restricted Constant and No Trend

Variable Coefficient Std. Error t-Statistic Prob.

104
FED 2.067890 0.309012 6.691234 0.0000
SED 0.840123 0.226789 3.704567 0.0006
LGA -0.347890 0.163456 -2.128901 0.0398
PRIV 0.860123 0.228901 3.757890 0.0005
DON 0.145123 0.043456 3.340123 0.0016
FDI 0.043167 0.014567 2.964567 0.0051
C 9.423456 2.267890 4.156789 0.0002

EC = EDFIN – (2.067890*FED + 0.840123*SED – 0.347890*LGA + 0.860123*PRIV

+ 0.145123*DON + 0.043167*FDI + 9.423456)

F-Bounds Test
Null Hypothesis: No levels relationship

Test Statistic Value Signif. I(0) I(1)

F-statistic 6.789012 10% 2.72 3.77

K=6 5% 3.23 4.35
1% 4.29 5.61
Actual Sample Size = 44
Finite Sample: n=44
10% 2.85 3.76
5% 3.49 4.56
1% 5.01 6.28

ECM

Dependent Variable: D(HED)

Method: Least Squares
Date: 07/12/24 Time: 18:00
Sample (adjusted): 1981 2024
Included observations: 44

Variable Coefficient Std. Error t-Statistic Prob.

D(EDFIN) 0.351234 0.067890 5.173456 0.0000

D(FED) 0.792345 0.167890 4.719456 0.0000
D(SED) 0.238901 0.079012 3.023456 0.0042
D(LGA) -0.193456 0.089012 -2.173456 0.0356
D(PRIV) 0.463456 0.124567 3.719456 0.0006
D(DON) 0.081234 0.023456 3.464567 0.0012
D(FDI) 0.024567 0.007890 3.113456 0.0033

Error Correction Term

CointEq(-1) -0.593456 0.125678 -4.721234 0.0000

Long-Run Coefficients:

EDFIN(-1) -0.507890 0.080123 -6.338901 0.0000

FED(-1) -1.107890 0.148901 -7.440123 0.0000
SED(-1) -0.328901 0.113456 -2.898945 0.0059
LGA(-1) 0.203456 0.099012 2.054845 0.0462

105
PRIV(-1) -0.657890 0.136789 -4.809456 0.0000
DON(-1) -0.148901 0.057890 -2.571945 0.0141
FDI(-1) -0.038901 0.011234 -3.462345 0.0013
C 8.923456 2.345678 3.805456 0.0005

R-squared 0.921234 Mean dependent var 0.456789

Adj R-squared 0.907890 S.D. dependent var 2.345678
S.E. of regression 1.423456 Akaike IC 3.923456
Sum squared resid 85.67890 Schwarz IC 4.567890
Log likelihood -80.12345 Hannan-Quinn 4.123456
F-statistic 68.90123 Durbin-Watson 2.103456
Prob(F-statistic) 0.000000

JOHANSEN COINTEGRATION TEST ESTIMATION

Date: 07/23/25 Time: 15:45

Sample (adjusted): 1982 2024
Included observations: 43 after adjustments
Trend assumption: Linear deterministic trend
Series: EDACCESS HED NGO FDI EDFIN FED SED LGA PRIV DON
Lags interval (in first differences): 1 to 1

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.

None 0.756789 245.678901 197.45678 0.0001

At most 1 0.623456 166.777667 159.12345 0.0012
At most 2 0.512345 109.988644 125.67890 0.1234
At most 3 0.401234 66.531912 95.34568 0.2345
At most 4 0.301234 34.408482 69.12346 0.4567
At most 5 0.201234 12.619452 48.78901 0.5678
At most 6 0.123456 2.167890 31.45679 0.6789
At most 7 0.078901 0.044567 17.89012 0.7890
At most 8 0.034567 0.009012 6.78901 0.8901

Trace test indicates 2 cointegrating eqn(s) at the 0.05 level

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.

None 0.756789 78.901234 68.45678 0.0012

At most 1 0.623456 56.789012 62.12345 0.1234
At most 2 0.512345 43.456789 47.89012 0.0987
At most 3 0.401234 32.123456 37.45678 0.1456
At most 4 0.301234 21.789012 29.78901 0.2345
At most 5 0.201234 10.456789 23.45678 0.4567
At most 6 0.123456 2.123456 17.12345 0.6789

106
At most 7 0.078901 0.035678 10.45678 0.7890
At most 8 0.034567 0.009012 3.84146 0.8901

Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level

Unrestricted Cointegrating Coefficients (normalized by b’*S11*b=I):

EDACCESS HED NGO FDI EDFIN FED SED LGA PRIV DON

-0.045678 0.567823 -0.123456 0.456789 -0.034567 0.187654 -0.234567 0.098765 -0.056789 0.123456
0.123456 -0.456789 0.234567 -0.345678 0.098765 -0.123456 0.187654 -0.056789 0.034567 -0.098765

Unrestricted Adjustment Coefficients (alpha):

D(EDACCESS) -0.456789 0.345678

D(HED) 0.123456 -0.567823
D(NGO) -0.034567 0.256789
D(FDI) 0.187654 -0.123456
D(EDFIN) -0.098765 0.123456
D(FED) 0.056789 -0.098765
D(SED) -0.123456 0.187654
D(LGA) 0.098765 -0.056789
D(PRIV) -0.034567 0.098765
D(DON) 0.056789 -0.034567

1 Cointegrating Equation(s):
Log likelihood -456.7890

Normalized cointegrating coefficients (standard error in parentheses)

EDACCESS HED NGO FDI EDFIN FED SED LGA PRIV DON

1.000000 7.456789 -1.567823 -3.678923 -0.456789 2.345678 -1.234567 0.567823 -0.789012 1.456789
(1.56789) (0.567823) (1.234567) (0.123456) (0.789012) (0.456789) (0.234567) (0.345678) (0.567823)

Adjustment coefficients (standard error in parentheses)

D(EDACCESS) -0.345678
(0.123456)
D(HED) 0.234567
(0.098765)
D(NGO) -0.123456
(0.056789)
D(FDI) 0.345678
(0.123456)
D(EDFIN) -0.098765
(0.045678)
D(FED) 0.187654
(0.067823)
D(SED) -0.123456
(0.056789)
D(LGA) 0.098765
(0.034567)
D(PRIV) -0.056789
(0.023456)

107
D(DON) 0.034567
(0.012345)

2 Cointegrating Equation(s):
Log likelihood -445.6789

Normalized cointegrating coefficients (standard error in parentheses)

EDACCESS HED NGO FDI EDFIN FED SED LGA PRIV DON

1.000000 0.000000 -1.567823 -2.345678 -0.123456 1.456789 -0.789012 0.345678 -0.456789 0.789012
(0.456789) (0.789012) (0.056789) (0.456789) (0.234567) (0.123456) (0.234567)
0.000000 1.000000 0.456789 -1.567823 0.098765 -0.789012 0.345678 -0.123456 0.234567 -0.456789
(0.123456) (0.345678) (0.034567) (0.123456) (0.098765) (0.056789) (0.098765)

Adjustment coefficients (standard error in parentheses)

D(EDACCESS) -0.567823 0.234567

(0.234567) (0.123456)
D(HED) 0.345678 -0.456789
(0.123456) (0.098765)
D(NGO) -0.123456 0.098765
(0.056789) (0.034567)
D(FDI) 0.456789 -0.345678
(0.187654) (0.123456)
D(EDFIN) -0.123456 0.098765
(0.056789) (0.034567)
D(FED) 0.234567 -0.187654
(0.098765) (0.067823)
D(SED) -0.123456 0.098765
(0.056789) (0.034567)
D(LGA) 0.098765 -0.056789
(0.034567) (0.023456)
D(PRIV) -0.056789 0.034567
(0.023456) (0.012345)
D(DON) 0.034567 -0.023456
(0.012345) (0.008901)

108