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Human Capital Development and Healthcare Outcomes in Nigeria

OJEAH, Augustine Ikechukwu and EJEDEGBA, Roland Ufuoma

Delta State University, Abraka, Delta State, Nigeria

DOI: https://dx.doi.org/10.47772/IJRISS.2025.915EC00758

Received: 20 October 2025; Accepted: 28 October 2025; Published: 17 November 2025

ABSTRACT

Life expectancy induces uncertainty and health is wealth stock of a nation (NHIS 2025). Human capital

consists of the knowledge, skills, and health that people accumulate throughout their lives, enabling them to

realize their potentials as productive members of the society. Nigeria continues to struggle with inadequate

infrastructure and poor healthcare financing resulting in medical professional migration abroad which depletes

stock of healthcare value despite budgetary health provisions. The study uses the ARDL model of estimation

with data covering the period, 1994-2024 to determine the health outcomes measured by life expectancy in

Nigeria. The result of the estimation reveals that health outcomes have positive relationship with past value of

life expectance, Current Healthcare Expenditure, citizens welfare and Urbanization. The study thus stresses the

need for government to double policy strategy on preventive healthcare framework with improved efficiency.

Keywords: Life Expectancy, Healthcare Expenditure, Urbanization, Gross Domestic Product Per Capita.

JEL Classification: O15, O44, H51.

INTRODUCTION

Human capital consists of the knowledge, skills, and health that people accumulate throughout their lives,

enabling them to realize their potentials as productive members of the society. We can end extreme poverty

and create more inclusive societies by developing human capital. This requires investing in people through

nutrition, health care, quality education, jobs and skills (World Bank, 2023). The cost of inaction on human

capital development is increasing. Without human capital, countries cannot sustain economic growth and

compete effectively in the global economy.

The world development indicators of the World Bank estimated Nigerian population to be about 250 million

people as at 2023. This places Nigeria as the most populous black nation and the seventh largest nation in the

world, (World Bank, 2025). One of the necessities for such a large population is the need for building human

capital through sustainable healthcare system to enhance socioeconomic development of the nation. The

significance of the health sector in the development of any nation is recognized in Article 3 of the United

Nations 17 Sustainable Development Goals which is a key policy target for all economies of the world.

According to the Article, all countries are to work towards ensuring healthy living and well-being for all

members of the society. This global framework thus, provide the basis for countries of the world to adopt

policies to achieve the overall goal and contribute to a healthy world. (United Nations, 2025).

Health promotion includes, education, communication, organizational changes, community development,

fiscal measures, legislation, and local actions leading to healthy public policy for health improvement of the

population based on the model of human ecosystem (Hancock and Perkin 2023).

The historical analysis of the Nigerian health sector can be in four different eras - the precolonial era, the

colonial era, post-independence era, and the modern day. The precolonial era was characterized by traditional

medicine and local health practitioners such as herbalists, divine healers, soothsayers, midwives, and bone

settlers. These traditional health systems were practiced on a localized bases and due to the small population of

most towns and villages. The health system was sustainable in terms of availability, however, the health

practitioners then had little or no education which limited the extent they could provide solutions to health

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challenges. The period was also characterized heavily by deep-rooted cultural beliefs and spirituality thus, the

traditional health system was largely indigenous. The colonial era came with civilization in the health system.

The emergence of colonial rulers and the early missionaries led to the introduction of western medicine and

modern health practices in Nigeria. Through the church missionaries, the first organized health services were

established with the first health structure notably a dispensary built by the Church Missionary Society in 1880.

However, these facilities did not attend to the health needs of the general populace and thus, there was the need

for government to establish health centers to cater for the general healthcare need of the public. The post-

independence era saw the Nigerian health sector improve drastically with the government now more involved

to resolve the dual structure of the health system created by medical schools built for the elite in the urban

population. While a major proportion of the population has little or no access to health care. This era was

characterized by huge government investment into the health sector to provide the needed infrastructure to

expand and improve the healthcare system. The post-independence era featured the first five national

development plans of Nigeria and each of these plans contributed to the development of the health sector.

While the first three developmental plans (1962-1968, 1970-1975, and 1975-1980) which focused on

improving the existing structures and facilities of the current health system. The fourth national development

plan (1981-1985) led by Late President Shehu Shagari laid a landmark in the health sector through the

introduction of the Basic Health Service Scheme (BHSS) which further led to the establishment of the 3 tiers

of health care structures: the Comprehensive Health Centres, the Primary Health Centres, and the Health

Clinics. These structures created a policy of shared responsibility for the health sector between the federal,

state, and local governments. The fourth development plan built on the structure of the former to introduce the

National Directorate of Health Planning as the centralized authority for health-related affairs. The Department

of Health Planning Research and Statistics (DHPRS) is currently responsible for National Health Policy in

Nigeria.

Healthcare Quality is foundational to achieving the goals of healthcare system worldwide: improving

population health, enhancing patient experiences and controlling costs. But Nigeria continues to struggle with

inadequate infrastructure and poor healthcare financing resulting in medical professional migration abroad

which depletes stock of healthcare value despite health budgetary provisions.

Under the foregoing scenario, this paper seeks to examine the impact of human capital development on

healthcare outcomes in Nigeria. Following the introductory section, section 2 clarifies some conceptual and

theoretical issues that relate to the study. Section 3 reviews the related literature while section 4 presents the

theoretical framework and research methodology. Section 5 discusses the empirical results and section 6

presents the concluding remarks.

Conceptual /Theoretical Issues

Health outcome is defined as event occurring due to health intervention (Oleske & Islam, 2019). This may be

measured clinically (physical examination, laboratory testing, imaging), self-reported or observed (such as gait

or movement fluctuations seen by a healthcare provider or caregiver). Some health outcomes require complex

assessments to determine if they are present or absent.

Human life expectancy is a statistical measure of the estimate of the average remaining years of life at a given

age. The most commonly used measure is life expectancy at birth (LEB). This can be defined in two ways.

Cohort LEB is the mean length of life of a birth cohort (in this case, all individuals born in a given year) and

can be computed only for cohorts born so long ago that all their members have died. Period LEB is the mean

length of life of a hypothetical cohort assumed to be exposed, from birth through death, to the mortality rates

observed at a given year. National LEB figures reported by national agencies and international organizations

for human populations are estimates of period LEB.

Grossman Theory of Healthcare Demand

The Grossman model or theory of health demand is a model for studying the demand for health and medical

care outlined by Michael Grossman in (1972). The model based the demand for medical care on the interaction

between a demand function for health and a production function for health. In this theory, health is durable

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capital good which is inherited and depreciates over time. Investment in health takes the form of medical care

purchases and other inputs and depreciation is interpreted as natural deterioration of health over time. The

model creates a dynamic system of equations which can be an optimization problem where utility is optimized

over gross investment in health in each period, consumption of medical care, and time inputs in the gross

investment function in each period. In this way, the length of life of the agent is partially endogenous to the

model. Dynamic optimization problems are often optimized using comparative statics, setting partial

derivatives of the outcome function of interest, in this case the utility function, equal to zero. Theoretical

expansions of the Grossman model was extended in a number of directions and among the first to this

includes, uncertainty in the model by Charles Phelps and Maureen Cropper. The relationship between

education and health was expanded in the model by Isaac Ehrlich. Regarding the relationship between

education and medical care demand, one important question is whether the marginal efficiency of capital

elasticity with respect to education is less than or greater than one. If the curve is elastic (elasticity greater than

one), education will increase medical care demand. On the other hand, if the curve is inelastic, education will

decrease medical care demand.

Theories of Human Capital

The value of human capital theory is widely accepted in order to increase organizational performance, so an

organization relies on employees’ skill, knowledge and ability as a key concept of value creation. Adam Smith

(1973) initiated an improvement in human capability that is to production, then a term of human capital was

introduced by Theodore W. Schultz (1961) published in the American Economic Review, called investment in

human capital. Human capital widely used after Gary Backer won the novel prizes which initiated “human

capital theory” and stated that a different level of education and training contributes to a different level of

wages and salaries, the more knowledge, skill and ability, the more likely to get a better job (Blair, 2012).

According to Gary Backer (1964), human capital is a physical means of production. Organizations invest in

human capital via education, training, and health.

Thomas Davenport (1999) advanced that the component of human capital consisted of abilities, knowledge,

skill, personal talent, behavior, and effort, when those three components plus time, he extended that,

knowledge included IQ, intelligence, specific and general knowledge to work. Skill is expertise used in

working, including the physical body, and movement of the job. Talent is a personal characteristic which is

innate and can be improved by development. Behavior is an expression and observable behavior, norm, ethics

and personal belief. Effort is when people try to use their innate or personal resources including their talent,

experience, knowledge and ability to work to be successful.

According to Becker (1964), human capital can be accumulated in different forms of education, training,

migration, and health. Through such forms, employees gain knowledge, skills and abilities in different ways.

Firms invest in human capital because these firm view humans as an asset and expect that what the firm has

invested will be returned and provide a positive value in the future. In other words, infrastructural provision on

education and healthcare plays a vital role in developing human capital, boosting productivity and innovation.

Lifespan Theories of Cognitive Development

Lifespan theories of cognitive development (LTCD) provide an integrative framework for the description,

explanation, and optimization of cognitive development across different functional domains and age periods.

Generally, LTCD make two central assumptions: (a) cognitive development reflects the operation of two

components, one biological and the other cultural; (b) during development, the biological component is

invested into various cultural domains, thereby leading to the acquisition of culturally transmitted bodies of

knowledge. Based on the general lifespan dynamics between biology and culture, the biological component is

expected to decline after maturity. In contrast, the cultural component is expected to increase with age as long

as knowledge maintenance and knowledge acquisition outweigh age-based losses in biological potential. This

prediction of multi-directionality in lifespan trajectories has gained ample empirical support.

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REVIEW OF RELATED LITERATURE

Vast literature exists on the subject but a brief review would be undertaken. For instance, Albert (2021)

investigated the impact of health status and human capital formation on regional performance using various

measures of population health status, controlling for potential endogeneity between health and growth,

employing instrumental variable regressions and dynamic panel data. He found a positive effect of a change in

health status on regional output and a negative effect of proxy variables for health limitations on regional

performance. This corroborates the importance of investing in health along with education with the aim of

improving not only the well‐being of individuals but the human capital and growth of a region.

Kelani et al., (2019) examined the role of health status and labour productivity on economic growth in Nigeria.

The study employed annual time series data, covering the period, 1981-2017 and adopted the ARDL bound

test along with co-integration test, and found that both health status and labour productivity impact growth

positively. Thus, the study suggested the need for a well-developed health system and increased funding of the

educational sector to improve labour productivity.

Ejedegba, Olele and Omotor (2017) examined the effect of human capital formation on economic growth

process in Nigeria, using data covering the period 1970 to 2004 and employed the ARDL model to test for

cointegration relationship. The study found a positive relationship between human capital indicators and

economic growth and thus stresses the need for a conscious effort of government to adequately invest in health

and education and also to establish more labour-intensive industries in Nigeria.

Akpata, J. U. and Ejedegba, R. U. (2024) employed data ranging from 1981to 2022 for Nigeria to examine the

effects of human capital formation and aggregate domestic investment on economic growth and development.

It explores the variables of expenditure on health, education and household welfare; domestic investment and

economic growth. In the study, it ws found that education and health human capital positively relate with

economic growth and household welfare. The study, therefore, stresses the need for improvement in the

indicators of human capital formation for a sustained economic growth and development in Nigeria.

Linhartová (2020) in a study, investigates whether human capital development expenditure can promote human

development in the Czech Republic with data spanning, 1995 to 2018.

Liljestrand et al., (2018), analysed the effect of quality health systems in the attainment of SDG’s amongst

low-income and middle-income countries. They found that high-quality health systems are a global need for all

countries and will contribute significantly towards the development of the nations. Popp et al., (2012),

employed the panel co-integration test in assessing the long-term impact of using population and food security

on the health status of developing countries. The result reveals that the rising population has a negative impact

on health status in developing nations due to the high rate of poverty and unemployment.

Akinwale et al., (2014), explored the public health status of Lagos metropolis and most industrial States of

Nigeria. The study employed primary data covering 2,434 households and concluded that even in the urban

region of the Nigerian state, there are various health challenges such as poor drainage and waste disposal

system, natural climate disaster i.e., flooding, overpopulation, the outbreak of perennial diseases such as

malaria, diarrhea, and so on, and poor house planning.

Vu (2022) in his study postulated that life expectancy augments incentives for human capital accumulation.

Albert (2021) study shows a positive effect of a change in health status on regional output and a negative effect

of proxy variables for health limitations. This corroborates the importance of investing in health and education

with the aim of improving not only the well‐being of individuals but the human capital.

Collectively, the empirical review of the health status and outcomes in developing nations, Nigeria included,

justified the need for a revisit of the evidence.

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THEORETICAL FRAMEWORK AND RESEARCH METHODOLOGY

Theoretical Framework

The purchasing power created by individuals acting on the motive of benefit maximization in terms of factor

income over the expected lifetime constitutes the main constraint of the benefit function (Kök et al. 2018: 2-4).

In this model, health is a durable capital good which is inherited and depreciates over time. Investment in

health takes the form of medical care purchases and other inputs and depreciation is interpreted as natural

deterioration of health over time. Therefore, in order to maintain health, individuals should maximize their

expected life expectancy over a certain rate of depreciation (discount) over time. In this context, each

individual wants to maximize the utility index over the basket of goods he or she consumes during his or her

lifetime.



(

...,





)







 











󰇛  󰇜





-1

- (1)

In equation 1, U refers to the utility index,

(



, …, 



) is

manufactured consumer goods,  common utility,

which equates the rate of substitution between each pair of goods in two periods, 



income constraint that

each individual provides depending on the choice between free time and working hours in active working life,









expenditures on manufactured consumer goods,

(

1 + 

1

)

−1

discount rate defined over the depreciation,

respectively. When we equate the partial derivative of (1) to zero and make a simplification:









󰇛󰇜

󰇛󰇜

----------------- (2)

where, (j,k=1…,n) (t,τ=1…,T).

We can associate the reduced-price phenomenon in the Equation 2 system with the reduced life expectancy

with an analogical approach. The depreciation (discount) rate

(

1 + ε

)

−1

that may arise in the health stock will

cause a decrease in the goods produced/consumed during the lifetime of the individual, as well as a decrease in

the life expectancy due to less consumption.

According to Grossman (1999), individuals' demand for healthcare services was evaluated as an effort to

compensate for the health stock worn out by the effects of diseases and similar factors. Therefore, an average

one-year additional increase in the expected life expectancy during life from the time of birth of each

individual decreases at the marginal

(

1+ 



)

−1

discount rate. In order to gain additional life, the total return on

an individual's investment in their own health can be expressed in an analogous approach as follows (Kök et al.

2018: 2-4)

The model specified for the study is based on the already established theoretical framework using the

Grossman health demand theory.

The Autoregressive (Adaptive Expectation) model is given as;

= 

+ 

+ (1-) Y

t-1

+ [U

– (1- 󰇜U

t-1

] ---- (3)

The common form of the model of equation (3) is expressed below as;

= 

+ 

t-1

+ U

----------------------- (4)

Following this route, the model of Worinde Olalekan Bashir et al (2024) is given below as;

= 

+ 

FDI

+ 

EDS

+ 

HES

+ 

URB

+ 

GDP

+ U

…………………………………………………………. (5)

where, LE (life Expectancy), FDI (Foreign Direct Investment), EDS (Education spending), URB

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(Urbanization), GDP (Gross Domestic Product).

The study adopted a model of adaptive expectation which is stated below as modified based on Grossman

theoretical framework.

= 

+ 

t-1

+ 

CHE

+ 

URB

+ 

GDP

+ U

--- (6)

where, LE= life Expectancy, CHE= Current Healthcare Expenditure % of GDP, URB= Urbanization, GDP=

GDP Per Capita.

A priori expectation is as contained in table 3

Table 3: Summary of the a priori expectation

Regressand

Relationship

Regressors

+ (-)

GDP per Capita

CHE

URB

From table 3.1, shows a positive relationship between life expectancy and GDP per capita. But according to

the Preston Curve Effect, at higher GDP per capita there exist a weaker and diminishing returns on the life

expectancy. While there is a general positive relationship between Healthcare Spending, Urbanization and Life

Expectancy.

The data for this study was sourced from the office of the National Bureau of statistics, World Development

Indicators (WDI), 2024 issue and the Central Bank of Nigeria, 2024 issue.

Empirical Results and Analysis

Life expectancy which is the dependent variable has a mean value of 51.00870 as presented and GDP per

Capita, which is an independent variable has a mean value of 30, 6986.7. The maximum and minimum values

also conform to the data on the data set.

Table 4 in appendix, shows the summary of descriptive statistics of the variables included in the model. It

shows the existence of wide variations in the variables as depicted by the mean values during the study period.

For instance, LE, CHE, URB, and GDP per Capital shows 51.00870, 3.653043, 4.521304, and 306986.7

respectively with GDP per capital and URB having the highest mean values during the study period. This is a

clear indication that current healthcare expenditure are among the major determining factors of life expectancy

in Nigeria.

The skewness is a measure of the symmetry of the histogram. A variable with negative skewness is said to be

far below the mean, while a variable with positive skewness is usually above the mean. All the distributions

are negatively skewed but with the exception of current healthcare expenditure rate that is positively skewed.

Variables with value of kurtosis less than three are called platykurtic (fat or short-tailed) and URB, and GDP

per capital variables are short tailed. On the other hand, variables whose kurtosis value is greater than three are

level

P-value

difference

P- value

Order of

integration

-2.4625

0.1382

-4.9146

0.0032

1(0)

GDP Per C

-1.0765

0.7112

-2.4272

0.0172

1(1)

URB

-0.5337

0.4759

-4.2396

0.0000

1(1)

CHE

-2.4625

0.1382

-5.6643

0.0002

1(1)

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called leptokurtic (slim or long tailed) and CHE variable qualified for this during the study period. That is they

are slim or long tailed.

The goal of the study is to examine the life expectancy determinants and its impact both at short term and long

run in Nigeria, which makes the analysis more comprehensive. In recognition of this, the study begins by

examining the time series properties of the variables. Data analysis became very necessary to understand their

statistical behavior.

Econometric studies have shown that most financial and macro-economic time series variables are non-

stationary and using non-stationary variables leads to spurious regression (Engel & Granger, 1987). Thus, the

variables were investigated for their stochastic properties using Augmented Dickey-Fuller (ADF) and Phillips-

Perron (PP). The two tests were used to test for consistency and where conflicts exist, to decide on the most

appropriate option. The results are shown in

Table 1: Test of Stationarity

From table 1 above, the result of the PP unit root test suggests that all the variables were non-stationary with

the exception of LE that was stationary at level. They however became stationary after the first difference was

taken.

The different order of integration of the variables necessitates the choice of the ARDL- Bounds testing

approach to co-integration which is suitable for testing long-run relationship among variables that are of mixed

order of integration. The result of the co-integration test is presented in Table 2 of the appendix. It shows the

F-statistics is greater than the lower bound and upper critical values at 1%, 5% and 10% respectively. Hence

the null hypothesis of no co-integration is rejected and long-run co-integration relationship is established

among the variables. It can therefore be inferred that the variables are co-integrated. Therefore F-statistics is

9.52 which is greater than lower bound 1(0) values of 3.41, 4.01, 5.17 and the upper bound 1(1) with 4.45,

5.62, and 6.36 using the 1%, 5% and 10% levels of significant.

In view of the co-integration relationship between the dependent variable and the independent variables, we

proceed to estimate the error correction and long-run model. The results of the estimations are presented in

Table 3. From the result, 99% variation in the depended variable (LE) are explained by the independent

variables, (that is CHE, URB and GDP per capita) as indicated. The regression results present a positive

relationship between the dependent variable LE

(life expectancy) and LE

t-1

(Lag of LE), CHE

t-1

(Current

Healthcare Expenditure % of GDP Lag), GDP

(per capita) and URB

(Urbanization). This simplifies that a

unit increase in the level of the outlined independent variables is associated with a positive change in the

dependent variable (LE) in the short run. But GDP

, GDP

t-1

(per capita) and URB

(Urbanization) are positively

related to LE

at long run. While an inverse relationship between LE

, CHE

(Current Healthcare Expenditure %

of GDP), CHE

t-1

(Current Healthcare Expenditure % of GDP) Lag and URB

t-1

indicating that a unit increase in

these variables will bring about a negative change in the dependent variable LE in the long run. The result of

Error Correction Model is given in table 3 in the appendix, showing coefficient of error correction is

significant at 1 percent with negative sign. The coefficient of ECM is (-0.896) which shows high speed of

adjustment from short run fluctuations to long run equilibrium (89% discrepancy is corrected each year)

approximately 89 percent of disequilibrium from the previous year's shock convergence back to the long run

equilibrium in the current year. This is a further indication of the existence of long-run relationship between

the dependent variable and the independent variables. The R

value of 0.90 shows that 90% variance in the

dependent variable explained by the explanatory variables. The D.W value of 2.2 shows fair absence of auto-

correlation. The F-statistic value of 14.9 is greater than the Probability (F-statistic) or p-value of 0.000039

affirming the overall statistical significance of the model for inference.

The outcomes of the Granger Causality test in table 4 in the appendix indicate that only URB and GDP per

capita have bi-directional causality, while LE has a unidirectional causality with URB, based on their p-values.

We therefore reject H0 for the two variables above and conclude that URB granger causes GDP per capital and

vice versa, and LE grange causes URB in a unidirectional. For the other variables, since the p-values is greater

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than 0.05, we do not reject the Null hypothesis and conclude that those variables do not granger cause each

other.

CONCLUSIONS

The study in conclusion found gross domestic product per capita,

current healthcare expenditure and

urbanization as contributory variables to variation in life expectancy in Nigeria. The regression result as

outlined in 5.1 summary of findings above in the case of positive relationship between the dependent variable

(life expectancy) and LE

t-1

(Lag of LE), CHEt

t-1

(Current Healthcare Expenditure % of GDP Lag), GDP

(per capita) and URB

(Urbanization), conforms to the a prior expectation. The study noted a deviation from a

prior expectation of the Grossman Model of healthcare demand. This suggest that high healthcare expenditure

dose not translate into improved health outcomes if the system is inefficient, quality of care is poor and lack of

framework for preventive care.

POLICY RECOMMENDATIONS

1. Based on the findings from this research, it is necessary to provide a set of policy recommendations

that would be applicable to the Nigerian economy. The research therefore suggests the following policy

options based on our empirical findings:

2. The study recommends that federal, state and local government via Federal Ministry of Health and

National Primary Healthcare Development Agency should increase policy strategy on preventive care

framework.

3. The study recommends that government at all tiers should invest in human capital specifically on

education and skills to increase capital formation on health and labor productivity.

4. The study recommends that government at all tiers should protect for integrated urban master planning

to encourage environment development for quality life.

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Table 3: The Error Correction Model.

Table 4: Granger Causality Test.

Dependent Variable: LE

Method: ARDL

Date: 05/28/25 Time: 09:32

Sample (adjusted): 2002 2022

Included observations: 21 after adjustments

Maximum dependent lags: 1 (Automatic selection)

Model selection method: Akaike info criterion (AIC)

Dynamic regressors (2 lags, automatic): GDP_PER_C CHE URB

Fixed regressors: C @TREND

Number of models evaluated: 27

Selected Model: ARDL(1, 2, 2, 2)

Variable Coefficient Std. Error t-Statistic Prob.*

LE(-1) 0.103294 0.237505 0.434913 0.6739

GDP_PER_C 7.25E-07 4.63E-06 0.156675 0.8790

GDP_PER_C(-1) -3.13E-06 4.85E-06 -0.646094 0.5343

GDP_PER_C(-2) -8.32E-06 3.74E-06 -2.221909 0.0534

CHE -0.127131 0.086487 -1.469945 0.1756

CHE(-1) 0.080080 0.042144 1.900136 0.0899

CHE(-2) 0.073460 0.040063 1.833616 0.0999

URB 3.634115 1.069490 3.397988 0.0079

URB(-1) -2.113847 1.315197 -1.607247 0.1425

URB(-2) 0.756447 0.319457 2.367914 0.0421

C 30.27117 8.334337 3.632103 0.0055

@TREND 0.489357 0.139251 3.514209 0.0066

R-squared 0.998974 Mean dependent var 51.35762

Adjusted R-squared 0.997720 S.D. dependent var 1.619006

S.E. of regression 0.077303 Akaike info criterion -1.986621

Sum squared resid 0.053781 Schwarz criterion -1.389751

Log likelihood 32.85952 Hannan-Quinn criter. -1.857085

F-statistic 796.7115 Durbin-Watson stat 2.232316

Prob(F-statistic) 0.000000

ARDL Error Correction Regression

Dependent Variable: D(LE)

Selected Model: ARDL(1, 2, 2, 2)

Case 5: Unrestricted Constant and Unrestricted Trend

Date: 05/28/25 Time: 09:25

Sample: 1994 2024

Included observations: 21

ECM Regression

Case 5: Unrestricted Constant and Unrestricted Trend

Variable Coefficient Std. Error t-Statistic Prob.

C 30.27117 4.185825 7.231830 0.0000

@TREND 0.489357 0.068652 7.128108 0.0001

D(GDP_PER_C) 7.25E-07 2.61E-06 0.277768 0.7875

D(GDP_PER_C(-1)) 8.32E-06 3.05E-06 2.729885 0.0232

D(CHE) -0.127131 0.038729 -3.282617 0.0095

D(CHE(-1)) -0.073460 0.029908 -2.456239 0.0364

D(URB) 3.634115 0.498486 7.290300 0.0000

D(URB(-1)) -0.756447 0.154861 -4.884677 0.0009

CointEq(-1)* -0.896706 0.125816 -7.127138 0.0001

R-squared 0.908564 Mean dependent var 0.310952

Adjusted R-squared 0.847606 S.D. dependent var 0.171491

S.E. of regression 0.066946 Akaike info criterion -2.272336

Sum squared resid 0.053781 Schwarz criterion -1.824683

Log likelihood 32.85952 Hannan-Quinn criter. -2.175184

F-statistic 14.90486 Durbin-Watson stat 2.232316

Prob(F-statistic) 0.000038

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Table 2: Co-Integration Test.

Pairwise Granger Causality Tests

Date: 05/26/25 Time: 22:16

Sample: 1994 2024

Lags: 2

Null Hypothesis: Obs F-Statistic Prob.

GDP_PER_C does not Granger Cause LE 28 2.51187 0.1031

LE does not Granger Cause GDP_PER_C 1.50913 0.2422

CHE does not Granger Cause LE 21 0.28281 0.7574

LE does not Granger Cause CHE 0.86582 0.4395

URB does not Granger Cause LE 28 0.42784 0.6570

LE does not Granger Cause URB 5.61003 0.0104

CHE does not Granger Cause GDP_PER_C 21 0.10790 0.8984

GDP_PER_C does not Granger Cause CHE 1.13491 0.3460

URB does not Granger Cause GDP_PER_C 28 7.23757 0.0036

GDP_PER_C does not Granger Cause URB 3.76313 0.0386

URB does not Granger Cause CHE 21 1.74026 0.2071

CHE does not Granger Cause URB 1.19678 0.3278

F-Bounds Test Null Hypothesis: No levels relationship

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

Asymptotic: n=1000

F-statistic 9.524268 10% 3.47 4.45

k 3 5% 4.01 5.07

2.5% 4.52 5.62

1% 5.17 6.36

Actual Sample Size 21 Finite Sample: n=35

10% 3.8 4.888

5% 4.568 5.795

1% 6.38 7.73

Finite Sample: n=30

10% 3.868 4.965

5% 4.683 5.98

1% 6.643 8.313

ARDL Error Correction Regression

Dependent Variable: D(LE)

Selected Model: ARDL(1, 2, 2, 2)

Case 5: Unrestricted Constant and Unrestricted Trend

Date: 05/28/25 Time: 09:25

Sample: 1994 2024

Included observations: 21

ECM Regression

Case 5: Unrestricted Constant and Unrestricted Trend

Variable Coefficient Std. Error t-Statistic Prob.

C 30.27117 4.185825 7.231830 0.0000

@TREND 0.489357 0.068652 7.128108 0.0001

D(GDP_PER_C) 7.25E-07 2.61E-06 0.277768 0.7875

D(GDP_PER_C(-1)) 8.32E-06 3.05E-06 2.729885 0.0232

D(CHE) -0.127131 0.038729 -3.282617 0.0095

D(CHE(-1)) -0.073460 0.029908 -2.456239 0.0364

D(URB) 3.634115 0.498486 7.290300 0.0000

D(URB(-1)) -0.756447 0.154861 -4.884677 0.0009

CointEq(-1)* -0.896706 0.125816 -7.127138 0.0001

R-squared 0.908564 Mean dependent var 0.310952

Adjusted R-squared 0.847606 S.D. dependent var 0.171491

S.E. of regression 0.066946 Akaike info criterion -2.272336

Sum squared resid 0.053781 Schwarz criterion -1.824683

Log likelihood 32.85952 Hannan-Quinn criter. -2.175184

F-statistic 14.90486 Durbin-Watson stat 2.232316

Prob(F-statistic) 0.000038