Effect of FDI Inflows on Human
Development in Ghana: Evidence from
Structural Equation Modeling Approach
Haw-la Baku Yussif*, Abdallah Abdul-Mumuni**
and Ibrahim Mohammed***
While existing studies have often focused on the link between Foreign Direct
Investment (FDI) and economic growth, empirical evidence on the effect of FDI
inflows on human development remains sparse. This paper examines the
relationship between FDI inflows and human development in Ghana for the
period 1970 to 2019. In order to ascertain the specific effects of FDI on various
human development indicators (life expectancy at birth, school enrolment and
GDP per capita growth), the structural equation modeling approach was
employed. The findings of the study indicate that FDI has a positive effect on
life expectancy at birth and school enrolment. This suggests that more FDI
inflows to Ghana will improve human development in the country. Thus, the
government needs to develop its bargaining power and negotiation skills in
relation to its dealings with multinational corporations to attract a favorable
type of FDI into the country.
Introduction
Human development can be seen as the process of building up and enhancing peoples’
choices, however, this basic understanding has evolved over time, shifting from its initial
association with economic growth and rising per capita incomes (Ortega et al., 2016).
Traditionally, GDP per capita is a measure of a country’s economic development. However,
it is a one-way dimensional measure of development which does not provide a full reflection
of the level of development of a country in terms of the welfare of its citizens. Amartya Sen
in his work emphasized the limitations on the use of per capita income as a measure of
overall welfare. He argued his point by comparing China, Sri Lanka, South Africa, Brazil,
*
Graduate Student, Department of Banking and Finance, University of Professional Studies, Accra, Ghana;
and is the corresponding author. E-mail: hawlayussif@gmail.com
** Senior Lecturer, Department of Banking and Finance, University of Professional Studies, Accra, Ghana.
E-mail: abdul.mumuni@upsamail.edu.gh
*** Associate Professor, Department of Banking and Finance, University of Professional Studies, Accra,
Ghana. E-mail: ibrahim.mohammed@upsamail.edu.gh
48
© 2022 IUP. All Rights Reserved.
The IUP Journal of Applied Economics, Vol. 21, No. 3, 2022
and Gabon. He stated that, in 1992, China and Sri Lanka had far lower GNP per capita but
had higher life expectancies when compared to South Africa, Brazil and Gabon (Sen, 1995).
Therefore, measures of development should look beyond per capita income. Given the
insufficiency of the use of per capita income as a measure of human development, there is
the need to focus on a broader and more comprehensive measure that includes both economic
and social indicators of wellbeing like the human development index and its indicators.
Introduced in 1990, the Human Development Index (HDI) is published by the UNDP and
used to compare nations’ real economic development status. It is widely recognized as a
proxy and more appropriate indicator of economic and societal progress of nations (Ranis
et al., 2000). It is a composite index which is based on three indicators, namely, health,
education at school, and standard of living. Health is measured by life expectancy, education
is measured by the literacy rate and standard of living is measured by GDP per capita.
External capital constitutes an important source of financing development projects in
Ghana. Sub-Saharan African economies including Ghana uniquely identify FDI as the major
source of financing for domestic activities (Deléchat et al., 2009). FDI inflows play an
important role in helping to achieve the Sustainable Development Goals, which is geared
towards improving human development. Ghana has received huge sums of money in the
form of FDI in the quest to develop the economy. According to Osei (2012), FDI in Ghana
in the year 2000 was about $630 mn. This amount increased to about $2.25 bn in 2007
surpassing the flow of remittances. In 2009 and 2019, FDI inflows accounted for about
$2.08 bn and $3.9 bn respectively (World Bank, 2020). Despite the substantial flows of FDI
into the country, the growth of the Ghanaian economy continues to diminish and the HDI of
Ghanaians, which is an aggregate of the general standard of living of the people, in terms of
access to education, healthcare, life expectancy and security, etc., has not fared well. In
view of this, it is reasonable to examine the extent to which FDI inflows can contribute to
human development in Ghana.
The main motivation for this study stems from the fact that one of the goals of the
“Ghana Beyond Aid” is to improve the quality of lives of Ghanaians. “Ghana Beyond Aid” is
a national agenda that calls for the transformation of the Ghanaian economy from the export
of raw materials to the one that is based on manufacturing and the provision of opportunities,
jobs and prosperity to all Ghanaians (Ghana Beyond Aid Committee, 2019). One of the ways
by which this goal can be realized is to make a conscious effort of improving the country’s
HDI and this can be aided by FDI inflows into the country. According to Simionescu and
Naros (2019), FDI has an important influence on the economic growth of a nation, as a
condition to attract investors to develop and improve the economy and the quality of human
resources.
There is extant literature on the effects of FDI on development and other macroeconomic
indicators such as inflation, domestic savings and investments (Murshid and Mody, 2011;
and Aizenman et al., 2013). Most of the studies focus on the effect of FDI on economic
growth using per capita income alone as an indicator of the level of development of a
country (Gizaw, 2015; Oladele, 2015; and Ogbokor, 2018). However, studies on the
Effect of FDI Inflows on Human Development in Ghana:
Evidence from Structural Equation Modeling Approach
49
relationship between FDI and HDI remains underexplored. HDI improves on GDP per
capita as an indicator of development by including health and education aspects of
development. Essentially, any factor that may lead to an increase in these three components
will promote human development (Razmi et al., 2012). Some studies have looked at the
link between FDI and Human Development. For instance, Gokmenoglu et al. (2018) examine
the impact of FDI on HDI in Nigeria using the Johansen integration test and find that there
exists a long-term relationship between FDI and HDI. Similarly, Timothy (2018) empirically
examines the effects of globalization which is captured as FDI on life expectancy in
Nigeria between the periods of 1986 to 2016. His study employed Augmented DickeyFuller (ADF) to test for the unit root of the variables, and Johansen co-integration test to
investigate the long-run relationship among the variables. The findings of his study indicate
that FDI has a positive and significant impact on life expectancy in Nigeria.
In this paper, we argue that FDI can lead to economic growth and welfare improvements
in Ghana by smoothing consumption patterns and domestic savings which may lead to an
increase in production capacity through technology, provide employment opportunity, and
increase per capita income.
The rest of the paper is as follows: first, the paper describes the methodology used in the
study, then it discusses the empirical findings; finally, it presents the conclusion and policy
recommendations.
Data and Methodology
Data Source
The study employs secondary annual time series data from 1970-2019 for its analysis. The
choice of this period is based on data availability and the data is sourced from the World
Bank Development Indicators (WDI) (2020).
Empirical Strategy
The study employs the SEM approach to test the hypotheses. This is because, SEM allows
the researcher to consider the relationships among multiple exogenous and endogenous
concepts concurrently. SEM has three main advantages over traditional multivariate
techniques: (1) It is used for the estimation of latent variables via observed variables; (2) It
provides an explicit assessment of measurement error; and (3) It is used for model testing
and to estimate error variance parameters for both independent and dependent variables
(Byrne, 2012).
Following Gokmenoglu et al. (2018) and Amoh et al. (2019), the following multivariate
models are adopted for the study.
50
LEBt   0  1 FDI t   2 POP   3GS   4 DUMMY   t
...(1)
SEt   0  1 FDI t   2 POP   3GS   4 DUMMY   t
...(2)
The IUP Journal of Applied Economics, Vol. 21, No. 3, 2022
GDPPCGt   0  1 FDI t   2 INTEREST   3GS   4 DUMMY   t
...(3)
Equation (1) represents the life expectancy at birth model. This model helps the researcher
to test the hypothesis, “FDI affects life expectancy at birth.” Equation (2) is the school
enrolment model. It enables the researcher to test the hypothesis, “FDI affects school
enrolment.” Equation (3) represents the GDP per capita growth model. This model helps the
researcher to test the hypothesis, “FDI affects GDP per capita growth.”
Justification of Control Variables
Population Growth
Among the key issues related to development is the significant factor of population with its
multidimensional aspects. Population growth rate is closely related to the quality of economic
life, available funds for individual and social consumption, national income to be used for
reproduction, and the labor employment situation. It is reasonable that the absolute value of
GDP in a country grows when its population grows as more workers are available.
Nevertheless, a higher population growth, in lot of studies, is supposed to have a negative
effect on wealth or on GDP per capita. Human development is dependent on reduced
population growth rates. Population growth helps the process of development in the
following ways: An increasing population means an increase in the number of working
populations who can function as active participants in the process of economic growth and
human development. The quantity, quality, structure, distribution, and movement of a
population can help or hinder the rate of economic development. A developed country with
low population density and a low percentage of employable people needs an increase in
population in order to keep up with economic development. On the other hand, for an
underdeveloped country with high population density and a high percentage of employable
people, any increase in population will be detrimental to its economy.
Government Spending
Government general final consumption as percentage of GDP is used as a proxy for
government spending. In a study on Nigeria, Omodero (2019) concludes that government
recurrent expenditure has strong and significant positive impact on HDI. Government spending
is expected to improve human development because the HDI measures the fruit of developing
countries’ investments in education and health as well as countries’ economic performances,
all of which mainly stem from government spending and/or FDI. Especially in developing
countries, citizens’ basic needs are principally ensured by government spending. Governments’
spending on social infrastructure (e.g., hospitals, schools) facilitates better systems for, and
access to, basic human needs, thus improving human development. Secondly, increased
expenditure from the population means that people have more opportunities to earn and
spend on education to develop their capabilities.
Dummy Variable for Economic Reforms
ECOREFORM represents a dummy variable constructed to capture the effect of periods of
economic reform on human development. It is constructed such that it takes the value one
Effect of FDI Inflows on Human Development in Ghana:
Evidence from Structural Equation Modeling Approach
51
(1) for the period of economic reform and zero (0) for periods without economic reform.
Most policymakers and scholars anticipate that overall economic reforms including openness
to international trade, macroeconomic stabilization, price liberalization and enforcement of
laws, regulation and proper institutions improve human and economic development by
enlarging capabilities of and choices among individuals (Carvalho et al., 2016).
Interest Rate
Interest rate is one of the important macroeconomic variables, which is directly related to
economic growth and therefore human development. The link between interest rates and
human development is derived from the use of interest rates as a means for achieving
desired economic conditions. That is to say that interest rates are tools used to make the
economy more stable by limiting undesirable factors like inflation.
Results and Discussion
Path Analysis of Statistical Models
After the model specification, the path coefficients of the three models are presented as
follows:
Model 1: FDI, POP, GGFC, ECOREFORM -> LEB
...(4)
Model 2: FDI, POP, GGFC, ECOREFORM -> SE
...(5)
Model 3: FDI, INTEREST, GGFC, ECOREFORM -> GDPPCG
...(6)
From Figure 1, the coefficients, means, variances and covariances among exogenous
variables emanating from the three equations can be identified.
The Fitness Level of Model and Path Analysis Stability
Model fit is defined and quantified as the extent to which the model-reproduced covariance
matrix differs from the sample data covariance matrix. In path analysis, Keith (2013) indicates
that the fit index statistic tests the stability between the predicted and observed data. Thus, a good
fitting model is practically consistent with the dataset. Path analysis evaluates model fit by examining
multiple tests such as Bentler-Raykov index, Root Mean Squared Error of Approximation (RMSEA),
Standard Root Mean Squared Residual (SRMR) and Coefficient of Determination (CD).
Hu and Bentler (1999) suggested at least two indices for the acceptance of a model fit;
this includes the SRMR, Tucker-Lewis Index (TLI) or the Comparative Fit Index (CFI).
The SRMR is defined as the difference between the observed correlation and the model
implied correlation matrix. It implies acceptable model fit when it is lower than 0.10. However,
Hu and Bentler (1999) suggest that the SRMR can be an indicator of good fit when it is
smaller than 0.05. Therefore, our reported SRMR index of 0.032 in Table 1 is a good
indicator of our models’ fit.
The CFI is an index of “good fit” which quantifies the proportional improvement in
structural equation model fit over a “null” model (Hu and Bentler, 1999). CFI values range
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The IUP Journal of Applied Economics, Vol. 21, No. 3, 2022
Effect of FDI Inflows on Human Development in Ghana:
Evidence from Structural Equation Modeling Approach
53
–0.98
1.8
0.63
GGFC
4.1
11
2.4
POP
0.084
2.6
20
INTEREST
99
Source: Authors’ Computation from WDI (2021)
–0.11
5.9
0.62
2.6
–0.34
–0.74
6.8
–0.11
–0.13
–4.4
0.5
2.5
0.74
ECOREFORM
0.19
0.031
–0.19
–0.089
0.47
0.77
0.25
2.6
FDI of GDP
7.6
Figure 1: Path Analysis Showing Multivariate Relationships
70
63
GDPPCG
–8.2
SE
LEB
3
2
1
12
56
2
Table 1: Equation-Level, CFI and SRMR Goodness of Fit
Endogenous Variables: LEB, SE, GDPPCG
Endogenous
Variable
Fitted
Variance
Predicted
Residual
R2
MC2
LEB
17.39715
15.39064
2.006504
0.8846648
0.8846648
SE
132.4161
76.90776
55.50832
0.5808038
0.5808038
GDPPCG
19.22986
6.996059
12.2338
0.3638122
0.3638122
Overall Coefficient of Determination
0.941
Standardized Root Mean Squared Residual (SRMR)
0.032
Comparative Fit Index (CFI)
0.841
Note: MC indicates the correlation between endogenous variable and its prediction;
MC2 indicates the Bentler-Raykov squared multiple correlation coefficient.
Source: Authors’ Computation from WDI (2021)
between 0 and 1 with values closer to 1 implying a good fit (Hu and Bentler, 1999). Thus,
the CFI reported index denotes the extent to which the model of interest is better than the
independent model. The CFI index of 0.841 therefore indicates an acceptable model fit.
The R2 is a measure in statistics that examines the predictive ability of the model. It
shows the proportion of variance in the dependent variable (endogenous variable) that is
predicted by the independent variable (exogenous variable). It is the focal criterion for judging
the quality of SEM. Generally, a high R2 value indicates that the model is a good fit for the
data, although interpretation of fit often times depend on the context in which it is used.
Table 1 reports the overall R2 as 94.1%, which shows that all the three models jointly and
significantly explain the FDI-Human development relationship.
SEM Model Stability Condition
Using the eigenvalue to determine the stability condition of the simultaneous equations, all
the three models produced a stability index of zero (0), indicating that all the eigenvalues
(zero) lie inside the unit circle, confirming the satisfaction of the stability condition for
further discussion. Therefore, our models are stable and robust to test the hypotheses.
Wald Test
The Wald test is a way of testing the significance of explanatory variables in a model. It
assesses the joint significance of the exogenous variables in affecting the endogenous variable
jointly. Generally, a p-value of less than 5% indicates the acceptance of the null hypothesis
that exogenous variables affect the endogenous variable jointly. From Table 2, all the exogenous
variables (FDI, POP, GGFC, ECOREFORM), INTEREST jointly and significantly affect the
endogenous variable in Equation 1 (LEB), Equation 2 (SE) and Equation 3 (GDPPC Growth).
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The IUP Journal of Applied Economics, Vol. 21, No. 3, 2022
Table 2: Wald Test for Equations
Observed Variable
Chi-square
df
p-Value
LEB
383.52
4
0.0000
SE
69.28
4
0.0000
GDPPCG
28.59
4
0.0000
Source: Authors’ Computation from WDI (2021)
Correlation Analysis
From Table 3, it can be seen that there is correlation amongst all the FDI variables. There is
a positive and significant correlation between economic reforms (ECOREFORM) and FDI
and between economic reforms (ECOREFORM) and population (POP) and between economic
reforms (ECOREFORM) and interest (INTEREST). However, there is a negative and
significant correlation between FDI and population (POP).
The results indicate there is no high correlation amongst the exogenous variables. Hence,
there is an absence of the problem of multicollinearity.
Table 3: Correlation Matrix
FDI
FDI
1.0000
POP
–0.3108**
GGFC
–0.1375
ECOREFORM
0.3879***
INTEREST
–0.0355
POP
GGFC
ECOREFORM INTEREST
1.0000
–0.1511
1.0000
0.2473*
–0.2124
0.2182
0.0893
1.0000
0.5964***
1.0000
Note: ***, **, * means correlation is significant at 1%, 5% and 10% levels, respectively.
Source: Authors’ Computation from WDI (2021)
Regression Analysis
Following the determination of the predictive quality of SEM with R2, it is important to
evaluate the unstandardized path coefficients when making a decision on whether there is a
hypothesized relationship in the data. In the three equations of the path analysis (in equations
1 to 3), FDI, POP, GGFC, ECOREFORM and INTEREST were stated as the exogenous
variables, while human development variables (LEB, SE and GDPPCG) as endogenous
variables. The main regression results show that FDI has a positive overall effect on human
development.
The results from Table 4 show that the coefficient of FDI is positive (0.503959) and it is
statistically significant at 1%. This implies that FDI inflows in Ghana are contributing to the
Effect of FDI Inflows on Human Development in Ghana:
Evidence from Structural Equation Modeling Approach
55
improvement of life expectancy at birth in the country. Therefore, a percentage increase in
FDI will cause a 0.50% rise in life expectancy at birth. This result is in line with the studies
of Lehnert et al. (2013), Alam et al. (2016) and Timothy (2018), which found that FDI has
a positive effect on life expectancy at birth. A plausible explanation to this is that FDI through
positive effect on economic growth and infrastructure creations contributes towards the
improvement of the overall life expectancy of the host country.
Table 4: Final Path Analysis for Model 1 (LEB)
Endogenous
Variable
LEB
Exogenous
Variable
Unstandardized
Path Coefficient
Standard Error
p-Value
FDI
0.503959
0.0890179
0.000
POP
–4.378488
0.8115713
0.000
GGFC
–0.133324
0.1023121
0.193
ECOREFORM
6.828864
0.5486398
0.000
CONS
62.85663
2.489267
0.000
Source: Authors’ Computation from WDI (2021)
The results also show that the coefficient of population growth is negative (–4.378488)
and is statistically significant at 1%. This indicates that the higher the population of the
country, the lower the improvement in life expectancy. The coefficient of economic reform
is positive (6.828864) and is statistically significant at 1%. This means that FDI inflow in
Ghana in the period of economic reforms will increase life expectancy at birth by 6.82 years
as compared to the period before the economic reforms.
Table 5 reveals that the coefficient of FDI is positive (2.412588) and statistically significant
at 1%. This indicates that FDI has a positive effect on school enrolment in Ghana. This
further can be explained that when FDI inflows in the country increases by 1%, there would
be an increase in school enrolment by 2.41%. This result is in line with the studies of Azam
et al. (2015) and Gokmenoglu et al. (2018), which find evidence that FDI has an inelastic
and statistically significant positive effect on school enrolment, which is a proxy for education.
A convincing reason for this is that FDI through positive effect on economic growth, job
creations and infrastructural developments contributes towards increasing the total school
enrolment rate of the host country.
Furthermore, the results show that population growth and government spending both
have a negative effect on school enrolment. However, holding other factors constant,
economic reforms will improve school enrolment in the country.
Table 6 shows that all other things being equal, an increase in interest rate by 1% will
result in a decrease in GDP per capita growth by 0.1134656% and this is statistically significant
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The IUP Journal of Applied Economics, Vol. 21, No. 3, 2022
Table 5: Final Path Analysis for Model 2 (SE)
Endogenous
Variable
Exogenous
Variable
Unstandardized
Path Coefficient
Standard Error
p-Value
FDI
2.412588
0.4682053
0.000
POP
–10.8214
4.268602
0.011
–0.7379715
0.5381284
0.170
ECOREFORM
2.582038
2.885668
0.371
CONS
69.74384
13.09274
0.000
GGFC
SE
Source: Authors’ Computation from WDI (2021)
at 10%. On the other hand, an increase in government spending by 1% will lead to an
increase in GDP per capita growth by about 0.62% and this is statistically significant at 5%.
Finally, the results reveal that economic reforms improve GDP per capita
growth.
Table 6: Final Path Analysis for Model 3 (GDPPCG)
Endogenous
Variable
GDPPCG
Exogenous
Variable
Unstandardized
Path Coefficient
Standard Error
p-Value
FDI
0.3408748
0.2084069
0.102
INTEREST
–0.1134656
0.0688605
0.099
GGFC
0.616026
0.2596203
0.018
ECOREFORM
5.925038
1.717233
0.001
CONS
–8.156976
3.007326
0.007
Source: Authors’ Computation from WDI (2021)
Conclusion
In most developing countries like Ghana, low human development is still considered a major
problem. Adequate planning and collective efforts are needed to improve human development.
To do so, countries need adequate investment for job creation, workforce training (in order
to increase productivity and improve human capital), and education and health improvements.
These countries often lack sufficient investment due to low domestic savings; therefore,
there is an urgent need to attract foreign investment. This paper specifically examines how
FDI affects the three indicators of the HDI, namely, life expectancy at birth, school enrolment
and gross domestic per capita income. The findings of the study indicated that FDI has a
positive effect on the three human development indicators which shows the importance of
Effect of FDI Inflows on Human Development in Ghana:
Evidence from Structural Equation Modeling Approach
57
FDI to the host country, that is to say, FDI contributes to educational development, health
improvement and income of the host country. These results are not surprising since previous
empirical studies depicted that FDI’s impact on human development is positive, but can
depend on host country’s characteristics. Several conclusions can be drawn from these
results. FDI has a greater positive effect on improving human development in Ghana, as
measured by health, education and standard of living. This analysis supports the argument
that FDI provides additional funds and resources for host countries’ government and
households to invest in welfare.
Policy Implications: The results of this study provide very useful information for
policymakers in Ghana. The positive effect of FDI on life expectancy at birth, school
enrolment and GDP per capita growth gives an indication that FDI through positive effect
on economic growth and infrastructure creations plays a vital role in the improvement of
human development. This then depicts that, open economic policies with increasing efforts
towards integration of national economies into the global marketplace are a necessary condition
for human development. Thus, FDI should be welcomed into Ghana and for that matter,
other developing countries that aim at attaining greater levels of progress in human development.
The paper recommends that policymakers in Ghana should strategize and enact policies
that will encourage substantial inflows of FDI into the country. Government of Ghana should
harmonize national development policies with FDI policies so as to improve human
development and realize the “Ghana Beyond Aid” vision.
Nonetheless, policymakers need to carefully investigate the issue of efficacy of FDI
from the viewpoint of national economic development priorities and be selective in terms of
its sectoral composition. Social benefits of FDI will be more influential if policymakers can
ensure that greater FDI are channeled into the health and educational sectors. The government
needs to also develop its bargaining power and negotiation skills in relation to its dealings
with multinational corporations to attract a favorable type of FDI.
References
1. Aizenman J, Jinjarak Y and Park D (2013), “Capital Flows and Economic Growth in the
Era of Financial Integration and Crisis, 1990-2010”, Open Economies Review, Vol. 24,
No. 3, pp. 371-396.
2. Alam M S, Raza S A, Shahbaz M and Abbas Q (2016), “Accounting for Contribution of
Trade Openness and Foreign Direct Investment in Life Expectancy: The Long-Run and
Short-Run Analysis in Pakistan”, Social Indicators Research, Vol. 129, No. 3,
pp. 1155-1170.
3. Amoh J K, Abdul Mumuni A and Fosu R A (2019), “Does Foreign Direct Investment
Cause Financial Sector Development – Evidence from an Emerging Economy”, Review
of Economic and Business Studies, Vol. 12, No. 1, pp. 33-55. DOI:10.1515/rebs2019-0081
58
The IUP Journal of Applied Economics, Vol. 21, No. 3, 2022
4. Azam M, Khan S, Zainal Z B et al. (2015), “Foreign Direct Investment and Human
Capital: Evidence from Developing Countries”, Investment Management and Financial
Innovations, Vol. 12, No. 3, pp. 155-162.
5. Byrne B M (2012), Structural Equation Modeling with Mplus: Basic Concepts,
Applications, and Programming, Routledge, New York.
6. Carvalho A, Nepal R and Jamasb T (2016), “Economic Reforms and Human
Development: Evidence from Transition Economies”, Applied Economics, Vol. 48,
No. 14, pp. 1330-1347.
7. Deléchat C, Wagh M S, Ramirez M G et al. (2009), “Sub-Saharan Africa’s Integration
in the Global Financial Markets”, IMF Working Paper, IMF.
8. Ghana Beyond Aid Committee (2019), Ghana Beyond Aid Charter and Strategy
Document, Accra.
9. Gizaw D (2015), “The Impact of Foreign Direct Investment on Economic Growth:
The Case of Ethiopia”, Journal of Poverty, Investment and Development, Vol. 15,
No. 1, pp. 34-48.
10. Gokmenoglu K K, Apinran M O and Taþpýnar N (2018), “Impact of Foreign Direct
Investment on Human Development Index in Nigeria”, Business and Economics Research
Journal, Vol. 9, No. 1, pp. 1-14.
11. Hu L T and Bentler P M (1999), “Cut-off Criteria for Fit Indexes in Covariance Structure
Analysis: Conventional Criteria Versus New Alternatives”, Structural Equation Modeling:
A Multidisciplinary Journal, Vol. 6, No. 1, pp. 1-55.
12. Keith T Z (2013), Multiple Regression and Beyond, Pearson New International Edition
PDF ebook, Pearson Higher Ed.
13. Lehnert K, Benmamoun M and Zhao H (2013), “FDI Inflow and Human Development:
Analysis of FDI’s Impact on Host Countries’ Social Welfare and Infrastructure”,
Thunderbird International Business Review, Vol. 55, No. 3, pp. 285-298.
14. Murshid A P and Mody M A (2011), “Growth from International Capital Flows: The
Role of Volatility Regimes”, IMF Working Paper, IMF.
15. Ogbokor C A (2018), “Foreign Direct Investment and Economic Progress: Application
of a Dynamic Model”, International Journal of Economics and Finance Studies,
Vol. 10, No. 1, pp. 1-15.
16. Oladele A A (2015), “Sectoral Inflow of Foreign Direct Investment and Economic Growth
in Nigeria”, Journal of Economics and Sustainable Development, Vol. 6, No. 17,
pp. 168-172.
17. Omodero C O (2019), “Government General Spending and Human Development: A
Case Study of Nigeria”, Academic Journal of Interdisciplinary Studies, Vol. 8, No. 1,
pp. 51-51.
Effect of FDI Inflows on Human Development in Ghana:
Evidence from Structural Equation Modeling Approach
59
18. Ortega B, Casquero A and Sanjuán J (2016), “Corruption and Convergence in Human
Development: Evidence from 69 Countries During 1990-2012”, Social Indicators
Research, Vol. 127, No. 2, pp. 691-719.
19. Osei R D (2012), “Aid, Growth and Private Capital Flows to Ghana”, WIDER Working
Paper, No. 2012/22.
20. Ranis G, Stewart F and Ramirez A (2000), “Economic Growth and Human Development”,
World Development, Vol. 28, No. 2, pp. 197-219.
21. Razmi M J, Abbasian E and Mohammadi S (2012), “Investigating the Effect of
Government Health Expenditure on HDI in Iran”, Journal of Knowledge Management,
Economics and Information Technology, Vol. 2, No. 5, pp. 1-8.
22. Sen A K (1995), “Demography and Welfare Economics”, Empirica, Vol. 22, No. 1,
pp. 1-21.
23. Simionescu M and Naros M S (2019), “The Role of Foreign Direct Investment in
Human Capital Formation for a Competitive Labour Market”, Management Research
and Practice, Vol. 11, No. 1, pp. 5-14.
24. Timothy P O (2018), “Impact of Economic Globalization on Life Expectancy in Nigeria”,
Health Economics & Outcome Research: Open Access, Vol. 4, No. 2, p. 152.
25. World Bank (2020), World Development Indicators on Online (WDI) Database. Retrieved
on March 12, 2021 from http://www.worldbank.org
Reference # 05J-2022-07-03-01
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