Vol. 12(22), pp. 1086-1090, 23 November, 2017
DOI: 10.5897/ERR201 7.3335
Article Number: 602436C66655
Educational Research and Reviews
Author(s) retain the copyright of this article
Full Length Research Paper
Economics of quality education and paths leading into
and out of quality education: Evidence from Debre
Markos University, Ethiopia
Department of Agricultural Economics, College of Agriculture and Natural Resources, Debre Markos University, Debre
Received 24 July, 2017; Accepted 26 October, 2017
The difference in economic development among nations entirely emanates from difference in human
capital development as it is the priority pathway out of poverty, diverse socio-economic and
environmental crises. Although, huge investment in human capital development has long been made,
mere investment will never lead to quality labor force unless paths for quality education are well
substantiated. This study identifies viable paths to quality education using cross-sectional survey
design by making data acquisition from 150 students selected using multistage sampling. Factor
analysis and path analysis were employed to identify principal components explaining most of the
variation in academic performance and to identify statistically significant paths leading into and out of
quality education, respectively. Accordingly, labor market demand (unemployment), student’s learning-
attitude, communication skill, curriculum teaching method and learning facility are statistically
significant factors, together explaining 74% of the variation in academic performance of students. Path
analysis result indicated that the availability of learning facilities and macroeconomic situations
(perceived unemployment and perceived employment-by-chance) is statistically significant. Thus,
paradigm shifts in both internal (students and institutions) and external forces are needed. Specifically,
ensuring cumulative grade point average (CGPA)-based employment as compared to chance-based
employment followed by fulfillment of learning facilities will equip students for better academic results.
Besides, the interaction of curriculum revision and learning facilities, and assisting students from low-
income family are necessary policy synergy interventions to realize the quest of “quality education-
quality labor force for economic development” if implemented with greater inter-sector integration from
micro to macro levels.
Key words: Quality education, paths to quality education, policy synergy.
Improving educational quality requires a focus on Investment in human capital development should be the
institutions and efficient education spending (WB, 2007). primary focus for every nation that aspires to achieve
E-mail: firstname.lastname@example.org. Tel: +251-921284526.
Authors agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License
economic growth. It is a proven fact that the difference in
economic development among nations stems from the
difference in human capital development. This is because
human capital investment is the path out of diverse socio¬
economic and political progress of nations.
Human development is still a challenge in Ethiopia.
Human Development Index (HDI) value of the country
stands at 0.442 (HDI, 2015). Since the intervention of
Millennium Development Goals, Ethiopia is classified as
a low human capital development country despite
significant improvements in educational programs.
Ethiopia’s vision, during the period of GTP II, in its quest
to become a middle-income country (UNDP, 2014), is to
build an education system which assures quality and
equity in education by 2019/20 with the aim of producing
competent human resource for the country.
Development of tertiary education is identified by the
education sector as major priority in the country to ensure
the relevance and quality of education at all levels
besides general education and TVET (FDRE, 2015).
Acting dynamically through education policy reform is
imperative towards achieving sustainable human capital
development in Ethiopia. Hence, this study aimed to
identify feasible paths for higher education institutions to
attain quality education.
Sample size and sampling technique
This study was done in Debre Markos University, Ethiopia. Debre
Markos University is one of higher education institutions established
in 2007. Multi-stage sampling procedure was used to select sample
of undergraduate students. The first stage involves purposive
selection of students of Agriculture College followed by stratification
of the sample into five departments for sample representativeness.
Finally, after identifying the sampling frame containing the complete
list of all students per stratum (department), 150 sample students
were randomly selected using probability proportional to size
Methods of data analysis
Cross-sectional survey design was used to collect data from sample
undergraduate students. Data collection was done by administering
questionnaire comprised of items pertaining to the study objective.
Before the actual data collection, the questionnaire was
restructured by conducting pilot survey with few undergraduate
students to obtain reliable data.
To achieve the objective of the study, factor analysis was
employed to analyze primary data collected from sample students.
This is because it is popularly used by many researchers
(Kyoshaba, 2009; Ibrahim et al., 2009; Irfan and Shabana, 2012;
Georgis et al., 2012; Samuel and Kibrom, 2015) to reduce many
variables to smaller principal factors and to pinpoint which of the
factors have the most impact (DiStefano et al., 2009; Williams et al.,
2010; An and Sean, 2013) for variation in academic performance of
students thereby easing policy interventions for urgent remedial
action. OLS regression model was fitted by regressing perceived
student’s academic performance (dependent variable) upon Likert-
scale score results for identification of theoretically valid and
statistically significant variables (factors) determining student’s
academic performance. Regarding measurement of academic
performance, some researchers have used five-point Likert scale
(Georgis et al., 2012; Irfan and Shabana, 2012), while others
preferred to use GPA (James, 2005; Jessica, 2006; Victor, 2011) as
a valid measure of student's academic achievement given that the
assessment and grading procedures used by teachers is accurate
(James, 2005). However, the appropriateness of cumulative grade
point average (CGPA) is conditional upon academic results limited
to specific subjects/courses, particular semester, year and single
test scores. Despite that, using CGPA has the problem of
convergence; hence, not indicative of differential academic
performance by students in every course and semester.
For the purpose of this study, academic performance of the
student was measured using a five-point Likert-scale (proxy for
quality education ranging from strongly agree to strongly disagree)
as a valid measure for capturing the variability in their academic
performance. Various factors drawn from literature and researcher’s
personal experience were considered by factor analysis for
extraction of principal factors. Factor analysis used in this study is
7 =7 F +p
^ pxl pxm 1 mxl ' e /?xl
Where, Z = pxl vector of variables; A = pxm matrix of factor
loadings; F = mxl vector of factors and e = pxl vector of error or
residual factors (Sharma, 1996).
Perceived score values of selected factors were used for path
regression analysis for predicting student academic performance
and validating statistically significant paths to attain quality
education. Path regression equation fitted to identify feasible paths
leading to better academic performance of students is given below:
AP = u ~\~ b^X^ -\-b^X2 + b^X^ + b ^ X ^
Where, AP = perceived academic performance; a = regression
constant (the value of intercept); bi, b 2 and b 3 are regression
coefficients and e is the error term.
RESULTS AND DISCUSSION
Pathways leading into and out of quality education
Dynamic studies on quality education deterrents are
required to take proactive and reactive measures for
delivery of quality education among higher education
institutions in Ethiopia. Factor analysis was done to
extract principal external and internal factors determining
quality education. The KMO value was 0.656 for all items
included for analysis and the corresponding test statistic
value for sphericity was found significant, indicating
appropriateness of the data for factor analysis as shown
in Table 1.
To account for factors that influence students’
academic performance, the factor components with Eigen
value greater than 1 were considered and 7 factors were
extracted (Table 2). Accordingly, these seven factors
explain 74% of variations in academic performance of
Educ. Res. Rev.
Table 1. KMO and Bartlett's test.
Kaiser-Meyer-Olkin measure of sampling adequacy. 0.656
Approx. Chi-square 616.626
Bartlett's test of sphericity df 210
Source: Survey, 2016.
Table 2. Total variance explained.
Rotation sums of squared loadings
Source: Survey, 2016.
students. Specific to components, labor market problem
is the external component explaining the largest variation
(more than 19%) in academic performance (Table 3).
Path regression analysis
To identify statistically significant components, path
regression analysis was done (Table 4). Path regression
result of academic performance predictors indicated that
the labor market (demand for job) negatively and
significantly determine students’ motive towards better
academic performance. This is because whenever
employment opportunities are scanty out there, their
hope for future employment will be dwindled which in turn
erode their academic motive. Learning facility is also a
statistically significant variable which has positive
influence on academic performance of students signifying
adequate provision of required facilities (like ICT,
laboratory technology and reference materials) through
prioritization. Besides, the interaction of fulfilling learning
facilities and curriculum reform will significantly improve
students' academic results than either alone strategy
signifying the need for policy synergy.
Labor market situations (adequacy of labor market
demand) and employability are external factors that
largely jeopardize student’s motive for better academic
performance followed by adequate learning facilities.
Even internal forces have conditional effect on quality
education as they are driven by external forces altogether,
Table 3. Factor loading.
Labor market problem
Employment not considering CGPA
Employability after graduation
Entrepreneurial intent after graduation
Lack of adequate laboratory
Lack of adequate ICT
Lack reference materials
Student guidance from teacher
High school background
Lack of practicum
Low income family
Table 4. Estimation result of path regression model.
Academic performance without
Academic performance with
Labor market problem
Learning facility and curriculum
Standard errors in parentheses. *** p<0.01, ** p<0.05 and *p<0.1.
to guarantee better academic performance of students. labor pertinent to sustain economic development of the
The interplay result will ultimately impact supply of quality country.
Educ. Res. Rev.
For production of quality labor from huge education
investment, identified paths leading into and out of quality
education should be relieved with more focus on external
and internal forces exacerbating learning morale of
The Ministry of Finance and Economic Development
(MoFED) has to promote expansionary fiscal and
monetary macroeconomic policies aimed at increasing
employment opportunities which will absorb more
graduates based on academic merit/performance.
Employer institutions should give value to CGPA-
academic performance to ensure fairness on behalf of
CGPA-based employment than pursuing prevailing
chance-based employment. Whenever vacant jobs are
announced, employers should recruit qualified labor
using a mix of criteria like CGPA, practical exam and
interview. This might encourage student to work harder
for high academic performance as it will be later required
to secure employment. To better solve the internal
problems, Higher Education Institutions/Ministry of
Education should capitalize and set priority to provide
learning facilities required for assuring quality education.
Equipping students with entrepreneurial morale,
integrating group-learning and assisting students from
low-income family background are necessary
interventions to realize supply of quality labor force for
economic development of the country.
Educational institutions have to go through curricular
revision and monitoring to add assessment methods
aligning practice with theory, abandon simultaneous
delivery of block and parallel courses, and relieve student
communication problems being language which pamper
progresses of quality assurance in education.
Policy synergy (hurdle-rule) involving grassroots
participation of education policy (educationalists),
employment policy (business), economic policy
(economists) and other stakeholders is also among
necessary policy interventions in the pursuit of realizing
quality education which will in turn lead to production of
quality labor force needed for economic development.
Therefore, it seems necessary to appropriately review
and solve problems associated with policies lacking
genuine and equitable implementation.
An GY, Sean P (2013). A Beginner’s Guide to Factor Analysis:
Focusing on Exploratory Factor Analysis. Tutorials Quantitative
Methods Psychol. 9(2):79-94.
DiStefano C, Zhu M, Mindrila D (2009). Understanding and Using
Factor Scores: Considerations for the Applied Researcher.
Practical Assess, Res. Evaluation 14 (20):11.
Federal Democratic Republic of Ethiopia (FDRE) (2015). The Second
Growth and Transformation Plan (GTP II) (2015/16-2019/20) (Draft).
National Planning Commission, September, 2015. Addis Ababa,
Georgis D, loannis T, Ferentinos S (2012). Factors That Influence
Students To Do Mathematics. Teach Math. 15(1 ):43-54.
Human Development Report (HDI) (2015). Work for Human
Development. Statistical Annex of Human Development Report,
Ibrahim D, Serpil K, Ozer D (2009). Factors affecting Turkish students’
achievement in mathematics. US-China Educ. Rev. 6(6):47-53.
Irfan M, Shabana NK (2012). Factors Affecting Students’ Academic
Performance. Global J. Manage. Bus. Res. 12(9):17-22.
James DA (2005). Grades as valid measures of academic achievement
of classroom learning. The clearing house. J. Educ. Strategies Issues
Jessica RW (2006). Measuring the Academic Achievement and English
Language Proficiency of Students at the Secondary Level. A
Research Paper Submitted in Partial Fulfillment of the Requirements
for the Degree of Education Specialist With a Major in School
Psychology, The Graduate School of University of Wisconsin-Stout.
Kyoshaba M (2009). Factors Affecting Academic Performance of
Undergraduate Students at Uganda Christian University. Dissertation
Submitted To Graduate School In Partial Fulfillment of The
Requirements For The Award Of The Degree of Master of Arts In
Educational Management of Makerere University.
Samuel E, Kibrom G (2015). Identification and Analysis of Factors that
Affect Student’s learning among University Students. Res.
Humanities Social Sci. 5(13):58-64.
Sharma S (1996). Applied multivariate techniques. John Wiley and sons
inc New York, SPSS (2004) 14.0 statistical package.
United Nations Development Programme (UNDP) (2014). Accelerating
Inclusive Growth for Sustainable Human Development in Ethiopia.
National Human Development Report of 2014, Addis Ababa,
Victor M (2011). An analysis of some factors affecting student academic
performance in an introductory biochemistry course at the University
of the West Indies. Caribbean Teach. Scholar 1(2):79-92.
Williams B, Brown T, Onsman A (2010). Exploratory factor analysis: A
five-step guide for novices. Austr. J. Paramed. 8(3):1-13.
World Bank (WB) (2007). Education Quality and Economic Growth.
CONFLICT OF INTERESTS
The author has not declared any conflict of interests.