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Full text of "ERIC EJ1161385: Economics of Quality Education and Paths Leading into and out of Quality Education: Evidence from Debre Markos University, Ethiopia"

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ac ademicjournals 


Vol. 12(22), pp. 1086-1090, 23 November, 2017 

DOI: 10.5897/ERR201 7.3335 

Article Number: 602436C66655 

ISSN 1990-3839 

Educational Research and Reviews 

Copyright ©2017 

Author(s) retain the copyright of this article 
http://www.academicjournals.org/ERR 



Full Length Research Paper 

Economics of quality education and paths leading into 
and out of quality education: Evidence from Debre 

Markos University, Ethiopia 

Tsegaye Molla 

Department of Agricultural Economics, College of Agriculture and Natural Resources, Debre Markos University, Debre 

Markos, Ethiopia. 

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. 


INTRODUCTION 

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: tsegayem4@gmail.com. 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 










Molla 


1087 


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. 


METHODOLOGY 

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 
sampling technique. 

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 
formulated as: 

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 

Factor analysis 

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 



1088 


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 

_Sig._ 0.000 

Source: Survey, 2016. 


Table 2. Total variance explained. 


Component 


Initial Eigenvalues 

Rotation sums of squared loadings 

Total 

Variance (%) 

Cumulative (%) 

Total 

Variance (%) 

Cumulative (%) 

1 

5.534 

26.353 

26.353 

4.024 

19.162 

19.162 

2 

2.190 

10.430 

36.783 

2.309 

10.995 

30.157 

3 

2.090 

9.952 

46.735 

2.088 

9.941 

40.098 

4 

1.730 

8.238 

54.973 

1.890 

9.002 

49.100 

5 

1.563 

7.442 

62.416 

1.784 

8.497 

57.597 

6 

1.336 

6.360 

68.775 

1.756 

8.360 

65.957 

7 

1.098 

5.227 

74.003 

1.690 

8.046 

74.003 

8 

0.905 

4.312 

78.315 




9 

0.783 

3.730 

82.044 




10 

0.659 

3.139 

85.184 




11 

0.658 

3.133 

88.316 




12 

0.439 

2.089 

90.405 




13 

0.432 

2.055 

92.460 




14 

0.348 

1.658 

94.118 




15 

0.289 

1.376 

95.494 




16 

0.244 

1.160 

96.654 




17 

0.192 

0.917 

97.570 




18 

0.180 

0.856 

98.427 




19 

0.154 

0.735 

99.162 




20 

0.130 

0.618 

99.780 




21 

0.046 

0.220 

100.000 





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. 


Conclusion 

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, 



Molla 


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Table 3. Factor loading. 


Factor 

Item 

Loading 


Employment inadequacy 

0.927 

Labor market problem 

Employment not considering CGPA 

0.886 


Employability after graduation 

0.816 

Entrepreneurial motive 

Entrepreneurial intent after graduation 

0.767 

Learning facilities 

Lack of adequate laboratory 

Lack of adequate ICT 

0.892 

0.846 


Lack reference materials 

0.820 


Student guidance from teacher 

0.789 

School environment 

Friend/peer relationship 

0.562 


Learning preference 

0.535 


High school background 

0.867 


Learning motive 

0.724 

Student personality 

Learning-attitude 

Communication skill 

0.657 

0.615 


Student expectations 

0.586 


Academic preference 

0.561 


Lack of practicum 

0.867 

Curriculum 

Group learning 

0.856 


Internship 

0.527 

Family background 

Low income family 

No family 

-0.649 

-0.622 


Table 4. Estimation result of path regression model. 

Variable 

Academic performance without 
interaction effect 

Academic performance with 
interaction effect 

Labor market problem 

-1.355***(0.587) 

-1.403***(0.610) 

Entrepreneurial motive 

1.511(1.884) 

1.547(1.902) 

Learning facility 

0.864**(0.455) 

0.817**(0.467) 

School environment 

0.293(0.471) 

0.270(0.422) 

Student personality 

0.190(0.170) 

0.168(0.129) 

Curriculum 

0.117(0.152) 

0.140(0.163) 

Family background 

-0.138(0.218) 

-0.106(0.189) 

Learning facility and curriculum 

- 

1.153***(0.271) 

Constant 

0.547(0.312) 

0.487(0.297) 

Observations 

150 

150 

R-squared 

0.488 

0.572 


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. 



1090 


Educ. Res. Rev. 


POLICY RECOMMENDATIONS 

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 
students. 

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. 


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CONFLICT OF INTERESTS 


The author has not declared any conflict of interests.