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The link between educational expenditures and student learning outcomes:

Evidence from Cyprus

Eliophotou-Menon, Maria, Stylianou, Andreas, Kyriakides, Leonidas

Abstract

The paper attempts to investigate the relationship between educational expenditures and student

learning outcomes in Cyprus, a Southern European country severely hit by the financial crisis.

Specifically, our research investigates the extent to which changes in the effectiveness status of

schools can be related to changes in educational investment. The population of the study

consisted of approximately 9500 public secondary school graduates who had taken the

Pancyprian Examinations (admissions examinations for Cypriot and Greek Universities). The

study used the results of the examinations for a five-year period (2008 to 2012) in order to

determine whether there is any causal relationship between educational expenditure and student

learning outcomes. The impact of additional variables (student gender, class size etc.) on

student achievement was also investigated. The methodology of this study was based on

quantitative methods, namely, Multilevel Analysis and Discriminant Function Analysis (DFA).

Based on the findings, educational investment had a positive effect on the effectiveness status of

a school if invested in least effective schools and not in other types of schools (Typical or Most

Effective). Investment in specific types of equipment was found to have a significant effect on

student learning outcomes. While gender appeared to have a significant effect on learning

outcomes, this was not the case with class size. The implications of our findings are drawn and

suggestions for further research are presented. We expect that the results of our study will

inform the literature on the link between educational investment and student outcomes and

provide the basis for strategies that can be used to improve school effectiveness. Specifically,

the findings of this study are important for educational policy makers since they provide useful

information and recommendations relevant to educational expenditure policy.

Keywords: Educational Finance; Educational Outcomes; Educational effectiveness

Introduction

The economic crisis brought the narrowness of financial resources and inevitably affected

education expenditure. The probability of impending reductions of such expenditures appears to

be the focus of interest for many parties, such as scientific researchers, community groups,

parents, students, teachers, employers, political parties, the church and others.

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Due to the different views expressed by the above parties for the appropriate level of costs of

education and because of the pluralism of these different positions and arguments expressed, a

great concern is created for the formation of an education policy. Consequently, the surrounding

atmosphere may, very often, affect crucial decisions made in relation to the funding of

education in Cyprus.

Based on Cyprus data, in recent years the total expenditure on education as a percentage of the

Gross National Product (GNP), were quite large, placing Cyprus at the most senior positions in

comparison with the average of European Union countries (EU). According to the European

Statistical Office (Eurostat, 2014), during the year 2010 the proportion of total expenditure in

Cyprus to its GDP amounted to 7.92%, whereas for the EU countries the average rate was

5.44%, yet, without proving that, education provided in Cyprus is qualitatively better than in

other countries, whose total expenditure on education corresponds to a lesser rate in their GDP.

Therefore, an urgent question arises as to whether or not the amount of money provided for

expenditure on education have the desired results and reflect any degree of impact on students’

performance.

Background

Each State seeks prosperity, progress, development and improvement of the quality of life of its

citizens. These efforts also include the provision of educational facilities by the state with a

decisive role in the education system and educational institutions in general, to ensure

effectiveness in the education provided. Achieving the goal of quality education through

effective schools is a major goal of modern societies which justifies the increased interest

around the research in question.

The concept of effectiveness is linked, but often contrasted with the sense of effectiveness. The

use of these terms belongs in the field of Economics and relates to a production process. The

efficiency of such process usually refers to the transformation of inputs into outputs in the best

possible way, to the maximum possible amount, using the lowest in number possible inputs

quantities and with the least cost (Antony & Herzlinger, 1989). Effectiveness means the ability

of someone to produce an expected result, while efficiency is the ability to produce a

satisfactory task to make a profit. (Levine & Lezotte, 1990).

To calculate the magnitude of the effects of schools in relation to their effectiveness, the

research covered factors originally referring to the school level and subsequently to the class

level and the effects those factors had on student performance (Creemers, 1994˙ Scheerens &

Bosker, 1997). In a later stage, using multilevel modeling techniques in investigations of the

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Educational Effectiveness, the research emerged that in many countries the most important level

is that of the class in relation to the school level and the education system in general (e.g.

Kyriakides, Campell & Gagatsis, 2000˙ Yair, 1997).

With the recent development of dynamic models of educational effectiveness of Creemers &

Kyriakides (2006), which belong to the multilevel models, nonlinear relationships between

effectiveness factors and student performance are sought. Therefore, the possibility of

examining relationships in effectiveness factors at the same level is provided.

Our research, attempted a connection with the Human Capital Theory , because it highlights the

importance of changes in the variables of input, not only as material resources, but also as

human capital. The concept of human capital includes all natural and acquired abilities of an

individual, talent, skills, knowledge and competencies. Apart from the natural abilities, all other

skills can be acquired through activities such as education, apprenticeship, training, etc., and

even physical abilities have a prospect of improvement in this way (Becker, 2009˙

Psacharopoulos, 1999). According to this position and as noted by Mark Blaug (1972), people

invest in themselves through education, in order to enjoy monetary or non-monetary benefits in

the future. A second opinion on the Human Capital Theory, is that through education and

training individuals acquire abilities and skills, a kind of added human capital, that render such

individuals more productive in their workplace. Therefore, our research, examines if under the

Human Capital Theory, inflows as a variable of a multilevel model of effectiveness are

associated with learning outcomes.

Methodology: Research design and data collection

In regards to the methodology for conducting our research, quantitative research methods were

used. Specifically, data analysis was performed using the statistical program SPSS and MlwiN

for multilevel data analysis. The use of multilevel analysis aimed to identifying the effects of

exercising on the effectiveness variable inputs used, taking into account two levels, the student

and the school. The variables of our research are presented both in detail and based on the

school and student level and can be found below:

At the student level: gender

At the school level:

(a) The percentage of girls

(b) The number of poor students

(c) The province of each school

(d) The educational expenditures have been grouped into the following five categories and

relate to:

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(i) Improvements / Extensions / Maintenance / Construction of buildings /

Classrooms

(ii) Workshops Equipment / Special Exhibits

(iii) Heating / air conditioning

(iv) Technology / Computers

(v) Grants provided by School Boards (Consumables)

The research questions that required an answer were the following:

(a) Do the changes, presented in public expenditures on education, either in kind or in total,

require changes in the effectiveness of schools?

(b) Does the class size (number of students) affect their performance in the pancyprian

examinations?

(c) Does the geographical area in which the school units falls and were students attend;

affect their performance in the pancyprian examinations?

(d) Does the gender of students affect their performance?

(e) Can changes of directors of schools affect student performance?

(f) Which type of Student grants appears to affect more students’ performances?

Two methods were used to investigate possible causal relations between changes of input and

changes in learning outcomes. The first method is based on the simple correlation between

investment and learning outcomes, a method previously used by other researchers in the field of

educational economy. The second method, not yet used in similar studies up until to date, is the

method of Discriminant Function Analysis which tested whether changes in educational inputs

are able to interpret changes in the effectiveness of school units.

Secondary sources and population research

The research data used constitute secondary data analysis as it was formulated, by the

Examinations Office, the Directorate General Education Medium, the Accounts of the Ministry

of Education and Culture and the Statistical Service of Cyprus. The total number of training

units and the population of our research includes 42 high schools from 2008 to 2010 and 44

high schools from 2011 until 2012 (two new high schools).

Two methods were used to investigate possible causal relations between changes of input and

changes in learning outcomes. The first method (first phase) is already used by other researchers

in the field of educational economy. However the second method (second phase), which is the

specific distinction in our investigation in relation with others that have been made so far, is the

method of discriminant analysis, which tested whether changes in educational inputs are

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possible to interpret changes in the effectiveness of schools. Multilevel analysis models will be

used in both stages. These phases are analyzed below.

Phase A: Investigation of the potential impact of each school inputs on learning outcomes

for each individual year from 2008 to 2012 (Grade Classification in the Pancyprian

Examinations)

This research aims to investigate the possible existence of cause and effect relationships

between inputs and outputs of a school unit. Specifically, it is examined whether the possible

changes to the educational expenditures can bring the proportional effect on students' learning

outcomes.

In this phase, an attempt was made to identify the factors each year from 2008 to 2012 and

explore how such factors may affect learning outcomes in two different levels, the student’s and

the school’s level. Then we proceeded to analyze student data for each year separately and

investigated if student’s performance can influenced by the attending school attending and from

the investments made in this school.

Phase B: Investigate whether the changes observed in the effectiveness of schools from

year to year can be explained by changes in their inputs

Phase B was based on Phase A and based on the results of model 1, the following categories for

schools were registered:

i. Typical

ii. Least Effective

iii. Most Effective

Typical schools are defined as the schools which intersect zero under the terms of the

investigation, as explained below. The Least Effective schools identified below zero and Most

Effective are above zero. In particular, to better explain the procedure followed, it should be

noted that each school has its own residual, accompanied by a standard error. This value

generally ranges from -3 to +3. Suppose for example that the first school 1 has unexplained

remainder -0.3 and standard error is 0.2, then the school’s range is from -0.1 to +0.5, so its

range includes zero. Therefore, such school is considered to be a Typical school. Subsequently,

removing 0.2 which is the worst case scenario, we have -0.5 and the best scenario is -0.1.

Therefore, in this case the value zero is not included and so we believe that the school belongs

to the Least Effective list of schools.

The above two elements, the residual and standard error, allow us to see which schools are

statistically significant in terms of their effectiveness and are above zero, which schools are

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below zero or which schools contain zero. In this way, it is possible to classify the schools in the

three above-mentioned categories. Yet, due to the fact that, the classification is performed

separately for each year, is possible to conclude whether there are schools that changed their

effectiveness level from one year to another. Thus, when we compared two years, e.g. 2008 to

2009, it became feasible to rank schools, to those that showed improvement, those who

remained the same (constant) and those with the worst image (decreased). The same tactic was

followed for classification of schools under the above categories, expanding the range to three

years, from 2008 to 2010 or from 2009 until 2011, or even in four years, etc.

Summary of the main findings of the investigation that resulted in the first method

(conducting separate analyzes for each year and related to the possible influence of inputs

to the learning outcomes).

From Tables 1, 2 and 3 shown below we see that the variables appear to have a statistically

significant effect on learning outcomes (p<0.5) relate to gender, the improvements / expansions,

Workshops equipment / special rooms and costs for Technology and Computers. The variables

that appear not to have any influence learning outcomes relate to the age of the students, the

class size and the school’s principals. Finally, the variables related to the province, heating / air

conditioning and consumables show a small statistically vital influence in some years but not in

all.

Summary of the main findings from the second the second method (conducting separate

analyzes for each year concerning whether the changes observed in the effectiveness of

schools from year to year can be explained by changes in their expenditures).

Table 4 shows the distribution of schools according to their effectiveness (Overall ranking

grade) during 2010-2011 and 2011-2012. Based on the data, it can be seen from the column

groups of schools that 32 out of 44 schools remained constant (rate 72.73%). Also, it was

observed that in 5 out of 44 schools, the level of effectiveness had been improved (percentage

11.36%), while 3 schools were Least Effective in 2010-2011 and enlisted in the list of Typical

schools in 2011-2012. In addition, 2 schools which were listed as Typical schools in 2010-2011,

were listed as Most Effective schools in 2011-2012. It should be noted that no such change had

been observed in any school, which in 2010-2011 was among the Least Effective schools, to be

classified between 2011-2012 as Most Effective school. Thus there is no rapid change in the

movements of the schools observed. Thereafter, in 7 of the 44 schools the level of effectiveness

was decreased, i.e. 4 schools were among the Most Effective schools in 2010-2011 and in 2011-

2012 the same schools became Typical schools, while 3 schools already classified as Typical

schools in 2010-2011 they had been classified as Least Effective schools in 2011-2012.

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In Table 5, wherein the formal estimates are shown for each variable included in each function,

the following procedure was followed for our analyzes. Based on the three categories, in which

we had previously classified the schools, we took the first case, i.e. we compared all schools

that showed improvement in their effectiveness level in relation to all other schools, either

retained their constant level or decreased their constant level. In other case we compared all

schools that decrease their level in relation to all other schools. Then, we examined which

variables can explain the changes of schools in their effectiveness level. Column 1 presents the

variables related to changes in connection with the operation of school factors. These factors are

taken into account in function 1, which can distinguish which schools improved their

effectiveness level during the two years from 2010-2011 to 2011-2012. Function 2 helps us to

identify the schools, which their effectiveness level of the biennium 2010-2011 to 2011-2012

may had decreased.

In Table 6, wherein the ranking of change of school effectiveness in each category is shown,

using both functions of Table 5, a person can classify schools into those that had improved their

level of effectiveness, into those schools that their level remained stable and into those schools

that decreased their level (see. first column). This provision may be compared with what

actually happened, following the course of the fifth column, where the distribution of schools is

based on the actual image.

Thus, one can see from Table 6 that 29 out of 44 schools, the prediction was correct.

Specifically, the prediction about two schools to improve their effectiveness level became a

reality and actually improved. Furthermore, the prediction for 25 schools to remain stable in

relation to their effectiveness was also correct. In addition, the prediction for two schools in

relation to the reduction of their level of effectiveness was also accurate.

Suggestions for developing educational policy

Following the discussion of the results of this research and based on the interpretations given,

the following recommendations are addressed to the responsible parties for the formation of an

educational policy and practice in secondary schools in Cyprus:

Any necessary reductions in educational expenditure should not be made proportionally to all

school units, as this can affect schools already listed in the category of the Least Effective

schools, depriving their ability to improve their level of effectiveness.

Also, another suggestion is that the educational expenditures should not be allocated based on

the number of students in a school, a common practice nowadays, but instead, such allocation

should be based on the real needs of a school.

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References

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Έκδοση Κυβερνητικού Τυπογραφείου.

English

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ed.). Oxford: Blackwell.

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Table 1: Parameter estimates for each variable on students’ achievement in the Pancyprian Examinations and standard errors of each estimation,

which appears in the parenthesis.

Year 2008 2009

Factors Model 0 Model 1 Model 2 Model 0 Model 1 Model 2

Fixed part/intercept 7.10(.30) 4.06(.25) 3.12(.20) 8.00(.30) 7.00(.20) 5.21(.20)

Student Level

Context

Sex (boys=0, girls=1) .20(.05) .20(.04) .12 (.04) .13 (.04)

School Level

Context

Percentage of girls .11(.03) .12(.04) .08(.03) .08 (.03)

District of Limassol N.S.S. N.S.S. -.06(.02) -.06(.02)

District of Larnaca -.08(.04) -.08 (.04) N.S.S. N.S.S.

District of Paphos N.S.S. N.S.S. .07(.03) .07(.03)

District of Famagusta .06 (.03) .07 (.03) Μ.Σ.Σ. Μ.Σ.Σ.

Expenditures

Improvements/extensions/maintenance/construction of classrooms N.S.S. .10 (.05)

workshops’ equipment/special exhibits .10 (.04) N.S.S.

Heating/air-conditioning N.S.S. N.S.S.

I.T./computers .11(.03) N.S.S.

Grants provided by school Boards (consumables) N.S.S. N.S.S.

Variance

School 15 12 9 18 16 13

Student 85 71 69 82 70 69

Explained 17 22 14 18

Significance test

X2 712,35 601,21 550,01 810,81 700,01 660,01

Reduction of X2 111,14 51,20 110,80 40,00

Degrees of freedom 4 2 4 1

p-value .001 .001 .001 .001

N.S.S = No statistically significant effect at the .05 level.

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Table 2: Parameter estimates for each variable on students’ achievement in the Pancyprian Examinations and standard errors of each estimation,

which appears in the parenthesis.

Year 2010 2011

Factors Model 0 Model 1 Model 2 Model 0 Model 1 Model 2

Fixed part/intercept 5.10(.30) 3.26(.25) 2.72(.25) 8.20(.38) 7.40(.32) 5.14(.30)

Student Level

Context

Sex (boys=0, girls=1) .11(.05) .10(.04) .12 (.04) .13 (.04)

School Level

Context

Percentage of girls N.S.S. N.S.S. N.S.S. N.S.S.

District of Limassol N.S.S. N.S.S. N.S.S. N.S.S.

District of Larnaca -.08(.04) -.07 (.03) N.S.S. N.S.S.

District of Paphos -.07 (.03) -.07 (.03) .10(.04) .10(.03)

District of Famagusta N.S.S. N.S.S. -.09(.04) -.09(.04)

Expenditures

Improvements/extensions/maintenance/construction of classrooms .11(.03) N.S.S.

workshops’ equipment/special exhibits N.S.S. .14 (.05)

Heating/air-conditioning N.S.S. N.S.S.

I.T./computers N.S.S. N.S.S.

Grants provided by school Boards (consumables) N.S.S. N.S.S.

Variance

School 19 16 14 17 16 14

Student 81 74 72 83 72 71

Explained 10 14 12 15

Significance test

X2 732,35 611,21 580,11 460,81 370,51 330,11

Reduction of X2 121,14 31,1 0 90,30 40,40

Degrees of freedom 3 1 3 1

p-value .001 .001 .001 .001

N.S.S = No statistically significant effect at the .05 level.

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Table 3:

Table 2: Parameter estimates for each variable on students’ achievement in the Pancyprian

Examinations and standard errors of each estimation, which appears in the parenthesis.

Year 2012

Factors Model 0 Model 1 Model 2

Fixed part/intercept 5.10(.30) 3.26(.25) 2.72(.25)

Student Level

Context

Sex (boys=0, girls=1) .11(.05) .10(.04)

School Level

Context

Percentage of girls N.S.S. N.S.S.

District of Limassol N.S.S. N.S.S.

District of Larnaca -.08(.04) -.07 (.03)

District of Paphos -.07 (.03) -.07 (.03)

District of Famagusta N.S.S. N.S.S.

Expenditures

Improvements/extensions/maintenance/construction of classrooms .11(.03)

workshops’ equipment/special exhibits N.S.S.

Heating/air-conditioning N.S.S.

I.T./computers N.S.S.

Grants provided by school Boards (consumables) N.S.S.

Variance

School 19 16 14

Student 81 74 72

Explained 10 14

Significance test

X2 732,35 611,21 580,11

Reduction of X2 121,14 31,1 0

Degrees of freedom 3 1

p-value .001 .001

N.S.S = No statistically significant effect at the .05 level.

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Table 4: The distribution of the school units according to their effectiveness status during the

school year 2010-2011 and during the school year 2011-2012.

Groups of schools Number of schools

Α) Stability

Remain Typical 23

Remain Least Effective 5

Remain Most Effective 4

Β) Improvement

From Least Effective to Typical 3

From Least Effective to Most Effective 0

From Typical to Most Effective 2

C) Declining

From Most Effective to Typical 4

From Typical to Least Effective 3

From Most Effective to Least Effective 0

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Table 5: Standard deviations of each variable included in each function

Μεταβλητές που αφορούν στις αλλαγές στη

λειτουργία των σχολικών παραγόντων

Γενικός Βαθμός Κατάταξης

Συνάρτηση

1

Συνάρτηση 2

Ποσοστό απόρων μαθητών .123 .111

Εξοπλισμός Εργαστηρίων/Ειδικών Αιθουσών .091 .088

Τεχνολογία/Πληροφορική .072 .074

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Table 6:

Κατάταξη των αποτελεσμάτων των αλλαγών της σχολικής αποτελεσματικότητας σε κάθε κατηγορία

Ομάδες Σχολείων Προβλεφθείσα Ομάδα κατηγορίας Σύνολο

Βελτίωση Σταθερότητα Μείωση

Βελτίωση 2 (40.0%) 2 (40.0%) 1 (10.0%) 5

Σταθερότητα 4 (12.5%) 25 (78.1%) 3 ( 9.4%) 32

Μείωση 2 (28.6%) 3 (42.9%) 2 (28.6%) 7