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School Size And Average Cost Of Output Finance Essay

Abstract

The purpose of this study is to investigate the relationship between school size and average cost of output. This relationship is highly relevant to the consideration of economic efficiency in school operation, and this investigation should therefore be of interest to groups such as school managers, parents, and government. Especially since Thai Education market seemingly is on the boom, with government plans to reform the system and structure, as well as the ever-growing role of private sector in providing education, making Thai education market very competitive. Thus, operating efficiently is certainly one of their concerns. This study will investigate relationship between size & cost specifically for Thailand case, using Private school in Chiang Mai as a case study. While earlier studies in other countries may have suggested the existence of scale economies, it has not been identified so for this case study.

Introduction and Background

Human resource is one crucial factor of production which contribute to a country’s growth and development. Education is globally recognized as a foundation structure for creating a prosper society. A developing country such as Thailand also sees the importance of education. There have been many policies and measures implemented to promote access to education as well as improving quality of education provided. In ways of doing so, the government sees the potential of private sector in providing high quality education as a way to help develop the country’s competent human resource, and meanwhile lower the state’s burden by some extent. From national examination records, it has been proven that private schools have created many high-intellectual and skillful students.

Educational business has been in interest of many entrepreneurs, and Thai education market has been booming ever since. Private institutions are the main subject of this study because they are appropriate in a cost-minimizing model. Their operating characteristic is to make production decision at individual institution level, and the competitive environment of private education market gives incentive for efficient production. Thus, the concept of economic efficiency should be in concern of institution’s financier and management team.

The main question we attempt to answer in this paper is: Whether economies of scale exist in Chiang Mai private school operation? Further, we want to investigate whether size of institution has impact on average cost, what relationship can be identified between size of school and average cost. The hypothesis for this problem is that: as expected to see in any consolidation of production, larger scale operation should experience a reduction in average cost, and we should see a negative relationship between cost and size. From this theoretical concept, economies of scale should also exist in this case of Chiang Mai private schools’ operation.

In general, the hypothesis has been confirmed by many previous research papers. For example, Riew (1966) found scale economies in the operation of public high schools and cost minimizing school size of 1675 students. Cohn’s (1968) study Iowa high schools discovered similar results with supporting evidence of scale economies in school operation. Outside the U.S., Kumar’s (1983) study on the Canadian schools concluded that economies of scale existed. In England, Bee and Dolton (1985) also found that average cost declines with increasing school size.

While those researches has confirmed the hypothesis of scale economies in school operation, other studies by Watt (1980), Tholkes (1991), and Monk (1990) are less confident that increase in enrollment would lead to unit cost reduction. They generally suggest that making schools bigger does not produce significant cost savings. Especially in Watt’s work, he found no significant relationship between size and cost.

From these existing studies, we have chosen the research of Riew, Cohn, Watt, and Bee and Dolton to study the methodology of their research in details, which this paper will be reviewing and adapting them for our case of Chiang Mai schools investigation in the following sections.

This paper will be organized into 5 sections as follows: 1) Framework of Thai Education; 2) Existing Studies and Methodologies; 3) The Approach and the Data; 4) Results and Analysis; and 5) Conclusion and Contribution.

Framework of Thai Education

At present, the framework of education in Thailand is based on the 1999 National Education Act [1] , which provides principles and guidelines for the provision and development of Thai education. The National Education Act includes 9 chapters prescribing the objectives and principles, which serve as the fundamental law for the administration and provision of education and training. For the scope of this study, this paper will discuss only on some areas of Thai education background, describing the overall system of Thai education and some information that involve the provision of formal education by the private sector.

Thai National Education System

Thai education system is comprised of 4 levels of education, in details as following:

1) Pre-school Education: is provided for children age 3-5 year old. Its aim is to help encourage development in physicality, emotion, and intellectual of the children before they begin their formal education. The pre-primary education can be provided different ways such as a child development center, pre-school classes, or a formal kindergarten education offered by private and public schools. But since the pre-primary education is optional and not compulsory, most education in this level is provided by the private sector, and they are supervision by the Office of the Private Education Commission, Ministry of Education.

2) Primary Education: is compulsory and is free of charge by the National Education Act, provided for 6-11 year old children. The primary school curriculum focuses on five competency areas, which are, Basic skills (Thai language and mathematics), Character development, Work-oriented experiences, Life experiences, and Special experiences. Private primary schools are supervised by the Private Education Commission, and Public ones are under the control of The Ministries of Education, Interior, and local municipalities.

3) Secondary Education: is provided for children between the ages of 12-17 year old, and is divided into two levels—three years for each period. The lower level’s aim is to emphasize on student’s intellect, basic skills and ethic morality. The upper level accentuate on providing appropriate academic knowledge and vocational skills, which will be beneficial for students to continue their study at a higher level or to enter the working field. Secondary education is provided by both public and private sectors, and Public schools are under supervision of the Department of General Education, Ministry of Education.



4) Higher Education: this level of education is organized in forms of universities, colleges and institutions for specialized studies. It is divided into two levels: associate degree and degree levels.

Educational Administration and Management Structure

Education in Thailand is managed and administered by the government at three levels: Central level, Education service areas, and Education in institution at all levels of education.

1) Central level: the Ministry of Education is responsible mainly on the formulation of education policies and standards, overseeing all level and types of education in term of inspection and evaluation of education provision. The responsibilities of the MOE are divided into 5 main bodies [2] :

The Office of the Permanent Secretary

The Office of the Education Council (OEC)

The Office of the Basic Education Commission (OBEC)

The Office of the Higher Education Commission (OHEC)

The Office of the Vocational Education Commission (OVEC)

2) Educational Service Areas (ESA’s): 185 ESAs were established under the jurisdiction of the Basic Education Commission in order to decentralize educational administration [3] . Each ESA has an Area Committee, which responsible for roughly 200 educational institutions with a population of 300,000-500,000 students. Main duties of each Area committee are such as:

Coordinating, supporting and evaluating the education institution in the service area so as to enable them to provide education in accordance to educational policies and standards as specified by the central level.

Allocating educational service budgets to institutions in the area.

3) Educational institutions: this level of management consists of direct duties regarding each institution’s academic matters, personnel and staffs, financial budgets, and general affairs.

Administration and Management of Education by the Private Sector

Private education institutions can be categorized into two categories based on types of education they provided, namely: General education ranging from kindergartens to primary, secondary schools, and universities; or Vocational education. According to the National Education Act, private institutions can offer any level of education, and are under the supervision of the MOE. They are allowed to develop their own administration system, and have flexibility in implementing the national academic curriculum. However, they are to be evaluated and monitored by the state on the subject of education quality and standards.

As a way to promote Private sector’s role in providing education at all levels and of all types, the government has taken obligations to provide supports for the private education providers in terms of both direct and indirect grants, tax rebates or exemptions, academic supports, and other benefits directly to the institutions across country. Furthermore, relevant policies and laws have also been defined to facilitate private education. For example, the government has formulated a strategic plan for the next 5-10 year period to reform and promote the private education sector.

Resources and Investment for Education

Financial resource invested in Thai education is derived from both private and public sources, details as following;

Contributions from public sources: consist of the central government budget and subsidies allocated to local funding and private expenditure.

Contributions from the private sources: comprise of funding from 1) Private educational institutions; and 2) Non-government source such as donations made by individuals and communities or foreign loans or international funds from the World Bank, for example.

Distribution of Government’s Budgetary Allocations for Private Educational Institutions

As stated in Section 60 of the National Education Act, the government has responsibilities to distribute its budgetary allocations for the operating and capital costs of educational institutions, and general subsidies for per head expenditure, detail as follows:

Operating and Capital Costs of Educational Institutions: To decrease the burden of private institutions in providing basic education, the government absorbs the cost of teachers’ salaries. However unlike public institutions, private institutions have to deal with all the capital cost by themselves.

General subsidies for per head expenditure are granted for those receiving compulsory and basic education in both public and private institution. Even though private institution may receive less subsidies than the public ones whose capital costs are covered by the state, the government allows the private schools to charge “additional fees for improving the quality of education” as appropriate. They are also allowed to collect other expenses to cover various costs in relation to school supplies, facilities, student lunches, milk/snack, laundry and annual checkup from students. However, the rate these additional fees to be collected cannot be above the rate specified by the Office of the Private Education Commission.

As we have now reviewed general structure and background of Thai education system, the next section will begin to review existing researches regarding scale economies, which use different methods and data in investigating cost-size relationship. The various methods will guide us to constructing an appropriate approach for our case study.

Methodology and Existing Studies

The relationship of school size and average cost of output has been investigated by many scholars in the past, with the majority of those existing works being done for the case of American and British schools. In this section, we will review and compare the methodologies used by John Riew (1966), Elchanan Cohn (1968), P.A. Watt (1980), M. Bee and P.J. Dolton (1985) for scale economies analysis. The existing studies; however, have different conclusions about the size-cost relationship, why this occurred could partially be explained by the different models and variables they used.

Cost Function

First and foremost step in analyzing cost relation is to develop a cost function, in other words, defining the dependent variable.

Riew (1966) defines his average cost function as the ‘operating expenditure per pupil in average daily attendance’, which represent the sum of current operating expenditures on administration, teacher’s salaries, other instruction, operation and maintenance.

Cohn (1968)‘s cost function is defined by per pupil cost of teachers' salaries, building values, bonded indebtedness, and also takes into accounted components of quality such as test scores and number of credit units offered.

Watt (1980) opts to use ‘school fees’ as a measurement of the dependent variable; though, the fees are adjusted to allow for capitation grants in his sample of direct grant schools. The cost is termed as ‘average variable cost’ because fixed costs in the form of loan charges are excluded from these adjusted fees.

Bee and Dolton (1985)’s average cost of an institution is measured by the annual fee charged, with no adjustment needed.

Variables & Models

Riew chose Wisconsin high schools as the object of his study. He selected the districts that only have one high school, and eliminated the schools where average teacher salary exceeded $6,500 as a way to narrow differences in standards. Thus, he analyzed on the data of 109 public high schools (grade 9-12). The following Table1 reproduces Riew’s model and results:

Table 1

Riew's estimated cost functions for Wisconsin schools

Y =

10.31

- 0.402X2*

+ .0001X22

+ .107X3*

+ .985X4

- 15.26X5

+ .613X6*

- .102X7

(0.062)

(.000023)

(.013)

(.640)

(11.95)

(.189)

(.109)

Note:

Adjusted R2=.557

; * denotes significance at the .01 level

Variables are defined as follows:

Y =

Operating expenditures per pupil year

X2 =

No. of pupils

X3 =

Average teacher's salary

X4 =

No. of courses offered

X5 =

Average no. of courses taught per teacher

X6 =

Percentage change in enrollment 1957-60

X7 =

Percentage of classrooms built after 1930

Results from Riew’s model show the statistically significant of ‘no. of enrollment’ to the dependent variable. He further concludes that the cost minimizing school size is 1,675 students.

Cohn’s study uses the Iowa State sample of 377 high school districts, his model and results are as showed in the following Table2:

Table 2

Cohn's factors affecting per pupil cost for 377 Iowa high school districts (Cohn's equation II)

C =

262.157

- .1582X1

+ .0000358X2

+ 1.0903X3

+ 1.1093X4

+ 19.968X5

+ 0.0041X6

(0.0263)

(.0000061)

(2.6311)

(0.4914)

(6.2233)

(0.0052)

+ 1.2192X7

+ .0541X8

+ .071X9

- .6219X10

(0.5106)

(0.0059)

(0.0258)

(0.2168)

Note:

Adjusted R2=.359

Variables are defined as follows:

C =

Per pupil cost

X1 =

Average daily attendance (ADA)

X2 =

ADA2

X7 =

Units offered

X3 =

School Quality

X8 =

Building Value

X4 =

College hours

X9 =

Bonded indebtedness

X5 =

Assignment per teacher

X10 =

Class size

X6 =

Teachers' salaries

The empirical results from Cohn indicate that economies of scale do exist for the case of Iowa high schools. However, the coefficient ADA2 is significantly positive, demonstrate the U-shaped average cost curve. That is, diseconomies of scale also occur at some point.

Watt’s paper specifically focuses on the private sector. The British boy’s and girls’ direct grant schools are chosen for the study. The paper also emphasize on the aspect of a local authority financial aid to students, and its impact on average cost. The model and variables are present as follows:

Table 3

Watt's estimated regression function for 74 UK girls’ direct grant schools (Watt’s equation 1)

AVC =

127

- .0473X1

+ .0000452X2

+ 96.1X3*

+ 745X4*

- .234X5*

(0.89)

(.99)

(2.88)

(2.97)

(3.90)

Note:

Adjusted R2= .613 ; * denotes significance at the 1% level

Variables are defined as follows:

AVC =

Average variable cost

X1 =

Number of pupil

X2 =

Squared of number of pupil (X1)2

X3 =

Proportion of sixth form places

X4 =

Teacher-pupil ratio

X5 =

Percentage local authority financed pupils

Results from Watt’s many models all show the proportion of pupils financed by local authorities to have a strong negative effect on costs. However, none of the scale variables tried reach significance. Watt concludes that for the range of school sizes investigated in his study, the hypothesis finds no support.

Bee and Dolton uses the data of 309 secondary schools in the UK independent sector. It this study, measures of student performance are used as indicators of output quality in additional to teacher-pupil ratio, which is just a measure on input. The model and variables are as presented in Table4.

Table 4

Bee and Dolton’s independent schools cost-functions (Equation 1)

Y =

1736.39

- .373X1*

+ .0001X2

- 2.394X3*

+ 1.910X4*

- 36.980X5*

- 6.873X6

(0.176)

(.0001)

(0.530)

(0.623)

(4.405)

(28.691)

- 31.331X7

- 1.226X8

(54.447)

(0.798)

Note:

Adjusted R2= .716 ; * denotes significance at the 5% level

Variables are defined as follows:

Y =

School fees per pupil

X1 =

Number of pupil

X2 =

Squared of number of pupil (X1)2

X3 =

Percentage of pupils from state primary schools

X4 =

Number of pupil in the sixth form

X5 =

Pupil-teacher ratio

X6 =

Average number of GCE A level passes per pupil

X7 =

Average number of A grades at GCE A level per pupil

X8 =

Percentage of GCE A level leavers going on to degree study

The results suggest a significant negative relationship between school size and average cost. The authors also emphasize that quality is independent of size, minimum cost sized schools are not necessarily the highest quality producer. The lack of significance in the output quality variables (X6, X7, X8) indicates that costs are not related to the examination performances.

Now that the methodologies of the existing studies have been reviewed, there are several forms to be considered in helping us build appropriate models for testing scale economies of Chiang Mai case. The next section of the paper will give details of the approach to finding scale-cost relationship for the case of private schools in Chiang Mai.

The Approach and the Data

Chiang Mai private schools are chosen as the subject of this study, one reason is because of the accessibility to the information source. Chiang Mai Educational Service Area 1 (CMESA1) is able to provide us some information of the private schools in the area for the purpose of this research. CMESA 1 supervises more than 150 educational institutions in four Aumper of Chiang Mai province, namely aumper Muang, Sankumpang, Doi Saket, and Mae Aon. However, this paper intends to study specifically on the private sector, thus there are 59 formal private institutions to choose from. Out of the 59 schools, 8 vocational schools and 3 schools for handicaps are eliminated. The rest 51 schools can be divided into three categories base on number of levels of education each provides:

Kindergartens 27

Primary schools 6

All-level schools 18

1) Kindergartens offer only pre-primary education;

2) Primary schools educate from Prathom1 to 6; and

3) All-level schools those offer from preschool up to Mathayom 6.

These schools need to be categorized due to the different in many criteria of each level of education provided. The most prominent difference is concerning the level of investment. For example, a kindergarten has no need to install a laboratory as primary and secondary schools need to, thus kindergarten’s expenditure per pupil does not include this figure in its calculation. To standardize the data set as much as possible for this analysis, it is mandatory to separate the investigation into each type of schools. Unfortunately for the primary schools, the sample size is too small to make relevant analysis, so we have to leave this out of the study.

Limitations

This study has some limitations regarding the collection of data as for the following reasons:

The specific data for the variable that need in analysis are not available for all schools and in all cases. It is necessary to eliminate some schools because of incomplete information, making the size sample even smaller. As for the kindergarten case, 6 schools provide insufficient data thus they are eliminated from the analysis.

The collectible data are detailed to just a certain extent. The documents from our data source usually contain cost figures for the school as a whole, which is impossible for us to separate the numbers and investigate for each level of education.

It is quite impossible to assure the quality of the data since this is an ex post facto study, and we can only work with the data that is available.

As the ex post facto is the nature of this research, we must opt to use operational definitions of variables developed in the previous existing studies.

Defining variables

First part of the data set is the figures representing the number of student enrolled in each school, number of teachers, number of classrooms, and school’s expenditure on teachers’ salary. These data able us to construct four functional independent variables, which are:

X1 : Number of enrollment

X2 : Square of “number of enrollment”

X3 : Student-teacher ratio

X4 : Average teacher’s salary

Second part of data collection is for the purpose of constructing cost function. From the information provides in the fee declaration report of each school, we are able to utilize the data to develop two dependent variables for the analysis. First dependent variable is the figure representing the sum of per-pupil expenditures on administration, operation (utilities), maintenance (costs related to property maintenance), instructional supplies, teachers’ salaries and IT & Language instruction. [4] Second dependent variable is the average annual fee, computed by averaging the fee of each level of education, pre-primary, primary, lower secondary, and upper secondary. Thus, the two dependent variables to be experimented are:

Y1 : Operating expenditure per pupil

Y2 : Average annual fee

To provide wider range of variables for further relationship analysis, one more independent factor is defined as:

X5 : Expenditure on IT & Language instruction per pupil

In table 5 and table 6, schools are grouped by size, and the average per pupil expenditures and annual fees are related to various size classes. Additional data is also presented for respective size classes concerning schools’ characteristics.

Table 5

Average Operating Expenditure and Annual Fee Across School Sizes (All-level schools)

Number of schools

Number of Enrollment

Operating Expenditure per pupil

Average Annual Fee

Average Teacher's Salary

Student-Teacher Ratio

Expenditure on IT & Languages Instruction

3

<1000

9,166.67

16,814.33

9,500.60

18.33

2,800.00

4

1001-2000

10,120.00

15,738.21

12,004.61

21.74

4,425.00

4

2001-3000

17,307.50

30,803.33

15,115.13

16.92

9,132.92

2

3001-4000

13,347.50

20,828.17

14,225.70

15.90

6,333.33

2

4001-5000

13,255.00

24,885.00

14,884.49

17.00

5,790.00

1

5001-6000

17,800.00

31,690.00

18,139.97

16.25

9,600.00

2

>6000

11,500.00

22,552.00

18,432.43

17.30

7,150.00

Table 6

Average Operating Expenditure and Annual Fee Across School Sizes (Kindergartens)

Number of schools

Number of Enrollment

Operating Expenditure per pupil

Average Annual Fee

Student-Teacher Ratio

Average Teacher's Salary

5

<100

11,664.61

14,935.20

16.55

6,628.81

3

101-150

12,853.31

16,724.00

21.73

8,648.70

4

151-200

13,318.12

23,199.00

22.91

9,465.06

5

201-250

18,977.16

27,724.00

16.88

10,439.74

2

251-300

17,722.88

27,224.00

17.69

11,654.21

2

>300

17,812.87

23,868.00

14.90

11,832.08

Results and Analysis

As there are two sets of data—kindergartens and all-level schools, there are to be two parts of the analysis, within each part presenting both simple and multiple regression results. The regressions will also be estimated for the two forms of cost function, namely Y1: Operating expenditure per pupil and Y2: Average annual fee.

All-Level Schools

Results from both single and multiple regression analysis of the all-level schools (Table 7 and 8) show no significant correlation between unit cost and size in this case. The regression outcomes regarding enrollment—both X1 and X2 in all five models—are rather weak in explaining the effect on costs. Furthermore, the all-level schools’ cost trend in Figure 1 illustrates volatility of expenditure and fee across school sizes with no particular pattern. Thus, it is difficult to draw absolute conclusion on the impact of scale on unit cost in this case.

Table 7: Simple Regressions of individual variable against Y1 and Y2—All-Level schools

Dependent variable:

Y1 (Operating Expenditure per pupil)

Dependent variable:

Y2 (Average Annual Fee)

p-value

Significant

R-squared

p-value

Significant

R-squared

X1

0.1544

0.122531

X1

0.221

0.092048

X2

0.2961

0.067967

X2

0.4289

0.039541

X3

0.0322

**

0.25582

X3

0.3125

0.063654

X4

0.0022

***

0.45453

X4

0.0165

**

0.309579

X5

2.41E-08

***

0.86428

X5

0.0002

***

0.585707

Note: ***denotes significance at the 1% level;

**denotes significance at the 5% levelOn the other hand, student-teacher ratio (X3) and average teacher’s salary (X4) are shown to have significant effect on cost, negative and positive respectively.

Table 8: All-level schools’ estimated regression functions

Dependent variable: Y1 (Oper.Exp.)

Variable

Equation A1

Equation A2

Equation A3

Equation A4

const

7587.625

5475.975

13899.928

***

15069.082

***

(6876.514)

(6365.385)

(3622.049)

(3790.328)

X1

1.939

1.208

(2.251)

(1.178)

X2

-0.0005

*

-0.0003

***

-0.0001

-0.0002

(0.0002)

(0.0001)

(0.0001)

(0.0001)

X3

-920.372

***

-855.312

***

-574.626

***

-620.037

***

(220.560)

(205.312)

(117.391)

(125.245)

X4

2.317

***

2.602

***

0.759

*

0.614

(0.540)

(0.422)

(0.374)

(0.399)

X5

1.606

***

1.578

***

(0.262)

(0.2631)

R2

0.826

0.816

0.952

0.956

Adjusted R2

0.772

0.777

0.938

0.938

Dependent variable: Y2 (Annual Fee)

Variable

Equation A5

Equation A6

Equation A7

const

10955.109

84647.135

612.782

(12513.698)

(80938.014)

(13123.650)

X1

2.517

0.256

-1.937

(3.889)

(1.681)

(1.616)

X2

-0.0004

(0.0005)

X3

-247.580

64.020

-387.509

(413.496)

(425.329)

(404.453)

X4

0.208

0.111

2.448

**

(1.319)

(1.309)

(0.977)

X5

1.639

*

1.127

(0.868)

(1.129)

R2

0.608

0.617

0.423

Adjusted R2

0.445

0.457

0.300

Kindergartens

Interestingly for kindergarten samples, regression results (Table 9 and 10) indicate a very much significant positive relationship of size to average cost, especially for the annual fee dependent form. The bigger number of enrollment, the higher per pupil cost. This outcome suggests an existence of diseconomies of scale rather than a cost-declining model. Nevertheless, quality measures are significant same as in the all-level school samples.

Table 9 : Simple Regressions of individual variable against Y1 and Y2—Kindergartens

Dependent variable:

Y1 (Operating Expenditure per pupil)

Dependent variable:

Y2 (Average Annual Fee)

p-value

Significant

R-squared

p-value

Significant

R-squared

X1

0.0184

**

0.259075

X1

0.0025

***

0.389584

X2

0.0327

**

0.218312

X2

0.0097

***

0.303306

X3

0.0428

**

0.198839

X3

0.0935

*

0.141007

X4

0.0001

***

0.547921

X4

0.0064

***

0.330819

Table 10 : Kindergartens’ estimated regression functions

Dependent variable: Y1 (Oper.Exp.)

Dependent variable: Y2 (Annual Fee)

Variable

Equation k1

Equation k2

Equation k3

Equation k4

const

5368.890

6839.078

**

9763.299

*

10546.095

**

(3337.819)

(3109.423

(4941.231)

(4474.215)

X1

42.228

-6.285

203.242

***

217.596

***

(43.863)

(11.536)

(64.933)

(54.131)

X2

-0.120

-0.421

**

-0.445

***

(0.104)

(0.155)

(0.141)

X3

-419.672

***

-364.436

***

-553.781

***

-560.956

***

(108.753)

(98.371)

(160.995)

(156.206)

X4

1.564

***

1.725

***

0.247

(0.392)

(0.369)

(0.580)

R2

0.769

0.750

0.689

0.685

Adjusted-R2

0.711

0.706

0.611

0.630

Our results from both scenarios do not suggest the existing of economies of scales as found in previous studies at all. While Riew’s unit cost declining as the number of enrollment increase and Cohn found a U-shape cost curve, Chiang Mai cost trend seems to rise with size. The most apparent difference is our smallest institutions are the one who operate with the least cost per pupil. [5] It is very likely that education sectors vary in each country, thus producing dissimilar analysis results. Education system in Thailand is no doubt has different set-up, and institutions are operating according to local scenario, in respond to the local education market.

To better explain the numerical results, we will resource to qualitative method from an interview with a school founder from Chiang Mai and from a research report done by Glyn Owen, Mick Fletcher, Stan Lester [6] . The interviewee gives us informative details regarding characteristics of schools in Chiang Mai, and why there seems to be diseconomies of scale for Chiang Mai case. The research by Owen, Fletcher, Lester will lay emphasis on concepts of institutional behavior.

There are three explanations to help clarify our results of Chiang Mai case:

Why middle to large size schools operate with higher per pupil cost;

Why small schools operate with lower per pupil cost;

Why some larger schools are able to operate at the lower cost than some mid-size schools.

Why middle to large size schools operate with higher per pupil cost:

Quality education is the utmost concern for running private school in a competitive market. Thai private educational market is highly competitive and every school thrives to serve parents with moderate to high income in providing the best education they are willing to afford for their children. To meet the high demand of the parents and to keep competitiveness in the market, these schools have incentive to put large amount of investment on things such as;

Improving/expanding the campus: acquiring new land, building new buildings and classrooms

Providing better facilities and the latest of educational technology and equipment for classrooms, laboratory, library, ICT centers, etc.

Hiring expensive and qualified high-degree teachers, those that have specific specialties in teaching each subjects. [7] 

Owen, Fletcher, and Lester summarize this institutional behavior as the concept of ‘A disincentive to produce large surpluses’. Institutions are similar to normal cooperate firms in a sense that they have incentive to pursue financial stability, to produce modest profits, and to accumulate funds for future projects. However, institutions feel obliged to re-invest those profits or surpluses in improving student experience. To offer a wider choice of subjects, better facilities, better buildings, and more teaching support staff are ways in which larger institution re-invest their profits. This means that large potential surpluses of these schools may not be visible in their accounting reports, thus possibly implying the constant return to scale for larger schools operation.

Why small schools operate with lower per pupil cost:

In contrast to earlier studies in the U.S. and the U.K., Thai small size schools operate with the least cost per pupil. Our interviewee lightens us that these schools has smaller budget to begin with, and purposely target to serve different group of parents than those of the larger schools. In other words, they serve the lower market, providing education for children of lower income parents at the basic educational fees. This means the small schools do not require parents to pay additional for extracurricular subjects or activities related cost while the larger and higher quality schools do. Thai basic educational fee is standardized as declared in the National Education Act. However, the annual fees across schools vary because the added amount each school collects for the different provision of extra curriculum beyond what is required by the national act.

From our observation of Chiang Mai case, it is apparent that the smaller schools collect lower fees and have lesser expenditures on instructional operations, especially on IT & Languages instructions even on the average cost basis. Our interviewee reasoned that these small and low-cost schools also face with much less demand for quality education from parents. Their targeted parents are the group that willing to buy education just for their children meet the national standard. This type of school is almost a substitution to small public schools wholly governed and financed by the state or local authority. Public schools, which provide entirely free education, are decent alternative to the small low-cost private schools. To put it simply, this type of schools serves the middle market between the high-cost/high-quality private schools and the totally free public schools. If they raise the fee by a little bit, parents can opt to choose free public education instead. But if parents are willing to pay for quality, they will choose larger schools those equipped high technology facilities. [8] 

Thus, as a way for the small private schools to keep the students from going elsewhere, they maintain the low fees. Therefore they operate with that little funds they collect, and are not obligated to invest in improving quality to be in the same level of large high-cost schools.

Why some larger schools are able to operate at the lower cost than some mid-size schools:

Even though the results from our regression models do not suggest an existence of scale economies or a significant negative relationship between average cost and size, we can see some of the larger schools do have lower per pupil cost compare to some middle size schools. [9] 

Owen, Fletcher and Lester’s research raise the concept of Systemic scale economies, saying that ‘it must be more economical to teach a larger class that usually comes in a large size school’. Obviously, costs—for all operation, instructions, and expansion—are divided by larger number of students, lowering per pupil cost. As seen in our regression result, student-teacher ratio is negatively related to average cost. The bigger the class size, the lower per pupil cost. Also observable in table 5 and 6, schools with higher expenditure tend to have lower student-teacher ratio, and vice versa.

However, Owen, Fletcher and Lester further note that a large school might not be able to benefit from economies of scale when students are spread in small numbers across a diverse set of different programs. This could possibly explain some of our sampled schools that has lower student-teacher ratio but still operate at high cost. [10] 

Our school-founder interviewee gives explanation specifically for the several large schools in Chiang Mai case. Among the school samples chosen for this study, several schools—especially the larger ones—happen to be established for more than 50 years at the least. These well-known and long-established schools may have lower per pupil expenditure due to their maturity in building and facilities investment. They most definitely differ from new schools those that still the phase of expanding campus and building more classrooms. While the old schools are now mature and using its aged buildings, new schools have to incur high cost in providing the needed facilities to catch up with those old ones. Apart from scale economies explanation, that is one reason why some large schools have lower expenditure compare to some smaller ones.

Conclusion

From the scope of our observation, we found that Chiang Mai institutions tend to be operating with a constant return to scale rather than an increasing return to scale. Each school not exactly benefit from the increase in size that would lower any per unit cost. In term of cost management, we can see that even small scale of operation can operate efficiently. All in all, Chiang Mai institutions seems

From both quantitative and qualitative analysis, we can therefore conclude that 1) a significant negative relationship of size-cost is not found for the scope of this study; 2) Economies of scale does not truly exist in case of Chiang Mai schools’ operation; and 3) Size alone is unable to explain the variation in cost. We have to take into account the characteristic of each institution size to better understand the scenario.

Due to limited time and resources, this study may not have been executed to achieve the most accurate results. Further improvement could very much be done on this research by

using a larger sample size of other education area, increase number of schools to be investigated to assure unbiased results;

analyzing cost-size relationship for each level of education separately, to better standardize the difference in investment criteria;

investigate public sector as well as private sector to get the absolute conclusion for the whole Thailand education market.

These developments would be a companionship of this study, and could further prove or contradict our findings. That could raise a good argument for much more interesting discoveries.

Finally, I hope this study could at least be beneficial to those who involved in the Thai education market, giving an economic analysis view on some aspect of educational service. And it should be of interest to groups such as school managers, parents and especially the government, to have an outlook for Thai education market and further educational policy development.

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