COST ANALYSIS AND ITS USE IN SIMULATION OF POLICY OPTIONS: THE PAPUA NEW GUINEA EDUCATION FINANCE...

REVI ART. NO. 95/10 PIPS. NO. 125610

COST ANALYSIS AND ITS USE IN SIMULATION OF POLICYOPTIONS: THE PAPUA NEW GUINEA EDUCATION FINANCEMODEL

THOMAS WEBSTER

Abstract – The article describes the structure and operations of a computer simula-tion programme used in Papua New Guinea that was developed with technical assis-tance from UNESCO. By establishing baseline data on student enrolments, teacherposts and costs of education at different levels, the model can be used to simulatevarious policies under consideration and provides output on the likely effects onstudent flows, teacher requirements and total costs over a ten-year period. The articledescribes the operations of the primary school model and highlights some of the policyoptions that can be simulated. It is country-specific but the purpose is to inform readersof how such a programme is helping planners to improve planning and policy-makingin a developing country. The author, from Papua New Guinea, worked closely withthe UNESCO expert in building the model.

Zusammenfassung – Der Artikel beschreibt die Struktur und Vorgehensweiseeines Computersimulationsprogramms, das in Papua Neu Guinea angewandt wirdund mit technischer Assistenz der UNESCO entwickelt wurde. Das Modell wirdgenutzt, um unterschiedliche politische Maßnahmen zu simulieren, die gegenwärtigdiskutiert werden und stellt Ergebnisse zur Verfügung über die wahrscheinlichenAuswirkungen auf Studenten, Lehreransprüche und Gesamtkosten über eineZehnjahresperiode. Dazu wurde eine

baseline data eingerichtet, über Studentenein-schreibungen, Lehrerposten und Bildungskosten auf verschiedenen Ebenen. Der Artikelbeschreibt die Vorgehensweise eines Grundschulmodells und hebt einige der politi-schen Wahlmöglichkeiten hervor, die simuliert werden können. Dies geschieht zwarländerspezifisch, der Zweck besteht jedoch darin, die Leser zu informieren, wiesolche Computer den Planern bei der Verbessergung ihrer Planung und politischenEntscheidungen in einem Entwicklungsland helfen können. Der Autor aus Papua NeuGuinea arbeitete bei der Schaffung dieses Modells eng mit einem UNESCO-Expertenzusammen.

Résumé – L’article décrit la structure et le fonctionnement d’un programme desimulation électronique utilisé en Papouasie-Nouvelle-Guinée, qui a été conçu avecl’assistance technique de l’UNESCO. En livrant des données de base sur les effec-tifs, les postes d’enseignants et les coûts de l’éducation à différents niveaux, le modèlepeut être appliqué pour simuler l’application de différentes politiques, et fournit desrenseignements pour une période de dix ans sur leurs conséquences probables surles effectifs, les besoins en personnel enseignant et les coûts totaux. L’article décritle fonctionnement du modèle pour l’école primaire et souligne certaines optionspolitiques pouvant être simulées. Cette étude est spécifique au pays mais le butest d’informer les lecteurs de l’assistance que peuvent apporter ces programmes auxplanificateurs pour améliorer les plannings et l’élaboration de politiques dans unpays en voie de développement. L’auteur est ressortissant de Papouasie-Nouvelle-Guinée et a collaboré étroitement avec l’expert de l’UNESCO dans la réalisation dece modèle.

International Review of Education – Internationale Zeitschrift für Erziehungswissenschaft –Revue Internationale de l’Education 43(1): 5–23, 1997. 1997 Kluwer Academic Publishers. Printed in the Netherlands.

Resumen – El artículo describe la estructura y las operaciones de un programa desimulación por computadora usado en Papua Nueva Guinea, que ha sido desarrolladocon la asistencia técnica de la UNESCO. Estableciendo datos básicos de matriculaciónde alumnos, de puestos de maestros y de costos de la educación en diferentes niveles,el modelo puede utilizarse para simular diferentes politicas a considerar y provee losresultados de los efectos probables que se producirán sobre el flujo de alumnos, lademanda de maestros y los costos totales para un período de diez años. El artículodescribe las operaciones sobre el modelo de la escuela primaria, destacando algunasde la opciones políticas que pueden ser simuladas. Si bien está destinado específica-mente a ese país, el propósito del artículo reside en informar a los lectores sobrecómo estos programas ayudan a los planificadores a mejorar su planificación ysus políticas en un país en vías de desarrollo. El autor, de Papua Nueva Guinea, hatrabajado en estrecha cooperación con el experto de la UNESCO en la elaboración deeste modelo.

Education consumes a large share of any country’s budget. In the face ofdwindling resources and increasing demands for education in the developingcountries, the discourse since the 1970’s has looked at ways of improving effi-ciency. Strategies proposed to improve the internal efficiency of educationsystems have included reducing dropout, improving the quality of education,restructuring education systems, increasing pupil-teacher ratios, and adoptinginstructional technologies such as shift teaching, multigrade teaching, radio,television and correspondence. Proposals to shift the burden of cost haveincluded increasing private contributions to education, corporate sponsorshipor private school places, and redistributing expenditures within the educationsystem (Bray and Lillis 1988; Coombs and Hallak 1987; Heyneman 1990;UNESCO 1992; Colclough and Lewin 1993).

The feasibility of such policy options in different contexts can be gaugedthrough simulation modelling. Planners have used various instruments to


6

calculate the projected costs and the outcomes of various options for decision-makers to consider. These have ranged from crude manual calculations tosophisticated computer models that perform multiple calculations simultane-ously. The basic task in most cases involves calculating the unit cost from ananalysis of current expenditures, and from that, future project costs. Computersare being used increasingly by planners to perform such jobs.

This article describes the structure and use of a computer simulation modelin Papua New Guinea (PNG) developed with technical assistance fromUNESCO. It was used to simulate the likely effects of policy options onstudent enrolments, teacher requirements and the recurrent costs of the edu-cational budget over a ten-year period. Through the process of updatingbaseline data for the model, a cost analysis of recurrent expenditure wascarried out, enabling planners to establish how much was being spent by thegovernment for each level of education and the average per-student expendi-ture out of the education budget.

The purpose of the article is to describe the operational aspects of themodel. It is country-specific and descriptive in orientation. The intention isto inform others involved in similar or related work of the method developedby one developing country to carry out cost analysis studies, which are usedin planning, and in particular to assist policy-makers in making decisions.By sharing experiences, critical stances can be adopted in one’s own methods,leading to refinement and improvements. This is particularly important in mostdeveloping countries, where optimal benefits are required from existing levelsof resources.

The article begins with a brief background on the development of the modeland its use in planning in PNG. It then looks briefly at the nature and purposesof educational cost analysis. This is followed by an examination of the costsof education in PNG and what educational expenditures were included in themodel. The paper then describes the general structure of the model, pointingout the linkages between the sub-models. This is followed by a more detaileddiscussion of the functional aspects of the sub-models and of some policyoptions that could be simulated. The paper concludes with a discussion onproblems associated with establishing the baseline data and with simulatingpolicy options.

Background

The model was initially developed by a 1986 UNESCO/World Bank studyteam looking at the financing of education in PNG, and in particular, identi-fying areas for cost saving within the education sector as well as areas fordonor input (World Bank 1987). In noting the absence of accurate data oneducational expenditures and the critical need for the use of such data toimprove planning capacity, the study recommended that assistance beprovided. Dr. Klaus Bahr of UNESCO, who had developed the model for the


7

study, went back to PNG in 1988 for three weeks to refine the model and totrain and develop local expertise. The author of this article worked at that timeas the in-country counterpart.

The Education Finance Model consists of a series of tables on a spread-sheet, with cells linked wherever an input or a change in one policy variableis likely to affect another (others). The basic structure of the model remainsthe same, but adaptations have been made to suit specific requirements ofthe various levels of education. There is a working model for communityschools (primary), vocational schools, high schools, national high schools,technical colleges, teachers’ colleges, specialist colleges and universities. Afinancial summary program was developed to put together the total costs ofeach level of education to show the total education sector budget. The descrip-tions and examples used in this article are from the community school model.

The Education Finance Model had been used extensively by the planningbranch of the Education Department between 1988 and 1991, with briefingsfor executives, politicians and various working committees. The model wasused particularly for the following purposes:

• It was used extensively by an interdepartmental committee established bythe incoming government of 1988 to advise on manpower developmentstrategies and the financial implications of these strategies (Department ofEducation 1988).

• A paper presenting options for expansion of community schools and provin-cial high schools, and the financial implications, was presented to theCouncil of Education Ministers Conference for consideration and decision(Webster 1989). This led to decisions on preferred options that were sub-sequently developed as projects and funded under the government’s devel-opment budget.

• It was used to project enrolments and costs for policy options consideredby a committee, leading to selection of preferred options for the develop-ment of the higher education sector (National Higher Education Plan1990).

• The education sector study of 1991, in which various options for restruc-turing the education system were modelled, was debated and decisions havebeen made. Recommendations from the study are currently being imple-mented under a major reform of the education system (Department ofEducation 1990; Avalos 1992). In 1990, the baseline data for the modelwas updated using 1989 expenditure, enrolment and staffing statistics, bya team led by the author, in preparation for the sector study.

The PNG Education Finance Model has not been used, nor has the baselinedata been updated since 1991. This is attributed to staff mobility, those knowl-edgeable in the construction and use of the model having left the planningbranch before it could be institutionalised. The recent adoption of policieswithout their being subjected to close scrutiny through such a simulation


8

model has led to costly mistakes. For instance, a decision to extend teachertraining from two years to three years in 1993, combined with the effects ofa school expansion program under current reforms being implemented, hasled to a teacher shortage problem. If that policy choice had been simulated,such a shortfall could have been predicted (see Webster 1995: 39 for tablebased on the model predicting shortfall).

The nature and use of educational cost analysis

The field of cost studies

The conceptual framework for educational cost studies developed from theview of education as an economic production model, with set objectives,inputs, a process and outputs (Tsang 1988). An era of educational crisis, whendemands outstripped supply, induced a proliferation of studies in educationalcosts, with the majority of these looking at the external efficiency of educa-tion. Such studies have served to encourage continued investment in educa-tion, because rates of return equal or exceed those of other developmentprojects (Psacharopoulos and Woodhall: 1985). Levels of education showinga higher social rate of return justify continued public funding, whilst levelsof education that show a higher private rate of return, the introduction of “userpay” policies is recommended (Psacharopoulos 1993).

Internal efficiency studies look at the inputs and the process of enhancingoutputs of education systems. These include areas such as the costs of alter-native technologies, and studies to reduce the costs of teachers and tomaximise their input through higher pupil-teacher ratio strategies such as shiftteaching or the use of untrained teacher assistants (for a good review, seeEicher 1984). There exists a vast amount of theoretical literature on whatstrategies developing countries should pursue to be more efficient (e.g.,Colclough and Lewin 1993; World Bank 1995). However, few offer practicaland realistic methodological tools to calculate the costs and benefits of thesestrategies (except, for instance, IIEP’s Fundamentals of Education Planningseries, and Coombs and Hallak 1987).

Planners need to identify the costs and effects on the education system ofsuch interventionist policy measures, and to offer decision-makers a range ofoptions. Decision-makers need to see the trade-off between options, and toselect options that do improve the efficiency of education systems at minimalcost.

Cost analysis and its use in planning

The applications of cost analysis in planning were propounded by Coombsand Hallak (1972) more then twenty years ago:


9

1. costing and testing the economic feasibility of education plans;2. evaluating and improving the allocation of available educational resources

(e.g. by principal levels and types of education;3. weighing the comparative advantages of alternative ways to pursue the

same educational objectives;4. determining both the short and longer-run cost implications of a particular

project;5. estimating the introductory costs and the likely longer-term cost impacts

of a major educational innovation;6. conducting a general search for ways to improve efficiency and produc-

tivity; and7. checking the economic implications and feasibility of specific policy deci-

sions before they are made (Coombs and Hallak 1972: xiv).

In their more recent text on the subject, which is a very useful guide forplanners in cost analysis, they add that cost analysis can be used to compareinternal and external efficiency, to:

. . . uncover serious internal waste and inefficiency and possible ways to eliminatethem . . . suggest ways to enhance the external productivity of education and thebenefits accruing to individuals and society from well directed investments(Coombs and Hallak 1987: 2).

Tsang (1988, 1994) develops a useful classification of these cost analysisstudies and puts them into three main categories that provide an overview ofthe field:

1. Education costing and feasibility testing studiesConcerned mainly with inputs, such as the initial and recurrent costs of educa-tion projects, the feasibility of education plans, and the total cost of an activity.

2. Behavioural studies of educational costConcerned with relationships among inputs, the utilisation of inputs to makesystems more efficient (internal efficiency).

3. Input-output studies in educationThe foregoing two areas deal with input and behavioural characteristics, whilethis third category of studies deals with inputs and outputs. Studies in this areadeal with cost benefit and cost effectiveness (external efficiency).

The strength of the PNG Education Finance Model lies in its capacity to beused in any of the three categories, separately or simultaneously. The modelcan show the total cost of an activity, not only at the point of its introduc-tion, but the recurrent costs over a ten-year period, and the effects on thetotal education sector budget. At the same time, the model can show the rela-tionships between inputs, and in some instances, show the changing natureof these relationships over time. The model can show outputs (student flows)and the cost involved, and can calculate the unit costs. By pooling the infor-mation provided by the models of the various levels, the total education sector


10

budget needs for a ten-year period can be shown for the policy options beingconsidered.

Expenditure costs included in the model

Educational cost studies encompass a much wider field, which includes publicversus private costs, and various classifications (e.g., recurrent and capital,direct and indirect costs: see Psacharapoulos and Woodhall 1985; Coombs andHallak; 1987), areas that are beyond the scope of this article. However,because different countries may apply different methods to cost analysis, anexplanation and discussion of educational costs in PNG, and the range ofcost factors that are built into the model, seem necessary.

Cost as referred to in this model relates only to direct recurrent expendi-ture from the government’s education sector budget. This includes centralgovernment expenditure on salaries and other associated costs, such as leaveand transfer costs of teachers and support staff, operating costs of the centraland provincial administration, and textbooks and other curriculum materialssupplied by the National Department of Education to schools. It excludes allcapital costs and funds raised and spent at the school level.

School Boards raise fund through fees for the purposes of purchasing andsupplying most basic items such as exercise books, pencils and rulers forstudents, and teaching aids required by teachers. They are also responsible forschool furniture such as desks, tables and chairs and for construction of class-rooms and teachers’ houses. School Boards receive an annual per-studentsubsidy from the national government. That subsidy is taken into account inthe model as a cost to the government.

The omission of the school-level financial data does not affect its use as atool to simulate the costs of policy options being considered. The model isused by education planners to help make decisions on the allocation of fundsfrom the education sector budget. All appropriate costs at that level are takeninto account. The basic structure of the model can be adapted for use at sub-regional and institutional levels, taking into account the costs to the budgetsat those levels.

Structure of the PNG education finance model

The model consists of three sub-models: students, teachers and costs. The costsub-model is divided further into three components, teacher-related, student-related and administrative costs. These will be discussed in greater detail whenthe operational aspects of the sub-models are considered.

Each sub-model has three basic parts. The first is the “decision variables”,allowing changes to be made to inputs simulating the effects of a decision.These then use the data for the base year, or what is called the “baseline data”,


11

to provide the “result variables” that emulate the likely outcomes of a givendecision. These linkages and the basic outline of the model’s structure areshown in Figure 1. The first row of boxes contains the decision variables,the second the baseline data, and the third the result variables.

The sub-models and their components are linked, so that changes made tothe decision variables of one sub-model, will result in a sequence of changes.For instance, a decision to increase the grade one intake capacity will resultin an increase in total enrolment, teacher requirements, costs of teachers’salaries, leave fares, school fee subsides and school materials. The total costper student (i.e., unit cost), and its breakdown into the unit cost for teachers,materials, and administration, can be calculated.

Each of the working models takes up more then 500 rows on the spread-sheet, so a summary table constructed at the top of the spreadsheet shows theoverall effects when changes are made to decision variables in the sub-models.The table shows information on likely enrolments, staffing and costs of thescenario being considered. Table 1 is an example, showing the likely effectsof maintaining a constant gross enrolment ratio scenario, whilst holding thestudent dropout and teacher student ratio constant.

The table shows that even to maintain existing levels of services, with about30% of the school age population still out of school, the community schools’share of the education budget would increase at 4% each year. The budgetwould increase from more than 90 million kina in 1990, to over 136 millionkina over the ten-year period.

The sub-models

The next section will outline the dynamics of the sub-models, and discusssome decision variables and assumptions that can be tested.

Student sub-model

The student model has several parts. The first basic data item is the projectedschool age population, ages 7–12, based on growth trends. The likely effectsof a policy to lower the population growth rate can be simulated on the model.

Intake capacity into first grade is another key variable. This is affected bydecisions to open new schools or to expand existing schools.

Whilst the summary table shows the total retention rate, one of the decisionvariables allows for setting inter-grade dropout rates. This allows for adjust-ments to cater for specific policies. For instance, it has been noted that thegrade one to two inter-grade dropout is higher than those of other grades. Ifa specific policy option was being considered to reduce the dropout rate atthat level (e.g., pre-schools for smoother transition from home to school),the inter-grade rate between grades one and two could be reduced, to simulatethe likely effects of such a policy. PNG has automatic progression between


12


13

Fig. 1. Structure of the Education Finance Model.


14

Tab

le 1

.S

umm

ary

tabl

e of

sce

nari

o.

Stu

dent

sT

each

ers

Cos

t

Inta

ke

Tot

alG

ross

Ret

enti

onN

umbe

rIn

c.N

ew

Stu

dent

Tea

cher

Sub

Gro

wth

G

row

th

Per

Enr

ol.

Enr

ol.

Rat

eof

ofT

rsT

each

erS

alar

yS

ecto

rT

each

erof

Sub

Stu

dent

Rat

io

Tea

cher

sT

rsN

eede

dR

atio

Exp

end.

Cos

tS

alar

yS

ecto

rE

xpen

d.(%

)(K

’000

)(%

)(K

INA

)

1990

093,

174

427,

130

76%

61%

13,3

2808

3033

075,

400

090,

495

212

1991

094,

897

437,

021

77%

61%

13,6

3430

708

2232

079,

204

094,

420

0.05

0.04

216

1992

096,

613

448,

366

77%

61%

14,0

1037

609

0332

082,

887

098,

924

0.05

0.05

221

1993

098,

570

458,

711

77%

61%

14,3

4333

308

7532

087,

127

103,

293

0.05

0.04

225

1994

100,

896

467,

637

77%

61%

14,6

1226

908

2532

091,

244

107,

374

0.05

0.04

230

1995

103,

111

472,

608

76%

61%

14,7

6915

706

6332

095,

091

110,

389

0.04

0.03

234

1996

104,

692

483,

219

76%

61%

15,0

1330

408

7532

097,

928

114,

972

0.03

0.04

238

1997

108,

329

494,

606

76%

61%

15,3

4032

709

0932

102,

250

119,

871

0.04

0.04

242

1998

111,

035

506,

782

76%

61%

15,6

9035

009

4632

106,

876

125,

109

0.05

0.04

247

1999

113,

800

519,

612

76%

61%

16,0

6037

009

8032

111,

829

130,

671

0.05

0.04

251

2000

116,

632

532,

917

76%

61%

16,4

4238

210

0732

117,

095

136,

516

0.05

0.04

256

Not

e: 1

990

is a

ctua

l w

hile

the

rem

aind

er a

re p

roje

cted

. T

he P

NG

kin

a (K

) w

as a

bout

equ

al t

o th

e U

S$

in 1

990,

but

bec

ause

of

a de

valu

atio

n an

d ot

her

mon

etar

y po

lici

es i

n 19

95,

is e

quiv

alen

t to

abo

ut U

S$0

.75

in 1

996.

grades and does not have a compulsory education policy. If changes to policywere being considered in these two areas, the effects on enrolments, teacherrequirements and financial requirements could be simulated.

Enrolment in private schools is taken into account. The effects of policiesdesigned to increase or decrease enrolments in private schools, and the effectson the cost of subsides, can be simulated.

With intake and dropout rates set, total enrolment in public and privateschools is then projected. From this and the school-age population data, thegross enrolment ratio is calculated.

The model allows for costing scenarios that involve lengthening the numberof years of schooling. This was particularly useful in the education sectorstudy of 1990, when costs were simulated for an eight-year community schoolcycle (previously six years), and for an additional two years at high school.

Teacher sub-model

Total enrolment from the student sub-model is then linked, to drive the teachersub-model. A student-teacher ratio is set (a decision variable) and from that,the total teacher requirement for future years is projected.

New recruitment needs are calculated, taking into account the number ofnon-teaching positions, the number of teachers likely to take study andfurlough leave (a six-month paid leave taken after 15 years of service), andpossible resignations each year. The resultant teacher recruitment needs canthen be simulated on the Teacher Training College working model.

Possible policy options that can be simulated with the manipulation ofdecision variables within the teacher sub-model include changes to student-teacher ratios, increases or decreases in furlough and study leave periods,pay rates on study leave, changes affecting teacher attrition rates, and thenumber of non-teaching positions in schools.

Cost sub-model

The cost sub-model is the largest segment of the model. It has three sub-components. The first accounts for teacher-related costs; the second forstudent-related costs (materials), and the third deals with administrative costs.

Teacher-related costsTeacher-related costs fall into two categories: salaries and annual leave fareexpenses.

Salaries. The salaries component has two main inputs. First, the model takesaccount of the different classifications and their starting salary levels. In com-munity schools, these range from a class teacher position of Education Officer(E.O.) Class 1 level, to a headmaster at E.O. Class 6 level. The annual salaryincremental points range from more then twenty points within the E.O. 1 scale,


15

to less then five in the top range. The overall total salary bill increases byabout 2% each year, because of the annual increments that must be paid.

Possible areas for testing policy options are the number of promotionallevels, salary levels, the number and structure of salary incremental points,shift teaching and multigrade teaching.

Leave fare costs. Leave fares for teachers and family members working awayfrom their home province are provided every second year. Travel betweenmost provinces in PNG is by air and this is very expensive. Teachers’ leavefares are allocated under provincial budgets and it was difficult to obtainaccurate figures on actual expenditure, so budget estimates were used. Averagestaff leave fare cost were calculated and these were linked to the total numberof staff, to provide the total cost of leave fares. Policy options could beto increase teacher contributions to these costs, or to reduce the costs byrestricting recruitment of staff to neighbouring provinces.

Student-related costsStudent-related expenditures are in the area of fee subsidies, school textbooksand library books.

Fee subsidies. Schools are now responsible for the purchase of basic supplies,such as writing materials for both students and teachers, which were in thepast supplied by the Education Department. The national government providesa per-student grant, known as a fee subsidy to schools, to assist in purchasingsuch items and to meet other operational costs of schools. In the model, it isassumed that all funds allocated under the fee subsidy programme were usedto purchase basic school materials. This assumption will need to be reviewed,considering a recent evaluation study of the scheme. The report shows thatonly 44% of expenditure from the community school fee subsidy funds forthe period 1987–1989 was used for “classroom/office supplies and teaching/learning aids”. More then 50% was spent on building and maintenance ofteachers’ houses and classrooms (Department of Education 1990: 20).

Textbook. School textbooks are supplied to schools by the CurriculumDevelopment Division. These are either printed at the Education Department’sown printing shop, or purchased in bulk from overseas printers. Funds spentby the National Department of Education for these purposes are categorisedas a student-related cost.

Library books. Funds are allocated under a schools library programme toassist schools to purchase library books. If a school places an order for bookswith the national library service, the school pays half the total cost, while theprogramme provides a matching grant. Expenditure by the Department ofEducation on library books under the scheme is considered a student-relatedcost.


16

Administrative costsAdministrative costs are treated as non-variable costs. This means thatincreases in enrolments, staffing, etc., will have no significant impact onadministrative costs, except for the annual increments of staff salaries.

The administrative cost component has two parts: salaries and generaladministrative support costs. Expenditure on travel, telephones and postage,utilities, maintenance and transport, are all lumped together as administrativesupport costs. It was difficult to obtain accurate data from provinces onexpenditure on support services. Therefore budget estimates were taken asexpenditures.

The different models for the various levels of education had separateadministrative cost sub-models, while the total administrative expenditure atnational and provincial levels is on a system-wide basis. The difficulty lay indeciding which administrative costs were related to which model (or level ofeducation).

Expenditures on administrative activities that relate to specific levels ofeducation, such as divisions or branches responsible for teachers’ colleges,technical colleges, etc., were apportioned to the respective working modelfor that level. However, it was difficult to establish clearly what proportionof time and resources was devoted to each level by officers in the centralexecutive, in curriculum development and in policy/planning, and by generaladministrative staff. In a purely arbitrary manner, it was decided to allocatesuch administrative costs on the basis of the proportion of the student popu-lation at the various levels. Provincial administrative costs were apportionedon the basis of enrolments, between the models for community schools, highschools and vocational centres – institutions for which provinces are directlyresponsible.

Cost structure

The costs of the different areas outlined above are then brought together toshow total costs for each category, and then shown as a proportion of totalcommunity schools’ expenditure. This is shown in Table 2, indicating thelikely total costs of the scenario of holding the gross enrolment ratio constant,as presented in Table 1.

Unit cost structure

The above cost structure is then divided by total enrolment, to give a per-student unit cost structure, for each category of expenditure. For example in1990, the annual unit cost was K185 (86%) for teacher’s salaries and relatedcosts, K15 (7%) for school materials, K1 (1%) for provincial administrationand K12 (5.5%) for national administration. These are then added to give aper-student cost of K212. The projected unit cost structure for the scenariosimulated in this example is shown in Table 3.


17


18

Tab

le 2

.T

otal

exp

endi

ture

str

uctu

re,

com

mun

ity

scho

ols.

Yea

rT

each

erS

choo

lN

DO

E A

dmin

.P

DO

E A

dmin

Com

m.

Sch

ools

Exp

end.

Mat

eria

lT

otal

Sup

port

Tot

al

Am

ount

% o

fA

mou

nt%

of

Am

ount

% o

fA

mou

nt

% o

f A

mou

nt%

Inc

.(K

000

)T

otal

(K 0

00)

Tot

al(K

100

)T

otal

(K 0

00)

Tot

al(K

000

)

1990

075,

400

86.7

%6,

233

7.2%

4,74

05.

5%58

40.

7%08

6,95

819

9107

9,20

487

.5%

5,86

06.

5%4,

835

5.3%

596

0.7%

090,

495

4.1%

1992

082,

887

87.8

%5,

993

6.3%

4,93

25.

2%60

80.

6%09

4,42

04.

3%19

9308

7,12

788

.1%

6,14

66.

2%5,

030

5.1%

620

0.6%

098,

924

4.8%

1994

091,

244

88.3

%6,

285

6.1%

5,13

15.

0%63

30.

6%10

3,29

34.

4%19

9509

5,09

188

.6%

6,40

36.

0%5,

234

4.9%

645

0.6%

107,

374

4.0%

1996

097,

928

88.7

%6,

465

5.9%

5,33

84.

8%65

80.

6%11

0,38

92.

8%19

9710

2,25

088

.9%

6,60

65.

7%5,

445

4.7%

671

0.6%

114,

972

4.2%

1998

106,

876

89.4

%6,

916

5.5%

5,66

54.

5%69

80.

6%12

5,10

94.

4%19

9911

1,82

989

.4%

6,91

65.

5%5,

665

4.5%

698

0.6%

125,

109

4.4%

2000

117,

095

89.6

%7,

085

5.4%

5,77

84.

4%71

20.

5%13

0,67

14.

4%

ND

OE

= N

atio

nal

Dep

artm

ent

of E

duca

tion

, P

DO

E =

Pro

vinc

ial

Div

isio

n of

Edu

cati

on


19

Tab

le 3

.C

omm

unit

y sc

hool

s un

it c

osts

and

str

uctu

re.

Yea

rT

each

erS

choo

lN

DO

EP

DO

EU

nit

Cos

tE

xpen

d.M

ater

ials

Adm

in.

Adm

in.

Am

ount

% o

fA

mou

nt%

of

Am

ount

% o

fA

mou

nt%

of

Am

ount

(K)

Uni

t C

ost

(K)

Uni

t C

ost

(K)

Uni

t C

ost

(K)

Uni

t C

ost

1990

185

86.7

%15

7.2%

125.

5%1

0.7%

213

1991

189

87.5

%14

6.5%

125.

3%1

0.7%

216

1992

194

87.8

%14

6.3%

125.

2%1

0.6%

221

1993

198

88.1

%14

6.2%

115.

1%1

0.6%

225

1994

203

88.3

%14

6.1%

115.

0%1

0.6%

230

1995

208

88.6

%14

6.0%

114.

9%1

0.6%

235

1996

212

88.7

%14

5.9%

124.

8%1

0.6%

239

1997

217

88.9

%14

5.7%

124.

7%1

0.6%

244

1998

221

89.2

%14

5.6%

124.

6%1

0.6%

248

1999

226

89.4

%14

5.5%

114.

5%1

0.6%

253

2000

231

89.6

%14

5.4%

114.

4%1

0.5%

258

Not

e: T

he t

otal

uni

t co

st i

s no

t th

e sa

me

as i

n T

able

1. T

his

is t

he e

ffec

t of

rou

ndin

g up

or

dow

n w

ithi

n ea

ch c

olum

n of

thi

s ta

ble,

whi

leth

e su

mm

ary

tabl

e is

lin

ked

to t

he t

otal

exp

endi

ture

str

ucut

re (

Tab

le 2

).

Establishing the baseline data

The accuracy and realism of simulations using the model depend on theaccuracy of the educational statistics used in the baseline data and adjustmentsmade to inputs during simulations. The baseline data needs to be updated everyyear if it is to be used on a regular basis. Enrolment and staffing statisticswere available from school reports sent to provincial education offices andwere forwarded to the planning branch of the Education Department, wherethey were collated and used. The accuracy of these has been questioned(Department of Education 1991), but they were the best available.

Collating financial expenditure data was more problematic as the Depart-ment had not established a systematic way of collating such data. Actualexpenditures vary considerably from budget appropriations, and thereforeexpenditure records were sought and put together from a variety of sources.

At the national level, funds are budgeted for and appropriated under variousbudget headings of the Department of Education. The Department’s accountssections manages a reliable computerised recording system for all financialtransactions, and these were available. However, it was not easily discerniblewhether expenditures were for administrative operational purposes or forschools. For instance, funds for textbook supply are appropriated under thecurriculum division’s operational appropriations. It required cross-checkingactual expenditure records from computerised printouts against budget appro-priations, asking personnel what services were purchased, before it could bedecided whether an item of expenditure was teacher, student or administra-tion-related.

There is a centralised payroll system for all teachers and administrators,with accurate data showing total staff paid by position, institution andprovince. It also shows staff paid, but who are on various types of leave.This was cross-checked with the school staffing statistics collated by theplanning branch to establish that these were not erroneous (very few differ-ences were noted and these could be explained).

At least 92.2% of the total community schools’ expenditure in the com-munity school model is based on actual expenditure in 1989 (see Table 2).Salaries of all teachers (86.7%), and the NDOE administrative operating costs(5.5%), are actual expenditures. Less then 8% (materials and provincial admin-istrative costs) is based on budget appropriations.

Modelling the assumptions

The PNG Education Finance Model is simple and easy to use and to inter-pret, unlike other modelling techniques using complicated econometric mod-elling formulae and algebraic functions. Many decision-makers in educationhave often had little training in economics and could find it intimidating ifthese were used.


20

Simulating options using the model may seem haphazard, as it involvesmaking assumptions about the impact of particular interventions, fiddlingand adjusting the mix of inputs to create a likely scenario. However, suchactivities are regularly carried out in normal planning exercises. The modelis simply being used to perform a multitude of the same tasks within seconds,instead of weeks. Simulating some options, such as the effects of salary awardson the total budget, is more accurate and straightforward. Simulating theeffects of textbook provision on reducing dropout, in comparison with freelunch programmes, would be more difficult. While the model may not providean exact picture of the likely outcomes, at least it provides a view of what islikely to happen, and the relationships between inputs.

To use the model, an understanding of the structure of the model as wellas awareness of the types of data required, where these can be obtained, andwhat effects inputs are likely to have, are essential. It also requires knowl-edge of research findings, training and experience in collation of educationalstatistics, planning techniques and a general understanding of the operationsof the education system. It could also be argued that those involved in mod-elling should have had working experience in schools. A feel for how thepolicy options will work out in the institutional arena needs to be taken intoaccount when an operator is tinkering with the model.

Using small but powerful portable computers, effects on enrolments,staffing and costs of policy option can be calculated and presented using anoverhead projector while discussions are in progress. Policy-makers can bedirectly involved in the process of making adjustments to the model and testingassumptions as they arise. This is a big improvement over the past, whendecisions could be postponed for several weeks, while planners performedcalculations and wrote up briefs on changes suggested.

The model is just a tool. Decisions still need to be made on resource allo-cations, and these are often political in nature. What it aims to do, is to ensurethat policy-makers are better informed in terms of the likely outcomes ofpolicies being considered, in both the short and long term. It also allows theexploration of a number of ways of achieving the same ends, as the most cost-effective option may not always be politically expedient.

Recent trends may have made some of the work of education planners,redundant, particularly the task of informing decision-makers on priority areasfor spending public funds. However, in most developing countries, the internalefficiency of education systems will continue to need fine tuning and improve-ment. Planners will still be required to perform the basic tasks. Hallak andCaillods (1995: xv) make the following conclusions about the role of plannerswhen the application of cost analysis methods feature prominently:

Planning becomes much more strategic and interactive. To help them in their task,strategic planners have to juggle a variety of traditional planning techniques, suchas population and enrolment projections, cost analysis, assessment of future labourmarket requirements, budget preparation, collecting reliable statistics and definingindicators in order to set up an efficiency-based management information system.


21

Conclusion

Increasing demands are being made on the education systems of developingcountries for more education of better quality. Education budgets alreadyconsume a large share of national budgets and additional resource inputs arevery unlikely. The answer lies in making better use of resources already avail-able and gaining maximum benefit from additional resources as they becomeavailable. The use of improved methods of analysing costs in planning, par-ticularly in simulation models such as the PNG model, remains a viable meansof testing and identifying such efficiency measures.

References

Avalos, B. 1990. The Need for Educational Reform and the Role of Teacher Training:The Case of Papua New Guinea. International Journal of Educational Development12(4): 309–318.

Bray, M. and Lillis, K. 1988. Community Financing of Education: Issues and PolicyImplications in Less Developed Countries. Oxford: Pergamon Press.

Colclough, C. and Lewin, K. 1993. Educating all the Children, Strategies for PrimarySchooling in the South. Oxford: Clarendon Press.

Coombs, P. and Hallak, J. 1972. Managing Educational Costs. Oxford: OxfordUniversity Press.

Coombs, P. and Hallak, J. 1987. Cost Analysis in Education: A Tool for Policy andPlanning. Baltimore, MD: The Johns Hopkins University Press.

Department of Education. 1986. 1985 Community School Data Profile, EvaluationUnit. Papua New Guinea: Department of Education.

Department of Education. 1988. Responding to the Challenge: Report of the WorkingGroup on Education and Manpower Development. Waigani, Papua New Guinea.

Department of Education. 1990. School Fee Subsidy Scheme in Papua New Guinea:An Evaluation, 1982–1989. Waigani, Papua New Guinea.

Department of Education. 1991. Education Sector Review: Deliberations and Findings.Waigani, Papua New Guinea.

Eicher, J. 1984. Educational Costing and Financing in Developing Countries. Focuson Sub-Saharan Africa. World Bank Staff Working Papers, No. 655. Washington. DC:World Bank.

Hallak, J. and Caillods, F. 1995. Educational Planning, The International Dimension.Garland Publishing.

Heyneman, S. 1990. Economic Crisis and the Quality of Education. InternationalJournal of Educational Development 10(23): 115–129.

National Higher Education Plan. 1990. Waigani, PNG: Commission for HigherEducation.

Psacharopoulos, G. and Woodhall, M. 1985. Education for Development: An Analysisof Investment Choices. Oxford: Oxford University Press.


22

Psacharopoulos, G. 1993. Returns to Investment in Education: A Global Update. WorldBank Working Papers. Washington. DC: World Bank.

Tsang, M. 1988. Cost Analysis for Educational Policymaking: A Review of CostStudies in Education in Developing Countries. Review of Educational Research 58(2):181–230.

Tsang, M. 1994. Cost Analysis in Education. In: The International Encyclopaedia ofEducation. Second Edition (1121–1127). Oxford: Pergamon Press.

UNESCO. 1992. Education for All: The Requirements. Paris: UNESCO.

Webster, T. 1989. Options for Expansion of Community Schools and SecondaryEducation Sectors. Paper presented to the National Council of Education MinistersConference, Rabaul, 13th–17th March 1989, Papua New Guinea.

Webster, T. 1995. Improving the Initial Teacher Training of Community SchoolTeachers: A ‘Drop in the Ocean’ Policy to Improve Teaching in Schools. Papua NewGuinea Journal of Teacher Education 2(2): 37–41.

World Bank. 1987. Papua New Guinea: The Costs and Financing of Education. ReportNo. 6767-PNG. Washington. DC: World Bank.

World Bank. 1995. Priorities and Strategies for Education. A World Bank Review.Washington. DC: World Bank.


23

COST ANALYSIS AND ITS USE IN SIMULATION OF POLICY OPTIONS: THE PAPUA NEW GUINEA EDUCATION FINANCE...

Documents

Transcript of COST ANALYSIS AND ITS USE IN SIMULATION OF POLICY OPTIONS: THE PAPUA NEW GUINEA EDUCATION FINANCE...