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Indicators of Educational Development: Concept and Definitions
Arun
C. Mehta
·
DIAGNOSIS
OF THE EXISTING SITUATION
INTRODUCTION
To
achieve the goal of `Education for All' envisaged in the National Policy on
Education (1986) and its Revised Policy Formulations (1992), proper planning
and effective implementation is required.
Generally planning exercises are initiated at two levels, micro and
macro levels of planning. In micro planning, plans are prepared at sub‑national
level, such as, institution, village, block and district level, whereas macro
plans are developed at the level which is just above the sub‑national
level i.e. state and national level. At
the district level, blocks, villages and educational institutions are units of
micro planning but at the state level, district is an unit of micro planning.
In India, barring a few states, educational planning is carried‑out at
the state level, which do not ensure adequate participation of functionaries
working at grassroots level. Of late,
National Policy on Education (NPE, 1986 & 1992) and Eighth Plan envisaged
disagregated target setting at least at the district level, which is also one
of the major objectives of a number of projects and programmes currently under
implementation in different parts of the country. Such programmes are IDA
assisted District Primary Education Programme (DPEP), ODA assisted Andhra
Pradesh Primary Education Project, UNICEF assisted Bihar Education Project (now
under the ambit of DPEP), World Bank sponsored Basic Education Project in Uttar
Pradesh (also under DPEP) and SIDA assisted Shiksha Karmi and Lok Jumbish
projects in Rajasthan among of which the scope and coverage of DPEP is much
more wider than other programmes of similar nature. The programme was first initiated in the year 1993 in 43
districts of seven states, namely, Assam, Haryana, Madhya Pradesh, Karnataka,
Kerala, Tamil Nadu and Maharashtra. At present, the programme is under
implementation in about 150 districts of fifteen states. Therefore, development of district plan at
the district and lower levels with emphasis on participative planning is of
recent origin. DIAGNOSIS OF THE EXISTING SITUATION
There are different stages of planning but
diagnosis of the educational development is one of the most important stages of
planning. It is taking stock of the present situation with particular reference
to different objectives and goal of `Education For All' in general and
`Universalisation of Elementary Education' in particular. One of the other main objectives of the
diagnosis is to understand district and its sub‑units with reference to
educational development that has taken place in the recent past. Generally, cross‑sectional data for
analysing existing situation and time‑series information for capturing
trend is required, period of which depends upon the nature of a variable, which
is to be extrapolated. The next
important question is the level at which information need to be collected which
depends upon the basic unit of planning.
Universal access to educational
facilities is one of the important components of `Educational for All’; hence a
variety of information relating to population of a village & habitation is
required, so that school mapping exercises are undertaken. Exercises based on
school mapping play an important role to decide location of a new school or
whether an existing school is to be upgraded or closed down. Thus, number of
villages distributed according to different population slabs is required so
that opening of school in a habitation is linked to existing norms. In case of hilly and desert areas, the
population norm of 300 is generally relaxed and lowered down. Habitations served by schooling facilities
and whether they are available within habitation or a walking distance of one
and three kilometers along with the total number of habitations in a district
is also required, so as to assess the existing situation with reference to
universal access. Similarly, percentage of rural population served by schooling
facilities can also be used as an indicator of access, which should be linked
to school mapping exercises. Similarly, information relating to adult learning
and non‑formal education centers is required which should be analysed in
relation to number of illiterates, out‑of‑school children and child
workers. Once the indicator of access is analysed, the next
variable on which information is required is number of institutions. Within the
institutions, the first important variable is availability of infrastructure in
a school and its utilisation.
Information relating to buildings, playgrounds and other ancillary facilities,
such as, drinking water, electricity and toilets need to be collected and
analysed. In other words, complete information relating to scheme of Operation
Blackboard (MHRD, 1987) with reference to its implementation; adequacy, timely
supply and utilisation need to be collected.
Similarly, information relating to number of classrooms and their
utilisation, class‑size, number of schools distributed according to class‑sizes
and number of sections is also required which can be used in institutional planning
related exercises. Enrolment is the next important variable on which
detailed information is required. Both aggregate and grade‑wise enrolment
together with number of repeaters over a period of time needs to be collected
separately for boys & girls, Scheduled Caste and Scheduled Tribe
population, rural & urban areas and for all the blocks and villages of a
district. The enrolment together with corresponding age‑specific
population can be used to compute indicators of coverage, such as, Entry Rate,
Net and Gross Enrolment Ratio, Age‑specific Enrolment Ratio and
indicators of efficiency. Similarly,
detailed information on number of teachers distributed according to age,
qualifications, experience and subjects along with income and expenditure data
is also required for critical analysis, so that optimum utilisation of the
existing resources is ensured. From the basic
information, a variety of indicators can be generated which can be of immense
help to understand a district and its sub‑units with particular reference
to its demographic structure. It is not
only the past and present information that is required but for proper and
reliable educational planning, information on a few variables is also required
in future. If the emphasis of planning exercises is on disaggregated target
setting, then the entire set of statistics would have to be collected both at
micro and macro levels of planning. The
POA (1992) identified poor urban slum communities, family labour, working
children, seasonal labour, construction workers, land‑less agricultural
labour, forest dwellers, resident of remote and isolated hamlets as some of the
target groups. Thus, information on
these groups also need to be collected, if considerable size of a group(s) is
concentrated in a district or its sub‑units. It is not that all the data required
for planning is available but information on a good number of variables may not
be available. Generally, secondary
sources are explored for diagnosis of the existing situation but for the variables,
which are not available, primary data need to be collected. For example, age‑grade
matrix is one such variable that is not readily available at the micro level
but plays an important role in setting‑out-disaggregated targets. Hence,
age‑grade matrix and other variables of similar nature is required for
which sample surveys at the local level needs to be conducted and data
generated. So far as the sources of
data are concerned, Census publications for demographic and publications of
State Education Department may be explored for educational data but the same
may or may not be available in detail as required in the planning
exercises. However, state‑wise
information is available on most of the variables from the publications of the
Department of Education, Ministry of Human Resource Development (MHRD) but
latest publications are not available.
For information relating to infrastructure, access, ancillary facilities
and age‑grade matrix, NCERT publications may be explored but that is
available only at a few points of time. A variety of information relating to both general and
educational scenario need to be collected.
Information, such as on, geography, irrigation, transportation, industry
and administrative structure is required, so as to prepare a general scenario
of the existing infrastructure available in a district and its sub‑units. So far as the educational variables are
concerned, required information can be grouped under information relating to
demography, literacy and education sectors. Under the demographic variables,
total population and its age and sex distribution separately in rural and urban
areas need to be first collected. Apart from the total population, age‑specific
population in different age groups is also required. For programmes relating to
primary and elementary education, population of age‑groups 6‑10, 11‑13
and 6‑13 years and for adult literacy and continuing education
programmes, population of age‑group
15‑35 years is required. Similarly, single‑age population (age `6') is an another important variable
on which information needs to be collected. In addition, information on a few
vital indicators, such as, expectation of life at birth, mortality (death)
rates in different age‑groups, fertility (birth) rate and sex ratio at birth is required so that the
same can be used to project future population. For adult literacy and
continuing education programmes, number of literate and illiterates in
different age groups is required which should be linked to population in
different age groups . As soon as the diagnosis exercise is over, the next stage
of planning needs review of past plans, policies and programmes implemented in
the district with respect to its objectives, strategies and major achievements.
It would be useful, if similar programmes are undertaken in future. (Mehta,
1997a). Generally, these programmes are
related to promotion of education of Scheduled Caste and Scheduled Tribe, Girls
and Total Literacy Campaigns. Reasons of failures and success of a particular
programme need to be thoroughly analysed.
If need be, the existing programme with or without modifications can be
continued which should be followed by setting up of the targets on different
indicators. The variables identified for both general and educational
scenarios cannot be used in its original form to draw inferences. Therefore, once the basic set of information
is collected and complied, the next important step is to analyse data so as to
derive meaningful indicators. The derived indicators are used to analyse
different aspects of educational planning with particular reference to goal of ‘Education for All’ and can also serve
as a decision support tool. Therefore,
in the present article, the concept of an indicator and the methodology on
which it is constructed is demonstrated by taking actual data at the all‑India
level. If information on a particular
variable is not available from the official sources, other agencies, like NCERT
are explored and indicators computed.
The indicators obtained are interpreted and its implications on the goal
of ‘Education For All’ are also
examined. In addition, the concept of
an indicator and different types of statistics, such as, primary and secondary
data, cross‑section and time‑series data and institution and stage‑wise
data has also been discussed in length. More specifically, indicators on the
following aspects of planning is constructed, developed and analysed : (a) Coverage of Educational System (b) Internal
Efficiency of Education System and (c) Quality of
Services and its Utilisation. Indicators on the above aspects can answer a variety of
questions. System's level of
development, accessibility and children taking advantage of educational
facilities are some of the questions, which relate to coverage of an education
system. Thus, simple indicators like,
enrolment ratio, entry rate, out‑of‑school and additional children
need to enrol have been discussed. The next set of questions relates to
internal efficiency of education system.
Information on number of children who enter into the system and complete
an education cycle, those who dropout from the system in between and number of
children who reach to the next higher level can be obtained, if indicators of
efficiency are computed. For this
purpose, methods like, Apparent Cohort Method, Reconstructed Cohort Method and
True Cohort Method have been discussed and indicators computed at the state and
all‑India level. Similarly
inequalities in the system, if any, can be detected and disadvantage group(s)
be identified with the help of indicators of efficiency. The last set of questions relates to
resources provided to education and how they contribute to the quality of
educational services and whether resources being used in the most effective way
possible, all of which answered efficiently, if indicators for disaggregated
target groups are available. Time Utilisation Rate, Space Utilisation Rate,
indicator of average audience in a class etc. are discussed under the group of
quality of educational services and utilisation indicators. To understand
what an indicator is and other questions of similar nature, let us first define
an indicator itself. An indicator is
that which points out or directs attention to something (Oxford
Dictionary). According to Jonstone
(1981), indicator should be something giving a broad indication of the state of
the situation being investigated. The
most common use of indicators is to examine the relative state of development
of different systems accomplished over a period of time in a specified field of
human concern (Prakash, Mehta and Zaidi, 1995). For example, primary enrolment of two districts do not produce
any information but the same, if linked to corresponding age‑specific
population, can be used to compare the status of primary education. In our day‑to‑day
life we also come across various indicators which can be classified into three
broad categories, namely, input, process and output indicators. Various process control machines, such as, videocassette
recorder, automatic milk booths and automatic weighing machines are some of the
examples of these indicators. However,
in the field of education, the classification of indicators under different
categories is not an easy task. Generally, we view education as a system, which
receives inputs in the form of new entrants, transforms these inputs through
certain internal processes, and finally yields certain outputs in the form of
graduates. The output from a given cycle of education is defined as those
students who complete the cycle successfully and the input used up in the
processes of education are measured in terms of student years. These indicators can further be classified
into four categories, namely, indicators of Size or Quantity, Equity,
Efficiency and Quality, some of which are presented in Box 1. Before the concept and definition of a variety of
indicators is presented, first the basic terms like, primary and secondary
data, stage and institution‑wise data and stock and flow statistics is
differentiated. (i) Primary and Secondary Data When information is first time collected, it is termed as
primary data otherwise it is known as secondary data. Primary data is generally
scattered in files, registers and
records so as to collect it either
from the concerned institutions or
even can be collected from
the sampling unit, such as,
a teacher, school and student. The
primary data is generally termed as raw which has no use to planners and
decision making authorities, as it do not serve as a tool of decision support system. The information thus collected is processed,
analysed and tabulated with the help of statistical indicators, so that it
becomes derived information. Simple
statistics, such as, averages, index numbers and growth rates can be used to
generate derived data. The derived information
in the form of indicators can also be used to analyse present status of
educational facilities and its utilisation.
At the micro level, just before beginning of an academic session,
village survey is conducted. Generally,
primary information on number of children in a specific age group and those who
are out‑of‑school is collected which is an example of primary data.
On the other hand secondary data generally lie in publications, which is
readily available for use. Literacy
rates during 1901‑91 that can be obtained from the Census publications is
an example of secondary data. (ii) Cross-sectional and Time‑Series Data Generally two types of statistics, namely cross‑section
and time‑series information is available. If information is available at a single point of time, it is
known as cross‑sectional data.
For example, state‑wise literacy rates and its male and female
distribution in 1991 is an example of cross‑sectional data. Cross‑sectional
data is also known as stock statistics.
Stock statistics do not have flow of information and whatever is
available that restrict to only a single point of time. On the other hand, information available on
more than two points of time is known as time‑series information, which
is also known as flow statistics. In
flow statistics, there is a flow of information from one time period to another
time period. State‑wise number of teachers during 1984‑85 to 1994‑95
is an example of time‑series data.
Similarly, grade‑wise enrolment in 1991 is an example of stock
statistics but if the same is also available for 1992, it becomes flow
statistics. While analysing educational
development, both types of statistics is needed. For projecting enrolment, we need enrolment over a period of time
whereas for analysing present status of educational development, cross‑sectional
data is required. (iii) Institution and Stage‑wise Data The third type of statistics generally we deal with is
institution‑wise and stage‑wise information both of which can be
cross‑sectional and/or time‑series in nature. For example, stage‑wise enrolment at
the primary level includes all children those who are currently in primary
classes irrespective of schools. Thus, while collecting stage‑wise
information, enrolment irrespective of schools is considered. This means that
primary stage enrolment includes enrolment in primary, middle, high and
secondary schools. Otherwise, if
consider enrolment in a particular type of school, it is termed as institution‑wise
enrolment. Thus, enrolment in primary
classes in primary schools is an example of institution‑wise data. In fact, a large number of children are in
primary classes who are otherwise not in primary schools but are in middle and
other higher levels of school education. Box 2 presents educational pattern in
different States & UTs, which reveals that two types of patterns are in
existence. In some states, primary level consists of Grades I‑V but in
other states, it is Grades I‑IV.
Similar is the case with middle and secondary levels of education. However irrespective of state patterns, the
data disseminated in case of most of the publications covered Grades I‑V
at the primary and VI‑VIII at the middle level.
Note :
All other States & UTs have Grades I‑V and VI‑VIII corresponding to Primary and Middle levels
of education. Source : Annual Report: 1994‑95, MHRD, New
Delhi. INDICATORS OF ACCESS (COVERAGE) In order to know whether the existing schooling
facilities are equally available to
clientele population/area/region or not, indicators of access are
used. These indicators are also used to
know whether schooling facilities are adequately utilised or not, because
availability of school within a habitation or a walking distance need not
guarantee that the entire clientele is utilising the facilities. While analysing accessibility, a number of
indicators, such as, distance from the house, mode of travel and time need to
reach schools are generally been used.
While analysing, distance to travel to reach school, norms prescribed in
policy can always be used. Generally, a
primary school is supposed to be available within one kilometer from the
habitation and a middle school within three kilometers. In India, habitations are treated as lowest
unit of planning where schooling facilities should be available. Habitations are also known as cluster, which
are below village level consisting of about ten houses. Thus, number of habitations having primary
schooling facilities within a habitation and/or a walking distance is
considered as an indicator of access, which if links to rural population, may
generate more meaningful information.
Thus, the second indicator of access one should consider is percentage
of rural population served by the schooling facilities. For example about 94 percent of the total
habitations in the country had schooling facilities either within the
habitation or a walking distance of one kilometer which means that only six
percent habitations were out of reach of schooling facilities. Similarly, about 95 per cent of the total
population is being served by the schooling facilities which means that only a
small segment of population is not accessible to schooling facilities. While developing plans at the micro level,
the two indicators presented above should be computed separately for all of its
sub-units. The identification of a
habitation where schooling facilities are yet to be provided should be based on
school mapping exercises referred above. MEASURING THE EDUCATIONAL ACCESS
By measuring educational
access, we mean interaction between demand and supply. Demand and supply in education means
children of a specific age group utilising the educational facilities, which is
termed as supply. Broadly, indicators
of measuring educational access are the indicators of coverage. The following indicators of access are
discussed in the present article: (i) Admission Rate (ii) Enrolment Ratio and (iii) Transition Rate. (i) Admission Rate The first important indicator of educational access is
Admission Rate which is also known as Entry or Intake Rate. Admission rate plays an important role to
know coverage of child population (age 6’) in an education system, which is
important to both policy makers and planners.
When enrolment is analysed, we notice two types of children in Grade I
i.e. new entrants and repeaters. But while
computing the admission rate, only present members of cohort (new entrants, in
Grade I are considered and repeaters are ignored, as they are members of some
previous cohort. A cohort is simply a
group element (children) moving together from one grade to another and from one
time period to another. In some cases
we may also have new entrants in other grades too, in situation like this we
assume that their number is negligible.
The admission rate plays an important role to know status of a district
with respect to other districts of the state and can also be computed separately
for boys & girls, rural and urban areas and Scheduled Castes &
Scheduled Tribes population. The
admission rate can be used to identify educationally backward blocks &
districts, so that specific strategies are formed. As mentioned, admission rate
also plays a significant role in enrolment projections and forms the basis of
future enrolment in Grade II to VIII in subsequent years. The computation procedure of Admission Rate is presented
below: New
Entrants in Grade “I” Apparent
Admission Rate = X
100 (1) Population
of Age “6” Year New
Entrants of Age `6' in Grade "I" Age‑specific
Admission Rate = X
100 (2) Population
of Age "6" Year The admission rate presented above indicate that Apparent
Admission Rate consider new entrants in Grade I irrespective of ages which
means children above and below age `6' are
included in enrolment which may in some cases resulted into rate more
than hundred. That is why the rate is known as Gross Admission Rate which is
considered a crude indicator of access and may not present the true picture of
the coverage. If considered total
enrolment (Grade I) instead of new entrants, the corresponding rate is known is
Gross Admission Rate which again is a crude indicator. Therefore, Age‑specific Admission
Rate is computed which is considered better than the gross entry rate. Age‑specific admission rate cannot
cross hundred because of its consideration of new entrants of age `6' in Grade
I which means children of below and above age `6' are excluded from Grade I
enrolment. This rate has serious policy
implications and unless brought to hundred, the goal of UPE cannot be
realised. Apart from the admission rate
presented above, Cohort Admission Rate is last in the series which watch
movement of a particular members of a cohort over several consecutive years and
account for the member of cohort who successfully sooner or later enter school
(Kapoor, n.d.) but due to limited data
which is available on Grade I enrolment, the same cannot be computed at any
level. In 1990‑91, total enrolment in Grade I was reported
27.06 million including those of 1.23 million repeaters of previous
cohort. The population of age `6’
officially entitled to get admission in Grade I was 20.98 million. Let us first compute Gross Admission Rate by
using the following formula: Total Grade "I"
Enrolment G.A.R = X 100
(3) Population of Age "6" 27.06 million = X 100 20.98 million = 128.98
% The computation of Apparent
Admission Rate needs new entrants in Grade I which can be obtained by
subtracting repeaters from the enrolment i.e. 27.06 ‑ 1.23 = 25.83 million. 25.83
million Thus, Apparent
Admission Rate = X
100 20.98
million = 123.10 % . The next rate, we compute
below is Age‑specific Admission Rate which requires new entrants of age
`6' in Grade I which is readily not available from any of the regular
sources. If we assume, 19.23 million
children in Grade I of age `6', then
the Age‑specific Admission Rate is computed as follows: 19.23 = X 100 20.98 = 91.66
% which indicates that about
92 per cent population of age‑6 were admitted in schools or a little more
than 8 per cent were otherwise out‑of‑school in the year 1990‑91. For some of the other cohorts, admission
rate at the all‑India level is computed which is presented in Table 1
(Mehta 1993a & 1995a). The table
reveals that a large number of children enter education system every year but
it remains to see how many of them retain in the system. Further, it has been noticed that a large
number of over‑age and under‑age children are also included in
Grade I enrolment which makes the statistic more than hundred. Due to limitations in data, the net
admission rate presented above couldn't be computed. Enrolment Ratio is the next
indicator of coverage that is presented below. Table 1 Apparent Admission Rate: All India (1984‑85 to 1990‑91)
(Figures in Percentage)
Note :
Due to over‑age and under‑age children, the rate comes
out more than 100 per cent. Source :
Mehta, Arun C. (1995a). (ii)
Enrolment Ratio: Concept, Definitions and Limitations Enrolment Ratio is simply division of enrolment by
population, which gives extent to which the education system is meeting the
needs of child population. Two
questions may crop‑up, first enrolment of which level and second,
population of what age group. Before we
present definition of enrolment ratio, let us first examine the position of
different States & UTs with reference to enrolment ratio at primary and
middle levels of education. It has
been observed that in many of our states, the enrolment ratio at the primary level is more than 100 per
cent (currently 105 per cent at the national level, Table 2) and on the other
hand in some states like Bihar (76%), Rajasthan (91%) and Uttar Pradesh (89%); the enrolment ratio is
less than hundred. Further, it has been
observed that at the middle level, except for Himachal Pradesh, Kerala, Mizoram, Lakshadweep, Pondicherry
and Tamil Nadu, none of the states have enrolment ratio more than hundred per
cent which shows the quantum of drop‑outs from one stage to another and
incidence of over‑age and under‑age children. It has also been
noticed that in some of the smaller States & UTs, such as, Goa, Dadar and
Nagar Haveli, Manipur, Mizoram, Sikkim, Lakshadweep and Pondicherry where the
base population (age‑specific) is small, only slight over‑reporting
of enrolment and over‑age and
under‑age children dramatically change the enrolment ratio (Table
3). In order to understand statistics
on enrolment, the following enrolment ratios are discussed in detail in
the present article. (A) Over‑All Enrolment Ratio (B) Age‑Specific
Enrolment Ratio and (C) Level
Enrolment Ratio. The concept of different
ratio is demonstrated by computing it at the primary and middle levels of
education. The merits and limitations
of a particular ratio is also discussed which may lead us to identify the best
indicator of coverage. The indicator
identified may thus help us to know the present position on different aspects
of Education for All. (A) Over‑All
Enrolment Ratio The first indicator of coverage we
discuss below is Over‑All Enrolment Ratio (OAER) which presents an
overall picture or a birds eye‑view of the education system under
study. For a school system consisting
of Grades I‑XII, the OAER is simply division of total enrolment in Grades
I‑XII to population in the respective age‑group i.e. 6‑17
years (equation 4). It may be observed
that in the numerator, total enrolment in Grades I‑XII is considered
irrespective of ages, but in the denominator corresponding school‑age
population i.e. 6‑17 years is considered which may sometimes lead to
serious problems. In some cases, quantity in numerator may be higher
than the quantity in the denominator, which gives enrolment ratio often more
than 100 per cent. Technically, if the
enrolment ratio for a particular stage in a block/district/state/country is 100
percent, it means that the goal of universal enrolment has been achieved but in
reality the case is not so. When
enrolment ratio is found to be more than 100 per cent, it may be due to over‑age
and under‑age children which are included in enrolment of Grades I‑XII. Other possible reason of this may be due to
large scale over‑reporting, may be intentional and/or unintentional, to
show the fulfillment of targets which can be checked only, if average daily
attendance is available. Thus, OAER is
not considered an ideal indicator of
enrolment and planning exercises based on it may lead to misleading picture.
Hence, it is termed a crude indicator of coverage. The second important
limitation of this ratio is that level and stage‑wise enrolment ratio
cannot be obtained. The ratio is recommended to use only when quick estimates
are required and detailed information on enrolment is not available. Table 2 Gross Enrolment Ratio at Primary Level in Selected States of India 1993‑94 (Figures in Percentage)
Source : Selected
Educational Statistics: 1993‑94, Department of Education , MHRD,
Government of India, New Delhi, 1994. Et I - XII O.A.E.R =
X 100 (4) Pt 6‑17 The total enrolment in
Grades I‑XII and the corresponding population in the age‑group 6‑17
years in 1986‑87 was 128.22 million and 170.55 million (estimated)
respectively (Table 5), thus giving an enrolment ratio of 128.22 S = X 100 170.55 = 75.36
%. Enrolment ratio of 75.36 per
cent means that more than seventy five per cent of the total 175.55 million
children of the age‑group 6‑17 years including over‑age and
under‑age children were enrolled in 1986‑87. Table 3 Enrolment (Grades I‑V) and School‑Age
Population (6‑11 Years) in Smaller States/UTs: 1993‑94 (Figures in Hundred)
* ‑
Includes Daman and Diu Source : Same as
Table 2. (B) Age‑Specific
Enrolment Ratio The second ratio we discuss below is Age‑Specific
Enrolment Ratio (ASER). It gives enrolment ratio for a particular age or age
group. It is simply division of
enrolment in year `t' in age‑group `a' in all the levels of education in
any grade by a population of a particular age `a' in that year `t'.
The limitation of the ASER is that it consider total enrolment
irrespective of its ages, hence enrolment in numerator is not free from error,
as it consists of enrolment in different grades. Still, ASER may be useful to planners and policy makers when
information on coverage and children not enrolled in a particular age group is
required. Eat A.S.E.R = X
100 (5) Pat For example, ASER for age
`13' in year 1995 can be computed as follows: E131995 A.S.E.R = X
100 P131995 Where E13 = E13I
+ E13II + E13III + .... Similarly, Age‑specific
enrolment ratio can be obtained for any other single age or age‑group. For example, in 1986‑87 total
enrolment of age‑group 6‑10 and 11‑13 years was 71.11 and
29.11 million respectively (Table 5) which if divided by the corresponding age‑specific
population i.e. 93.70 and 56.88 million, gives
71.11
A.S.E.R = X 100 = 75.89 % (6-10 Years) 93.70 and 29.11
A.S.E.R
= X 100 = 51.17 %. (11-13 Years) 56.88 Thus, of the hundred
children of age‑group 6‑10 and 11‑13 years, about 75.89 and
51.17 per cent were in the system but
the enrolment data shows that a good number of children belong to grades other
than I‑V and VI‑VIII. In
fact, children of age‑group 6‑10 years are expected to be in Grades
I‑V, if school entrance age is strictly restricted to age `6' but the
situation in reality is not so. Similar
is the case with children of age‑group 11‑13 years in Grades VI‑VIII. The next ratio we present below is Level Enrolment Ratio.
(C) Level Enrolment Ratio The Level Enrolment Ratio is an improved version of OAER,
which gives enrolment ratio level‑wise.
Two types of ratios, namely, `Gross and Net' enrolment ratios are
available. The Gross Enrolment Ratio
(GER) is a division of enrolment at school level `i' in year `t' by a
population in that age group `a' which officially correspond to that level `i'. Ei,ta G.E.R = X
100 (6) Pat On the basis of above
formula, GER for primary and middle levels of education in any given year, say
1987 can be computed as follows: E1987 I
- V 85.91 G.E.R (Primary) =
X 100 = X
100 = 91.69 % P19976‑10 93.70 E1987
VI‑VIII 27.27
G.E.R (Middle) = X 100 = X 100 = 47.95% P198711‑13 56.88 Again, it has been observed
that GER includes over‑age and under‑age children in enrolment
which often resulted into enrolment ratio more than hundred and the same should
not be used in planning exercise unless the net amount of children in the
respective age‑group is considered. Net Enrolment Ratio (NER) is an
improved version of the GER. The
difference between GER and NER is in its consideration of enrolment (equation
7). In NER, over‑age and under‑age children are excluded from
enrolment and then ratios to the respective age‑specific population are
obtained. One of the limitations of the NER is that it excludes over‑age
and under‑age children from the enrolment though they are very much in
the system. Despite these
limitations, the ratio seems to be more logical than the other ratio presented
above. Ei,at N.E.R = X 100 (7) Pat For example, NER for primary
and middle level, for 1987 can be obtained as follows: EI‑V,6‑101987 68.18 N.E.R = X 100 = X
100 = 72.76 % (Primary) P6‑101987 93.70 EVI‑VIII,11‑131987
20.50
N.E.R = X 100 = X 100 = 36.04 % (Middle) P11‑131987 56.88
The GER and NER computed above at the primary level comes
out to be 91.69 and 72.76 per cent respectively which indicate that about 18.93
per cent children were from out‑side the prescribed age‑group i.e.
6‑10 years. Compared to primary
level, the percentages of over-age and under-age children at the middle level
were low at 11.91 per cent. But,
computation of NER requires data on age‑grade matrix, which, as
mentioned, is not -available. Thus, GER
is the only indicator of enrolment which is available annually apart the age‑specific
ratio from the Census sources. However, few estimates of over‑age and
under‑age children at the school level are available which are briefly
presented below. ESTIMATES OF OVER‑AGE AND UNDER‑AGE CHILDREN
The estimates of over‑age and under‑age
children at the primary and middle levels of education are available most of
which are computed at the all‑India level, only few are available at the
state level. In order to compute number
of children outside the prescribed age‑group, information on under‑age
and over‑age children is required but the available estimates cannot be
used directly at the block, district and state level because of the methodology used in generating estimate
vary from source to source. At the elementary level, the most authentic source
of grossness is the Ministry of Education (MOE) estimate which is based on the actual age‑grade matrix
but the same is available only up to the year 1970‑71, after that the
series was discontinued. Using the MOE age‑grade data during the period
1950 to 1970 and by employing Method of Least Squares, Kurrien (1983) estimated 22 per cent as an
amount of grossness at the primary level and 39.2 per cent at the middle
level.The next source of grossness that is also based Table
4 Estimate
of Over‑age and Under‑age Children at Different Levels of Education
: NSS0 42nd Round (July 1986 ‑ June 1987) (Figures in Percentage)
Source : Mehta,
Arun C.(1993b). on complete enumeration of
educational institutions like the MOE estimate, is the NCERT estimate but that
too is available occasionally, 1986‑87 being the latest one (recently, it
is also made available for 1993-94).
The estimate is based on survey data, which provides age-grade matrix
only at the all-India level, and no information is available at the state
level. Kurrien estimate of 22 per cent
at primary level is amply supported by the MOE and NCERT estimates. A number of
other estimates based on sample surveys are also available which is provided by
NIEPA (1992 & 1993) and NSSO (1991).
But, due to small size of sample, the estimate of grossness cannot be
generalised at the all‑India and even at the state level. The estimates of over‑age and under‑age
children are also referred and used in the Working Group on Early Childhood and
Elementary Education (1989), Eighth Five Year Plan document (1992) and EFA
document of MHRD (1993a) but are available only at the all‑India
level. While, the working group used
22 per cent as an estimate of total grossness at all levels of education, the
Eighth Plan used only 15 per cent compared to 25 per cent referred in EFA
document. Also, reference has been not
been given on the basis of which the estimates are computed. Hence, the estimates can be best used at the
all‑India level only. The next estimate, which is available separately for
rural and urban areas, is the NSSO estimate based on the household survey
conducted during 1986‑87. Like
NCERT estimate, it is also available at a single point of time and that
too at the all‑India level
only, hence no time‑series can be
built‑up. The NSSO estimate compares well with the MOE and NCERT
estimates especially at the primary level, however, a significant difference
has been noticed at the middle level. Thus, MOE estimate at the state level and
NCERT and NSSO estimates are left for use at the all‑India level. However, these estimates are too outdated to
use but in the absence of the latest data, there is no option but to use the
available estimates in whatsoever form they are available. In addition, the estimate of grossness can
also be derived from the states where enrolment
ratio (gross) at present is more than hundred per cent. A perusal of 1993‑94
enrolment ratio reveals that in a number of States & UTs it is very
high. For instance Tamil Nadu
(145.00%), West Bengal (123.90%), Maharashtra (119.40%), Karnataka (119.90%),
etc. had very high enrolment ratios, which means at least 45.00, 23.90, 19.40
and 19.90 per cent grossness at the primary level. Further, it has also been
noticed that enrolment targets at the state level provided in the Eighth Plan
document when added together do not matches well with the targets fixed at the
all‑India level and the deviation is significant, hence pro‑rata adjustment is required
which means the estimate of grossness at the all‑India level may not be
applicable at the state level. At the
micro‑level, especially at the
block and district level, where setting‑up of targets is an important
task, needs estimates of over‑age and under‑age children. The available estimates of all‑India
level cannot be used at the micro‑level,
for that purpose, a survey at the local level would be most appropriate
to conduct. However, out‑of‑school
children in the present article is computed on the basis of NSSO estimates
(Table 4) i.e. 24.1, 17.1 and 21.9 per cent for boys and 23.2, 13.3 and 20.6
per cent for girls are considered estimates of over‑age and under‑age
children respectively at the primary, middle and elementary levels of education. Table 5 Age
and Grade Distribution of Enrolment and Population NCERT
Fifth Survey: 1986‑87
Source: Fifth All India
Educational Survey, NCERT, New Delhi, 1993. The estimate of over‑age and under‑age
children also plays an important role to work‑out out‑of‑school
children which is an important component of planning exercise It has been noticed that in most of our
planning exercises, out‑of‑school children are conspicuous by their
absence and additional children need to enroll is only computed which is also
not free from limitations. The detailed procedure of computing out‑of‑school children is presented in Box 3 which is
based on the data of Sixth All India Educational Survey for year 1993‑94. For obtaining out‑of‑school
children, first enrolment is refined with particular reference to estimate of
over‑age and under‑age children.
Thus, the corresponding percentage of over-age and under-age children
have been taken‑out from enrolment and the refined enrolment is
obtained. The balance of age‑specific
population and refined enrolment is termed as out‑of‑school
children. For demonstration purposes,
out‑of‑school children is computed only for Primary Level/Age‑group
6‑10 years, Box
3
otherwise the same needs to
be computed separately for boys and girls, Scheduled Caste & Scheduled
Tribe children and rural & urban areas.
If computed at the disaggregated levels, the same will help to identify
educationally backward areas where out‑of‑school children
concentrate, so as to adopt appropriate strategies to bring them under the
umbrella of education. The out‑of‑school children computed indicate
a net enrolment ratio of 72.05 per cent as against 94.40 per cent gross
enrolment ratio which means that only 72 per cent of the total 103.54 million
children of the age‑group 6‑10 years were enrolled in 1993-94. The existing data do not allow us to know
how many of them were actually attending schools regularly which can be known
only, if average daily attendance is available. The out‑of‑school children is also used to project
additional number of children need to enroll over a period of time, if goal of
universal enrolment is to be realised.
For projecting additional children, projected population of the
respective age‑group in the target year i.e. 2001 is required. The results (Box 3) indicate that for
achieving goal of universal enrolment about 40 million additional children in
the age group 6‑10 will have to be enrolled by the year 2001. The next indicator of coverage is Transition Rate, which
is based on `Student Flow Analysis'.
Before we present the concept of transition rate and its computation
procedure, it is better first to discuss student flow analysis and different
flow rates, which are presented below. (A) Student Flow Analysis Student Flow Analysis starts at the point where students
enter into an education cycle. The flow
of student into, through and between an educational cycle is determined by the
following factors (UNESCO, 1982): I : Population of Admission Rate (`6' Year) II : Student Flow into the System : The Admission
Rate III : Student Flow through the System : Promotion,
Repetition and Drop‑out Rates
and IV : Student Flow between Systems : The
Transition Rate. The rates mentioned above are important to understand the
education system which can also answer a variety of typical questions, such as,
at which grade in the cycle is the repetition or drop‑out rate highest?,
who tends to drop‑out and repeat more frequently boys or girls? and what is the total, accumulated loss of
students through drop‑out?. The answer of these questions can be
obtained, if flow rates for different target groups and for each grade are
computed. The transition of students
between cycles has, of course, a great
significance of its‑own. It is
not in
planners hand that
he/she will control different flow rates but through policy
interventions they can be altered over a period of time. Since, the concept and definition of Admission Rate is
already presented in the last section, we start student flow analysis by
assuming that a student has already entered into the system and there are now only following three
possibilities in which he/she will move: * Students
have been promoted to the next higher grade * Students
have to repeat there grades and * Students
have dropped‑out from the system. In the educational statistics, it is convention that
enrolment is presented in a rectangle box, which has three directions each of
which indicate flow of students. The
diagonal indicate flow of students who successfully complete a grade and are
now promoted to next higher grade. While down below direction represents number
of children who repeat a particular grade but only in the next year. The last direction indicates the balance of
those who are neither promoted nor repeated and are termed as dropouts from the
system without even completing a particular grade (Figure 1). For convenience point of view, total enrolment in Grade I
is considered 1,000. It may be noted
that total of promotees, repeaters and dropouts are equivalent to enrolment in
a particular grade. In order to workout
flow rates, grade‑wise enrolment for at least two consecutive years along
with number of repeaters is required.
If information on number of repeaters is not available, the flow
analysis is based on Grade Ratios, which is also known as pass percentage. Grade Ratio is simply division of enrolment
in the next grade (say Grade II) to enrolment in the previous grade (say Grade
I) in the previous year. Since,
enrolment is not free from limitations because of the repeaters, the method may
not present the true picture of the
existing situation. Hence, Grade Flow
Diagram (I)
Figure
1 Ratio is termed a crude
indicator of promotion rate. If number
of repeaters is available, the analysis is based on promotion rate which is
bound to be more reliable than based on the Grade Ratio. The method also assumes that transfer from a
school to another school within or outside a block/district is negligible. In case, if their number is significant,
they should be considered in calculation, otherwise the same can produce
unrealistic rates. Below the detailed
procedure of calculation of flow rates is demonstrated by considering actual
set of data presented in Box 4. Grade‑wise
enrolment along with number of repeaters at the all‑India level is used
for years 1989‑90 and 1990‑91. (a) Promotion Rate The first rate we discuss below is promotion rate, which
needs to be computed separately for all the grades. The main task is to obtain number of promotees who are promoted
to next grade. Of the total 23,592
thousand children in Grade I in 1989‑90,
it look like that about 20,999 thousand children were promoted to the
next higher grade i.e. Grade II in 1990‑91. But in reality, the number of promotees was 20,401 and not 20,999
thousand because of 598 thousand repeaters who were also included in Grade II enrolment,
which needs to be taken out from enrolment.
Thus, the actual number who were promoted to Grade II in 1990‑91
was 20,999 ‑ 598 = 20,401 thousand.
In the similar fashion, promotees in the remaining grades are to be
worked out. Once the number of
promotees is worked out, the next step is computation of promotion rate in
different grades. Thus, promotion rate in a particular grade can be computed as
follows: Number of
Students Promoted to Grade `g+1' in Year `t+1' = X
100 Total Number of Students in Grade `g' in Year `t' In notations, it is
expressed by the following equation: BOX 4 Enrolment and Repeaters at the All India Level 1989‑90 & 1990‑91 (In Thousand)
t+1 P t g+1 Promotion
Rate (
p ) = X
100 (8) g t E g Let us now compute promotion
rate for Grade I. Putting values in equation (8) from Box 4, we have 1990‑91 P
1989‑90 II P = X 100
I 1990‑91 E I 20,401 = X
100 23,592 = 86.47
%. Thus,
86.47 per cent promotion rate in Grade I indicates that the remaining 13.53 per
cent children were either dropped‑out from the system and/or repeated
Grade I next year. By adopting the same procedure, promotion rate in remaining
grades can also be computed (Box 4). (b) Repetition Rate Once the promotion rate is computed,
the next indicator that is required
to compute is grade‑to‑grade repetition rate. Since the number of repeaters is already
given, the computation of repetition rate is as simple as division of number of
repeaters in a grade to enrolment in the previous year in the same grade. The
Box 4 presents complete analysis of student flow which has repeaters for two
consecutive years, namely 1989‑90 and 1990‑91. Care should be taken to select one out of
them. For computing repetition rate in a grade, say Grade I in 1989‑90,
repeaters of 1990‑91 is considered because a repeater can repeat a
particular grade only in the next year.
In notations, it is presented as follows: Number of Repeaters in Grade `g' in Year `t+1' = X 100 t E g t+1 R
t g or ( r
) = X
100 (9)
g t E g Now
let us compute repetition rate for one of the grades, namely, Grade III 1990‑91 R 1989‑90 III R = X 100 III 1989‑90 E III 576 = X 100 17,832 = 3.23
%. Thus,
the repetition rate in Grade III indicates that of the 17,832 thousand children
in 1989‑90, only 3 per cent repeated next year. The remaining children
may be either promoted to Grade IV or they were dropped‑out from the
system before completion of Grade III. Similarly repetition rate in any other grade can also be
worked‑out. (c) Drop‑out Rate One of the important indicators of educational
development is dropout rate which like other rates should be computed grade‑wise. Before the dropout rate is computed, the
first requirement is to obtain number of dropouts between the grades. In the last two steps, we have calculated
number of promotees and repeaters in different grades. In fact, the balance of enrolment in a particular grade is termed as
drop‑outs. Or in other words, of
those who are not promoted and/or repeated is known as drop‑outs. For example, Grade I enrolment in 1989‑90
was 23,592 thousand of which 20,401 children were promoted to next higher Grade
II and 1,230 thousand children repeated Grade I which means, the resultant
23,592 ‑ 20,401 ‑ 1,230 = 1,961 is termed as dropouts of Grade
I. The number of dropouts can now be
easily linked to enrolment in a particular grade and dropout rate can be
obtained. First the formula is
presented: t D t g Drop‑out Rate ( d ) = X
100 (10) g t E g Number of
Students Dropping‑out from Grade `g' in Year t = X
100 t E g Thus for Grade III, the drop‑out
rate is 1989‑90 1990‑91 1990‑91
1989‑90 E ‑ (P +
R ) D III IV III
III = X
100 1989‑90 E III 17,832 ‑
(15,524 + 576) = X
100 17,832 = 9.71
%. Drop‑out rate in Grade
III indicate that about 9.71 per cent children of those who were in Grade III
in 1989‑90 dropped‑out from the system without completing Grade
III, thus contributing a lot to wastage of resources. It should however be noted that addition of promotion,
repetition and drop‑out rates in a particular grade is always 100 per
cent. Knowing two of them means knowing
the third one as well. t t t p + r + d = 100 (11) g g g The different flow rates
presented above also play an important role when exercise of internal
efficiency of education system is undertaken which cannot be obtained unless,
flow rates are available. However, educational planner and policy makers are
particularly interested in those successful who proceed to the next higher
cycle. In order to trace the flow of
students from one cycle to other; Transition Rate is very useful. The
transition of students between cycles has, of course, a great significance of
its own. It can be manipulated for
purposes of educational policy. After a
student is reached at the final grade i.e. Grade V, or Grade IX or Grade XII,
there are the following four possible ways in which they move (Figure 2): (i) a
student may repeat the grade (ii) a
student may drop‑out from the system (iii) a student may complete the grade
successfully and then leave the school
system and (iv) a student may complete the grade
successfully and then enrol in the
first grade of the next higher cycle. Flow
Diagram (II)
Figure
2 The transition rate is
defined as follows: New Entrants into Grade VI in
Year `t+1' Transition
Rate = X
100 Enrolment in Grade V in Year `t' t+1 E g+1 = X 100 (12) t E g 12,279 = X 100 13,514 = 90.86%. With the limited set of
data, it is not possible to exactly know successful completors who continue in
Grade I of next higher cycle. The
transition rate, thus computed is nothing but the promotion rate between final
grade of a cycle and first grade of next cycle. The number of drop‑outs so calculated consists of both who
complete the cycle successfully but not taken admission in the next cycle and
also who dropped‑out in between without completing the last grade of
first cycle. In the present article, for demonstration purposes
transition rates have been calculated (1985‑86) for primary to middle and
from middle to secondary levels of education. Table 6 reveal that at the all‑India level,
out of hundred children in Grade V, only 83 could take admission in Grade VI as
compared to 78 girls, which means in transition 17 per cent children dropped‑out
from the system as compared to 22 per cent girls which also included fresh
admissions to Grade VI. The state‑wise
results reveal that from primary to middle level, the transition rates vary
from 55.79 per cent in Nagaland to 92.48 per cent in Karnataka for total
enrolment compared to 54.23 per cent in Nagaland to 91.38 per cent in Tripura
for girls enrolment. Karnataka (92.48%)
had the highest transition rate which is followed by Maharashtra (89.78%),
Punjab (90.34%), Haryana (87.09%), Tripura (91.79%) and Uttar Pradesh (86.32%). The transition rates
for Maharashtra and Tripura are included that of the repeaters, hence may not
present the correct picture. It has also been revealed that during transition, about 28.67
per cent (Sikkim), 18.3 per cent (Andhra Pradesh), 26.58 per cent
(Orissa) and 27.59 per cent (Gujarat) children dropped‑out from
the system. In Kerala too, about 16.20
per cent children dropped‑out during the transition compared to
13.46 per cent girl children. In Jammu & Kashmir, the rate so
calculated is not reliable as it comes out to be 100.91 per cent, similar is
the case with Meghalaya. The transition rate from middle to secondary level
calculated at the all‑India level shows that of 100 students in Grade
VIII, only 77 could take admission in Grade IX which means more than 23 per
cent students dropped‑out from the system during the transition as
compared to 25 per cent girls. The state‑wise analysis also present grim
situation where as many as 54.58 per cent (Himachal Pradesh), 47.95 per cent
(Sikkim), 25.47 per cent (West Bengal), 19.30 per cent (Gujarat), 44.36 per
cent (Madhya Pradesh) etc. children dropped‑out from the system in
transition. But at the same time,
Karnataka had only 3.47 per cent drop‑outs followed by Tripura (4.22%),
Rajasthan (7.95%), Nagaland (8.23%) and
Maharashtra (8.80%). On the
other hand, Kerala one of the educationally advanced state had about 23 per
cent children whom dropped‑out during the transition, as compared to 21
per cent girls. Table
6 Transition
Rates at Primary and Middle Level of
Education: 1985‑86 (In Percentage)
* Including
repeaters ** Excluding
Repeaters, but data not reliable Source :Mehta,
Arun C. (1995b). Despite spectacular quantitative expansion of educational
facilities in the country, the desired level of achievement with respect to
coverage, retention and attainment couldn't be achieved mainly due to low level
of efficiency upon which the system is currently based. Though, a number of common indicators of
efficiency presented above are computed widely both at the state and all‑India
level, still a number of other indicators considered to present more reliable
picture of internal efficiency of education system are generally not computed
largely due to their lengthy computation procedure. Therefore, an attempt has been made in the present article to
demonstrate how different indicators of wastage and status of internal
efficiency of education system are measured and computed. Broadly, the
following methods are discussed in detail: (a) Apparent Cohort Method (b) Reconstructed Cohort Method and (c) True Cohort Method. (a) Apparent Cohort Method The simple but crude method of measuring extent of
educational wastage which require minimum amount of information is Apparent
Cohort method. The method requires
either cross‑sectional (year‑grade) or time‑series data on
grade‑wise enrolment both of which is easily available on fairly a long
period. If the cross‑sectional grade data is considered, the percentage
of enrolment in all other grades to enrolment in Grade I which is considered
cohort is measured and termed and treated as evidence of wastage. The indicators at the all‑India level
for three cohorts, namely 1985‑86, 1986‑87 and 1987‑88 is
presented in Table 7 which reveals that
in 1987‑88, at the primary level, the wastage was 42.66 per cent for boys
compared to 51.22 per cent for girls.
The corresponding figures at the middle level were 22.31 and 28.60 per cent
respectively for boys and girls which indicate
that the incidence
of wastage on part of girls is higher than boys. It has also been revealed that over a period
of time wastage ratio has declined from 53.58 per cent in 1985‑86 to
47.05 per cent in 1986‑87 which has further declined to 46.29 per cent in
1987‑88. But at the middle level,
it has remained almost stagnant, still the gap between the two is highly
significant. However, the indicator presented
above could only be treated as crude estimate of wastage as the Apparent Cohort
Method has its own limitations. The
most significant limitation is that percentages in different grades are
obtained by taking Grade I as base, where in fact they all are not members of
the same cohort. Practically they come
from different cohorts entered into the education cycle some two, three, four
and five years ago. The other important
limitation is that in Grades II, III, IV and V, there may be some fresh
entrants and repeaters from previous years which are now repeating but are not
the members of the present cohort. Thus, in the next exercise, time‑series
enrolment in different grades separately for boys and girls have been used to
measure the extant of wastage. The Apparent Cohort method using time‑series data
on grade‑wise enrolment assumes
enrolment in Grade‑I in a base year as cohort and determines the
relationship through diagonal analysis between cohort enrolment in successive
grades in successive years (Sapra, 1980). However, the major limitation of
Apparent Cohort method in using time‑series grade enrolment is that it
does not take into account the element of repetition. As is evident from our
system that enrolment in each higher grade consists of new entrants coming from
outside, termed as migrants or first time comers, those who have promoted from
the previous grade and those who are repeating the same grade but in the next
year. The method under study ignores first time comers and migrants, so as to
the repeaters. Further, the method assumes that there is a direct relationship
between enrolment in higher grade to enrolment in the previous grade, as bulk
of the children come from the previous grade. But at the same time, the
difference between (higher grade
enrolment ‑ previous grade enrolment) them is treated as being
dropped‑out from the system, but infect, in higher grade repeaters are
also included. In addition, all those who termed as drop‑outs from the
system, may not be actually one as they may have joined other schools or they
may have even died. The method further assumes that difference between the
migrants from the school/area and migrants to other schools/area is negligible.
But, it has been observed that despite no detention policy, a good number of students tend to repeat
primary grades (Mehta, 1994). Table 7
Apparent
Cohort Method: All India 1987‑88 (In
Percentage)
Source: Mehta,
Arun C. (1995a). Thus, ignoring repeaters in
the analysis of measurement of wastage will not present true picture. It has
also been observed that the method fails to give any idea about the efficiency
of the education system as such, as it provide only retention rate which is
useful to a limited extent. Some of these limitations, especially those of
repeaters are taken care‑off in the Reconstructed Cohort Method. In order to compute the wastage, time‑series data
on enrolment is required. For example,
for computing wastage for cohort 1979‑80, Grade I enrolment is
required. To watch movement of those
who have taken admission in Grade I in 1979‑80, enrolment in successive
grades in subsequent years is required.
Thus Grade II enrolment in 1980‑81, Grade II in 1981‑82 etc.
is required, so that percentage to the base year enrolment in Grade I is
obtained for Grade II and onwards which is treated as retention. The detailed results are presented in Table
8 which reveal that 100 students who have taken admission in Grade I in 1979‑80,
only 52 could reach to Grade V in year 1983‑84 and only 37 reached to
Grade VIII in year 1986‑87 which shows that about 48 and 63 per cent
children dropped‑out before they reach to Grade V and Grade VIII
respectively. It has also been observed
that move from one grade to another
increases the incidence
of drop- Table
8 Wastage
at Primary and Middle Levels of Education: All‑India Cohort
: 1979‑80 (In Percentage)
out and is true for both primary and middle levels of
education and that too for boys and girls.
(b) Reconstructed Cohort Method Based on the methodology presented in the previous
section, the repeaters are taken‑out from enrolment and then ratio to
Grade I enrolment is computed. Table
9 presents the extent of wastage and
retention when time‑series grade enrolment along with repeaters is
considered. The table reveals that of 100 children enrolled in Grade I in 1984‑85,
only 77 reached to Grade II compared to 69, 54 and 52 to Grade III, Grade IV
and Grade V which means about 23 per cent children (5.50 million) dropped‑out
before reaching to Grade II and 31 (7.41 million), 46 (11.00 million) and 48 (11.48 million) per cent before reaching
to Grade III, IV and V respectively.
Similar pattern is obtained for girls, where it has been noticed that
girls tend to drop‑out more than boys and only 49 out of 100 could reach
to Grade V compared to 54 boys. It has
also been noticed that diagonal analysis of grade‑wise enrolment along with number of repeaters
could only present the amount of wastage or retention but it fails to give any
idea about promotion, repetition and drop‑out rates for all grades. The method also assumes that difference
between in‑migrants and out‑migrants is negligible and the children
who are not in the higher grades coming from the previous grade are termed to
be dropped‑out from the system.
Some of these limitations can be handled efficiently, if analysis is
based on `True Cohort Method' presented below. (c) True Cohort Method The first question which may crop‑up is what do we
understand by the word efficiency. The
origin of efficiency lies in economics but it has relevance in every spheres of
life. In simple terms, efficiency can be
defined as a optimal relationship between input and output. An activity is said to be performed
efficiently, if a given quantity of output is obtained with a minimum inputs or
a given quantity of input yields maximum outputs. Thus, by the efficiency we mean to get maximum output with
minimum inputs or with a minimum input, maximum output is obtained. The best system is one which has both input and output exactly the same which is
known as a perfect efficient system.
But, what are input and outputs in an education system? Let us suppose that a student has taken
admission in a particular grade and he/she remains in the system for at least
one complete year. A lot of
expenditure on account of cost of teachers, room, furniture and equipments is incurred on those who stayed in the
system which can be converted into per student cost and is termed as one
student year. On the other hand every
successful completor of a particular cycle is termed as output which is also
known as a `graduate'. Though we may have two types
of efficiency, namely internal and external efficiency but in
the present article we shall be discussing only internal efficiency of
education system. We may have a system,
which is internally efficient but externally inefficient, or vice‑versa. We may have a system which has no drop‑out,
low repetition and high output but the output that is produced may not be
acceptable to the society and the economy. Table
9 Retention
and Wastage Rates : All‑India Level Cohort
: 1984‑85 (In
Percentage)
Note : *
Wastage rate Below concept and definition of a variety of indicators
of wastage and efficiency is presented which are based on a hypothetical
(theoretical) cohort of 1,000 pupils who enter the beginning of the stage is
followed up in subsequent years, till they reach the final grade of the
stage. More specifically, the method is
based on the following assumptions: (i) the method assumes that the existing rates of promotion,
repetition and drop‑out in different grades would continue through out the evolution of
cohort. Thus, the flow rates computed above for cohort 1989‑90 have been
(Box 4) assumed to remain constant; (ii) a student would not allow to continue in the system after he/she has repeated for three years, thereafter, he/she will either leave the system or would be promoted to next higher grade; and (iii) no student
other than 1000 would be allowed to enter
the cycle in between the system. Based on the above assumptions, the computation procedure
of the following indicators have been demonstrated in detail (Box 5): (i) Input/Output
Ratio (ii) Input
per Graduate (iii) Wastage
Ratio (iv) Proportion
of Total Wastage Spent on Account of Drop‑outs and Repeaters (v) Average
Duration of Study on Account of Graduate and Drop‑outs and (vi) Cohort
Survival and Drop‑out Rates. (i) Input/Output Ratio The evolution of cohort (Box 5) reveals that total number
of student years invested was 4,212 as against 380 those who dropped‑out
from the system. Thus, those who have
not dropped‑out i.e. 1000 ‑ 380 = 620 are termed as outputs who
have completed the cycle successfully and are known as graduates of cohort
1990. Based on the numbers, let us
first compute the Input/Output ratio with the help of the following formula: Number of Graduates X
5 Input/Output Ratio = (13) Total Student Years
Invested 620 X 5 = X
100 4,212 = 73.60
%. The above ratio indicates
that the system is efficient to the extant of only 74 per cent and there is a
scope of improvement and the wastage is of the tune of about 26 per cent. A
ratio is said to be ideal, if it's value is five in case of primary cycle. It may be recalled that we have started
analysis with a hypothetical cohort of 1000 students which means the system
should take at least five years to produce a primary graduate which otherwise
gives an Input/Output ratio of 5. But in reality most of the systems have an
Input/Output ratio well above the five.
The Ideal Input/Output Ratio is defined as follows: 1000 X 5
Student Years Ideal
Input/Output Ratio =
(14) 1000
Successful Completors = 5 Years. (ii) Input per Graduate Now let us compute the actual Input/Output ratio which should
be linked to number of student years invested and number of those who
successfully complete an education cycle.
One of the important indicators which reflect on wastage and efficiency
of the education system is Input per Graduate, which means average number of
years the system took to produce a graduate.
Ideally a student should take five years to complete primary cycle and
three years to middle cycle but the situation in reality is not so. Box 5
Reconstructed Cohort Method :
Flow of Students
Cohort
1990
Total
Student Years Invested Input per
Graduate = (15) Number of
Graduates 4,212 = 620 = 6.79. An Input/Output
ratio of 6.79 indicates that the system is taking about 1.79 years more than
the required five years which shows a wastage of about 26 per cent
(1.79/6.79*100) which is similar to one presented above. (iii)
Wastage Ratio Let us now compute one of the other
important indicator of wastage, namely the Wastage Ratio. The ratio of actual Input/Output ratio to
ideal Input/Output Ratio is termed as wastage
ratio which always lies between 1 and
Ą. In an ideal situation where the system has
maximum efficiency, the ratio is one, otherwise more than one indicate extent
of inefficiency and wastage. Thus, the
ratio is computed as follows: Actual
Input/Output Ratio Wastage Ratio = (16) (w) Ideal
Input/Output Ratio 6.79 = 5 = 1.36 (1Ł w
Ł
Ą). The wastage ratio computed
above reveals that it is well above the
ideal ratio i.e. one. The ratio also
indicates that there is a scope of improvement in the system which can be
handled efficiently, if complete information on wastage on account of drop‑outs
and repeaters is available. Further, if
the ratio is computed for different target groups would help us to identify
where it is exorbitantly high and alarming.
This can be checked through policy interventions and by improving
efficiency. (iv) Wastage on Account of Repeaters & Drop‑outs In order to work out wastage on account of repeaters and
drop‑outs, let us first analyse the evolution of cohort over a period of
time. It is evident that of the total
investment of 4,212 student years, graduates have consumed only 3,100 student
years i.e. (620 x 5). Thus, the balance
of 1,112 student years have gone waste which may be due to either repeaters or drop‑outs. Or in other words 1,112 more student years than required were
used to produce 620 graduates. Let us
first examine the break‑up of 620 graduates: 620 = 506 (5) + 100 (6) + 13 (7) + 1 (8)
The above equation reveals
that of the total 620 graduates, only 506 students have taken exactly five
years compared to 99 students who have taken six years, 13 students seven years and only 1 student has taken eight
years. Thus, 100, 13 and 1 students
have respectively taken 1, 2 and 3
years more than ideally required to become a primary graduate which is termed
as wastage on part of repeaters. Thus, 100 x 1 + 13 x 2 + 1 x 3
= 129 students years were wasted. Similarly wastage on account of drop‑outs
can also be worked‑out as follows: 83 x 1 + 127 x 2 + 80 x 3 + 50 x 4 + 34 x 5 + 6 x 6 + 0 x 7 + 0 x 8 = 983 Thus, the wastage on account
of repeaters and drop‑outs comes out to be equal to total wastage i.e.
1,112 which can be used to compute percentage wastage on account of repeaters
and drop‑outs as follows: Wastage on Account of 129 Repeaters = X
100 = 11.60% 1,112 983 Drop‑outs = X 100 = 88.40%. 1,112 (v) Average Duration of Stay: Graduates, Drop‑out
and Cohort The next indicator of efficiency we present below is
Average Duration of Stay in the system
which can be computed separately for graduates, drop‑outs and also for
entire cohort as a whole. The detailed
computational procedure is presented below. Graduates 506 x 5 + 100 x 6 + 13 x 7 + 1 x 8 = 620 = 5.19
Years Drop‑outs 83 x 1 + 127 x 2 + 80 x 3 + 50 x 4 + 34 x 5 +
6 x 6 + 0 x 7 = 380 = 2.59 Years Cohort 1000 x 1 + 917 x 2 + 790 x 3 + 710 x 4 + 660
x 5 + 120 x 6 + 14 x 7 + 1 x 8 = 4,212 = 2.89
Years. It may be noted that 620,
380 and 4,212 are number of graduates, drop‑outs and total student years
invested during the entire evolution of hypothetical cohort of 1,000 students
who entered into the system in the year 1990.
The average stay in the system indicates that on an average a dropped‑out student stayed for at least 2.59 years which
is slightly lower than the average stay for the entire cohort. Hence, drop‑outs have contributed a
lot to wastage which is also evident from the percentage wastage on account of
drop‑outs presented above. (vi) Cohort Survival & Drop‑out Rates The last indicator of efficiency based on hypothetical
cohort method is cohort survival and drop‑out rates. The number of survivals and drop‑outs
up to a particular grade can easily be obtained from the evolution of hypothetical cohort presented above in Box
5. The last two rows indicate that only
913 students remained in the system up to Grade II which is obtained by
subtracting number of drop‑outs who left the system before reaching to
Grade II from the initial 1,000 students in Grade I. Thus, giving cohort survival and drop‑out rate to be 8.70
per cent (87/1,000 X 100) and 91.30 per cent (913/1,000 X 100) respectively.
Similarly, rates for the remaining grades can also be obtained. The cohort survival and drop‑out rates
tell us in which grade is the drop‑out rate highest. The exercise can be repeated for different
target groups. Once the grade and
target group along with sub‑area which has lowest survival is identified,
the next step is to initiate programmes, so that the survival rate is improved. Some of the indicators of efficiency discussed above are
computed at the all‑India level for three cohorts, namely 1985‑86,
1986‑87 and 1987‑88. The
results are presented in Table 10. The
perusal of results reveal that over a period of time, at primary level, the
wastage ratio has declined from 1.45 in 1985‑86 to 1.44 in 1986‑87
which has further declined to 1.34 in 1987‑88. Similarly, a declining trend has been observed in number of
years the system took to produce a primary graduate which is considered a healthy
sign, however it is still on
high side. The results further
show that though the efficiency of the education system is improving but still
around 34 per cent of the efforts are going waste which is also evident from
the amount of wastage on account of repeaters and drop‑outs. Of the total wastage in 1987‑88,
repeaters share was only 16.59 per cent compared to 83.41 per cent drop‑outs,
relative figures for girls are 14.50 per cent and 85.50 per cent which show that girls tend to
repeat less than their counterparts but preferred to drop‑out from the
system which severely affect the
efficiency of the education system.
Unless this is checked, the efficiency of system at the primary level
would continue be at low ebb. However,
different studies suggest that this could not be possible by one single method
or once for all action but it should involve the whole educational system. School community relationships,
instructional material, curriculum, infrastructural facilities, quality of
teaching and supervision are some of the areas where policy interventions may
be required (NIEPA, 1992a). Table 10 Efficiency of Education System: Primary Education Selected Indicators: 1985‑86 to 1987‑88
Source: Mehta,
Arun C. (1995a).
Few indicators of quality of facilities and their
utilisation is presented below.
However, it may be noted that it is not an easy task to develop
indicators of quality of facilities because of lack of precise definition of
quality of education which is generally judged in terms of learners
achievements (Kapoor, n.d.). The next important question one may ask is
about factors which influence quality of education in general and learners
achievement in particular. A large
number of studies conducted in the recent past have identified a number of
factors some of which can be grouped under the following two broad headings: (a) School Buildings and Equipments Related
Indicators; and (b) Indicators Relating to Staffing Conditions. (a) School
Buildings and Equipments As mentioned, the first stage of planning is diagnosis of
the existing position which also includes school buildings and equipments which
enables to answer a variety of questions.
The first important objective of this exercise is to assess the
availability of school buildings and also the quality of available buildings
which plays an important role to
identify priority areas, so that renovation and extension of buildings are
taken up. The analysis of equipments will help to identify
disadvantaged areas and will also help to assess true capacity of a school, so
that schools which are underutilised or conversely overloaded can be identified
(Kapoor, n.d.). Once the facilities are
identified, the next important question is about their utilisation which should
be compared in relation to national standards.
So far as the utilisation of premises in a primary school is concerned,
the simple indicators, such as, percentage of schools which work double shifts
and rooms which are used double shifts be computed. In case of a middle and secondary school, Time Utilisation
Rate may be computed which is presented
in equation (17). Number of Periods
Actually Used Time Utilisation
Rate =
X 100 (TUR) Number
of Periods for which Use Theoretically
Possible (17) 50 = X 100 100 = 50
%. If Time Utilisation Rate for a particular school comes out to be 50
per cent which means that theoretically the school is in a position to allow 50
per cent additional enrollment even without improving the existing number of
rooms. But, in practice this may or may
not possible because of the contingencies of time‑table, the utilisation
rate may not go beyond 90 per cent.
One of the serious limitations of this indicator is that it fails to
give any indication of how far room space is occupied (Kapoor, n.d.). For this purpose, Space Utilisation
Rate (equation 18) may be used which
relates average students who utilise room to its maximum capacity. Average Number of Students Per Room SUR = X 100 (18) Room Capacity For example, ten rooms are
built‑up in a school to accommodate about 1200 students but in reality
the rooms on an average are occupied by only 600 students, which means SUR
comes out to be only 50 per cent
i.e.(600/1200* 100). The next important
question which may crop‑up now is that for how much time the rooms are
being utilised by these 50 per cent students for which Overall Utilisation Rate
(equation 19) needs to be computed.
Thus for the above data, the Overall Utilisation Rate comes out to be: Overall Utilisation Rate = TUR x SUR (19) 50 600 OUR = X X 100 100 1200 = 25 %. This rate should be used to
asses utilisation of school premises which is based on both time and space
utilisation, hence should be improved
to the extent possible to decide adequacy of premises. Needless to mention that the indicators presented above
can be used only for micro level analysis. (b) Equipments The next set of indicators presented below relates to
equipments and their utilisation. Generally,
equipments cover furniture, teaching aids and educational supplies. Indicators of equipments of premises may be computed either school‑wise or
classroom‑wise which depends upon the nature of a particular
variable. Percentage of schools having
electricity, drinking water and toilet
facilities, playground and staff
quarters are some of the basic indicators
of facilities which can be computed for a block and district. But for facilities within schools,
percentage of classrooms with pupils desk and teachers chair and table can be
considered. So far as the teachers
equipment is concerned, the same may be linked to scheme of Operation
Blackboard in relation to timely supply of items, its adequacy and utilisation. Similarly, percentage of classrooms having
blackboard may also be considered an indicator of teaching equipment, so as the
percentage of teachers having maps, charts and globes. Percentage of children having textbooks,
slates, exercise books and pencils are some of the indicators relating to pupil
supplies. Similarly, percentage of
children in a school receiving mid‑day meals, free textbooks and uniforms
is an indicator of beneficiaries under a particular scheme. (c) Staffing Conditions Teacher plays an important role in functioning of school
and imparting education. Therefore information on a number of variables, such
as, qualifications, length of service, training, experience and subject needs
to be collected. However, these factors themselves do not guarantee quality in
a teacher, as it depends upon a number of other factors predominantly relating
to working environment and type of school in which he/she works. Therefore, simple percentage distribution of
teachers according to their education, sex and other variables mentioned above
can be computed. The next area under
which information needs to be collected is workload of teachers for which pupil‑teacher
ratio and class‑size are calculated which can be used to know average
audience. Class‑size is
calculated by dividing total enrolment in a school to total number of classes
in that school. But number of pupils
being taught vary according to subject and teaching method and hence, class‑size may not always present true
picture. Average Audience indicator is
better indicator than the class‑size (Kapoor, n.d.) which consider
average enrolment of the groups which is being taught by one teacher (equation
20). However, the indicator is recommended to compute at the
institutional level only. Number of Weekly Class
Periods x Number of Pupil Average Audience
= (20) Number of Weekly Periods Taken by
Teachers It may be noted that in the
numerator, number of weekly class periods is considered and not the weekly
hours, as the same vary from 40 to 60 minutes.
Total
Enrolment Pupil‑Teacher
Ratio (I) = (21) Total
Teachers Total
Enrolment Classes Pupil‑Teacher
Ratio (II) = x (22) Classes Total
Teachers The other simple indicator of utilisation of teachers
is Pupil‑Teacher Ratio (equation
21) which is simply a division of total enrolment to total teachers. In a more refined way, the ratio can also
be computed by relating enrolment per class to classes per teacher. But in case of primary education, the
teacher usually occupied with same
class or classes, in that situation the
indicator presented above in equation 22 will simply be the be Pupil‑Teacher Ratio (I). The detailed analysis of number of teachers
in an institution according to subjects will reveal that the existing number of
teachers is adequate or inadequate. The indicators of educational development presented above
are useful to undertake diagnosis of the existing situation. More specifically, the indicators of access,
coverage, out‑of‑school children and drop‑out rate are used
to identify educationally backward areas.
However, one of the limitations of indicators based on the Reconstructed
Cohort Method is that they do not take into account the quality of output that
the system is producing. The method
takes cognisance of only number of students who successfully complete an
education cycle, which means learners attainment is totally ignored. The method also assumes that all the members
of a cohort have identical facilities in schools, which may not always be
true. Therefore, for comparing the
efficiency of schools situated in the rural and urban areas and for the
Government and Private schools, the method may not be appropriate to use. Government schools are practically open to
all children irrespective of caste, creed, income and educational level of
parents but the same is not true in case of the private schools. Admission test is one of the many
considerations on the basis of which mostly children of the elite society used
to get admission in the private schools.
In contrast, only those children take admission in the Government
schools whose parents do not have capacity to pay high tuition fee or
alternatives to the Government schools are not available. Hence, an output of
700 out of 1000 input students may be considered efficient for a Government
school but the same is not true for a school managed by the private
organisation. Similarly, the socio‑economic
background of students, if incorporated in the method would help to know
whether the efficiency vary from one income group to another and educational
level of parents. It may also be noted that
the method takes into account only the number of drop‑outs and repeaters
as possible causes of an inefficient system but ignores all other reasons which
may pre‑dominantly relate to the delivery mechanism or the same may
attribute to systems failure. May be,
it is due to the medium of instructions which is not local or it is the
teaching methods which are not effective or the infrastructure facilities are
not adequate to retain children in the system.
The method is also not suitable for the developed education systems,
which have low drop‑out and repetition rate, and hence, it can be best
applied to the systems, which have high incidence of drop‑out and
repetition. Hence, the method needs
further refinement. Johnstone, James N. (1981),
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Education, NIEPA, September, New Delhi. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
