ResearchPDF Available

Measuring Equity of Education Resource Allocation: An Output-Based Approach

Authors:
  • FHI 360, Washington DC, United States

Abstract and Figures

The promise of public education is in expanding opportunity and accelerating pathways for individuals regardless of their background, physical ability, or place of residence. This promise drives SDG 4: "Ensure inclusive and equitable quality education for all", which has galvanized governments and donors around equity, and pushed the education community towards greater clarity around disparities in education inputs and outcomes. Recognizing that equity is not feasible without a resource commitment, the Incheon Declaration calls for governments to "allocate resources more equitably across socioeconomically advantaged and disadvantaged schools" (Education 2030). Building equity through education involves directing resources-talent, materials, financing-to where the needs are greatest. As such, an equitable funding model is an intentional redistribution of resources with higher priority placed on schools and communities that have historically fallen behind, due to socioeconomic and demographic factors outside their control. In this paper, we offer a methodological framework for assessing the equity of resource allocation in education, using data on key resource elements that are available to students at different levels of disadvantage. Using an outputs-driven model, we seek to provide a snapshot of the relative equity of different education systems and offer a gauge of how equity in resource allocation may be measured, both across systems and over time. Our model is simple and replicable with many existing data sources.
Content may be subject to copyright.
Measuring Equity of Education Resource
Allocation: An Output-Based Approach
Education Equity Research Initiative
DRAFT FOR COMMENT
Carina Omoeva, Nina Cunha, and Wael Moussa
September 2019
DRAFT FOR COMMENT
2
The case for examining resource allocation in education
The promise of public education is in expanding opportunity and accelerating pathways for
individuals regardless of their background, physical ability, or place of residence. This promise drives SDG
4: “Ensure inclusive and equitable quality education for all”, which has galvanized governments and
donors around equity, and pushed the education community towards greater clarity around disparities in
education inputs and outcomes. Recognizing that equity is not feasible without a resource commitment,
the Incheon Declaration calls for governments to “allocate resources more equitably across socio-
economically advantaged and disadvantaged schools” (Education 2030). Building equity through
education involves directing resources talent, materials, financing - to where the needs are greatest. As
such, an equitable funding model is an intentional redistribution of resources with higher priority placed
on schools and communities that have historically fallen behind, due to socioeconomic and demographic
factors outside their control.
In this paper, we offer a methodological framework for assessing the equity of resource allocation
in education, using data on key resource elements that are available to students at different levels of
disadvantage. Using an outputs-driven model, we seek to provide a snapshot of the relative equity of
different education systems and offer a gauge of how equity in resource allocation may be measured,
both across systems and over time. Our model is simple and replicable with many existing data sources.
Background
How does one measure equity in resource allocation? There are two indicators currently
proposed for this in the SDG Framework: (1) 4.5.3 Extent to which explicit formula-based policies allocate
resources towards disadvantaged populations, and (2) 4.5.4 Education expenditure per student by level of
education and source of funding. While 4.5.4 has already been adopted for reporting, 4.5.3 is drawing
substantial discussion, understandably so: as UIS recognizes in its Information Paper 32, Improving the
International Monitoring Framework to Achieve Equity (SDG 4.5): Indicator 4.5.3 (UIS 2016), the complexity of
financing flows in education, and the different ways in which per pupil funding norms are constructed,
make it difficult for there to be a single metric of how resources are directed towards the disadvantaged.
In highly decentralized systems, formula-based federal allocation may represent a small fraction of the
total amount of resources that reach the schools. A further UIS discussion paper prepared for the
Technical Cooperation Group on Indicator 4.5.3 (UIS 2018) examines the complexity of funding formulas
at different levels and stops short of recommending a single approach to measuring equity in education
financing.
Even if the challenges of complexity and diversity of systems were to be overcome, there are
limitations to using policy as a source of data on resource inequities. Formula-based policies may
demonstrate the intent, but not the actual implementation of resource allocation in a system. By contrast,
direct observation of resources actually available to learners would provide a gauge of the real disparities
at play, and, in combination with data on education outcomes, would offer a powerful lens onto how
resource inequities may shape learner trajectories.
Some recent publications, including the Education Sector Analysis Methodological Guidelines
(Pole de Dakar, World Bank, UNICEF, and GPE, 2014) and Chapter 4 of the UIS Handbook for Measuring
Education Equity (UIS 2018) recommend the benefit incidence analysis (BIA) as a resource availability
measure that allows for equity comparisons. BIA draws on a single nationwide figure of per student
expenditure by level of schooling primary, secondary, or tertiary, which is then multiplied by the
estimated number of people, disaggregated by equity dimension (such as wealth quintile or ethnicity),
that have attained each of the respective levels. The estimated population of people attaining a given
level is drawn from household surveys, which offer statistics of educational attainment by region,
DRAFT FOR COMMENT
3
subgroup, and socioeconomic status. Using a unit of expenditure by level, one can estimate the total
amount of expenditure benefiting a given subgroup, relative its size within a population. An odds ratio of
one group’s share of expenditure relative its size to that of the other is then used as a metric of disparity
in resource allocation known as the Relative Appropriation Index (RAI).
While the RAI can be a useful general gauge of the resource imbalances within a country, it is less
useful in contexts where educational attainment (years of schooling) is generally higher for all groups, or
for analyses within a level of schooling (e.g. primary only). Also, because the RAI assumes a constant
amount of funding per student within a level of schooling, it does not allow for accounting of the
differences in the quality of inputs and resources available to students due to their socioeconomic and
demographic characteristics. However, we know that differences in the quality and costs of inputs
teachers, learning materials, infrastructure, support systems between communities and schools can be
substantial.
School surveys, when they are regularly administered, provide an opportunity to examine
differences in key resource inputs that are crucial for delivering a quality education. Many existing surveys
already include a range of variables that are essential to learning such as the quality of teacher capacity
and the conditions of the learning environments. Coverage of such data is still far from ideal, and variables
are not always consistent survey to survey and across contexts. Increasingly, however, there are efforts
to standardize some level of reporting across systems and allow for within-country analyses of resource
availability to take place. Notable examples include the SABER School Financing module, which seeks to
strengthen consistency of school-level data collection on finance.
Because an equity analysis requires disaggregation of data across equity dimensions, linking
school-level resource data with information on the population of learners at the school level is a key
condition for measuring equity in education resource allocation. School surveys that include data on the
composition of the student body, as well as their learning outcomes, are the strongest sources of data for
resource equity analyses. In this paper, we describe an approach for performing equity analysis using data
from a single source, as well as the options and limitations of linking data from different sources.
Analytical Framework
The analysis will follow a three-stage sequence. First the analysis characterizes the level and
distribution of “need” at the student and school level which is quantified based on student demographic
proxies that are associated with poverty. For instance, a high needs student is a student in the bottom
quintile of household wealth and a high needs school would be one with a high percentage of these
students. This provides us with a consistent method to assess the level of inequity in the allocation of
school resources. Second, we determine resource inputs at the school level using a standardized approach
that may still be adaptable to changing systems and contexts based on a simple education production
function with human and physical capital as inputs and learning outcomes as outputs. Lastly, we assess
the overall allocation of these resources or inputs by disaggregating their distribution by equity dimension
(such as SES, disability, ethnicity, etc.) or by level of needs.
However, the challenges with measuring equity of education finance and resource allocation, as
the UIS rightly concedes in the Framing document on SDG indicator 4.5.3
1
, lie in the complexity of
education finance systems, the multitude of funding sources, and the lack of data on school financing for
key resource elements. Education financing often comes from multiple funding agencies and multiple
levels, and identifying precise amounts allocated to different units may not be feasible in all systems.
Our analytical approach narrows the task at hand measuring equity in resource allocation to
measuring equity in access to education resources, moving the locus of measurement to the receiving
end. Rather than tracing policy and flows, which reflect policy and resource intent, the focus on outputs
1
http://tcg.uis.unesco.org/wp-content/uploads/sites/4/2018/08/TCG4-14-Development-of-Indicator-4.5.3.pdf
DRAFT FOR COMMENT
4
provides a sense of the resource allocation implemented and observed at the school level, where students
can interact with teachers and their school environment.
1. Resource Indices
There is no standard list that make up the inputs that go into the education production function.
There is some agreement in the literature that teacher quality, the classroom environment, learning
materials, and overall financial spending constitute inputs or resources that are conducive to student
learning (Hanushek, 2009). As such, we argue that a student’s access to these resources can benefit their
overall learning outcomes. Empirically, however, access to standardized data across these inputs is
especially challenging, and in some cases may not exist.
To simplify the analysis and maximize the applicability of this framework across education
systems, we conceptualize access to education resources broadly as: 1) teacher quality; 2) instructional
environment, and 3) physical environment. Each of these factors captures a vector of specific inputs,
which may have slightly different underlying structures, but reflect the same key inputs. Within Teacher
Quality, we seek to measure access to well-trained and experienced teachers, with demonstrated
effectiveness. Within Instructional Environment, we capture access to teaching and learning materials,
as well as learner’s resources within a school, such as computer labs, libraries, and education technology.
Within Physical Environment, we seek to examine access to the quality of physical infrastructure, which
is particularly crucial as a differentiator in lower-resource contexts. Using available data within this general
framework, we construct indices for each factor, allowing us to examine the distribution of resources
within a system along each axis, as well as their combination. Table 1.1 shows the key variables that are
required for the application of this framework.
It is important to note that the teacher resource variables in our analysis are standardized by
school enrollment, wherever possible. That is, the number of teachers with a certain level of experience
and education is divided by school enrollment. This is consequential because in some systems, wealthier
schools have higher enrollments such that the standardization may influence the magnitude of the
disparities in teacher quality. Our approach represents the average student’s likelihood of accessing a
high-quality teacher in what we consider a High-Needs vs Low-Needs school; however, future applications
may choose to standardize by the number of teachers, which would be representative of the quality of
the faculty overall and might somewhat alter the magnitude of the measured gap in some systems. Other
resource variables, such as physical infrastructure and instructional environment, are available at the
school level only, and are therefore not adjusted by school enrollment in our analysis.
In essence, resources that are measured as quantities are standardized on a per student basis
such as the number of teachers with a graduate degree. Whereas, school resources that refer to whether
a certain attribute is available or not are kept in their binary form. This enables us to group inputs into
three main vectors of inputs that are teaching quality, instructional environment, and physical
Table 1.1: Key variables required for application of the framework
Teaching Quality
Instructional Environment
Teacher experience
Library/ Books
Sturdy walls/ Physical structure
Teacher education and/or
certification
Computer lab/ Computers
Steady electricity/ Water supply
Teacher performance metric*
Classroom Teaching and Learning
Materials
Non-academic infrastructure
(stadium, field, gym, playground)
DRAFT FOR COMMENT
5
environment. The three vectors are then aggregated into three resource indices, respectively, using a
Principal Components Analysis (PCA) that is measured as the relative rank of a school in the distribution
of that resource.
Once the Access to Resources Index is constructed, comparisons between relevant categories of
disadvantaged students and schools on their values along the index provide a gauge of the equity in
resource allocation within a system. These comparisons may juxtapose, for example, urban and rural
students, able-bodied and those with disabilities, and dominant and minority ethnic groups. However,
we propose a combination of socioeconomic and demographic factors to determine need and seek to
construct a composite Need Index.
2. Need Index
The Need Index is a summary measure that combines important socio-economic and
demographic predictors of performance, such as poverty, educational status of the parents, ethnicity or
language, and disability. Where available, we use the system’s own definitions of need and cutoff
categories to identify needy students and schools. In other cases, we construct the Need Index using
principal components analysis with available data on socio-economic status (SES; wealth index and
parents’ education, ethnicity or caste where relevant). The distribution of the Need Index allows for a
comparison of relatively high-need with relatively low-need learners and schools on learning outcomes
and on access to resources.
For this paper, we illustratively use the bottom 20% on the Need Index distribution as our cutoff
point for the analysis, thus designating the bottom 20% of students on the SES index as “High-Need”.
Aggregating the Need Index at the school level, we examine the school Need Status, by pooling the
proportion of disadvantaged students in its student population. Again, for this exercise, we set the
benchmark for High-Needs school as that which has 40% or more of its students falling in the bottom
quintile on the SES index. Conversely, schools that have 5% or fewer students falling in the bottom quintile
on the SES index are designated as Low-Needs. As Figure 2.1 illustrates, the High-Needs schools may form
the majority of schools within a system, if a large number of schools include more than 40% High needs
students, as is the case with New York State schools.
Figure 2.1. Distribution of Schools by Need Index, New York State
DRAFT FOR COMMENT
6
It is important to note that this distribution is tied to the public school systems. In contexts where high
proportions of well-to-do families enroll their children in private schools, the analysis that focuses on
disparities within the government-run schools only may mask inequalities at large. However, because our
interest lies in the public sector and the ways in which governments are able to direct resources to where
the needs are, we zoom in and construct all metrics of the framework as restricted to public schools only.
3. Examining Resource Equity: Do Resources Follow the Need?
For the final step in the application of the framework, we examine the level of resources available
by need, comparing the average level of resources on each of the three factors for High Needs schools
and Low Needs schools. It follows that in an equitable system, greater levels of resources are available to
units (schools) where there are higher concentrations of needy students. By contrast, if Low Needs
schools receive higher levels of teacher quality, and benefit from richer instructional and physical
environments, we may conclude that inequality is exacerbated.
Visually, we can examine the relative position of High-Needs vs Low-Needs schools on each of the
three Resource Axes, where the distance between them on each of the resource indices provides a gauge
of the equality and relative equity of resource distribution within the system. Figure 3.1 demonstrates
this approach to measuring equity of resource allocation, where each axis on the spider chart represents
percentiles in a distribution of the Resource Indices. Greater percentile rank shows higher access to
resources, and the difference between the lines indicates the disparities between High-Needs and Low-
Needs schools. As the Figure illustrates, in the hypothetical System A, Low-Needs schools receive
substantially greater access to teacher quality, instructional environment, and physical environment. In
System B, the distribution is the reverse. Consequently, we determine that System B is more equitable
than System A, because it disproportionately directs resources to areas of greater need. This assumes
that the underlying elements within each of the three factors are the same, as is the composite measure
of Need.
Once the disparities across each category of need are measured, the difference between the
highest and lowest need is summarized in a single score representing the percentile rank distance
between the two benchmarks. The score can then be summarized across categories into an aggregate
equity of resource allocation measure:
Figure 3.2
  


Figure 3.1: A three-pronged framework to measuring the equity of resource allocation
System A
System B
DRAFT FOR COMMENT
7
In constructing a measure of equity in the distribution of resources as the equity in access to
observed resources at the school level, we take a step forward from a proposal that first appeared in UIS
2016 (p.13-14). The proposed Index of Access to Educational Resources referenced in that publication
combines all available data on resources in a single factor and examines resource disparities by comparing
groups along several dimensions of equity. In contrast, our framework distinguishes key resource
elements as separate and independent but uses the full composite Need Index to identify High-Needs
students and schools, through a combination of multiple background characteristics.
By focusing on the observed access to educational resources, our approach allows for gauge of
the extent to which resource distribution across schools is reinforcing existing inequalities or working to
correct historical disparities by directing more public resources to the neediest schools.
Application
We apply the output-based analytical framework described above to four cases of education
systems: New York State, Brazil, Peru, and Pakistan. In each case, we use data that is available at the
school level, both for the estimation of the needs distribution and the analysis of the distribution of
resources. Table 1 provides a brief summary of the data sources and the characteristics of the sample.
Table 1: Summary of the data sources and the characteristics of the sample
In three cases we worked with a systemwide school census surveys, and one ASER Pakistan
was a large-scale household survey with an assessment component administered to both enrolled and
out-of-school children. Across all cases, we held the Need Index benchmark for High-Needs schools at
40% and above of High-Needs students. The case studies provided below offer more detail on the
construction of the resource indices and the findings for each country; however, the following
observations emerge across the countries:
1. The Need Index is highly predictive of student outcomes in all cases. While the magnitude of the
relationship varies, and the assessments differ in their distributions, there is a clear pattern where
High-Needs students are more likely to underperform.
2. The distribution of High-Needs students across schools within a system ranges dramatically. From a
resource allocation perspective, it matters whether disadvantaged students are clustered in high-
poverty, High-Needs environments, or placed in schools where the majority of students are not
disadvantaged. Across the four cases, the proportion of schools with 40% and above of High-Needs
students ranges from 60% in Peru to 18% in Pakistan. As the second column of the table illustrates,
the 20% neediest schools in Peru have 88%, on average, of High-Needs students, compared to only
Country
Data Source
Grade/ Student
Age
Number of
schools
Brazil
1. The National Assessment System for Basic Education
(Sistema Nacional de Avaliação da Educação Básica, SAEB)
2. Prova Brasil
Grade 5 and Grade
9
53,469
New York
1. The Common Core of Data
Elementary and
Secondary grades
3,041
Pakistan
1. Annual Status of Education Report (ASER)
5-16 years of age
3,032
Peru
1. Peru’s Educational Census (Censo Educativo)
2. Student Evaluation Census (Evaluación Censal de
Estudiantes ECE)
Grade 4
13,239
DRAFT FOR COMMENT
8
36% in Pakistan. Generally it appears that disadvantaged students are more represented in public
schools in Peru and New York than in the other countries.
Table 2: Proportions of schools with 40% and above of High-Needs students, and the cutoff for highest quintile
Country
% Schools with 40% and above of High-
Needs students
%of High-Needs students in
the top 20% neediest
schools
Brazil
24%
44%
New York
54%
67%
Pakistan
18%
36%
Peru
60%
88%
3. The relationship between need and resource allocation varies across subnational units within
systems; however, across systems this relationship is generally inverse: areas with concentrations of
disadvantaged students are also the ones that receive the least amount of resources.
4. The summary index provides a useful comparative measure of system inequity in education resource
distribution. It is most informative within systems but provides a reasonably good perspective for
understanding the magnitudes of inequity across systems. As Table 3 shows, Pakistan demonstrates
a lower overall level of resource inequity than the other three systems. However, the greatest insight
comes from a comparative analysis across each of the axes: in New York, the highest inequity is in
teacher quality, while in Peru and Brazil, the instructional environment presents the greatest
disparities between High-Needs and Low-Needs schools.
Table 3: Summary indices across systems
Country
Teacher Quality
School Physical
Environment
School
Instructional
Environment
Summary Score
New York
38.14
-3.76
12.26
46.64
Brazil
10.03
8.62
25.43
44.08
Peru
7.73
8.48
27.74
43.95
Pakistan
13.6
-1.9
19.5
31.2
Figure 3 provides a visual demonstration of the application of the framework, following the illustrative
example set above in Figure 3.1. The distances between the lines and the resulting shapes of the triangles
gives us a sense of the ways that resources are distributed in each system and across systems.
With subnational analyses, presented later in the case studies, the size of the spider visuals across
subnational units can provide a sense of the overall level of resources in that unit, as compared to a
national average. With cross-country comparisons, however, one must be aware of the differences in the
underlying structure of the axes variables to gauge the level of resource availability across systems: for
example, while it may appear that New York has a higher level of Teacher Quality overall than Brazil, there
are differences in the markers of teacher quality (e.g. the New York index includes teacher salary and
performance) that make that comparison less than meaningful. However, where it is possible to fully
standardize the underlying variables, the size and shape of the spider graphs would offer an immediate
gauge of resource availability across systems.
Thus, the key value of the country-level summary Inequity Index, as well as each of the composite
indices is its measure of the relative intra-system inequity, using measures that are important within that
system. To the extent possible, we envision these measures being standardized internationally; however,
DRAFT FOR COMMENT
9
even as is, the Index offers a gauge of where the key imbalances are, and how drastic the disparities are
between High-Needs and Low-Needs schools across systems.
Figure 3: Distribution of education resources across High-Needs and Low-Needs schools in four education systems
Below we provide the four country-level applications of the output-based equity analysis with
further detail. Each country case includes a description of the data sources used, the construction of the
Need Index and Resource Indices, and a visual presentation of the results, including the Need Index
school-level distributions, learning outcomes distributions by Need Index, spider charts demonstrating
the application of the three-pronged resource equity framework, and maps with spatial demonstrations
of need and resource distribution. Importantly, within each of the countries the three-pronged radar
visuals of resource distributions are available at the subnational level, where they can be examined both
DRAFT FOR COMMENT
10
for their absolute size and shape (e.g. showing that the overall level of resources, even for Low Needs
schools, is substantially lower in some regions than others), and for the disparities between High Needs
and Low Needs school categories within each region. A distribution of the Need Index shows the extent
to which students with high levels of disadvantage are clustered within schools vs. represented more
evenly across all schools. Learning outcomes distributions by Need Index demonstrate the potency of the
socio-economic determinants of achievement, particularly when one examines the ways that Low Needs
and High Needs student score distributions on standardized exams are offset, in some cases (such as New
York) only partially overlapping. Finally, we present a spatial/ geographic analysis of the need and
resource distribution, showing the extent to which the resources are directed to the areas of need.
Each of the cases allows for a redefinition of the resource axes, and we envision that analysts at
the national level may wish to examine a variety of definitions of the composite factors. However, at the
cross-national comparative level, we see a value in greater standardization of key resource parameters,
allowing for a higher-level examination of the magnitude of public school systems’ addressing inequality
in the student’s starting conditions. We welcome further applications of the framework, and resulting
critique and refinements.
4. Brazil
Background
The National Assessment System for Basic Education (Sistema Nacional de Avaliação da Educação
Básica, SAEB) is the national system for evaluation of basic education in Brazil based on a rigorous
sampling methodology. It is a nation-wide sample survey conducted on a bi-annual basis since 1993. SAEB
focuses on the education quality in Brazilian schools by measuring the performance of students on
standardized language and math tests at different schooling stages. It also monitors changes in the
students’ performance over time, considering the existing conditions of the Brazilian education system.
SAEB assesses students from both public and private schools at the end of primary (5th grade), lower
secondary (9th grade), and upper secondary (12th grade) school cycles.
Prova Brasil, a national assessment program based on student achievement tests, is implemented
as a component of SAEB. The tests are the same from SAEB. However, the Prova Brasil is administered
only to public-school students attending the end of primary (5th grade) and lower secondary (9th grade)
schooling levels.
SAEB is representative at the state level. SAEB samples schools with a minimum of 10 students
per class. Prova Brasil, on the other hand, samples all public schools with a minimum of 20 students per
class in fifth and ninth grades. In 2015, SAEB assessed 3,913,615 students enrolled in the 5th, and 9th
grades of 57,744 schools, of which 96.6% were public schools and 3.7% were private schools. In addition
to language and math assessments, the data set also provides information on the socio-economic
background of students and information on teachers, principals, and schools.
For the analysis, we use data from SAEB and Prova Brasil 2015, for fifth and ninth graders enrolled
in public schools. After merging data on student background, teacher survey, and school survey, the final
sample includes 53,469 schools.
Instruments
Need Index
The student survey includes a set of questions on the availability of the following household
assets: tv, home stereo, DVD, fridge, washer, car, computer, bathroom, bedroom, and if maid provides
regular services for the household. Using these variables, we employ factor analysis to construct an index
representing the socioeconomic status of each household. We use this variable to construct Need Index,
which is the percentage of low-income students in each school (percentage of students in the bottom
DRAFT FOR COMMENT
11
quintile of the socioeconomic index). As a final step, we divide the Need Index into three groups: High-
Needs, average-needs, and Low-Needs. The High-Need category represents schools with at least 40% of
the student body classified as low-income. The average-need category is composed of schools with the
percentage of low-income students between 5% and 40%. The schools with at most 5% of the student
body identified as low-income are categorized as Low-Needs schools. Figure 4.1 shows the distribution of
the Need Index across Brazilian schools. 18% of schools fall under the Low-Need category, whereas 24%
of schools fall under High-Need category. Figures 4.2 and 4.3 show the distribution of Portuguese and
math test scores by need category. For both the subjects, the Low-Needs schools perform better than the
High-Needs schools.
Teacher Quality
The teacher quality factor is composed of teacher education level and teaching experience. These
variables are standardized by student enrolment. To construct the teacher education level, we divided
the number of teachers with a graduate degree in the school by student enrollment. Likewise, to construct
the teacher experience variable, we divided the number of teachers with 20 or more years of experience
by student enrollment. Figure 4.4 shows the distribution of the teacher quality index by need category.
Although the distributions of the High and Low-Needs schools are quite similar, the distribution of teacher
quality index for the Low-Needs schools is shifted to the right, indicating that Low-Needs schools have
access to better teacher quality.
School Physical Environment
The school physical environment factor captures the conditions of infrastructure items as good,
regular, bad, or nonexistent. These infrastructure items include roof, wall, floor, building entrance,
schoolyard, halls/corridors, classrooms, doors, windows, bathroom, kitchen, hydraulic, and electrical.
Figure 5 shows that the distribution of the school physical environment index for the Low-Needs schools
is slightly shifted to the right and, the average score is better compared to the High-Needs schools.
School Instructional Environment
The school instructional environment factor captures the condition of the instructional
environment as good, regular, bad or nonexistent. The instructional items include whether the school has
a computer, internet, a copy machine, a printer, a projector, data show, a DVD, a tv, broadband, landline,
stereo, a library, a reading room, a sports court, a computer lab, a sciences lab, an auditorium, a music
room, and an art lab. Figure 6 shows a large disparity between Low-Needs and High-Needs schools as far
as the school instructional environment is concerned.
Findings
Spatial Analysis
Figures 4.7 through 4.10 show the distribution of the Need Index and each of the school resource
factors across the Brazilian micro-regions. Figure 4.7 shows a high concentration of Low-Needs schools in
the south and southeast, while High-Needs are more present in the north and northeast. The resources
maps show the opposite picture: more resources are present in the south and southeast schools, for all
the three factors, while fewer resources are available at schools in the north and northeast, showing that
High-Needs schools have less access to school resources as compared to Low-Needs schools.
Three-pronged analysis of inequity
Figures 4.11 through 4.16 show the radar charts for Brazil and each of its five regions. Nationally,
the disparity between Low-Needs and High-Needs schools is the largest for the school instructional
environment. In the Center-West region, the High-Needs schools have equal access to teacher quality as
the Low-Needs schools, but access to poorer infrastructure and instruction, although the disparity is
smaller when compared to the national level. In the Northeast, High-Needs schools have equal access to
school infrastructure as the Low-Needs schools, but access to poorer teacher quality and instructional
DRAFT FOR COMMENT
12
environment. North shows a large disparity between high and Low-Needs schools across all three
dimensions. In the Southeast, the disparity between Low-Needs and High-Needs schools is lower for
teacher quality as compared to school infrastructure and instruction. In the south, high and Low-Needs
schools have equal access to teacher quality and school infrastructure resources, but Low-Needs schools
have access to better school instructional resources.
Table 4.2 indicates the difference between Low-Needs and High-Needs schools across each of the
three dimensions, and the summary score indicates the magnitude of inequity between Low-Needs and
High-Needs schools across all three dimensions. A higher summary score represents higher inequity in
resource distribution. The South is the most equitable region, followed by the Center-West, Northeast,
Southeast, and North. North shows the largest disparity across all three dimensions and is the only region
showing a larger disparity when compared to the national level.
Figures and Tables
Table 4.1: Composition of School Resource Factors
Factor
Teacher Quality
School Physical Environment
School Instructional Environment
Variables
Teacher experience
Teacher education
Roof
Wall
Floor
Building entrance
Schoolyard
Halls/corridors
Classrooms
Doors
Windows
Bathroom
Kitchen
Hydraulic
Electrical
Computer
Internet
Copy machine
Printer
Projector
Datashow
DVD
TV
Broadband
Landline
Stereo
Library
Reading room
Sports court
Computer lab
Sciences lab
Auditorium
Music room
Arts lab
DRAFT FOR COMMENT
13
Figure 4.1: Distribution of schools' needs-index
Figure 4.2: Distribution of language test score, by need category
DRAFT FOR COMMENT
14
Figure 4.3: Distribution of math test score, by need category
Figure 4.4: Distribution teacher quality index, by need category
DRAFT FOR COMMENT
15
Figure 4.5: Distribution school physical environment index, by need category
Figure 4.6: Distribution school instructional environment index, by need category
DRAFT FOR COMMENT
16
Figure 4.7: Distribution of Needs Index across Brazilian micro-regions
Figure 4.8: Distribution of teacher quality across Brazilian micro-regions
DRAFT FOR COMMENT
17
Figure 4.9: Distribution of school physical environment index across Brazilian micro-regions
Figure 4.10: Distribution of school instructional environment index across Brazilian micro-regions
DRAFT FOR COMMENT
18
Figure 4.11: Three-pronged distribution of schooling
resources, by need category - Brazil
Figure 4.12: Three-pronged distribution of schooling
resources, by need category Center-West
Figure 4.13: Three-pronged distribution of schooling
resources, by need category - Northeast
Figure 4.14: Three-pronged distribution of schooling
resources, by need category - North
Figure 4.15: Three-pronged distribution of schooling
resources, by need category - Southeast
Figure 4.16: Three-pronged distribution of schooling
resources, by need category - South
DRAFT FOR COMMENT
19
Table 4.2: Magnitude of inequity in resource distribution
5. New York (excludes New York City)
Background
The Common Core of Data (CCD) is a comprehensive, annual, national database of all public
elementary and secondary schools and school districts in the United States. The state education agencies
in the 50 states and other US areas submit data annually to the National Center for Education Statistics.
The analysis focuses on the state of New York and utilizes data from two surveys, the Public
Elementary/Secondary School Universe Survey (2015-16), which contains information on student
enrollment and demographics, and the Local Education Agency Finance Survey (2014-15), which contains
information on teacher salaries and school expenditures. Our analysis focuses on 3,041 schools in 721
school districts.
Instruments
Need Index
The Need Index indicates the percentage of low-income students in each school. We use the
percentage of students per school that qualify for free and reduced lunch as a proxy for the proportion of
low-income students in the schools. Further, we divide Need Index into three categories- low, average,
and High-Needs. The Low-Need category is composed of schools with at most 5% of its student body
classified as low-income. The average-need category comprises schools with the proportion of low-
income students between 5% and 40%. The High-Need category contains schools with at least 40% of low-
income students. These categories form the basis of our analysis. We compare student learning outcomes
and school resource allocation between the low, average, and High-Needs categories.
Figure 5.1 shows the distribution of the schools’ Need Index. 3.9% of New York public schools are
Low-Needs schools, whereas 54% of schools are High-Needs. Figures 5.2 and 5.3 show the distribution of
English and math test scores by needs-categories. For both subjects, Low-Needs schools perform better
than High-Needs schools.
Teacher Quality
A school-level factor representing teacher quality is created using the following variables teacher
salary standardized by student enrolment, the proportion of teachers with Master of Arts (MA) diploma,
the proportion of teachers with greater than three years of experience, and the proportion of teachers
deemed highly effective. In Figure 5.4, we see that Low-Needs schools have access to better teacher
quality scores than average needs-schools and High-Needs schools. We also note that the disparity
between Low-Needs and High-Needs schools is the highest for teacher quality indicator.
Teacher Quality
School Physical
Environment
School
Instructional
Environment
Summary Score
National
10.03
8.62
25.43
44.08
Center-West
.2
7.26
9.44
16.9
Northeast
13.18
2.41
9.23
24.82
North
12.4
15.07
26.94
54.41
Southeast
5.78
13.04
11.45
30.27
South
.97
2.1
13.78
16.85
DRAFT FOR COMMENT
20
School Physical Environment
The school physical environment factor comprises spending on land structures, spending on
construction, and spending on other equipment. Figure 5.5 shows that High-Needs schools have slightly
better access to the physical environment than the Low-Needs counterparts.
School Instructional Environment
The school instructional environment factor is composed of spending on instruction and spending
on instructional equipment. The difference in the average school instructional environment score for
High-Needs and Low-Needs schools is indiscernible in Figure 5.6. While both, High-Needs and Low-Needs
distributions are concentrated towards the lower end of the score spectrum, the proportion of High-
Needs schools is higher at the lowest score spectrum end than the Low-Needs schools.
Findings
Spatial Analysis
Figures 5.7 through 5.10 show the distribution of the Need Index and each of the school resource
factors across New York school districts. Figure 5.7 shows a high concentration of Low-Needs schools in
the southeast near New York City, while High-Needs are dispersed throughout the entire State in both the
southeast and the rest of upstate New York. The resource maps in Figure 5.8 show that there is a higher
concentration of teacher quality in the southeastern region, and a lower concentration of these resources
in the north and western regions of the State. For school physical and instructional environment,
however, we observe less of a clear pattern.
Three-pronged analysis of inequity
Figure 5.13 shows the radar chart for New York, and Table 5.2 shows a difference between Low-
Needs and High-Needs schools for each factor and the overall summary score. A higher summary score
indicates higher inequity in resource allocation. The difference between Low-Needs and High-Needs
schools is the highest for teacher quality. The positive summary score indicates that there is inequity in
resource allocation.
Figures and Tables
Table 5.1: Composition of School Resource Factors
Factor
Teacher Quality
School Physical
Environment
School Instructional
Environment
Variables
Teacher salary (standardized
by student enrolment)
Proportion of teachers with
MAs
Proportion teachers with > 3
years of experience
Proportion of highly effective
teachers
Spending on
Land structures
Construction
Other Equipment
Spending on
Instruction
Instructional equipment
DRAFT FOR COMMENT
21
Figure 5.1: Distribution of schools’ needs-index
Figure 5.2: Distribution of language test score, by need category
DRAFT FOR COMMENT
22
Figure 5.3: Distribution of math test score, by need category
Figure 5.4: Distribution of teacher quality index, by need category
DRAFT FOR COMMENT
23
Figure 5.5: Distribution of school physical environment index, by need category
Figure 5.6: Distribution of school instructional environment index, by need category
DRAFT FOR COMMENT
24
Figure 5.7: Distribution of the needs-index, by school district
Figure 5.8: Distribution of teacher quality index, by school district
DRAFT FOR COMMENT
25
Figure 5.9: Distribution of school physical environment, by school district
Figure 5.10: Distribution of school instructional environment, by school district
DRAFT FOR COMMENT
26
Figure 5.11: Three-pronged distribution of schooling resources, by need category
6. Pakistan
Background
The Annual Status of Education Report (ASER) is a household-based survey conducted annually in
Pakistan since 2008. It provides a snapshot of the state of learning in Pakistan, assessing basic
competencies in literacy and numeracy of children ages 5 to 16. The ASER toolkit comprises learning
assessment tools in reading and arithmetic, as well as a household survey and school questionnaires for
both government and private schools. The questionnaires assess household demographic and
socioeconomic characteristics as well as school characteristics based on classroom observation and
teacher response. In 2016, ASER reached 255,269 children aged 3-16 from 5,540 schools in 144 rural
districts.
The ASER is representative at the district level. Within each of the 144 sampled districts, 30
villages are selected randomly using the PPS sampling technique, with 20 villages from the previous year
maintained, and ten new villages added each year to compose a rotating panel of villages. Within each
sampled village, 20 households are randomly selected. In every surveyed household, all children ages 3-
16 are surveyed, and all children age 5-16 are tested for competency in reading, in both their local
language and in English, and arithmetic. Our analysis focusses on 54,771 students enrolled in 3032
surveyed government schools.
Instruments
Need Index
The ASER asks each household a series of questions on the availability of certain household assets:
electricity, television, mobile phone, car, motorcycle/bike, computer, and solar panel. From this series,
we create an index of students’ socioeconomic status applying factor analysis. Using this index, we then
construct a Need Index at the school level by calculating the percentage of low-income students in each
school (percentage of students in the bottom quintile of the socioeconomic index). Next, we divide the
Need Index into three groups: High-Needs schools (schools with at least 40% of student-body composed
Table 5.2: Magnitude of inequity in resource distribution
Teacher Quality
School Physical
Environment
School Instructional
Environment
Summary Score
Statewide
38.14
-3.76
12.26
46.64
DRAFT FOR COMMENT
27
of low-income students), average- needs schools (schools with proportion of low-income students
between 5% and 40%), and Low-Needs schools (schools with at most 5% of student body composed of
low-income students). These three school-level needs categories form the basis of our analysis, as we
compare student learning outcomes and school resource allocation between low, average, and High-
Needs schools.
Figure 6.1 displays the distribution of the Need Index across schools in Pakistan. 18% of schools
fall under the High-Need category, and 55% of schools fall under the Low-Need category. Summary Figure
6.5 shows the percentage of students at each test score benchmark for tests assessing local language
skills, English, and math by need category. For both 5-12 and 13-16-year-olds, we see a higher percentage
of students in the Low-Need category achieve scores at the highest benchmark than High-Needs students.
Teacher Quality
The ASER survey collects information on teacher qualifications. As seen in Table 6.1, we create a
teacher quality index using two ordinal scores representing teachers’ level of education and level of
professional qualification. In every school, the number of teachers at each level of education and
professional qualification are assigned a score in increasing order of their credentials. We generate an
average for each of these two variables at the school level. The average teacher education and teacher
qualification scores are used to create teacher quality index. Figure 6.2 displays that High-Needs schools
have access to poorer teacher quality on average than Low-Needs schools.
School Physical Environment
The school physical environment factor captures the availability of certain infrastructure a fence,
toilet, electricity, drinking water, and solar panels as well as the number of certain school resources
classrooms and rooms. In Figure 6.3, we see that a vast majority of schools demonstrate a similar, low
physical environment factor score. However, High-Needs schools have access to better physical
infrastructure than Low-Needs schools. The difference in the average physical environment factor score
between High-Needs and Low-Needs schools is indiscernible in Figure 6.3.
School Instructional Environment
The school instructional environment captures the availability of resources supporting instruction.
We assess if a school has internet, library books or a science laboratory. Then, based on classroom
observation, we assess if a class has a usable blackboard and textbooks. These variables are binary, with
1 representing the existence of a certain asset. Figure 6.4 indicates that the High-Needs schools have
access to poorer school instruction than the Low-Needs schools, on an average.
Findings
Spatial Analysis
Figures 6.6-6.9 show the distribution of the Need Index of each of the school resource factors
across Pakistan’s districts. Figure 6.6 shows a high concentration of High-Needs schools in the southwest
of Pakistan, with a higher concentration of Low-Needs schools in the northeast. On the contrary, the
resource maps presented in Figures 6.7 and 6.9 show that more teacher quality and instructional
environment resources concentrated in the northeast schools, while fewer resources are available at
schools in the south and southwest, indicating that High-Needs schools have lower access to these
resources compared to Low-Needs schools. For physical infrastructure, however, the pattern is
indiscernible.
Three-pronged analysis of inequity
Figures 6.10 through 6.16 show the radar charts for Pakistan and each of its seven provinces. At
the country level, we see that the orange High-Needs triangle is within the blue Low-Needs triangle for
teacher quality and school instructional environment, signaling that High-Needs schools have lower access
to resources than Low-Needs schools across these two dimensions. The physical environment, on the
DRAFT FOR COMMENT
28
other hand, is in the expected direction. The High-Needs schools have better access to the physical
environment than the Low-Needs schools.
Table 6.2 shows the difference between low and High-Needs schools for each factor, as well as
the overall difference for all factors in sum. Certain provinces are more equitable than others. The
province of Gilgit-Baltistan shows the highest levels of overall inequity, with a summary score of 46.5
indicating a greater allocation of resources toward Low-Needs schools. Meanwhile, the province of Azad
Jammu and Kashmir indicates the lowest overall inequity in resource allocation.
Figures and Tables
Table 6.1: Composition of School Resource Factors
Figure 6.1: Distribution of schools’ needs-index
Factor
Teacher Quality
School Physical Environment
School Instructional Environment
Variables
Teacher Education Level
Teacher Professional
Qualification Level
Fence
Toilet
Electricity
Drinking Water
# Classrooms
# Rooms
Solar Panels
Internet
Usable blackboard (Classroom
Observation)
Test books (Classroom Observation)
Library books
Science laboratory
DRAFT FOR COMMENT
29
Figure 6.2: Distribution of teacher quality index, by need category
Figure 6.3: Distribution of school physical environment index, by need category
DRAFT FOR COMMENT
30
Figure 6.4: Distribution of school instructional environment index, by need category
DRAFT FOR COMMENT
31
Summary Figure 6.5: Percentage of students at each test score benchmark, by need category
As Figure 6.5 illustrates, children with high levels of disadvantage (High -Needs) were substantially less
likely to reach the highest performance benchmarks on ASER assessments, which are administered to children
ages 6-16. They were also somewhat more likely to not reach the lowest benchmark.
DRAFT FOR COMMENT
32
Figure 6.6: Distribution of needs-index schools, by district
Figure 6.7: Distribution of teacher quality index, by district
DRAFT FOR COMMENT
33
Figure 6.8: Distribution of school physical environment index, by district
Figure 6.9: Distribution of school instructional environment index, by district
DRAFT FOR COMMENT
34
Figure 6.10: Three-pronged distribution of schooling resources,
by need category - National
Figure 6.11: Three-pronged distribution of schooling
resources, by need category - Punjab
Figure 6.12: Three-pronged distribution of schooling
resources, by need category - Sindh
Figure 6.13: Three-pronged distribution of schooling
resources, by need category - Balochistan
Figure 6.14: Three-pronged distribution of schooling
resources, by need category - Khyber Paktunkhwa
Figure 6.15: Three-pronged distribution of schooling
resources, by need category - Gilgit Baltistan
Physical
Environment
Teacher Quality
National
Instructional
Environment
Low Needs
High Needs
Punjab
Low Needs
Teacher Quality
High Needs
Physical
Environment
Instructional
Environment
Sindh
Teacher Quality
Physical
Environment
Instructional
Environment
Low
Needs
High Needs
Balochistan
Teacher Quality
Physical
Environment
Instructional
Environment
High Needs
Low
Needs
Khyber Paktunkhwa
Teacher Quality
Physical
Environment
Low Needs
High Needs
Instructional
Environment
Gilgit-Baltistan
Teacher Quality
Physical
Environment
Instructional
Environment
Low Needs
High Needs
DRAFT FOR COMMENT
35
Figure 6.16: Three-pronged distribution of
schooling resources, by need category - Azad
Jammu and Kashmir
Table 6.2: Magnitude of inequity in resource distribution
Teacher Quality
School Physical
Environment
School Instructional
Environment
Summary Score
National
13.6
-1.9
19.5
31.2
Punjab
-1.5
5.7
19.6
23.7
Sindh
10.2
-1.5
8.0
16.8
Balochistan
3.9
-4.4
11.6
11.1
Khyber Paktunkhwa
5.7
5.8
8.4
19.9
Gilgit-Baltistan
9.7
11.5
25.3
46.5
Azad Jammu and
Kashmir
8.8
1.3
-3.3
6.8
Physical
Environment
Instructional
Environment
Azad Jammu and Kashmir
Teacher Quality
High Needs
Low Needs
DRAFT FOR COMMENT
36
7. Peru
Background
Peru’s Educational Census (Censo Educativo) takes place annually and collects data from public
and private education institutions, programs, and schools throughout the country. It provides information
on the current situation and evolution of the school population, the results of educational exercises, the
composition of teaching and non-teaching staff, quality of educational infrastructure, and quantities of
educational resources at the district, provincial, departmental, and national levels. The Educational
Census is designed by the Statistics Unit of the Ministry of Education and implemented jointly as part of
the decentralized educational management system across the country. Data is collected through self-
reported forms completed by officials from each educational institution, including programs of basic
education, technical production, and non-university higher education institutions. Regional Directorates
of Education (Dirección Regional de Educación DRE) and Local Educational Management Units (Unidad
de Gestión Educativa Local UGEL) are responsible for the distribution of the census forms and training
educational institution officials on completing the forms. Information sources for completing the census
include administrative records, registration forms, tuition payroll, teacher lists, and equipment
inventories. The 2016 Educational Census was the tenth round of the census, which has been conducted
each year since 1998, excluding 2003. In 2016, data was collected from May to July for the period of
January 1 to September 15.
The Student Evaluation Census (Evaluación Censal de Estudiantes ECE) is an annual evaluation
of student learning outcomes for students in the second and fourth grades of primary school and second
grade of secondary school. The ECE has been conducted annually by the Office of Measurement of the
Quality of Learning (Oficina de Medición de la Calidad de los Aprendizajes UMC) since 2006 and includes
student-level reading (Spanish) and mathematics outcomes, as well as socioeconomic information. The
2016 ECE collected information on 505,376 fourth grade students from 20,180 educational institutions.
For this study, we use data from the 2016 Educational Census for public schools and merge this
with 2016 ECE learning outcomes and SES data for students in the fourth grade of primary school. The
final sample includes 13,239 schools.
Instruments
Need Index
The 2016 ECE includes a student-level socioeconomic index variable constructed using factor
analysis. The index includes the maximum education level of one parent, housing materials, basic services
in the household, assets in the household, and other services in the household. Using this variable, we
construct a Need Index by calculating the percentage of low-income students in each school (percentage
of students in the bottom quintile of the socioeconomic index). Next, we divide the sample into three
groups: High-Needs (schools with at least 40% of student-body classified as low-income), average needs
(schools with proportion of low-income students between 5% and 40%), and Low-Needs (schools
comprising at most 5% of student body categorized as low-income). Figure 1 shows the distribution of the
Need Index across Peruvian schools in our sample. 11% of schools are Low-Needs schools, whereas 60%
of schools are categorized as High-Needs.
Figure 7.2 shows the Spanish language test score distributions by the needs- category. As seen in
the figure, schools with Low-Needs have higher language scores than schools with High-Needs, on
average. Figure 7.3 displays a similar pattern for math outcomes. Low-Needs schools have higher math
scores than High-Needs schools, on average. These distributions show that on average, Low-Needs
schools outperform High-Needs schools in language and math.
DRAFT FOR COMMENT
37
Teacher Quality
The 2016 Educational Census includes information on teacher education and magisterial levels.
As seen in Table 7.1, we use these variables to construct a teacher quality factor standardized by student
enrollment. To construct the teacher education level variable, we divide the number of fourth-grade
teachers with a pedagogical degree by fourth-grade student enrollment. Similarly, we use teacher
magisterial level, measured on a 0-8 scale, as a proxy for teacher experience. To calculate this variable,
we divide the number of fourth-grade teachers with a magisterial level higher than Level 2 by student
enrollment. Figure 7.4 shows that the low, average, and High-Needs schools have similar teacher quality.
However, it is slightly better for Low-Needs schools.
School Physical Environment
To construct the school physical environment factor, we used variables including, classroom
conditions (good/regular or bad), walls/floors need repair, ceiling needs repair, walls and ceiling material,
electricity, water system, and bathrooms. Figure 7.5 shows that High-Needs schools have a slightly worse
school physical environment than Low-Needs schools, on average.
School Instructional Environment
The school instructional environment factor captures the availably of instructional resources at
each school. Using 2016 Educational Census data, we determine whether or not a school has the following
resources: science lab, library, computer, internet, TV, desktop, laptop, laptop XO, tablet, digital
whiteboard, pedagogical innovation classroom, and robotics kit. Compared to teacher quality and school
physical environment, we see that the school instructional environment has the largest disparity between
low and High-Needs schools.
Findings
Spatial Analysis
Figures 7.7-7.10 display the distributions of the Need Index, teacher quality index, school physical
environment index, and school instructional environment index across Peruvian provinces. As seen in
Figure 7.7, the north and center of the country have higher concentrations of High-Needs schools,
particularly in the northeast. The south and southeast have some provinces with high concentrations of
High-Needs schools and many provinces in the middle quintiles. The western coastal provinces have the
highest concentrations of Low-Needs schools. The maps showing school resource factors display the
opposite: where there are higher concentrations of High-Needs schools, there are less resources, and
where there are higher concentrations of Low-Needs schools, there are more resources. This shows that
schools with higher needs are getting less resources than schools with lower needs.
Three-pronged analysis of inequity
Figures 7.11-7.16 display spider graphs for Peru at the national level and across its five regions.
When looking at the national graph, we see that the level of each school resource factor is smaller for
High-Needs schools than Low-Needs schools, showing that High-Needs schools have less access to
resources than Low-Needs schools across the three dimensions. The disparity is largest for the
instructional environment, followed by physical environment, then the teacher quality. We see a similar
pattern for the center and Lima. However, in Lima, we see that the teacher quality factor is higher for
High-Needs schools than Low-Needs schools, the only such case across all dimensions and regions.
Table 7.2 shows substantial inequity of resource allocation across all regions of Peru. The
summary scores show that the Lima region is more equitable compared to the others, followed by the
north, south, center, and east. In addition to inequity within regions, we also see that the east, the most
inequitable region, also has the lowest levels of teacher quality and instructional environment compared
to the other regions. Additionally, the east is the only region showing a larger disparity of resource
allocation when compared to the national level.
DRAFT FOR COMMENT
38
Figures and Tables
Table 7.1: Composition of school resource factors
Figure 7.1: Distribution of schools' needs-index
Factor
Teacher Quality
School Physical Environment
School Instructional Environment
Variables
Teacher Education Level
Teacher Magisterial
Level
Condition of classrooms
Walls/floors and/or ceilings
need repair
Walls, ceiling material
Electricity
Water system
Bathroom
Science lab
Library
Computer (at least one)
Internet
TV
Desktop
Laptop
Laptop XO
Tablet
Digital whiteboard
Pedagogical innovation classroom
Robotics kit
DRAFT FOR COMMENT
39
Figure 7.2: Distribution of language test score, by need category
Figure 7.3: Distribution of math test score, by need category
DRAFT FOR COMMENT
40
Figure 7.4: Distribution of teacher quality index, by need category
Figure 7.5: Distribution of school physical environment index, by need category
DRAFT FOR COMMENT
41
Figure 7.6: Distribution of school instructional environment index, by need category
DRAFT FOR COMMENT
42
Figure 7.7: Distribution of needs-index across Peruvian provinces
Figure 7.8: Distribution of teacher quality index across Peruvian provinces
DRAFT FOR COMMENT
43
Figure 7.9: Distribution of school physical environment index across Peruvian provinces
Figure 7.10: Distribution of school instructional environment index across Peruvian provinces
DRAFT FOR COMMENT
44
Figure 7.11: Three-pronged distribution of
schooling resources, by need category - National
Figure 7.12: Three-pronged distribution of schooling
resources, by need category - North
Figure 7.13: Three-pronged distribution of schooling
resources, by need category - South
Figure 7.14: Three-pronged distribution of schooling
resources, by need category - Center
Figure 7.15: Three-pronged distribution of schooling
resources, by need category - Lima
Figure 7.16: Three-pronged distribution of schooling
resources, by need category - East
DRAFT FOR COMMENT
45
References
UIS (2016) Improving the International Monitoring Framework to Achieve Equity (SDG 4.5): Indicator
4.5.3. UIS Information Paper No 32. November 2016. UIS: Montreal, Canada.
UIS (2018) TCG4: Development of SDG Thematic Indicator 4.5.3. Country review on explicit formula-based
policies to reallocate education resources towards educational needs. Available at:
http://tcg.uis.unesco.org/wp-content/uploads/sites/4/2018/08/TCG4-14-Development-of-
Indicator-4.5.3.pdf
UNESCO IIEP Pole de Dakar, World Bank, UNICEF, and GPE (2014) Education Sector Analysis
Methodological Guidelines. Volumes 1 and 2.
UIS (2018) Handbook for Measuring Equity in Education. UIS: Montreal, Canada.
Table 7.2: Magnitude of inequity in resource distribution
Teacher Quality
School Physical
Environment
School Instructional
Environment
Summary Score
National
7.73
8.48
27.74
43.95
North
11.85
1.65
22.89
36.39
South
7.86
15.48
14.94
38.28
Center
8.83
9.61
21.2
39.64
Lima
-8.45
13.45
19.51
24.51
East
20.04
10.37
23.75
54.16
Chapter
In this chapter, we use statistical data and surveys to show differences in access to educational resources and the quality of the academic achievements of students living in different regions of Russia. We demonstrate that behind the benign indicators of inclusion (academic and social) used for international comparisons, there may be noticeable intra-country imbalances. Using Russia as a case, we argue for the need to consider intra-country differences in international comparative studies and the obligation to consider the diversity of conditions within countries when implementing policies and inclusion and justice.
TCG4: Development of SDG Thematic Indicator 4.5.3. Country review on explicit formula-based policies to reallocate education resources towards educational needs
UIS (2016) Improving the International Monitoring Framework to Achieve Equity (SDG 4.5): Indicator 4.5.3. UIS Information Paper No 32. November 2016. UIS: Montreal, Canada. UIS (2018) TCG4: Development of SDG Thematic Indicator 4.5.3. Country review on explicit formula-based policies to reallocate education resources towards educational needs. Available at: http://tcg.uis.unesco.org/wp-content/uploads/sites/4/2018/08/TCG4-14-Development-of-Indicator-4.5.3.pdf UNESCO IIEP Pole de Dakar, World Bank, UNICEF, and GPE (2014) Education Sector Analysis Methodological Guidelines. Volumes 1 and 2.
Handbook for Measuring Equity in Education
UIS (2018) Handbook for Measuring Equity in Education. UIS: Montreal, Canada.