Journal of Applied Economics and Business Studies, Volume. 4, Issue 1 (2020) 1-28 https://doi.org/10.34260/jaebs.411

Journal of Applied Economics
and Business Studies (JAEBS)

Journal homepage: https://jaebs.com
ISSN (Print): 2523-2614

ISSN (Online) 2663-693X

Educational Inequality and Inclusiveness:

The Case of Khyber Pakhtunkhwa, Pakistan

Tahira Tauheed 1 & Muhammad Nasir 2*1
1 Assistant Professor of Economics, Incharge Department of Economics, Lahore College for Women University, Lahore
2 Senior Research Economist, Pakistan Institute of Development Economics (PIDE), Quaid-i-Azam University Campus, Islamabad

ABSTRACT

It is argued that masses in Pakistan are excluded from the mainstream progress of

education resulting in social unrest and adverse state of human development. This

paper examines prevailing inequality in and exclusion from education in the Khyber

Pakhtunkhwa province of Pakistan, and provides an empirical base for designing

an appropriate policy framework to mitigate the underline issues. Towards this end

the household-based education and Inequality-adjusted education indices are

derived using Foster-López-Calva-Székely (FLS) methodology at the provincial,

and district levels from the most recent Pakistan Social and Living Standard

Measurement (PSLM) survey 2014-15. The provincial analysis is elaborated at the

urban and rural regions as well. The distribution of these indices across households

are utilized to measure inequality and inclusiveness coefficients by

employing “Atkinson’s inequality measure” and “sixty percent of median as

threshold of exclusion,” respectively. At the district level the impact of economic,

social, demographic, and locational factors on inclusiveness of education are also

investigated using linear regression. The results demonstrate that KPK households

reside on average in low category of actual education level experiencing high inter-

regional and intra-regional disparities and exclusions. At the district levels, the

inequalities in educational achievement and exclusions are even more pronounced,

indicating that aggregated analyses suppress the intra-regional disparities and

segregations. Based on these findings, it is asserted that investment in social

infrastructure specifically educational, health, and law and order facilities,

development of agriculture sector, and eradication of gender discrimination, are

important factors to promote inclusive education in the province.

Keywords
Inclusive

education,

Household-based

Education index,

Inequality

coefficient,

Inclusion

coefficient,

Socioeconomic

determinants

JEL

Classification
C00, C21, I24,

I29

1. Introduction

Inclusive education is listed among the top four sustainable development goals (SDGs)

in a recent UNSECO report on sustainable development. It is also considered highly

influential towards attaining other SDGs that are aimed at eradicating poverty by 2030

1*Corresponding author: nasir84@pide.org.pk

mailto:nasir84@pide.org.pk

Tahira Tauheed & Muhammad Nasir

(UNESCO, 2017). Inclusive education could be derived from the definition of inclusive

development by Rauniyar & Kanbur (2010). It is a broad concept that includes enhancement

of education level coupled with its equitable distribution in all segments of society,

especially the deprived ones. To ensure inclusive education is a world-wide challenge. A

substantial proportion of the world population is excluded from the arena of educational

achievements (UNESCO, 2017). Masses in Pakistan are also suffering from educational

exclusion resulting in social unrest and non-coherence (Burki, et al., 2015; UNDP Pakistan,

2016; UNDP, 2016). Due to its complex socio-political structure Khyber Pakhtunkhwa

(KPK), the northernmost province of Pakistan, faces this challenge more hardly than most

of its provincial counterparts (Gouleta, 2015).

To raise the level of inclusive education, a comprehensive and regionally integrated plan

is required. The foremost steps in this regard are to evaluate the present status of education

at all possible administrative levels and to identify the factors influencing inclusive

education. The existing literature is unable to serve this purpose adequately. Based on

aggregated data, a few studies including Jamal (2016) and Pakistan National Human

Development Index Report (NHDIR) (2017) elaborate on the potential level of education

(represented by education indices) at the national, provincial, and district levels1. However,

these studies are unable to provide information about the distributional aspects of

educational development including disparities and inclusion, especially at the intra-district

level. There is hardly any study that empirically analyze the inclusiveness of education in

KPK. Some studies analyze the education system of KPK. Zia uddin and Tahir (2014) has

investigated the primary data on the public-school monitoring in KPK. Ahmed & Rashid

(2018) in a research study has analyzed the performance of public schools in districts Swat

and Lower Dir of KPK. Gouleta (2015) analyzed KPK’s educational assessment policies

and practices. A comprehensive measurement of inclusive education and analysis of its

determinants remains limited for KPK. Health and education are central in building people’s

physical and mental capacities, and hence any serious inequality of opportunity in these

areas will aggravate inequality in their future (Kato, 2014). Keeping in view the significance

of the subject matter and the research gap, this study undertakes the task of executing a

household-based analysis of educational achievements of KPK at the provincial and district

levels. The data used in this study is taken from the latest Pakistan Social and Living

Standard Measurement (PSLM) survey 2014-15. The analyses are further extended to urban

and rural regions at the provincial levels. This study also examines factors (including

economic, social, demographic, and locational factors) influencing inclusiveness of

education at the district level. The three underlying aspects of inclusive education: education

enhancement, inequality reduction and inclusion of the marginalized are covered in this

study by measuring and analyzing household-based education index (IIE), inequality-

1 These studies are mentioned specifically as these are utilizing same measure of human development (HDI) and same data source (PSLM
2014-15) as the present study.

Journal of Applied Economics and Business Studies, Volume. 4, Issue 1 (2020) 1-28 https://doi.org/10.34260/jaebs.411

adjusted education index (IIE), loss due to inequality, and coefficient of inclusiveness. The

Classical Linear regression model (CLRM) is employed for analyzing the determinants of

all aspects of inclusive education.

KPK is the northernmost province of Pakistan with a privileged geostrategic position

and abundant natural and human resources. Majority of the population in KPK resides in

rural area and its main economic sources are forestry and agriculture. However, the current

level of human development reveals that it could not exploit its advantageous position and

abundant resources successfully to construct a shared and resilient society. A number of

challenges including influx of Afghan refugees, high incidence of terrorist activities, and a

very complex and diversified socio-political structure could be accounted for its low

performance (Gouleta, 2015). The picture of education in KPK presents a gloomy outlook.

According to Pakistan Education Statistics (2017) KPK currently has about 2.38 million

children of the ages of 5 to 16 that are out of schools. Economic Survey of 2016-17 reports

that the adult literacy rate in KPK is 53 per cent since 2012, depicting that 47 per cent of

adults in the province are illiterate. According to World Development Bank, in 2015 KPK

is well ahead of Islamabad Capital Territory, all four provinces, and other regions in the

country in term of Gross Intake rate (GIR) (Hunter, 2020). It shows that effort has been

started, however, a lot has to be done. As Pakistan NHDR (2017) reports for the same year,

the achievement level of KPK in terms of its provincial and district level educational indices

depicts that it is just ahead of FATA and Balochistan and falls substantially below the

national level, Azad Jammu & Kashmir, Islamabad, and Punjab. The national level and

KPK’s education indices are 0.538 and 0.49 and these lie in medium and low medium

categories, respectively . KPK’s overall development level including the standard of living,

health, and education reveals that it falls in medium category and is slightly ahead of

National level achievements. A comparison of development in three dimensions of human

development exhibits that education is the least achieved dimension in KPK. The

consequences of low education manifest themselves in lower achievements in other social

and economic dimensions. Education can drastically change the growth and development

cycle of a region, as established by the East Asian countries during the 1990s (Najam &

Bari, 2017).

According to 18th Amendment to the constitution of Pakistan the education has become

a provincial subject. Therefore, the provinces should form statutes and articulate educational

policies that guarantee the best education system. Despite the fact that government of KPK

spends substantially higher on education than any other province in the country and it has

passed “Khyber Pakhtunkhwa Right of Children to Free and Compulsory Education Act

2017”, province is unable to meet the millennium development goals (MDGs) in education

due to high prevalence of extreme poverty (Gouleta, 2015). To emerge as a stable society

Tahira Tauheed & Muhammad Nasir

in the twenty-first century and to get advantage of its increasing population, KPK must

provide a skilled and educated workforce. To achieve this end, serious efforts are required,

and a careful examination of present status of inclusive education is necessary to understand

the state of the world today. Furthermore, based on these analyses it is imperative to design

a new development framework for the future. The foremost step in this regard is to formally

and methodically assess the existing status of education and level of its inequality &

inclusion. The next step is to inspect the factors influencing inclusive education so that

appropriate policies and action plans could be designed.

There are three main segments of this study. First is the empirical assessment of

existing level of education and prevailing inter-regional and intra-regional educational

inequalities. To achieve this end, household-based IE and IIE for the year 2014-15 are

constructed at the provincial and district levels, using data from PSLM (2014-15) on the

lines proposed by Alkire and Foster (2010). Regional indices (rural and urban) are also

constructed at the provincial level. The Atkinson’s measure of inequality and loss in

educational achievements due to this inequality are also estimated. The second segment

supplements this analysis by examining the profile of inclusive education at provincial and

district levels using a unified measure of inclusion, ‘Coefficient of Inclusive education’. It

is based on distribution of households’ education indices computed in the first segment.

Rural and urban coefficients of inclusiveness are also estimated at the provincial level. The

third segment comprises of empirical analysis of the prerequisites of inclusive development.

To determine the proximate factors that influence inclusive education, district-wise

education indices, inequality coefficient and coefficients of inclusive development are

regressed on various economic, social, demographic, and locational factors considered to be

influential for inclusive development in literature.

The major contributions of this study are: First, it is a leading study in estimating

education index at the household level (the smallest possible unit for which required data is

available in Pakistan). This study is also credited for being pioneer in constructing

household-based provincial and district level education and inequality adjusted education

indices for KPK. All the previous works on this subject involve aggregated data at a certain

level that suppress the inter-regional variations. Several factors that play a vital role in

raising disparities at micro level have remained unaddressed. Second, for the first-time

across households’ inequalities in education indices at the provincial (overall, rural, and

urban) and district levels are estimated for KPK. Third, to our knowledge, it is a seminal

work that calculates a unified measure of inclusive education. As a unified measure of

inclusive education, coefficient of inclusiveness is an efficient tool for the analysis of its

dynamics and determinants. Forth, this study investigates and identifies major economic,

social, demographic, and locational determinants of inclusive education at the district level.

Journal of Applied Economics and Business Studies, Volume. 4, Issue 1 (2020) 1-28 https://doi.org/10.34260/jaebs.411

This study is also important in the wake of adoption of SDGs and Vision 2030 by

Pakistani government, and devolution resulting from Pakistan's 18th Constitutional

Amendment. The district level study of inclusive development and its determinants would

assist local and provincial governments in identifying areas and sectors that require greater

attention, enabling them to allocate resources accordingly. Last but not the least, this study

is also expected to generate dialogue and further research to deepen the understanding of

the dynamics and key drivers of inclusive education in KPK.

The rest of the paper is organized as follows: Section 2 presents the data sources and

research methods utilized in this work. This section also describes the procedures to

estimate education indices and their distributional inequality. Section 3 provides analyses

of the estimated education indices and their inequality coefficients. Section 4 outlines the

estimation of inclusiveness coefficients of education and presents its analysis . Section 5

presents the analyses of determinants of inclusive education. Finally, Section 6 highlights

the conclusions of the study and lays out recommendations for policy and future research.

2. Data and Research Methodology

The main data utilized in this study is taken from the latest Pakistan Social and Living

Standard Measurement (PSLM) survey 2014-15. It is a district as well as provincial and

national level representative survey which covers 78635 households. (Pakistan Bureau of

Statistics, 2016). Most of the District-wise data for the determinants of inclusive education

is collected from various publications of Pakistan Bureau of Statistics for the years 2014

and 2015. The data about education and health institutions, total area, forest area, cultivated

area, road length, registered factories, police stations, and reported crimes for year 2014-15

is collected from KPK development statistics 2015 and 2017. Data about population and sex

ratio is collected from Pakistan Census 2017 as these figures are close approximates for year

2014-15. For detail description of data see Table A.1 and A.2 in appendix.

The general methodology utilized here to construct the household-based education index

is taken mainly from Lopez-Calva & Ortiz-Juarez (2011). Technical notes for human

development reports (2014; 2015) are consulted for details of index construction, inequality

measurement, and loss due to inequality. Traditional component of education index are adult

literacy and enrollment indicators. However, the household-based calculation of enrollment

imposes the problem of missing data in households without children, as enrollment depends

on the presence of individuals of school going age. In this work education index is calculated

by replacing enrollment with a continuous variable capturing the years of schooling for

individuals of or above the age of 7 (the age required to complete the first year of primary

education). Using this variable, missing values are avoided by imputing the household i’s

average schooling to children below the age of 7, under the assumption that children could

Tahira Tauheed & Muhammad Nasir

achieve at least such average over the course of their lives (Lopez-Calva & Ortiz-Juarez,

2011). Household’s achievements are normalized to a score between 0 and 1 using extreme

values across country, called the domestic goalposts. Hence education indices are

contextualized regarding domestic goalposts to consider the national realities and priorities.

Domestic goalposts provide a realistic assessment of the relative educational progress made

by households and districts in KPK.

Information about schooling years and adult literacy of a household’s members are

collected from section ‘C’ of PSLM 2014-15. To construct schooling index of a household,

firstly for each household member of age 7 years or above, an indicator of the years of

schooling is computed and is compared it with a minimum value of zero and a maximum

value that depends on age. For instance, a 7-year aged person must have 1 year of schooling

as maximum; an 8-year aged person must have 2 years of schooling as maximum, and so

on up to a maximum of 18 years of schooling which corresponds to individuals aged 24 or

above. If a person aged 7 has 2 or more years of schooling, the value would be fixed up to

1; if a person aged 8 has 3 or more years of schooling, it would be fixed up to 2, and so on.

The schooling index for individual j in household i (𝑆𝑐𝑖𝑗 ) is calculated by normalizing

his/her schooling years as:

𝑆𝑐𝑖𝑗 =
𝑆𝑐𝑗−𝑆𝑐𝑚𝑖𝑛

𝑆𝑐𝑚𝑎𝑥−𝑆𝑐𝑚𝑖𝑛
-----------------------------------------(1)

with 𝑆𝑐𝑗 being the observed years of schooling for individual j, and 𝑆𝑐𝑚𝑖𝑛 and 𝑆𝑐𝑚𝑎𝑥

the reference values. The average of the individual indices is calculated and imputed to

children aged below 7 years. The schooling index for household i (𝑆𝑐𝑖) is the average of

schooling for all the individuals in that household.

In the case of adult literacy, if an adult with or above the age of 15 declared to be able

to read and write in any language with comprehension a short simple statement on his/her

everyday life, he/she is considered as literate (Anon., 1997). Hence, the adult literacy index

is denoted as the proportion of population aged 15 years and older who can read and write

with understanding in any language. Household literacy rate (𝑙𝑖) is then calculated as:

𝐿𝑖 =
1

𝑇
∑ 𝑙𝑗
𝑚
𝑗=1 ------------------------------------------------(2)

with T being the total number of adults in household i, m the total number of literate

adults, and 𝑙𝑗 an indicator taking the value of 1 if the adult j is literate, and 0 otherwise. This

rate is equivalent to the literacy index.

The education index for household i ( 𝐸𝑖 ) is computed as weighted average of

household’s adult literacy index and schooling index. The weights proposed and used by

UNDP in human development reports 1991-1994 are 2/3 for literacy and 1/3 for schooling.

Using these weights education index is calculated as:

Journal of Applied Economics and Business Studies, Volume. 4, Issue 1 (2020) 1-28 https://doi.org/10.34260/jaebs.411

𝐸𝑖 =
2

3
𝐿𝑖 +

3
𝑆𝑐𝑖--------------------------------------------(3)

To conduct a household-based analysis of educational progress across KPK districts, the

first task is to construct education index at household level. At next stage this measure is

used to analyze the aggregate level education at provincial and district levels. A household’s

education index is composed of its adult literacy index and schooling index. Information

about adult literacy and schooling years of a household’s members are collected from

section ‘C’ of PSLM 2014-15.

For schooling index, at first step data about years of schooling for individuals of or above

the age of 7 is collected from three questions. First of these questions is, “what the highest

class /level of education is completed?”. The answer to this question comprises twenty

different categories (classes/levels) with specific value labels. The years of schooling are

assigned to each class/level according to educational system prevailing in the country. At

second step the schooling index for everyone of or above the age of 7 is calculated by

normalizing his/her schooling years by using equation 1. The schooling indices of all the

individuals in a household are averaged out to obtain a household’s schooling index (𝑆𝑐𝑖).

To avoid the underestimation of index, all the zero values are replaced by 0.02 under the

assumption that individuals have accumulated some learning and experience throughout

their lives, regardless of if they have attended school or not. The value of 0.02 is selected

arbitrarily keeping in view very low mean years of schooling index in Pakistan i.e. 0.3133

according to Human Development report 2014. Moreover, this number involves no

truncation of the distribution as the smallest non-zero observed household’s schooling index

equals 0.0253.

For adult literacy index, information is collected from the question, “Can this person

read & write in any language with understanding?”. There is no missing response for this

question in case of individuals of age 15 years or above. Household literacy index (Li) is

derived by dividing number of adult literates in a household by its total number of adults

and normalizing it by natural goal posts of 0 and 1. For the same reasons outlined in the case

of schooling, a minimum level of 0.05 is attached instead of 0 in those households with all

illiterate adults. This does not truncate the distribution as smallest observed non-zero adult

literacy index is 0.0625.

Education index of a household (Ei) is calculated as a weighted average of its schooling

index and adult literacy index, assigning weights of 1/3 and 2 /3 respectively. Quintiles

based on households’ education indices are computed considering the sampling weights.

Households’ literacy indices, schooling indices, education indices and education quintiles

would be provided on request. These education indices and quintiles are utilized for analysis

of development in the dimension of education at the provincial and district levels.

Tahira Tauheed & Muhammad Nasir

In the present study to estimate the provincial or district level education index and

inequality adjusted education index the general means are utilized for aggregation of

household education indices based on (Foster, et al., 2005). The inequality in distribution of

educational progress across households is captured by an inequality measure suggested by

Alkire & Foster (2010). Foster, Lopez-Calva, and Szekely (2005) proposed the use of a

general mean or equally distributed equivalent (ede) achievement level for aggregation of

achievement (x) to account for inequality in progress/development. The generalized mean

can be referred as μα (x), and for a population of size n it is commonly expressed as:

--------------(4)

where α may take any value in the interval (− ∞, + ∞) (Foster, et al., 2013). The general

means for α < 1 are generally interpreted as measures of social welfare. The Foster-López-

Calva-Székely (FLS) class of indices satisfies all basic axioms of a welfare index including

subgroup consistency and distribution sensitivity (Alkire & Foster, 2010; Seth, 2009).

Atkinson used the parameter ε = 1 - α ≥ 0 (α≤ 1) to index the class of edes; he interpreted

ε as an inequality aversion parameter in the aggregation method of achievements (which he

considered to be welfare (Alkire & Foster, 2010). The case of ε = 0 yields the index that is

based on the arithmetic mean, which is insensitive to inequality in achievements. The value

of ε=1 yields index which is obtained by the geometric mean to evaluate achievements. For

ε > 0, the inequality adjusted index discounts for inequality within-dimension according to

the level of inequality aversion indicated by its associated parameter ε.

In this study the arithmetic mean of households’ indices is employed to obtain a

provincial or district education index (𝐼𝐸) without accounting for inequality. It is given as:

𝐼𝐸 = (𝐸1 + 𝐸2 + ⋯ +𝐸𝑛)/𝑛 -------------------------------(5)

where as 𝐸𝑖 is the ith household education index and n is the number of households. To

obtain inequality adjusted education index ( 𝐼𝑖𝐸) at the provincial or district level the

households’ education indices are aggregated by using geometric mean as:

𝐼𝑖𝐸 = √𝐸1 × 𝐸2 × ………….× 𝐸𝑛
𝑛

-------------------------(6)

Atkinson (1970) family of inequality measures is used to capture inequality in

underlying distributions of education across households. Atkinson measure of inequality

with inequality aversion parameter ε=1 is employed in this research. It can be expressed as:

----------------------------------------------(7)

𝜇𝛼(𝑥) =

𝑥1
𝛼 + 𝑥2

𝛼 + 𝑥3
𝛼 ……… . +𝑥𝑛

𝛼

𝑛

1/𝛼

𝑖𝑓 𝛼 ≠ 0

(𝑥1 × 𝑥2 × …… . .× 𝑥𝑛)
1/𝑛 𝑖𝑓 𝛼 = 0

𝐴𝐸 = 1 −
𝐼𝑖𝐸
𝐼𝐸

Journal of Applied Economics and Business Studies, Volume. 4, Issue 1 (2020) 1-28 https://doi.org/10.34260/jaebs.411

The inequality measure 𝐴𝐸 represents the share of per household educational

achievement that is wasted because of inequalities in its distribution across households. It is

regarded as the percentage loss in potential level of educational achievement or welfare

arising due to inequitable distribution.

To compute unified measure of inclusive development at the district and provincial

levels method proposed by Suryanarayana (2008) is adopted in this study. The underlying

idea is that the growth process under review will be inclusive if it is beneficial for deprived

sections of the society. To identify the deprived, this approach compares the achievement

of individual units of the society (individuals/ households/ regions) relative to the average

achievement of the society. The population having achievement below sixty percent of

median achievement of the society is considered as deprived. The same approach is adopted

to measure inclusiveness of educational achievement in this study. Thus, the segment of

population which is deprived of education is defined regarding a threshold of education

index, specified as a function of median education index. The population (households)

having 𝐸𝑖 below sixty percent of median 𝐸𝑖 is considered as deprived. The 60% of median,

and 50% of the mean are two commonly used thresholds for relative income deprivation;

the former measure is probably the most extensively used measure nowadays (Townsend

& Kennedy, 2004). The advantage of this threshold is that it will not change by the rise of

incomes in the deprived section unless they cross the median income. (Mack, 2016;

Bradshaw & Mayhew, 2011).

In this study the application of this threshold is extended to measure the proportion of

households deprived of education. The deprived of education proportion of population is

given as:

𝜃 = 𝐹(𝛿𝐸0.5) = ∫ 𝑓(𝐸)𝑑𝐸
𝛿𝐸0.5
0

---------------------------(8)

where θ = incidence of the deprived (ID), 0<δ< 1. The 𝐸0.5 represents the households’

median education index such that:

∫ 𝑓(𝐸)𝑑𝐸
𝐸0.5
0

=
1

2
= ∫ 𝑓(𝐸)𝑑𝐸

∞

𝐸0.5
-------------------------(9)

The value of δ is kept 0.6. F is the cumulative distribution function and 𝑓(𝐸) is the density

function of ‘𝐸𝑖’. Some important features and implications are as follows:

The value of θ lies in the open interval (0, 0.5).

(i) θ tends to 0 implies bottom half of the distribution concentrates in the “inclusion
zone”, given by [δE0.50, E0.50]

Tahira Tauheed & Muhammad Nasir

(ii) θ approaches to 0.5 implies bottom half of the distribution concentrates in the
“exclusion zone”, given by [0, δ ξ0.50].

Assuming society consisting of a homogeneous group with heterogeneity in educational

achievement across households, a “Coefficient of Inclusion” is defined by suitable

standardization regarding its limits. Inclusion Coefficient (IC) denoted by ‘Ψ’ is given as:

Ψ = 1 − 2∫ 𝑓(𝐸)d𝐸
𝛿𝐸0.5
0

---------------------------(10)

where 0 < Ψ < 1. It has the following relevant properties:

(i) The value of Ψ tends to the value 0 (unity), when no (all) household (households)
with relatively poor educational achievement is (are) participating and hence,

benefiting from the mainstream educational progress, implying a state of perfect

exclusion (inclusion)

(ii) A value of Ψ greater (less) than ½ , indicates a situation where the proportion of
the bottom half of the population falling in the inclusion zone is greater (less) than

the proportion in the relative deprivation-zone, implying a state of inclusion

(exclusion).

The economic and social welfare is not evenly distributed across regions in Pakistan

(Jamal, 2016; UNDP Pakistan, 2016), exhibiting a scenario of non-homogeneous society.

Consequently, inclusiveness of education in KPK is analyzed in two ways i.e. across the

regions (inter-regions) and within the regions (intra-region). Inter-regional inclusion is

examined with reference to disparities in median levels education across regions. It is

measured by closeness of regional median ( 𝐸0.5
𝑅 ) to national median 𝐸0.5

𝑀 (of the

national/mainstream population). For a given 𝛿 such that 0 < 𝛿 < 1, there can be two

scenarios:

(i) 𝐸0.5
𝑅 < δ𝐸0.5

𝑀 implies exclusion of the specific region.

(ii) 𝐸0.5
𝑅 ≥δ𝐸0.5

𝑀 implies inclusion of the specific region.

Intra-regional inclusion is examined in terms of inclusion coefficients (ICs) defined with

respect to regional as well as mainstream (provincial) median. Intra-regional inclusion for

any given region ‘i’ is measured with respect to either own median (𝐸0.5
𝑅 ) providing a

measure of 𝛹𝑖
𝑅 (IC Regional) or mainstream median (𝐸0.5

𝑀 ) providing a measure of 𝛹𝑖
𝑀 (IC

Mainstream). These two measures are distinct and different for situations when there is

inter-regional exclusion; and converge with progressive inter-regional inclusion. IC

Regional (𝛹𝑖
𝑅) measures the extent of inclusion of the bottom half population of the region

under review in its own progress. Its limits and properties are the same as discussed for the

inclusion coefficient of a homogeneous society. IC Mainstream (𝛹𝑖
𝑀) measures the extent

of inclusion of the population (laying below national median) of concerned region in the

progress of the country/ society. The limits for IC Mainstream (𝛹𝑖
𝑀) are as follows:

Journal of Applied Economics and Business Studies, Volume. 4, Issue 1 (2020) 1-28 https://doi.org/10.34260/jaebs.411

𝛹𝑖
𝑀= -1 implies exclusion of the entire region

𝛹𝑖
𝑀 = 1 implies inclusion of the entire region

An important objective of this study is to analyze the determinants of inclusive

education. The determinants of inclusive education are analyzed at district level by

estimating regression models for three of its aspects i.e. level of educational achievement,

its distributional inequality, and inclusion of marginalized in educational progress. To

achieve this end KPK districts’ education index (IE), inequality coefficients (AE), IC-

Mainstream (𝛹𝑖
𝑀), and IC-Regional (𝛹𝑖

𝑅) are regressed on various potential factors for

inclusive education. The selection of these probable determinants is based on evidence from

existing literature and availability of data at district level for KPK. The choice of variable is

constrained severely due to data availability at district level. Inclusive development is

influenced by several diversified factors, however, the factors considered in this study are

grouped in to four major categories, economic factors (EF); social factors (SF);

demographic factors (DF); and locational factors (LF). The generalized form of the model

is given below:

Inclusive education= f (EF, SF, DF, LF) ----------------------------(11)

Economic factors comprise of industrial development (measured by no. of registered

factories per hundred thousand of population), agricultural development (measured by

percentage of cultivated area), percentage of forest area, and level of physical infrastructure

(measured by road density, airport, and railway station) at district level.

Social factors include public education and health facilities, and law & order condition

at district level. Public education facilities are proxied by district-wise total number of

government schools, high schools, middle schools, primary schools, and colleges per

hundred thousand population. District-wise number of government hospitals per hundred

thousand population and number of beds in government health institutions per ten thousand

population are utilized to proxy public health facilities. Law and order facility are assessed

by number of police stations per hundred thousand population at district level.

Demographic factors utilized in this study are the population density, ratio of male to

female population (sex ratio), and urbanization (ratio of urban population to total

population).

Locational Factor include the dummy variable for divisional capital.

The functional forms of the regression models are given as:

IE = 𝛼1 + 𝛽1 EF + 𝛾1 SF + 𝛿1 DF+ ξ1 LF + ε1 -----------------------(12)
AE = 𝛼2 + 𝛽2 EF + 𝛾2 SF + 𝛿2 DF+ ξ2 LF + ε2-----------------------(13)
𝛹𝑖
𝑀= 𝛼3 + 𝛽3 EF + 𝛾3 SF + 𝛿3 DF+ ξ3 LF + ε3----------------------(14)

𝛹𝑖
𝑅= 𝛼4 + 𝛽4 EF + 𝛾4 SF + 𝛿4 DF+ ξ4 LF + ε4----------------------(15)

where ε1, ε2, ε2, and ε4 represents the random error terms in the models.

Tahira Tauheed & Muhammad Nasir

In this study Classical linear regression model (CLRM) is employed to estimate the

above stated equations individually. The SUR could be an appropriate technique for the

estimation of this system of equation. However, selection of CLRM to estimate individual

equation is based on the two reasons. First, Greene (2005) argues if the set of regressors is

same across the two (or more) dependent variables, the outcomes from SUR will be identical

to those from OLS (Greene, 2005). Second, the sample size in this study is too small to

restore reasonable degrees of freedom in estimating a system of equation with large number

of coefficients. To produce robust estimates, possible violations of the assumptions of the

CLRM relevant for cross-sectional data are explored. Shapiro-Wilk test is utilized to check

the normality of residuals since it is recommended the best choice for testing the normality

of data by some researchers (Thode, 2002). To deal with the possibility of

heteroskedasticity, robust standard errors (heteroskedasticity-consistent) are utilized as it is

a common and popular technique in this respect (Berry, 1993). Multicollinearity is tested

by analyzing the Variance Inflation Factor (VIF) that is the most extensively used diagnostic

for multicollinearity (Allison, 2012). The data issues pose a serious limitation on testing

endogeneity of the model as the appropriate instruments could not be found for the district

level data. Therefore, to establish the causality between inclusive education and its

determinant is beyond the scope of this study. In this scenario the objective of this work is

to identify the significant covariates of inclusive education.

3. Analysis of Education Index and Its Inequalities

For aggregated analysis of educational progress households’ education indices are

classified in to five categories. These categories/classes of education index are very low,

low, medium, high, and very high. The cut-off values for classes of education index are

determined by five provincial quintiles of households’ education indices following the lines

proposed by UNDP (2014). The various categories of household’s education index along

with their cut off values are given in Table 1. A simple comparison of lowest and highest

cut off values reveals substantial disparities in education indices of households. The gap

between maximum and minimum cut off for education index it is 0.74. The wide differences

in education indices across and within quintiles show the prevalence of high educational

disparities across households.

Table 1: Categories of Educational Achievements with Cutoff Values

Categories of Education Index
Cutoff values

(Based on Quintiles of IE)
Very low Less than 0.19

Low 0.19 to 0.50

Medium 0.51 to 0.69

High 0.70 to 0.93

Very High greater than 0.93

Source: Authors’ Calculations

Journal of Applied Economics and Business Studies, Volume. 4, Issue 1 (2020) 1-28 https://doi.org/10.34260/jaebs.411

The estimates of education index (IE), Inequality-Adjusted education index (IiE), and

the estimated percentage losses due to inequalities in educational achievements across

households (AE) in KPK and, its rural and urban regions are exhibited in figure 1. These

estimates establish the incidence of low actual educational achievements in KPK and of

high disparities across regions. The overall KPK education index reveals that KPK falls in

low category of potential educational achievements. There is a substantial urban-rural

educational disparity in KPK. The urban households’ education index lies in medium

category, while rural index falls in low category of educational achievements. The urban

households’ education index is 1.4 times higher than that of rural households.

Figure 1: Provincial Education indices and Inequality Measures (Source: Authors’

Calculations)

Inequality-Adjusted education index demonstrate that achievement level in education is

affected considerably due to inequalities. The loss is substantial with a varied magnitude in

different regions. At provincial level, this loss is estimated around 28 percent. The loss due

to inequality is markedly high in rural KPK as compared to its urban counter parts.

0.4681

0.6068

0.4366

0.3372

0.4903

0.3097
0.2796

0.1920

0.2906

0.00

0.20

0.40

0.60

0.80

Overall Urban Rural

KPK

Education Index Inequality Adjusted Education Index % Loss due to Inequality

Tahira Tauheed & Muhammad Nasir

Table 2: District-wise Education Indices and Inequality Measures

District

Education

Index

(IE)

Inequality

Adjusted

Education

Index

(IiE)

% Loss due to

Inequality

(AE)

Rank

IiE

Change in

rank

due to

Inequality

Haripur 0.6552 0.5660 0.1362 1 1 0

Karak 0.5650 0.4758 0.1580 3 2 1

Malakand 0.5425 0.4429 0.1836 6 3 3

Abbottabad 0.5970 0.4381 0.2662 2 4 -2

Chitral 0.5345 0.4292 0.1970 7 5 2

Peshawar 0.5462 0.4258 0.2204 5 6 -1

Lower Dir 0.5047 0.4203 0.1672 10 7 3

Mansehra 0.5522 0.4113 0.2552 4 8 -4

Lakki Marwat 0.5057 0.3982 0.2124 9 9 0

Bannu 0.4959 0.3948 0.2038 11 10 1

Nowshera 0.5064 0.3871 0.2356 8 11 -3

Mardan 0.4541 0.3331 0.2664 13 12 1

Kohat 0.4669 0.3295 0.2943 12 13 -1

Charsadda 0.4353 0.3214 0.2617 14 14 0

Swat 0.4319 0.3197 0.2598 15 15 0

Swabi 0.4306 0.3009 0.3014 16 16 0

Tank 0.3807 0.2725 0.2843 18 17 1

D. I. Khan 0.4121 0.2719 0.3401 17 18 -1

Hangu 0.3569 0.2557 0.2836 20 19 1

Upper Dir 0.3584 0.2537 0.2921 19 20 -1

Batagram 0.3341 0.2326 0.3039 21 21 0

Buner 0.3044 0.2096 0.3115 22 22 0

Shangla 0.2975 0.1924 0.3533 23 23 0

Tor Ghar 0.2211 0.1395 0.3694 24 24 0

Kohistan 0.2152 0.1270 0.4099 25 25 0

Source: Authors’ Calculations

The district-wise Education indices (IE), Inequality-Adjusted Education indices (IIE),

and coefficients of inequality (AE) are reported in Table 2. There is no district in KPK with

potential education index above the medium level of education index or in very low

category. The data reveals that potential education index for most of the KPK districts fall

in low category, ten districts are in medium category. Inequality adjustment pulls down all

districts’ education indices to further in low category or in very low category. The estimated

inequality coefficients validate the prevalence of wide disparities within districts. In ranking

of both education indices with and without Inequality adjustment; Haripur is at the top.

KPK’s capital Peshawar is ranked at 4th place in terms of IE and with inequality adjustment

it ranks at 8th place. It is the largest loss of rank among all districts of KPK indicating the

intensity of educational inequality in Peshawar. It lies in medium category of education and

fall in low category with inequality adjustment. Kohistan is at lowest rank of education in

terms of both education indices IE and IiE. It is preceded by Tor Ghar, Shangla, Buner and

Batagram. All these bottom ranked districts are in low category of education and first three

of them come down to very low category after accounting for inequality.

Journal of Applied Economics and Business Studies, Volume. 4, Issue 1 (2020) 1-28 https://doi.org/10.34260/jaebs.411

Analysis of inequality coefficients reveals that loss due to inequality ranges from the

loss of almost 14 percent in Haripur to the substantial loss of 41 percent in Kohistan. In top

five districts maximum loss due to in equality is around 27 percent, in contrast the minimum

loss in bottom ten districts is around 41 percent. In general, the magnitude of disparities

within districts and across districts rises with deterioration of education level. However,

considerable disparities are observed in some top ranked districts too. The KPK districts

exhibiting very low level of human development are situated in its north and south. It is

observed that mostly districts that have natural resource endowment are in low or very low

category of human development. In contrast the majority districts with better status of

human development and low disparities are either centers of administration, or are home to

small industries, or are hub of commerce and trade. It indicates the skewed utilization of

public and private funds, underutilization and wastage of natural resources, and the ignored

agriculture sector. There are some obvious socio-political factors that could be responsible

for adverse human development status in certain regions. In KPK the terrorist activities,

Afghan refugees, and armed conflict specifically in southern districts are some of the

probable reasons for poor human development situation (Akbar, 2015; Khattak, 2017;

Yousaf, 2013). To address these issues further research at regional levels is required.

The distribution of districts in categories of education, according to their education index

(IE) and Inequality-Adjusted education index (IIE) is given in Table 3.

Table 3: Distribution of Districts in Categories of Education Index

Education Category

Districts

(According to IE)

Districts

(According to IiE)

Number Percentage Number Percentage

KPK

Very High 0 0 0 0

High 0 0 0 0

Medium 9 36 1 4

Low 16 64 22 88

Very Low 0 0 2 8

Source: Authors’ Calculations

The findings of this study are comparable with the facts presented by Pakistan NHDR

(2017), see Table A.3 in appendix. According to this report most of the KPK districts’

education indices falls in low medium or low categories, the same result is established by

the present study. Majority of the districts ranks according to the education index (IE) is

almost similar in both studies. The interesting comparison is between the education index

(IE)-wise rank of districts cited by the NHDR and the inequality adjusted Inequality-

Adjusted education index (IIE)-wise rank given by the present study. The inequality

adjustment changes the education ranking of the districts remarkably. For example,

Abbottabad that stands first in NHDR in terms of education index slides down to fourth

Tahira Tauheed & Muhammad Nasir

place with inequality adjustment calculations in the present study. These findings urge for

policy measures focused not only to elevate the average educational achievements but also

to alleviate the educational inequalities simultaneously.

A mapping of KPK districts’ inequality adjusted education and human development

indices and their ranks respectively calculated by present study and Pakistan NJDR (2017)

is cited in appendix Table A.4. It reveals substantial differences in the level of education

and overall human development level in majority of KPK districts. District Karak with HDI

ranking of 13 and IIE ranking of 2 is showing the highest divergence. At the lowest end for

the districts of Shangla, Tor Ghar, and Kohistan this deviation is minimal. The considerable

inequalities in districts’ HDI and IIE suggest that the dimensions of HDI are not perfect

substitutes of each other.

4. Inclusiveness Analysis of Education

The third important aspect of inclusive education in a society i.e. inclusion of marginalized,

is examined by utilizing distribution of households’ education indices. To determine the

inter-regional inclusion status the median education index for each region is compared to

sixty percent of national median education index. The value of national median education

index estimated in this study is 0.5714. The comparison demonstrates that overall, urban,

and rural regions in KPK are inclusive in mainstream education, see Table 4. Inclusiveness

describes that at least some proportion of households in bottom half (below median

education index) of these regions have education index that fall in inclusion zone.

Table 4: Provincial Level Estimates of Inter-Regional Inclusion/Exclusion in Terms

of Education index

Education
Median

Education Index

Inter-regional

Inclusion/Exclusion*

KPK

Overall 0.4707 Inclusion__

Urban 0.6143 Inclusion

Rural 0.4306 Inclusion

Source: Authors’ Calculations

*Criterion for inter-regional inclusion is regional median>= (0.6*national overall

median).

Table 5 displays inter-district inclusion/exclusion in terms of education index in KPK.

Out of 25 districts of KPK 18 are exhibiting inter-regional inclusion. Hence, 74 percent and

28 percent of the total KPK districts demonstrates the inter-regional inclusion and exclusion,

respectively. The districts with very low median education indices showing exclusion

include Hangu, Upper Dir, Batagram, Buner, Shangla, Tor Ghar, and Kohistan. It implies

that education index of all households in these districts fall in exclusion zone.

Journal of Applied Economics and Business Studies, Volume. 4, Issue 1 (2020) 1-28 https://doi.org/10.34260/jaebs.411

Table 5: District-wise Estimates of Inter-Regional Inclusion/Exclusion in Terms of

Education index

District
Median

Education Index

Inter-regional

Inclusion/Exclusion*

Haripur 0.6788 Inclusion

Abbottabad 0.6667 Inclusion

Mansehra 0.5714 Inclusion

Karak 0.5714 Inclusion

Chitral 0.5556 Inclusion

Malakand 0.5417 Inclusion

Lower Dir 0.5333 Inclusion

Peshawar 0.5333 Inclusion

Lakki Marwat 0.5238 Inclusion

Nowshera 0.5000 Inclusion

Bannu 0.5000 Inclusion

Mardan 0.4675 Inclusion

Kohat 0.4630 Inclusion

Swabi 0.4444 Inclusion

Charsadda 0.4287 Inclusion

Swat 0.4222 Inclusion

Tank 0.3968 Inclusion

D. I. Khan 0.3662 Inclusion

Hangu 0.3353 Exclusion

Upper Dir 0.3333 Exclusion

Batagram 0.2917 Exclusion

Buner 0.2540 Exclusion

Shangla 0.2500 Exclusion

Tor Ghar 0.1444 Exclusion

Kohistan 0.1167 Exclusion

Source: Authors’ Calculations

Intra-regional inclusiveness analysis at the provincial and district levels is presented in

figures 2 and 3, respectively. The estimate of provincial IC establishes that 75 percent of the

lower half of households (with education index below median) in KPK falls in exclusion

zone of education, consequently the percentage of inclusion is markedly low. The

substantial rural-urban disparities are evident in inclusiveness of education. In urban region

mainstream inclusion is more than three times higher than its rural counterpart. In urban

region and rural regions respectively, 59 percent and 18 percent of the bottom half of

households lie in mainstream inclusion zone. The regional inclusion of rural region is less

than two-third of that of the urban region. The IC-regional for rural households and urban

households is 56 percent and 34 percent, respectively.

Tahira Tauheed & Muhammad Nasir

Figure 2: Provincial Level Estimates of Education’s Regional and Mainstream

Inclusion Coefficients (Source: Authors’ Calculations)

The intra district inclusion analysis reveals that in KPK only 2 out of 25 districts are in

state of inclusion with respect to IC-mainstream and seven districts with negative IC-

mainstream exhibit nearly perfect exclusion. In KPK, Haripur district has the highest level

of mainstream inclusion and regional inclusion of 73 percent and 66 percent respectively.

The second highest level of mainstream and regional inclusion is exhibited by district Karak

for which both measures have the same value of 0.58. Provincial capital Peshawar also

exhibit mainstream exclusion with IC-mainstream at 0.43 and IC-regional at 0.50. The

districts with the lowest level of inclusion with respect to mainstream education and regional

education are Tor Ghar (-47 %) and Kohistan (-45 %), respectively. It shows that education

indices of almost 97 percent and 95 percent of households of Tor Ghar and Kohistan falls

in exclusion zone of education, respectively.

0.56

0.37 0.34

0.59

0.25
0.18

0.00

0.50

1.00

KPK Urban KPK overall KPK Rural

Inclusion Coefficient Regional Inclusion CoefficientMainstream

Journal of Applied Economics and Business Studies, Volume. 4, Issue 1 (2020) 1-28 https://doi.org/10.34260/jaebs.411

Figure 3: District-wise Regional and Mainstream Inclusion Coefficients of Education

(Source: Authors’ Calculations)

5. Analysis of Determinants of Inclusive Development

Analyzing the determining factors of inclusive education is a prerequisite to identify

critical areas for optimal utilization of available resources (Oluseye & Gabriel, 2017). One

of the objectives of this study is to identify the factors that could ensure and enhance the

inclusive education across districts in Pakistan. The district level diagnosis recognizes the

most significant local factors that boost or hampers the inclusive education. This analysis

provides a base to suggest appropriate policies. The methodology for the analysis is

discussed in section 2.

In this regression analysis dependent variables are the indicators of inclusive education

estimated by this study and the independent variables are factors that influence these

indicators. The individual regression models are estimated for indicators of inclusive

education representing its three aspects including education index, inequality coefficient,

and inclusiveness coefficients (mainstream & regional). A set of 18 variables mentioned in

section 2 is initially selected to include in regression analysis. The descriptive statistics of

these regression variables are cited in Tables A.1 and A.2 in appendix. The descriptive

analysis shows high variability in the regressors, it improves the precision with which the

parameters are estimated (Anon., 2016).

The four regression models presented by equations 12-15 are estimated in this study by

utilizing Stata 13. The assumptions of CLRM vital for cross-sectional data are identified

-0.6

-0.4

-0.2

0.2

0.4

0.6

0.8
H

a
ri

p
u
r K

a
ra

M
a
la

k
a
n

C
h
it

ra
l

A
b

b
o
tt

a
b
a
d

L
a
k

k
i

M
a
rw

a
t

P
e
sh

a
w

a
r

L
o

w
e
r

D
ir

M
a
n

se
h

B
a
n
n

N
o

w
sh

e
ra

M
a
rd

a
n K

o
h

a
t

S
w

a
t

S
w

a
b
i

C
h
a
rs

a
d

d
a T

a
n

D
.

I.
K

h
a
n

H
a
n

g
u

U
p

p
e
r

D
ir

B
a
ta

g
ra

m S
h

a
n

g
la

B
u
n

e
r

K
o

h
is

ta
n

T
o

r
G

h
a
r

Part (a): KPK's Districts

Regional Inclusion Coeffient Mainstream Inclusion Coefficient

Tahira Tauheed & Muhammad Nasir

and are taken care of to generate robust estimates of regression coefficients. The robust

standard errors are used to address the probable prevalence of heteroscedasticity (Williams,

2015). To estimate a parsimonious CLRMs for the education index, inequality coefficient,

IC-mainstream, IC-regional are estimated by including different subsets of independent

variables from initially selected 17 variables. To control for high multicollinearity and to

restore degree of freedom final models are selected on the basis of VIF values, overall

significance of the model, and the model selection criteria of Akaike and Schwarz. In these

selected models individual and mean VIFs are less than 2.5 and residuals are approximately

normal and homoskedastic. The estimated regression models are reported in Table 6.

The regression results demonstrate that being divisional headquarter, public investment

in social infrastructure, eradication of gender bias, development of agriculture sector, and

urbanization are the significant determinants of one or more aspects of inclusive education

in districts of KPK. The public expenditure on health and education have a significant

impact on all aspects of inclusive education. The impact of physical infrastructure ( road

density) is found to be statistically insignificant for all aspects of inclusive education.

According to analysis the inequality coefficients of districts which are divisional

headquarters are significantly high. It implies that in administrative centers the proportions

of households excluded from mainstream of educational achievement is higher than other

districts. The regression results provide an empirical evidence for the strong role of social

infrastructure to ensure a higher level of inclusive education. It is asserted by regression

finding that total number of government /government high schools (an indicator of public

education facilities) has a substantial and statistically significant effect on all indicators of

inclusive development. The substantial effect of public spending on education provide an

empirical evidence for multiplicity of benefits that investment in education yields (Mitra,

2011). This finding leads to the policy recommendation of keeping education at the highest

priority in development agenda. The regression results revealed that impact of number of

hospital beds (an indicator of public health facilities) is statistically significant on all aspects

of inclusive education in KPK districts. These findings are in line with the existing studies

that witness the positive significant role of public health facility (James, 2016). The police

stations as institutions for safeguarding law and order in the society are also found to be a

significant factor of mainstream inclusive education. A noticeably clear picture of

relationship between education status and sex ratio (gender discrimination) is portrayed

from present analysis. The negative impact of high sex ratio on educational achievements

leads to lower inclusive education. It signifies that female inclusiveness is a key prerequisite

for inclusive education. It is depicted by regression results that cultivated area has a

statistically significant positive effect on education index. A larger percentage of cultivated

area leads to higher educational achievement.

Journal of Applied Economics and Business Studies, Volume. 4, Issue 1 (2020) 1-28 https://doi.org/10.34260/jaebs.411

Table 6: Regression Models for Determinants of Inclusive Education

Regressand

Regressor

Education

Index

Inequality

Coefficient

IC-

Mainstream
IC-Regional

District HQ
-0.0211

(0.0320)

0.0460**

(0.0186)

-0.0659

(0.0641)

-0.0479

(0.0315)

Urbanization
0.0038*

(0.0020)

-0.0005

(0.0010)

0.0074

(0.0045)

0.0007

(0.0017)

Sex ratio
-0.0046*

(0.0024)

0.0004

(0.0014)

0.0034

(0.0064)

0.0002

(0.0028)

Govt. schools
0.0018**

(0.0009)
_______ _______ _______

Govt. High Schools _______
-0.0145***

(0.0043)

0.0782***

(0.0160)

0.0212**

(0.0096)

Beds in Govt.

Health Institutes

0.0168**

(0.0069)

-0.0090**

(0.0041)

0.0351***

(0.0132)

0.0146**

(0.0070)

Cultivated area
2.6688**

(1.1347)

-0.3584

(.6693)

1.7009

(2.7991)

0.8350

(1.2751)

Road Density
-0.0013

(0.0014)

(-0.0010)

(0.0009)

0.0022

(0.0038)

0.0011

(0.0015)

Police Stations
-0.0398

(0.0465)

0.0398

(0.0247)

-0.2345**

(.1028)

-0.0454

(0.0523)

Constant
0.5892

(0.2584)

0.3693

(0.1530)

-0.8752

(0.7379)

0.1414

(0.3021)

R-Squared 0.7215 0.7540 0.8055 0.6365

F value 12.77*** 15.55 *** 21.41*** 11.37***

RMSE 0.0738 0.0413 0.1726 0.0816

Note: ***, **, * indicate 1%, 5% and 10% significance level. Robust standard errors are

cited in parenthesis.

6. Conclusions and Recommendations

This paper provides an empirical study of the education inclusiveness in the province of

Khyber Pakhtunkhwa (KPK) in Pakistan based on data from the most recent Pakistan Social

and Living Standard Measurement (PSLM) survey 2014-15. It finds that in general, the

magnitude of disparities and exclusion within districts and across districts in KPK rises with

deterioration of education status. However, considerable disparities and/or exclusion in

some top ranked districts are also observed. It is noted that mostly districts that are in low

or very low category of education index are rich in any one or more than one natural resource

endowment including minerals, forests, and cultivable lands. In contrast the majority

districts with better status of education, low disparities, and high inclusion are either centers

of administration, or home to small industries, or hub of commerce and trade. It points out

Tahira Tauheed & Muhammad Nasir

to the skewed utilization of public and private funds, underutilization and wastage of natural

resources, and the ignored agriculture sector.

To achieve a higher level of inclusive education, the regions with different status of

education and different hindering factors require different strategies. The policies must be

formulated keeping in view all the aspects of inclusive education. The status of inclusive

education of a region must be one of the criteria of allocating public funds. In the regions

that are at a very low level of education, it is more important to get educational growth

acceleration, as the inclusivity of education may have to come later. For the regions with

higher level of education accompanied with high disparities and exclusions, a progressive

taxation policy would be more effective. For the regions exhibiting high level of education

and lower inclusion, it is vital to facilitate the emergence of inclusive institutions. The

education policies must be designed that target the balance of educational achievements in

urban and rural areas of KPK.

A comparison of KPK districts’ human development estimates from Pakistan NHDR

(2017) and inequality adjusted education level estimated by present study suggests that the

dimensions of human development are not perfect substitutes of each other. These results

suggest formulating public policies that focus on balanced development in all the

dimensions of human development.

It is recommended to allocate reasonable percentage of provincial and local bodies

budget for the enhancement of quantity and quality of social infrastructure ( specifically

basic education, health, and law and order). Based on highly significant negative impact of

sex ratio (utilized as an indicator of gender discrimination) on inclusive education it is

recommended to formulate effective policies for elimination of gender bias at all levels. The

increasing trends of urbanization in KPK and the empirical evidence of its positive

significant impact on inclusive education in this study imply that policies must be

formulated to control these factors so that their negative effect could be avoided in the long

run as well. As a major proportion of population in KPK is dependent on agriculture sector,

its development must be at top priority in inclusive education agenda. A policy framework

must be designed for uplifting and mechanizing agriculture sector. To reform the police

department is also one of the important implications of the present analysis.

There are some major limitations of this study that must be acknowledged. Some

important economic factors of inclusive development could not be included in the analysis

due to unavailability of data at the district level. For many indicators the available data is

not standardized across provinces. It is suggested to formulate policy for collection and

standardization of data about macroeconomic indicators at provincial, districts, and sub-

district level.

Journal of Applied Economics and Business Studies, Volume. 4, Issue 1 (2020) 1-28 https://doi.org/10.34260/jaebs.411

This study provides an empirical analysis of the existing status of inclusive education

and its determinants in KPK in the best possible way. However, a great deal of additional

research is required in this direction. Some recommendations for future research are the

natural extensions of this study. First is the dynamic and comparative static analysis of

inclusive education in KPK and other provinces of Pakistan utilizing different rounds of

PSLM-HIES. The second is to estimate the inequality coefficient and coefficient of

inclusion by utilizing various non-conventional values of risk aversion parameter and

threshold for deprivation and compare its findings with that obtained by conventional

measures utilized in the present study. Third is to include the qualitative aspects of education

in its index. The forth is to utilize various measures of inequality such as Gini coefficient in

addition to the Atkinson’s inequality index to measure the inequalities and compare the

results. To recommend policies in accordance to each specific region and administration

level, it is suggested for future studies to investigate the factors of inclusive development

that could not be covered adequately in the present research specifically the institutions,

economy, local customs and traditions, and geography (spatial analysis). The literature

highlights some socio-political factors that could be responsible for adverse inclusive

education status in certain regions (as indicated by the present study) of KPK. It is

recommended for further research to execute case studies for specific regions to explore the

impact of these factors on inclusive development.

References

Ahmed, A. & Rashid, S., 2018. Citizen report card study education sector Swat and Lower Dir -

Khyber Pakhtunkhwa , Karachi: Transparancy International Pakistan.

Akbar, A., 2015. Over 600,000 Afghan Refugees in KP. [Online] Available at:

https://www.dawn.com/news/1167103

Alkire, S. & Foster, J., 2010. Designing the Inequality adjusted Human Development Index (HDI),

Oxford: Oxford Poverty and Human Development Initiative.

Allison, P., 2012. When Can You Safely Ignore Multicollinearity?. [Online] Available at:

https://statisticalhorizons.com/multicollinearity

Anon., 1997. National Literacy Policies Pakistan. [Online] Available at:

http://www.accu.or.jp/litdbase/policy/pak/

Atkinson, A. B., 1970. On the measurement of inequality. Journal of Economic Theory 2(3), pp.

244-263.

Berry, W. D., 1993. Understanding Regression Assumptions. London: SAGE Publication, Inc..

Bradshaw, J. & Mayhew, E., 2011. The Measurement of Extreme Poverty in Europian Union.

York: European Commission.

Tahira Tauheed & Muhammad Nasir

Burki, A. A., Memon, R. & Mir, K., 2015. Multiple inequalities and policies to mitigate inequality

traps in Pakistan, Lahore: Oxfam.

Foster, J. E., López-Calva, L. F. & Székely, M., 2005. Measuring the Distribution of Human

Development: Methodology and an Application to Mexico. Journal of Human

Development.

Foster, J., Seth, S., Lokshinl, M. & Sajaia, Z., 2013. A uniform approach to measuring poverty and

inequality Theory and practice. Washington, DC: World Bank.

Gouleta, E., 2015. Educational assessment in Khyber Pakhtunkhwa Pakistan’s north-west frontier

province: Practices, issues, and challenges for educating culturally linguistically diverse

and exceptional children. Global Education Review, 2(4), pp. 19-39.

Greene, W. H., 2005. Econometric Analysis. 5th ed. s.l.:Pearson Education. Jamal, H., 2016.

Quantifying Sub-national Human Development Indices from Household Survey data,

Karachi: Social Policy and Development Centre.

James, C., 2016. Health and Inclusive Growth: Changing the Dialogue. s.l.:World Health

Orgnization.

Kato, H., 2014. Executive Summary. In: Perspectives on the Post-2015 Development Agenda.

Tokyo: Japan International CooperationAgency Research Institute, pp. 1-15.

Khattak, D., 2017. Reviewing Pakistan's Anti-Terror Fight, 3 Years After the Peshawar School

Attack. [Online] years-after-the-peshawar-school-attack/

Lopez-Calva, L. F. & Ortiz-Juarez, E., 2011. https://www.springer.com: Springer

Science+Business Media B.V. 2011 .

Mack, J., 2016. Income threshold approach. [Online]

Available at: http://www.poverty.ac.uk/definitions-poverty/income-threshold approach

Mitra, D., 2011. Pennsylvania’s Best Investment: The Social and Economic Benefits of Public

Education. [Online] Available at:

https://www.elcpa.org/wpcontent/uploads/2011/06/BestInvestment_Full_Report_6.27.11.p

Najam, A. & Bari, F., 2017. Pakistan Human Development Index Report, 2017, Islamabad: UNDP,

Pakistan.

Oluseye, I. C. & Gabriel, A. A., 2017. Determinants of Inclusive Growth in Nigeria: An ARDL

Approach. American Journal of Economics, pp. 97-109.

Pakistan Bureau of Statistics, 2016. PSLM 2014-15, Islamabad: Pakistan Bureau of Statistics.

Rauniyar, G. & Kanbur, R., 2010. Conceptualizing Inclusive development: with application to

Rural infrastructure and Development Assistance. Ithaca(Newyork): Department of

Applied Economics and Management, Cornell University, Ithaca, Newyork.

Seth, S., 2009. Inequality, Interactions, and Human Development. s.l.:Oxford Poverty & Human

Development Initiative (OPHI).

Suryanarayana, M. H., 2008. What is Exclusive about 'Inclusive Growth'?. Economic & Political

Weekly, 25 October, pp. 93-101.

Thode, H. C., 2002. Testing for Normality. New York: Marcel Dekker, Inc..

http://www.poverty.ac.uk/definitions-poverty/income-threshold

Journal of Applied Economics and Business Studies, Volume. 4, Issue 1 (2020) 1-28 https://doi.org/10.34260/jaebs.411

Townsend, I. & Kennedy, S., 2004. Poverty: Measures and Targets. [Online] Available at:

http://www.parliament.uk

uddin, Z. & Tahir, M., 2014. Monitoring of the delivery of educational services: A case study of

sovernment schools in Khyber Pakhtunkhwa. The Dialogue, Volume IX, No. 3, pp. 271-

284.

UNDP Pakistan, 2016. Development Advocate Pakistan, Volume 3, Issue 2, Inequality: Missing

from the Public Agenda, Islamabad: UNDP Pakistan.

UNDP, 2014. Technical notes. [Online]

Available at: http://hdr.undp.org/sites/default/files/hdr14_technical_notes.pdf

UNDP, 2015. Technical Notes. [Online]

Available at: http://hdr.undp.org/sites/default/files/hdr2015_technical_notes.pdf

UNDP, 2016. Human Development Report 2016: Human Development for Everyone, New York:

UNDP.

UNESCO, 2017. A guide for ensuring inclusion and equity in education. [Online]

Available at: https://www.european-agency.org/sites/default/files/news/news-

files/UNESCO%20guide.pdf [Accessed April 2020].

Williams, R., 2015. Heteroskedasticity. [Online] Available at:

https://www3.nd.edu/~rwilliam/stats2/l25.pdf

Yousaf, N., 2013. Kyber Pukhtunkhawa's Sad South. [Online] Available at:

https://www.dawn.com/news/1029031

https://www.european-agency.org/sites/default/files/news/news-files/UNESCO%20guide.pdf
https://www.european-agency.org/sites/default/files/news/news-files/UNESCO%20guide.pdf

Tahira Tauheed & Muhammad Nasir

APPENDIX

Table A.1: Descriptive Statistics of Variables in regression analysis of

Inclusive Development

Variable Mean Minimum Maximum S.D.

Education Index (IE) 0.4442 0.2152 0.6552 0.1119

Inequality coefficient (AE) 0.2627 0.1362 0.4099 0.0666

IC-Mainstream 0.1950 -0.4699 0.7327 0.3131

IC-Regional 0.4094 0.2095 0.6642 0.1083

Forest density (percentage) 33.4716 0.0000 86.946 25.739

Population density (per sq. km) 676.3255 30.1254 3396.24 678.29

Urban Population (Percentage) 13.0295 0.0000 46.1468 11.168

Sex ratio (Male to female) 102.2356 92.6900 124.360 5.5109

Total No. of Govt. Schools 104.7996 30.8497 181.509 34.034

No. of Govt. Primary schools 87.4001 23.9630 150.017 29.933

No. of Govt. Middle schools 9.7419 3.6073 19.0003 3.1487

No. of Govt. High schools 7.6576 2.9172 16.3179 2.8099

No. of Colleges 1.8197 0.0000 2.8317 0.7508

No. of Hospitals 0.6377 0.0000 1.7503 0.3589

No. of beds in Govt. Health

Institutions
5.2704 0.0000 12.4336 2.8171

Cultivated area 0.0311 0.0000 0.0736 0.0201

No. of Factories 6.0789 0.0000 21.5085 6.5550

Road density (per sq. km) 29.7069 5.5793 71.3936 15.955

No. of Police stations 1.1245 0.5927 2.6824 0.4616

Note: Number of schools, hospitals, factories and police stations are reported as per

hundred thousand of population, and cultivated area is reported as a percentage of total

area.

Table A.2: Frequency Distribution of Categorical Determinants of Inclusive

Development

Variable Frequency Relative Frequency

Divisional Headquarter

No 20 80

Yes 5 20

Railway Station

No 11 44

Yes 14 56

Airport

No 4 16

Yes 21 84

Journal of Applied Economics and Business Studies, Volume. 4, Issue 1 (2020) 1-28 https://doi.org/10.34260/jaebs.411

Table A.3: Comparative Analysis of Education Indices of KPK Districts

District
Inequality Adjusted

Education Index (IIE)
Education Index (IE)

Present Study Present Study
Pakistan NHDR

2017

Haripur 0.5660 0.6552 0.6367

Karak 0.4758 0.5650 0.5567

Malakand 0.4429 0.5425 0.5750

Abbottabad 0.4381 0.5970 0.6400

Chitral 0.4292 0.5345 0.5500

Peshawar 0.4258 0.5462 0.5833

Lower Dir 0.4203 0.5047 0.5033

Mansehra 0.4113 0.5522 0.5500

Lakki Marwat 0.3982 0.5057 0.5117

Bannu 0.3948 0.4959 0.5133

Nowshera 0.3871 0.5064 0.5033

Mardan 0.3331 0.4541 0.5117

Kohat 0.3295 0.4669 0.4950

Charsadda 0.3214 0.4353 0.4683

Swat 0.3197 0.4319 0.4600

Swabi 0.3009 0.4306 0.4817

Tank 0.2725 0.3807 0.4000

D. I. Khan 0.2719 0.4121 0.4033

Hangu 0.2557 0.3569 0.3850

Upper Dir 0.2537 0.3584 0.3600

Batagram 0.2326 0.3341 0.3533

Buner 0.2096 0.3044 0.3717

Shangla 0.1924 0.2975 0.3083

Tor Ghar 0.1395 0.2211 0.2483

Kohistan 0.1270 0.2152 0.2483

Source: Authors’ calculations and National Human Development Index Report (2017)

Tahira Tauheed & Muhammad Nasir

Table A.4: A Mapping of KPK Districts' Human Development Indices & Education

Indices

District

Present Study
Pakistan NHDR

2017
Difference

in IIE &

HDI

Ranks
Inequality Adjusted

Education Index (IIE)
Rank HDI Rank

Haripur 0.5660 1 0.732 3 -2

Karak 0.4758 2 0.615 13 -11

Malakand 0.4429 3 0.69 6 -3

Abbottabad 0.4381 4 0.761 1 3

Chitral 0.4292 5 0.674 8 -3

Peshawar 0.4258 6 0.756 2 4

Lower Dir 0.4203 7 0.6 15 -8

Mansehra 0.4113 8 0.676 7 1

Lakki Marwat 0.3982 9 0.577 17 -8

Bannu 0.3948 10 0.613 14 -4

Nowshera 0.3871 11 0.697 5 6

Mardan 0.3331 12 0.703 4 8

Kohat 0.3295 13 0.666 9 4

Charsadda 0.3214 14 0.65 11 3

Swat 0.3197 15 0.618 12 3

Swabi 0.3009 16 0.654 10 6

Tank 0.2725 17 0.459 21 -4

D. I. Khan 0.2719 18 0.496 20 -2

Hangu 0.2557 19 0.594 16 3

Upper Dir 0.2537 20 0.375 23 -3

Batagram 0.2326 21 0.505 19 2

Buner 0.2096 22 0.528 18 4

Shangla 0.1924 23 0.438 22 1

Tor Ghar 0.1395 24 0.24 24 0

Kohistan 0.1270 25 0.229 25 0

Source: Authors’ calculations and Pakistan National Human Development Index Report

(2017)