Chapter: 1

INTRODUCATION

The rationalization of higher government expenditure on basic education is often

based on its impact on individual life time earning i.e. social rate of return. Different

studies indicated that social return for primary education is higher than secondary and

tertiary education but expenditure on tertiary education is inappropriately high in most

of the countries (Gupta et al.1999).

Higher budget allocation for primary health care is justified on the basis that such

expenditures ameliorates the impact of diseases on productive years of people. Many

studies suggested that burden of disease could be minimize in developing countries if

government ensure the availability of basic and cost effective health services for all

population(World Bank1993). Preventive measures from diseases are more cost

effective but in developing countries mostly resources are allocated for curative

services (Sahn et al.1993; Pradhan 1996).

Impact of public spending on education attainment and basic health care is

inconclusive. It is possible that public spending on education and health crowed out

the private spending, or government resources are used inefficiently and inequitably.

Infant mortality rate, child mortality and life expectancy are used by many researchers

as a proxy for health care, likewise for education attainment, primary, secondary and

tertiary school enrollment are used as an indicators. Beneficial impact of sufficient

resource allocation on health and educational outcomes are mixed according to the

social, political and economic conditions of the country.

1

Health is vital elements of human capital. A healthier worker can contribute more in

the production process than his unhealthy counterpart. There are several channels

that define the contribution of health in production and output. For a given level

of all other factors, the economy can produce higher output if it has higher

levels of health. Health is an important factor for determining the level of

returns from education. Improvement in health increases output due to increased

strength and also due to more learning from a given level of education.

The relationship between health care expenditure and health status has received some

attention in developing regions. At the country level, Akinkugbe and Mohano (2004)

performed time series analysis using the error correction model (ECM) and found that

in addition to public health care expenditure, the availability of physicians, female

literacy and child immunization significantly influenced health outcomes in Lesotho.

At the regional level, (Anyanwu and Erhijakpor, 2007) in a panel data analysis and

using a fixed effect model found that total health expenditures are a significant

contributor to health outcomes with a 10 percent increase in total health care

expenditure per capita resulting in21 percent and 22 percent decrease in under-five

and infant mortality rates respectively. Similarly (Rajkumar and Swaroop 2008;

Craigwell et al. 2012) confirm the positive impact of government spending on infant

mortality rate, child mortality rate and life expectancy.

Weak and insignificant impact of government expenditure on health status is explored

by (Carrin and Politi, 1995; Mello et al. 2003; Mello and Pisu, 2009). Empirical

evidence suggests that health expenditure effects on health indicators may vary

2

between countries, possibly due to differences in population, political and economic

factors that modify the expenditure effects.

Education which is probably the most important determinant of human capital

(Bergheim, 2005) affects output through various channels. It increases knowledge

which helps to produce more output in relatively smaller time and it is intuitionally

suggested that an educated person could learn much faster. Increase in the level of

education also leads towards better health due to increase in the awareness of the

benefits of healthy living, which in turn increases output. Moreover, education also

enhances labor force participation in the economy.

The causal relationship between educational expenditures and school enrolment

continues to attract the attention of many. However, despite decades of intensive

study, there is no general consensus regarding the effectiveness of monetary

educational inputs for student outcomes. (Tiongson et al .1999; Mello et

al .2003 ;Gupta et al .2004) are in favour of the effectiveness of public education

expenditures (Noss ,1991; Mingat & Tan ,1998) found weak and insignificant

relationship between government spending and education attainment and suggested

per capita income, parents education level and school age population as major

determinants of school enrollment.

Human capital is widely accepted as an important determinant of economic growth

and importance of human capital accumulation is unconditionally acknowledged

in existing exogenous and endogenous growth theories (Mankiw et al.

1992 ;Howitt , 2005). In most of the studies education or health related indicators are

employed as a proxy for human capital. Studies undertaken on both developed and

developing countries have indicated that efficient and sufficient government resource

3

allocation on education and health encourages human development and economic

growth as well as lessens the poverty burden.

Researchers (Schultz, 1961; Barro and Lee, 1997; Swaroop, 1996; Gupta et al. 2004

Greenidge and Stanford, 2007; Moore, 2006) have evaluated the positive outcomes of

government expenditure on education and health care. Effective public expenditure on

education and health care in the Pakistan is imperative as resources are limited and

economic growth is necessary to sustain economic development, and thus improve

standards of living and human development. Despite the importance of education

and health sectors for economic growth, these are still the most neglected

sectors of the Pakistan’s economy. This study therefore attempts to analyze the

discussion on the role of government expenditure in education and health care in

Pakistan.

1.1 OBJECTIVES OF THE STUDY

To investigate the effects of the public education expenditures on primary and

secondary school enrolment in Pakistan for period of 1980 to 2012.

To determine the effect of government expenditure on health status measured

by infant mortality rate and child mortality rate in Pakistan for the period

1980-2012.

1.2 SIGNFICANE OF THE STUDY

To the best of my knowledge, a very few studies have been done to investigate the

impact of government expenditure on education and health care in Pakistan with these

4

variables. The significance of the study is to bridge this knowledge gape and fill this

empty field of research in Pakistan for policy implication

1.3 ORGANIZATION OF THE STUDY

The study is organized as follows. Chapter I provide the brief introduction of the

study. Chapter II reviews the existing literature on governmental spending and

educational and health outcomes. Chapter III presents the general theoretical

framework of the study, model used to conduct analysis and data sources. Chapter IV

describes results and discussion of the study, and comparison with previous literature.

Chapter V concludes and offers policy suggestions for government.

5

Chapter: 2

LITERATUR REVIEW

2.1 INTRODUCATION

This chapter focuses on the previous views of the researchers about the impact of

government expenditure on health status and education attainment. Previous studies

used different proxies to measure health care, like life expectancy, infant mortality,

under five mortality rate and maternal mortality rate. Similarly primary school

enrollment, secondary and tertiary school enrolment is used as a proxy for education

attainment by many researchers.

2.2 REVIEW OF PREVIOUS LITERATURE

Without use of empirics (Schultz, 1961) argues that human capital has been the basis

of the faster growth in Western countries. So investment in direct expenditure on

health is necessary to achieve economic growth through increase in level of

productivity. According to (Schultz ,1961) access to education plays a very important

role in equipping persons with opportunities that shape their character and develop

their personal, economic, socia and cultural status. This is demonstrated by

education’s progressive impudence on health; income, family structure and political

participation

Using the sample of 40 countries for the years 1985-1990 (Carrin and Politi, 1995)

analyze the impact of poverty reduction and government health expenditure on health

care in developing countries. Dependent variable, Health status is measured by life

expectancy, infant mortality and under-five mortality. Public health expenditure to

6

gross national product ratio, incidence of total absolute poverty and per capita income

is used as explanatory variables. Study concluded that per capita income and

reduction of poverty have significantly positive impact on health status while Public

health expenditure is found to be statistically insignificant in regression analysis.

To establish the link between per capita income and several indicators of educational

development (Mingat and Tan, 1998) use the large sample of 125 developing and

developed countries for year 1993.results indicate that per capita income have greater

influence on literacy rate then public spending on education.

Using cross-sectional data of 98 developing countries, (Filmer and Pritchett, 1999)

examine the impact of government health expenditure on infant and under-5 mortality

rate. Authors find very small and statistically insignificant effect of government

spending over the period of 1992/3. They suggested that 95% of the variation in infant

and child mortality is explained by income inequality, income per capita, female

literacy and ethnic fractionalization.

To support the evidence that government expenditure positively influence health and

education indicators (Tiongson et al .1999) employ 2SLS for cross section data of 50

developing and transition economies. Study confirms that education investment

increase school enrollment and health expenditure reduce the infant child mortality

rate.

(Mello et al .2003) investigate the social outcomes of health and education

expenditure for 94 developing countries in the period of 1996–98.Findings of the

study show that public spending is major determinant of social outcomes in education

sector particularly but not in health sector.

7

To show the effectiveness of government spending on education (Baldacci et

al .2003) uses a panel data of 94 developing countries. By employing covariance

structure model for the period 1996 to 1998 empirical findings reveal that government

spending on education alone does not advance social outcomes. Gender inequality

deteriorates social outcomes so government needs to remove these unfavorable social

conditions along with increase in public spending to accelerate human development.

(Roberts ,2003) did comprehensive global survey of the literature on the determinants

of education in developing countries, findings of the study suggested that despite the

fact that developing countries need to assign more resources to primary education,

they also need to improve efficiency of recourses and educational quality

simultaneously. Although since 1970 developing countries have been spending more

(relative to GDP) on education, Roberts examine that education expenditure has no

strong relationship with primary school enrolment.

For the fifteen states of India (Kaur and Misra 2003) have done empirical analysis to

analyze the impact of public expenditure on primary Intermediate, and secondary

school enrollment rates. Regression analysis for the period of 1985-86 and 2000-01

point out that government expenditure on education is effective especially in poorer

states. Study also reveals that government expenditure has a greater outcome in

primary education than secondary. The authors Hypothesize that private funding plays

a greater role in secondary education therefore role of public spending decreases at

higher stages of education.

(Gupta et al .2004) explore the impact of government spending on education

attainment and acknowledge that government expenditure is necessary to increase

8

education attainment. To accelerate the economic growth, government need to assign

recourses for education efficiently. They also argue that per capita income, adult

literacy, urbanization and private spending have significant contribution towards

education attainment.

According to (Gupta et al. 2004) government spending on health care strengthens a

country’s health status .By Using the two stage least squares method on 50 developing

and transition countries. They concluded that health care is also influenced by per

capita income, adult literacy, and access to sanitation, water urbanization and private

spending.

(Greenidge and Stanford ,2007) attempted to investigate the determinants of health

status in Latin America and the Caribbean by using panel data of 37 countries from

1994 to 2005.The results show that health status which is measured by life expectancy

is positively influenced by increment in health expenditure. Literacy rate, urbanization

rate and per capita calorie availability (calorie intake) also add to health status, while

per capita carbon dioxide emissions negatively impact the longevity.

To assess the relationship between health expenditure and health outcomes (Anyanwu

and Erhijakpor, 2007) use the data of 47 African countries from 1999 to 2004.

Empirical findings suggest that health expenditure reduce the infant mortality and

under five mortality while female literacy and higher number of physicians are

inversely related with health outcomes.

(Anyanwu and Erhijakpor, 2007) confirm the significantly positive relationship

between public expenditure on education and school enrollment. They use the panel

data of African countries for the period of 1990 to 2002 and employ ordinary least

square to statistically analyze the data. Estimation of data show that 10% increase in

9

public spending on education increase the secondary school enrollment by 33 to 42 %

while increasing primary enrollment by 21 to 28 %.

A study conducted by (Baldacci et al .2008) reveal that public expenditure on

education directly results increased better educational outcomes. They Used panel

data of 118 developing countries to find out the relationship between government

spending and education attainment. By utilizing a non-linear model and fixed-effects

model for time period of 1971–2000, they evaluate that government spending

increase the school enrollment however public spending are inefficient in countries

with poor governance.

(Rajkumar and Swaroop ,2008) used annual data of 1990, 1997 and 2003 for 91

developed and developing countries to find out the impact of public spending on

health status. By employing Ordinary least square regression on cross-section data,

results show that public expenditure on health is inversely related with child mortality

in countries with high quality of bureaucracy, good governance and low corruption

levels. Similarly, government expenditure on education is more effective to increase

primary school enrollment in countries with good governance.

(Mello and Pisu ,2009) explore the impact of government expenditure on health and

education outcomes by combining data of census, household survey and budget of 4

000 Brazilian municipalities for year 2000.By employing two stage least square

(2SLS) findings of the study suggest that education expenditure increase the

education outcome, but on the other hand health expenditures are ineffective.

By utilizing the primary data of 115 districts across three states in India( Iyer and

Tarozzi, 2009) investigate the effectiveness of public spending in education. They

10

employ fix effect model and concluded that government expenditure on education

have negligible impact on primary enrollment.

(Pueyo et al .2009) investigate the contribution of public health expenditure to

increase longevity for the data panel of 29 OECD countries. To statistically analyze

the data, they use the generalized method of moments (GMM) and conclude that life

expectancy is positively influenced by public health spending.

To determine the causal relationship between education expenditure and economic

growth (Abhijeet, 2010) uses linear and non-linear Granger Causality method for the

period of 1951-2009. The findings of the study reveal that economic growth

contributes to the government spending on education irrespective of any lag effect but

investment in education accelerate the economic growth after some time leg.

(Waheed and Qadri 2011) confirm the long run direct relationship between human

capital investment and economic growth by using standard Cobb-Douglas production

function. Analysis of the data 1978 to 2007 for Pakistan suggested that in order to

ensure long run growth, special attention should be given to health and education

sector.

For the data set of seventy countries (Fink et al .2011) conducted a very

comprehensive study on impact of water and sanitation facility on child mortality

over the period 1986 to 2007. As compare to other studies, impact of improved water

and sanitation is smaller but still positive on reduction of mortality. The authors also

find that the positive result of clean water is slighter and affect only children between

1 and 12 months.

11

By evaluating the life expectancy and school enrollment (Craigwell et al.2012)

measure the efficiency of government spending on health and education for 19

Caribbean countries. By employing Panel Ordinary Least Squares on the data set of

1980 to 2009 study concluded that health expenditure has significant positive

outcomes while education spending have slight impact on school enrollment.

By using the data set of 177 countries (Obrizan and Wehby, 2012) examined the

influence of health expenditure on life expectancy. Results of regression analysis

show that longevity and public health expenditure have direct relationship.

(Ijaz, 2012) analyzes the impact of female literacy rate in 35 districts of Punjab

Pakistan. By simple regression analyses it is concluded that female literacy rate has no

significant impact o reducing the child mortality in Punjab while male literacy rate is

effective in year 2007-2008.it is also suggested that quality of service delivered and

presence of better institutions are the major factors to decrease the infant mortality

rate.

Improved water and sanitation access are key strategies to reduce child and maternal

mortality. (Cheng et al, 2012) abstracted the data of 193 countries from global data

base and linear regression analysis was used for the outcomes. Results suggested that

both clean water and sanitation negatively influence the infant and maternal deaths.

(Gitau , 2012) investigate the impact of health aid expenditure on child mortality over

the period of 1980 and 2010 for Kenya. They employ semi log regression analysis on

the Model and later an Error- Correction methodology on time series data of thirty

year. Results of the study reveal that immunization coverage and health aid

expenditure negatively impact the under five mortality in Kenya.

12

(Kaushal et al, 2013) investigate the association between government health

expenditure and child mortality rate in India. Over the period of 1985 to 2009 they

used generalize least square, ordinary least square and fixed effect regression model

for analysis. They suggested insignificant relationship between health expenditure and

childhood mortality rate while per capita income, female literacy rate and poverty

have significant impact on reduction of mortality rate in India and EAG states.

One of the prime benefits of educating women is healthier children. (Shetty and

Shetty, 2014) found the inverse relationship between female literacy rate and infant

mortality rate in India. Data was collected for 28 Indian states for year 1981 to 2001.

States which have high female literacy rate front with lower infant mortality so

government should encourage female education in India.

Manoux et al used the data of 26 states of India over the period of 1998-1999 to

explore the relationship of adult education, cast, wealth and urbanization with child

mortality. By utilizing a two-level multilevel logistic regression model they suggested

that adult education decrease the child mortality but household wealth and

urbanization have no significant relation with mortality rate in India.

2.3 CONCLUSION OF PREVIOUS LITERATURE

Previous findings of the studies show that impact of government expenditure on

education and health care is mixed. Some researchers concluded the positive

outcomes of health and educational expenditures done by government and some

studied suggested the insignificant and negligible impact of government spending.

Therefore impact of government spending can be different according to the economic,

political and environmental conditions of the country

13

Table: 2.1 SUMMERY OF LITERATURE REVIEW

Author Year Key findings Schultz 1961 Investment in direct

expenditure of health is necessary to achieve economic growth through increase in level of productivity.

Access to education plays a very important role in equipping persons with opportunities that shape their character and develop their personal, economic, socia and cultural status.

Carrin and Politi 1995 Per capita income and reduction of poverty have significantly positive impact on health status.

Public health expenditure is found to be statistically insignificant in regression analysis.

Mingat and Tan 1998 Per capita income has greater influence on school enrollment then public spending on education.

Filmer and Pritchett 1999 Very small and insignificant impact of government spending on infant and child mortality rate.

95% of the variations

are explained by

income inequality,

income per capita and

female literacy rate.

Author Year Key findings

14

Mello et al 2003 Public spending is major determinant of social outcomes in education sector particularly but not in health sector.

Baldacci et al 2003 Government needs to remove unfavorable social conditions along with increase in public spending to accelerate human development.

Roberts 2003 Education

expenditure has not

strong relation with

primary school

enrolment.

Kaur and Misra 2003 Government spending on education is effective especially in poorer states.

Government spending has a greater outcome in primary education than secondary.

Gupta et al 2004 To accelerate the economic growth, government need to assign recourses for education efficiently.

Per capita income, adult literacy, urbanization and private spending have significant contribution towards education attainment.

Author Year Key findings

15

Greenidge and Stanford 2007 Life expectancy is positively influenced by increment in health expenditure.

Literacy rate, urbanization rate and per capita calorie availability also add to health status, while per capita carbon dioxide emissions negatively impact the health status.

Anyanwu and Erhijakpor 2007 Health expenditure reduce the infant mortality and under five mortality.

Female literacy and higher number of physicians are inversely related with health outcomes.

Anyanwu and Erhijakpor 2007 Significantly positive relationship between public expenditure on education and school enrollment.

Baldacci et al 2008 Government spending increase the school enrollment however public spending is inefficient in countries with poor governance.

Rajkumar and Swaroop 2008 Public expenditure on health is inversely related with child mortality in countries with high quality of bureaucracy, good governance and low corruption levels.

Government spending on education is more effective to increase primary school enrollment.

Author Year Key findings

16

Mello and Pisu 2009 Education expenditure increases the education outcomes.

Health expenditures are ineffective to get desired results.

Iyer and Tarozzi 2009 Government spending on education has negligible impact on primary enrollment.

Pueyo et al 2009 Life expectancy is positively influenced by public health spending.

Abhijeet 2010 Economic growth contributes to the government spending on education irrespective of any lag effect but investment in education accelerate the economic growth after some time leg.

Craigwell et al (2012) 2012 Health spending has significant positive outcomes while education spending has slight impact on school enrollment.

Obrizan and Wehby 2012 Longevity and public health expenditure has direct relationship.

Author Year Key findings

Ijaz 2012 Female literacy rate

17

has no significant impact o reducing the child mortality.

Quality of service

delivered and

presence of better

institutions are the

major factors to

decrease the infant

mortality rate.

Cheng et al 2012 Clean water and sanitation negatively influence the infant and maternal deaths.

Gitau 2012 Immunization coverage and health aid expenditure negatively impact the infant and child mortality rate.

Kaushal et al 2013 Insignificant relationship between health expenditure and childhood mortality rate.

per capita income, female literacy rate and poverty have significant impact on reduction of infant and child mortality rate

Shetty and Shetty 2014 High female literacy rate front with lower infant mortality rate.

Chapter: 3

DATA AND METHODOLOGY

18

3.1 INTRODUCATION

Our study is divided in to two sections, one is health model and other is education

model. Health model is based on the study of (Craigwell et al. 2012; Anyanwu and

Erhijakpor, 2007). To estimate the impact of government expenditure on educational

outcomes, we adopted the methodology from (Craigwell et al. 2012).

3.2 UNIVSESE OF THE STUDY

Time series data for Pakistan is used for analysis in both education and health

models.

3.3 TIME PERIOD OF ANALYSIS

Study used annual observations of secondary data for Pakistan over the period of

1980-2012 for both health and education models.

3.4 DATA SOURCES AND ANALYSIS

Data is collected from the World Bank, The United Nations Educational Scientific

and Cultural Organization (UNESCO) database, State bank of Pakistan, Federal

Bureau of Statistics Government of Pakistan, world Development indicator and

WHO. Ordinary least square for health model and ARDL approach for education

model are used to statistically analyze the data. An E view 6 is used for estimation in

present study.

3.5 CONCEPTUAL FRAME WORK OF HEALTH MODELS

19

Government expenditure on

health

Based on Craigwell et al (2012) and Anyanwu and Erhijakpor (2007)

DEPENDENT VARIABLE

Infant mortality rate and under five mortality rate.

INDEPENDENT VARIABLES

Government expenditure on health, per capita income, female literacy rate,

immunization DPT3 and measles, carbon dioxide emission, access to improved

sanitation and clean water, urban population.

3.6 MAIN HYPOTHESIS FOR HELATH MODEL

20

Infant mortality and under five mortality rate

Income per capita

Female literacy rate

Immunization DPT3 and measles

Access to sanitation and clean water

Carbon dioxide emission

H0: Government health expenditure has significantly negative impact on infant and

child mortality rate in Pakistan.

3.7 ECONOMETRIC SPECIFICATION OF HEALTH MODELS

i. Htj= αt + β1Xt + β2Zt+ β3Yt + ɛt

t = 1980……………………………2012.

Where:

Htj = is health care, proxied by infant mortality and under five mortality rate.

Xt = is a vector of investment variables comprising of public expenditure spent on

health, income per capita and female literacy rate.

Zt = is a vector of accessibility indicators composed of urban population as a percent

of total population, Carbon dioxide emissions and percent of population with access

to sanitation facilities and clean water sources.

Yt = is an immunization vector that consists of DPT [3] and measles.

3.8 EXPLAINATION OF HEALTH THE VARIABLES

Public health expenditure consists of recurrent and capital spending from

government (central and local) budgets as percentage of gross domestic

product (GDP).

Female literacy rate is the percentages of females ages 15 and above who can,

with understanding, read and write a short, simple statement on their

everyday life (The World Bank, 2011).

Carbon Dioxide Emissions taken as CO2 emissions (metric tons per capita) at

time t. Carbon dioxide emissions are those stemming from the burning of

21

fossil fuels and the manufacture of cement. They include carbon dioxide

produced during consumption of solid, liquid, and gas fuels and gas flaring

(The World Bank, 2011)

DPT refers to a combination of vaccines that fight against three infectious

diseases: diphtheria, pertussis (whooping cough) and tetanus. DPT3 and

immunization measles is taken as % of children ages 12 to 23 months.

The infant mortality rate is the number of infants dying before reaching one

year of age, per 1,000 live births in a given year.

The income variable is measured by gross domestic product per capita

(purchasing power parity).

3.9 EXPECTED RESULTS OF HEALTH MODELS

a) INVESTMENT VARIABLES

In terms of the a priori signs of the explanatory variables, many studies have indicated

that government spending on health care is pertinent for health enhancement and

human development (especially for those who have lower incomes) and consequently

economic growth (Schultz, 1961; Anand and Ravallion, 1993; Swaroop, 1996; Gupta

et al., 2004). Therefore, it is expected to reduce infant and child mortality rate.

Income per capita measured by gross domestic product per capita (purchasing power

parity) suggests that as household income increases, a country’s health position

should improve. If people have more disposable income then they will have the

capacity to personally invest more in health care and caloric intake per capita may

22

increase which improves health status (Greenidge and Stanford, 2007). Thus, a priori

the coefficient on income per capita is negative.

For female literacy rate, many studies show that a negative relationship exists

between female literacy and infant and child mortality rate. ).Female literacy reduces

the infant mortality by allowing them to read and understand the necessary

information for healthy living. As suggested by (Schultz, 1993; Ijaz, 2012) that one

prime benefit of educating women is healthier children.

b) ACCESSIBILITY VARIABLES

With respect to the accessibility variables, increased access to sanitation facilities and

water creates a more salubrious environment thus improving health status (Gupta et

al., 2004). Deprived access to sanitation and water promote the spread of health

problems like hepatitis and diarrheal diseases like cholera and a weakened immune

system (World Health Organization (WHO, 2011)). Evidence has suggested that

water-poor and sanitation facility deprived communities are typically simultaneously

economically poor. This variable is expected to be negatively related to infant and

under five mortality rates.

With respect to urbanization, defined as the percent of the entire population existing in

urban areas, it is believed that in such areas access to health facilities is much easier

than rural areas (Greenidge and Stanford, 2007) and related to improved health status

(Schultz, 1993). Though, Thornton (2002) states that urban areas are characteristically

polluted with carbon dioxide emissions (metric tons per capita) and thus have positive

impact on health indicators measured as infant and child mortality rate. Consequently,

the relationship between urbanization and health expectancy depend on the overall

23

effect of pollution. On other aspect urbanization expected to reduce the infant and

under five mortality rate and carbon dioxide emissions increase.

c) IMMUNIZATION VECTOR

Concerning the immunization indicators, vaccination from the diphtheria, pertussis

(whooping cough) and measles diseases should reduce the infant and child mortality,

assuming other factors remain constant.

24

3.10 CONCEPTUAL FRAME WORK OF EDUCATION MODELS

To find out the impact of government expenditure on educational outcomes,

methodology is adopted from (Craigwell et al. 2012).

Source: Craigwell et al (2012)

DEPENDENT VARIABLES

Primary and secondary school enrollment

INDEPENDENT VARIABLES

Government expenditure on education, Income per capita, adult literacy rate, School

aged population, Pupil teacher ratio and urban population.

25

Primary and secondary school enrollment

Income per capita

Adult literacy rate

Government expenditure on

education

School aged populatio

Pupil teacher ratio

Urban population

3.11 MAIN HYPOTHESIS FOR EDUCATION MODEL

H0: Government educational expenditure has significantly positive impact on primary

and secondary school enrollment

3.12 ECONOMETRIC SPECIFICATION OF EDUCATION MODELS

The education equation is modeled as follows:

Etj= α tj+ β1Xtj + β2Zt+ β3Ytj + β4Atj+ɛtj

t = 1980……………………………2012.

Where

Etj = is education attainment for j enrollment where j is primary and secondary

percentage gross school enrollment, respectively.

Xt j= represents a vector of investment variables consisting of public expenditure

spent on education as a percentage of GDP, income per capita ,per pupil public

spending and adult literacy.

Zt = is an accessibility indicator measured by urban population as a percent of total

population.

Ytj= is a quality variable proxied by pupil-teacher ratio.

Atj = represents the school aged population.

The index t is as defined above and j represents the different levels of education-

primary and secondary.

26

3.13 EXPLAINATION OF THE EDUCATIONAL VARIABLES

Primary gross enrollment ratio is the ratio of total enrollment, regardless of

age, to the population of the age group that officially corresponds to the level

of education shown. Secondary gross enrollment ratio is defined in the same

way however secondary education completes the provision of basic education

that began at the primary level.

Public expenditure on education consists of current and capital expenditure

and includes government spending on educational institutions (both public and

private), education administration as well as subsidies for private entities.

Adult literacy rate is the percentage of people ages 15 and above who can,

with understanding, read and write a short, simple statement on their everyday

life (The World Bank, 2011)

The total school pupil-teacher ratio is the number of pupils enrolled in

primary and secondary school divided by the number of primary and

secondary school teachers (regardless of their teaching assignment).

The infant mortality rate and child mortality rate is the number of infants and

children dying before reaching one year of age, per 1,000 live births in a given

year.

The income variable is measured by gross domestic product per capita

(purchasing power parity).

3.14 EXPECTED RESULTS

a) INVESTMENT VARIABLES

The amount of money government spends on education (construction of schools and

provision of teachers) should have a positive effect on education attainment. As

27

income per capita rise the relative cost of enrolling children into school is decreased

indicating that increasing incomes should expand school enrollment. Parents incur

direct and indirect costs when they send their children to school which include

uniforms, supplies, transportation and the forgone income of the child’s work in the

labor market (McEwan, 1999). In addition, if education is a normal good, at higher

income levels the demand for education will augment (Gupta et al. 2002). If persons

in the household are literate or acknowledge the importance of literacy, then it will

positively influence the education attainment. This suggests a positive relationship

between literacy and school enrollment.

b) ACCESSABILITY IMDICATORS

In urban areas access to education is relatively better (Plank, 1987) and the

transportation costs may also be lower so enrollment in urban areas will be higher

(Gupta et al. 2002).

c) QUALITY VARIABLE

The lower the pupil-teacher ratio the more attention each child receives and the more

effective individual teachers can be. If households believe that the pupil-teacher ratio

is too high and thus ineffective for educating then they may utilize private school,

home-schooling or make their children get jobs. As a result, the coefficient of this is

expected to be negatively signed. However, the decrease in this ratio necessitates an

increase in public education expenditure. Additionally, (Mingat and Tan ,1998) found

that a reduction in this variable has a small impact on student learning and has a long

run effect of lowering levels of education attainment levels. It is expensive and

difficult to increase enrollment rates when the population is relatively young (Mingat

28

and Tan, 1992). (Gupta et al .2002) claim that a high incidence of young people

(population aged 5-14) should have a negative a coefficient.

3.15 BOUND TESTING APPROACH:

The use of the bounds technique is based on three validations. First, Pesaran et al.

(2001) advocated the use of the ARDL model for the estimation of level relationships

because the model suggests that once the order of the ARDL has been recognised, the

relationship can be estimated by OLS.

Second, the bounds test allows a mixture of I (1) and I (0) variables as independent,

the order of integration may not necessarily be the same. Third, this technique is

suitable for small or finite sample size (Pesaran et al., 2001).

Following Pesaran et al. (2001), we assemble the vector auto regression (VAR) of

order p, denoted VAR (p), for the following growth function:

Z t=μ+∑i=1

p

β i zt−i+εt...................................... (1)

where zt is the vector of both xt and yt , where yt is the dependent variables

defined as school enrolment primary and secondary , x t is the vector matrix which

represents a set of explanatory variables i.e. per capita income ,Adult literacy rate ,

school aged population, public spending on education, pupil teacher ratio primary and

secondary and urban population, and t is a time or trend variable. According to

Pesaran et al. (2001), y t must be I(1) variable, but the regressor x t can be either I(0)

or I(1). We further developed a vector error correction model (VECM) as follows:

29

Δz t=μ+αt+λz t−1+∑i=1

p−i

γt Δyt−i+∑i=1

p−1

γt Δxt−i+εt................................. (2)

where Δ is the first-difference operator. The long-run multiplier matrix λ as:

λ=¿ [ λYY λYX ¿ ] ¿¿

¿¿

The diagonal elements of the matrix are unrestricted, so the selected series can be

either I(0) or I(1). IfλYY=0 , then Y is I (1). In contrast, ifλYY <0 , then Y is I(0).

The VECM procedures described above are imperative in the testing of at most one co

integrating vector between dependent variable y t and a set of regressors x t . To

derive model, we followed the postulations made by Pesaran et al. (2001) in Case III,

that is, unrestricted intercepts and no trends. After imposing the restrictions

λYY=0 , μ≠0 andα=0 , the GIIE hypothesis function can be stated as the following

unrestricted error correction model (UECM)

∆ (SE) jt=β0+β1(SE) jt−1+β2(ALR)t−1+β3(PCI )t−1+β4(PSE)t−1+

β5(PTR) jt−1 β6(SAP)t−1 β7(UP)t−1+∑i=1

p

β8 ∆ (SE) jt−i+∑i=0

q

β9 ∆ (ALR )t−i+

∑i=0

r

β10 ∆(PCI )t−i+∑i=0

s

β11 ∆ (PSE)t−i+∑i=0

t

β12 ∆(PTR )jt−i+∑i=0

u

β13 ∆(SAP)t−i +

∑i=0

v

β14 ∆ (UP)t−i+ μt………………………………… (1)

Where ∆ is the first-difference operator and μt is a white-noise disturbance term.

30

Table: 3.1EXPLAINATION OF VARIABLES

SE School enrollment where j is primary and secondary percentage gross school

enrollment, respectively.

ALR Adult literacy rate

PCI Per capita income

PSE Public spending on education

PTR Pupil teacher ratio in primary and secondary schools

SAP School age population

UP Urban population

Equation (1) also can be viewed as an ARDL of order (p, q, r). Equation (1) indicates

that education tends to be influenced and explained by its past values. The structural

lags are established by using minimum Akaike’s information criteria (AIC). From the

estimation of UECMs, the long-run elasticises are the coefficient of one lagged

explanatory variable (multiplied by a negative sign) divided by the coefficient of one

lagged dependent variable (Bardsen, 1989). For example, in equation (3), the long-run

inequality, investment and growth elasticise are (β2/ β1 ) and (β3 /β1 ) respectively.

The short-run effects are captured by the coefficients of the first-differenced variables

in equation (3).

After regression of Equation (1), the Wald test (F-statistic) was computed to

differentiate the long-run relationship between the concerned variables. The Wald test

can be carry out by imposing restrictions on the estimated long-run coefficients of

31

school enrolment, Adult literacy rate, Per capita income, public spending on

education, Pupil teacher ratio in primary and secondary schools, school age

population and urban population.

The null and alternative hypotheses are as follows:

H0: β1 =β2 =β3 =β4 = β5 =β6 = β7 = 0 (no long-run relationship)

Against the alternative hypothesis

Ha: β1 ≠ β2 ≠ β3 ≠ β4 ≠ β5 ≠ β6 ≠ β7 ≠ 0 (a long-run relationship exists)

The computed F-statistic value will be evaluated with the critical values tabulated in

Table CI (iii) of Pesaran et al. (2001). According to these authors, the lower bound

critical values assumed that the explanatory variables x t are integrated of order zero,

or I(0), while the upper bound critical values assumed that x t are integrated of order

one, or I(1). Therefore, if the computed F-statistic is smaller than the lower bound

value, then the null hypothesis is not rejected and we conclude that there is no long-

run relationship between school attainment and its determinants. Conversely, if the

computed F-statistic is greater than the upper bound value, then school attainment and

its determinants share a long-run level relationship. On the other hand, if the

computed F-statistic falls between the lower and upper bound values, then the results

are inconclusive.

32

Chapter: 4

RESULTS AND DISCUSSION

4.1 INTORDUCATION

We used ordinary least square for health models and ARDL approach for education

models based on the previous studies as discussed in methodology section. Results of

the estimation by e views are discussed in this chapter.

4.2 UNIT ROOT TEST FOR HEALTH MODELS

Application of conventional econometric methods for estimation of coefficients by

using time series data is based on assumption that the model variables are stationary.

A time series variable is stationary only if its mean value, variance and correlation

coefficients remain constant through the time. If time series variables used in

estimation of coefficients are non-stationary, then its R square coefficient may be of a

high value and can cause an incorrect understanding about level of relation between

variables although there may be no significant relation between variables Econometric

software e-views6 was used for estimation of this study. To check the order of

integration, standard Augmented Dickey-Fuller (ADF) unit root test was exercised for

all the variables included in the study.

33

Table 4.1 ADF TEST FOR HEALTH MODELS

Level 1st difference

Variable Constant Constant linear

trend

Constant Constant linear

trend

Decision

Pci 3.906

1.000

1.256

0.999

(-3.455)**

0.016

-5.019

0.001

Stationery at first

difference

CO2 -0.881

0.781

-2.572

0.294

(-6.917)***

0.000

-6.884

0.000

Stationery at first

difference

Dpt3 -1.695

0.423

-3.120

0.118

(-4.671)***

0.000

-5.029

0.001

Stationery at first

difference

Flr -0.466

0.885

-1.723

0.717

(-6.753)***

0.000

-6.859

0.000

Stationery at first

difference

Im -2.190

0.213

-2.462

0.343

(-5.075)***

0.000

-5.348

0.000

Stationery at first

difference

Imr -2.029

0.273

(-2.757)***

0.007

-6.847

0.000

-8.149

0.000

Stationery at level

Mru5 (-3.331)**

0.022

-2.477

0.335

-1.499

 0.518

-1.901

0.628

Stationery at level

Psh -0.408

0.896

-2.269

0.437

(-5.054)***

0.000

-5.224

 0.001

Stationery at first

difference

Up 1.481

0.998

0.283

0.997

(-5.657)***

0.000

-6.759

0.000

Stationery at first

difference

Note: *, ** & *** indicate the rejection of the null hypothesis of non-stationary at

10%, 5% and 1% significant level, respectively.

34

The results are reported in Table 4.1. Based on the ADF test statistic, it was initiate

that out of nine variables, seven have unit root i.e. PCI,CO2, DPT3,FLR,IM,PSH, UP

and stationary at first difference, while our dependent variables IMR,MRU5 is I(0).

These results imply that OLS provides consistent estimate for health models.

Table 4.2: DESCRIPTIVE STATISTICS FOR HEALTH VARIABLES

statistics CO2 DPT3 FLR IM IMR ASF ACW

S

MRU5 PCI PSH

Mean 0.69

1

50.96

7

34.00

3

52.60

6

95.22

7

34.51

5

81.77

8

123.04

5

551.595 0.71

9

Median 0.70

8

54 32.8 52 95.1 34.3 87.1 122.8 453.494 0.73

Maximum 0.96

9

86 48 83 121.3 49 92 160.4 1290.36

5

1.19

Minimum 0.40

0

2 19.6 1 69.3 19.3 35 85.9 296.179 0.23

Std. Dev. 0.17

5

24.09

5

7.651 22.39

9

16.36

8

9.083 13.86

1

23.487 269.890 0.20

7

Observatio

ns

33 33 33 33 33 33 33 33 33 33

4.3 DIAGNOSTIC TEST STATISTICS FOR HEALTH MODELS

The robustness of the health models have been definite by several diagnostic tests

such as, Jacque-Bera normality test ,Breusch-Pagan-Godfrey Heteroskedasticity

Test, Breusch- Godfrey serial correlation LM test and Ramsey RESET specification

test . All the tests disclosed that the model has the aspiration econometric properties, it

has a correct functional form and the model’s residuals are normally distributed,

35

homoskedastic and serially uncorrelated. Therefore results reported by OLS are valid

for reliable interpretation.

Table 4.3: A .DIAGNOSTIC TEST STATISTICS FOR HEALTH MODEL: 1

Test Test-stats p-values

Heteroskedasticity Test 0.724 0.681

Normality test 0.923 0.630

Ramsey RESET Test 2.592 0.125

Serial Correlation LM Test 1.926 0.176

Table 4.4:B. DIAGNOSTIC TEST STATISTICS FOR HEALTH MODEL:

Test Test-stats p-values

Heteroskedasticity Test 1.434 0.233

Normality test 0.832 0.659

Ramsey RESET Test 0.415 0.527

Serial Correlation LM Test 1.780 0.198

36

Table: 4.5 IMPACTS OF EXPLAIANTORY VARIABLES ON INFANT

MORTALOTY RATE

Variables Coefficient Standard

error

T statistic Probability

(ACWS) (-0.043)** -2.341 -2.341 0.029

(ASF) (-1.998)** 0.945 -2.115 0.046

(PCI) (-0.488) 0.297 -1.642 0.115

(FLR) (-2.498)** 1.175 -2.125 0.048

(IM)

(-0.212)** 0.100 -2.116 0.046

(PSH) (-0.194)*** 0.042

-4.613

0.000

(DPT3)

(-0.096)

0.124

-0.772

0.450

(CO2) (1.156)** 0.499

2.315

0.030

MA(2) 0.922 0.096

9.541

0.000

R-squared 0.916

F-statistic 15.525

Durbin-Watson

stat

  

1.969

Adjusted

R-squared

0.877

Prob(F-

statistic)

0.000

37

Note: * statistically significant at 10%, ** statistically significant at 5%, **

*statistically significant at 1%.

Dependent variable: Infant mortality rate:

Table: 4.6 EXPLANATIONS OF VARIABLES

Name of variables Explanation

ACWS Access to clean water sources

ASF Access to sanitation facility

PCI Per capita income

FLR Female literacy rate

IM Immunization measles

PSH Public spending on health

DPT3 Vaccination against diphtheria, pertussis and tetanus

CO2 Carbon dioxide emissions

4.4 DISSCUSSION OF RESULTS

Table 4.5 shows the results of our regression analysis. The value of R- squared is 0.91

which indicates that regressors fit the models fairly well. Except per capita income

and DPT3 all variables are statistically significant i.e. improved water source,

improved sanitation facility ,female literacy rate ,immunization measles and co2 per

capita are significant at 5 %while public spending on health is statistically significant

at 1%.

Our Results are consistent with the previous literature and signs of the coefficients are

similar as expected. Coefficient value of improved water source indicates that 1%

increase in population access with clean water decrease the infant mortality by

0.043%, likewise improved sanitation facility reduce the infant mortality by 1.99 %

in Pakistan. These results are similar as (Kim and moody, 1992; hojman, 1996; Cheng

38

et al .2012; Fink et al .2011). Better access to sanitation facilities and clean water

creates a more hygienic environment thus improving health status (Gupta et al.,

2004). According to WHO ,2011 Deprived access to sanitation and clean water

endorse the spread of health problems like hepatitis ,cholera and a weakened immune

system .Almost one tenth of the global disease burden could be prevented by

improving water supply, sanitation, hygiene and management of water resources.

Worldwide, 1.4 million children die each year from preventable diarrheal diseases and

some 88% of diarrhea cases are related to unsafe water, inadequate sanitation or

insufficient hygiene.

Female literacy rate lessen the rate of infant mortality by 2.49%. As suggested by

(Ijaz, 2012; Schultz, 1993) one prime benefit of educating women is healthier

children. Improvement in literacy status of women results in a downward trend in

infant mortality rate, (Shetty and Shetty, 2014).female literacy reduce the infant

mortality by allowing them to read and understand the necessary information for

healthy living. They always try to bring up their children in hygienic conditions and

know the importance of proper nourishment, clean water, immunization against

different diseases and other necessities of healthy living.

Increase in immunization measles lessens the rate of infant mortality by 0.21% while

co2 emissions per capita positively impact the infant mortality. Research from other

studies has demonstrated substantial reductions in mortality associated with measles

immunization programs (Aaby, 1995; Koenig, Fauveau and Wojtyniak, 1991). We

therefore considered it important to assess the independent contribution of measles

immunization to survival in this population. Likewise polluted environment can affect

39

the respiratory system of the human and cause many diseases so clean environment

is essential for infant survival in Pakistan.

Public spending on health reduces the infant mortality by 0.19 % as indicated by

many studies e.g. ( Tiongson et al.1999; Anyanwu and Erhijakpor ;Gitau ,2012 ;

Kaushal et al .2013).government expenditure on health is benefiting the poor people

more by providing them easy access to health facilities thus reduce the infant

mortality in Pakistan. Free provision of vaccinations against child diseases, medicines

and other health care facilities can reduce the infant mortality rate in Pakistan.

DPT3 and PCI are inversely related with infant mortality rate but not statistically

significant to explain the variations. It shows that DPT3 vaccination in Pakistan is not

as much effective as it should be to reduce infant mortality. Possibly in ruler areas of

Pakistan, poor people have not easy access to vaccination or due to their social

conservative culture they do not consider important to immunize their children against

diseases.

In case of per capita income we can say that PCI shows the average income of the

country and in presence of huge income disparities it cannot be significant

determinate. Most of the infant deaths occurred in ruler areas of Pakistan where

income level of the people is less than average income shown by the PCI so it is not

considerable to show variation in infant mortality rate.

40

Table 4.7 IMPACTS OF EXPLANATORY VARIABLES ON

CHILD MORTALITY RATE

Health Model No 2: Dependent variable: under five mortality rate

Variables Coefficient Standard

error

T statistic Probability

(IWS) (-0.074)** 0.032 -2.283 0.033

(ISF) (-0.067)* 0.034 -1.932 0.067

(PCI) (-0.615)** 0.290 -2.119 0.046

(FLR) (-2.786)** 1.325 -2.102 0.050

(IM)

(-0.240)** 0.112 -2.137 0.044

(PSH) (-0.172)*** 0.065

-2.612 0.016

(DPT3) (-0.145) 0.139

-1.038 0.314

(CO2) (1.285)** 0.563

2.281 0.033

AR(1) 0.999 0.0038 257.618

0.000

  1.965

Adjusted

R-squared 0.905

Prob(F-statistic) 0.000

Note: * statistically significant at 10%, ** statistically significant at 5%, **

*statistically significant at 1%.

Table 4.7 shows the impact of explanatory variable on less than five child mortality

rate. R- Squared of health model 2 is 0.93 which indicates that 93% variation in

41

dependent variable is explained by the all independent variables. F – Statistic is 30.8

which ensure the significance of the model.

As suggested by the theory Improved water source, and female literacy rate reduce the

under five mortality rate at the significance level of 5%.improved sanitation facility

have significantly negative impact on dependent variable at 10 % level while public

spending on health is highly significant to reduce the death rate.CO2 emission per

capita has significantly positive impact on under five mortality rate but coefficient of

DPT3 is insignificant in as the previous model. These results are supported by the

previous literature as discussed in the previous model .All variables have similar

signs as in the previous model where dependent variable is infant mortality rate

except per capita income. According to this model with increase in PCI child

mortality rate shrink as suggested by Filmer and Pritchett (1999), Kaushal et al

(2013).As disposable income of the person increase, they can spend more money on

better health facility and healthy living so child mortality diminish.

4.5 ADF TEST FOR THE EDUCATION MODELS

The standard Augmented Dickey-Fuller (ADF) unit root test was utilized to confirm

the order of integration for variables included in education models. The test contains

null and alternative hypothesis while the rejection of the null hypothesis is based on

MacKinnon (1996) critical values.

42

Table 4.8: ADF TEST FOR EDUCATION MODELS

Level 1st difference

Variable Constant Constant linear

trend

Constant Constant linear

trend

Decision

Pci 3.906

1.000

1.256

0.999

(-3.455)***

0.0164

-5.019

0.001

Stationery first

difference

Alr 1.911

0.999

(-3.977)**

0.020

-5.221

0.000

-6.037

0.000

Stationery at level

Pse -2.691

0.086

-2.585

0.289

(-6.492)***

0.000

-6.508

0.000

Stationery at

first difference

Ptrp 1.200

0.661

-1.226

0.887

(-4.614)***

0.000

4.550

0.005

Stationery at

first difference

Ptrs -0.479

0.882

-2.171

0.484

(-3.894)***

0.005

-3.831

0.028

Stationery at

first difference

Sap -0.127

0.936

(-5.285)***

0.001

-2.245

0.195

-3.535

0.057

Stationery at level

Sep 0.498

0.984

-2.101

0.525

(-6.722)***

0.000

-6.851

0.000

Stationery at

first difference

Ses -0.164

0.933

-1.829

0.666

(-4.823)***

0.000

-4.741

0.003

Stationery at

first difference

Up 1.481

0.998

0.283

0.997

(-5.657)***

0.000

-6.759

0.000

Stationery at

first difference

Note: *, ** & *** indicate the rejection of the null hypothesis of non-stationary at

10%, 5% and 1% significant level, respectively.

Null hypothesis = series is non-stationary, or contains a unit root.

43

Alternative hypothesis = series is stationery.

The results Based on the ADF test statistic are presented in Table 4.7; results

indicated that our dependent variables, school enrollment primary and school

enrollment secondary are stationery at first difference whereas our explanatory

variables have mixture of both. Adult literacy rate and school aged population are l

(0) while per capita income, public spending on education, pupil teacher ratio primary

and secondary and urban population are integrated of i(1).Noticeably, under the

Johansen procedure the mixture of both I(0) and I(1) variables would not be

possible .These results give us a good justification for using the bounds test

approach, or ARDL model, proposed by (Pesaran et al. 2001).

44

Table: 4.9 IMPACTS OF EXPLANATORY VARIABLES ON PRIMARY

SCHOOL ENROLMENT

Variables Coefficient Standard error T statistic Probability

C (0.309)** 0.131 2.357 0.027

LOG(SEP(-1)) (-0.508)*** 0.126524 -4.017 0.000

LOG(ALR(-1)) (0.393)*** 0.123 3.185 0.004

LOG(PCI(-1)) (0.093)** 0.043 2.177 0.040

LOG(PSE(-1)) (0.095)*** 0.025 3.665 0.001

LOG(PTRP(-1))

(-0.162) 0.148 -1.089 0.287

LOG(SAP(-1)) (-0.479)*** 0.134 3.564 0.001

LOG(UP(-1)) (1.850)*** 0.626 2.953 0.007

DLOG(ALR(-1))

(0.427)** 0.177 2.403 0.024

DLOG(PCI(-1))

(0.079) 0.049 1.612 0.120

DLOG(PSE(-1))

(0.099)*** 0.030 3.309 0.003

DLOG(PTRP(-1))(-0.085) 0.128 -0.669 0.509

DLOG(SAP(-1))

(-0.558) 0.857 -0.650 0.521

DLOG(UP(-1))

(-36.955)** 15.907

-2.323 0.029

R-squared

0.878

F-statistic 15.123 Durbin-

Watson

stat

1.917

Adjusted R-

squared 0.820

Prob(F-statistic) 0.000

Note: *, ** & *** indicate at 10%, 5% and 1% significance level, respectively.

45

The estimation of Equation (3) using the ARDL model is reported in Table 4.9 Using

Hendry’s general-to-specific method, the goodness of fit of the specification that is,

R-squared and adjusted R-squared, is 0.87 and 0.82 respectively. Several diagnostic

tests were exercised to ensure the robustness of the model such as Breusch- Godfrey

serial correlation LM test, Breusch-Pagan-Godfrey Heteroskedasticity Test Jacque-

Bera normality test and Ramsey RESET specification test. All the tests disclosed that

the model has the aspiration econometric properties, it has a correct functional form

and the residuals of the model are serially uncorrelated, homoskedastic and normally

distributed and Therefore, the outcomes reported are serially uncorrelated, normally

distributed and homoskedastic. Hence, the results reported are valid for reliable

interpretation.

Table 4.10: DIAGNOSTIC TESTS FOR EDUCATION MODEL NO 1

Test Test-stats p-values

Heteroskedasticity Test 1.572 0.193

Normality test 2.187 0.334

Ramsey RESET Test 0.132 0.719

Serial Correlation LM Test 1.198 0.325

4.6 SHORT RUN AND LONG RUN IMPACT ON PRIMARY SCHOOL

ENROLMENT: MODEL 1

46

Table 4.9 illustrate the short run as well as long run impact of explanatory variables

on primary school enrolment of Pakistan. Adult literacy rate (ALR) has a significant

impact on primary school enrolment at 5% and 1% in short run and long run

respectively in Pakistan. Our results are similar with (Gupta et al .2004 and Craigwel,

2012) which indicating the direct relationship between the variables. If persons in the

household are educated they will definitely acknowledge the importance of education.

They will try their level best to educate their children according to their recourses and

hence school enrolment will increase. Uneducated people are less likely to enroll their

children in school.

Per capita plays a significant role to improve primary school enrolment in long run

but in short runs it is not significant detriment of enrolment in Pakistan. These results

are confirmed by many other studies .i.e. (Mingat and Tan, 1998; Gupta et al. 2004;

Craigwel, 2012). As PCI go up the relative cost of enrolling children into school is

decreased indicating that growing income s expand school enrollment in Pakistan.

Parents incur direct and indirect costs when they send their children to school which

include uniforms, supplies, transportation and the forgone income of the child’s work

in the labor market (McEwan, 1999). In addition, if education is a normal good, at

higher income level the demand for education increases (Gupta et al. 2002).

Major determent of school enrolment, government spending on education is highly

significant both in short and long run. Our result is consistent with (Mello and Pisu,

2009; Anyanwu and Erhijakpor, 2007; Mello et al. 2003; Tiongson et al.1999).this

expenditure consist of government provision of teachers, construction of school

47

building and all other expenditures which are needed to run the school. Therefore as

number of schools and teachers increases, access to school will be easy and

inexpensive so school enrolment will increase.

Coefficient of Pupil teacher ratio is negative as suggested by theory but not significant

both in short run as well as in long run. As Pakistan is developing country and most of

the population is illiterate so pupil teacher ratio is not considered both by government

due to lack of resources and by parents due to lack of understanding and education.

However, the decrease in this ratio necessitates an increase in public education

expenditure. (Craigwel, 2012) found the same results for Caribbean countries.

School age population (SAP) do not have a significant relationship with school

enrolment in short run because increase in school age population is only possible in

long run. In short span of time SAP cannot lessen the school enrolment in Pakistan

Craigwel (2012).in long run our results are consistent with the previous findings. As

in long time period of time school age population increases so school enrollment

diminish in Pakistan. It is expensive and difficult to increase enrollment rates when

the population is relatively young (Mingat and Tan, 1992). Gupta et al. (2002) claim

that a high share of young people (population aged 0-14) should have a negatively

impact the enrollment.

Last variable included in the model is urban population (UP) which is significant at

5% in short run while in long run it is highly significant at 1% of level. According to

plank (1987) urbanization increase the school enrolment because access to education

is typically better in cities. Quality of education is also comparatively better in urban

48

areas than ruler, among all other reasons transportations cost is low for urban

household so they are most likely to send their children to school. Gupta et al (1999)

In Table given below the results of the bounds co-integration test demonstrate that the

null hypothesis of against its alternative is easily rejected at the 1% significance level.

The computed F-statistic of 15.05396 is greater than upper l bound value of 5.06, thus

indicating the existence of a steady-state long-run relationship among SEP, ALR,

PCI.PSE, PTRP, UP and SAP.

Table 4.11: Bounds Test for Co integration Analysis

Critical value Lower Bound Value Upper Bound Value

1% 3.74 5.06

5% 2.86 4.01

10% 2.45 3.52

Note: Computed F-statistic: 15.053 (Significant at 0.01 marginal values).Critical

Values are cited from Pesaran et al. (2001), Table CI (iii), Case 111: Unrestricted

intercept and no trend.

The estimated coefficients of the long-run relationship between SEP, ALR, PCI, PSE,

PTRP, SAP and UP are expected to be significant, that is:

49

Test Statistic Value      Probability

F-statistic 15.053   0.000

D log (SEP)t =0.309** + 0.774***log(ALR)t + 0.185** log(PCI)t+

0.187***log(PSE)t--0.3193 log (PTRP)t -0.9436***log(SAP)t +3.641***

log(UP)t…………………………………………………(4)

Equation (4) indicates that adult literacy rate, public spending on education and urban

population are highly significant to determine the primary school enrolment in long

run.1% increase in adult literacy rate increase the primary school attainment by 0.77%

,likewise public spending on education and increase in urbanization enhance the

school enrolment by 0.18% and 3.64% respectively. Per capita income is also

significant determinant of primary school enrolment i.e. 1 % increase in per capita

income improve the enrolment by 0.18%. School age population negatively impact

the enrolment by 0.94% but pupil teacher ratio is not significant to decline the

primary enrolment in long run.

Long-Run Elasticities and Short-Run Elasticities of school enrolment in Pakistan

TABLE: 4.12 LONG-RUN ESTIMATED COEFFICIENTS FOR EDUCATION

MODEL NO 1

Variables Coefficients

50

ALR 0.773

PCI 0.184

PSE 0.187

PTRP -0.319

SAP -0.943

UP 3.641

TABLE: 4.13 SHORT-RUN CAUSALITY TEST (WALD TEST F-STATISTIC)

FOR EDUCATION MODEL NO 1

Variable F-statistic Probability

DLOG(ALR(-1)) 13.472 (0.001)***

DLOG(PCI(-1)) 4.111 (0.056)**

DLOG(PSE(-1)) 3.208 (0.087)*

DLOG(PTRP(-1)) 4.227 (0.051)**

DLOG(SAP(-1)) 0.034 (0.855)

DLOG(UP(-1)) 1.591 (0.223)

Note: *, **, *** denote significant at 10%, 5% and 1% level

The dynamic short-run causality among the relevant variables is shown in Table 4.13.

The causality effect can be generated by restricting the coefficient of the variables

with its lags equal to zero (using Wald test). If the null hypothesis of no causality is

rejected, then we concluded that a related variable Granger-caused School enrolment.

51

From this test, we commence that per capita income and pupil teacher ratio is

statistically significant to Granger-caused the primary school enrolment. Adult

literacy rate and public spending on health is significant at 1 % and 10 % respectively.

To sum up the findings we can say that public spending on education, and adult

literacy rate, pupil teacher ratio and per capita income granger cause in short run.

Table4.14: DESCRIPTIVE STATISTICS FOR EDUCATION VARIABLES

statistics PTRP PTRS SAP SEP SES UP PSE

Mean 38.181 29.112 41.042 67.766 26.032 32.166 2.407

Median 38.339 28.655 43.062 66.891 27.654 32.096 2.398

Maximum 41.62 42.266 43.634 94.809 36.600 36.549 3.022

Minimum 32.999 16.898 34.320 47.886 16.504 28.066 1.837

Std. Dev. 2.442 10.050 3.066 14.659 5.960 2.523 0.335

Observation

s

33 33 33 33 33 33 33

Table: 4.15 IMPACT OF EXPLANATORY VARIABLES ON

SECONDARY SCHOOL ENROLMENT

Variables Coefficient Standard T statistic Probability

52

error

C 0.352** 0.169 -2.072 0.049

LOG(SES(-1)) -0.596*** 0.170 -3.493 0.002

LOG(ALR(-1)) 0.588** 0.289 2.035

0.053

LOG(PCI(-1)) 0.174*** 0.055 3.120 0.004

LOG(PSE(-1))

0.135***

0.049 2.742 0.011

LOG(PTRS(-1))

-0.338*** 0.092 -3.680 (0.001

LOG(SAP(-1)) -1.188*** 0.334

-3.548

0.002

LOG(UP(-1))

3.864***

0.868

4.452

0.000

DLOG(ALR(-1)) 0.504* 0.275 1.833 0.079

DLOG(PCI(-1)) 0.322*** 0.103

3.122

0.004

DLOG(PSE(-1))

0.192***

0.068 2.829 0.009

DLOG(PTRS(-1))

-0.558*** 0.171 -3.248 0.003

DLOG(SAP(-1)) 1.054 1.133

0.930 0.361

DLOG(UP(-1))

3.041***

1.170

2.598

0.016

R-squared 0.822

F-statistic 9.744 Durbin-

Watson

stat

2.051

Adjusted R-

squared 0.738

Prob(F-statistic) 0.000

Note: *, ** & *** indicate at 10%, 5% and 1% significance level, respectively.

The estimation of Equation (3) using the ARDL model for secondary school

enrolment is reported in Table 4.13. R-squared of the model is 82% while adjusted R-

squared is 73 % so goodness of fit is fairly well. The robustness of the model has been

definite by several diagnostic tests such as Breusch- Godfrey serial correlation LM

53

test, Breusch-Pagan-Godfrey Heteroskedasticity Test Jacque-Bera normality test and

Ramsey RESET specification test. Results of the tests revealed that functional form of

the model is correct and the residuals of the model are serially uncorrelated,

homoskedastic and normally distributed and therefore, the results presented are valid

for reliable interpretation.

Test Test-stats p-values

Heteroskedasticity Test 1.511 0.212

Normality test 3.225 0.199

Ramsey RESET Test 0.568 0.460

Serial Correlation LM Test 0.418 0.663

Table 4.16 DIAGNOSTIC TESTS FOR EDUCATION MODEL NO 2

4.7 SHORT RUN AND LONG RUN IMPACT ON SECONDARY SCHOLL

ENROLMENT: MODEL 2

Our findings in case of secondary school enrolment are also consistent with the

previous literature as discussed in model 1 where our dependent variable is primary

school enrolment. All the references of previous literature discussed in model 1 to

support our findings are equally applicable when we take secondary school enrolment

as a dependent variable because all the researchers used both primary and secondary

school enrolment in their analysis.

Adult literacy rate positively impact the secondary school enrolment in short run at

10% of significance level while in long run it is significant at 5%.As literate parents

54

know the importance of educating their children so with the increase in adult literacy

arte secondary school enrolment increases.

Per capita income is significant determent of secondary school enrolment both in

short and long run in Pakistan. As income level of the household increases, they have

to allocate relatively small percentage of total income for children education.

Government expenditure on education is highly significant to improve the secondary

school enrolment in short run as well as in long run.

In case of secondary school enrollment pupil teacher ratio is significant at 1% both in

short run and long run as indiacted by (Craigwel et al .2012). The lower the pupil-

teacher ratio the more attention each child receives and the more effective individual

teachers can be. If households believe that the pupil-teacher ratio is too high and thus

useless for educating, then they may exploit private school, home-schooling or make

their children get jobs. Consequently, the coefficient of pupil teacher ratio negatively

signed. However, the decrease in this ratio necessitates an increase in public education

expenditure. Additionally, Mingat and Tan (1998) found that a reduction in this

variable has a small impact on student learning and has a long run effect of lowering

levels of school attainment. In Pakistan we can observe that pupil teacher ratio is

lower in secondary schools than primary, most of the rural areas have only one

teacher to run the primary school still not have significant impact on reduction of

school enrolment. Numbers of primary schools are much more than secondary schools

therefore it is hard to consider pupil teacher ratio. Likewise parents are less motivated

to enroll their children in private school in early age so they do not give considerable

attention to pupil teacher ratio in primary school.

55

Outcome of school age population in secondary school enrollment is alike with

primary enrolment. SAP has no significant impact on school enrollment in short run

but in long run it is highly significant to reduce the secondary school enrolment in

Pakistan. Literature suggested that when a country have large number of young

people in its population, is becomes challenging for government to enroll all children

in schools.

Population living in urban areas has easy and relatively less expensive access to

schools so same findings are true in case of Pakistan. Urbanization is positively

related with secondary school enrollment as in case of primary enrollment.

In Table given below the results of the bounds co-integration test exhibit that the null

hypothesis of against its alternative is easily rejected at the 5% significance level. The

computed F-statistic of is 4.418 greater than upper bound value of 4.01, thus

indicating the existence of a steady-state long-run relationship among SES, ALR,

PCI.PSE, PTRS, UP and SAP.

The estimated coefficients of the long-run relationship between SES, ALR, PCI, PSE,

PTRS, SAP and UP are expected to be significant, that is:

56

Test Statistic Value      Probability

F-statistic 4.418 0.003

D log (SES)t =0.352** +0.847 *log(ALR)t + 0.293*** log(PCI)t+

0.227***log(PSE)t - 0.569*** log (PTRS)t -1.994***log(SAP)t +6.483***

log(UP)t…………………………………………………(4)

Equation (4) exhibits the long run estimate coefficients of the secondary school

enrolment.

Coefficient of adult literacy rate show that 1% increases in adult literacy rate increase

the enrolment by 0.84% similarly per capita income increase the secondary

enrolment by 0.29% at a significance level of 1%.increase in public spending on

education and urban population is significantly positive impact on enrolment at 1%

level. Pupil teacher ratio declines the enrolment by 0.56% at a significance of 1%

while school age population decrease the enrolment by 1.9% significant at 1%.

Table 4.17: LONG-RUN ESTIMATED COEFFICIENT FOR MODEL 2

Table 4.18: SHORT-RUN CAUSALITY TEST (WALD TEST F-STATISTIC)

FOR MODEL 2

57

Variables Coefficients

ALR 0.8467

PCI 0.2929

PSE 0.2269

PTRP -0.5684

SAP -1.9934

UP 6.4828

Note: *, ** & *** indicate at 10%, 5% and 1% significance level, respectively

Short run causality test for secondary enrolment show that per capita income and

adult literacy rate is statistically significant at 1% to Granger-caused the primary

school enrolment public spending on education and pupil teacher ratio, school age

population and urban population is significant at 5%.cocluding our findings we can

say that all variables included in the model are significantly granger cause in short

run.

4.8 CONCLUDING REMARKS:

58

Variable F-statistic Probability

DLOG(ALR(-1))

(7.675)*** 0.011

DLOG(PCI(-1))

(25.222)*** 0.000

DLOG(PSE(-1))

(4.741)** 0.040

DLOG(PTRP(-1))

(4.682)** 0.041

DLOG(SAP(-1))

(5.104)** 0.034

DLOG(UP(-1))

(5.414)** 0.029

Evidence from this study suggested that government spending plays noteworthy role

to improve health and educational outcomes in Pakistan. Other explanatory variables

e.g. adult literacy rate, per capita income, pupil teacher ratio, school age population

and urbanization also effect the primary and secondary school enrolments similarly in

health model, female literacy rate, improved water source sanitation facility, per

capita income immunization for measles and co2 emission are significant detriments

of infant and child mortality rate but DPT3found to be statistically insignificant in our

analysis.

Chapter: 5

59

CONCLUSION AND RECOMMENADTIONS

5.1 SUMMARY

Many researchers advocated the greater public expenditures on basic education and

primary health care but little empirical support exists on the beneficial impact of such

expenditure on social outcomes. Using time series of 33 years for Pakistan we

investigated the effectiveness of government expenditure in health and education

sector. Infant and child mortality rate is used as health indicators, while educational

outcomes are expressed by gross primary and secondary school enrollment in

Pakistan. Study employ simple regression analysis for Health models but impact of

government spending on education sector is estimated by ARDL methodology as per

the requirement of stationery level of the variables, included in the study. The

evidence is stronger both for health and education sector in Pakistan.

5.2 CONCLUSION

Our investment variables for health models are government expenditure on health, per

capita income and female literacy rate. Impact of government expenditure and female

literacy rate on infant and child mortality rate is same as prescribed by the literature

but per capita income shows the insignificant relationship with infant mortality rate in

Pakistan. Provisions of efficient and sufficient resources for health sector reduce

infant and child mortality both, which are universally accepted as a measure of health

status. Literate women can have a better awareness of child growth, diseases, clean

and healthy food and sanitation therefore with the increase in female literacy child

and infant mortality reduces. Insignificance of per capita income may be the result of

highly unequal distribution of income in Pakistan.

60

Improved water source and sanitation facility plays a vital role to diminish the infant

and child deaths in Pakistan. DPT3 do not illustrate the results as prescribed by the

previous literature yet this result can be justified in case of Pakistan. Coefficient of

DPT3 is negative but not significant both for infant and child mortality rate in our

analysis. Among all other reasons one source of this insignificant relation is due to

lack of awareness and access about immunization in ruler areas. People do not

immunize their children against these diseases because of their conservative and

ignorant Attitude towards these vaccinations.

Immunization measles is an important component of health status in Pakistan .Co2

emission is positively related with infant and child mortality as indicated by the

literature.

Increase in government spending on education directly affects the primary and

secondary school enrollment in Pakistan. Most importantly, increase in government

expenditure alone do not ensure desired level of school enrollment, adult literacy rate,

higher per capita income and urbanization level are significant indicators to achieve

greater school enrollment. Decline in pupil teacher ratio and school age population

can also play a significant role to boost enrolments, which are treated as quality

variables in our study

Efficient and sufficient public expenditure on education and health care in the

Pakistan is imperative. As resources are limited and economic growth is necessary to

sustain economic development, and thus improve standards of living and human

development.

5.3 RECOMMENDATIONS

61

According to the findings of this study we suggest the following recommendations.

Pakistan have lowest health budget and higher child deaths in the region so

government should increase its health budget to ensure basic health care for

all.

Special attention should be given to educate the women by government and

public both.

Government should provide adequate drainage and sewerage systems to

people; moreover government should ensure easily accessible clean water

facilities for low income people.

Government health institutes and NGOs should organize different awareness

programs for illiterate community to provide them knowledge of healthy

living and importance of clean water and sanitation.

There should be positive media campaign and other awareness programs to

change the mind of uneducated people so they can acknowledge the

importance of these vaccinations.

Government should divert sufficient funding for prevention of measles to

reduce infant and child deaths.

Government should allocate higher budget for education sector to ensure

basic education for all.

Better and easy access to schools, reduction of illiteracy and increase in

household income can be helpful to get our national goal.

Construction of more primary and secondary schools especially in ruler areas

from foreign and local assistance is needed to manage the growing number of

school age population, likewise to ensure that every student get required

62

attention in school, government should hire more qualified and trained

teachers.

To improve the educational system, government should allocate the money in

a Manner which benefits each level of education equitably.

To guarantee the effectiveness of public spending on human development

infrastructure needs to be set up.

Education should be easily accessible to the persons who cannot afford it. .

Even though books are provided free of cost in government schools, many

poor families cannot afford to send their children to schools because of the

direct and indirect costs related with school enrollment. To minimize this

incidence and account for income disparity, government should allow free

school enrollment and offer subsidized education for those households who

can afford it.

5.4 LIMITATIONS OF THE STUDY

Limitations of the study are as follow:

We can have regional comparison regarding the effectiveness of government

expenditure on education and health care.

Different proxies can be used to measure the health status like life expectancy

at birth.

63

Tertiary and higher education attainment, primary and secondary pass out ratio

etc can be employed as proxies for education attainment as suggested by

previous literature.

Government expenditure on education and health as a percentage of total

expenditures can be used instead of expenditure as percentage of GDP.

Due to lack of availability of total health expenditure data as a percentage of

GDP, we utilized the only public spending on health as a percentage of GDP

in our analysis although sixty percent of expenditure on health is privet so if

anyone have access to total health spending ,they can use it in their analysis.

Chapter: 6

64

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