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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