Determinants of Educational Enrollment in Pakistan

7/27/2019 Determinants of Educational Enrollment in Pakistan

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Determinants of Educational Enrollment in Pakistan

Authored by: M. Asfandyar Khan


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Table of Contents


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Determinants of the Educational Enrollment

The Case of Pakistan

Abstract. This study provides an analysis of education in different

regions of Pakistan; across different ages, gender and income groups.

Different geographic zones in which education varies like rural &

urban are formed for the four provinces of Pakistan across the

different categories. The study finds visible differences in the

education situation across different areas of Pakistan. The study

suggests that educational participation can improve if the disparity of

opportunity in the geographic zones can be eradicated and if the

general tendency to discriminate is rebuked and hence removed along

with changing the mindset of the people about the attainment and

benefits of education. For this purpose, more funds may be allocated

to health and education sectors, and special attention needs to be

given to awareness programs and disparity in income and facilitiesacross regions, for the improvement of the enrollment rate in

Pakistan.

I. INTRODUCTION

This paper explores the variables that affect the decision to acquire

education in Pakistan. Education and human capital being one of the

driving forces of the modern economy is a matter of great importance to

the government and hence its policies.The research question is to findthe relation between variables like region, gender, age and income with

the attainment of education. But first we delve deeper into the theory of

how human capital and education are essential in the first place.

The prevalent vast differences in standard of living between developed

and developing countries can be attributed to large differences in

nutrition, infant mortality rate, life expectancy and level of education.

Level of education is primarily linked with the changes in the marginal

productivities of workers, so the quality and stock of skills and of a

labour force is dependent on it. According to Solow model:

Y= F(K, AL)

Where: Y=Output, K=Capital, L=Labor, and A=

Knowledge/Effectiveness of labor.

More unambiguously, as education i.e. knowledge rises so does the

technological progress. AL is technological progress or effective labour.


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Solow model treats technological progress as exogenous; it assumes that

technological progress is the outcome of the allocation of resources to

the creation of new technologies. However, new growth theories treat

technical progress as endogenous, a key factor to growth.

In sum, education is an instrumental determinant of growth. Growth

miracles of industrialized nations are due to the unprecedented level of

education accumulation. A modern vision of growth lays emphasis on

the sustained technological and institutional growth. Growth does not

solely depend upon human capital and physical capital but high growth

is chiefly dependent on viable technological and organizational growth

(Chaudhry, 2009). Education, however, in modern economies plays a

vital and decisive role. Level of education is a stock of accumulated

knowledge and skills possessed in the workforce which can be translated

into productivity. It is the attained quality of a workforce via investment

in education. The countries which succeeded in attaining high economic

growth rate laid emphasis on the education and health.

Recently focus has been shifted from growth to education, because

growth has a trickle-down effect in reducing poverty, while education

has a direct effect on poverty reduction. Education enhances productivity

which leads to higher rewards or returns. In turn this increased

productivity yields higher wages and higher output so two

macroeconomic goals are achieved. Increased real income inflated

consumption capacity while the output expansion provides more goods

at lower prices whereas inflation is kept under control.

World Bank (2002) report on attacking poverty identifies the

following resources:

(a) Natural resources (land).

(b) Human assets (ability for basic labor, skills and good health).

(c) Financial assets (formal and informal social security and

political power).


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(d) Physical assets (access to infrastructure and physical capital).

All this reveals that human assets hold the key ranking in reducing

poverty, accelerating economic growth, generating employment,

enhancing productivity and controlling price hike. Unfortunately, the

level of human development is the lowest in Asia, as measured by

access to education basic health and family planning services, safe

drinking water and sanitation.

Ironically, Pakistan witnesses a typical story of lack of education

due to neglected focus on it and the health sector. Pakistan spends just

1.7% of its GDP on education. In this already low amount to education,

only 0.22% of GDP (about 13% of the total education spending) goes to

higher education. Hence, improvement in the quality of education is

extremely necessary at present, so as to make Pakistani workers

competitive in the labor market internationally.

The main focus of this term paper is to analyze the levels of

educational enrollment in different categories of Pakistans demographic

makeup and comparison of these categories through Logit regression

and bivariate analysis. As education is such an important part of

achieving economic growth the determinants of it are to be looked at

and the relation to be analyzed so that the relevant and appropriate

policy incentives and instruments can be elucidated.

The objective is to find the relation between variables like region,

gender, age, Household Heads Education and household income with

the educational enrollment to find out the impediments to educational

attainment.

II. LITERATURE REVIEW

Education is widely treated as part of the Production function as it is a

good measure of the marginal productivity of the worker. This treatment

is served out in the work of Mankiw, Romer and Weil (1992) as well. A

substitute method related with the theory of endogenous growth is to use


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technological progress, or the total factor productivity growth as a

function of the level of education. It reveals that educated labor force is

effective in executing creating, and espousing new technologies, thereby

stimulating growth.

Existing literature on the role of level of education on several sectors of

the economy suggests that human capital has substantial effects on

economic growth; Thereby establishing that any disparities in it across

different regions will result in the divergence of living standards in those

regions. As can be viewed in the paperRegional disparities in human

Capital: A case for Pakistan by M. Azhar Khan and Hafeez ur

Rahman. The study suggests that skills of workforce can be boosted

through investment in human capital that may result in an increase in the

marginal productivity of capital.

The paperThe Determinants of Educational Attainment: Modeling and

Estimating the Human Capital Model and Education Production

Functions by Kathryn Wilson in addition incorporates neighborhood,

school characteristics, and family to have an impact on the educational

decisions and choices

Becker's (1964) in his popular and groundbreaking Human Capital

proposed that an individual has to decide whether to make an investment

in education or not. This looks into the inter-temporal choice that a

worker faces i.e. whether to work now and earn or to forgo income and

invest in education for higher expected future returns. The main idea is

that the education decision is time dependent of present and future

consumption and spending. So Education is here treated, for the first

time, as an investment good. A lot of empirical studies have followed

this along with statistical and econometric analysis approach being

applied to this theory.

Willis and Rosen (1979) conducted their on study in light of Beckers

Model and analyzed the individual level investments in education. They


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said that a person decides their higher secondary education based on the

returns or incomes that are associated with the jobs that hire from that

particular field pool. They forecast from the lifetime income hypothesis

as they find an enrollment to earnings estimated elasticity of

approximately two; so education is assumed to be completely and

singularly dependent on the returns that that education will

prospectively yield. E.g., a person receiving education from a poor

quality education from a poor quality of school will expect low returns

to education and hence low education.

Romer (1990) postulated that level of education will have a direct effect

on the workers productivity by determining the capacity of notion come

up with new and improved technological methods for the output of the

domestic production of goods. Mankiw, Romer and Weil (MRW) (1992)

find a support for the level of education augmented model in a country

time series. However, Pugno (1996) shows strong reservations on these

results and argues that model tested by MRW is miss-specified and show

structural break. Nelson and Phelps (1966) explored that level of

education is instrumental to stimulating the response times of

technology.

The World Bank (1980) concludes that studies have shown that

economic return on investment in education seems in most instances, to

exceed returns on alternative kind of investment, and that developing

countries obtain higher returns than the developed ones. Abbas (2008)

investigated relationship between level of education and economic

growth in Pakistan using time series data. The study uses Johansen co-

integration approach for estimation purpose and rejects the vision of

endogenous growth model. The fitted model indicates that output

elasticity of level of education may be expected to increase with

technical progress. Higher secondary schooling shows same level of

productivity as it is observed in OECD economies, which is against the

convergence theory. The reason might be the low level of average

schooling in Pakistan. Higher return to health spending compared very


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favourable with the industrial investment.

Bagde (2008) analyzes the relationship between level of education and

growth of Indian software industry. The study through empirical

analysis has shown that software engineering baccalaureate has positive

effect on growth of software industry in 13 states of India.

Asgharet al. (2012) uses dynamic modeling in order to make an effort in

analyzing economic growths relation with educational attainment. The

results of the study show the existence of positive and strong relationship

between human capital and economic growth. The study suggests that

spending more on educational attainment may lead to an increase in

sustained economic growth in Pakistan.

Although in the preceding discussion empirical studies produce

contrasting results but a large number of studies find that growth of

region is influenced by the initial level of level of education.

III. MODEL AND METHODOLGY

Now that it has been established the attainment of education is

quintessential to sustainable growth we look into the matter of why, if it

is such an obvious answer to all our problems is it not as practically

reflected in the global populace and in this case Pakistan.

The first issue is to find a relevant variable which elucidates the

problem of lack of education as our dependent. The simplest way to

tackle is to return to the basics, i.e. why do people not get educated?

What it is that disinclines them from the pursuit of education? A few

things can be that there is simply a lack of opportunity; they dont have

the resources, another could be that they are not able or they simply

dont want to, which in turn begs the question of why not: are the

oblivious to its benefits or is it inconvenience or perhaps some other

issue? To answer all of these questions an econometric model has been

developed and data from the PSLM 2010/2011 is used.


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LEVEL OF EDUCATION

We use PSLM data and STATA software to analyze the situation.

The model used is:

Dependent Variable: Enrollment Rate

Independent Variables:

Region. Dummies are used for urban and rural.

Province. Dummies are used for the four provinces.

Gender. Dummies are used for Males and Females.

Age. Age groups* are used.

Education.

Heads Education. Groups* are used for convenience.

Household Income. Groups* are used in this as well.

* denotes that Coding for the groups is given in the Do File.

The Logit Model regression is used along with a bivariate analysis so as

to capture the effects and trends of all the variables. The reason for using

the logit model is that the dependent variable is a qualitative yes no

variable and the logit model checks the probability of likelihood by

using log of odds ratio with the dependent variable.

Logit scq05= Bo+B1(region)+B2(province)+B3(Gender)

+B4(Household Income)+B5(Head Education Category)

+B6(Education Category)+B7(Age)

IV. RESULTS & DATA SOURCE

The Data used was from the Pakistan Social and Living

Standards Measurement Survey (PSLM) 2010-2011.

STATA software was used to get the following regression results,

and tables of relation between Educational enrollment and other

variables:


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The Coefficients show how the probability of likelihood of beingenrolled in an institution or school is affected by the independent

variables.

Region is urban as rural is taken as base category. A person is

more likely to be enrolled in urban as opposed to rural area.

Sbq03 is male as female is taken as base category. A male is

more likely to be enrolled as compared to a female.

The constant term shows a Female living in rural Punjab.

Hhinc2 is household income/10000 meaning that a Rs.10000

change in household income leads to a small decrease in the

probability of likelihood of being enrolled.

The probability of likelihood is more when the Head of the

household is more educated as well as when the personhimself/herself is more educated.

Punjab is the base category. A person living in Sindh is less

likely while a person in KPK and Baluchistan is more likely

to be enrolled than Punjab.

BIVARIATE Analyses


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74.54% of the people are not enrolled in total and 25.46% are.

This trend doesnt deviate much with respect to changes in

household income.

Most of those who are enrolled belong to the 1-15 age group.

There isnt much gender disparity in enrollment outcomes.

23% of the rural population is enrolled as opposed to the 30% of

the urban population.


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KPK has the highest %age of people enrolled while Baluchistan

has the lowest.

This has an increasing trend as the education of the Head of

household increases so does the percentage of enrollment.

yes 25,098 14,436 6,989 5,150no 56,664 36,748 15,128 11,260

Enrolled? punjab sindh khyber pakhtunkhwa balochistan

urbanregion and province

yes 30,222 15,473 18,464 11,254no 89,087 61,820 50,238 51,188


ruralregion and province

In Punjab the urban rural disparity is large but Baluchistan has thehighest whereas KPK has the lowest.


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yes 29,872 18,650 16,020 11,747no 71,641 49,164 29,872 30,445


malegender of person and province

yes 25,448 11,259 9,433 4,657no 74,110 49,404 35,494 32,003


femalegender of person and province

The highest gender disparity is in Baluchistan and KPK while Punjab

has the lowest in the provinces.

So in general Baluchistan is the worst of in terms of enrollment level

disparity between gender and regional gaps which may perhaps be

explained by the fact that the infrastructure in it is also the lowest as

compared to the other provinces as well as the extremely conservative

attitudes of the general populace and the political turmoil that exist there.

V. CONCLUSION AND SUGGESTIONSThe socio-political differences are reflective in the different

geographical zones of Pakistan. These differences are natural, but

prolonged differences in provision of education facilities and economic

conditions among and within regions are posing some serious problems.

Gender based discrimination and a lack of opportunity for those who are

born in poverty is apparent and this has to be counter acted if progress

has to be achieved. These differences may foster a feeling of lack ofeconomic opportunity disparity and it may bring so many social evils

and bad blood in the society that is dangerous for the social fabric.

Concentrated growth has no economic value as it does nothing for the

unemployment stats and inflation stats but rather exacerbates the

situation. This can only be done when along with other facilities health

and education facilities are provided especially to the rural masses.

Majority of the population lives in rural areas where these basic

facilities are deficient. In Pakistan less than 2% of GDP is spent on


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education, which is quite less than the rest of the world. Hence,

improvement in the quality of education is the need of the hour, so that

Pakistani workers can be raised to the world standards.

VI. POLICY IMPLICATIONS

The government needs to increase its education budget to provide

incentive, opportunity and infrastructure to all the provinces to

reduce dissention between the masses. Special attention is needed

towards building a foundation for education in the rural areas as

70% of our population lives there. Educational programs can be

introduced to facilitate those in the rural areas.

As Pakistan has a broad based population pyramid it is necessary

that the enrollment rates especially of the youth increase as this is

a demographic dividend and by capitalizing on this great

opportunity Pakistan can bring about stability in its currently

tumultuous economy.

Anti-Discrimination laws have to be implemented and enforced

to reduce male female disparity. The disparity between male and

females is especially detrimental as half the population of

Pakistan is female and lack of opportunity for them leads to

economic stagnancy. The government needs to carry out

awareness campaigns so as to mitigate and moderate the cultural

dogmas of our society.

Special attention is required for higher education, as skilled

workers and professionals lead the way in economic progress.

Wages are dependent on the marginal productivity of the

individual (VMP) so in order to alleviate poverty there is a need

to increase the VMP which can only happen if the Level of

education is increased through educational investment.

Enrollment rates will increase if the education budget increases

i.e. the spending on educational institutes is increased as the


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better the quality of the schools the more will be the expected

return from education hence more people will forgo present

income for future higher incomes. So the enrollment rates will

reflect this in its statistic composition.

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Determinants of Educational Enrollment in Pakistan